Biome Level Characterization (BLC) of Western India – a Geospatial Approach

JOSHI et al. 213

Tropical Ecology 43(1): 213-228, 2002 ISSN 0564-3295 © International Society for Tropical Ecology

Biome level characterization (BLC) of western India – a geospatial approach

P.K. JOSHI, P.S. ROY, S. SINGH, S. AGARWAL & D. YADAV

Indian Institute of Remote Sensing (NRSA), Dehradun-248001, India

Abstract: Environment is constantly changing as a function of climate and more recently due to human activities. Timely and accurate monitoring of these changes is a difficult task for regional planning and management. Up-to-date information on distribution and rate of changes of any ecosystem are required for a wide variety of applications viz., biome status, environmental, climatic etc. Aerospace technology has been found to be a vital tool for assessment and monitoring of these natural resources. The present study focuses on the potential of IRS 1C WiFS data set for the regional level mapping. The temporal resolution along with the recommended spatial and spectral resolution configures for the assessment of the phenological growth of the vegetation in the terrestrial ecosystems. The utilization of the climatic data along with the biogeographic map is proposed to delineate the biomes in the western Indian subcon- tinent. The product offers the basic input for the eco-physiological processes for studying the land surface interactions and conservation strategies of the arid regions.

Resumen: El ambiente está en constante cambio en función del clima y más recientemente como consecuencia de las actividades humanas. El monitoreo preciso y oportuno de estos cam- bios es una tarea difícil para la planeación y el manejo de una región. Se requiere información actualizada sobre la distribución y las tasas de cambio de cualquier ecosistema para una am- plia variedad de aplicaciones, por ejemplo las referentes a la condición del bioma, las ambien- tales, las climáticas, etc. La tecnología aeroespacial es una herramienta vital para la evalua- ción y el seguimiento temporal de estos recursos naturales. El presente estudio se enfoca en el potencial que tienen los datos IRS 1C WiFS para la elaboración de mapas regionales. La re- solución temporal, así como las resoluciones espacial y espectral recomendadas, se configuran para evaluar el crecimiento fenológico de la vegetación en los ecosistemas terrestres. Se pro- pone el uso de datos climáticos junto con el mapa biogeográfico para delimitar los biomas de la porción occidental del subcontinente indio. El producto ofrece los insumos básicos sobre procesos ecofisiológicos para el estudio de las interacciones de la superficie terrestre y las es- trategias de conservación de las regiones áridas.

Resumo: O ambiente está mudando constantemente em função do clima e, mais recente- mente, devido às actividades humanas. A monitorização tempestiva, e exacta, destas mudanças é uma tarefa difícil para o planeamento regional e a gestão. Informação actualizada quanto á distribuição e taxas de mudança de qualquer ecossistema é necessária para um conjunto variado de aplicações como sejam o status do bioma, o ambiente, clima, etc.. A tecnologia aeroespacial tem-se mostrado uma ferramenta vital para a avaliação e monitorização destes recursos naturais. Este estudo foca o potencial do conjunto de dados obtidos pelo IRS 1C WiFS para o mapeamento a nível regional. A resolução temporal, a par com a resolução espacial e espectral recomendada, mostra-se adequada para a avaliação do crescimento fenológico da vege- tação nos ecossistemas terrestres. A utilização dos dados climáticos, juntamente com o mapa biogeográfico, é proposta para o delimitar os biomas na parte ocidental do subcontinente in- Address for Correspondence: P.K. Joshi, Indian Institute of Remote Sensing, 4, Kalidas Road, P.O. Box – 135, Dehradun 248 001, India; Tel: +91-135-744583; Fax: +91-135-741987; e-mail: [email protected] 214 BIOME LEVEL CHARACTERISATION

diano. O produto oferece o input básico para o estudo dos processos ecofisiológicos, das in- teracções com a supercície do solo e para as estratégias de conservação das regiões áridas.

Key words: Biome level characterization, geospatial approach, green wave, land use/cover, phenology, remote sensing, western India.

Introduction general temperature range and precipitation level, we can predict what kind of biological community In nature, there exists a complex of more than is likely to develop on a particular site if that site one community. Communities are never stable, but is free of disturbance for a sufficient time. Biome dynamic. Although a typical community maintains distribution also is influenced by the prevailing itself more or less in equilibrium with the prevail- landforms of an area. Mountains, in particular, ing conditions of the environment, but in nature exert major influences on biological communities. this is hardly true. They are never found perma- Furthermore, with the human population ris- nently in complete balance with their component ing by almost a billion a decade, over the next species or with the physical environment. Envi- three decades at least sustained increases in food ronment always keeps on changing over a period of production is required. According to the projections time due to variations in climate and physiography from the Integrated Model for Assessment of the and the activities of the species of the communities Greenhouse Effect (IMAGE model: Alcamo 1994) themselves. These influences bring about marked and from other analysis, this will result both in changes in the dominants of the existing commu- further conversion of natural ecosystems to agri- nity, which is thus sooner or later replaced by an- culture. Both of these processes always accelerate other community at the same place. This process the release of carbon in the atmosphere. In addi- continues and successive communities develop one tion, as more land is converted to agriculture, after the other over the same area until the termi- there is less area of natural ecosystems available nal final community again becomes more or less to act as carbon-sink; thereby, reducing the poten- stable for a period of time. This occurrence of rela- tial sink strength of the terrestrial biosphere. tively definite sequence of communities over a pe- Changes in the composition and structure of eco- riod of time in the same area is known as Ecologi- systems are driven by a combination of manage- cal Succession. Thereafter, a concept of biome, de- ment practices and changes in climate and atmos- fining as ‘biotic community of geographical extent pheric composition. Hence it makes the assess- characterized by distinctiveness in the life forms of ment of natural resources, changes in the ecosys- the important climax species’ came up (Sharma tem so important to incorporate that new arriving 1997). The biome is not based on the species of policies and strategies would not hinder the bio- plants and animals present but rather on the life spheric activities rather would allow it to flow on forms of the most important plants (i.e. trees, sustainable basis. shrubs, grasses) that give to the landscape its special character. The chief character of a biome is Present knowledge on biomes depicted by climax communities with their domi- The emergence of biome or ecoregional level nant life forms. Thus a biome is viewed, as a biotic studies is an important step in dealing with diffi- community – a unit larger than a community, con- cult natural resource issues. Ecoregional studies stituting the great regions of the world distin- have distinctive emphasis on interaction between guished on an ecological basis, such as tundra bi- development and conservation, including biological omes, forest biomes, grasslands, deserts etc. and physical resources. The study requires both Temperature and precipitation are among the greater depth of understanding of interactions of most important determinants in biome distribu- humans and ecosystems and more focus on imme- tion along with the topography. If we know the diate social needs. Nevertheless this has grown in JOSHI et al. 215 part by efforts to fill a critical gap in dealing such The wide field of view of the AVHRR provides problems by scaling our understanding from local large-area coverage but involves considerable diffi- to global and how human activities interact with culty in the collection of ground data for valida- the atmosphere. tion. Results of measurement of primary produc- Last decade has been a uniquely productive tion using a harvest technique are described by era for mapping and modeling of earth’s surface Diallo et al. (1991), as the results from the applica- with roots in bioregional sciences, such as biogeog- tion of slightly different methods in Niger that raphy, regional economics of natural resources etc. were suitable for the more extensive and uniform and contemporarily having substantial improve- terrain in the study region (Wylie et al. 1991). ments in computing technologies and also in our These field methods rely maximum on the above- understanding of global and regional systems and ground standing annual vegetation as the estimate the availability of improved earth science data of seasonal production and, therefore, are subject sets. This is clearly articulated by the Interna- to some well-known sources of error (Beadle et al. tional Geosphere Biosphere Program (IGBP) core 1985). However, they are capable of estimating net projects on Biospheric Aspects of the Hydrological production in large areas (in excess of 10 km2) as Cycle (BAHC), Global Change and Terrestrial Eco- is necessary for analysis of the AVHRR data. The systems (GCTE), International Global Atmos- use of AVHRR NDVI data to model regional pri- pheric Chemistry (IGAC), Land-Use and Land mary production is discussed by Prince (1991), in Cover Change (LUCC), Global Analysis, Interpre- which the subject of scale is addressed and rele- tation and Modeling (GAIM), Land-Ocean Interac- vant variables are identified at the regional scale. tions in the Coastal Zone (LOICZ), Past Global Factors that obscure the relationship between the Changes (PAGES) etc. which have all identified satellite measurement of NDVI and various meas- the need for improved baseline landcover datasets urements of the vegetation were identified. At- for the studies of global change. Landcover is a mospheric water vapor, in particular, the seasonal case in point. These have typically used the corre- change associated with the movement of the inter- lation between major biome patterns and climate tropical convergence zone has an important effect for the description of landcover. Such models tend owing to the presence of a water vapor absorption to be based on simplified set of climatic parame- band in channel 2 of the AVHRR. The extent of ters to delineate potential landcover patterns. This this problem for remote sensing of vegetation and is because of existing global landcover databases, some possible solutions were assessed by Justice et although valuable, have coarse resolution. IGBP al. (1991). determined that 1.1 km AVHRR data were the ap- Since then, the emphasis has been on refining propriate choice for generating landcover datasets the data interpretation techniques, including stud- at global level. ies of the necessary corrections for attenuation of radiation in the atmosphere, improvements in Review on NOAA-AVHRR cloud masking, and addressing some problems associated with the NDVI in general and the calcula- The potential of the data provided by the Ad- tion of surface NDVI from satellite data in particu- vanced Very High Resolution Radiometer lar. Specifying and modeling the relationship be- (AVHRR) for vegetation monitoring at the regional tween primary production and AVHRR NDVI data scale was first demonstrated (Tucker et al. 1985) and developing operational methods for applica- in Africa and the research involved the application tion of the data have been important issues. of Normalized Difference Vegetation Index (NDVI). Research using AVHRR data has provided us In the five years since 1986 three factors have con- with important experiences relevant to the design tributed to this interest: first, the unique multi- of the new generation of instruments for vegeta- layer archive derived from the sensor; second, its tion monitoring (Potdar 1990). The spectral chan- large spatial coverage, which has made possible nels should avoid as much as possible atmospheric certain new types of investigations at a very broad absorption bands. Spatial pre-processing of scales of regional, continental; and global; and multitemporal datasets, including navigation, rec- third, the high temporal frequency (Prince & Jus- tification and registration, is a cumbersome task tice 1991). and is recommended that these be regarded 216 BIOME LEVEL CHARACTERISATION largely as operational responsibilities to be under- ent from those in corresponding natural ecosys- taken prior to the distribution of the data. tems. The past one hundred years technological revo- Wide field sensor (IRS 1C – WiFS) for lution has given human beings the capability to ecoregional studies modify the biosphere substantially and in particular to exterminate all life not only directly with India launched IRS-1C remote sensing satel- weapons but, more permanently, indirectly, lite in December 1995 with WiFS sensor having through disrupting life support systems. In the potential to provide inputs for vegetation studies evolution of culture, human beings also lost the because of its spectral bands B1 (0.62-0.68 µm) and sense of evolutionary justice, which stems from the B2 (0.77-0.86 µm). Its high temporal resolution, i.e. conviction that it is our responsibility to share the 5 days and large area coverage (810 × 810 km2) is biosphere with all other biota. Widespread and helpful in developing improved yield models and accelerating species extinction and irreparable dis- assessing crop condition (Ray et al. 1994). The de- ruptions of life processes by human agency during tails available in this data are best suited for land recent times are reversing the trend of biological cover characterization at regional scale. evolution and diversification that had taken place WiFS data provide advantage of covering a during the last 3.5 billion years. Even the rich cul- very large area in single instantaneous field of tural diversity of human species, which reflected view (IFOV) avoiding any illumination difference. the diversity of lifestyles adapted to drastically Suitability of such moderate resolution data for differing habitats evolved during the last 20 or 30 regional vegetation as compared to AVHRR data thousand years, has been greatly reduced and di- should make a better choice for monitoring and luted. Almost universally a single cultural precept research. The pixel size of 188 m suits regional has come to dominate human endeavor, that of scale mapping. Probability of obtaining cloud free material short-term gain. All the current socio- data has increased with WiFS data with five days economic, political and cultural compulsions glob- revisit capability. Besides for certain episodic ally are the destruction of the diverse value sys- events like, forest fire, drought etc. frequent data tems and visions of life retained during human of WiFS enable crop monitoring and forecasting history. We all end up merely aping a single domi- (Oza et al. 1996). nant style or trend. To assess the potential of WiFS sensor and its What we most often hear from the authorities use at regional level, Indian Institute of Remote is the accusation that the common people are de- Sensing (NRSA) has taken a project entitled ‘Bi- stroying the forests by collecting fuelwood. They ome Level Characterization of Indian Vegetation are also accused of committing the crime of keep- using IRS-1C/1D WiFS data’. Various zones viz. ing vast numbers of ‘unproductive’ cattle, sheep Gujarat (Singh et al. 1999a; 1999b), Northeast and goats, and by grazing them in the forests, de- (Roy & Joshi 2000b; 2001) and Western Himalayas grading them. The forest fires regularly sweeping (Joshi et al. 2001a; 2001b) have been mapped. The across practically forests are supposedly started by paper here presents the recent study in the state the people. The common people who are directly Rajasthan, India using WiFS. The study highlights dependent on the forest resources for income and the characterization of desert biome in India. are considered as exploiting the so-called Minor Forest Produce (M.F.P) and wiping out the forest Anthropological impacts on biome biodiversity. The landless and the tribal people are Humans have become the dominant organisms depicted as regularly encroaching into governmen- over most of the earth, having disturbed more than tal forests and clearing them for cultivation. The half of the world’s terrestrial ecosystems to some tribal people everywhere are specially accused of extent. It has been observed elsewhere that human practicing shifting cultivation that is pointed out preempt about 40 percent of the net terrestrial as being a most destructive practice for forests. primary productivity of the biosphere either by Destruction of biotic potential of land leads to consuming it directly, by interfering with its pro- desertification. Such problem arises due to over- duction / use, or by altering the composition of grazing, indiscriminate felling of trees and over- human-dominated ecosystems to organisms differ- exploitation of land resources. The devastating effects of deforestation in India include soil, water JOSHI et al. 217 and wind erosions. This may result either due to a creased mortality of some of their components, fol- natural phenomenon linked to climatic change or lowed by establishment and growth of new assem- due to abusive land use. Removal of vegetal cover blages, rather shift as intact biomes. Mortality of brings about marked changes in the local climate the present vegetation, which releases carbon to of the area. Thus, deforestation, overgrazing etc. the atmosphere, is a fast process, while the growth bring about changes in rainfall, temperature, wind of new assemblage of vegetation, which absorbs velocity etc. and soil erosion. Desertification often the carbon from the atmosphere, is slower. Thus, starts as patchy destruction of productive land. the processes by which ecosystem structure and Increased dust particles in the atmosphere lead to composition will change, will probably release a desertification and drought in margins of the zones transient pulse of carbon to the atmosphere on a that are not humid. Even the humid zones are in timescale of decades to centuries, irrespective of danger of getting progressively drier if drought whether the new theoretical equilibrium biome continues to occur over a series of years. Indica- distribution eventually stores more or less carbon tions are that the temporary phenomenon of mete- than the present distribution. orological drought in India is tending to become permanent one. This trend is not restricted to the Satellite remote sensing for mapping and fringes of existing deserts only. The threat of de- monitoring sertification is thus real because as the forest di- Understanding the interaction between the minishes, there is steady rise in the atmospheric atmosphere and biosphere is a crucial part of ef- temperature. forts to improve models of Earth’s physical systems. Song (1999) illustrates a specific example of Climate change vis-a-vis biome one component of this, examining the effect of An important aspect of the effects of environ- phenological changes on land surface albedo. Fur- ment on the life of an organism is the interaction thermore, the advent of satellite measures created of ecological factors. All the factors are interre- opportunities to use empirical data for improved lated. Variations in any one may affect the other. understanding of bioclimatic processes. The most For instance, if weather is a description of physical widely used measure is the NDVI (Normalized Dif- conditions of the atmosphere (humidity, tempera- ference Vegetation Index) which is being calcu- ture, pressure, wind and precipitation) then cli- lated to take advantage of active vegetation high mate is the pattern of weather in a region over reflectance in the near infrared and low reflectance long time periods. The interactions of atmospheric in the red visible light band to discriminate it from systems are so complex that climatic conditions dormant and non vegetated areas and on the basis are never exactly the same at any given location of phenological variations among vegetation cover from one time to the next. While it is possible to itself. discern patterns of average conditions over a sea- Phenology, the traditional study of seasonal son, year, decade, or century, complex fluctuations plant and animal activity driven by environmental and cycles within cycles make generalizations dif- factors has found new relevance in research into ficult and forecasting hazardous. global climate change. The satellite view is synop- Changes in the composition and structure of tic and integrating, therefore observes ‘broad ecosystems are driven by a combination of man- brush’ changes in the ecosystem dynamics. What agement practices and changes in climate and at- satellites cannot observe are the details driving mospheric composition. For example, the biomass the events, therefore, unless biospheric changes increase currently observed in many forested areas are quite profound, they are likely to be missed by largely reflects successional changes due to past satellite observations and this is the area where changes in forest management. Future global phenology can contribute. Generalized NDVI im- change effects will be superimposed on these pre- ages show the gross progression of vegetative sea- sent trends. In particular, the current trend of sons around the world. Several studies in mid biomass increases may be reversed in some areas, 1980s used these data to lay the foundation of sat- as the effects of global change become increasingly ellite phenological analysis. While a few other pa- important. Under global change, present vegeta- pers subsequently incorporated derived phenologi- tion assemblages will likely change through in- cal parameters in satellite based vegetation dy- 218 BIOME LEVEL CHARACTERISATION namics and land cover analysis, refined phenologi- biosphere interaction. Increasing solar radiation cal measures have only began to develop (Roy & receipts allow vegetation to resume growth, but Joshi 2000b, 2001). these biospheric events may in turn feed back The concept of ‘Green Wave’ or ‘Green Up’ states upon the lower atmosphere, through modification an integration of conventional and satellite derived of the surface energy and moisture balances, re- measures is to understand the mid latitude spring sulting in detectable changes in surface tempera- onset of photosynthesis. Satellite NDVI data and ture and moisture (Schwartz & Karl 1990; surface observations show that the mid-latitude Schwartz 1992, 1996). onset of greenness often occurs abruptly in early In principle, plants are special, highly sensi- spring and then greenness gradually increases to a tive weather instruments that integrate the com- maximum in mid summer. The approach identifies bined effect of weather factors such as tempera- sharp increases in the NDVI that can be related to ture, rainfall, humidity, wind and sunshine in the onset of ‘significant photosynthetic activity’. The their growth response. These can be observed year onset and offset of the ‘green period’ are the key de- after year and dates recorded when certain growth rived measures, with all others easily computed. stages such as opening of leaf buds or appearance These ‘features’ are extracted by comparing of first flower occurs. Ideally this would mean col- smoothened data with a moving average of the time lecting phenological events from a small set of series so that departures from an established trend plant species whose ranges cover the major portion are identified. Thus, indices that measure the onset of mid-latitude continental areas. Next, biome di- of the green wave are ideal biological measures of versity should be represented, an objective that is the climatic variability. somewhat at odds with the previous one. The best compromise might be to use a few wide-ranging Green wave plant species as ‘indicators’ and then describe their relationship to the representative species within Recently the scientific community has become their each major biome. Lastly, physical mecha- concerned with the prospects of global change. nisms responsible for the phenological events must Currently, a broad definition is emerging which be well understood, if the information gathered is emphasize changes within the hydrosphere and to be put in to the best use. biosphere as well as the atmosphere. Perhaps Long-term phenological records frequently more importantly, it is also generally recognized provide a useful measure of the species level bio- that a number of feedback mechanisms are at logical response to climate variation at specific work between the ‘spheres’ and even subtle sites. The challenge in interpreting these measures changes in one may have long lasting effects of the lies typically in developing a method that can both entire system. The advent of satellite measures transform species data in meaningful information created opportunities to use empirical data for im- at the biome level and generalize the results to proved understanding of bioclimatic processes. In broader geographic areas. Schwartz (1994) out- order to realize this potential, these data must be lined a conceptual approach to this problem, which calibrated with surface information. The paper is a incorporates satellite, native species and indicator first part of an on-going project aimed at connect- plant data. The integrated approach of phenologi- ing these NDVI data with the first appearance of cal data analysis (satellite-indicator-native) can mid-latitude deciduous foliage in spring, commonly help in bridging gap between local and larger scale called the ‘green wave’ or ‘green-up’. The appear- measurements. However, connection between con- ance of foliage causes rapid increases in near- ventional phenology and remotely sensed green- infrared reflectance and transpiration (Kaufmann ness measures at the ecosystem level are being 1984; Rosenberg 1983). An Earth Resource Obser- confirmed and further studies are underway. For vation Satellite research team has a functional example, Inouye & McGuire (1991) found that de- methodology to determine the onset time of these creased snow cover adversely affects production in NDVI reflectance changes, allowing calculation of an early-blooming herbaceous perennial of mon- satellite green-up dates for specific sites (Reed et tane Western north America; Kramer (1997) con- al.1994). cludes that differences in tree species’ phenological The green wave is of particular interest as a responses to temperature changes can have long dynamic feature resulting from close atmosphere- term consequences on their geographic distribu- JOSHI et al. 219 tion; and so on. Thus, phenology-climate relation- relatively lower wind velocity and high humidity ships can also reveal the potential impacts of fu- with better rainfall. ture climate changes as well. A marked variation in diurnal and seasonal range of temperatures occurs throughout the state Approach for biome level classifica- that is the most characteristic phenomenon of tion of India warm-dry continental climate. The summer begins with the month of March with temperature rising Study area progressively through April, May, June and reaches up to 49o C at some places. The winter sea- Location son remains from December through February Rajasthan lies between 230 30' and 300 11' N with marked decline in minimum temperature in latitude and 690 29' and 780 17' E longitude, in the December and January. A sharp decline in night track of the Arabian sea branch of the southwest temperatures is experienced throughout the arid monsoon. The Aravalli, and in the southeast, the and semi-arid zone of western Rajasthan on ac- plateau of Hadauti being the only highland chan- count of quick release of the thermal radiation nel the SW monsoon coming from Kathiawar and from sandy soil soon after the dusk. stop the drier eastern flow, creating the desert in The climate is marked by low rainfall with er- the west. While the western boundary of the state ratic distribution. The general trend of Isoheights is part of Indo-Pak international boundary, Punjab is from NW to SE. There is a very rapid and and Haryana surround the state in the north, marked decrease in rainfall west of Aravalli range Uttar Pradesh in the east, Madhya Pradesh in the making western Rajasthan the most arid part. Southeast and Gujarat in the southwest (Anon. However, on the eastern side, the rainfall is much 1994). higher during the rainy season than the potential evaporation demands during that time, thus, pro- Physiography viding water surplus during the rainy season re- Physiography of the state is the product of long sulting in more vegetation and cultivation. years of erosion and depositional processes. The Apart from the above prominent climatic fac- present landforms and drainage systems have tors, the humidity, wind velocity and duration of been greatly influenced and determined by the sunshine, etc. affect the cropping pattern in sig- geological formation and structures. Four major nificant way which in turn affect agricultural prac- physiographic regions can be identified within the tices and also life style of the people. state, namely, 1. The Western desert (Thar) Satellite sata 2. The Aravalli hills IRS-1C WiFS data is being used to do the 3. The Eastern plains and mapping of regional biome types. To capture the 4. The Southeastern plateau. phenological variability, data set comprising of The Aravalli hill ranges, running from north- different seasons is used. Cloud cover data have east to southwest divides the state approximately been discarded and the analysis is performed us- into the western arid and eastern semi-arid re- ing monthly data sets (dated 13-1-98 & 17-1-98; 4- gions. It is also a major water divide. The area, to 4-98 & 23-4-98; 18-10-98 & 21-10-98 and 7-12-98 & its east, is well drained by integrated drainage 10-12-98). systems, while the area, to the west, has a single Ancillary data integrated drainage system, i.e., the Luni drainage system in the southeastern part of the desert. Survey of India (SOI) toposheets on 1:1,000,000 scale; Biogeographical maps of climatic Climate factors like, temperature, rainfall and humidity of The climate of Rajasthan varies from arid to different zones have been used. sub-humid. To the west of the Aravalli range, low Hardware/software humidity and high wind velocity characterize the climate. The climate is semi-arid to sub-humid in Silicon Graphics, Octane Machine (Work- the east of the Aravalli range and characterized by station)/IRIX Operating System with ERDAS more or less the same extremes in temperature but IMAGINE 8.3 for the Digital Image Processing 220 BIOME LEVEL CHARACTERISATION

(DIP) and ARC/INFO for Geographical Informa- the NDVI requires no a priori information con- tion System (GIS) have been used. cerning the imaged scene and partly by the fact that the calculation of the NDVI is such that Methodology there is some normalization for variations in viewing conditions (Curran 1980; Holben & Radiometric correction Fresher 1980; Justice et al. 1985). Townshend et al. (1985) have discussed in detail about the util- Unwanted artifacts like additive effects due to ity of NDVIs while doing biome level classifica- atmospheric scattering removed through a set of tion of African and South American continents preprocessing or cleaning up routines. First order using NOAA-AVHRR data of different seasons. corrections were done by dark pixel subtraction Matrices derived from the NDVI temporal profiles technique. This technique assumes that there is a included many of those suggested by Lloyd (1990) high probability of at least a few pixels within an and Reed et al. (1994). image, which should be black, i.e. with zero reflectance. However, because of atmospheric scattering, Vegetation and land use mapping the image system records a non-zero DN value at the supposedly dark-shadowed pixel location. This Analysis of the satellite data is being carried represents the DN value that must be subtracted out using various digital analytical procedures. For from the supposedly dark shadowed pixel location. the classification of the satellite data, stacking of This represents the DN value that must be sub- maximum NDVI and WiFS data set is taken. The tracted from the particular spectral band to re- mapping step involves using a clustering based on move the first order scattering component (Roy & the ISODATA clustering run on maximum value of Joshi 2001). NDVI, NIR and Red band. Each cluster is assigned a preliminary cover type label taking care of the Geometric correction spatial pattern and spectral or multi-temporal sta- tistics of each class and on comparison with ancil- Images are registered geometrically using to- lary data and extensive ground truth. posheets of Survey of India (SOI) on 1:1,000,000 Ancillary data includes descriptive land cover scale. The common uniformly distributed Ground information, NDVI profiles and class relationships Control Points (GCPs) are marked and imagery to the other landcover legends. Related ‘single was resampled by nearest neighborhood method. category’ classes are then grouped using a conver- The monthly data are then co-registered for fur- gence of evidence approach. The cloud were ther analysis. The state/regional boundaries are masked out. The unsupervised classification is fol- extracted by overlaying the digital boundary pro- lowed by post classification refinement for the co- vided by SOI (Roy & Joshi 2001). herent set of classes (Joshi et al 2001a,b).

Vegetation indices Results Most remote sensing techniques for vegetation monitoring use data recorded through those parts Analysis of NDVI images of the electromagnetic spectrum which provide a Accurate quantitative information on the dis- strong signal from vegetation while contrasting tribution and phenology of vegetation formations is with background material. Huete (1988) critically limited, yet is fundamental for the effective man- examined the role and importance of canopy back- agement of forest resources. Four different time ground signals in the global assessment and moni- period NDVI images are the representative for toring of vegetation from satellites. The coupling of seasonal changes. The NDVI images show the foli- soils and vegetation occurs in both in a ‘biome’ as age cover in the respective time period. The well as in a radiometric sense. bounded values of NDVI range from –0.16 to 0.32 The NDVI is the vegetation index most fre- (January), -0.12 to 0.11 (April), -0.15 to 0.51 (Octo- quently used for regional scale vegetation moni- ber) and –0.13 to 0.42 (December). The values vary toring. This can be in part explained by its effec- relatively for every class, the graph of which shows tiveness as a surrogate measure of biophysical the respective phenological changes in the dif- parameters, partly by the fact that calculation of JOSHI et al. 221 ferent communities residing together (Fig. 1a & b). senescence in April is well discriminated by the The large range of NDVI values is observed in the spatial distribution of NDVI values. month of October and December. The ageing or The comparative analysis revealed the influ-

(a)

(b)

Fig. 1. (a) NDVI profiles of forest classes (b) NDVI profiles of non-forest classes. 222 BIOME LEVEL CHARACTERISATION ence of seasonal variation on the vegetation. Dur- Table 1. Forest classes mapped using WiFS vis- ing January, the vegetation is showing a growing à-vis Champion & Seth (1968). trend; in April, there is decline in NDVI values. Further, the influence of monsoon is very much Classes Mapped Champion & Seth clear in October. Again the cyclic reduction in Dry Deciduous Forest Desert Thorn Forest NDVI values has been analyzed and decline in fo- Thorn Forest Ravine Thorn Forest liage cover has been observed. The maximum Desert Forest Rann Thorn Forest NDVI image has been computed to represent the Sand Dune Scrub Zizyphus Scrub maximum foliage cover for all the classes. Tropical Euphorbia Scrub

Spectral and temporal discrimination of Northern Tropical Thorn Forest with following vegetation cover types types (Table 1): The area-averaged temporal plots were se- C1 Desert Thorn Forest lected and analyzed. NDVI values obtained for C2 Ravine Thorn Forest different over types (forest and non-forest) are C3 Rann Thorn Forest plotted in Fig. 1a & b. The representative sites DS1 Zizyphus scrub selected were having variation in the NDVI re- DS2 Tropical Euphorbia scrub sponse in the individual cover types. For each lo- The forests are unevenly distributed in various cation area-averaged NDVI value was plotted for districts and most are over hilly areas which make the composite time period. The maximum values up for about 50% of the forests of the state (Figs. are true indicators of foliage cover irrespective of 2a, b & 3a, b). the area. The dry deciduous forest of teak and 1. Desert Thorn Forest: The region is flat to kardhai showed high values in the month of De- undulating, with low hillocks or hills. The soils are cember while the dry deciduous dominated by partly in situ but usually fluvial or aeolian depos- Kardhai & Salai and Kardhai & Babool have its in various stages of consolidation. On riverain shown a highest value in October. The non-forest strips and shifting sand different types are devel- classes have shown a phenological trend, having oped. Locally, consociations of certain species are high values in October and December. The differ- prominent, notably Acacia senegal and Prosopis ence in NDVI values within agriculture and sand spicigera. Calligonum polygonoides, Leptadenia dune classes has helped to differentiate them, in pyrotechnica, Crotolaria burhia etc. are typical of spite of topographical similarities. After critical dune vegetation. evaluation it is apparent that different cover 2. Ravine Thorn Forest: Acacia is conspicu- types exhibit characteristic NDVI curves. The ous, standing fairly close together where not sub- trend of the temporal variation, i.e. phenology has jected to maltreatment, with more or less complete not been affected by the climatic variations. In- grass cover between. This type occurs typically tensive data sets may help in tracing the season- with about 600 to 750 mm annual rainfall on in- ality and to establish the timing of ‘green-up’ and tensively drained ground and dry porous soils gen- ‘senescence’ and estimate the length of growing erally; it gives way to the semi-desert type where period year by year. the rainfall decreases. 3. Rann Thorn Forest: Where the soil is red- Land use/land cover pattern in Rajasthan dish and gravelly the awned grasses such as Aris- tida, Heteropogon and Chrysopogon take an upper The geographical area of the state is 342,239 hand. Salvadora and Rhus mysorensis predomi- km2. According to Forest Survey of India (FSI), the nate on waste lands and Sweda, Salsola, Ginus, actual forest area in the state occupies only 13,871 Sporobolus on saline soils. Nearby the habitat is km2, i.e. 4.05% of the geographical area while the favourable enough for dry deciduous forest (Bos- recorded forest cover is 31,700 km2 which is 9.26% wellia and Anogeissus pendula types). of the geographical area (Anon. 1999). 4. Zizyphus Scrub: Common throughout the Forest thorn belt, intermingled with the Euphorbia type which is slightly more conspicuous in rocky areas According to Champion & Seth (1968), major and owing its origin to intense biotic pressure. part of Rajasthan falls under subgroup 6B, i.e. JOSHI et al. 223

(a)

(b)

Fig. 2. (a) Forest cover map of Rajasthan (b) Forest type map of Rajasthan. 224 BIOME LEVEL CHARACTERISATION

(a)

(b)

Fig. 3. (a) Land use/cover map of Rajasthan (IRS 1C WiFS) (b) Biome level characterization of Rajasthan.

Zizyphus may have colonised old cultivation or old aphic factors also possibly involved. It occupies cutting. stony sites in general. 5. Tropical Euphorbia Scrub: Like the Zizy- Non-Forest phus scrub, this also owes its present form to ex- cessive grazing and felling of tree growth but ed- Wastelands are lands, which are degraded and at present lying unutilized or which are not being JOSHI et al. 225 used to its optimum potential due to different con- mapping at global and continental level studies. straints. Broadly these lands can be grouped as The introduction of WiFS offered an advantage cultivable or non-cultivable. As per estimates, Ra- with its spatial temporal resolutions for vegetation jasthan has about 92.56 lakh hectares of waste- studies. Earlier studies have established the appli- lands of different types. The cultivable wastelands cation of WiFS for regional studies (Joshi et al. are capable of or have the potential for develop- 2001a,b; Roy & Joshi 2000b, 2001; Singh et al. ment for agricultural or pasture purposes or even 1999a,b). Besides, multidate WiFS data has been can be afforested. The non-cultivable wastelands used for assessing crop growth within the season on the other hand are barren lands that can not be and comparison across the season. It has been put to any productive use either for agriculture or used to provide pre-harvest multiple forecasts of to develop forest cover. wheat production over large region (Bhagia et al. Various land use patterns are in practice in 1997; Oza et al. 1996) and for other crops as well the state, depending on the type of the soil and (Sehgal & Dubey 1997). water resources and the human endeavors to The concept of NDVI images is quite signifi- tackle them. Mainly six groups of land use classes cant in case of regional level mapping for the rea- are predominant in the state namely, forest, land son that the bioclimatic phenomenon are under- put to non-agricultural uses, permanent pastures, stood with the help of phenology which in itself is gullied or ravinous land, net sown area and fallow an effective subject. Understanding the dynamics land. of global change and modeling the Earth’s physical systems will require concentrated interdisciplinary Biome level characterization effort (Kramer 1997; Myking 1997; Regniere & Sharov 1999; Song 1999; Spano 1999). Phenologi- The geospatial analysis has delineated two bi- cal analyses can play an important role in the on- ome zones in the western India, Rajasthan viz. going studies. Development of systematic observa- Tropical thorn/savanna forest and Tropical arid tion networks on the national and global scale will zone (Fig. 3a). Thorn forests or thorn scrub com- be critical in realizing such potential contributions. prised of low grazing trees, shrubs and frequently The terrestrial biosphere is an active compo- stem-succulents. It is distributed in the entire nent of the Earth system. Vegetation has a strong eastern Rajasthan bordering dry humid climatic control over the flows of water, carbon and energy region in the east and dry arid climate in the west. between land and atmosphere and between land This region is comprised in the areas where an- and oceans. Vegetation develops in response to nual precipitation averages 800 mm and dry sea- climate and hydrology and it feeds back to the son extends for 6 to 8 months. The woody species Earth’s climate system. We need to develop man- are deciduous and small leafed; many have thorns agement strategies that allow sustainable use of or spines. The high anthropogenic factor has re- natural resources and that help us to adapt and sulted in the desertification of the area. The tropi- mitigate changes in the Earth’s functioning that cal arid zone is the representative of the desert may already have been set in motion. This re- ecosystem, with scattered adapted buses and xero- quires a multidisciplinary understanding of the phytic plants. This area is rarely devoid of life. interactions between the land surfaces, the climate Shrubs are the dominant growthform, with typi- system and water resources. To analyze temporal cally small leaves and frequently spines and variations in biosphere-atmosphere-hydrology in- thorns. They form an open canopy and except after teractions, long observational records are required. rains when annuals may cover the desert floor, the Issues such as long-term carbon uptake by a par- ground between shrubs in bare of vegetation ticular ecosystem, soil moisture ‘memory effects’ growth. carried from one season to the next or the inter- annual climate variability caused by cyclic pat- Discussion terns in ocean temperatures require sustained monitoring of vegetation-atmosphere fluxes over The temporal data provides a wider view to multiple years. By switching on or off certain land find out the qualitative status as well as quantita- surface processes, or by perturbing the geographi- tive status of the vegetation. NOAA-AVHRR has cal distribution of certain biome types, we ‘tickle’ been widely used over the world for vegetation the system. The reaction of other parts of the sys- 226 BIOME LEVEL CHARACTERISATION tem can then be analyzed to elucidate the mecha- date information on the current status with possi- nisms behind, for example, desertification or to ble monitoring of their status is currently very assess the consequences of tropical deforestation. pressing issues in such countries. At a finest reso- In another category of coupled models, vegetation lution, WiFS data is best suited to monitor vegeta- development and distribution interactively re- tion dynamics even in a very small interval of time spond to the simulated climate. Such studies show period at a regional scale (1:1 million) comparable that in the past in some regions a specific climate- with that available with LISS or Landsat or vegetation combination may have been stable that NOAA-AVHRR (Roy & Joshi 2001; Joshi et al. differs from the combination actually found at that 2001a,b). The present study emphasizes the use of time. Here also phenology has got an important WiFS data for land cover and land use mapping at place to hold in terms of showing variability mesoscale for regional level assessment and moni- among the vegetation structure. Bolstered by toring. The NDVI has been found related to green global surface data collection, phenological re- leaf activity and as such provides a useful means search will be poised to improve the understanding to monitor the vegetation cover/phenology. The of atmosphere-biosphere interactions that have advantage of the sensor lies in suitable spatial and implications for global change – such as the onset spectral resolutions for regional level, competitive of Spring plant growth in temperate climates band combination for vegetation studies and tem- (Schwartz 1999). poral resolution for vegetation dynamics. It offers In the case of Rajasthan, the WiFS data has wider coverage for comparison of regional or conti- proved significant for the discrimination of differ- nental level studies. Its effectiveness is in dis- ent land cover/use classes. There is an intermixing crimination of forest types and major crops and among the classes of Desert forest and Sand dune other land cover and uses. The accuracy of medium classes but the NDVI variability in these classes resolution data in regions dominate with uniform led to the proper discrimination of the land features. Hybrid approach of classification proved cover/use classes. The vegetation in Rajasthan is to be more useful than other approaches. WiFS mostly dry deciduous type and scrub. data have been found to be satisfactory (accu- In spite of the characteristic features of WiFS racy~80 to 87%) to perform forest assessment, to characterize the vegetation, it also has some mapping and delineation. limitations. The wider pixel may create complexity in interpretation, overestimation or underestima- References tion of the vegetation cover leading to inaccuracy in nomenclature/classes. This paper has empha- Alcamo, J. (ed.) 1994. Image 2.0: Integrated Modeling of sized exclusively the role of medium resolution Global Climate Change. Kluwer Academic Publish- sensor for vegetation monitoring. The regional ers, Dordrecht, The Netherlands. phyto-phenological classified map provides details Anonymous. 1994. Resource Atlas of Rajasthan. De- on vegetation stratum. This can be further ex- partment of Science and Technology, Government of ploited in conjunction with high-resolution sensors Rajasthan. and extensive ground truth to provide for quanti- Anonymous. 1999. Summary. Forest Survey of India tative forest resource information. (Ministry of Environment and Forests), Dehradun. Beadle, C.L., S.P. Long, S.K. Imbamba, D.O. Hall, &

R.J. Olembo. 1985. Photosynthesis in Relation to Plant Terrestrial Environments. Oxford United Na- Conclusion tions Environment Programme and Tycooly Pub- lishing. Proper assessment and evaluation of the re- Bhagia, N., M.P. Oza, D.R. Rajak, R.P. Singh, V.K. source potential of a country is an essential pre- Sehgal, N. Ravi, H.S. Srivastava, J.H. Patel, S.S requisite for national development. For these rea- Ray, & V.K. Dadhwal. 1997. An attempt to make sons most developing countries are now seeking National Wheat Production Forecast using multi- new technologies to improve their resource evalua- date WiFS data for 1996-19997 season. NNRMS (B), tion programs. This is true in the tropics where 21: 54. forest resources are depleting both quantitatively Champion, H.G. & S.K. Seth. 1968. A Revised Survey of and qualitatively at an ever-increasing rate. Up-to- the Forest Types in India. Govt. of India Press, Nasik. JOSHI et al. 227

Curran, P.J. 1980. Multispecateal remote sensing for Myking, T. 1997. Spring index models: dormancy, bud- estimation of biomass and productivity. pp. 65-69. burst and impacts of climatic warming in coastal In: H. Smith (ed.). Plants and Dry Light Spectrum. inland and altitudinal Betula pendula and B. pubes- Academic Press. cens ecotypes. pp. 51-66. In: H. Leith & M.D. Curran, P.J. & P. Franquin. 1980. Multispectral remote Schwartz (eds.) Phenology of Seasonal Climates. I. sensing of vegetation amount. Progress in Physics of Bakhuys, The Netherlands. Geography 4: 315-341. Oza, M.P., N. Bhagia, S. Dutta, J.H. Patel & V.K. Diallo, O., A. Diouf, N.P. Hanan, A. Ndiaye & Y. Dadhwal. 1996. National wheat acreage estimation Prevost. 1991. AVHRR monitoring of Savanna for 1995-1996 using multidate IRS-1C WiFS data. primary production in Senegal, West Africa: 1987- Journal of Indian Society of Remote Sensing 24: 88. International Journal of Remote Sensing 243-254. 12:1259-1279. Potdar, M.B. 1990. Analyses of sorghum yield depend- Holben, B.N. & R.S Frasher. 1980. Red and near infra ence on crop growth characteristics using NOAA- red response to off-nadir viewing. International AVHRR data. Journal of Indian Society of Remote Journal of Remote Sensing 5: 145-160. Sensing 18:1. Huete, A.R. 1988. A soil adjusted vegetation index Prince, S.D. 1991. A model of regional primary production (SAVI). Remote Sensing Environment 25: 295-309. for use with coarse resolution satellite data. Interna- Inouye, D.W. & A.D. McGuire. 1991. Effects of snowpack tional Journal of Remote Sensing 12: 1313-1330. on timing and abundance of flowering in Delphin- Prince, S.D. & C.O. Justice. 1991. Coarse resolution re- ium nelsonii (Ranunculaceae): Implications for cli- mote sensing of the sahelian environment. Interna- mate change. American Journal of Botany 78: 997- tional Journal of Remote Sensing 12: 1133-1421. 1001. Ray, S.S., S.S. Pokhrana & Ajai. 1994. Cotton produc- Joshi, P.K., Sarnam Singh, Shefali Agarwal & P.S. Roy. tion estimation using IRS-1B and meteorological 2001a. Forest cover assessment in western Himala- data. International Journal of Remote Sensing 15: yas, Himachal Pradesh using IRS 1C/1D WiFS data. 1085-1090. Current Science 80: 941-947. Reed, B.C., J.F. Brown, D. VanderZee, T. R. Loveland, Joshi, P.K., Sarnam Singh, Shefali Agarwal & P.S. Roy. J.W. Merchant, & D.O. Ohlen. 1994. Variability of 2001b. Land cover assessment in Jammu & Kash- land cover phenology in the United States. Journal mir using phenology as discriminant – an approach of Vegetation Science 5: 703-714. using wide swath satellite (IRS – WiFS).Current Regniere, J. & A. Sharov. 1999. Simulating tempera- Science 81: 392-398. ture-dependent processes at the sub-continental Justice, C.O., J.R.D. Townshend, B.N. Holben & C.J. scale: male gypsy moth flight phenology as an ex- Tucker. 1985. Analysis of the phenology of global ample. International Journal of Biometeorology 42: vegetation using meteorogical satellite data. In- 146-152. ternational Journal of Remote Sensing 6: 1271- Rosenberg, N.J. 1983. Microclimate: The Biological En- 1381. vironment. Second Edition. John Wiley and Sons. Justice, C.O., T.F. Eck, D. Tanre, & B.N. Holben. 1991. Roy, P.S. & P.K. Joshi. 2000a. Space remote sensing for The Effect of water vapor on the normalized differ- sustainable forest management. pp. 357-382. In: R.K. ence vegetation index derived for the sahelian re- Kholi, H.P. Singh, S.P. Vij, K.K. Dhir, D.R. Batish & gion from NOAA-AVHRR data. International Jour- D.K. Khurana (eds.). Man and Forest. DNAES, nal of Remote Sensing 12: 1165-1188. IUFRO, ISTS, Panjab University, Chandigarh. Kaufmann, M.R. 1984. A canopy model (RM-CWU) for Roy, P.S. & P.K. Joshi. 2000b. Tropical forest cover as- determining transpiration of subalpine forests. II. sessment using wide field sensor–IRS 1C. NNRMS Consumptive water use in two watersheds. Candian (B) 25: 22-29. Journal of Forest Research 14: 227-232. Roy, P.S. & P.K. Joshi. 2001. Forest cover assessment in Kramer, K. 1997. Phenology and growth of european north east India – potentials of temporal wide swath trees in relation to climate change. pp. 39-50. In: H. satellite data (IRS 1C – WiFS). International Jour- Leith & M.D. Schwartz (eds.) Phenology of Seasonal nal of Remote Sensing (In press). Climates. I. Bakhuys, The Netherlands. Roy, P.S., Sarnam Singh, Shefali Agarwal & P.K. Joshi. Lloyd, D. 1990. A phenological classification of terres- 2001. Biome level classification of vegetation in trial vegetation cover using shortwave vegetation western India – An application of wide field sensor index. International Journal of Remote Sensing 11: (WiFS). IGBP in India 2000 – a status report on pro- 2269-2279. jects. Indian National Science Academy, New Delhi. 228 BIOME LEVEL CHARACTERISATION

Schwartz, M.D. & T.R. Karl. 1990. Spring phenology: Singh, Sarnam, Shefali Agarwal, P.K. Joshi & P.S. Roy. nature’s experiment to detect the effect of ‘Green-up’ 1999b. Vegetation mapping through phenological on surface maximum temperatures. Monthly variability - An application of multidate IRS 1C/1D- Weather Review 118: 883-890. WiFS data. XIX INCA International Congress Schwartz, M.D. 1992. Phenology and springtime surface Vasco-Da-Gama (Goa), India - 26th to 28th Oct. layer change. Monthly Weather Review 120: 2570- 1999. 2578. Song, J. 1999. Phenological influences on the albedo of Schwartz, M.D. 1994. Monitoring global change with Prairie grassland and crop fields. International phenology: The case of the spring green wave. Inter- Journal of Remote Sensing (In press). national Journal of Biometeorology 38: 18-22. Spano, D. 1999. Phenological stages of natural species Schwartz, M.D. 1996. Examining the spring discontinu- and their use as climate indicators. International ity in daily temperature ranges. Journal of Climate Journal of Biometeorology 42: 124-133. 9: 803-808. Townshend, J.R.G., T.E. Golf & C. J. Tucker. 1985. Mul- Schwartz, M.D. 1999. Advancing to full bloom: planning tispectral dimensionality of images of normalized phenological research for the 21st Century. Interna- difference vegetation index at continental scales. tional Journal of Biometeorology 42: 113-118. IEEE Transaction on Geoscience Remote Sensing 23: Sehgal, V.K. & R.P. Dubey. 1997. Evaluation of poten- 888-895. tial usefulness of IRS-1C WiFS simulated data for Tucker, C.J., R.G Townshend & T. Goff. 1985. Continen- dry land rabi sorghum crop discrimination. Photon- tal land cover classification using NOAA-7 AVHRR irvachak 25: 137-147. data. Science 227: 369-375. Sharma, P.D. 1997. Ecology and Environment. Rastogi Wylie, B.K., J.A. Harrington, S.D. Jr, Prince & I. Denda. Publications. 1991. Satellite and ground based pasture production Singh, Sarnam, Shefali Agarwal, P.K. Joshi & P.S. Roy. assessment in Niger: 1986-88. International Journal 1999a. Biome level classification of vegetation in of Remote Sensing 12: 1281-1300. western India - An application of wide field view sensor (WiFS). Joint Workshop of ISPRS Working Groups I/1, I/3 and IV/4: Sensors and Mapping from Space, Hanover (Germany). 27-30 Sept 1999.