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Remote Sensing of Forest Decline in theCzech Republic

fiNBC6lVE0 m OST i ■■ '' Jonas Ardo

MEDDELANDEN FRAN LUNDS UNIVERSITETS GEOGRAFISKAINSTITUTIONER avhandlingar 135 DISCLAIMER

Portions of this document may be illegible electronic image products. Images are produced from the best available original document. MEDDELANDEN FRAN LUNDS UNIVERSITETS GEOGRAFISKA INSTITUTIONER Avhandlingar 135

Remote Sensing of Forest Decline in the Czech Republic

Jonas Ardo

1998 LUND UNIVERSITY, SWEDEN DEPARTMENT OF PHYSICAL GEOGRAPHY Author's address:

Department of Physical Geography Lund University Box 118, S-221 00 Lund Sweden

Email: [email protected] URL: www.natgeo.lu.se

Lund University Press Box 141 S-221 00 Lund Sweden

Art nr 20532 ISSN 0346-6787 ISBN 91-79-66-528-4

© 1998 Jonas Ardo

Printed by KFS AB, Lund, Sweden 1998. Remote Sensing of Forest Decline in the Czech Republic

Jonas Ardo N aturgeografiska institutional! Matematisk-naturvetenskapligfakultet Lunds universitet, Lund

Avhandling FOR FILOSOFIE DOKTORSEXAMEN

som kommer att offentligen forsvaras i Geografiska institutionemas forelasningssal, 3:e vaningen, Solvegatan 13, Lund, onsdagen den 20 maj 1998, kl. 13.15

Fakultetsopponent: Professor Curtis Woodcock, Department of Geography, Boston University, Boston, USA Document name LUND UNIVERSITY DOCTORAL DISSERTATION Physical Geography Date of issue Remote Sensing and GIS 1998-04-27 CODEN: ISRN lu : OTU3/NBNG-96/ 1135-SE Autbor(s) Sponsoring organisation Jonas Ardo Swedish National Space Board Title and subtitie Remote Sensing of Forest Decline in the Czech Republic

This thesis describes the localisation and quantification of deforestation and forest damage in Norway spruce forests in northern Czech Republic using Landsat data.

Severe defoliation increases the spectral reflectance in all wavelength bands, especially in the mid infrared region. These spec­ tral differences allow the separation of three damage categories with an accuracy of 75% using TM data and regression-based relationships. Estimating the same categories using an artificial neural network, multitemporal TM data and topographic data yields slightly higher accuracy (78%). The methods are comparable when using identical input data, but the neural network more efficiently manage large input data sets without pre-processing.

The estimated coniferous deforestation in northern Bohemia from 1972 to 1989 reveals especially affected areas between 600 and 1000 m.a.s.1. and on slopes facing south and southeast. The sector downwind a large source of sulphur dioxide was strongly deforested.

Comparing regional forest damage statistics to three methods estimating harmful effects of sulphur dioxide on Norway spruce yielded significant relationships versus level of forest damage and accumulated salvage felling. Quantifying the effect of data uncertainties permit mapping the probabilities of areas to be significantly over or below thresholds for harmful effects on spruce forests.

Satellite based estimation of coniferous forest health is a good complement to field surveys and aerial photography.

Key words Remote sensing, Landsat, spectral characteristics, GIS, forest decline, deforestation, Norway spruce, air pollution, sulphur dioxide, neural networks, Czech Republic, Ore Mountains

Classification system and/or index terms (if any)

Supplementary bibliographical information Language English

ISSN and key title ISBN 0346-6787, Meddelande fr&n Lunds Universitets Geografiska Institutioner, Avhandlingar 135 91-79-66-528-4 Recipient’s notes Number of pages 150 Security classification

Distribution by.- Lund University Press, Box 141 S-221 00 Lund, Sweden I the undersigned, being the copyright owner of the above-mentioned dissertation, herby grant to aii reference sources permission to publish and disseminate the abstract of the above-mentioned dissertation.

Signature Date April 27, 1998 Contents

List of papers...... 4

Abstract...... 5

1. Introduction...... 6

2. Background...... 7 2.1. Forests and forest decline...... 7 2.2. Forest decline in Europe and the Czech Republic...... 8 2.3. Causes of forest decline...... 9 2.4. Czech Republic and North Bohemia...... 14 2.5. Assessment of forest decline...... 19 3. Remote sensing of forest decline...... 20 3.1. Factors affecting the spectral reflectance from forests...... 20 3.2. Factors related to forest decline...... 21 3.3. Previous satellite remote sensing studies of coniferous forest decline...... 24 4. Present investigation...... 29 4.1. Introduction...... 29 4.2. Objectives...... 29 4.3. Volume quantification of coniferous forest compartments [I]...... 29 4.4. Influence from forest stand variables on vegetation indices used for coniferous forest damage assessment [II]...... 30 4.5. Spectral characterisation and regression based estimates of forest damage in the Czech Republic [III]...... 31 4.6. Neural networks, multi temporal TM data and topographic data to classify forest damage [IV]...... 32 4.7. Forest cover changes in the Ore Mountains 1972-1989 [V]...... 33 4.8. Critical levels of S02 - uncertainty and to regional forest decline [VI]...... 34 5. Conclusion and future perspective...... 35 5.1. Conclusion...... 35 5.2. Future perspective...... 35 Acknowledgement...... 36

References...... 37

Appendices: paper I-VI List of papers

This thesis is based on the following papers, which are referred to in the text by their Roman numeral.

I. Ardo, J. 1992. Volume quantification of coniferous forest compartments using Spectral Radiance recorded by Landsat Thematic Mapper. International Journal of Remote Sensing 13:1779-1786. II. Ardo, J. 1992. Influence from forest stand parameters on vegetation indices used for coniferous forest damage assessment. Proceedings of the ASPRS/ ACSM/RT Conference in Washington, USA, August 2-14, 1992, pp. 523- 531, (ISBN 0-944426-88-3). III. Lambert, N.J., Ardo, J., Rock, B.N. and Vogelmann, J.E. 1995. Spectral Characterization and regression-based estimates of forest damage in Norway Spruce stands in the Czech Republic using Landsat Thematic Mapper data. International Journal of Remote Sensing 16:1261-1287. IV. Ardo, J., Pilesjo, R and Skidmore, A. 1997. Neural networks, multi temporal TM data and topographic data to classify forest damage in the Czech Republic. Canadian Journal of Remote Sensing 23:217-229. V. Ardo, J., Lambert, N. J., Henzlik, V. and Rock, B. N. 1997. Satellite Based Estimations of Coniferous Forest Cover Changes: Krusne Hory, Czech Republic 1972-1989. Arnbio 26:158-166. VI. Ardo. J., Barkman, A. and Arvidsson, R 1998. Critical levels of S02 - uncertainty and relationship to regional forest decline in the Czech Republic. Submitted.

Reproduced with permission from the publishers: Taylor & Francis [I, III], Canadian Remote Sensing Society, [IV] and the Royal Swedish Academy of Sciences [V],

4 Abstract

This thesis describes the localisation and quantification of deforestation and forest damage in Norway spruce forests in northern Czech Republic using Landsat data. Severe defoliation increases the spectral reflectance in all wavelength bands, especially in the mid infrared region. These spectral differences allow the separation of three damage categories with an accuracy of 75% using TM data and regression- based relationships. Estimating the same categories using an artificial neural network, multitemporal TM data and topographic data yields slightly higher accuracy (78%). The methods are comparable when using identical input data, but the neural network more efficiently manage large input data sets without pre­ processing. The estimated coniferous deforestation in northern Bohemia from 1972 to 1989 reveals especially affected areas between 600 and 1000 m.a.s.l. and on slopes facing south and southeast. The sector downwind a large source of S02 was strongly deforested. Comparing regional forest damage statistics to three methods estimating harmful effects of S02 on Norway spruce yielded significant relationships versus level of forest damage and accumulated salvage felling. Quantifying the effect of data uncertainties permit mapping the probabilities of areas to be significantly over or below thresholds for harmful effects on spruce forests. Satellite based estimation of coniferous forest health is a good complement to field surveys and aerial photography.

5 1. Introduction

This thesis describes the attempts to localise and quantify damage to coniferous forests and deforestation in the northern part of the Czech Republic using reflected electromagnetic radiation, recorded by orbiting satellites. An effort to characterise especially affected areas, in terms of topography and S02 concentration is incorporated. This work is relevant for understanding some of the spectral and spatial properties related to the severe forest decline and deforestation in Czech coniferous forests. Papers I and II describe the influence of various forest stand variables on stand reflectance. Papers III and IV quantify the reflectance change due to defoliation and estimate defoliation using empirically derived relationships. Paper V describes some of the spatial and temporal properties of the severe deforestation in northern Czech Republic. In paper VI, the aerial concentration of S02 is related to regional forest decline statistics, identifying areas significantly exceeding thresholds for harmful effects on coniferous forests. Section two of the thesis describes the forest decline problem, some of its potential causes and gives an overview of northern Bohemia, Czech Republic. Section three briefly reviews the spectral characteristics of needle loss and studies using satellite remote sensing to localise and quantify forest decline. The main result of the thesis is summarised in section four with conclusions and future perspectives in section five.

6 2 Background

2.1. Forests and forest decline

Forests are important, they provide raw materials and fuel as well as form inevitable environments for many organisms. Covering extensive areas, forests play a significant role in the carbon cycle, influence the climate, the water regime and protect soils against erosion. Their decline may disturb these functions. Forest decline and forest damage are terms widely used to describe the deteriorating health status of forest ecosystems including metabolic changes, reproduction problems, early leaf senescence, discoloration, decreased and altered growth, altered branch and crown morphology, loss of foliage, and finally tree death. Forest decline is mostly attributed to human activities, often air pollution, but natural forest decline also occurs [57,61].

2.1.1. Developmentof the forest decline issue

Forest decline first received public attention in the 1980s when Norway spruce (Picea abies) trees showed damage on widespread areas in Germany. Symptoms, such as chlorosis and needle loss, at that time unexplained by known pathogens and other factors, appeared extensively [111]. The discussion of potential causes of this ‘new forest decline’ (‘neuartige waldschaden’, ‘waldsterben ’) centred on air pollutants and acidic precipitation which were widely believed to play a major role [15,112]. Scientific reports questioned the sustainability of forest ecosystems in central Europe [112] and the need for major extra felling of declining and dead trees was predicted [64]. The alarming reports of forest decline and widespread damage in Europe on several species in the early 1980s triggered a demand for information regarding forest health status. Consequently, many monitoring programs were launched for assessment of forest health and investigation of possible causes [15, 111]. These initial predictions of a continuously increasing damage, and thus a large economic loss, were not confirmed and the occurrence of several damage types and causing agents were accepted [16]. During the last decade, the public interest has waned, but the investigations have continued and the issue is still a matter of discussion [14,105], with manifold opinions. Gregory et al. estimated the loss of forest growth in Europe to 80 million m3 with a value of £1840 million (roughly 3000 million US$) [44]. On the other hand, Skelly and Innes even suggested that the general idea of the phenomenon ‘Waldsterben' or forest decline related to air pollution is a fantasy [113]. These two studies mentioned above exemplify the wide range of opinions. 2.1.2. Is there a forest decline?

Despite the prodigious amounts of research, much of which has have been focusing on air pollution, there are still questions as to the wide-spread forest decline in temperate regions. The mere presence of widespread, large-scale forest declines at the present time in Europe and North America has been questioned in recent reviews [57,58,62-64,113]. The dominating role of air pollution has also been questioned and an increased attention to climate factors is suggested [11, 57]. Kandler [63] concluded as two of five points that: However, a decade of research on novel forest damage in western Germany has shown that: (1) no spatial and temporal correlation between novel forest damage and air pollution could be shown and (2) retrospective studies on forest conditions suggest that similar levels of crown transparencywere found in Norway spruce at the beginning of this century as are today and that recurrent decline episodestake place in the main tree species. Instead, forest decline is proposed to be a problem of awareness, where earlier “normal” forest conditions (including declines) now are symbolising the fear for harmful human environmental impacts [63, 64]. In contrast to decline and decreased growth, increasing growth trends have been reported for European forests [118], with faster growth during the second half of this century than during the first [63]. This increase, caused by nitrogen deposition, increased carbon dioxide concentration and improved management may offset growth reductions caused by air pollution. A recent review [14] of nitrogen effects in Swedish forests concluded that N deposition represents no immediate threat of forest decline. Current rates of N deposition do not reduce forest growth on N-sufficient sites, instead they argue that the Swedish forests are still, in the 1990s, as limited by N nitrogen availability as in the 1960s. They consider the liming of forest soils to generally decrease forest growth and this would clearly not be good for forest health in Sweden.

2.2. Forest decline in Europe and the Czech Republic

Irrespective of the reports mentioned above, the health status of the European forests has deteriorated over the last 11 years, particularly in Central Europe [36, 84]. According to the International Co-operative Programme on assessment and Monitoring of Air Pollution effects on forests (ICP forest) 25.1% of the sample trees in Europe were defoliated by more than 25% in 1996 [36]. This deterioration and subsequent deforestation negatively influence forestry, forest industry [80], availability and quality of water [79], soil erosion [108], biodiversity and have negative social consequences [120]. Consequently, large values are ruined [2,35]. The proportion of conifers in the Czech Republic with a defoliation above 25%

8 has increased substantially from 1988 (38%) to 1996 (75%) (Figure 1). These data are the result of a survey of >12000 trees [36]. According to Czech statistics, a deterioration of forest health, on the national level, has occurred between 1984 and 1994 [5]. Older data is not readily available for the entire country but included below for the most affected north-western part of the Czech Republic (see §2.4).

Figure 1. Per cent of sample trees in the Czech Republic with >25% defoliation from 1988 to 1995 (1989 data is missing) [36].

The declining health status of European forests, especially the conifers, is often attributed to air pollution even if numerous explanations have been proposed.

2.3. Causes of forest decline

Many potential causes of forest decline have been investigated. Air pollution, which causes damage to trees through the direct effects of gaseous pollutants and the indirect effects of altered soil properties, is often considered a crucial factor with harmful effects on forests. Additional factors related to climate and management can also be detrimental to forests. Below, some of the proposed explanations from the extensive literature are briefly reviewed.

2.3.1. Direct effects of air pollutants on foliage

Direct effects on foliage and plant metabolism may occur due to gaseous pollutants such as S02[114,116], NOx [32] and 03 [23]. Direct damage to the leaf protecting epicuticular wax causes leakage of nutrients and facilitates attacks from fungi, insects and parasites. Photosynthesis and stomatal behaviour are disturbed, causing decreased productivity and increased sensitivity to drought and frost. For example, in areas with high aerial concentration of S02, the majority of the annual stomatal uptake is detoxified via soi accumulation in the needle vacuoles. K+ and Mg2+ are consumed for the charge balance of SO<\ reducing the amount of these elements available for growth and hence reducing tree vitality [114, 116]. The increased needle sulphur content reduces the photosynthetic capacity and hence the vitality is reduced [92]. Wet acidic deposition, through rain and occult deposition are often large at high altitudes. The occult deposition, including interception of fog, mist, rime and cloud, often have higher element concentrations, sometimes up to ten times higher than rain and is potentially a contributing factor in forest decline [72, 75].

2.3.2. Soil effects

Soils are acidified by the deposition of hydrogen, sulphur and nitrogen oxides as well as by plant cation uptake. The deposition of ammonia (NH3) is not acidifying until it is nitrified in the soil. Soil acidification could be defined as a decrease in the acid neutralising capacity. The neutralising capacity is a measure of capacity compared to pH, which is a measure of intensity. This distinction is important as pH can changes readily with the addition of mobile anions whereas the neutralising capacity change more slowly upon acid addition [127]. Acidic inputs are largely neutralised and buffered within the soil, primarily as a result of weathering and cation exchange reactions. In most acidic forest soils, the weathering rates of silicate minerals are too low to compensate for elevated acidic deposition rates [27]. Further acidification is caused by removal of base cations through biomass removal (harvest). The acid input decreases the base saturation, increases the exchangeable acidity and decreases the soil pH. Base cations become more mobile and leach to lower soil horizons before removal by runoff. Decreases in base saturation and pH also depend on the capacity of the cation exchange buffer range and the aluminium buffer range. The aluminium buffer range is summer drought. Mg deficiency reduces fine-root growth and decreases the photosynthetic capacity [56]. Additionally, decreased pH increases the solubility of potentially harmful metals such as Cd, Pb, Cu, Mn and Zn in the soil. The micro flora and fauna of the soil may also be altered, causing decreased decomposition of organic matter [83]. In addition to soil acidification, nitrogen deposition disturbs the nutrient balance with an increased demand for nutrients such as K, Mg and Ca. These nutrients, which may also leach from the soil due to acidification, may not be sufficiently available in the long term. Excess nitrogen also causes reduced winter hardiness [9] and less occurrence of mychorrizae [45]. An increased nitrogen supply causes the above ground biomass to be disproportionate to the root biomass, increasing drought susceptibility and the risk of other weather related injuries.

2.3.3. Other possible causes

Various additional factors have been proposed to play a role in forest decline. Climatic fluctuations such as drought, frost events during late spring and early autumn and high winter temperatures can be of major importance [58]. Nitrogen induced crown enlargement and reduced root systems might increase the sensitivity of trees to storms [60]. Innes stresses the importance of climate and claims that “There is strong correlation between the occurrence of climatic stress, particularly drought, and the occurrence of declines, both in space and time” [57]. Numerous insects, fungi, viruses and other pathogens may damage and reduce the vitality of the trees and make them more susceptible to other types of stress. Unsuitable species and proveneances with a genetic adaptation to sites different from the present growing conditions can act as predisposing factors. Norway spruce have been widely planted outside its natural range, this is especially common in central Europe and in the main centres of forest damage [50]. The low biodiversity of many Norway spruce (and other species) stands, increases the susceptibility to a range of stresses [57]. Many eastern European countries, including the Czech Republic have very high game populations (deer, roe deer), severely damaging forest regeneration and younger spruce stands. Former unsuitable management, such as the occurrence of litter raking and present management such as liming and fertilising contribute to the current forest vitality. 2.3.4. Multiple stress

It is difficult to determine the relative contribution of the factors affecting the interactions of the lithosphere, biosphere and atmosphere within forest ecosystems. This is especially notable during studies and experiments in the field. The large variation in proposed causes of forest decline supports the idea that there is not one explanation, but many, to the decline of forests [86 , 112]. European and Czech forest ecosystems have a large variation in their abiotic and biotic environments. It is hence apparent that forest decline can not be explained using one global or continental explanation but regional and local explanations are needed. We might divide the proposed factors into predisposing, triggering and causes of death (Figure 2). The predisposing factors decrease the physiological conditions of the trees and include acidic and nitrogen deposition, direct effects of gaseous pollutants, heavy metals and climatic changes. The predisposing factors make the trees more susceptible to triggering factors such as drought, frost, excess precipitation, storm, fast growing monocultures, unsuitable species and proveneances. The final death of the tree might be caused by extreme weather situations, insects or pathogenic fungi on root and stem [86 ]. The relative contribution of the factors vary with space, time and specie [81]. Consequently, the classification of the factors in Figure 2 is not constant, triggering factors can be predisposing and sometimes lethal to trees [86 ]. The predisposing factors generally show a larger inertia than the triggering factors, resulting in slower alteration of their state. This is valid for soil acidification, nutrient imbalance and altered root/stem biomass proportion. The direct effects of gaseous pollutants may have acute effects at high concentrations and chronic, long time effects at lower concentrations [114,115]. Accordingly, the death causes may act faster than the triggering factors. A severe temperature drop, which in combination with S02 exposure is proposed to be harmful to Norway spruce forests, exemplifies this in [78] (see §2.4 below). Considerable spatial variation is often found in the field with dead trees next to apparently healthy ones [62]. This variation may be caused by the spatial variation of the triggering factors and of the causes of death. Management factors such as plantation, selection of species and provenances, thinning and cutting practices may vary among forest stands and among forest management units. Vulnerability to drought and water logging may vary due to local topography and soil conditions. This may also infer differences in soil chemistry, weathering rates and nutrient availability. Trees at the edge or in a gap of a stand are more exposed to wind, temperature changes and receive elevated amounts of dry deposition compared to the interior of the stand [51,59]. The individual trees are also affected by competition and ecological sheltering [129]. Critics of the multiple stress hypothesis argue that it is “vague enough to apply to any damage that can not be explained in any other way" and its versatility makes it impossible to test [16].

12 To summarise the numerous efforts trying to explain the relationships of forest decline versus the environment we might state that some forest decline can be explained sometime, but all the forest decline can’t be explained all the time.

Causes of Death

Pathogenic fungi Noxius insects (on root and stem) (on stems)

Triggering factors

------\ Heavy precipitation; Drought Frost waterlogging j Storm

Unsuitable specie i Monocultures, (in relation to soil | increased growth and climate)

Predisposing factors

Acid N-dep Gases+UV Heavy Climatic deposition (SO., 03etc) metals change Erosion on Increased growh, Cell damage, Negative Physiological leaves, soil soil acidification, increased oxygen decom­ stress acidification, nutrient imbalance, radicals, changed position effects, root damage, imbalance root metabolism nutrient /stem, imbalance decreased frost hardiness V

Figure 2. Schematic overview of common relationships between stress factors and tree damage. Modified from [86]. 2.4. Czech Republic and North Bohemia

2.4.1. Czech forests

During the second half of the IS"1 century the generic composition of the Czech forest changed. The originally broadleaf and mixed growths were substituted by unmixed stands of pine and later on spruce. Already in the first half of the 19th century, clear cutting was predominantly occurring followed by the artificial restoration and planting of monocultures of spruce and pine. Only remnants of the original beech and oak forest remain today [98]. Today, one third of the Czech Republic is forest land, a proportion that has increased the last 100 years. The forestry sector provides one per cent of the gross national product and employs approximately 45,000 people. The percentage of deciduous forest has increased from 12.5 (1950) to 21.3 (1994) with an equivalent reduction of conifers. This is the result of the Czech forest policy, aiming to increase the resistance of the forests to “unfavourable factors” [5]. According to national statistics, the forest health conditions have deteriorated since 1984 [5], but locally this has occurred much longer [26, 79], with damage reported from the Ore Mountains (Krusne Hory in Czech, Erzgebirge in German) in 1947 and 1956 [72, 79, 123]. Still, the Czech Republic has the fourth largest growing stock volume per hectare (m3 ha1) and the fourth largest annual increment per hectare (m3 ha1 y ') in Europe [5].

5D Kilometers

Klasterec and Janov forest enterprises

/ 18.00" E

49 .00-

100 Kilometers 12 .00- E Figure3. Overview of the Czech Republic and the study areas. Papers [II1-V] concerns the dark shaded area, paper [VI] the light grey area as well.

14 2.4.2. The Czech Environment

The soil, water and air of the Czech Republic have been, and still are, severely polluted. The spatial distribution of the pollution problems is irregular and depends on the location of natural resources, industrial centres and coal burning power plants. Whereas South Bohemia is comparably less polluted, the environment in northern Bohemia, the industrial region of northern Moravia, and Prague with surroundings are severely disturbed [3,4, 24, 25, 119] (Figure 3). Comparatively, on a European level, low life expectancies, high infant mortality and cancer rates suggest a harmful impact on the Czech population as well [25]. For northern Bohemia, the health statistics are even worse [66 ]. The present investigation (§4) mainly concerns the Ore Mountains in northern Bohemia (III, IV, V and VI) and hence the following description focus on this area.

2.4.3. Northern Bohemia

Northern Bohemia is an important region for energy production, chemical, metallurgical and textile industries along with glass and ceramic manufacturing. Most important is the open cast mining for lignite brown coal, producing 60 per cent of the domestic coal supplies. The thermal power stations in the region provide two thirds of the domestic electricity demand [25]. These power plants and the industry complexes emit large amounts of S02 (Figure 4), NOx, particulate matter [3,6 , 12,68 ] and heavy metals [107]. These emissions cause large deposition of acidifying substances (H+, NOJ, NHl, SO4 ) [3, 85]. Base cations (Ca2+, Mg2+, K+, Na+ ) originating from the fly ash emitted by the power plants are also deposited, neutralising a part of the acidic deposition. Recent reductions of S02 emissions have been partly counteracted by the accompanying reduction in base cation deposition [52]. High aerial concentrations of S02, NOx, and Os, exceeding critical levels [126], frequently occur in Northern Bohemia [3,34], (Figure 5). Consequently, large parts of the North Bohemian environment are strongly to extremely disturbed [130]. High deposition of arsenic, cadmium, copper, lead, nickel and vanadium occur in some areas [13, 107]. 1000

1960 1965 1970 1975 1980 1985 1990 1995 Year Figure 4. Emission ofS02 in the Ore Mountain region from 1963 to 1991. The solid line is a 3 year movingaverage [71 ].

— 120

Year

Figure 5. Measured mean annual concentration ofS02 in the Ore Mountains from 1971 to 1995. The solid line is a 3 year moving average. The average number of measuring stations per year is 61.

2.4.4. Topography, geology, soils and climate of the Ore Mountains

The Ore Mountains are part of the Bohemian Massif, a large geologic feature occupying Bohemia, western Moravia and adjacent parts of Austria, Poland and Germany. The Ore Mountains consists of a plateau, gradually sloping towards the German side with steeper slopes towards the north Bohemian basin on the Czech side. These steeper slopes were created by movements along the Ore Mountains

16 fault, stretching from south-west to north-east. The elevation difference between foot and summit ranges from 500-700 m (meters) with the highest peaks in the western part (Klinovec, 1244 m) [122]. The area is comprised primarily of schists, phyllites and granitoids which are mostly covered by mineral soils of local origin, mainly podzols and cambisol [122]. Peat bogs and peat soils, of importance for local water management, are also present [71]. The natural soil development has been disturbed by the replacement of original forest to pine and spruce forest [71]. The surface horizons of the mineral soil are strongly to extremely acidic and very poor in Mg, Ca and K [76]. In general, soil pH in the area is lower than 3.5 - 4.0 with extremes reaching 2.2 [72]. Soil pH = 4.0, similar to those measured in normal podsolized brown forest soils at other sites was recently found in the Ore Mountains [93]. Enrichment of heavy metals in the soil occur as a result of fall out of solid particles of local origin [71]. The average annual rainfall in the area is 840 mm, ranging from 500 mm in the basin to 1100 mm at higher elevations [70, 90]. Prevailing winds blow from the west or southwest [24, 66 ]. The average annual temperature is 5.4° C, ranging from 3° C at higher elevations to 8° C at lower elevations. There are annually more than 50 days of continuous fog and 70% of the days are cloudy. Inversions preventing air pollution dispersal often occur during winter, causing accumulations and high concentrations of air pollutants [3, 6 , 66 ]. A meterological event that been associated with increasing conifer damage the following growing season is the large and fast drop in temperature that occurred when 1978 turned 1979 (Figure 6 ) [78,79]. Following five days with a minimum temperature >0°, the temperature dropped from 9.2° (max. on Dec. 31) to -19.1° (min. on Jan. 1), a difference of 28.3° C within two days.

B -5

Figure 6. Minimum and maximum temperature in the Ore Mountains from December 25, 1978 to January 5, 1979. Meteorological station Nova Ves s Horach (id 11432,13.48°E, 50.6°N, altitude 729mas l). 2.4.5. Forests in North Bohemia

Large changes and disturbances have occurred in the Bohemian forests during the last centuries. Today, no Bohemian forest areas fully undisturbed by man exists and areas with genetic material of local origin are small. Large parts of the native oak and beech forest have deliberately been replaced by coniferous forest, mainly Norway spruce, during the last century [72, 79]. Unsuitable seed material have often been used when regenerating Norway spruce [98]. The share of deciduous trees used in present reforestation is increasing [5].

2.4.6. Forest damage in North Bohemia

Already in 1923 there was a warning for the possible harmful influence of air pollution on vegetation issued and the first large scale injury to spruce forest occurred in the Ore Mountains 1947 [79]. According to Czech national statistics, all forest areas in North Bohemia are damaged today, 95 per cent in East Bohemia but only 22 per cent in West Bohemia and 15 per cent in South Bohemia [4], Large areas have been damaged since the 1950s and since 1958 approximately 47300 hectares have been cut and removed due to poor health status, mainly in North Bohemia (Figure 7). To avoid insect infestations and a reduced timber price due to low quality and thus securing the economic benefit, these forest stands were cut and sold before dying [70,72].

Year

Figure 7. Salvage felling of damaged stands in the northeastern Ore Mountains from 1963to 1991 [71 ]. The solid line is a three year moving average.

18 2.4.7. Remedial actions and substitute species

During the last decades, damaged and removed Norway spruce stands in polluted areas have been replaced with ‘substitute species’. These species, supposed to be less sensitive to air pollutants than Norway spruce include Pinus silverstris, Picea pungens, Pinus mugo [116], Picea mariana and Picea omorika [79]. In the substitute forest regeneration process in the Ore Mountains, Betula sp., Sorbus acuparia, Populus tremula, Fagus silvatica (at lower elevations), Larix deciua and Pinus contorta, among others, have also been used [71].

2.5. Assessment of forest decline

The most frequently used methods for the assessment of forest health, its magnitude, spatial distribution and development, are visual assessments, non-visual assessments and remote sensing [57]. Visual assessments commonly identify symptoms such as foliage discoloration, morphology, needle retention, needle loss and tree mortality on samples plots, distributed in various sampling schemes. Non visible methods include measurements of the physiological and biochemical status of the trees, sometimes aiming at an ‘early warning’, possibly indicating a predisposition to severe injuries. An advantage is the actual measurement instead of visually assessed variables. The disadvantages are the costly and time consuming field measurements, destructive measurements and the large variation within a branch, a tree or at a specific site [57]. Remote sensing methods include among others, the use of aerial photography, airborne sensors and satellite borne sensors. Aerial photography has frequently been used in applied forest decline inventories [28,73], commonly in scales between 1:4000 and 1:10000, sometimes as large as 1:2000 [110]. The method provides good information on chlorosis, needle loss and tree mortality [57]. The advantages are good estimates of foliage loss and discoloration, especially when using colour infrared film. Disadvantages are the high cost and the short suitable time for taking the pictures. Satellite remote sensing are treated separately below (§3). Needle loss and discoloration, the predominately assessed variables, are the results of many stresses with many potential causes [86 , 112]. Additional environmental variables are assessed to investigate possible relationships with crown condition data. These include abiotic site information on soil, geology, altitude, slope, aspect, air pollution (S02, NOx, 03) landform, climatic variables and biotic data regarding vegetation types and animals as bio indicators. This is performed at permanent monitoring plots, uniformly throughout Europe within the ICP Forest activities [36, 84]. 3. Remote sensing of forest decline Below, the reflectance properties of forest canopies with emphasis on the spectral characteristics of declining coniferous forests are briefly described. The domains of the electromagnetic spectrum referred to are the visible (0.4-0.7 pm), near infrared (NIR, 0.7-1.2 pm) and the middle infrared (MIR, 1.2-2.5 pm). A review of previous studies estimating needle loss in coniferous forests using satellite borne sensors is also provided. The characteristics of the most frequently used sensors in these studies, Landsat Thematic Mapper (TM), Landsat Multispectral Scanner (MSS) and Systeme Propatoire d'Observation de la Terre High Resolution Visible (SPOT HRV) are described in Table 1.

Table 1. Characteristics of the MSS, TM and SPOT sensors, modified from [99]. Instrument Spectral bands Geometric resolution [pm] [m] MSS 4a 0.5-0.6 (green) 79x79 5. 0.6-0.7 (red) 79x79 6 . 0.7-0.8 (NIR) 79x79 7. 0.8-1.1 (NIR) 79x79 TMb 1. 0.45-0.52 (blue) 30x30 2. 0.52-0-60 (green) 30x30 3. 0.63-0.67 (red) 30x30 4. 0.76-0.90 (NIR) 30x30 5. 1.55-1.75 (MIR) 30x30 7. 2.08-2.35 (MIR) 30x30 SPOT HRV 1. 0.50-0.59 (green) 20x 20 (Multispectral 2. 0.61-0-68 (red) 20x 20 mode)c 3. 0.79-0.89 (NIR) 20x 20 a MSS bands 4 to 7 have been renumbered to MSS bands 1 to 4 from Landsat 4 onwards b TM band 6, 10.4-12.5 pm, (thermal), not included here. c Can also acquire data in panchromatic mode (0.51-0.73 pm, 10 x 10 m resolution).

3.1. Factors affecting the spectral reflectance from forests

3.1.1. Forest stand factors

The visible, NIR and MIR reflectance of a forest stand generally decreases with age [20,67,69,97], biomass [69], canopy closure [22], leaf area index (LAI) [117] and forest stand volume [97]. These factors are usually positively correlated to

20 each other. This negative relationship is especially strong in the visible and MIR domain and when the canopy closure is low [42,54]. It will reach a point of saturation as the canopy closure approaches 100 per cent. Accordingly, since basal area and volume continues to increase when the stand grow older, no further decrease in reflectance occurs due to the large sensitivity of the remotely sensed signal to the canopy closure [42]. The effect of the understory is closely linked to the canopy cover, with an increasing impact from the background, simultaneous as the amount of the low reflective canopy decreases [22]. This effect is enhanced in forests with low canopy reflectance and high understory reflectance. Thus, the relationships of canopy cover versus visible and MIR reflectance will be stronger for coniferous forests than deciduous, and further enhanced by a bright background. In the NIR region, the correlation between volume and volume related variables versus reflectance may vary from positive [117], no correlation [42] to negative [7, 97]. Negative correlations occur in forests with an open canopy where the sub ­ canopy has higher NIR reflectance than the canopy, while positive correlations occur at high leaf area index when the canopy is closed [117, 124]. Spruce canopies generally have lower reflectance than pine canopies [67] and deciduous canopies have higher reflectance than coniferous, especially in the NIR domain [7, 18]. The NIR reflectance increases with the proportion of deciduous trees in the stand [7].

3.2. Factors related to forest decline

3.2.1. The visible domain (0.4-0.7 jam)

The main part of the incoming radiation in the visible domain is absorbed by the leaf pigments, roughly 10 per cent is reflected. Chlorophylls, caroteniods and anthocyanins, with strong absorption in the blue (0.35-0.50 pm) and red part (0.62- 0.70 pm), dominate the reflectance properties in this domain. The reflectance in the green region (0.50-0.62 pm) is slightly higher. Due to the low reflectance in the visible portion of the spectrum, reflectance from only the uppermost portion of the canopy is detected and measured by the sensor [136]. In dense canopies, the influence from soil, understory and lower parts of the canopy is small. This influence increases when the leaf cover is reduced. A decrease in pigment and nutrient concentration, chlorosis, due to leaf senescence or damage, increase the reflectance in the blue and red regions and decrease the green reflectance [53, 69, 104]. As the needle biomass declines, the proportion of shadows within the stand might increase, causing a small reflectance decrease in the visible domain [39,69]. Simultaneously, this effect may be masked by an increased spectral contribution from the bark. Bark reflectance is higher than leaf reflectance in the visible part of the spectrum [47, 102]. As the needle loss progresses, the relative contribution to the sensor from the needles will decrease and the contribution from other targets such as branches, bark, understory vegetation, soil and shadows will increase [69]. Hence, an initially small reflectance decrease may turn into a reflectance increase in severely defoliated forest stands.

3.2.2. The near infrared domain (NIR, 0.7-1.2 jim)

The high reflectance of leaves in this domain is caused by the internal leaf structure, i.e. the cell wall to air interface within the leaves [43, 91], Approximately 50 per cent of the incoming radiation is reflected by healthy canopies, the absorption is low and the transmittance is high. Therefore, incoming solar radiation can penetrate through and reflect back up through multiple layers of leaves within the canopy. An increase in the number of leaf layers significantly increases the NIR reflectance [136]. Deviations from this reflectance increase with greater canopy closure have been attributed to shadows, occurring if the increased vegetation cover is accompanied by more shadows [31]. Chlorosis is of minor importance in this region since the reflectance properties do not depend on leaf pigments. Laboratory measurements of yellow-brownish needles show unaltered or increased NIR reflectance and field measurements showed both increased and decreased NIR reflectance due to chlorosis [69]. Defoliation decreases the NIR reflectance [39,69,102]. In addition to changes in the internal leaf structure and canopy closure, increased fractions of shadow and dark background may explain the decreased reflectance [31,69]. The influence of shadows is strong in the NIR region due to the high reflection of vegetation compared to low reflecting shadows [69]. An increased exposure of bark also decreases the canopy reflectance since bark has lower reflectance than leaves in the NIR domain [47]. The reverse, a reflectance increase may occur if the background is bright, such as grass for example [31]. Rock et al. [102] found a strong inverse correlation between NIR reflectance and visual damage and a decrease of the NIR plateau at highly damaged sites. Ekstrand [39] found that a decreased reflectance in the NIR region was the only reliable reflectance change for assessment of spruce defoliation in southern Sweden. Westman and Pierce found a reduced NIR reflectance of needles exposed to acid mist fumigation and suggested that this was caused by cell volume reductions [135].

3.2.3. The middle infrared domain (MIR, 1.2-2.5 pm)

The optical properties in this domain are mainly affected by the water content of the target, absorbing a large part of the incident energy. The strongest absorption occurs near 1.4 and 1.9 pm with reflectance peaks around 1.65 and 2.2 pm [47, 136]. As the moisture content decreases, the MIR reflectance increases. The 1.55-

22 1.75 gm region (TM band 5) has been proposed as the best suited wavelength interval for satellite remote sensing of canopy water status in the MIR region [125]. Due to the strong relationship between leaf moisture content, stomatal conductance, respiration and the rate and efficiency of photosynthesis, assessment of the relative photosynthetic activity can be performed using remotely sensed data [136]. Chlorosis in spruce needles does not cause any reflection increase but chlorotic leaves from deciduous trees and brownish pine needles showed increased reflectance in the MIR region [69]. Severe chlorosis, resulting in brown needles, which often are drier than green needles, increases the MIR reflectance [47]. Decreasing leaf biomass (defoliation) increases the reflection in this domain due to reduced absorption [69]. Bark reflectance [47], and often background reflection as well, are higher than leaf reflectance in the MIR. Hence, an increased exposure of these targets, due to defoliation, will not mask the effect of defoliation. Contradictory to what has been stated above, several other studies have found a weak spectral response due to water stress. Hence the ability of operational detection of water stress in conifer canopies using MIR reflectance are limited under normal conditions of water loss [55, 94, 100].

3.2.4. Additional factors affecting the spectral response

In addition to forest stand characteristics and the damage symptoms mentioned above, reflectance properties are influenced by several factors. These include leaf orientation [31], canopy geometry, sun-sensor-target geometry [47, 67], soil properties [47,101], reflectance properties of other parts of the canopy (reproductive parts and bark), background reflectance (soil, rock and fitter) [31], type and quantity of field vegetation and tree row orientation [47]. Additionally, disturbances are caused by the atmospheric conditions during scene registration and spectral measurements. The influence of topography on the sensor response is a common obstacle in remote sensing. The incoming irradiance received by a target on the ground varies with the cosine of the incidence angel. Additionally, targets perpendicular to the sun will receive less radiation with lower sun elevation due to atmospheric scattering [41]. A recent study using Landsat TM found the topographic effect in Norway spruce forest to be non-Lambertian for medium and low solar elevations [41]. A correction model based on a Minnaret constant changing with the cosine of the incident angel [82] and another model using an empirical function were developed. Both models produced accurate corrections and improved the capabilities for damage assessments using Landsat TM data. An often neglected problem when correcting for variations in topography is the poor quality of digital elevation models used. Interpolating digital elevation models from contour lines often results in an over-representation of flat areas [37, 95]. These flat grid cells have no slope and no aspect, resulting in errors for these grid cells despite the correction algorithm.

23 3.3. Previous satellite remote sensing studies of coniferous forest decline

Some previous studies regarding assessments of needle loss using Landsat and SPOT data are summarised in Table 2. Using Landsat MSS data, Williams and Nelson [137] classified defoliated and healthy forest in Pennsylvania with 77 per cent accuracy. Spectral overlap among defoliation classes limited the number of detectable classes to two. Vogelmann [131] compared MSS data from 1973 and 1984 for the Green Mountains of Vermont and found a decrease in the NIR (MSS bands 3 and 4) for high damage sites. A decrease in basal area and the loss of green leaf biomass with an accompanying increase in the amounts of dead branches and trees are suggested explanations to the decreased reflectance. Using multitemporal MSS data, Boresjo [17] examined the utility to detect and map forest damage in Germany. She concluded that it was impossible to perform absolute radiometric calibrations of MSS data from the Fucino receiving station in Italy. The Fucino station receives data for large parts of central Europe. The importance of using data from approximately the same time of the year when conducting multitemporal studies were emphasised to minimise sun angle and phenological variation. A tendency for a decreased NIR/red ratio with increased needle loss was found. Hagner and Rigina [48] related the long term mean aerial S02 concentration to the red and NIR reflectance changes using linear regression after iterative histogram matching of MSS data. Highly significant relationships were found for increased red reflectance (R2= 0.93) and decreased NIR reflectance (R2= 0.92) from 1978 to 1992. The changes in reflectance are suggested to depend on reduced vegetation vitality, a reduction of tree shadow density due to defoliation, a reduction of species with high NIR reflectance and an increasing proportion of dead needles, woody debris, technogenic barrens and bare soil. Using TM data, Vogelmann and Rock [133] found very strong linear relationships between the ratios TM7/TM4 and TM5/TM4 versus ground based assessments of conifer damage in Vermont. They suggested that these ratios measure not one but several different highly correlated and largely inseparable vegetation parameters such as biomass, damage and water content. Vogelmann [132] compared the relative effectiveness of the normalised difference vegetation index (NDVI) and the SWIR/NIR ratio for measuring forest damage in northeastern United States. NDVI was proposed as appropriate for broadleaf vegetation and the SWIR/NIR ratio appropriate for both deciduous and conifer vegetation. NDVI is calculated as (NIR-red)/(NIR+red). The low sensitivity of NDVI to conifer damage is attributed to subtle red reflectance differences between low and high damage sites.

24 Khorram et al. [65] regressed per cent spruce/fir defoliation in North Carolina on TM band 4 reflectance, altitude and aspect resulting in a high coefficient of determination (R2=0.85). Twenty-one field plots were used but no evaluation using independent data was performed. In a similar study defoliation was related to TM4 reflectance and elevation increasing the coefficient of determination from 0.65 to 0.87 when adding elevation as an independent variable in the regression [21]. Using SPOT HRV band 3 instead of TM4 resulted in similar, but slightly lower coefficients of determination. The absence of a significant understory component was considered an important factor contributing to the decreased NIR reflectance in stands with needle loss and openings in the canopy. In southwest Sweden, Wastenson et al. [134] did not find any significant relationships when studying eight Norway spruce stands, with defoliation from 20 to 40 per cent, using TM data. They conclude that the vast range of variation in forest types, relief and phenolgy considerably diminish the chances of developing a generally applicable inventory method. Rosengren and Ekstrand [106] found that errors were introduced into damage classifications because of the spectral similarity of pine and damaged spruce stands in their analysis of forest decline in the Izerskie Mountains of southern Poland and the Harz Mountains of Germany. A classification using the chromaticity index TM4/(TM4+TM5+TM7) discriminated four damage classes with a total accuracy of 76 per cent. Ciolkosz et al. [28, 29] used Landsat TM data in the Sudety Mountains of southern Poland to separate eight forest classes, including three spruce damage classes, with accuracy ranging from 65 to 90 per cent depending on the forest class. Ekstrand has in detail studied the possibilities to assess forest damage in Sweden using TM data, digital elevation models and digital forest maps [38-41]. Correction methods for topographic effects and varying forest stand characteristics were developed and applied. After excluding stands younger than 70 years, very sparse and very dense stands, a classification accuracy of 85 per cent was reached when dividing the stands into two defoliation classes (<25% and >25%). Equivalent accuracy for rugged terrain instead of flat terrain slightly decreased the accuracy (82.5 per cent) after corrections for topographic effects were applied (see above). Applying the method in operational conditions, using readily available data for corrections (digital forest maps and standard digital elevation models) reduced the accuracy to 80 per cent for horizontal areas and 75 per cent in rugged terrain. It should be stressed that no damage levels >40 per cent defoliation occurred in the study area.

25 8

Table 2. Overview of satellite-based forest damagestudies in coniferous forest areas, modified from [ 103 /. Reference Study Vegetation Sensor (1) Selection Additional Corrections Accuracy Area Type Band/Index Criteria (2) Data (3) [33] New York Red spruce, TM5/TM4 - - r="strong" USA Balsam fir, [28] Sudety Mts. Norway spruce, TM . 65-90% Poland Hardwood 8 classes, (3 spruce) [106] Izerskie Mts. Norway spruce TM 4/(4+5+7) 76% Poland 4 classes

[134] Southwest Norway spruce TM4 Homogeneity r=-0.17 Sweden .... TM5 r=0.31 [133] Vermont USA Red spruce TM5/TM4 - - R2= 0.92 Balsam fir .... TM7/TM4 R2= 0.84 [19] North Red spruce, TM4/TM3 Homogeneity - r= -0.62 Carolina USA Fraser fir TM4 .... elevation, R2= 0.85 aspect [65] North Red spruce, TM4 Homogeneity elevation, R2= 0.85 Carolina USA Fraser fir aspect [38] Southwest 85-100% Norway TM4 0-15° slope r= -0.52 Sweden spruce 0-15% hardwood _ K _ 85-100% Norway TM3 < 5°slope r= -0.68 spruce < 5% Hardwood TM4 r= -0.72 TM5 r= -0.75 Table 2, continued. Reference Study Vegetation Sensor(1) Selection Additional Corrections Accuracy Area Type Band/Index Criteria (2) Data (3) [40] Southwest Mixed forest, TM4 < 3° slope Species R12 3= 4 0.66, 5 Sweden spruce, pine and age >70 years (85%, 2 class) hardwood R2 = 0.52, (80%, 2 class) [41] Southwest Mixed forest, TM4 70-100 year Species and r=0.61 Sweden spruce, pine and 0-25° slope topography (75% in 2 hardwood classes) [21] North Red spruce, Fraser TM4 homogeneity R2= 0.65 Carolina fir USA -u - HRV3 " - R = 0.55 TM4 DEM R2= 0.87

HRV3 " DEM R = 0.80

[46] Giant Mts. 100% Norway TM >20 years Topography 79%, 3 classes Czech Rep. spruce III Ore Mts. 100% Norway TM 1,3,4,7 >40 years old, 71-75%, 3 Czech Rep. spruce even-aged classes IV 2x6 TM Altitude, 78% in 3 bands slope, classes aspect Altitude, 70% in 6 slope, classes aspect

(1) TM = Landsat Thematic Mapper, HRV3 = SPOT High Resolution Visible band 3 (0.79-0.89 pm) (2) Prior criteria for including an area in the analysis. (3) Aspect and elevation used as additional independent variables in the multiple regression. (4) The area units used differ among the studies, pixels, forest stands or 100 x 100 meter squares. Some studies use independent data for evaluation, some studies does not. (5) Maximum Likelihood classification 3.3.1. Concluding remarks on remote sensing of forest decline

Structural, morphological and physiological changes occur in a forest stand during the decline process. These include alterations in light absorbing pigments, internal structure and moisture content at the cellular level. On tree and forest stand levels, decreased leaf biomass, exposure of trunks, branches and understory vegetation increase when the canopy closure decreases, altering the reflectance directly and indirectly through altered shadows in the forests. The spectral variation caused by these decline related phenomena is overlapped by the spectral variation caused by topography, canopy cover, species composition, stand age and biomass [39, 109]. This limits the possibilities for successful forest damage mapping of Norway spruce using satellite data only. It increases the need to correct for this non decline related variation [40,41]. Proper corrections may require large amounts of data, whereof some must be collected in the field or by aerial photography (i.e. fraction deciduous forest) to reach desired quality of the data for the corrections. Satellite remote sensing is still often considered a good technique for detection and quantification of vegetation and forest changes [30, 39, 49, 77, 88, 128]. Advantages include access to historical data and easy integration with other data. Remotely sensed data is the only available data providing global coverage. Limitations are the small spectral differences among different damage classes and the large need of ancillary calibration data [39]. The unique, scene dependent, relationship between satellite data and field measurements exclude using a single sensor-target relationship that is stable over time and space. Compared to information derived from aerial photography [110], satellite based estimates of forest decline are rough but relatively fast and inexpensive.

28 4. Present investigation

4.1. Introduction

This section presents the main results of the appended papers [I-VI]. Reliable methodology is required when assessing the health status of forest ecosystems and a prerequisite when relating the decline symptoms to potentially explanatory variables [V, VI]. One possible method to collect this health status information is by remote sensing, relating the measured reflectance to needle loss [III, IV] and forest stand variables not directly related to the decline [I, II].

4.2. Objectives

The main objectives of the present investigation are to: 1. Establish and evaluate remote sensing methodology for detection and quantification of forest damage in spruce forests in the Czech Republic [III, IV]. 2. Localise and quantify deforestation in the Ore Mountains 1972-1989 [V]. 3. Investigate possible relationship between the spatial distribution of deforestation and forest decline versus the spatial distribution of some abiotic factors [V, VI].

4.3. Volume quantification of coniferous forest compartments [I]

The volume of a forest stand (compartment) is an important variable in boreal forest planning [124]. Its variation strongly influences the reflectance properties of forest stands, particularly in the visible and middle infrared regions [54,97]. The mean spectral radiance (mW cm 2 sr1 [tor1) of 198 forest stands in southeastern Sweden was calculated using Landsat TM data. The volume of Scotch pine and Norway spruce dominated (some birch also present) stands varied from 1 to 300 m3 ha1 and had an average area of seven ha. Spectral radiance had a significant negative relationship with volume in all TM bands. This relationship was stronger in the visible and MIR (r <-0.7) than in the NIR domain (r ~ -0.5). A significantly stronger relationship was found for stands with smaller volume than for stands with larger volume. The volume of 99 independent stands was predicted with a linear regression on

29 TM band 5 (MIR). The correlation between estimated and observed volume was strong (r = 0.83), indicating that TM band 5 explains 69 per cent of the variation in volume (R2= 0.69). Accepting a maximum deviation of the estimates from ground- based sampling methods of +50 m3 ha-1 resulted in correct estimation for 77 per cent of the stands. The decrease in reflectance depends on a larger proportion shadow and a smaller proportion bright background as stand volume increases. Increased water content may contribute to the decreased MIR reflectance and absorption to the decrease in the visible region. The significant weaker relationship between spectral radiance and volume for larger volumes depend on the strong sensitivity of the remotely sensed signal to crown closure [42]. In closed stands, volume continues to increase without affecting the crown closure and thus not affecting the remotely sensed signal either. Spectral changes due to volume variations overlap those caused by needle loss [39, 109]. Stratifications based on canopy closure [109], age, volume or a combination of these variables [39] can be applied to minimise the effect of this overlap.

4.4. Influence from forest stand variables on vegetation indices used for coniferous forest damage assessment [II]

Various vegetation indices, used in assessments of needle loss, might differ in then- sensitivity to variation in stand variables. Low sensitivity to stand variables not related to forest damage and high sensitivity to damage related variables are desirable. The influence of species composition, age, annual increment and volume on six vegetation indices was empirically quantified by a stepwise multiple regression. The stepwise procedure look for variables not yet in the equation whose F-statistics were >4. The six vegetation indices were (TM4/[TM4+TM5+TM7 }), (TM4/ {TM2+TM5+TM7 }) [106], TM7/TM4, TM5/TM4 [133], TM1+TM3 [38] and the normalised difference vegetation index, NDVI, ({TM4-TM3 }/{TM4+TM3}) [132]. The field data, consisting of 198 mixed stands (Scotch pine and Norway spruce and birch) in south-eastern Sweden (same data as in paper I) are heterogeneous. To decrease this variation, stratifications were performed based on volume and species composition (>75% spruce, >75% spruce and >50 m3 ha1, >90% spruce). The regression was applied for each stratum. The variations in volume, age, species composition and annual increment significantly influence all indices analysed. The stratification most comparable to studies assessing needle loss where stands with >75 per cent spruce and with a

30 volume >50 m3 ha"1 is included. In this case the no significant influence on the TM7/TM4 ratio was found and weak influence (R2 = 0.14) on (TM4/ {TM4+TM5+TM7 }), suggesting a relatively low influence of forest stand variables on these two indices. The other indices were more strongly influenced by age, volume and species composition, yielding R2 values >0.47.

4.5. Spectral characterisation and regression based estimates of forest damage in the Czech Republic [III]

The ability of Landsat TM data to discriminate among three damage categories of Norway spruce was assessed using dichotomous logit regressions. Six damage classes (1-6, higher number =more defoliation) were spectrally characterised using the spectral reflectance of 344 stands. The strongest relationship between reflectance and damage is in the MIR region (TM5 and TM7), with increasing reflectance with increasing damage level. The relationship in the NIR region (TM4) is unclear with minor differences among damage classes one to four and an increased reflectance for classes five and six. The visible bands (TMI, TM2 and TM3) do not indicate a very clear separation among the six damage classes except for damage class six. This class has a noticeably higher reflectance than the other damage classes in all the visible bands. Moderate and light damage stands, being the most spectrally similar, were separated with 83 per cent accuracy using TMI, TM4 and TM7. Moderate and heavy categories were best separated by TM3 (88 per cent accuracy). Light and heavy damage classes were separated with up to 95 per cent accuracy. Ratios and indices did not improve the regression accuracy. The regression equations, when used to classify three categories of damage, accurately classified 71-75 per cent of Norway spruce stands. The significant relationship between forest damage and reflectance could partly be explained by the increasing exposure of bark and understory as defoliation progresses. The spectral contribution from the decreasing, low reflective needle biomass, is successively substituted with an increasing spectral contribution from the more exposed, high reflective bark and grasses. This spectral contrast, between background and forest vegetation, is especially strong in the MIR. The magnitude of this contrast is positively related to the development of useful models for predictions of canopy closure and related stand variables [22]. Assessments of forest damage in areas with moderate defoliation commonly report strong negative relationships between NIR reflectance and defoliation [21, 39], seemingly contradictory to the results presented here. This can partly be explained by the absence of a significant understory component [21]. Additionally,

31 a low sun elevation during satellite data acquisition cause a larger part of the sensor response to originate from the canopy compared to the understory vegetation which becomes more shadowed [38]. In this study, the high sun elevation (55°), the high reflective background (grass) and the comparatively severe defoliation (cf. [21, 39]) strongly influence the recorded spectral response. Consequently, the spectral contribution from a reduced and damaged needle biomass, is too weak to determine the final spectral answer in the NIR region during these conditions.

4.6. Neural networks, multi temporal TM data and topographic data to classify forest damage [IV]

Due to the powerful learning algorithms and the ability to describe non-linear patterns, the interest of using artificial neural networks in remote sensing is large [89]. Its non-parametric nature is also important, allowing different and unknown distributions and combinations of various distributions. This study consists of two parts. Firstly, the results from the backpropagation neural network are compared with a statistical multinomial logit regression-based classifier. Only single date Landsat TM data are used as input data to the neural network. Secondly, the advantages of including multitemporal Landsat TM data and topographic data (slope, aspect and altitude) in the classification process are investigated. The spectral reflectance of344 forest stands (same data as in III) were calculated using Landsat TM data from July and September 1989. Additionally, altitude, slope and aspect were collected for 170 of the stands. These data, randomly divided into two groups, were used to train and evaluate the neural networks and the regression. In the first part, there was no significant difference between the accuracy of the best neural network (63%) and the regression (65%) when classifying three categories of forest damage using single date TM data. In the second part, the classification accuracy increases to 77-78 per cent when using two TM scenes instead of one. The topographic data do not increase the classification accuracy. No obvious dependence on the number of hidden layers and hidden nodes in the networks were found. Computing 30 identical networks shows that the randomly set initialisation weights cause variation in accuracy from 59 to 70 per cent. Comparing the neural network-based classification with the statistical approach indicates that the back propagation algorithm is comparable, but not superior. The capability to merge data sets of different types, to condense and extract information from large data sets is advantageous compared to traditional statistic methodology,

32 which may require comprehensive preparatory work. A neural network, classifying six damage classes with 70 per cent accuracy using two Landsat TM data sets and topographic information, increasing to 78 per cent using three damage categories, exemplifies this.

4.7. Forest cover changes in the Ore Mountains 1972-1989 [V]

The extent and rate of deforestation from 1972 to 1989, including some of its spatial properties in the heavily polluted Ore Mountains were studied. The deforestation was assessed using maximum likelihood classification of Landsat MSS and TM data, integrated with digital elevation data. The results show that over 50% of the coniferous forest disappeared during the period. Especially affected areas were between 600 and 1000 meter above sea level and slopes facing south and south-east. The relative rate of deforestation was highest from 1979 to 1982 (5.2% yr1) and the absolute deforestation rate decreased from 26.7 km 2 yr1 in the beginning of the period to 7.8 km 2 yr1 in the end (1989). The sector downwind of a large point source of S02 and NOx was strongly deforested. The relationship between deforestation and distance to die point source was however unclear. The forest damage level of the coniferous forest remaining in 1989 (308 km 2) was estimated as described in [III]. Resulting in 79 per cent (by area) light damage, 4 per cent moderate and 17 per cent heavy damaged forest. The coniferous forest in the Ore Mountains is characterised by high relative sensitivity to acidic depositions [74], high acidic depositions [34, 68, 85], harsh climate [90] and in some parts high deposition of arsenic, cadmium, copper, lead, nickel and vanadium occurs [107]. The origin of the Norway spruce genetic material is uncertain and only limited areas are of local origin [71]. In addition to insect infestations, all these factors are possible and probable contributors to the severe forest decline and the resulting deforestation. Comparing the spatial and temporal distribution of these stress factors with forest decline (needle loss and deforestation) data allow testing hypotheses of potentially causing factors. Using remotely sensed data for deriving needle loss and deforestation complement aerial photography and field plots, currently used for similar purpose [84].

33 4.8. Critical levels of S02 - uncertainty and relationships to regional forest decline [VI]

This paper [VI], does not include any remote sensing, but compares three methods of estimating S02 induced forest stress with regional forest statistics of sixteen forest enterprises in the northern Czech Republic. These methods assume adverse effects on ecosystem structure and function to occur when a predetermined threshold (critical level), is exceeded [87]. The methods are: 1. Critical level of S02 [10, 126]. 2. The (S02/ vegetation period) ratio [116]. 3. A model predicting needle loss from S02 and environmental data [114, 115]. All methods are strongly driven by the aerial concentration of S02 and show strong linear relationships to each other. Comparing the estimated impact with observed regional forest decline reveals significant relationship for all methods. The per cent salvage felling in the enterprises has a strong linear relationships with all methods (R2 >0.76). The relationships of the estimated impact versus per cent light, moderate and heavy damage are weaker (R2 = 0.4 - 0.5) The influence of data uncertainty, quantified using Monte Carlo simulations was used to map areas significantly over or below the critical thresholds. Assuming the aerial concentration of S02 to be within ±50% of the modelled values, 10-15 per cent (slightly different for the three methods) of the study area significantly exceed the threshold. S02 data of high quality, with known uncertainty, and integration with high- resolution land cover data are the main requirements for improved mapping and identification of various receptors at risk.

34 5. Conclusion and future perspective

5.1. Conclusion

Defoliation in Norway spruce forest significantly affect its reflectance properties, allowing three needle loss classes to be assessed with 75% accuracy, using Landsat Thematic Mapper data in the Ore Mountains. Variations in stand age, volume and species composition can significantly disturb this assessment. Forest information derived from remotely sensed data substantially contribute to our knowledge about the spatial and temporal variation of these resources including their health status. Combining this and other types of forest health information with environmental data allows significant relationship to be detected and hypotheses regarding potential causes of forest decline to be tested. In order to be a useful tool in addition to field measurements and aerial photography, the data uncertainties involved should be considered and quantified when investigating these relationships.

5.2. Future perspective

The large and various values of forest potentially at risk due to several factors, guarantee a continued debate and numerous monitoring programmes, including remote sensing. New sensors, such as the Earth Observing System and the Enhanced Thematic Mapper, planned to be launched during 1998, extend the possibilities of deriving high quality vegetation and forest (health) information. Integrating this information with ecosystem models and the growing databases containing time-series of distributed environmental data could improve our knowledge about the forest decline phenomena. The use of remotely sensed data in modelling of critical loads and critical levels will expand [1, 96]. Remote sensing is here beneficial when identifying spatial distribution of the ‘stock at risk' (i.e. the potential quantity and type of biomass which might be lost)’ [96] for modifying patterns of deposition [8], Additionally, numerous of other spatial distributed variables will more frequently be derived from satellite data.

35 Acknowledgement

I would like to thank friends and colleagues, at the Department of Physical Geography and elsewhere, for help and support provided during this work.

Especially, I appreciate: Lennart Olsson for supervision. Nancy Lambert, James Vogelmann, Vladimir Henzlik, Andreas Barkman and Per Arvidsson for co-authoring. Barret N. Rock for providing possibilities to work at Complex Systems Research Centre, University of New Hampshire and for co-authoring. Andrew Skidmore for providing possibilities to work at School of Geography, University of New South Wales and for co-authoring. Frantisek Zemek for providing possibilities to work at the Institute of Landscape Ecology, Ceske Budejovice and for, together with Martin Sima, making the Czech Republic an even nicer place. Petter Pilesjo for fruitful collaboration and co-authoring. Anna & Erik.

Economic support have been provided by the Swedish National Space Board (Dnr. 66/93,84/94), the Crafoord Foundation, Kungliga Fysiografiska Sallskapet i Lund, Jacob Wallenbergs Forskningsstiftelse, Svenska Sallskapet for Antropologi och Geografi samt Gunnar Sillens stiftelse. Landsat Thematic Mapper data were kindly provided by National Aeronautics and Space Administration (NASA) Landsat Thematic Mapper Data Grant (Global Change Landsat Data Collection) The Grant Data is proprietary to EOSAT. This data were made available through the UNEP project: Large Area Experiment for Forest Damage Monitoring in Europe Using Satellite Remote Sensing.

36 References

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