Detecting Pollution Damage to Forests in the Kola Peninsula Using the ERS SAR

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Detecting Pollution Damage to Forests in the Kola Peninsula Using the ERS SAR P. Saich,* W. G. Rees,† and M. Borgeaud‡

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e demonstrate the potential of the C-band Vertically polarised transmission, vertically-polarised reception (VV) polarized European Remote-Sensing Satellite (ERS) Synthetic Aperture Radar (SAR) to provide a capability for forest mapping with particular regard to forest degradation caused by pollution near the Severonikel smelter in the Kola Peninsula, Northern Russia. ERS SAR images covering the period from summer 1993 to summer 1995 are analyzed, and it is shown that a combination of acquisitions from summer and winter reveals areas of forest destruction, driven mainly by the summer (May) acquisition. The damaged forest is approximately the same brightness as undamaged forest in the winter but is found to be approximately 3 dB brighter in the summer. Regions of forest that have been damaged by wildfire are indistinguishable from the pollution-damaged areas. The physical basis for these observations is highlighted using a combination of knowledge of the in situ conditions and theoretical modeling. Destruction of the vegetation canopy (associated with pollution or fire) makes the radar measurements sensitive to the state of the underlying ground. During early summer (the principal time of snowmelt) a rough and wet snow surface causes an increase in backscattering coefficient in areas where the forest canopy has been destroyed. The modeling is also extended to alternative polarizations to study the potential of future Synthetic Aperture Radars, such as the Advanced SAR to be carried on board Envisat, and other systems to enhance this mapping capability. Elsevier Science Inc., 2001

* Department of Geography, University College London, London, UK † Scott Polar Research Institute, University of Cambridge, Lensfield Road, Cambridge, UK ‡ ESA/ESTEC TOS-EEP, Postbus 299, AG Noordwijk, The Netherlands Address correspondence to P. Saich, University College London, Department of Geography, 26 Bedford Way, London WC1H 0AP, UK. E-mail: [email protected] Received 19 June 1999; revised 31 May 2000. REMOTE SENS. ENVIRON. 75:22–28 (2001) Elsevier Science Inc., 2001 655 Avenue of the Americas, New York, NY 10010

INTRODUCTION Environmental degradation is a critical and central phenomenon to be addressed by remote sensing. Space-borne instruments enable one to distinguish and monitor land cover types over large areas and to relate these to changes in local conditions, whether these are caused by climate variation or anthropogenically. In Northern Russia, one of the most important sources of environmental change is that invoked by metal smelting. The Kola Peninsula is rich in reserves of metals such as iron, copper, and nickel and there has been extensive industrial activity and smelting in this area over the past 60 years (Rees and Kapitsa, 1994). The largest smelters are the Severonikel smelter (at Monchegorsk) and the Pechenganikel smelter (at Nikel). Annual emissions throughout the Kola Peninsula have been upward of 850,000 tons per annum, including over 650,000 tons of SO2 per annum. The effects of this pollution over the past few decades have been catastrophic. The environmental changes in the surrounding area have been driven by pollution (airborne metal particles and noxious gas) and by urbanization and industrialization. These have all had serious and large scale effects. Emissions from the Severonikel smelter in the Kola Peninsula have damaged the surrounding forest, lichen, dwarf shrub, and moss vegetation to such an extent that some areas have become barren “technogenic deserts” where the vegetation is completely destroyed and the ground underneath exposed. Even where the vegetation is only partially destroyed, there are significant aftereffects because these areas become more prone to wildfires, which are a major contribution to the overall destruction of local ecosystems. In the wake of extensive devastation of vegetation, exposed soil (which is also heavily polluted) is prone to erosion. In addition, the growth period for vegetation is short due to the local climate and therefore damage to ecosystems is not only severe but long-lasting. The severely damaged area around the Severonikel smelting complex is estimated to be approximately 40,000 ha in size (though it can be 0034-4257/00/$–see front matter PII S0034-4257(00)00152-8

Detecting Pollution Damage to Forests

visually distinguished at distances of 40 km to 60 km from the smelter) and to be expanding at approximately 2 km per year (Rees and Kapitsa, 1994). Much of this damage caused by airborne emissions lies to the north and southeast of Monchegorsk due to atmospheric conditions, the prevailing wind direction, and the local topography. The motivation for this study is twofold. First, forest degradation has previously been studied using optical (Landsat) data with some success. Mikkola (1996) and Rees and Williams (1997) have sought to characterize the extent of the pollution damage near Monchegorsk and to monitor the change in this area. Hagner and Rigina (1998) have attempted link the changes seen in imagery to the predictions of a model for the atmospheric propagation of the emissions. Similar work has been conducted for other sites on the border between Northern Russia and Norway (Tømmervik et al., 1995; Tømmervik et al., 1998) and in Northern Siberia (Toutoubalina and Rees, 1999). However, the use of optical data is often hampered by extensive and persistent cloud cover and by variable sun illumination (particularly at these high latitudes where the polar night becomes important). It is therefore of interest to determine whether similar or complementary information is made available by microwave observations. The continuity of data supply from C-band radars (including the SARs on-board ERS-1, ERS-2 and the future ASAR on-board ENVISAT) Advanced SAR (ASAR) (i.e., Advanced Synthetic Aperture Radar) is of clear benefit if the role of the imagery can be demonstrated. Second, a capability of space-borne imaging radar systems to map fire scars has been demonstrated over the past few years (Bourgeau-Chavez et al., 1997). It is believed that the physical mechanism for this is that the fire destroys the vegetation canopy, making the underlying ground visible to the radar. The fire also leads to an increase in surface soil moisture (through partial thawing of the permafrost layer or through poor drainage of the soil). The increase in the soil moisture then causes an increase in the backscattering coefficient, a feature that is also observed empirically (French et al., 1996). It is therefore of interest to see whether a similar physical scattering mechanism may apply to the case of forest devastation by pollution. DESCRIPTION OF THE SITE The site of interest is centered on the Severonikel smelter, in the town of Monchegorsk in the Kola Peninsula (Fig. 1). The terrain typically varies in height from approximately 100 m to 300 m, though there are also local plateaus (tundras) that extend to heights of up to 1,200 m. The region has characteristic areas of forest, subalpine, and alpine environments (Rees and Williams 1997). The forest areas extend up to approximately 400 m (which is the tree line) and are composed of coniferous species such as Norway Spruce (Picea spp.), Scots Pine (Pinus spp.), and some deciduous trees including birch (Betula spp.). The subalpine environments (near the limit of the tree line) are

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Figure 1. Map of the study site.

mainly birch. Higher than this, the alpine areas are composed of dwarf arctic birches, lichens, and moss. Temperatures away from the coast of the Kola Peninsula are approximately ⫺10⬚C during January and between 8⬚C and 14⬚C in June. The principal time at which snow melts is May– June and the snow-free period extends from June to September. ERS SAR DATA Five ERS SAR images have been acquired for the period 1993 to 1995, all from descending passes. These include one image from May of each year as well as an additional image in June and November 1994. The images have been coregistered manually but no georectification has been performed. This would clearly need to be conducted in a more detailed study to take account of the local topography. The acquisitions for May and November 1994 are shown in Figs. 2a and 2b. These intensity images have been smoothed using a simple 13⫻13 pixel block average. In both images, Lake Imandra is visible in the center (marked “L” in Fig. 2a), along with the Monchetundra and Khibiny Mountains (left and right, respectively). The Severonikel smelter at Monchegorsk is marked “M.” Interpretation of these images depends critically on having sufficiently detailed knowledge of the in situ conditions at the time of imaging. Beginning with the November image (Fig. 2b), the most noticeable feature is the overall brightness of Lake Imandra. This is to be contrasted with the relatively darker land and the smaller outlying lakes (which are much darker). At this time, virtually all of the terrain would be covered with snow. A possible explanation

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Figure 3. Temporal behavior of the backscattering coefficient over undamaged (diamonds), pollution damaged (crosses), and fire-damaged (squares) regions.

Though not visible in the figures, during May much of the lake appears to be covered with ice floes that are as bright as the surrounding land. Several other features are clearly visible in the images. The bright patch at the center top of the images is the mining town Olenegorsk. East of this is an arrowheadshaped region (“F” in Fig. 2a) in which the forest has recently (within the last few years) been damaged by fire. South of Monchegorsk is a second such region. The bright areas in the bottom right are tailing ponds associated with apatite quarries. The principal areas of forest destruction are marked “D” on the images. TEMPORAL BEHAVIOR OF THE RADAR SIGNATURES

Figure 2. (a) ERS-1 image of the study site (18 May 1994); (b) ERS-1 image of the study site (2 November 1994).

for the relative brightness of different parts of the lake is that the depth of the ice layer on the lakes differs. In this case, contributions to the scattering coefficient measured by the SAR resulting from the ice-water interface will depend on the depth of the ice layer. Such a mechanism has been proposed to link the temporal variations in backscattering coefficient of shallow lakes in Montana (Hall et al., 1994) and Alaska (Jeffries et al., 1994) to the development of the ice layer on the lakes as they freeze. In the May image (Fig. 2a), the lake is much darker, suggesting that the ice has either melted or is in the process of melting.

The parts of the forest that have been most severely damaged by pollution from the smelter are to the immediate southeast and north of Monchegorsk. These areas appear bright in the summer (Fig. 2a); in the winter image (Fig. 2b) the same areas are nearly indistinguishable from the surrounding areas. In Fig. 3 the changes in backscattering coefficient over the five images are shown for areas of relatively undisturbed forest, areas with severe damage caused by pollution, and fire-damaged regions. The backscatter coefficients shown are averaged over regions in excess of 200 pixels in size. This figure quantifies the observations made earlier, namely that the pollution-damaged forest is brighter to the radar in May 1994 than the surrounding (less damaged) forest but is indistinguishable in November. The figure also makes several other important points: (1) the undisturbed forest does not vary by more than (approximately) 1 dB throughout the period 1993 to 1995 but seems to be darkest during winter; (2) the damaged forest is uniformly bright during the summer in all three years and is noticeably darker during the winter; and (3) the pollution-damaged and fire-damaged forest regions are indistinguishable. These observations suggest that the most

Detecting Pollution Damage to Forests

important time for imaging to distinguish the damaged forest is during summer. At this time, the damaged forest is brighter than the undamaged forest by ⵑ3 dB. The observations also highlight the similarity in the appearance of pollution- and fire-damaged regions, even over a period of a few years. INTERPRETATION AND THEORETICAL SUPPORT The overall temporal dynamics in Fig. 3 show that the backscattering coefficients tend to be lower in winter than in summer. This is likely to be caused by freezing during winter. When the trees and ground freeze, the dielectric constants of both are decreased, leading to a decrease in the backscattered power (Rignot et al., 1994; Way et al., 1994). Of somewhat greater interest is the difference between the backscattered power between those regions that are damaged (either by pollution or by fire) and those that are not. The damage to the forest caused by pollution has been separated into approximate classes that detail the progression from (1) damage to the needlelike leaves, (2) progressive destruction of the canopy accompanied by (3) desiccation until twigs and smaller branches are destroyed. The final levels are (4) only the trunks and the main branches are left but these are severely dried out and (5) the forest is completely destroyed and only barren “technogenic desert” remains. Though it has not yet been possible to delineate these separate levels of degradation in the radar images, it is clear that it is principally the most pronounced deterioration that is distinguished. In these areas, the forest is either completely destroyed or all that remains are tree trunks and larger branches with low moisture content. The similarity with fire-damaged forest (Bourgeau-Chavez et al., 1997) is clear in that for both cases the forest canopy is largely destroyed and the remaining woody material is dry. This suggests that the most significant contribution to the radar backscattering arises from interactions with the ground. If the undisturbed forest has a sufficiently well-developed canopy, the ground will be less visible and the temporal dynamics will be correspondingly weaker. During the winter, recently fallen dry snow in the technogenic desert areas and in severely destroyed forest appears the same brightness as the undamaged forest canopy. In summer, however, the snow is melting (which typically occurs through May–June). If the snow surface is rough then this will lead to much higher levels of backscattered power wherever the ground is visible. A similar mechanism has been proposed to account for the temporal dynamics of ERS SAR backscattering over the Porvoo forest in Finland (Pulliainen et al., 1996). If true, this mechanism may also account for the similarity of the fire- and pollution-damaged regions. To support the conjecture, simulations of the backscat-

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tering coefficient have been conducted to determine the effect that canopy destruction and decreasing vegetation moisture content have on the radar signature. The radiative transfer model RT2 (Saich et al., 1995) has been used to simulate the backscattering coefficient from stands of trees. RT2 is a second-order solution to the vector radiative transfer equations (Tsang et al., 1985) and is implemented specifically for vegetation media over rough surfaces. It has previously been applied to agricultural crops (Cookmartin et al., 2000; Saich and Borgeaud, 2000). The model treats the vegetation in terms of horizontal layers, which in this case are taken to be a canopy layer and a trunk layer. The components of the vegetation are modeled as simple geometric shapes, such as dielectric needles or thin discs, using the Generalized Rayleigh-Gans approximation (Karam et al.,1988) and finite length cylinders (Karam and Fung, 1988). For the simulations that are conducted here, the trees are modeled as trunks, primary (large) branches, secondary branches (twigs), and needles. In view of the fact that detailed in situ data are not available for the state of the forests at this time, the simulations are intended only to demonstrate the plausibility of the mechanisms outlined above. Therefore, the canopy is treated as a 6-m-thick layer containing central trunks, large branches, smaller twigs, and needlelike leaves. The lower trunk layer (also 6 m thick) contains trunks with a range of inclinations up to 10⬚. The input data to drive the model represent Maritime Pine at Les Landes (Beaudoin et al., 1994) and are only intended to be representative of a stand of trees. The ground is treated as a rough surface with a backscattering coefficient of approximately ⫺5 dB (in the absence of vegetation) using the Kirchhoff Geometrical Optics approximation. No attempt is made to link this to the actual state of the ground surface, to the presence of snow, or to snowmelt since the model simulations are only intended to investigate the effect that destruction of the forest stand has on the radar signature. In passing, we note that while melting snow is typically associated with a decrease in the backscattering coefficient [e.g., a rule for detecting snowmelt in Alpine areas based on a decrease of 3 dB is used by Nagler and Rott (1998)], the backscattering coefficient is in general expected to depend upon both the moisture content and the roughness [e.g., Shi and Dozier (1995)]. We expect that the snow surface may not be smooth in these areas, and therefore we expect that snowmelt could be associated with either an increase or decrease in backscattering coefficient. Further work is required to establish which of these is the case here. The gravimetric moisture constant of the vegetation is calculated using the dual-dispersion relation (Ulaby and El-Rayes., 1987) with an assumed temperature of 5⬚C. The results of the simulations are shown in Fig. 4. The simulations have been conducted as a function of (1) the gravimetric moisture content of the vegetation and (2) the fraction of twigs and needles that are present in the canopy. Therefore, “100%” indicates a fully developed canopy,

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Figure 4. Simulated C-VV backscattering coefficient (incident angle 23⬚) as a function of gravimetric moisture content of vegetation. The curves are labeled by the fraction of twigs/needles present compared to a fully developed canopy.

while “0%” indicates that the canopy (upper layer) contains only the central tree trunk and the largest branches. The results show clearly that healthy, well-developed canopies typically have brightnesses of order ⫺10 dB. Destruction of the canopy (both in terms of decreasing twig/needle density and decreases in vegetation moisture) cause the backscattering coefficient to increase. The reason for this is that the canopy is penetrated to a greater extent as it becomes sparse/dry, and the radar sees through to the ground. In the limit of a very dry canopy with no twigs and needles, the backscattering coefficient approaches that of the underlying surface. As discussed above, this simulation does not include snow or model snowmelt. However, it does establish that a plausible mechanism for increased brightness of the damaged regions during summer is that damaged forest canopies (or completely bare areas) have a backscattering coefficient that is nearer to being characteristic of the surface under the forest. On the basis of the modeling described above, it is also possible to make generalized statements regarding the additional information that may be available from the ASAR. Here, we focus only on the polarization flexibility of the instrument and not on any other imaging modes (or imaging incidence angle flexibility). In Figs. 5 and 6, the equivalent results are given for Horizontally polarised transmission, horizontally-polarised reception (HH) and Vertically polarised transmission, horizontally-polarised reception (VH) backscattered powers. (These simulations have also been performed for an incident angle of 23⬚.) The former of these is dominated (at higher vegetation moisture) by the strength of the trunk-ground scattering terms, especially where the canopy has been severely destroyed. This contribution becomes weaker as the gravimetric moisture of the vegetation decreases and, correspondingly, the direct scattering from the rough ground surface becomes the controlling factor (in much the same way as for VV⫽Vertically polarised transmission, vertically polarised reception). However, the different dependence

Figure 5. Simulated C-HH backscattering coefficient (incident angle 23⬚) as a function of gravimetric moisture content of vegetation. The curves are labeled by the fraction of twigs/needles present compared to a fully developed canopy.

on canopy constituents and vegetation moisture will enhance the discriminatory capability of the ASAR. The strength of the depolarization is less clearly interpretable and ignores contributions from the underlying rough surface (as the theoretical model used for the ground surface predicts no depolarization). However, the important aspect is again that the dependence on vegetation moisture and canopy destruction differs from that in VV. Therefore, a preliminary conjecture we can make on the basis of theoretical simulations is that the combination of the copolarized VV, HH, and possibly also of the HV channels will be especially useful due to the different ways in which they interact with the forest canopy structure. This applies to both distinguishing the damaged areas and to providing additional information on the level (type) of damage. It should be noted, however, that the incident angle flexibility of future SARs, such as the ASAR, may also have an important role in vegetation/nonvegetation discrimination. DISCUSSION Although the theoretical model results given in the previous section support our conjecture for the increased brightFigure 6. Simulated C-HV backscattering coefficient (incident angle 23⬚) as a function of gravimetric moisture content of vegetation. The curves are labeled by the fraction of twigs/needles present compared to a fully developed canopy.

Detecting Pollution Damage to Forests

ness of destroyed areas of forest during summer, there are many aspects of the local conditions that have not been addressed. These become particularly important if we wish to account for why it is that during winter, the C-band radar signatures of the undisturbed forest and the destroyed regions are so similar. This may be due to recent (dry) snowfall (on both the ground and in the forest canopy). In addition, although a potential role for SAR data has been shown here, it would be greatly enhanced if it can be shown that different levels of damage can be detected. This is likely to require more sophisticated multiwavelength, polarimetric data sets such as one might acquire with the forthcoming ASAR and PALSAR⫽Phased Array type L-band Synthetic Aperture Radar instruments. To accompany the use of such data, the modeling support will need to take into account the presence of snow (and the structure of snow layers both within the well-developed forest and in the damaged areas) and temporal variations in the state of the forests (such as variations in dielectric constants). These must all be the subject of more detailed investigations in the future. It is also intended to extend this work to other sites to establish the robustness of these findings. Though the emphasis of this study has been on detecting the effects of pollution-induced forest stress in the imagery, there are also wider implications for forest and ground monitoring. Examples of this would include monitoring tree line dynamics (at high latitudes), detecting soil moisture and ground conditions, and freeze/thaw cycles of vegetation. The lengths of frozen and thawed periods play a role in the total metabolic activity of forest vegetation and therefore influence carbon fluxes. The use of radar remote sensing data in this context has been studied by Way et al. (1994) using L-band airborne data. It is to be expected that studies such as these will be greatly enhanced by the availability of polarimetric data sets from a range of sensors that will be launched in the near future. CONCLUSIONS We have shown that it is possible to distinguish pollutionstressed forest using ERS SAR imagery. The undisturbed forest has a brightness of approximately ⫺10 dB and, although it varies by no more than about 1 dB throughout the year, it seems to be darker during the winter. The damaged areas show up most clearly in summer images where they are brighter than undamaged forest by approximately 3 dB. The areas that have been damaged by pollution are essentially indistinguishable from those that have been damaged by fire. It has been proposed that the damaged areas are brighter than undamaged forest because the rough snow surface on the ground is visible to the radar, and when the snow is melting the amount of backscattered power is increased. This would also account for the similarity with the temporal dynamics of the fire-damaged regions. Limited theoretical modeling of the radar backscatter sug-

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gests that the information content will be enhanced by the polarization flexibility of the ASAR. However, the current investigation must be considered as only a feasibility study and needs to be extended to other sites and supported by improved knowledge of in situ conditions. Much of this work was conducted while P. Saich was supported by a Research Fellowship at ESTEC, and he would like to thank ESA for this support. The authors would also like to thank ESA/ ESRIN for provision of the ERS data and Nikolay Kashulin (Institute of North Industrial Ecology Problems, Kola Science Centre) for information on the in situ conditions.

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