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Phytogeography
Phytogeography (from Greek φυτό, phyto = plant and γεωγραφία, "geography" meaning also distribution) or botanical geography is the branch of biogeography that is concerned with the geographic distribution of plant species and their influence on the earth's surface. Phytogeography is concerned with all aspects of plant distribution, from the controls on the distribution of individual species ranges (at both large and small scales, see species distribution) to the factors that govern the composition of entire communities and floras. Geobotany, by contrast, focuses on the geographic space's influence on plants.
Overview
The basic data elements of phytogeography are occurrence records (presence or absence of a species) with operational geographic units such as political units or geographical coordinates. These data are often used to construct phytogeographic provinces (floristic provinces) and elements.
The questions and approaches in phytogeography are largely shared with zoogeography, except zoogeography is concerned with animal distribution rather than plant distribution. The term phytogeography itself suggests a broad meaning. How the term is actually applied by practicing scientists is apparent in the way periodicals use the term. The American Journal of Botany, a monthly primary research journal, frequently publishes a section titled "Systematics, Phytogeography, and Evolution." Topics covered in the American Journal of Botany's "Systematics and Phytogeography" section include phylogeography, distribution of genetic variation and, historical biogeography, and general plant species distribution patterns. Biodiversity patterns are not heavily covered. Ex: The basic data element of phytogeography are specimen records. These are collected individual plants like this one, a Cinnamon Fern, collected in the Smokey Mountains of North Carolina. Branches of biology relevant to phytogeography
Phytogeography is part of a more general science known as biogeography. Phytogeographers are concerned with patterns and process in plant distribution. Most of the major questions and kinds of approaches taken to answer such questions are held in common between phyto- and zoogeographers.
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Phytogeography is often divided into two main branches: ecological phytogeography and historical phytogeography. The former investigates the role of current day biotic and abiotic interactions in influencing plant distributions; the latter are concerned with historical reconstruction of the origin, dispersal, and extinction of taxa. Floristics is a study of the flora of some territory or area. Traditional phytogeography concerns itself largely with floristics and floristic classification, see floristic province.
PHYTOGRAPHICAL REGIONS OF INDIA
A phytogeographical region is defined as an area of uniform climatic conditions and having a distinctly recognisable type of vegetation. According to D. Chattarjee (1962), India can be divided into nine phytogeographical regions.
1. Western Himalayas
This region comprises north and south Kashmir, part of Punjab and Kumaon region of Uttaranchal. Average annual rainfall in the region is 100-200 cm. The region is wet in outer southern ranges and slightly dry in the inner areas. At high altitudes, snowfall occurs during winters. The region is subdivided into three zones.
1. Submontane (lower, tropical and subtropical) zone: This zone includes outer Himalayas i.e. regions of Siwalik Hills and adjoining areas from 300 to 1500 m altitude. Average annual rainfall of the zone is around 100 cm. The vegetation consists of subtropical dry evergreen, subtropical pine and tropical moist deciduous forests. 2. Temperate (montane) zone: This zone extends in the western Himalayas between the altitudes 1500 and 3500 m. The climate is wet between the altitudes 1500 and 1800 m and is drier at higher altitude. The vegetation consists of wet forests, Himalayan moist and Himalayan dry temperate forests. 3. Alpine zone: This zone extends between 3500 m and 5000 m altitudes. The rainfall is very scanty and climate is very cool and dry. The vegetation consists of alpine forests.
1. Eastern Himalayas
This region extends in the Himalyas from east of Nepal up to Arunachal. The climate is warmer and wetter than in western Himalayas. Tree line and snow line are higher by about 300 m than in the western Himalayas. The tropical temperature and rainfall conditions result in vegetation of the region having greater general species diversity, greater variety of oaks but lesser variety of conifers than in the western Himalayas. This region is also divided into three zones.
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1. Submontane (lower, tropical and subtropical) zone: This zone extends from the foothills up to the 1850 m altitude. The climate is nearly tropical and subtropical. The vegetation consists of subtropical broad-leaved forests, pine forests and wet temperate forests. 2. Temperate (montane) zone: The zone extends from 1850 m to 4000 m altitude, about 500 m higher than in the western Himalayas. The vegetation consists of typical temperate forests with oaks and Rhododendron at lower and conifers at higher altitudes. 3. Alpine zone: This zone extends from 4000-5000 m altitude. The climate is very cool and dry. The vegetation consists of alpine forests.
Indus plain
This region comprises a part of Punjab, Delhi, Rajasthan, a part of Gujrat and Cutch. The climate has very dry and hot summers alternating with dry and cold winters. The annual rainfall is generally less than 70 cm and may be 10-15 cm in some areas. Most of the region is desert today though it had dense forests about 2000 years ago that were destroyed due to biotic factors particularly extensive cattle grazing. The vegetation today consists of tropical thorn forests and grasslands in some areas.
Gangetic plain
This region covers part of Delhi, Uttar Pradesh, Bihar, West Bengal and part of Orissa. Average annual rainfall ranges from 50 cm to 150 cm from east to west. The vegetation consists of tropical moist deciduous forests, dry deciduous forests, thorn forests and mangrove forests.
Assam
The region covers most of the Assam. The climate is characterized by very high temperature and rainfall. The vegetation consists of tropical evergreen and wet temperate forests in the lower plains while hilly tracts up to 1700 m altitude have subtropical pine forests.
Central India
This region comprises part of Orissa, Madhya Pradesh, Vindhyan region and Gujrat. The areas are mostly hilly with some places at 500-700 m altitude. The average annual rainfall is 100-170 cm. Biotic disturbances are very common in this region resulting in degradation of forests into thorny forests in the open area. The vegetation consists of tropical moist deciduous forests, chiefly Sal forests in areas of annual rainfall above
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150 cm and mixed deciduous forest in areas of 125-150 cm annual rainfall. Tropical thorn forests are found in the areas of annual rainfall below 125 cm.
Western coast of Malabar
This is a small region extending from Gujrat to Kanyakumari along Western Ghats. The climate is warm humid having annual rainfall over 400 cm. The climate is tropical on the coasts and temperate in the hills. The vegetation consists of tropical wet evergreen, moist evergreen and moist deciduous forests. Wet temperate forests (Sholas) are present in Nilgiri while mangrove forests are found in the saline swamps on the coasts.
Deccan
The region comprises southern Peninsular India from southern Madhya Pradesh up to Kanyakumari excluding the Western Ghats. The average annual rainfall in the region is about 100 cm. The vegetation consists of tropical dry evergreen, dry deciduous and swamp forests.
Andman and Nicobar
This region includes Andman and Nicobar Islands. The climate of the region is warm and humid with very high temperature and annual rainfall. The vegetation consists of littoral mangrove, evergreen, semi-evergreen and deciduous forests.
Case study:
PHYTOGEOGRAPHIC ANALYSIS OF TREE FLORA OF THE EASTERN GHATS
Phytogeography is a branch of biogeography which deals with the geographical distribution of plants. The aim of this study was to analyze the geographic distribution of tree flora in the Eastern Ghats of south India, for their conservation significance. Of the total 272 tree species assessed, 36% of the species were found restricted to Asia, 19% were endemic to India and only one species (Hildegardia populifolia) was found endemic to Eastern Ghats. The present phytogeographical analysis revealed that 59% of the tree species inventoried
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Chapter V from Eastern Ghats of south India were common to Sri Lanka and supported their close geographic affinity. Species recovery programs are of urgent need for conservation of species with geographically limited distribution.
Currently, the earth is experiencing tremendous changes in the natural environments. Destruction and degradation of natural habitats are widespread and profound and their implications for the sustainability of natural resources are of global significance [1]. Biodiversity loss is a global phenomenon but its impact is greatest in the tropics, where the majority of species are distributed [2].
Though, the tropical forests occupy only 7% of the total land surface area of earth, they hold more than 50% of total species of the world. However, now, they are getting disappeared at a rate of 0.8 to 2% per year [3]. Many conservationists have increased concern on biodiversity loss in tropical forests due to deforestation and improper infrastructure development in the name of modernization [4].
Phytogeography is the branch of biogeography that is concerned with the geographic distribution of plant species. It is an effective tool for clarifying the historical-ecological interpretation of a large area [5], and for prioritizing species for conservation.
A few phytogeographical studies available worldwide include: Neotropics [6], Friulian plain, NE Italy [7], Western Europe and Eastern Greenland [8], Tasmanian alpine flora [9], Sonoran and Chihuahuan deserts [10], Yukon territory, NW Canada [5], Grasslands of Mexico [11], Yucatan Peninsula [12], Arid mountain of Oman [13], Coastal vegetation of Yucatan Peninsula [14], Bryophytes of Hong Kong [15]. Although, there are many studies on plant biodiversity focusing on the population status [16, 17], only a few records are available on the distribution patterns of plant species in India. The aim of this study
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MATERIALS AND METHODS Study area
he phytogeograph analy a arr ed o t for the tree pe e n entor ed from the d ont n o range of mo nta n of a tern Ghat ( - - n o th nd a he mo nta n range o ered an area of km , with altitudes varying from 200 m to 1649 m above mean sea level. They were composed of masses of Charnockite associated with gneisses and varied metamorphic rocks, and the soil was red loamy and lateritic. The mean annual temperat re and ra nfall for the reg on a and mm, re pe t ely he bulk of the rainfall was received from August to October.
Phytogeograghic analysis
The entire stretch of the Eastern Ghats of South India was divided into 6.25 km × 6.25 km grids, and within each grid a 0.5 ha belt transect (5 m × 1000m) was e tabl hed, and all l e tree th≥ m g rth at brea t he ght (GBH ere enumerated [18].Phytogeographic analysis of the tree flora was made by studying the distributional pattern of the inventoried 272 species by referring to several national and regional floras and other publications [19, 20, 21, 22, 23, 24, 25, 26]. The following classification of geographic area was made for analysis: 1. Eastern Ghats, 2. Peninsular India, 3. India, 4. Sri Lanka, 5. Peninsular India & Sri Lanka, 6. Asia, 7. Africa, 8. Australia, 9. America, and 10.Europe.
RESULTS
Summary of tree inventory
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A total of 27,412 trees representing 272 species in 62 families were inventoried from 120 transects (0.5 ha each, covering 60 ha area) distributed in the Eastern Ghats of South India [18]. Tree diversit was 29 (±12) species per transect, and it ranged from 9 to 71 species per transect. Tree density was 228 (±75) trees per transect, and it ranged from 96 to 477 trees per transect. The abundance of the 272 tree species varied from one individual for 33 species to a maximum of 2122 individuals for Albizia amara (Mimosaceae) for the total 60 ha sampled (Table 1). The dominant species, A. amara had a wide range of geographic distribution (Table 1).
Phytogeographic analysis
The tree species of Eastern Ghats of South India had a unique geographic distributional spectrum. Of the total 272 species recorded, 269 species (98.9%) are common to the entire Eastern Ghats region (Figure 1), and the rest three species, Antiaris toxicata and Dimocarpus longan were new additions to Eastern Ghats, which were earlier recorded only from the Western Ghats-Sri Lanka hot spot, and Memecylon parvifolium, formed a new record to tree flora of India [27]. Out of the total 272 species analyzed, 100 species (37%) were found common to Peninsular India, and 271 species (99.6%) were common to India (Figure 1). A total of 161 species (59%) were common to Sri Lanka, and 203 (75%) species were common to Peninsular India & Sri Lanka. In case of continent, all the species were found common to Asia, 11% of species were common to Africa, 10% were common to Africa, 4% were common to America and only 2 species (Callitris rhomboidea and Ficus microcarpa) were found common to Europe. Overall, only one per cent (3 species, Acacia farnesiana, Beilschmiedia bourdilloni and Gyrocarpus asiaticus) out of the total 272 species had pantropical dist ribution (i.e. geographic distribution in Africa, Asia and
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Americas), four per cent (12 species) of the species had neotropic distribution (species restricted to the Americas) and ten percent (27 species) of the total species analysed were of palaeotropics (species distributed in Africa and Asia).
Species endemism
Thirty-six percent of the total species studied were endemic to Asia i.e. species restricted to Asia and not found elsewhere in the world, 19% were restricted to India, 14% were restricted to peninsular India, 27% were endemic to peninsular India and SriLanka geographic regions, and only one species, Hildegardia populifoliawas found endemic to Eastern Ghats. Hildegardia populifolia and Grewia laevigata were the only two rare, endemic, threatened (RET) species found in Eastern Ghats of South India
DISCUSSION
In the past, plant biodiversity inventory mostly focused on the population status of species in a particular geographic area, and less attention was shown to study their patterns of geographical distribution. In the present study, a phytogoegraphical analysis was carried out for tree species inventoried [18] from Eastern Ghats of South India.Out of the 272 tree species inventoried, fifty nine percent of the species were found common to Sri Lanka, revealing the close geographic affinity of tree species of Eastern Ghats in South India with that of Sri Lanka. Similarly, Parthasarathy [28] had reported that a maximum (240 species, 43 %) of the total 550 species of vascular plants enumerated from Kalakad, Western Ghats of South India, were found common to Sri Lanka. However, according to a research based on mineral occurences, mainly g ems and graphite, Sri Lanka was found closely associated with southern part of Madagascar than with the Archean granulites of South India [29].
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Conservation significance :
Biologists have long known that the smaller the population, the more susceptible it is to extinction [30]. Out of the 272 tree species inventied from a total of 60 ha in the Eastern Ghats of South India,12% of species were represented by only one individ ual, the two RET species H. populifolia and G. laevigata were represented by 4 and 2 individuals, respectively [18], and M. parvifolium, a new addition to Indian tree flora, was represented by only one individual [27]. This situation forces an immediate conservation measure to avoid those species from local extinction.
Topographic variables (elevation, slope, aspect, etc.) have a strong influence on the forests and also contribute to the maintenance of species richness [31]. However, the major threats to plant species are the growing human population, expanding crop agriculture, improper harvesting methods and over-exploitation of the plant resources [32]. Though, deforestation, is happening mostly for the conversion of land to food crops, it is the most destructive force in tropical forests worldwide, and the other important disturbance such as the selective harvest of the timber have increased in rate as well as in magnitude [33]. The Eastern Ghats of South India is subjected to various forest disturbances, such as cattle grazing, collection of fuel wood and non-timber forest products, illegal extraction of timber, soil mining, besides invasion of the weeds. Hence, there is an immediate need of action plan for checking all the threatening forces that exist in Eastern Ghats of South India, not only for conserving the geographical distribution range of tree species in this region, but, also for the survival of a variety of wild fauna supported by the plant communities.
The present study concludes that more than half of the total tree species in Eastern Ghats of South India were found common to Sri Lanka, and revealed the geographic affinity of Sri Lanka with South India or simply the Gondwana affinity of the two na
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Chapter V tions. This study reflects the floristic affinity and phytogeographic significance of Eastern Ghats in South India. Further, it provides valuable data on geographic distribution of tree species of Eastern Ghats, which can be potentially used for conservation planning and management of species with geographically limited distribution in this region.
The following steps are recommended for tree specie s conservation in Eastern Ghats of South India: (i) extensive research has to be carried out on the population status of important tree species such as M. parvifolium, H. populifolia, G. laevigata, A. to xicata and D. longan, (ii) species recovery program by tissue culture is of immense need for conservation of the endemic and RET species, (iii) specific protection for very important sites such as Alathi and Kannimar shola, which hosts species such as A. toxicata and D. longan that were new additions to Eastern Ghats of South India, and (iv) awareness program for forest inhabitants is of most urgent need for biodiversity conservation of this part of India.
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Global patterns of plant diversity and floristic knowledge
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Continental drift
Continental drift is the movement of the Earth's continents relative to each other, thus appearing to "drift" across the ocean bed. The speculation that continents might have 'drifted' was first put forward by Abraham Ortelius in 1596
. Evidence of continental drift
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Continental Drift, Sea Floor Spreading and Plate Tectonics
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Human Influence
The Human Influence Index (HII) is a measure showing direct human influence on ecosystems using 8 measures of human presence.
Introduction
Human-caused environmental changes are creating regional combinations of environmental conditions that, within the next 50 to 100 years, may fall outside the envelope within which many of the terrestrial plants of a region evolved. These environmental modifications might become a greater cause of global species extinction than direct habitat destruction. The environmental constraints undergoing human modification include levels of soil nitrogen, phosphorus, calcium and pH, atmospheric CO2, herbivore, pathogen, and predator densities, disturbance regimes, and climate. Extinction would occur because the physiologies, morphologies, and life histories of plants limit each species to being a superior competitor for a particular combination of environmental constraints. Changes in these constraints would favor a few species that would competitively displace many other species from a region. In the long-term, the ―weedy‖ taxa that became the dominants of the novel conditions imposed by global change should become the progenitors of a series of new species that are progressively less weedy and better adapted to the new conditions. The relative importance of evolutionary versus community ecology responses to global environmental change would depend on the extent of regional and local recruitment limitation, and on whether the suite of human-imposed constraints were novel just regionally or on continental or global scales.
The earth is undergoing rapid environmental changes because of human actions (1–6). Humans have greatly impacted the rates of supply of the major nutrients that constrain the productivity, composition, and diversity of terrestrial ecosystems. Specifically, the natural rates of nitrogen addition and phosphorus liberation to terrestrial ecosystems
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(1, 7, 8) have been doubled, and atmospheric CO2 concentrations have been increased to about 40% above preindustrial levels (9). Soil calcium levels are declining in some ecosystems because of increased rates of leaching caused by acidic deposition (10). Humans have relaxed biogeographic barriers to dispersal by accidentally or deliberately moving exotic species to new biogeographic realms (e.g., ref. 11). Through both active fire suppression and increased use of fire as a land clearing or management tool, humans have regionally changed fire frequency (12, 13), which is a major force structuring communities and ecosystems (14). Humans now appropriate more than a third of all terrestrial primary production (15), and, in doing so, have simplified or destroyed large portions of some types of ecosystems, leaving behind fragments that often lack herbivores or predators that provided important top-down constraints. Moreover, many human environmental impacts are projected to be two to three times stronger within 50 years (16). In total, humans may be imposing combinations of constraints that already do, or may soon, fall outside the ranges within which many species evolved.
Here we explore how and whether such changes could result in the loss of local diversity and accelerated extinction (3), and thus potentially decrease ecosystem functioning (e.g., refs. 17–19). The effects of environmental change on species composition, diversity, and ecosystem functioning are poorly understood. As a tool to explore this issue, we use theories that potentially can explain multispecies coexistence (20–29). These models are based on the interplay of environmental constraints and the trade-offs organisms face in dealing with these constraints. They can predict both the persistence of a large number of species (24–29) and the conditions that could lead to extinctions. Although mechanisms differ, all solutions to Hutchinson's (20) paradox of diversity have a similar structure (26, 28, 29). All mechanisms assume that two or more factors constrain fitness, and that intraspecific and interspecific trade-offs constrain each individual or species to having optimal performance at a particular value of these constraints. These processes provide a basis for interpreting the impacts of global human ecosystem domination on community composition, extinction, and speciation.
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The physiology, morphology, and life history of a plant necessarily constrains it to survival in only a range of environmental conditions. In the classical literature, these conditions were called its fundamental niche. Each species is, at best, a superior competitor for a narrower range of conditions, classically called its realized niche (30– 32). The attributes of sites and regions thus limit the types of species that can occur in them. These classical concepts of fundamental and realized niches underlie recent mechanistic approaches to competition, coexistence, and community structure (24, 25, 28, 33–36) and are a useful way to summarize natural history (e.g., refs. 37–39). Moreover, they suggest that human-caused environmental changes could create ―vacant niches‖ (40)—i.e., evolutionarily novel suites of environmental conditions for which no species in a region are well adapted. In this paper, we use recent mechanistic theory to explore the potential impacts of human-driven environmental change on the composition and diversity of terrestrial plant communities, and on their patterns of speciation.
Environmental Constraints in Plant Communities
What are the major environmental variables that limit the abundance of terrestrial and aquatic plants, and which of these variables are being impacted significantly by human actions? In essence, plants may be limited by nutrients and other resources, by pathogens and herbivores, by disturbances, by dispersal abilities, and by the physical environment, including its climate. These constraints are elaborated below.
Resource Limitation.
Plants require N, P, K, Ca, Mg, S, trace metals, CO2, water, light, and other resources. Depending on the habitat and species, any one or several of these may be limiting. The most commonly limiting resources of terrestrial habitats are N, P, and water (24, 41–44). N limitation is common because the parent materials in which soils form contain almost no N. Rather, the chemically stable form of nitrogen is atmospheric N2, which is usable only by N-fixing plants via microbial symbionts. Non-N-fixing plants
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Chapter V obtain N as nitrate, ammonium, or organic N. Some soils are either initially low in other mineral elements, especially phosphorus and calcium, or become low in these after millennia of leaching. The Park Grass plots of Rothamsted, England have joint limitation by N, P, K, and early spring rainfall (43, 44). The greatest changes in plant community biomass, composition, and diversity came from N addition in the grasslands of both Rothamsted and Cedar Creek, Minnesota (45–47). Water is a limiting factor in many terrestrial habitats, as can be the atmospheric concentration of
CO2. Light may also be limiting, especially on productive soils in areas with low disturbance and low grazing rates.
Recruitment Limitation.
All sessile plants have the potential to have their abundance limited by dispersal (25, 48–51). This occurs because dispersal is a neighborhood process, and because interspecific interactions also occur locally. Such ―contact‖ processes can cause plants to have spatially patchy distributions (52), and thus to be missing from suitable habitat because of recruitment limitation. A one-time addition of seed of plant species that occurred in a savanna, but were absent from the local sites, led to an 83% increase in local plant species diversity and to a 31% increase in total community plant abundance (53). Because the added species occurred nearby, but were absent locally, their ability to germinate, grow, survive, and reproduce after a one-time seed addition showed that their abundance was limited by recruitment. Long-term observations in a Panamanian rainforest (51) also demonstrated strong recruitment limitation, as have seed addition experiments in other habitats (54, 55). Other evidence of dispersal limitation and of the rate of movement of plant species comes from studies of secondary succession. For instance, 10 to 15 years are required for Schizachyrium scoparium, a prairie plant that is a strong nitrogen competitor, to disperse from margins into abandoned fields, and another 30 years are required for it to attain peak abundance (46). This 40-year time delay between creation of a site and dominance is reduced to 3 years simply by adding seed of little bluestem. Cornell and Lawton (56) found that local diversity was limited less by local interspecific interactions than by recruitment from regional pools. Davis (57, 58) followed the dynamics of North
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American forests after glacial recession, and observed time lags of thousands of years between a region having the appropriate climate for a tree species and the arrival of that species. Such time lags could greatly influence responses of plant communities to human-caused environmental changes (58). Habitat fragmentation would lengthen such time delays.
Predators and Pathogens.
Plant abundance in both terrestrial and aquatic ecosystems is also limited by the densities and species identities of pathogens and herbivores, which in turn can be limited both by their predators and by dispersal. Thus, top-down forces can greatly constrain both terrestrial and aquatic ecosystems.
Disturbance.
Physical disturbances also limit terrestrial plant communities and sessile (benthic) freshwater and marine plant communities. For many terrestrial ecosystems, fire frequency has been a major constraint, as have been such physical disturbances as wind storms, landslides, mudslides, avalanches, clearings caused by gophers or other fossorial animals, disturbances caused by hooves, wallows, etc.
Temperature/Climate.
The growth rates of terrestrial and aquatic plants are temperature-dependent, with species (and genotypes) having optimal growth and competitive ability at particular temperatures, and thus in particular climates. This is likely the greatest cause of the geographic separation of species along continental climatic gradients, such as north– south gradients and elevational gradients. In addition, the geographic ranges and abundance of many terrestrial plants are limited by temperature extremes, especially by tissue damage associated with freezing or subfreezing temperatures. In addition, within a region, differences in temperature-dependent growth could cause different plant species to be specialized on different portions of the growing season.
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Temporal Variation.
Plants respond not just to the mean levels of limiting factors, but also to the extent and patterning of their temporal variation. Some species may be limited or inhibited by such temporal variation, whereas other species may have traits that allow them to exploit such temporal variation (21, 22). This means that temporal variation, itself, can function as an additional limiting factor.
In total, there are a large number of factors and processes that constrain abundance of plants in both terrestrial and aquatic habitats. All of these limiting factors have been implicated as potential determinants of the species composition and diversity of various plant communities. Various combinations of two or, at times, three of these limiting factors have been formally incorporated into theories that are potentially capable of explaining the diversity and composition of terrestrial and aquatic plant communities. Changes in any of these constraints could thus change the abundance of species and genotypes in a habitat.
Anthropogenic Global Change and Plant Constraints
Many of these constraints are undergoing large, rapid changes because of human actions. Recent human activities have more than doubled the preindustrial rate of supply of N to terrestrial ecosystems (7). Nitrogen had a preindustrial terrestrial cycle that involved the annual fixation of about 90 to 140 Tg (teragrams) of N/yr (1, 7), with an additional 10 Tg of N/yr provided by atmospheric N fixation via lightening. Industrial N fixation for fertilizer currently totals about 88 Tg/yr. About 20 Tg/yr of N is fixed during the combustion of fossil fuels, and about 40 Tg/yr of N is fixed by legume crops. In addition, land clearing, biomass burning, and other human activities mobilize and release about an additional 70 Tg of N/yr. The projected expansion of global population to about 9 billion people by year 2050 and shifts to diets higher in animal protein suggest that, by 2050, global food production will be double its current rate (19). If so, anthropogenic terrestrial N inputs in 2050 would be about three to four times the preindustrial rate (16, 19). Much of this N would enter rivers and be carried
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Nitrate is readily leached from soil, carrying with it positively charged ions such as Ca. Atmospheric N deposition may be depleting Ca and other cations in hardwood forests of the eastern United States (10). This depletion of base cations could cause elements that had not been limiting in a region to become limiting. Plant species often have distributions constrained by soil pH and Ca.
Phosphorus is a commonly applied agricultural fertilizer, and current P application is a doubling of the natural global rate for terrestrial ecosystems (8). Projections to year 2050 are that agricultural P fertilization will more than double. Much of this P may enter aquatic ecosystems, which can be P-limited.
The accumulation of such greenhouse gases as CO2 and methane may lead to global climate change, with the greatest changes, especially warmer winter temperatures, forecast for temperate and polar ecosystems (e.g., ref. 2). Because climate change and its potential impacts on terrestrial ecosystems are widely studied, we will not review them here. Rather, we merely note that rainfall patterns, the frequency and severity of droughts, and other aspects of climatic mean and variance, which all constrain plant communities, are also forecast to change. In addition, CO2 is a plant nutrient, and elevated levels of CO2 represent atmospheric eutrophication with a limiting plant resource.
Fire frequency is a major variable controlling the species composition and diversity of forests and grasslands (e.g., ref. 14). In the United States, active fire suppression, habitat fragmentation, and other human activities have decreased by 10-fold the area burned each year, from about 22 × 106 ha/yr in 1930 to about 1.5 × 106 ha/yr since about 1960 (13). In contrast, fire frequency is greatly increasing in other habitats, especially tropical habitats, where fire is used as a land-clearing or land-management tool (59).
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Modern transportation and commerce have immensely increased both accidental and deliberate introductions of species to novel biogeographic realms (11). About one quarter of the vascular plant species of California, for instance, are exotics. Exotic species are the second largest cause of native species of the United States being listed as endangered (60). Exotic species can impact the abundance of native species in a large number of ways, including via competitive suppression, via changes in disease incidence or some other trophic interaction, via inducing changes in the physical habitats, such as in fire frequency, and changes in nutrient cycles (61, 62). For instance, the invasion of the N-fixing Myrica fava into the Hawaiian Islands greatly increased local N fixation and thence soil N fertility. This increased soil fertility allowed other exotic species to increase in abundance once they were freed from N competition with native plants that where efficient N users (63).
Human actions have also fragmented habitats via conversion of native ecosystems to agricultural lands, urban or suburban lands, roads, power line rights-of-way, etc. Fragmentation is likely to escalate as population and per capita incomes increase globally. Habitat destruction can cause immediate extinction of those species that lived only in areas destroyed, and delayed extinction of poorly dispersing, perhaps competitively superior, species of extant ecosystems (64).
Finally, humans have decreased the geographic ranges and abundance of top predators, especially large carnivores. Decreased abundance of predators have had impacts in both aquatic and terrestrial habitats that have cascaded down the food chain (e.g., refs. 65 and 66), increasing abundance of some herbivores, decreasing abundance of their preferred plant species, and freeing herbivore-resistant species from competitive pressure.
In total, human actions are modifying many environmental constraints that, in combination with intraspecific and interspecific trade-off, led to the evolution of extant plant species and thus influenced the composition, diversity, and functioning of terrestrial and aquatic plant communities. If current trends continue, within 50 to 100 years the suites of factors constraining the structure of many plant communities may
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Ecological Responses to Environmental Change
How would such changes in environmental constraints impact plant communities? Although there would be a continuum of responses, it is instructive to consider two ends of this spectrum: the more immediate, or ―ecological‖ responses, and the more long-term, or ―evolutionary‖ responses, especially patterns of speciation. Clearly, both ecological and evolutionary responses happen simultaneously. We separate them because the evolutionary response in which we are most interested is speciation, which is much slower than changes in species abundance. Ecological responses would depend on the constraints and trade-offs that had structured a given community and on how these had changed. Let us consider a case in which the composition and diversity of a plant community are determined by competition for nitrogen and light (e.g., ref. 28) and by dispersal limitation (25, 49), and explore the impacts of elevated N deposition. The qualitative changes that would occur in this plant community in response to elevated N deposition are the same as those that would occur in response to changes in any other environmental constraint.
Concepts and Theory.
Assuming similar underlying physiologies, each plant species can be represented by the proportion of its biomass that is in either roots (for uptake of nitrogen), stem (which determines plant height and thus light capture), seed (which determines dispersal ability), or leaves (light capture via photosynthesis). For a given spatially homogeneous habitat—a site with a uniform soil of a given fertility (measured by the annual in-site mineralization rate of nitrogen)—and for a given physiology, there would be one pattern of biomass in root, stem, seed, and leaf that led to maximal competitive ability (28). On a low N soil, such as nutrient-maintained (rather than grazing-maintained) grasslands, the best competitor would have high root biomass, enough leaf biomass to provide photosynthate to meet the needs of roots, little
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Even if soils were spatially homogeneous, theory predicts that many other plant species could coexist with the best N competitor if they had appropriate trade-off between their competitive ability for N and their dispersal ability (23, 25, 27). Although there is an analytical limit to similarity for this mechanism of coexistence (25), there is no simple limit to the number of species that can stably coexist via this metacommunity process. This is the predominant mechanism of coexistence illustrated in Fig. 1A. It allows numerous species, each represented by a dot, to coexist with the major axis of differentiation being between root biomass (i.e., competitive ability for soil N) and seed biomass (i.e., dispersal ability). This defines the region of trait space in which species can coexist (28), which has a highly elongated shape (closed curve in Fig. 1A). This region of multispecies coexistence spans species with seed biomass from a few percent (the best competitor for N, which is more than 60% root) to more than 40% (the poorest competitor, but the best disperser). The region of coexistence includes species with different stem biomasses because of assumed spatial heterogeneity in the N content of soils. On more N rich soils, species with greater stem biomass are favored over those with more root biomass, because greater stem biomass allows better access to light. This, though, is a minor axis of coexistence compared with the seed–root trade-off for low N habitats.
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Figure 1
(A) Plant species can be represented by the proportion of biomass in leaves, roots, stems, and seeds (28). In low nutrient habitats, superior competitors have high biomass in root, low biomass in stem and seed, and moderate biomass in leaves. Such superior competitors stably coexist with species that are progressively poorer
competitors, but better dispersers (25). (B) In a fertile habitat, plant height and thus stem biomass is a determinant of competitive ability for light. (C) A nutrient-poor region, experiencing high rates of nutrient deposition. The region of coexistence includes only a few of the species originally present in the nutrient-poor region. These species would be competitively dominant and displace all of the other species, but be subject to invasion by species in the vacant region enclosed by the solid curve.
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Because Percent Root + Percent Stem + Percent Seed + Percent Leaf = 100%, Percent Leaf is about 30% for all cases shown.
A comparable pattern occurs for habitats with soils that have high N content (Fig. 1B). The elongated region of coexistence shown again represents coexistence mainly via a competition–colonization trade-off, but in this case the trade-off is between stem allocation (for light capture during competition for light) and seed allocation (dispersal ability that depends on the number and size of seed). Soils of intermediate fertility would favor species intermediate between the extremes shown in Fig. 1 A and B.
About a third of the globe has sandy soils with low N content. What would happen if a region with such soils were to receive projected increased rates of atmospheric N deposition? If all possible species were present throughout the region (i.e., if the whole triangular trait space of Fig. 1 were reasonably well covered with species), there would be a transition, as N accumulated, from a suite of species like those of Fig. 1A to a suite like that of Fig. 1B. However, given that the region receiving elevated N inputs started with low-N soil, the species of Fig. 1B, which occur on N- rich soils, would not be present. Rather, the responses observed would come from those species that happened to be present in the region—those shown in Fig. 1A.
The long-term response of this low-N habitat to greatly elevated N deposition should be dominance by superior light competitors, which have greater stem biomass. However, only two of the original species of the originally low N region would fall within the new trait space favored by N addition (Fig. 1C). These are both weedy species—i.e., species with high seed biomass compared with those that would be expected to be the competitive dominants of the elevated-N habitat. These species are favored initially because, of all of the species present in the original low-N habitat, they have relatively high stem biomass. Under conditions of elevated N, these two species would be expected to increase greatly in abundance where present and to
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A second striking feature is the extent to which there are ―vacant niches‖ caused by environmental change—i.e., there are almost no species present in the regional flora that have traits that would normally be favored in such habitats. This is shown by the large empty area within the solid closed curve of Fig. 1C. Any species with traits that fell in this empty area should be able to invade into the region. In total, because of N deposition, the majority of the species that had been the dominants of a region when it was a low N habitat would be competitively displaced by a few formerly rare species, creating an ecosystem highly susceptible to invasion and species turnover until a community like that of Fig. 1B had developed.
Results of Experimental N Additions.
Just such changes in plant diversity and composition are seen when one or a few such factors have been experimentally manipulated for extended periods of time. For instance, fertilization of the Park Grass plots with 4.8 g⋅m−2 of N, as ammonium sulfate, led to dominance by the grass Agrostis (84% of community biomass compared with an average abundance in unfertilized control plots of 12%) and to the loss of 14 of the 19 plant species found, on average, in unfertilized control plots (44, 68). The addition of 14.4 g⋅m−2 of N as ammonium sulfate together with P, K, Mg, and other nutrients led to extreme dominance by Holcus lanatus (Yorkshire fog, a grass), which had an average abundance of 96% in the two replicate high-N plots, compared with an average abundance in the three unfertilized and unlimed control plots of 2%. Both of the high-N plots contained only two plant species, whereas the controls averaged 19
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Chapter V plant species. Experimental N addition in a set of 207 grassland plots in Minnesota showed similarly strong loss of grassland species diversity and similar shifts in species composition at high rates of N addition (28, 69). Moreover, similar shifts in plant community diversity and composition have been reported for ecosystems experiencing high rates of atmospheric N deposition because of nearby intensive agriculture (70, 71). For instance, the heathlands of The Netherlands are an ecosystem type that had dominated sandy soils for millennia. Agricultural intensification in The Netherlands in the 1960s and later was associated with high rates of N fertilization. Much of this N was first captured by crops, then entered cattle as feed, and later was volatilized as ammonia from their wastes. This led to about an order of magnitude increase in the rate of atmospheric N deposition, which contributed to the conversion of species-rich heathlands first into low-diversity stands of a weedy grass (Molinia) and then into shrubby forest (71).
A Generalization of Constraint Surfaces.
These losses of diversity and shifts in species composition have, at their core, a conceptually simple basis (24, 44). The plant species that coexist in the unfertilized control plots do so for a variety of reasons, including interspecific trade-off in their ability to compete for limiting resources (e.g., ref. 24), or trade-off between competitive ability versus local dispersal ability (e.g., refs. 23, 25, and 27), or a trade- off between competitive ability versus resistance to herbivory or disease (e.g., refs. 24 and 72). If plant species coexist in the Park Grass plots because of competition for soil nutrients and light in a spatially heterogeneous environment (24), competitive abilities can be summarized by the relative shapes and positions of the resource-dependent growth isoclines of the species (24). Addition of N pushes this system toward an edge for which all plant species are limited by the same resource, light, and a single species is the superior competitor (24, 44). Moreover, the resource requirements of the Rothamsted species also depend on soil pH (24). The average soil pH of the unmanipulated Rothamsted soils was 5.3, whereas soil pH fell to 4.1 in the plot
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Comparable patterns of dominance by a few formerly rare species, of competitive displacement of most existing species by these newly dominant species, and of high susceptibility to invasion by exotic species would be expected to occur for each of the types of human-caused changes in environmental constraints summarized above. In essence, a given habitat has various factors that constrain the fitness of the organisms that live there, and there is a trade-off surface that defines the potential responses (both within and among species) to these constraints (Fig. 2). Ecological processes, such as interspecific competition, map these environmental conditions onto the constraint surface and thus show the region of traits within which species must fall to persist in a region that has a given suite of environmental conditions (Fig. 2). Changes in any environmental conditions that limit organismal fitness, such as decreased fire frequency, increased N deposition, elevated CO2, increased leaching loss of Ca and P, decreased herbivory, etc., would move the region of coexistence, as illustrated in Fig. 2.
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Figure 2 : The qualitative mapping of environmental conditions onto the traits of competitively superior species. The set of values of Constraints 1, 2, and 3 for Environmental Condition A, map into species traits on the trade-off surface, indicated by the shaded plane. Human-caused environmental change moves environmental conditions from Region A to Region B, causing a corresponding shift in the traits of the competitively dominant species.
The High Dimensionality of Environmental Change.
The greater the dimensionality of a habitat is (i.e., the greater its number of constraints), the more its diversity and composition would be impacted by a given amount of environmental change in each variable. As reviewed above, human actions are changing many environmental constraints simultaneously, including N, P, Ca,
CO2, pH, fire frequency, trophic structure, and climate. The high dimensionality of these changes may lead to much greater impacts on plant communities than anticipated from a consideration of only one or a few of these factors.
A simple example illustrates this. Consider a habitat in which there are three constraints, factors 1, 2, and 3. The low and high values of these factors might map
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A more formal, although still highly abstracted, treatment of this matter can be provided by a simple extension of Hutchinson's (30) abstraction of the niche as a hypervolume. Suppose species abundance is limited by multiple environmental factors defining orthogonal niche axes and forming a niche space whose boundaries are determined by the largest and smallest possible values of the environmental factors. Suppose that physiological and morphological trade-offs, as well as adaptation to past interspecific interactions, imply some optimal point in the niche space at which the species performs best, and away from which performance drops off. In two dimensions, for example, the axes might be soil pH and temperature, and performance might drop off as in a bivariate normal surface whose peak is at the optimal point (19). In a discrete approximation, the bivariate normal surface becomes a circle within which the species can survive, outside of which it cannot. In multiple dimensions, the circle becomes a hypersphere.
In this abstract view of the niche, prevailing environmental conditions are points in the niche space, and if the species can survive in the prevailing environment, those points fall within the species' niche hypersphere. Anthropogenic actions that change environmental conditions move those points to new locations in the niche space. What is the chance that the moved points will fall within the hypersphere of the species?
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With random and independent changes, that chance can be calculated simply by dividing the volume of the species' niche hypersphere by that of the entire niche space. Assuming the species niche is smaller than the entire niche space, then using formulae for the volumes of n-dimensional hyperspheres and hypercubes, that chance can be shown to be always less than where n is the number of environmental conditions changed, and where the factorial is computed via the gamma function when n is odd. Under these assumptions, if two environmental conditions were changed (n = 2), at most about 80% of the species on average would survive, but if eight conditions were changed randomly at once, at most about 1% of the species on average would survive. This multiplicative effect of changes in limiting factors means that several small changes can have as great an impact as one larger change, and that various combinations of small and large environmental changes can, in combination, have an immense impact. Thus, the ecological impacts of human-caused environmental change should depend on the dimensionality of the suite of factors that constrain species abundance, and, in a multiplicative manner, on the magnitudes of changes in all these factors.
In the short-term, such shifts in environmental constraints would eliminate many species and favor once-rare species. The longer-term dynamics of these terrestrial plant communities would depend on the dispersal rates of species both within a region and from other regions, if any, that formerly had characteristics similar to those that occur in the human-impacted region. They also would depend on the evolutionary responses of the species that remain in these habitats.
Evolutionary Responses to Global Change
What might the long-term outcome be of evolution under novel environmental conditions? For one possibility, let us consider again, but on an evolutionary time scale, the effects on a low-N terrestrial plant community of a large increase in the regional rate of N deposition. This could cause light and dispersal ability to become major limiting factors, as illustrated in Fig. 1C. As already discussed, the immediate effect of a high rate of N deposition would be dominance by a few formerly rare, fast-
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Chapter V growing, rapidly dispersing plant species. These species would rapidly spread and overtop low-N-adapted species and thus out-compete them for light. However, a large portion of the viable trait space of this community would be empty, as in Fig. 1C. Assuming that N deposition is occurring on a geographically large region, or that habitat fragmentation or other dispersal barriers prevent colonization by suitable superior light competitors, or that the region has experienced other environmental changes (e.g., Ca leaching, soil acidification, invasion by pathogens) that make it inhospitable for otherwise suitable superior light competitors, its longer-term dynamics would be driven as much, or more, by internal evolutionary processes than by colonization.
The evolutionary dynamics of such systems have been explored for situations in which it is assumed that there is a strict trade-off between competitive ability and dispersal ability (36, 73, 74). Let us ask what might happen to a weedy plant species that was the initial dominant of a formerly N-poor habitat that experienced elevated N deposition, as shown in Fig. 1C. Numerical solutions to a partial differential equation model (36) show that, within the initially dominant weedy species (species 1 of Fig. 3A), those individuals that are better light competitors have greater fitness than those that are better dispersers. This causes the weedy species to evolve into a progressively better light competitor (acquiring such traits as a larger proportion of biomass in stem, greater height, and larger seed), but to produce fewer seeds and/or allocate less to vegetative spread. Thus, species 1 evolves to the right in Fig. 3A. As species 1 evolves into a better local competitor (and thus a poorer disperser), it occupies fewer sites in the spatial habitat. After this has progressed sufficiently far, an interesting phenomenon occurs. Individuals at the far end of the range of phenotypes, which are good dispersers but poor light competitors, are also favored (species 2 of Fig. 3B). These individuals are poor light competitors, and thus do not competitively inhibit species 1. However, they are good dispersers, which allows them to live in the sites not occupied by species 1.
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Figure 3 : Numerical solutions of evolutionary change in a weedy species growing in a spatially implicit habitat in which fitness is limited both by dispersal ability and by competitive ability, based on a model of phenotypic diffusion (36). (A) Given this trade-off, an initially weedy species, species 1, undergoes evolutionary change, with its peak shown moving to the right. (B) After 50,000 years, species 1 has evolved into a much
better competitor, but a much poorer disperser than it originally was, and a new species, species 2, has appeared. Species 2 is a superior disperser, but an inferior competitor. It survives in vacant sites in this spatial habitat. (C) Species 1 and 2 each evolve toward being superior competitors. After some time a third species appears that is a poor competitor, but excellent disperser. This third species evolves into a superior competitor and a fourth species appears, etc. Shown here is the result after
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475,000 years, at which time 21 peaks of abundance appear, each peak representing a different phenotype, thus corresponding with different species.
In essence, there is a bimodal selective pressure created by competition in a spatial habitat and by an analytical limit to similarity for coexistence of organisms with traits at different points on the trade-off curve (36). This leads to two peaks on the trade-off curve, each peak corresponding to an incipient species (Fig. 3B). Such peaks appear even when all phenotypes are initially rare, and result from the interplay of selection, mutation/recombination, and the competitive limit to similarity. Within each of these peaks, those individuals that are superior light competitors but inferior dispersers are favored, causing the peaks to move to the right in Fig. 3B. Once the second peak, incipient species 2, moves sufficiently far to the right, a third peak appears. It also evolves toward the right, and a fourth peak appears, etc. In numerical solutions of the underlying reaction-diffusion model, after a 475,000 year period, a single weedy species had speciated into 21 species (Fig. 3C) that spanned the empty niche space of Fig. 1C. Such speciation processes would occur within each of the original weedy species, and eventually would yield a local flora as species-rich as occurred before N deposition.
In total, this process suggests that the imposition of novel environmental constraints would lead to the eventual diversification of the flora of a region, with the new flora filling in the empty niches created by novel human-caused environmental conditions. The process by which this is predicted to occur is one in which the ancestral progenitors of this new flora are small, fast-growing, weedy species. Interestingly, this is just what has been suggested to have occurred during the evolution of the angiosperms, during diversification in corals, and during the diversification of terrestrial mammals.
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Previous SectionNext Section
Conclusions
Anthropogenic changes in environmental limiting factors are likely to cause significant loss of plant diversity, leaving many niches empty and creating plant communities dominated by weedier species (poor competitors but good dispersers). The extent of this effect will depend both on the number of constraints that are changed (i.e., dimensionality) and on the magnitude of such changes. Because the impact of multidimensional environmental changes are expected to be multiplicative, a series of relatively small changes may be as important as a single major change. The vacant niches of a region experiencing a major change in an environmental constraint, such as a high rate of N deposition (Fig. 1C), indicate several things about such habitats. First, species that have traits that fall within the newly created vacant niches should be able to invade into, spread through, and persist if propagules are regionally available. Secondly, any heritable variation within existing species that allowed individuals to fill the vacant niches would be favored. For instance, following N deposition, there would be especially strong selection favoring those individuals with greater competitive ability for light, even if this cost dispersal ability. Until the available genetic variation for such traits was consumed, such evolution would be rapid. However, it seems unlikely that such species could rapidly evolve to be equivalent to the species of habitats that had a long evolutionary history of nitrogen rich soils. As such, these newer systems might long be susceptible to invasion by such species, with such invasion often leading to the displacement of the species that were evolving in situ.
Clearly, all of the ideas we have discussed are speculative extensions of a few simple models of community structure and assembly. Such models merit further testing and deeper exploration of their ecological and evolutionary implications.
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Human Influence on Global Climate Change Evidence that human activities influence the global climate system continues to accumulate. Data indicate that Earth's surface temperature is rising. This increase can be attributed, in part, to human-caused increases in greenhouse gases such as carbon dioxide. It is becoming apparent that these climatic changes are negatively affecting physical and biological systems worldwide.
NREL Warren Gretz, NREL Carbon dioxide and other pollutants Electricity generated at power plants result from the burning of coal to is carried by power lines to users, produce electricity. sometimes hundreds of miles away.
Warren Gretz, NREL Dr. Edwin P. Ewing, Jr.,CDC The burning of gasoline by Pollution created by human activities automobiles releases carbon dioxide is particularly evident around the
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and other types of air pollution that world's largest cities.
contribute to global climate change.
Twentieth Century Warmest of the Past Ten Centuries
Understanding climate requires long-term measurements of Earth‘s atmosphere. Direct measurements of global temperatures have been recorded for only the past 140 years or so. To extend these records back in time, scientists have learned that certain natural processes preserve indirect evidence of past atmospheric conditions. Using data from glacial ice cores, tree rings, lake-bottom sediments, and ocean corals, they can estimate global temperatures going back thousands of years.
The graph below shows reconstructed temperature data for the Northern Hemisphere for the past 1000 years. Instead of actual temperatures, the graph shows annual temperature anomalies—differences from the average temperature for each year. The gray lines are error bars, showing the possible degree of error in each measurement.
! Examine the graph to interpret how the temperature has changed over the last 1000 years.
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IPCC Reconstructed Northern Hemisphere temperature anomalies for the past 1000 years.
1. Why do you think the error bars become smaller in the more recent part of the graph?
Major Findings about Climate Change
Scientists have noted several significant trends in these data.
20th century surface temperatures were the warmest of any century in the past 1,000 years.
The rate and duration of warming in the 20th century is greater than in any of the previous nine centuries.
The 1990s were the warmest decade in the past 1,000 years.
1998 was the warmest year in the past 1,000 years.
The 11 warmest years of the past 140 have occurred since 1983, with the warmest years being 1998, 1997, and 1995.
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Average global surface temperatures have increased 0.4 to 0.8 degrees Celsius over the past 140 years.
Human Activities Are Increasing Greenhouse Gases
Another type of data that scientists can reconstruct is the amount of carbon dioxide
(CO2) in the atmosphere.
Major Findings about Greenhouse Gases
The concentration of CO2 in the atmosphere has increased by 31% since 1750.
The current CO2 concentration is at the highest level in the last 420,000 years.
Atmospheric CO2 is increasing at a faster rate today than at any time over the past 20,000 years.
The current concentration of methane (CH4) is at its highest level in the last 420,000 years.
The graphs below show the changes in concentration of three different greenhouse gases over the past 1,000 years.
2. When did concentrations of these greenhouse gases start rapidly increasing? What might have caused these increases?
Each of these greenhouse gases affects climate differently. Some gases can trap the sun‘s energy better than others. The degree to which a gas traps solar energy is called its radiative forcing value. Radiative forcing values are indicated on the right axis of the graphs. The higher the radiative forcing value, the stronger its effect on global warming.
1. Which gas affects global climate most? Which has the least effect?
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Human Activities Are a Major Cause of Global Warming
Major Findings about Climate Change
Reconstructions of the past 1,000 years of climate data indicate that the warming which has occurred over the past 100 years was unlikely to be entirely natural in origin.
Most scientific studies estimate that the rate of global warming over the past 50 years coincides with increasing concentrations of greenhouse gases.
A report issued in 2001 by the Intergovernmental Panel on Climate Change (IPCC) stated that it is "virtually certain" that emissions of carbon dioxide due to fossil fuel burning are the main cause of increasing atmospheric carbon dioxide during the 21st century.
The graph below shows temperature records and atmospheric carbon dioxide concentrations over the past 1,000 years.
Composite graph showing temperature anomaly and carbon dioxide level over the past 1,000 years.
Predict the effect that continued increases in atmospheric carbon dioxide will have on global temperatures.
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Climate Change Models Predict the Future
Scientists develop mathematically-based climate models to help predict future climate changes. Each model uses different assumptions about the future to predict how atmospheric CO2 levels and temperatures will change.
The variables in each model include:
Population growth rate
Economic development
Energy use
Efficiency of energy use
Mix of energy technologies
The graph below shows the results from three climate models used by the IPCC, with predictions starting in 1990 and ending in the year 2100. In all three, the global population rate rises during the first half of the century, then declines.
The A1B model assumes rapid economic growth and increased equity—the reduction of regional differences in per-person income. New and more efficient technologies are introduced, without relying heavily on a single energy source.
The A1F1 model is the same as A1B, but assumes the continued use of fossil fuel-intensive technologies.
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In the B1 model, the world moves rapidly from a producer-consumer economy toward a service and information economy. There is a reduction in the use of raw materials, and an emphasis on clean and efficient technologies and improved equity.
Other models have been developed, each based upon a different set of assumptions.
Adapted from IPCC, Third Assessment Report on Climate Change, 2001. Global temperature increases predicted by three different IPCC climate models.
Although differing in degree, these three climate prediction models show similar trends:
The projected rate of global warming in the future is much larger than the rate of global warming during the 20th century. Predicted rates of global warming are greater than any seen in the past 10,000 years.
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5. Why do scientists develop numerous models rather than rely on just one?
6. Based upon all the models shown, what range of temperature increase is expected to occur by 2100?
Mapping Surface Temperature Changes Over Time
Atmospheric warming is not evenly spread around the world. Analysis of historical records suggests that the temperature of land areas will increase more rapidly than the global average. The greatest warming has occurred in the high northern latitudes, especially in northern Canada and Alaska. The map below shows differences between surface temperatures in 2001 and average global temperatures calculated for the period from 1951 to 1980.
NASA Global temperature anomalies for 2001.
7. Which hemisphere (north or south) appears to have experienced the most significant temperature changes? Suggest why this is so.
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Role of GIS, GPS, and Remote Sensing
Introduction
The rapid development of spatial technologies in recent years has made available new tools and capabilities to Extension services and clientele for management of spatial data. In particular, the evolution of geographic information systems (GIS), the global positioning system (GPS), and remote sensing (RS) technologies has enabled the collection and analysis of field data in ways that were not possible before the advent of the computer.
How can potential users with little or no experience with GIS-GPS-RS technologies determine if they would be useful for their applications? How do potential users learn about these technologies? Once a need is established, what potential pitfalls or problems should the user know to avoid? This article describes some uses of GIS- GPS-RS in agricultural and resource management applications, provides a roadmap for becoming familiar with the technologies, and makes recommendations for implementation.
Spatial Technologies
Geographic Information Systems
GIS applications enable the storage, management, and analysis of large quantities of spatially distributed data. These data are associated with their respective geographic features. For example, water quality data would be associated with a sampling site, represented by a point. Data on crop yields might be associated with fields or experimental plots, represented on a map by polygons.
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A GIS can manage different data types occupying the same geographic space. For example, a biological control agent and its prey may be distributed in different abundances across a variety of plant types in an experimental plot. Although predator, prey, and plants occupy the same geographic region, they can be mapped as distinct and separate features.
The ability to depict different, spatially coincident features is not unique to a GIS, as various computer aided drafting (CAD) applications can achieve the same result. The power of a GIS lies in its ability to analyze relationships between features and their associated data (Samson, 1995). This analytical ability results in the generation of new information, as patterns and spatial relationships are revealed.
The Global Positioning System
GPS technology has provided an indispensable tool for management of agricultural and natural resources. GPS is a satellite- and ground-based radio navigation and locational system that enables the user to determine very accurate locations on the surface of the Earth. Although GPS is a complex and sophisticated technology, user interfaces have evolved to become very accessible to the non-technical user. Simple and inexpensive GPS units are available with accuracies of 10 to 20 meters, and more sophisticated precision agriculture systems can obtain centimeter level accuracies.
What is GPS?
It is a Global Positioning System
GPS, which stands for Global Positioning System, is a radio navigation system that allows land, sea, and airborne users to determine their exact location, velocity, and time 24 hours a day, in all weather conditions, anywhere in the world. The capabilities of today‘s system render other well-known navigation and positioning ―technologies‖—namely the magnetic compass, the sextant, the chronometer, and radio-based devices—impractical and obsolete. GPS is used to support a broad range of military, commercial, and consumer applications.
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24 GPS satellites (21 active, 3 spare) are in orbit at 10,600 miles above the earth. The satellites are spaced so that from any point on earth, four satellites will be above the horizon. Each satellite contains a computer, an atomic clock, and a radio. With an understanding of its own orbit and the clock, the satellite continually broadcasts its changing position and time. (Once a day, each satellite checks its own sense of time and position with a ground station and makes any minor correction.) On the ground, any GPS receiver contains a computer that "triangulates" its own position by getting bearings from three of the four satellites. The result is provided in the form of a geographic position - longitude and latitude - to, for most receivers, within a few meters.
If the receiver is also equipped with a display screen that shows a map, the position can be shown on the map. If a fourth satellite can be received, the receiver/computer can figure out the altitude as well as the geographic position. If you are moving, your receiver may also be able to calculate your speed and direction of travel and give you estimated times of arrival to specified destinations. Some specialized GPS receivers can also store data for use in Geographic Information Systems (GIS) and map making.
What is the difference between GPS and GIS?
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Remote Sensing
Remote sensing technologies are used to gather information about the surface of the earth from a distant platform, usually a satellite or airborne sensor. Most remotely sensed data used for mapping and spatial analysis is collected as reflected electromagnetic radiation, which is processed into a digital image that can be overlaid with other spatial data.
Reflected radiation in the infrared part of the electromagnetic spectrum, which is invisible to the human eye, is of particular importance for vegetation studies. For example, chlorophyll strongly absorbs blue (0.48 mm) and red (0.68 mm) wavelength radiation and reflects near-infrared radiation (0.75 - 1.35 mm). Leaf vacuole water absorbs radiation in the infrared region from 1.35 - 2.5 mm (Samson, 2000). The spectral properties of vegetation in different parts of the spectrum can be interpreted to reveal information about the health and status of crops, rangelands, forests and other types of vegetation.
Applications
The uses of GIS, GPS, and RS technologies, either individually or in combination, span a broad range of applications and degrees of complexity. Simple applications might involve determining the location of sampling sites, plotting maps for use in the field, or examining the distribution of soil types in relation to yields and productivity. More complex applications take advantage of the analytical capabilities of GIS and RS software. These might include vegetation classification for predicting crop yield or environmental impacts, modeling of surface water drainage patterns, or tracking animal migration patterns.
Precision Agriculture
GIS-GPS-RS technologies are used in combination for precision farming and site- specific crop management. Precision farming techniques are employed to increase
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Site-specific data, such as soil characteristics, fertility and nutrient data, topographic and drainage characteristics, yield data, harvester-mounted yield sensor data, and remotely-sensed vegetation indices, are collected from different sources and stored and managed in a spatial database, either contained within the GIS or connected to the GIS from an external source. The analytical power of a GIS is applied to the data to identify patterns in the field (e.g., areas of greater or lesser yield; correlations between yield and topography or characteristics such as nutrient concentrations or drainage) (Zhang et al., 1999).
Once patterns and correlations are elucidated, management practices can be modified to optimize yield and production costs, and minimize environmental impacts caused by excessive applications of fertilizers and pesticides. Site-specific applications of fertilizers, pesticides and other applications can be implemented by dividing a field into smaller management zones that are more homogeneous in properties of interest than the field as a whole (Zhang et al., 2002).
Forest Management
Spatial technologies are well suited for applications to resource management issues. The ability to interface GIS with relational databases enables integration of large data sets and many variables to support management decisions (e.g., Arvanitis, Ramachandran, Brackett, Rasoul, & Du, 2000). One example is the Florida
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Agroforestry Decision Support System (FADSS) (Ellis, Nair, Linehan, Beck, & Blance, 2000). FADSS is a GIS application that integrates geographically linked data on climate and soil characteristics in the state of Florida with a database of over 500 trees and 50 tree attributes. FADSS enables landowners, farmers and extension agents to make management decisions based on site-specific and tree-specific information.
Habitat Analysis
The modeling capabilities of GIS can be combined with remotely sensed landscape imagery to evaluate the effects of management practices and to assist resource managers and public decision makers in making informed decisions. For example, a GIS-enabled program, VVF, was developed to assess the suitability of a landscape as a species habitat (Ortigosa, De Leo, & Gatto, 2000). VVF integrates user-selected environmental variables to produce habitat suitability maps, and enables the user to create habitat suitability models for a specified area. Another model, LEEMATH (Landscape Evaluation of Effects of Management Activities on Timber and Habitat), evaluates both economic and ecological effects of alternative management strategies on timber production and habitat quality (Li, Gartner, Mou, & Trettin, 2000).
Data Analysis and Display
The spatial visualization capabilities of GIS technology interfaced with a relational database provide an effective method for analyzing and displaying the impacts of Extension education and outreach projects. This application was demonstrated in the Florida Yards & Neighborhood (FY&N) program developed by the University of Florida Extension to teach homeowners and landowners how to reduce non-point source pollution and storm water runoff and protect the environment through landscape practices they exercise in their own yards.
Homeowners filled out surveys both before and after receiving training in landscaping methods. Responses to questions concerning landscape practices were rated as good, fair, or poor, and statistical analysis was conducted on before and after scores for each landscape practice using a relational database interfaced with GIS software.
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Geospatial analysis of the extent of homeowner/landowner adoption of these best management practices taught by the program enabled assessment of impact by acreage and location, identification of areas needing greater emphasis, tracking of change, and the ability for policymakers to see impacts in map format.
Where to Start
To the uninitiated Extension specialist, the complexity and vast array of potential applications can be confusing and intimidating. Because the applications of GIS-GPS- RS cut across a great many disciplines, chances are good that these technologies can be beneficial in your own area of expertise. How do potential users with little prior knowledge identify specific ways in which they can be useful in their own work?
The decision must begin with a process of self-education. This includes gaining an understanding of the basic concepts of the technologies and doing a careful evaluation of your own needs and the needs of your clientele. A good place to start is at http:www.digitalgrove.net/. This Web site is a mapping gateway for resource managers and provides information on the fundamentals of GIS-GPS-RS technologies and data and provides numerous links to other sources of information, tools, utilities, data, and software applications. Another useful resource is ESRI's Virtual Campus online education and training Web site (http://campus.esri.com). This site offers course both free and inexpensive modules on the use of ESRI's GIS software, as well as a courses on how to go about planning and implementing a GIS.
An Internet search on uses of GIS, GPS, and RS in your field is a good way to start. Many agencies already using these methods post project reports, research results, and information on specific applications. Good sources include the USDA-ARS Hydrology and Remote Sensing Laboratory at the Beltsville Agricultural Research Center (http://hydrolab.arsusda.gov/) and the U.S. Fish and Wildlife Service GIS home page (http://www.fws.gov/data/gishome.html). Many online tutorials and educational materials are also accessible through the worldwide web. Two excellent tutorials are the National Center for Geographic Information and Analysis GIS Core
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Curriculum (http://www.ncgia.ucsb.edu/pubs/core.html) and the Remote Sensing Core Curriculum (http://www.r-s-c-c.org/).
Once you have researched the potential for GIS-GPS-RS technologies in your field, it is important to become familiar with the workings and capabilities of different software applications and equipment technologies before a decision is made about implementation. A number of software companies have free data readers and browsers that provide an opportunity to examine and use some of the functionality of their software packages. Some examples include ESRI's Arcexplorer (http://www.esri.com/) and Leica Geosystems' ViewFinder (http:www.leica- geosystems.com). Many software companies will also provide time-limited trial copies of software packages to allow the user to evaluate the applications before purchasing. If your needs are limited to obtaining and viewing images and GIS data layers and performing simple analysis functions, then one or several of these free data viewer programs may be sufficient for your purposes. Using ESRI's ArcExplorer, for example, you can view, identify, locate and query geographic and attribute data; create thematic maps; and perform basic statistical analysis.
If you need to create or edit new data layers, or perform some basic analysis and data conversion functions, several freeware applications are available that may fulfill your requirements. One of these applications, Forestry GIS (fGISTM), was developed for operational field managers in the natural resources by the University of Wisconsin. fGIS is freely downloadable (http:www.digitalgrove.net/fgis.htm) and can be used to edit existing GIS data, digitize new data layers, query and search spatial data, build customized data views and create maps. The application also includes utilities for working with database tables, transforming spatial data to different coordinate systems, and designing diagrams. Another freeware GIS application, DIVA-GIS (http:www.diva-gis.org/) was designed for mapping and analyzing the distributions of species. DIVA-GIS can create, edit, and transform GIS data files, and has capabilities for various statistical and biological modeling functions.
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If you need to perform more sophisticated spatial analytical functions, then you will most likely need to purchase commercial software with more functionality. A number of different GIS and RS software packages are commercially available, each with different features and functionality. Some of these applications can be expensive for organizations with a limited budget. If your organization is affiliated with an educational institution, then a special educational price may be available. Although ESRI GIS software products and file formats are probably the most common for manipulating and distributing GIS data, most other GIS software applications can translate data from and to these file types. For reviews and discussions and downloads of different GIS software products, see http://software.geocomm.com/.
GPS Equipment Selection
Recent advances, refinements, and expansion of GPS technology have provided a broad array of choices to users. The Global Positioning System was developed by the U.S. Department of Defense for military applications and consists of a number of continuously orbiting satellites that transmit low power radio signals. Ground-based receivers can use these signals to calculate a location on the surface of the Earth with a high degree of accuracy and precision. In general, obtaining higher degrees of accuracy requires the use of more complex, and therefore more expensive, equipment.
The type of equipment selected depends on a number of considerations, including the degree of accuracy required by the user, budget considerations, ease of use, and working conditions (e.g., is waterproof equipment required?). The issue of instrument accuracy is one that appears to cause some concern among new users. Many users seem to assume that an inexpensive ($100-$200) handheld unit is not able to deliver the necessary accuracy and precision. The fact is that even inexpensive units are capable of attaining good accuracies.
This has not always been the case. Prior to May, 2000, the horizontal accuracy of locations from non-corrected data obtained using the Global Positioning System was limited to at least 100 m. This limited accuracy was due to the effects of selective
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Chapter V availability (i.e., artificial signal degradation) applied by the US Government. On May 1, 2000, selective availability was turned off by Presidential order, and greatly improved accuracies are now possible from even inexpensive handheld GPS units. Studies have demonstrated that accuracies on the order of 10-20 m can be obtained from typical stand-alone GPS units (Ochieng and Sauer, 2002; Adrados, Girard, Gendner, & Janeau, 2002).
If accuracies less than 10 meters are needed, then differential correction of data (DGPS) is required. There are several ways of obtaining differential corrections. One method requires a base station receiver or beacon placed at a known location, which then transmits corrections in real time to a roving receiver via a ground- or satellite- based radio signal. Another method is to obtain pre-recorded correction files for post processing. Files can be obtained from commercial and governmental agencies. The increased accuracy of DGPS data comes at an increased cost, and the user can expect to pay significantly more for equipment. A good overview of GPS can be found on
The Geographer's Craft Web site (http://www.colorado.edu/geography/gcraft/contents.html), developed by the University of Colorado at Boulder.
Geospatial Technologies (gis, Gps, Rs) And Their Application
The rapid development of spatial technologies in recent years has made available new tools and capabilities to Extension services and clientele for management of spatial data. In particular, the evolution of geographic information systems (GIS), the global positioning system (GPS), and remote sensing (RS) technologies has enabled the collection and analysis of field data in ways that were not possible before the advent of the computer.
MSSRF keeps evolving its focus for research depending on the emerging needs by enhancing its capacity through new and innovative techniques and technologies. In the pace of innovations and use of modern technologies, application of RS and GIS for research and development is not an exception. It plays a vital role in planning,
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Chapter V monitoring and evaluation of the projects (natural resource management, socio- economic , PGIS ) in MSSRF.
However, spatial technologies have also widened the "digital divide," leaving many with little understanding of the technology and its potential applications. This seminar presents applications of Geospatial Tool, their potential and possible applications in various initiatives, giving practical examples from across the world.
R Nagaraj, Senior Scientist, MSSRF, elaborated on the potential for Geospatial technologies giving examples of work done around the world and at the Foundation at a seminar organized on 20th September, 2014. The presentation was followed by active discussion on various possibilities for adopting the various technologies specifically in the context of agriculture and nutrition.
What is the diffrence between GIS and GPS?
GPS: The Global Positioning System is a network of satellites that orbits around the earth at let a receiver device determine its location, speed, direction, and time. The receiver device (which is commonly known as "GPS") needs to be connected to at least 4 satellites to determine its location, and hence speed, direction and time. GPS is an American system, and it's the only completely implemented global navigation satellite system. There are other GNSS like Russian GLONASS, European Galileo, the proposed Chinese COMPASS, and Indian IRNSS. GPS devices are now widely, not just used but also, integrated into other devices like cars, smart phones, and cameras.
Geographic Information System (GIS) is the implementation of database for spatial data. If a database can have text, numbers, dates, and photos, it can have maps as well. It's not just about the location, it's about querying the location and analyzing that location with respect to other locations. It's just like querying and analyzing tabular data. The only difference is that if a picture is worth a thousand words, a map is worth a thousand pictures.
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Things to Keep in Mind
From our collective experience in using and teaching GIS and remote sensing for agricultural sciences, landscape design, urban forestry, geology, and Extension services, we have prepared a list of guidelines that may be helpful when considering or implementing spatial technologies for your program.
Educate Yourself: GIS-GPS-RS technologies have rapidly become more accessible, less expensive, and more sophisticated. As a result of the relatively fast evolution of geospatial technologies, many professionals may either be unaware of their capabilities or may have an obsolete understanding of their potential and current implementation. It is important for potential users to educate themselves before investing in equipment and software.
Clarify Your Needs: Make sure there is a clear need for GIS-GPS-RS technologies. Lack of understanding can lead users to overestimate the usefulness of geospatial technologies. Using these technologies requires a broad understanding of many different concepts, including map projections and coordinate systems, data types and formats, computer literacy, and proper documentation of data. If all you require is the ability to make maps or locate features, and don't need sophisticated spatial analysis capabilities, then you may not need a full-featured GIS package. In many instances, conventional methods of data collection, analysis, and presentation are more appropriate and efficient
Know Your Users: Carefully consider the needs of the intended users. Do you need technical support for your own staff, or do you want to create deliverables for your clientele? Do the intended users have a high or low degree of technical savvy? Are they teachable or not? Applications should be kept as simple as possible for your needs.
Be Realistic in Your Expectations: It has been our own experience that it is impractical to expect all members of your staff or faculty to learn to use
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GIS-GPS-RS technologies. Workshops we have held for this purpose have been poorly attended, despite enthusiasm expressed by the would-be attendees. Before investing in infrastructure, it may be wise to consider if your work could be farmed out to a consulting agency. There are now many commercial and independent contractors doing geospatial consulting work. In the long run, hiring a consultant may be more cost efficient.
Maintain Spatial Integrity of Your Data: One of the most frustrating aspects of working with geospatial data is dealing with different geographic coordinate systems and map projections. Because the Earth is not a perfect spheroid, numerous different projection systems have been devised to transfer points from an irregular curved surface to a plane surface. Different projections and coordinate systems are used for different purposes. For example, the State Plane coordinate system is used for many surveying applications, whereas a Transverse Mercator projection is useful for showing equatorial and mid- latitiutde continental regions. When data are stored and distributed in different projections, they must be reprojected so that all layers will plot in the same coordinate space. It is extremely important to carefully keep track of both the original and reprojected systems.
Document Your Data: Developing metadata documentation of your spatial data cannot be emphasized enough. Without proper documentation of coordinate and projection systems your data may be useless to both you and others. A commonly used method for preparing metadata documentation has been developed by the Federal Geographic Data Committee (FGDC) and is described in the Content Standard for Digital Geospatial Metadata (http://www.fgdc.gov/). It is very important that users of GIS data understand the documentation procedure before using and creating data.
Organization Is Key: Another important point to keep in mind before establishing and working with a GIS database is that your data can quickly become very disorganized. You will find that you will accumulate a large number of files as well as different versions of the same data (for example, in
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different projections systems). It is vitally important to establish a system for organizing data from the beginning.
Case Studies:
Applications of geographic information systems and remote sensing techniques to conservation of amphibians in northwestern Ecuador
Abstract
The biodiversity of the Andean Chocó in western Ecuador and Colombia is threatened by anthropogenic changes in land cover. The main goal of this study was to contribute to conservation of 12 threatened species of amphibians at a cloud forest site in northwestern Ecuador, by identifying and proposing protection of critical areas. We used Geographic Information Systems (GIS) and remote sensing techniques to quantify land cover changes over 35 years and outline important areas for amphibian conservation. We performed a supervised classification of an IKONOS satellite image from 2011 and two aerial photographs from 1977 and 2000. The 2011 IKONOS satellite image classification was used to delineate areas important for conservation of threatened amphibians within a 200 m buffer around rivers and streams. The overall classification accuracy of the three images was ≥80%≥80%. Forest cover was reduced by 17% during the last 34 years. However, only 50% of the study area retained the initial (1977) forest cover, as land was cleared for farming and eventually reforested. Finally, using the 2011 IKONOS satellite image, we delineated areas of potential conservation interest that would benefit the long term survival of threatened amphibian species at the Ecuadorian cloud forest site studied.
1. Introduction
The cloud forest of the Chocó region in South America is considered one of the 18 sites of greatest biodiversity and high endemism of species on the planet (Dodson and Gentry, 1991, Myers et al., 2000 and Olson and Dinerstein, 1998). The Andean Ecuadorian Chocó, in particular, presents environmental conditions that allow the
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Chapter V existence of a diverse flora and fauna (Mittermeier et al., 1998), with exceptional richness and endemism, especially of amphibians (Ron et al., 2012). However, in this region, amphibian species have been reported to be declining or becoming extinct since the late 1980s (Bustamante et al., 2005 and Lips et al., 2005). Likely threats to native amphibians are mostly related to drastic changes in land cover (Toral et al., 2002 and Young et al., 2001), including deforestation caused by farming, fires, selective logging, urbanization, and construction of roads. A more recent threat is the introduction of exotic predatory fish in streams (Martín Torrijos, 2011). Finally, infections caused by the chytrid fungus Batrachochytrium dendrobatidis may have contributed to local extinctions in the region ( Guayasamin et al., 2014).
There are many hypotheses that aim to explain global amphibian declines, but it is evident that the most significant factors are habitat destruction, disturbance, and fragmentation (Blaustein, 1994, Brodman et al., 2006, Crump et al., 1992, Davidson et al., 2001, Dodd and Smith, 2003, Lips, 1998, Marsh and Trenham, 2001, Schiesari et al., 2007, Wake, 1991 and Weyrauch and Grubb, 2004). In fact, habitat modification is the best documented cause of amphibian population declines (Alford and Richards, 1999, Gibbons et al., 2000 and Smith and Green, 2005). Habitat loss influences amphibian abundance and diversity directly, by reducing populations in the areas affected (Hecnar and M‘Closkey, 1996) and indirectly, by altering microclimatic regimes, compacting and desiccating soils, and reducing habitat complexity (Alford and Richards, 1999).
Amphibian conservation research focusing on drivers of population declines has generated a diverse body of information, including issues related to translocation of populations (Ficetola and De Bernardi, 2005 and Miller et al., 2014), captive breeding for reintroduction (Becker et al., 2014 and Kissel et al., 2014), habitat fragmentation and restoration (Bower et al., 2014 and Greenwald et al., 2009), and area selection for prioritization (Pyke, 2005 and Russell et al., 2002). Increasingly, in recent years, questions regarding animal habitat use and changes in vegetation cover have been addressed with satellite imagery, Geographic Information Systems (GIS), and historical aerial photography (Hooftman and Bullock, 2012, Pellikka et al.,
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2009 and Pringle et al., 2009). Combining these powerful tools provides means of investigating the magnitude and consequences of temporal land cover change in areas of interest, and in the context of preservation of species of concern (e.g., grassland birds, giant panda, resplendent quetzal; Pool et al., 2014; Solórzano et al., 2003; Zhang et al., 2013). Analyses of land cover changes can also identify areas that may be included in conservation planning (Fuller et al., 1998), but to our knowledge, this research avenue has received less attention in the amphibian conservation field. This observation is based on our review of ISI indexed journals, via Web of Science database searches with combinations of keywords (―amphibian‖, ―conservation‖, ―land cover‖, ―land use‖, and ―prioritization‖), restricted to 1995–2015. Our study illustrates the use of remote sensing techniques to study long-term, landscape scale changes of land cover associated with endangered and vulnerable amphibians in a cloud forest of western Ecuador and to delineate areas of conservation priority for protecting amphibians. Thus, we investigated land cover conversion as a strategic step to conserving critical habitat for amphibians in northwestern Ecuador.
2. Methods
2.1. Study area
The study area was comprised of Reserva Las Gralarias, a privately-owned reserve, and adjacent multi-use private lands, encompassing a region of approximately 5000 ha where the presence of 12 species of amphibians listed as endangered or vulnerable by the International Union for Conservation of Nature (IUCN) has been documented (Table 1; Guayasamin et al., 2014; IUCN, 2012). Reserva Las Gralarias protects 425 ha out of the total study area of 5000 ha in the parish of Mindo, Pichincha province, on the western slopes of the Andes in the Chocó region (Fig. 1; Josse et al., 2003). From a hydrological standpoint, the area lies within the Esmeraldas river basin and the sub-basins of the Guayllabamba and Blanco rivers. The physiography and vegetation of the area correspond to the Western Montane Forest region of Ecuador (Sierra et al., 1999), covering an elevation range of 1300–3400 m (Sierra et al., 1999). In this evergreen montane forest, the canopy is generally less than 25 m tall, with a
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Chapter V high abundance of epiphytes, especially mosses, ferns, orchids, and bromeliads. At intermediate elevations, particularly during the evenings, the forest is covered in fog and precipitation is horizontal, from low clouds. These conditions are favorable to direct-development amphibians, such as Pristimantis spp. (Craugastoridae; Ron et al., 2012). Glassfrog species (Centrolenidae), adapted to developing from larvae in permanent streams (Haddad and Prado, 2005), are also present in high numbers in this region (Guayasamin et al., 2014), probably because of the intermediate elevations, climatic conditions (Hutter et al., 2013), and abundance of fast-flowing streams. The area contains primary and secondary forests, with both high biodiversity and anthropogenic pressures.
Table 1.
List of endangered and vulnerable amphibian species recorded in Reserva Las Gralarias (Guayasamin et al., 2014), with threat categories according to the IUCN Red List (IUCN, 2012).
Species Status
Centrolene ballux Critically endangered
Centrolene heloderma Critically endangered
Centrolene lynchi Endangered
Pristimantis crenunguis Endangered
Pristimantis eugeniae Endangered
Pristimantis sobetes Endangered
Pristimantis pteridophilus Endangered
Centrolene peristictum Vulnerable
Nymphargus griffithsi Vulnerable
Pristimantis eremitus Vulnerable
Pristimantis calcarulatus Vulnerable
Pristimantis verecundus Vulnerable
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Fig. 1. Location of the study area (white star) in northwestern Ecuador, on an elevation map with province boundaries outlined, and zoomed in over the 2011 IKONOS satellite image. The two polygons represent Reserva Las Gralarias and the black squares with white dots the known locations of 12 threatened amphibian species.
2.2. Satellite image and aerial photo acquisition and processing
Frequent cloudy conditions in the moist tropical regions complicate capturing of satellite or aerial optical sensor data (Lu and Weng, 2007). Thus, a combination of multisensor data with various image characteristics is usually beneficial for
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Chapter V investigations in such environments (Lefsky and Cohen, 2003). Typical applications of remote sensing involve the use of images from passive optical systems, either satellite or aerial imagery (Goward and Williams, 1997). The present study was conducted using historical aerial photographs and a recent IKONOS satellite image, with the aim of quantifying the changes in land cover that could have affected the amphibian presence in the region in the last three decades.
Two black-and-white aerial photos (scale 1:60,000), taken on 9 November 1977 (flight line No. 5701 R-28 9-11-77) and 9 November 2000 (flight line No. 15279 R64RC30 9-11-2000), respectively, were acquired from Instituto Geográfico Militar (IGM), Quito, Ecuador. The aerial photos were georeferenced and rectified for inherent geometric errors using four digital topographic maps (scale 1:50,000; UTM coordinate system) acquired from IGM, corresponding to the quadrants of San Miguel de los Bancos, Calacalí, Mindo, and Nono. Registration to the digital topographic maps was carried out using road intersections that are usually very distinctive and clearly visible on images (Gautam et al., 2003). Finally, we applied first-degree rotation scaling and translation transformation, with the nearest neighbor resampling method (Gautam et al., 2003 and Richards, 2013). This procedure allowed for direct comparison of features between aerial photographs and IKONOS, during the selection of sample plots to use in image classification and accuracy assessment of classified images (Gautam et al., 2003).
The IKONOS satellite image was acquired on 26 June 2011, 1553 h GMT. The IKONOS sensor has advanced spectral, spatial, and radiometric characteristics (Dial et al., 2003, Lu and Weng, 2007 and Thenkabail et al., 2004), and collects 1-m panchromatic and 4-m multispectral images in four bands with 11-bit resolution (Dial et al., 2003). We created a multiband raster composite of the four IKONOS multispectral bands in ESRI ArcMap 9.3 and, although the IKONOS image was
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Chapter V geometrically corrected and projected to UTM zone 17S and datum WGS84, we performed a second orthorectification. This process removes distortions in the imagery caused by topography (Jensen, 1996), resulting in a more accurate product (Jensen, 1996 and Vassilopoulou et al., 2002). We used the orthorectification function in ENVI 5.0 (Exelis Visual Information Solutions, Boulder, CO, USA) which required a Digital Elevation Model (DEM) and Rational Polynomial Coefficients (RPC). We used the NASA SRTM 90-m resolution DEM (Jarvis et al., 2008), masked to the study area, and the RPC captured by the satellite at the time of image acquisition to improve the relative accuracy of the initial IKONOS image registration (Cheng et al., 2008, Grodecki and Dial, 2003 and Vassilopoulou et al., 2002).
2.3. Field data collection
Field reference data (ground-truthing) for IKONOS image classification were collected in Reserva Las Gralarias between 3 and 10 July 2012 (Table 2). Inventory field plots were 30×30 m and each plot encompassed approximately 52 IKONOS pixels (Thenkabail et al., 2004). Plots were established within homogeneous areas for the class under consideration, thus avoiding mixed or small patches of other vegetation classes (Thenkabail et al., 2004). The specific location of each plot was recorded as a point in the center of the plot, using a Global Positioning System (GPS) Garmin e-Trex® unit. In addition, qualitative observations of land cover were noted at these and other locations in the region to identify vegetation classes to consider (Ramírez, unpub. data). Vegetation classes included in this study were as follows: (1) forest with no (or minimal) evidence of anthropogenic disturbance, (2) riverine forest, (3) pasture (grazed by cattle), and (4) pasture in regeneration since 2000, when grazing was eliminated as land was acquired for establishing Reserva Las Gralarias (Table 2). In the latter plots, reforestation has been occurring by natural or assisted means (i.e., planting native species of trees and shrubs), and vegetation is dominated by the introduced African grass (Setaria glauca) of >1 m height, surrounded by medium or high canopy trees ( Fig. 2). The field plots were supplemented with on-
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Chapter V screen selection of additional reference sites for pasture (Table 2), as well as 50 references for a non-vegetation class, roads.
Table 2. Land cover classes, number of field reference plots used for the IKONOS satellite image classification, and dominant taxa in each class. The number of plots for pasture was increased by on-screen selection of additional sites, indicated by the number in parentheses.
Class Number Dominant taxa of plots Riverine forest 6 Cyathea sp., Melastomataceae, Clusiaceae, bryophytes, Araceae, bromeliads, Meriania maxima Montane forest 77 Croton floccosus, C. magdalensis, Solanum lepidotum, Cecropia aff. montana, M. maxima, Cyathea sp., Prestoea cf acuminta, Weinmannia balbisiana Pasture 14 (+201) Setaria glauca Pasture in 42 C. floccosus, P. cf acuminta, C. magdalensis, S. regeneration glauca
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Fig. 2. Field examples of the four classes selected for IKONOS satellite image classification: montane forest (A), riverine forest (B), pasture (C), and pasture in regeneration (D).
2.4. Supervised classification
Image classification consists of automatically categorizing all pixels in an image into different classes of land cover (Lillesand et al., 2004). Since the spectral signature of same objects often varies (Tiwari, 2008), a supervised approach involves the selection of training areas (pixels) on the image which statistically characterize the target land cover classes (Richards, 2013). This information allows estimating the extent occupied by objects with different spectral signatures and assigning them to land
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Chapter V cover classes. Herein, we applied a supervised classification using ENVI 5.0 (Exelis Visual Information Solutions, Boulder, CO, USA).
To classify the aerial photos, we selected the training samples for each class by means of on-screen drawing of polygons. We used three classes: (1) forest with no (or minimal) evidence of anthropogenic disturbance, (2) pasture, and (3) road. Training sites were chosen for each aerial photo separately to ensure that all classes were adequately represented. In contrast with the classification of IKONOS satellite image (see below), we did not include pasture in regeneration as a separate class because it represents a relatively new vegetation cover that could not be identified in the aerial photographs.
We used a minimum distance algorithm that calculates the mean vectors of each spectral end member (corresponding to the class selected) and the Euclidean distance from each unknown pixel to the mean vector for each class, a method recommended when limited training samples are available (Richards, 2013). All pixels were classified to the nearest spectral end member (class) and the results were refined with the aggregate minimum size approach. For the IKONOS satellite image we used a maximum likelihood algorithm with training samples collected in the field and additional pasture sites and roads digitized on-screen (described above). The maximum likelihood classifier assigns a pixel to a particular class based on both the variance and the covariance of the spectral information (Shalaby and Tateishi, 2007). This classifier is one of the frequently used supervised classification techniques (Richards, 2013). We used the maximum likelihood classifier because the number of field reference plots was higher than ten times the number of spectral bands (four IKONOS bands), a sample size considered adequate for this classifier (Richards, 2013). However, we only located six field plots for the riverine forest class, thus we combined these with the montane forest plots into a single training dataset for a forest class, given the similarities in the spectral signature between the two types of forest cover. Finally, we also digitized on-screen areas with clouds to train the classifier and mask out cloud pixels from subsequent land use change analyses.
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2.5. Classification accuracy assessment
Image classification accuracy is assessed by comparing obtained classes to reference data that are assumed to be true (Foody, 2002 and Lillesand et al., 2004). We generated a typical error matrix for the aerial photos and the IKONOS satellite image classification results, showing pixels correctly identified for a class either as a fraction of the ―true‖ (known) number of pixels in that class (producer‘s accuracy), or as a fraction of the number classified in that class (user‘s accuracy; Jones and Vaughan, 2010). Misclassification with producer‘s accuracy is termed omission error and indicates the number of known pixels for that class that were not correctly identified (Jones and Vaughan, 2010). We also calculated the kappa statistic, a measurement of agreement between the producer‘s accuracy and user‘s accuracy (Jones and Vaughan, 2010). The kappa statistic takes values from 0 to 1, with suggested strengths of agreement proposed by Landis and Koch (1977) as follows: moderate below 0.6, substantial 0.61–0.80, and almost perfect 0.81–1.
2.6. Detecting changes in the land cover
We first quantified the percent forest, pasture, and roads of the study area to compare the overall changes in land cover between consecutive time frames (1977–2000 and 2000–2011). Our justification of the selection of these classes was trifold: (1) achieving the objective of this study (quantification of changes in forest cover); (2) relying on a reasonable degree of classification accuracy, and (3) avoiding identification errors associated with the aerial photographs (Gautam et al., 2003). In addition to calculating overall percent land cover change between two consecutive time frames, we also quantified the change in forest and pasture cover types from one time frame to another. Of all possible transitions, we tracked the ones that
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Chapter V cumulatively represented 95% of the entire study area. Specifically, for initial (1977) forest cover areas, we calculated the following transitional patterns, by the three time frames: forest–pasture–forest, forest–forest–pasture, forest–pasture–pasture, and no change (forest in all three time frames). For initial (1977) pasture cover areas, we calculated transitions of pasture–forest–forest, pasture–pasture–forest, pasture–forest– pasture, and no change (pasture in all three time frames). By analyzing these transitional patterns, we were able to more specifically quantify the land cover dynamics in the study area and to make inferences about the effect of recent conservation initiatives led by the Ecuadorian government (see below) on land cover changes in the region studied. We excluded the regions with cloud cover from all our land use change calculations.
2.7. Identifying critical areas for conservation of endangered and vulnerable amphibians
Semiaquatic species require combinations of terrestrial and aquatic habitats to survive (Roe and Georges, 2007), which has fueled a growing interest in delineation of riparian terrestrial buffers surrounding aquatic habitats. Buffer zones surrounding rivers and wetlands are frequently limited to a few tens of meters (Correll, 2005). This width delimits the amount of terrestrial habitat considered important for the conservation of water resources (Correll, 2005). Nevertheless, recent analyses suggest that much larger areas may be needed for the conservation of semiaquatic species. Semlitsch and Bodie (2003) showed that at least 200–300 m of terrestrial habitat surrounding wetlands and rivers should be preserved to allow survival of terrestrial life stages of amphibians. We applied this concept for prioritization analysis of riverine regions in our study area.
We digitized 33 rivers and streams as polylines from the classified IKONOS satellite image and topographic maps, and defined as aquatic amphibian habitat a 200 m buffer surrounding these features (Semlitsch and Bodie, 2003). We then intersected the buffers with the land cover map derived from the IKONOS image classification. Within the study region, we identified areas belonging to the Socio Bosque program, a
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Chapter V conservation initiative led by the Ministry of Environment of Ecuador since 2008, which protects 338 ha in the study region. This information was provided by the Socio Bosque Program (Ministerio del Ambiente, Ecuador). We also mapped presence records for the 12 threatened species of amphibians, available from a previous study conducted at Reserva Las Gralarias (Guayasamin et al., 2014). All spatial analyses were performed in ArcMap 9.3 (ESRI, Redlands, CA, USA).
Finally, we defined various areas for conservation and restoration based on their current land cover, as identified through the classification of IKONOS 2011 satellite imagery (i.e., forest, pasture, pasture in regeneration, and road), and their importance for the protection of endangered and vulnerable amphibians. Firstly, ―areas for conservation‖ require minimal habitat restoration, that does not change the fundamental characteristics of the area, and human use is limited to ecological services such as water supply and climate regulation ( Van Der Hammen and Andrade, 2003). Secondly, ―areas for restoration‖ are those modified by degradation and environmental conflict, requiring intervention to restore their ability to serve as conservation areas (Van Der Hammen and Andrade, 2003).
3. Results
3.1. Classification accuracy
Overall, the classification accuracy was 97% for 1977, 80% for 2000, and 94% for 2011 and kappa statistic indicated substantial (0.63 for 2000) to excellent classification performance (0.93 for 1977 and 0.88 for 2011). Details of accuracy assessment of classification results obtained for 1977, 2000, and 2011 are shown in Table 3, Table 4 and Table 5, respectively. We obtained the lowest classification accuracy for pasture in regeneration (user‘s accuracy of 0.5; Table 5), a class that we attempted to identify only in the IKONOS satellite image. Cloud cover represented 8% of the study area in the IKONOS satellite image; no clouds were identified in the aerial photographs. The regions affected by cloud cover in the IKONOS satellite
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Chapter V image were discarded from subsequent analyses, including from those of aerial photographs, to control for extent of area when comparing land cover changes between time frames. This is a limitation when performing passive monitoring by remote sensing sensors, especially in areas where cloud cover is persistent throughout the year.
Table 3. Error matrix of the classification accuracy of the aerial photograph from 1977. Shaded cells along the diagonal represent the number of correctly classified reference training pixels.
Clas Forest Pasture Road Total User accuracy Commission s error
Class Fore 4866 39 1 4906 4866/4906=0.994866 40/4906=0.0 ificat st /4906=0.99 0840/4906=0 ion .008 Past 81 1407 28 1516 1407/1516=0.931407 109/1516=0. ure /1516=0.93 07109/1516= 0.07 Roa 0 50 212 262 212/262=0.80212/26 50/262=0.19 d 2=0.80 50/262=0.19 Tota 4947 1496 241 6684 l Pro 4866/4947=0. 1407/1 212/2 Overall accuracy: duce 984866/4947 496=0.9 41=0.8 6485/6684=0.976485/6684=0.97 r’s =0.98 41407/ 7212/ accu 1496=0. 241=0. racy 94 87 Omi 81/4947=0.01 89/149 29/24 Kappa: 0.93 ssio 6381/4947=0. 6=0.059 1=0.12 n 0163 89/149 29/24 erro 6=0.059 1=0.12 r
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Table 4. Error matrix of the classification accuracy of the aerial photograph from 2000. Shaded cells along the diagonal represent the number of correctly classified reference training pixels.
Class Forest Pasture Road T User accuracy Commission ot error al
Classif Forest 4646 325 10 4 4646/4981=0.9 335/4981=0.06 icatio 9 34646/4981=0. 7335/4981=0.0 n 8 93 67 1 Pasture 1436 2749 72 4 2749/4257=0.6 1508/4257=0.3 2 42749/4257=0. 51508/4257=0. 5 64 35 7 Road 1 193 538 7 538/732=0.735 194/732=0.265 3 38/732=0.73 194/732=0.265 2 Total 6083 3267 620 9 9 7 0 Produce 4646/6083= 2749/3267=0.8 538/620=0.86 Overall accuracy: r’ 0.764646/6 42749/3267=0. 7538/620=0.8 7933/9970=0.807933/9970=0.80 accurac 083=0.76 84 67 y Omissio 1437/6083= 518/3267=0.15 82/620=0.132 Kappa: 0.63 n error 0.231437/6 8518/3267=0.1 82/620=0.132 083=0.23 58
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Table 5. Error matrix of the classification accuracy of the IKONOS image from 2011. Shaded cells along the diagonal represent the number of correctly classified reference training pixels.
Class Pasture Forest Pasture Clouds Road T User Commissi in o accuracy on error regenera ta tion l
Class Past 188 0 1 0 14 2 188/203=0. 15/203=0. ificat ure 0 92188/203 0715/203 ion 3 =0.92 =0.07 Fore 6 70 7 0 3 8 70/86=0.81 16/86=0.1 st 6 70/86=0.81 816/86=0. 18 Past 20 12 34 0 1 6 34/67=0.53 33/67=0.4 ure 7 4/67=0.5 933/67=0. in 49 rege nerat ion Clou 0 1 0 958 0 9 958/959=0. 1/959=0.0 ds 5 99958/959 011/959= 9 =0.99 0.001 Road 1 0 0 7 32 4 32/40=0.83 8/40=0.28 0 2/40=0.8 /40=0.2 Total 215 83 42 965 50 1 3 5 5 Prod 188/215=0. 70/83=0. 34/42=0. 958/965=0. 32/50=0. Overall accuracy: er’ 87188/215 8470/83 8134/42 99958/965 6432/50 1282/1355=0.941282/1 s =0.87 =0.84 =0.81 =0.99 =0.64 355=0.94 accu racy Omis 27/215=0.1 13/83=0. 8/42=0.1 7/965=0.00 18/50=0. Kappa: 0.88 sion 227/215=0. 1513/83 98/42=0. 737/965=0. 3618/50 error 12 =0.15 19 0073 =0.36
3.2. Detecting landscape land use changes over time
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The supervised classification of land cover for three time frames, from aerial photos (1977 and 2000), and from IKONOS satellite image (2011), illustrated an increase of pasture cover in 2000, followed by a decrease in 2011 (Fig. 3). Based on the classification results, we calculated that in any of the three time frames the forest cover represented >70% of the study area, specifically 87.4% (4152 ha), 70.2% (3335 ha), and 71.5% (3397 ha) of the total area for years 1977, 2000, and 2011, respectively (Table 6). Pasture cover increased during 1977–2000 from 12.2 to 28.1% and decreased during 2000–2011 from 28.1 to 14.4%. The total area covered by roads increased slightly from 1977 to 2000, by 1.2%, and from 2000 to 2011, by 0.2% (Table 6). Since pasture in regeneration class was only produced with IKONOS satellite image classification, we could not include it in the temporal analysis. For 2011, this land cover class represented 12.3% of the study area, but this area estimation may be confounded by the low user‘s accuracy for this class (0.5; Table 5), as derived from the IKONOS satellite image.
Fig. 3. Classifications of aerial photographs (1977 and 2000) and IKONOS satellite image (2011). Due to lack of detail of aerial photographs, the class
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pasture in regeneration was used only for IKONOS satellite image classification.
Table 6. Comparison of area (ha) and percentage of study area in each land cover class, analyzed by year. Changes in each class were calculated for two time periods, 1977–2000 and 2000–2011. Land cover Extent of land cover by time frame Change between time type frames
1977 2000 2011 1977–2000 2000–2011
ha % ha % ha % ha % ha %
Forest 4152 87. 3335. 70. 3396. 71. −816. −17. +61.2 +1.3 4 5 2 7 5 5 2
Pasture 581. 12. 1335. 28. 683.4 14. +754. +15. −652. −13. 2 2 6 1 4 4 9 2 7
Road 16.7 0.4 78.9 1.6 87.0 1.8 +62.2 +1.2 +8.1 +0.2
Pasture in n/a n/a n/a n/a 582.8 12. regeneratio 3 n
Total 4750 100 4750 100 4750 100
While the forest cover was >70% in all three time frames, the analysis of transitional patterns in land cover change by time frame showed that in 2011 only 50% of the study area was represented by forest cover unchanged since 1977. Ten percent of the forest cover was converted to pasture, while a similar extent of the forest cover
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Chapter V transitioned to pasture in regeneration stage by 2011 (Fig. 4). The calculation of change from forest to pasture in regeneration may be confounded by the low user‘s accuracy for the pasture in generation class (0.5; Table 5). We consider this class important from a conservation standpoint (transition to forest is ongoing) thus we retained it in this analysis, but we present it in the context of changes to pasture, a class that had higher classification accuracy (0.85±0.110.85±0.11). Other regions experienced reversed changes from pasture in 2000 to forest in 2011, thus by comparing only initial (1977) and final (2011) land cover types, the extent of forests unaffected by farming would have been overestimated. Reforestation of pasture cover in 1977 occurred on about 6% of the study area by 2011 (Fig. 4).
Fig. 4. Transitions between forest (F) and pasture (P) classes among the three time frames studied, summing to 95% of the study area; panel A shows transitions that occurred in areas initially (1977) forested and panel B
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transitions in areas used as pasture. No change between the three time frames is labeled as F–F–F (forest in all three time frames) or P–P–P (pasture in all three time frames). Light gray bars indicate pasture in regeneration as the final (2011) stage.
3.3. Identifying critical areas for conservation of endangered and vulnerable amphibians
We outlined areas of conservation and restoration priority by overlapping the 2011 IKONOS derived land cover map with the limits of Reserva Las Gralarias and Socio Bosque program, the riverine habitat (200 m buffers around 33 digitized rivers and streams), and the presence records of endangered and vulnerable amphibians (Fig. 5). We identified a limited number of regions that qualified as areas for conservation, especially in the core region of Reserva Las Gralarias and in the southern part of the study area. Most of the regions outlined in this study qualified as areas for restoration,
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Chapter V whereby conversion of patches of pasture or pasture in regeneration to forest would be
requ ired (Fig. 5).
Fig. 5. Map showing priority areas within the 200 m buffers around digitized rivers and streams. These areas are proposed in the present study to either conserve (A; dark gray) or restore (B; light gray and white) habitat for endangered and vulnerable amphibians in the greater region of Reserva Las Gralarias.
Overall, we identified 2830.3 ha of conservation and restoration priority, representing 59.5% of the entire study area (Table 7). Most of the patches outlined fall in the area for conservation category (2184.6 ha; 46%; Table 7).
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Table 7. Type and size of priority areas selected within river buffers, summarized by land cover class (based on IKONOS 2011 satellite image classification). The percentages are calculated relative to the total surface of the study area (4750 ha). Land cover class Management Area within river Percent of total proposal buffers (ha) study area
Montane forest Conservation 2184.6 46
Pasture Restoration 260.7 5.5
Pasture in Restoration 348.9 7.3 regeneration
Road Restoration 36.1 0.7
Total 2830.3 59.5
4. Discussion
Multiple factors are involved in amphibian population declines (Kiesecker et al., 2001 and Lips et al., 2005). We focused on the major effect of land cover change, with the main goal of illustrating the role of GIS and remote sensing techniques as tools for analyzing such changes that could affect amphibians. We analyzed land cover changes at three points in time, over a 34-year period (1977–2011), in the area of Reserva Las Gralarias and adjacent private, multi-use lands. This area encompasses a region that contains 12 species of amphibians listed as threatened by IUCN (Guayasamin et al., 2014). Our aim was to inform conservation efforts by providing an understanding of historical land cover changes, as well as incorporating current herpetological and geographical information available for this area.
The supervised classification of IKONOS satellite imagery and aerial photographs had high accuracy for forest and pasture classes (>80%, except for forest class in 2000, at 74%). Overall, classification using the minimum distance for aerial photos and
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Chapter V maximum likelihood for the IKONOS satellite image generated thematic maps with relatively good reliability (overall classification accuracy ≥80%≥80%; kappa statistic ≥0.63≥0.63). We were able to discriminate well up to three land cover classes in both the aerial photos and in the IKONOS satellite image. A fourth class derived from IKONOS image only, pasture in generation, had the lowest user‘s accuracy (0.5; Table 5).
The analysis of land cover changes among three time frames, over 34 years (1977– 2011), represented the basis for assessing the potential deterioration of amphibian habitat at a landscape scale. Our study showed that, over three decades, the montane forest cover was preserved in about 50% of the region of Reserva Las Gralarias and adjacent lands, although forest represented over 70% of the study area in each time frame analyzed. A visible decrease in montane forest cover took place during 1977– 2000, but this trend slowly reversed during 2000–2011. It is important to note that the first period coincides with the implementation of the agrarian reform and colonization program that occurred during 1960–1990 (Gondard and Mazurek, 2001). This program, promoted by the Ecuadorian government, consisted of turning forests into ―productive land‖ and stimulating agricultural expansion (Gondard and Mazurek, 2001), and it is likely partially responsible for the forest loss in the region. However, it is difficult to assess how these changes in forest cover contributed to amphibian declines in the study area because no amphibian demographic studies were conducted during 1977–2000. On the other hand, the increase in mountain forest cover observed over 2000–2011 could be related to the implementation of private conservation initiatives that have been thriving in the study area in the last decade (Toral et al., 2002). In particular, the transition from pasture to forest or to pasture in generation, totaling 24% of the study area in 2011, represents a change in the land management that could positively affect persistence of amphibians. In addition, forest regeneration may have been promoted by the opening of alternate and faster roads (e.g., Calacalí- La Independencia road), which have re-directed human land use patterns out of the study area.
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The recovery rate of montane forest cover may be enhanced in the near future by the great potential for assisted and natural forest regeneration derived from both public and private initiatives. In 2008, the Ministry of Environment of Ecuador (MAE) established the Socio Bosque program. This initiative consists of providing direct monetary incentives to landowners to conserve forests and other natural ecosystems and seeks to maintain biodiversity, reduce carbon emissions from deforestation, and reduce poverty in rural areas (MAE, 2012). In addition, in recent years a growing number of private protected areas have been established in the high Chocó region (independently or associated to Socio Bosque program), that have been dedicated to habitat conservation and restoration, tourism, or ecological research (MAE, 2012).
The ongoing process of forest conservation and regeneration in the region, combined with other specific actions for the conservation of amphibians, may lower the probability of amphibian extinctions in the future. For example, it is well known that semi-aquatic organisms such as amphibians depend on both aquatic and terrestrial habitats to complete their life cycle and maintain viable populations (Burke and Gibbons, 1995 and Semlitsch and Bodie, 2003). However, environmental policies and regulations in Ecuador tend to focus only on the protection of rivers or arbitrarily defined portions of the adjacent terrestrial habitat (Echeverría, 2008). Terrestrial habitats adjacent to rivers are usually not protected, in part because of lack of a clear understanding of distances from river banks that are biologically relevant to maintaining wetland and river fauna (Semlitsch, 1998), as well as ecosystem functions and services. Such information is critical for delineation of terrestrial ―buffer zones‖ for rivers, and thus for conservation of semi-aquatic organisms (Semlitsch, 1998).
To assist the habitat preservation for 12 endangered and vulnerable amphibian species, we generated a priority map based on the overlap of the 2011 IKONOS derived land cover classes with river buffers that delineated 200 m of habitat around streams and rivers, following recent recommendations (Burke and Gibbons, 1995, Ficetola et al., 2009, Roe and Georges, 2007 and Semlitsch and Bodie, 2003). The map outlines specific areas for conservation and restoration that would benefit amphibian communities (Fig. 5), which may promote conservation initiatives that are centered on
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Chapter V protecting amphibians in the region studied. If the land cover transitions that have occurred within the river buffers are considered, within areas for conservation, priority could be given to patches of forest that have not been converted to pasture throughout the three decades analyzed here (Fig. 6), since they would not require restoration investments. These patches amount to approximately 1550 ha, representing 55% of the total area of river buffers outlined for conservation and restoration. The near contiguous forest areas in the southern and northeastern part of the region studied could be of particular interest for future conservation initiatives.
Fig. 6. Location of patches of forest that have not been converted to pasture during 1977–2011, within the 200 m buffers around digitized rivers and streams. In this study, these patches are identified as of priority conservation for amphibians in the greater region of Reserva Las Gralarias.
More broadly, the recognition that terrestrial habitat is vital for semiaquatic species (Gibbons, 2003) implies that conservation focusing only on aquatic habitats is not
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Chapter V sufficient. It has been shown that large terrestrial buffers are needed for terrestrial life stages of semiaquatic species (Burke and Gibbons, 1995, Crawford and Semlitsch, 2007, Denoël and Ficetola, 2008 and Semlitsch, 1998). Furthermore, different life stages require different landscape components, and permeable corridors are needed for maintenance of population processes (Ficetola et al., 2009). Therefore, a landscape- based approach should expand on the habitat approach (Joyal et al., 2001 and Roe and Georges, 2007). The former may be complex because different landscape elements require different spatial extents. Nevertheless, a shift of attention toward the management of different elements is necessary for the long-term persistence of semiaquatic populations (Semlitsch and Bodie, 2003).
5. Conclusions
The integration of GIS and remote sensing techniques facilitated both quantifying land cover changes that threaten amphibian habitats and prioritizing areas for conservation and restoration. Such an assessment provides conservation planners and natural resource managers with specific information on the location and size of the candidate areas for restoration and protection. This strategy could improve the allocation of financial resources at both broad and local scales. However, to further refine conservation prioritization initiatives, additional information is needed, for example to correlate the change of landscape and the loss of species with water quality and environmental parameters, and possibly carry out comprehensive studies on the presence of invasive species, amphibian diseases (e.g., infection by the fungus Batrachochytrium dendrobatidis), and effects of global warming ( Lips et al., 2005 and Young et al., 2001). Finally, frequent amphibian monitoring is necessary to ultimately create an adaptive management framework to understand how these variables, as well as land management and restoration initiatives, influence amphibian population survival.
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Reference:
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Case Study. 2. :
Remote Sensing of Ecology, Biodiversity and Conservation:
1. Introduction
In general, ecological research refers to the investigation of organisms and their surrounding environment, including biotic and abiotic entities. Due to the multifaceted nature of biodiversity, it is difficult to simply express and measure biodiversity. Biodiversity should be related to not only the variation of life forms, but also the ecological complexes of which they are a part. Conservation has become an indispensable way of dealing with the accelerated native ecosystem conversion and degradation, which have a significantly negative effect on biodiversity. Remote sensing, the science of obtaining information via noncontact recording [1], has swept the fields of ecology, biodiversity and conservation (EBC). Remote sensing can provide consistent long-term Earth observation data at scales from the local to the global domain. In addition, remote sensing is not labor-intensive and time-consuming, compared with field-based observations. The review papers of Kerr and Ostrovsky and Turner et al., published in the journal of ―Trends in Ecology and Evolution‖, has been cited hundreds of times by scientists from around the world who are involved in remote sensing of EBC [2,3]. Turner et al. stated two categories of approaches, namely direct and indirect remote sensing approaches [3]. The direct approach refers to the direct observation of individual organisms, species assemblages, or ecological communities from airborne or satellite sensors, such as the application of high spatial resolution and hyperspectral sensors (e.g., [4]). Indirect approaches rely on environmental parameters derived from remotely sensed data as proxies. For example, habitat parameters, such as land cover, species composition, etc., can be considered as a surrogate for precise estimates of potential species ranges and patterns of species richness [5]. The Foothills Research Institute Grizzly Bear Program (FRIGBP, formerly called Foothills Model Forest Grizzly Bear Research Program) has successfully applied this kind of approach in west-central Alberta (Canada) [6]. Kerr and Ostrovsky described ecological remote sensing in three main areas [2]. First, land cover classification, the physiographical characteristics of the surface environment, can be used to identify very specific habitats and predict the distribution of both individual species and species assemblages at a large spatial extent (e.g., [7]). Secondly, integrated ecosystem measurements offer the urgently needed measurements
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Chapter V of functions at different spatial scales, including whole ecosystems, such as the derivation of leaf area index (LAI) and net primary productivity (NPP) mostly based on the normalized difference vegetation index (NDVI, e.g., [8]). Thirdly, change detection provides near- continuous, long-term measurements of key ecological parameters in order to monitor ecosystem through time and over significant areas, such as the application of climate change and habitat loss (e.g., [9]). Additionally, several quality review papers have contributed to this field, such as [10–14].
Most existing review papers too often discuss an issue from the viewpoint of ecologists or biodiversity specialists. For instance, Aplin reviewed the remote sensing of ecology as it relates to the significance of remote sensing in ecology, to spatial scale, and to terrestrial and marine ecological applications [11]. Gillespie et al. discussed the development of measuring and modeling biodiversity from space with a focus on species and land-cover classifications, modeling biodiversity, and conservation planning [14]. This review, on the other hand, focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS).
Go to:
2. Advanced Instruments in Remote Sensing of EBC
Based on the current status of remote sensing instruments, their existing applications in the literature, and future potential contributions to EBC, the aforementioned five types of instruments: high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors, were selected. In order to avoid overlapping between high spatial resolution and hyperspectral sensors, the hyperspectral sensors discussed below will mainly refer to sensors with medium spatial resolution, such as Hyperion with 30 m spatial resolution. Radar sensors are not selected because their applications mostly concentrate on geology, ice and snow, marine surveillance, and agriculture. In addition, some uncertainties
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2.1. High Spatial Resolution
Generally speaking, high spatial resolution, also called fine spatial resolution, is less than 10 m, and ranges from 0.5–10 m in the commercial domain for environmental research. IKONOS, QuickBird, OrbView-3 and SPOT-5 (Satellite Pour l‘Observation de la Terre-5) are the commonly used systems (see [15] for the high-spatial resolution optical sensors). The benefit of high spatial resolution imagery is that it greatly increases the accuracy of identification and characterization of small objects at spatial scales which were previously only available from airborne platforms [3,14]. For example, Gillespie et al. provided several examples of accurately identifying plant species based on the high spatial resolution imagery [14]. Turner et al. have pointed out it is applicable and feasible to directly identify certain species and species assemblages at the scale of high spatial resolution [3]. In addition, high spatial resolution imagery can be employed to assess the accuracy of remote sensing precuts derived from moderate or coarse spatial resolution imagery. For instance, Wabnitz et al. assessed the accuracy of Landsat-based large-scale seagrass mapping against patterns detectable with very high-resolution IKONOS images [16]. However, the high spatial resolution imagery is still expensive to acquire from commercial satellites, at the price of approximately 3,000–5,000 US$ for 10 km2 [14], although it has tended to decrease with the emergence of more sensors and the upcoming competition. Moreover, data coverage and security restrictions are still a significant hurdle before easily accessing high spatial resolution satellite data [17].
Due to the large amount of high spatial resolution sensors, the commonly-used IKONOS imagery was selected to display their typical applications in 2008 and 2009. First of all land cover, as the representative of basic landscape information, can be extracted quickly and reliably based on the high spatial resolution data. For example, the object-oriented classification of IKONOS-2 satellite images was utilized to explicitly recognize the transitional areas between tree crowns and tree shades (tree shadows), and then for the quantification of canopy cover [18]. Further, IKONOS imagery can be used to quantify and evaluate the spatial structure of critical habitats and how it affects endemic species, which is essential baseline information for biodiversity monitoring and management (e.g., [19]). In the
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Chapter V context of marine applications, areas of coastline, with their fertile soil and unique flora and fauna which need to be highly protected, were planned for in a sustainable way through mapping the changes in land use of the area based on IKONOS imagery in the Cesme Peninsula (Turkey) [20]. Improving the science and conservation of coral reef ecosystems, such as the significant fish-habitat relationship, is often the objective of marine ecology, and also is an important facet in the application of IKONOS imagery [21]. Harborne et al. examined intra-habitat variability in coral-reef fish by mapping habitat heterogeneity, which is always considered a surrogate of biodiversity, in order to aid the design of networks of marine reserves [22]. Although high spatial resolution satellite remote sensing has been hailed as a very useful source of data, Nagendra and Rocchini pointed out that high spatial resolution remotely sensed data are one of the most potentially powerfully yet underutilized sources for tropical research on biodiversity, and stimulating discussion on the applications should be the first step in promoting a more extensive use of such data [17].
2.2. Hyperspectral
Hyperspectral data have the ability to collect ample spectral information across a continuous spectrum generally with 100 or more contiguous spectral bands. It is different from multispectral sensors which detect relatively few discrete bands [17]. Hundreds of spectral bands with 10–20 nm spectral bandwidths offer new possibilities to detect subtle differences between objects of interest. The best example is to discriminate fine-scale, species-specific land cover [3], such as vegetation categories or soil types [11], which make remarkable contribution to the study regarding biodiversity patterns. Moreover, Nagendra and Rocchini summarized that hyperspectral data have been successfully applied in recording information regarding critical plant properties (e.g., leaf pigment, water content and chemical composition), discriminating tree species in landscapes, and fairly accurate identification between different species [17]. What is more, spectral signatures acquired from atmosphere- corrected hyperspectral data can be directly compared to the existing spectral library (e.g., the Jet Propulsion Laboratory Spectral Library) in order to rapidly identify ground information useful in land-cover classification, characterization and change detection [3]. Similar to the situation with high spatial resolution imagery, the hyperspectral imagery encountered the same underutilization, and a high cost which may put it out of research for many ecologists [14], especially those in developing countries who eagerly need the data [17].
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Shippert listed the existing hyperspectral sensors acquiring imagery from space, including the Hyperion sensor on NASA‘s EO-1 (National Aeronautics and Space Administration‘s Earth Observing-1), the CHRIS (Compact High Resolution Imaging Spectrometer) sensor on the European Space Agency‘s PROBA (PRoject for On-Board Autonomy) satellite, and the FTHSI (Fourier Transform Hyperspectral Imager) sensor on the U.S. Air Force Research Lab‘s MightySat II satellite [23]. Of these sensors, the first-civilian and most commonly used data are derived from the Hyperion, which is operated by the EROS (Earth Resources Observation and Science) at a relatively low cost to the general public [23]. The EO-1, on which the Hyperion sensor is, was launched in November, 2000 as a one-year technology validation and demonstration in support of the LDCM (Landsat Data Continuity Mission; [24]). The Hyperion sensor, an upgrade from the LEWIS Hyperspectral Imaging Instrument (HSI), records visible light and other reflected electromagnetic energy in 220 spectral bands from 0.4 to 2.5 μm at a 30 m resolution [25]. Table 1 lists the Hyperion characteristics.
Table 1.Hyperion Imaging Spectrometer Characteristics (adapted from [26]).
Characteristics Values Sensor Type Push-broom imager Wavelength Range 400–2,500 nm Number of Spectral Bands 220 Spectral Resolution 10 nm Spatial Resolution 30 m Swath 7.5 km Digitization 12 bits Altitude 705 km Repeat 16 day
The recent applications of Hyperion hyperspectral imagery mainly include ecology and biodiversity in forest, grassland [27], agriculture [28], and vegetation [29], fragmented ecosystem and ecosystem succession, coastal environment [30], etc. For example, vegetation types and densities were classified in support of the wildfire management, that is, fire propagation simulation models and fire risk assessment were based on a Hyperion classification map with 93% accuracy [31]. Foster et al. proposed hyperspectral imagery from
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EO-1 Hyperion is capable of mapping low-lying woody lianas, which are critical to tropical forest dynamics because of their strong influence on forest regeneration, disturbance ecology, and biodiversity [32]. Pignatti et al. analyzed the capability of Hyperion data for discriminating land cover in a complex natural ecosystem according to the structure of the currently used European standard classification system (CORINE Land Cover 2000), and the results showed the potential of the imagery up to the 4th level of the CORINE legend, even at the sub-pixel level, within a fragmented ecosystem [33]. Besides the application of land cover classification, the relationships between LAI and spectral reflectance were studied by [34] using narrowband (EO-1 Hyperion) and broadband (Landsat ETM+ [Enhanced Thematic Mapper Plus]) remotely sensed data in Sulawesi (Indonesia). Nagendra and Rocchini preliminarily discussed the strengths and drawbacks of hyperspatial (i.e., high spatial resolution) and hyperspectral data [17]. Hyperspatial data was considered to be best suited for facilitating the accurate location of features such as tree canopies, but less suited to the identification of aspects such as species identity. However, conversely, hyperspectral data appear capable of identifying features with significantly increasing accuracy. Therefore, the integration of Hyperion and IKONOS imagery was proposed to differentiate the subtle spectral differences of land-use/land-cover types on household farms in the Northern Ecuadorian Amazon with an emphasis on secondary and successional forests, and the promising results supported the integrated use of hyperspectral and hyperspatial data [35].
2.3. Thermal Remote Sensing
Thermal remote sensing detects the energy emitted from the Earth‘s surface in the thermal infrared (TIR, 3 μm to 15 μm), which can be radiated by all bodies above absolute zero. Theoretically, TIR sensors measure the surface temperature and thermal properties of targets [36], which are essential for developing a better understanding, and more robust models, of land-surface energy balance interactions [37]. Moreover, TIR remote sensing is capable of uncovering the principles of ecological patterns of structure and function due to the development of ecological thermodynamics [37]. A thermal grey level image is generated based on relative radiant temperatures (a thermogram), and light tones correspond to warmer temperatures and dark tones to cooler temperatures [36]. TIR remote sensing plays an important role in observation of Earth surface characteristics, and is very useful for research regarding analysis of biophysical Earth processes, in particular landscape characterization and measurement of land surface processes [37]. The well-known sensors with TIR bands
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Chapter V include the Advanced Very High Resolution Radiometer (AVHRR) onboard the Polar Orbiting Environmental Satellites (POES), the Landsat Thematic Mapper (TM) and ETM+, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on the Terra Earth observing satellite platform, etc. [37].
TIR remote sensing has been developing since 1880, and has proven to be an integral part of understanding landscape characteristics [37], although it is relatively rarely used by ecologists [2]. However, interests have increasingly focused on the use of TIR remote sensing in EBC. For instance, biophysical variables were derived from thermal and multispectral remote sensing data and coupled with a Soil-Vegetation-Atmosphere-Transfer (SVAT) model [38]. Duro et al. pointed out the TIR region is an important source of information to study environmental disturbance because of the negative relationship between vegetation density and land surface temperatures [13]. Mildrexler et al. proposed a disturbance detection index using Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day Enhanced Vegetation Index (EVI) and 8-day Land Surface Temperature (LST), and it was successfully applied to detect continental-scale disturbance events such as wildfire, irrigated vegetation, precipitation variability, and the incremental process of recovery of disturbed landscapes [39]. Another good use of TIR remote sensing data is to measure evapotranspiration, evaporation, and soil moisture. For example, Crow and Zhan analyzed the continental-scale performance of surface soil moisture retrieval algorithms depending on satellite passive microwave, scatterometer, and thermal remote sensing observations [40]. Petropoulos et al. reviewed Ts/VI (surface temperature/vegetation index) remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture, and suggested one piece of future work should evaluate the accuracy of these methods under diverse environments [41].
2.4. Constellation of Small Satellites
A small satellite generally refers to its mass in the range of 1–500 kg and satellite constellation is defined as groups of satellites working in concert [42]. Since 1997, six symposia on small satellites have been organized by the International Academy of Astronautics (IAA) in Berlin, Germany. Kramer and Cracknell reviewed the development of small satellites in remote sensing [43]. With the launch of DMC (Disaster Monitoring Constellation, Table 2), the concept of the Earth-observation constellation of low-cost small satellites has been put into action. It is capable of obtaining multispectral images of any part
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Chapter V of the world every day [24]. The DMC was initially proposed in 1996 and led by SSTL (Surrey Satellite Technology Limited), which is a world leader in high performance small satellites [42]. Wang et al. briefly introduced the characteristics of DMC imagery and its potential applications in environmental science [44]. Also, HJ-1 (Huan Jing-1, also called Environment-1, operated by China) is another outstanding constellation system. It is designed mainly for environmental protection and disaster monitoring, and the payload instruments onboard consist of a CCD (Charge-Coupled Device) camera, an infrared camera, a hyperspectral imager and an S-band SAR (Synthetic Aperture Radar, [45]).
Table 2.Disaster Monitoring Constellation (DMC) on Orbit (adapted from DMC International Imaging Ltd.).
Designation Type Imager Launch Waveband Alsat-1 DMC 32m MS 2002 ✓ MS UK-DMC DMC 32m MS 2003 NIR: 0.77–0.90 μm Nigeriasat-1 DMC 32m MS 2003 Red: 0.63–0.69 μm Beijing-1 DMC+4 32m MS/4m Pan 2005 Green:0.52–0.60 μm Deimos-1 DMC 22m MS 2008 ✓ Pan UK-DMC2 DMC 22m MS 2008 0.50–0.80 μm
P.S. MS = Multispectral; Pan = Panchromatic
Besides the benefits in cost and operation, the constellation of small satellites has two obvious advantages in applications, i.e., global surveying and increased revisit frequency [24]. It is relatively easy to obtain observation data across the world in a short time for constellation systems. The increased revisit frequency can not only satisfy the application of detecting rapid surface changes such as crop-growth monitoring and detecting intraseasonal ecosystem disturbance, but also promotes acquisition of good-quality imagery with limited cloud-contamination. Wang et al. discussed the issue of clouds and cloud shadows in the environmental remote sensing community, and advised looking for good solutions to the unavoidable problem in optical remote sensing [44]. The development of a constellation of low-cost small satellites is believed to make contributions to this issue at the sensor level. Only a few studies of EBC applied the imagery of small-satellite constellation, though Aplin
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Chapter V has predicted the bright future of this kind of satellite imagery [11]. Qian et al. demonstrated that simulated HJ-1B satellite data performed better on smaller and cooler fires than MODIS or AVHRR data, and believe it will offer a great opportunity for fire detection [46]. The FRIGBP has started testing the applicability of DMC imagery for wildlife large-area habitat mapping in west-central Alberta (Canada) [44].
2.5. LIDAR
Light Detection and Ranging (LIDAR), also called Laser altimetry, is an active remote sensing technology that utilizes a laser to illuminate a target object and a photodiode to register the backscatter radiation [47,48]. The current LIDAR remote sensing can be categorized into two general groups: non-scanning LIDARs, and scanning LIDARs. The non- scanning LIDARs record pulsed ranging that measures the travel time between the transmitted and received signal backscattered from the object surface, and the scanning LIDARs register continuous wave ranging that is produced in a transmitted sinusoidal signal and carried out by modulating the laser light intensity [49]. According to the characteristics of LIDAR technology, it has been proven to provide horizontal and vertical information at high spatial resolutions and vertical accuracies [47]. For example, Miller stated that 5–30 cm range is the typical accuracies for LIDAR-derived vertical information [50]. Airborne LIDAR remote sensing systems such as LVIS (Laser Vegetation Imaging Sensor) have been used for bathymetry, forestry, and other applications [48,51,52]. For instance, Turner et al. briefly discussed the airborne LIDAR remote sensing for biodiversity science and conservation [3]; Lim et al. reviewed the application within forest structure (vertical information) [47], e.g., canopy and tree height, biomass, and volume; Goetz et al. claimed species distribution models have been improved through airborne LIDAR quantifying vegetation structure within a landscape [53]. LIDAR was underlined by [11] as one of the strong interests of the remote sensing community in ecology. Besides airborne LIDAR with the limitations of large data volumes, footprint size and high costs [54], spaceborne LIDAR has come through with the launch of the ICESat/GLAS (Ice, Cloud, and Land Elevation Satellite/Geoscience Laser Altimeter System), which is the first laser-ranging instrument for continuous global observations. The applications of the GLAS data in EBC, which are seldom reviewed, will be discussed below.
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LIDAR focused on the forest vertical structure, especially forest canopy height and aboveground biomass estimation. Lefsky et al. estimated forest canopy height with an RMSE of 5 m (83% of variance explained) in varied forest types including evergreen needle leaf, deciduous broadleaf and mixed stands in temperate North America, and tropical evergreen broadleaf forests in Brazil [55]. Mangrove forests are considered as one of the most biodiverse and productive wetlands on Earth, and the mangrove height and aboveground biomass were measured and mapped based on SRTM (Shuttle Radar Topography Mission) elevation data, GLAS waveforms and field data [56]. Pflugmacher et al. compared GLAS height and biomass estimates with reference data from the Forest Inventory and Analysis (FIA) program of the U.S. Forest Service at a regional scale, and promising results were obtained [57]. Helmer et al. proposed the combination of Landsat time series and the GLAS to estimate the biomass accumulation of the Amazonian secondary forest, and the estimation agreed well with ground-based studies [58]. Duncanson et al. tested simulated GLAS data under tough conditions, e.g., areas with dense forests, high relief, or heterogeneous vegetation cover, and demonstrated the capability of GLAS waveforms as supplemental model input to improve estimates of canopy height [54].
3. Advanced Techniques in Remote Sensing of EBC
Similar criteria were applied to choose the remote sensing techniques discussed below, including promising algorithms or methods in image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of RS and GIS. Although these techniques are reviewed separately, they are frequently integrated in practice. For example, data fusion can be implemented to remotely sensed data before they are classified by advanced classifiers in order to improve classification accuracy.
3.1. Image Classification
Regardless of the variety of uses for remote sensing images, the first goal is to extract landscape information from the satellite images [59]. Image classification has been recognized as the most effective means to do so since mid-1800s, when humans first identified different types of land-use and land-cover in aerial photography [60]. Jensen discussed in detail the fundamental elements of image interpretation including grayscale tone, color, height and depth, size, shape, texture, pattern shadow, site, association and arrangement [1]. With the widespread of digital computers, special-purpose image
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Chapter V classification algorithms have been used to extract land-use/land-cover and biophysical information directly from remotely sensed data [60]. In order to derive more accurate classifications, new approaches have increasingly emerged, and such approaches have made significant contributions to the science of EBC, examples of which would be support vector machines (SVMs), one-class classifier, object-oriented classification, and fuzzy classifications.
SVMs consist of many theoretically superior machine learning algorithms, and make use of optimization algorithms to find an optimal separating hyperplane (OSH) between classes based on training samples [61]. The hyperplane is called support vectors [62]. Foody and Mathur have demonstrated the robustness of SVMs through comparison with artificial neural networks (ANNs) and machine learning decision trees, especially for small training sets [63]. The SVM was selected by [64] to help understand the relationships among spectral resolution, classifier complexity, and classification accuracy obtained with hyperspectral sensors for the classification of forest areas. Ichii et al. applied SVM-based evapotranspiration estimation to refine rooting depths for ecosystem modeling in California [65].
Commonly, only one specific class is the foci of research interest [66]. Due to the fact that conventional multiclass classifier may be suboptimal in terms of the classification accuracy of the class of interest, a one-class-classification approach was suggested to focus tightly on the specific class. For example, Sanchez-Hernandez et al. applied the one-class classifier based on the support-vector data description (SVDD) to map fenland habitat in support of conservation activities [66]. An accuracy of 97.5% and 93.6% from the user‘s and producer‘s perspectives was obtained, and it performed much better than conventional maximum- likelihood classification. In the same year, the classifier was used to map and monitor coastal saltmarsh habitats of high conservation value under the European Union‘s Habitats Directive [67].
With the wide availability of high-spatial resolution satellite data, pixel-based classification algorithms seem not to be ideal to extract information desired from the data exhibiting high frequency components with high contrast and horizontal layover of objects [60]. Therefore, object-oriented classification algorithms have been developed to meet this need, and have established improved classification accuracy when compared with the traditional methods [5,60]. The basic processing units of object-oriented classification are segments, so-called
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Chapter V image objects that represent a relatively homogenous unit on the ground [68]. Then classification was performed on image objects, and not on pixels. One of the most popular algorithms was developed to the software of Definiens‘ Developer (also called eCognition; [69]). Advantages of object-oriented classification are to make full use of meaningful statistic and texture calculation, uncorrelated shape information (e.g., length-to-width ratio, direction and area of an object, etc.) and topological features (neighbor, super-object, etc.), and the close relation between real-world objects and image objects [68]. Jensen et al. pointed out that the advantages include rapid process, and scale flexibility in which users can select different scale levels according to their images [60]. A variety of studies applied object- oriented classification into the science of EBC. For example, Collingwood et al. classified agricultural areas in Alberta grizzly bear habitats based on one of the object-oriented classification techniques – sequential supervised masking (SSM), in order to help ecologists understand the relationship between crop types and grizzly bear presence [5]. Wang et al. proposed that object-oriented classification may traverse the possible Landsat-gap on applications such as landscape pattern analysis or ecological models [44].
Traditionally, land cover information is assigned into a finite number of non-overlapping classes, and the classes are mutually exclusive [70], which is described as the one-entity-one- class method [71]. However, pixels may contain more than one class because of the heterogeneity and the limitation in spatial resolution of remotely sensed data, especially in medium and coarse spatial resolution imagery [70]. And the presence of mixed pixels could not be removed totally no matter how accurately map classes are defined [71]. Therefore, fuzzy classification, also called subpixel classification, arose in the context of the uncertainty associated to class mixtures. In fuzzy systems, every pixel is supposed to consist of multiple and partial memberships of all candidate classes [70]. Spectral mixture analysis (SMA) is one of the most popular and most effective approaches for dealing with mixed pixel problem [60,70]. For example, Lu and Weng used linear SMA to explore the relationship between urban thermal features and biophysical descriptors based on ASTER images [72]. Plourde et al. estimated species abundance in a northern temperate forest using SMA for better understanding changes in biodiversity, habitat quality, climate, and nutrient cycling [73].
3.2. Vegetation Index
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Vegetation indices (VIs) are ‗dimensionless, radiometric relative abundance and activity of green vegetation, including LAI, percentage green cover, chlorophyll content, green biomass, and absorbed photosynthetically active radiation (APAR)‘ [1]. Jensen summarized VIs benefit in maximizing sensitivity to biophysical parameters, normalizing or modeling external effects, normalizing internal effects, and assisting validation effort and quality control [1]. Additionally, VIs are simple to understand and implement, easy to quickly calculate, and useful to track temporal characteristics. To date, hundreds of VIs have been used in all kinds of applications of remote sensing. VIs can be roughly categorized into two groups, i.e., biophysical indices and biochemical indices [74]. Biophysical indices represent those designed to link with vegetation biophysical characteristics including structure and condition. They can be grouped into simple ratio-based indices (e.g., Simple Ratio [SR]; [75]), soil-line-related indices (e.g., Soil Adjusted Vegetation Index [SAVI]; [76]), and chlorophyll-corrected indices (Ratio TCARI/OSAVI [Transformed Absorption in Reflectance Index/Optimized Soil Adjusted Vegetation Index]; [77]). Biochemical indices are those mainly employed to estimate vegetation biochemical properties such as Cellulose Absorption Index (CAI) and Lignin-Cellulose Absorption Index (LCAI) [78].
No doubt that NDVI is the most well-known vegetation index. Its use in EBC has been considerably reviewed by [2,13,14], etc. Nonetheless, other indices that are commonly used in the relevant applications are not taken seriously enough in the aforementioned review papers. For example, SR was validated to perform best in early and intermediate forest stages for the assessment of LAI based on ASTER data in East African rainforest ecosystems [79]. The modified soil adjusted vegetation index (MSAVI) was selected as the optimal vegetation index in a linear mixture model to map canopy fractional cover in tropical forests in the Amazonian state of Mato Grosso (Brazil) [80]. Haboudane et al. demonstrated that the existing VIs (e.g., NDVI, SAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI], etc.) were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels, whereas a modified triangular vegetation index (MTVI2) and a modified chlorophyll absorption ratio index (MCARI2) are proved to be the best predictors of green LAI [77]. Additionally, other recently proposed VIs such as WDRVI (Wide Dynamic Range Vegetation Index; [81]), L- ATSAVI (Litter-corrected Adjusted Transformed Soil Adjusted Vegetation Index; [74]), and VIUPD (Vegetation Index based on a Universal Pattern Decomposition; [82]). However, traditional measures such as the coefficient of determination and root mean square based on
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Chapter V regression statistics, are not capable of evaluating the performance of VIs on the estimation of biophysical parameters because the sensitivity of a VI may change substantially with vegetation density [83]. Therefore, a statistical sensitivity function was developed to summarize the overall relationship between VIs and biophysical parameters instead of a constant [83].
3.3. Inversion Algorithms
Various process-oriented models are developed to characterize Earth environments because traditional methods based on simple statistical relationships are often sensor-dependent, and site-specific [84,85]. These models represent the in-depth understanding of physical processes deriving the Earth system, and are unquestionably useful in Earth observations in support of EBC [85]. Generally speaking, models can be run under two modes, namely inverse mode and forward mode. An inverse mode applies outputs to retrieve inputs that cause them, while a forward mode applies inputs to obtain resulting outputs. For example, Boyd and Danson suggested that a remote sensing model can be used to simulate the reflectance of forest canopies [84]. The forward mode treats data on the forest canopy variables as the inputs and the spectral signature as the output but the inverse mode is converse process, i.e., the spectral signature is the input and estimates of the forest biophysical variables are the outputs. Obviously, the inverse model is more frequently used in remote sensing. The core of inverse model is inversion algorithms, which mostly follow the physical laws and establish cause-and-effect relationships [85]. In order to understand remote sensing signals and develop practical inversion algorithms to estimate land surface variables, physically-based models are advised to discuss the following three areas [86]: atmosphere (atmospheric radiative transfer modeling), land surface (surface radiation modeling), and sensor (sensor modeling). Liang grouped inversion algorithms into four categories: model simulation and statistical analysis, optimization algorithms, look-up table algorithms, and data assimilation [86]. Several recent examples are provided below to display the applications of inversion algorithms in EBC.
In order to monitor and model storm-water pollution, Park and Stenstrom proposed a Bayesian network approach, which falls into the category of model simulation and statistical analysis [87]. A leaf radiative transfer model called the LIBERTY (Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields) was selected and
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Chapter V incorporated with three pigments to better understand relationships between leaf biochemical, biophysical, and spectral properties [88]. A look-up table approach was developed to estimate LAI [89]. Migliavacca et al. assimilated remotely sensed vegetation index time series, such as MODIS NDVI, into a process-based model BIOME-BGC (Biome-BioGeochemical Cycles) to estimate the gross primary production (GPP) of agro-forestry ecosystems [90]. However, an intrinsic problem in inverse models is the process from inputs to outputs is often non- invertible, i.e., more than one combination of inputs results in the same output of spectral signature. Liang stated that, because it is still a nonlinear, ill-posed problem to inverse land surface parameters, further research is required to focus on use of regularization [86].
3.4. Data Fusion
Each kind of imagery has its own benefits and drawbacks, which provide great potential to fully exploit increasingly sophisticated multisource data through data fusion. For example, MODIS imagery has significant advantage in temporal resolution (one day) but is very poor in spatial resolution (250, 500 or 1,000 m) for certain applications, whereas Landsat TM imagery performed very well in spatial resolution (30 m) but with 16-day revisit. Therefore, Hilker et al. developed Spatial Temporal Adaptive Algorithm for Mapping Reflectance Change (STAARCH) model to fuse high spatial- (Landsat) and temporal-resolution (MODIS) for mapping of forest disturbance [91]. A general definition of remotely sensed data (image) fusion is given as ‗the combination of two or more different images to form a new image by using a certain algorithm‘ [92]. Since the late 1980s when data fusion emerged as a new topic [93], several comprehensive review papers have been published to review the data fusion techniques, such as [92–95]. In general, the fusion techniques can be categorized into two classes [92]: (1) colour-related techniques, such as colour composites (RGB), intensity-hue- saturation (IHS); (2) Statistical or numerical methods, such as principal component analysis (PCA), band combinations using arithmetic operators and others. Besides typical techniques, wavelet transform, SVM (support vector machine) and ANN (artificial neural network) represent the heart of new data fusion methods (e.g., [96–98]).
Data fusion has matured into a widely used application of EBC. Pan-sharpening technique, which is to integrate a panchromatic (Pan) image with high spatial resolution and a multispectral (MS) image with high spectral resolution [94] to produce a high spatial resolution MS image, is likely to be the first data fusion method to make installing to the
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Chapter V commercial remote sensing software such as PANSHARP module in PCI Geomatica software. For example, Wunderle et al. pan-sharpened SPOT-5 imagery to classify stand age of western red cedar in British Columbia (Canada) [99]. Due to the complementary nature of optical and radar imagery, their both fusion is always at the leading edge of remotely sensed data fusion [44]. Huang et al. estimated the quantity and quality of coarse woody debris in Yellowstone post-fire forest ecosystem from fusion of SAR and optical data [100]. Optical (Landsat-5 TM) and SAR (RADARSAT-1 Wide 1) images were fused through the combination of PCA and IHS transforms to map geomorphological and environmental sensitivity index in the Amazonian Mangrove Coast (Brazil) [101].
3.5. Integration of RS and GIS
RS and GIS have a complementary nature and should develop interdependently. RS routinely provides extracted information from remotely sensed data at scales ranging from local to global and the purpose of GIS is to store, analyze and visualize spatial data [102]. Although Hinton has reviewed well the combined use of remotely-sensed data and vector GIS data [103], Merchant and Narumalani claimed the integration of RS and GIS has actually become increasingly apparent since Aronoff [104,105]. Merchant and Narumalani listed key factors to benefit the integration, including development of theory and analytical methods, advances in computing (hardware and software) and global positioning system (GPS) technology [104]. A state-of-the-art definition of the integration is given as ‗the use of each technology to benefit the other, as well as the application of both technologies for modeling and decision support‘ [104]. Ehlers et al. proposed a three-level taxonomy of the integration [106]. First- level integration happens in the level of separate but equal data exchange between GIS and image analysis systems, e.g., displaying GIS (usually vector) data and remotely sensed (raster) data simultaneously. Second-level integration permits seamless tandem or combined raster-vector processing based on a common use interface. Certain RS or GIS software has capability of performing the second-level integration. For example, the aforementioned Definiens‘ Developer is capable of incorporating GIS data directly into image processing – image segmentation [69]. Third-level integration operates RS and GIS as a unified system, and finally generates an integrated model of the real world, e.g., accommodating raster and vector data in a hierarchical structure. Moreover, Gao pointed out GPS must be involved with the integration to build up seamless RS-GIS-GPS integration for geospatial information analysis [107]. Campbell, and Merchant and Narumalani summarized the contribution of RS
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Chapter V to GIS, and GIS to RS [25,104]. The contribution of RS to GIS includes: (1) RS develops thematic layers for GIS, such as surface elevation (Digital Elevation Model [DEM]), land use and land cover mapping, biophysical parameters, feature extraction and landscape change; and (2) RS provides orthoimagery as base data, which plays key role in positioning, registration and geo-referencing. The contribution of GIS to RS consists of (1) mission planning; (2) ancillary data for geometric and radiometric correction, and image classification; and (3) collection, organization and visualization of reference data.
Foody demonstrated many commonly used examples of RS and GIS for biodiversity applications. The following review focuses on the promising applications of the integration in EBC in 2009 [102]. For example, an adaptable method integrating low-cost remote sensing imagery and GIS was developed to assess forest cover change and conversion in support of decision-makers in assessing regional and local land use and planning forest conservation measures [108]. Giriraj et al. applied data generated from RS and GIS to categorize habitats, and then determined the relationship between the habitat categorizations and species- distribution patterns in tropical rain forests of Southern Western Ghats (India) [109]. Dong et al. pointed out that the integration of high-resolution RS images and GIS technique is an effective way to analyze the landscape changes at river basin scale [110]. In the management of water resources, RS and GIS integration techniques were used to design sustainable development plan of area and locale watershed [111], river inundation impact reduction [112], rainwater harvesting for drinking [113].
4. Conclusions
Remote sensing plays an increasing role in EBC research, especially regarding large spatial and/or long-term temporal scales. Moreover, the use of remote sensing deepens with the support of state-of-the-art remote sensing products and technology. Certainly, it is impossible to make progress without the assistance of GIS and GPS. It is believed that remote sensing will develop in a path similar to that of computer science, which has penetrated all aspects of human life. EBC performs as a propeller to push up the naissance of advanced remote sensing instruments and techniques. For example, the object-based image analysis (OBIA) is maturing in hopes to answer the question ―why are remote sensing and digital image processing still so focused on the statistical analysis of single pixels rather than on the spatial patterns they build up‖ raised by [114]. Blaschke summarized the status of OBIA for remote
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Chapter V sensing through a comprehensive review several thousand abstracts [115]. However, with the popularity of remotely sensed data and commercial remote sensing packages, it is easy to obtain processed remote sensing products based on certain algorithms or modules. These products can be applied to answer questions in the field of EBC. But, it is noteworthy that these products may not be suitable or accurate enough to use. Therefore, it is still urgent to make EBC practitioners and remote sensing specialists communicate efficiently.
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