Cold Regions Science and Technology 32 Ž2001. 191–203 www.elsevier.comrlocatercoldregions
Vegetation degradation in a permafrost region as seen from space: Noril’sk ž1961–1999/ O.V. Tutubalina, W.G. Rees ) Scott Polar Research Institute, UniÕersity of Cambridge, Lensfield Road, Cambridge CB2 1ER, UK Received 1 September 2000; accepted 1 July 2001
Abstract The presence and distribution of airborne and surface contaminants can often be inferred from their effects on vegetation, and this is particularly true in areas of frozen ground where the vegetation is especially vulnerable. In this paper, we take as a study area the region around the city of Noril’sk in northern Siberia. Non-ferrous metal smelting has been carried out extensively in Noril’sk since the 1930s, and it is now one of the world’s largest producers of nickel. The principal contaminants, which are extensive, are sulphur dioxide and heavy metals. Heat contamination from buildings and industrial activity is also significant in the immediate surroundings of the city where it has led to degradation of permafrost. We describe two approaches to the use of satellite imagery to monitor vegetation degradation in the Noril’sk region. The first of these compares a panchromatic spy satellite image from 1961 with a multispectral satellite image acquired 34 years later to quantify the gross changes in land cover around Noril’sk. This analysis shows a decrease of approximately 80 km2 in the vegetated area around the city. The second approach is a regional multitemporal study based on the use of the Normalised Difference Vegetation Index, to which we apply a new correction for phenological variation. This analysis is used to identify a previously unreported area of vegetation decrease to the southwest of Noril’sk. q 2001 Elsevier Science B.V. All rights reserved. Keywords: Noril’sk; Normalised Difference Vegetation Index; Satellite
1. Introduction The city of Noril’sk in northern Siberia Žsee Figs. 1 and 2. provides an extreme example of the effects of certain industrial contaminants in a permafrost region. The principal industrial activity in Noril’sk is smelting of nickel and other non-ferrous metals, carried out since the first smelters were constructed in 1935–1942. By 1998, the population of Noril’sk )
Corresponding author. Tel.: q44-1223-336-540; fax: q441223-336-549.
and its satellite towns has reached 250,000, and the factory complex, owned by the Norilsk Nickel concern, had become one of the world’s largest producers of nickel and platinum-group metals. The Noril’sk region suffers from three main types of contamination: very large atmospheric emissions of sulphur dioxide and heavy metals Žrespectively about 2 = 10 6 and 3 = 10 4 tons per annum., water pollution Žabout 10 8 m3 of waste water yearly. and heat contamination from industrial and residential activities. Surface temperatures at disposal sites near the old nickel smelter can exceed 600 8C, while the
0165-232Xr01r$ - see front matter q 2001 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 5 - 2 3 2 X Ž 0 1 . 0 0 0 4 9 - 0
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Fig. 1. Location map Žmodified from CAFF, 1996..
overall temperature increase in the centre of Noril’sk reached 0.5–1 8C at depths of 10–60 m, according to borehole measurements of 1959–1985 ŽGrebenets and Savchenko, 1995.. The total extent of the contaminated areas is large but not precisely known. For example, the estimates of the forest vegetation area damaged by air pollution range from 6800 ŽMel’nikov et al., 1996. to 20,000 km2 ŽKharuk, 1998.. While the direct detection of the presence of these contaminants obviously requires in situ measurement, indirect and, hence, potentially more rapid detection is also possible. At high latitudes, the distribution and type of vegetation is a particularly important indicator of the impact of contaminants. Vegetation is most vulnerable to air pollution during the snow-free vegetation season, generally from late June to early September. Larch trees and fruticose lichens in the boreal forest that occupies the southern
and eastern parts of the area are the plants most sensitive to air pollution. Deciduous shrub tundra in the northwestern part of the region is more resilient. ŽMel’nikov et al., 1996.. The prevailing direction of winds in summer is to the southeast and the transport of sulphur dioxide in that direction created a large area of dead forest ŽSimachev et al., 1992.. The presence of permafrost as an additional stress factor increases the sensitivity of local vegetation to air pollution. According to data presented by Ershov et al. Ž1996., the Noril’skaya River valley has discontinuous 80% permafrost coverage with a mean temperature at the bottom of the active layer of about q0.5 to y2 8C. Further away from the city, the permafrost cover is interrupted only by taliks under large rivers, with mean temperatures from y5 to y9 8C in the Putorana mountains. The high temperatures and heterogeneity of the permafrost in the city area,
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Fig. 2. Detailed location map, overlaid on an image from the MSU-SK sensor carried on the Russian RESURS satellite.
and the effects of heat conduction into the ground, are likely to predispose the soil to instability. Spaceborne remote sensing techniques can be used to monitor the impact of chemical and heat contamination on the land and water, with great efficiency and detail, and relative cheapness. There has been a number of remote sensing investigations of the damage caused to high-latitude ecosystems by air pollution. Studies by Hagner and Rigina Ž1998., Kharuk et al. Ž1996., Kravtsova Ž1999., Mikkola Ž1996., Rees and Williams Ž1997a,b., Solheim et al. Ž1995. and Tømmervik et al. Ž1995. on the ecosystem degradation caused by non-ferrous metal smelting in parts of the Russian Fennoscandia are particularly relevant. In some areas further south, remote sensing
is used for vegetation change mapping to assess revegetation efforts, e.g. by Inco in Sudbury, Canada ŽAllum and Dreisinger, 1986.. All these authors have focussed principally on the analysis of multispectral imagery from passive sensors in the visible and infrared parts of the spectrum Žincluding thermal infrared., and on the development of optimal classification algorithms, often based on hybrid supervised–unsupervised classification. Some success has also been reported in studying vegetation damage with active radar sensors. For example, the study of fire scars in boreal forests and tundra ŽBourgeau-Chavez et al., 1997., study of vegetation and permafrost soil damaged by oil spills ŽAllen and Wilson, 1995. or the detection of dead-tree zones in
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the areas of industrial impact ŽSaich et al., 2001.. However, analysis of radar imagery without reference to other data sources provides simple interpretations only in very simple cases, such as the detection of completely burnt areas. Where a more subtle classification of damage is required, multispectral optical imagery is usually a primary source at the present time, although radar imagery may be used for inferring additional properties, or to compensate for the frequent cloud cover, which is typical for high latitudes Že.g. Marshall et al., 1994.. In this study, we focus on the development of spaceborne remote sensing methods to monitor increasing vegetation degradation, as a result of urban and industrial development and of contamination from atmospheric pollution, over a period of 38 years. This research uses results of field geobotanical research in 1995, 1997 and 1998, conducted jointly by Cambridge and Moscow Universities. We concentrate on the use of the multispectral optical images only, and give particular attention to phenological variability in the image time series. Permafrost degradation is qualitatively illustrated on the basis of satellite images and literature data.
2. Methods and materials We conducted our study at two spatio-temporal scales: Ø Local Ž368 km2 . multitemporal, using satellite data Žwith a spatial resolution of 10–30 m. from 1961 to 1995; Ø Regional Ž5788 km2 . multitemporal, using satellite data Žwith a spatial resolution of 30–80 m. from 1972 to 1995. The methods and source images varied according to these scales as described below. 2.1. Growth of Noril’sk city from 1961 to 1995: hybrid classification and post-classification comparison In this local study, we compared two satellite images. The first of these was a vertical panchromatic photograph acquired on 7 July 1961 by the
Keyhole-2 ŽKH-2. reconnaissance camera carried on the Corona satellite. KH-2 images have a ground resolution of roughly 10 m and a nominal coverage of between 15 = 210 and 40 = 580 km. They form part of the archive of 860,000 US intelligence photographs, collected between 1960 and 1972, that were declassified by Presidential Executive Order in February 1995 and that can be accessed at http:rredcwww.cr.usgs.govrwebglis or at http:rr Earthexplorer.us.gov. Fig. 3a shows an extract of the KH-2 image, corresponding to an area of 30 = 17 km centred on Noril’sk. The second image was a Thematic Mapper ŽTM. image acquired on 9 July 1995 from the Landsat-5 satellite. The TM provides vertical imagery with a coverage of 185 = 185 km in seven wavebands, centred approximately at wavelengths of 0.49, 0.56, 0.66, 0.83, 1.65, 11.5 and 2.2 mm. It, thus, covers the visible, near infrared and thermal infrared parts of the spectrum. The spatial resolution is 30 m for all wavebands except band 6 Ž11.5 mm., where it is 120 m. Fig. 3b shows a greyscale representation of this multispectral image. It has been derived by transforming the image into its principal components, which are uncorrelated linear combinations of the original wavebands Žsee, e.g. Richards 1993 Ž133– 148. for a description of this technique.. In Fig. 3b, we present an extract, corresponding to the area around Noril’sk, of the third principal component. This is dominated by the contribution from band 6 and is particularly effective at showing the contrast between urbanrindustrial and background areas. To quantify changes in the vegetation cover in the immediate vicinity of Noril’sk over the period from 1961 to 1995, we performed a classification on each image and then a post-classification comparison. Classification methods differed depending on the type of image. Since the KH-2 image ŽFig. 3a. does not provide any spectral resolution of the visible waveband, only a very simple classification was possible, namely into areas of green forest–tundra vegetation and unvegetated areas. Areas dominated by vegetation are characterised by their low brightness in this summer image against the background of light-coloured sandy alluvial soil substrate and also in contrast to light-coloured built-up areas. The only other areas that have low brightness are shadows in mountainous areas and water bodies. Correctness of
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Fig. 3. Change of Noril’sk city boundaries as seen in Ža. the Corona KH-2 image of 7 July 1961 and Žb. the Landsat TM image of 9 July 1995. ŽA. Residential and industrial areas in 1961. ŽB. Tailing ponds in 1961. ŽC. Mines and quarries in 1961. ŽD. Increase in mines, quarries and built-up areas by 1995. ŽE. Increase in tailing pond area by 1995.
the forest–tundra boundaries was thoroughly checked against a coterminous Russian topographic map at a scale of 1:100,000 scale, compiled in 1959–1960. The same map was used to create a mask of mountainous areas that were excluded from further analysis due to the deep shadows. The water bodies that had low brightness were also masked out using the
‘water bodies’ class from the 1995 vegetation map described below. It was found that water bodies in the 1995 map fully overlap those in 1961 and, thus, confusion between dark water and forest in the 1961 map has been removed. The map of 1995 vegetation cover was constructed by multispectral classification, a procedure
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in which, for each pixel in the image, a decision is made on the land cover type that it is most likely to represent Že.g. Richards 1993 ŽChapters 8–9... The decision is based on matching the brightness of the radiation recorded in each of the image’s spectral bands to the known ‘spectral signatures’ of the different land cover types. These signatures were identified by a hybrid procedure which combined the results of 100-class unsupervised clustering of the Landsat TM image bands and manually defined characteristic areas, chosen on the basis of ground radiometry and geobotanic measurements on 85 field site locations. The low-resolution TM band 6 was excluded from processing to improve the identification of landrwater boundaries. The resulting classes were interpreted using a geobotanic map and data on seasonal vegetation change, obtained from an image obtained by the MSU-E sensor Žcarried on the Russian RESURS satellite. on 1 August 1998. This instrument provides vertical and oblique imagery within a 45-km swath with 45 = 35 m spatial resolution in the green Ž0.50–0.60 mm., red Ž0.60–0.70 mm. and near-infrared Ž0.80–0.90 mm. bands. Interpretation and aggregation of hybrid spectral classes into classes of vegetation health produced a 24-class map of the present state of the vegetation ŽToutoubalina and Rees, 1999.. The 24 classes were then combined into four categories to provide comparability with the two-category 1961 map. Dead forests with living understorey were singled out in a separate category on the 1995 map to illustrate the stratification of the northern boreal forest ecosystem and of the industrial damage by height levels and by vegetation type. Identification of such forests was possible by the integration of ground data, the 1: 500,000 geobotanic map of Shchelkunova Ž1974. and multispectral data from the Landsat TM image. Both the 1961 and the 1995 classification maps were georeferenced to a 1:200,000 Russian topographic map. The resulting spatial discrepancy between classification was on average within one pixel of the coarser 1995 map Ži.e. within 30 m.. 2.2. Vegetation degradation 1972–1995: NDVI mapping and phenological correction The time variation of vegetation disturbance can be investigated using two multispectral images sepa-
rated in time. Commonly, this is attempted by performing a multispectral classification of each image to identify the size and location of different vegetation types. The procedure of multispectral classification depends on fieldwork to identify the appropriate spectral signatures, and if the number of classes to be identified is large, the amount of fieldwork is correspondingly large. As we start to look at the large region around Noril’sk, a great variety of mountain and lowland forest and tundra vegetation types has to be taken into account. A further difficulty is introduced when, as here, the main goal is to identify changes over a long period of time. Although training areas for the spectral signatures can be identified for contemporaneous or near-contemporaneous imagery, it is unusual to have adequately detailed land cover data that can be used for deriving signatures from imagery 10 or 20 years earlier. For the reasons of phenology that we discuss in greater detail below, as well as the possibility that images may have unknown and differing calibration errors, it is unsafe to assume that a set of spectral signatures derived from one image can be transferred directly to another. For this reason, we have chosen to use a much simpler and more robust technique. This is based on the normalised difference vegetation index ŽNDVI.. This is defined as:
NDVI s
IR y R IR q R
,
Ž 1.
where IR denotes the reflectance value in the near infrared band of a multispectral image and R denotes the corresponding value in the red band. Values of the NDVI lie in the range y1 to q1, and are strongly positively correlated with the amount of green-leaved vegetation present in the image. The reason for this is that green-leaved vegetation exhibits a very low reflectance in the red part of the spectrum, as a result of absorption by chlorophyll, and a high reflectance in the near-infrared region, mainly as a result of multiple scattering in the mesophyll layer Že.g. Curran, 1985.. Global NDVI data, with a spatial resolution of 4 km and a repeat interval of the order of 1 week, are routinely available through the US National Oceanic and Atmospheric Administration from processing data of the
O.V. Tutubalina, W.G. Rees r Cold Regions Science and Technology 32 (2001) 191–203
AVHRR ŽAdvanced Very High Resolution Radiometer. instruments carried on board the NOAA series of satellites. These data were used by Justice et al. Ž1985. to study global phenology from April 1982 to January 1983. Townsend and Justice Ž1986. used similar datasets for Africa to study inter-annual differences in vegetation cover between January 1984 and January 1985. Other researchers have used NDVI to study tropical forest clearance, for leaf area index ŽLAI. estimation, biomass estimation, determination of ground cover types and for the estimation of photosynthetically active radiation. These derived estimates can be further used in various biophysical models ŽGoward et al., 1991.. There are some situations in which the NDVI is a poor indicator of vegetation biomass or LAI. These include very dense and very sparse vegetation canopies, cases where the amount of dead material is unknown or the species composition is particularly diverse, and cases where the leaf angle distribution is unknown and the solar elevation is high ŽSellers, 1985.. Rees et al. Ž1998. found little correlation between NDVI and mountain tundra biomass, a result confirmed by Toutoubalina and Rees Ž1999.. On the other hand, the NDVI has proved effective at discriminating between different types of high-latitude vegetation in Alaska Že.g. Stow et al., 2000.. At the least, we can say that the NDVI is capable of discriminating between vegetated and largely unvegetated areas and, because of its simplicity, we adopt it here. Our approach is to use two Landsat images, namely the TM image from 9 July 1995 and an MSS Žmultispectral scanner. image from 13 September 1972, to calculate the NDVI for each, and then to calculate the difference in the NDVI over this 23-year period. The MSS instrument has spatial resolution of 80 m and four spectral bands in visible and near infrared region; TM bands 4 and 3, and MSS bands 4 and 2 were used as infrared and red bands to construct the respective NDVIs. The reliability of this procedure is significantly compromised by the rapid phenology and frequent cloud cover associated with high-latitude vegetation. The frequent presence of cloud cover means that, if a visible-near infrared satellite image of a particular location can be obtained at all during a given summer, its timing within that summer will not in general be controllable. When this fact is coupled with
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the very rapid arctic phenology, it is apparent that images may come from early, middle or late summer, more or less at random. There is, thus, a strong likelihood that seasonal changes in the NDVI will be superimposed on any long-term changes that may have occurred, and as a result, phenological correction of the satellite sensor imagery is highly desirable. We have developed a new technique to perform this correction, based on the use of data from the NOAA AVHRR. The wide swath Ž2600 or 4000 km. of these instruments ensures frequent opportunities to reacquire data from any particular location, so that it can achieve a temporal resolution of 1 week or better. The first step in the phenological correction consisted of preliminary processing to ensure comparability of the two high-resolution multispectral images. This included calibration of the satellite data to give dimensionless reflectances, followed by bandwise linear regression of the reflectance values from one image to the other, using targets with stable reflectance throughout the snow-free season, such as clear water and bare rock, to remove any systematic Žnon-phenological. differences that can be taken as uniform over the whole image areas, e.g. instrumental constants, sun viewing geometry and atmospheric haze. Next, we determined the phenological position of each high-resolution satellite sensor image, on the basis of the sum of positive Celsius temperatures accumulated through the season Žapproximated by the number of days with stable positive average temperature, recorded at the local meteorological station., and the amount of residual snow cover. At this stage, we could construct a phenological change image. This involved studying the patterns of phenological change and finding two AVHRR images in one year that corresponds to the phenological positions of the high-resolution images. To minimise the effects of cloud cover and different viewing geometries, 10-day AVHRR composites were used. AVHRR NDVI datasets with 1-km resolution for the summer of 1995 were obtained from U.S. Geological Survey ŽUSGS.: the 10-day composites for 1–10 July 1995 and 11–20 September 1995 were phenologically the closest to the high resolution TM image from 9 July 1995 and the MSS image from 13 September 1972, respectively. The NDVI difference
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between these composites expresses the phenological change. By taking the composites in the same year the influence of long-term vegetation degradation is excluded. The simplest way to perform this is to subtract the NDVIs of the high-resolution images, and then subtract the phenological change image from the result. 3. Results Visual comparison of the Corona KH2 and the Landsat TM images ŽFig. 3. clearly shows an in-
crease in the built-up areas Žparticularly the new district of Oganer, and the new Nadezhdinsky industrial plant. and in the area of tailings near the copper plant during the period 1961–1995. This increase in the area occupied by the city and by the industry correlates with a documented increase of the mean annual ground temperatures in the city centre by 0.5–1 8C at the depths of 10–60 m. At the same time, there is no evidence of a regional climate-driven temperature increase, so the warming of the ground is caused by human influence ŽNoril’sk Regional Environment Protection Commit-
Fig. 4. Vegetation change around Noril’sk Ž1961–1995. by post-classification comparison of Fig. 3a and b.
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tee, 2001.. Some of the principal influences, as is evident from field observations, are leakages from hot water pipes and inappropriate use of the cold ventilated basements, originally designed to protect the ground from heat descending from the buildings. Warming of a typical ice-rich soil within the city leads to destabilisation of the foundations, cracks in walls, and eventual destruction of some buildings ŽA. Kerimov, personal communication, 1998.. Development of the road network and corresponding increase of temperature on the road surface are evident from Fig. 3b Že.g. the road leading to Oganer.. Outside the city territory the most apparent changes have occurred in the state of vegetation cover. Fig. 4 illustrates the result of change detection constructed from the post-classification comparison of the 1961 KH-2 and 1995 TM images. The change detection map is presented in a generalised form adequate for black-and-white display. Quantitative results are presented in Table 1 in more detail. It is not possible to undertake a formal, quantitative, assessment of the accuracy of the figures given in this table, for example in the form of an error matrix Že.g. Richards, 1993 Ž271–275... The principal reasons are the fact that it is clearly impossible to perform a randomised test of the classification of the 1961 image without access to new contemporaneous field data, and the necessity of using a manual texture-based classification on this image. However,
the accuracy of both the 1961 and 1995 classifications is likely to be high since the number of classes is small, and the separability of the classes Žon the basis of radiometric and texture parameters. high. Further reassurance is provided by the excellent correspondence between the classified 1961 image and the topographic map revised in 1959–1960; and between the classified 1995 image and field data collected between 1995 and 1998. It is clear from the preceding analysis Žshowing, for example, the loss of at least 80 km2 of green vegetation over the last 35 years. that the environment in the immediate neighbourhood of Noril’sk is in a highly disturbed state. However, significant degradation of vegetation and soil extends to much greater distances from Noril’sk. Fig. 5 illustrates the range of levels of disturbance to forest ecosystems observed within 110 km of Noril’sk. A full description of each level is given by Golubeva Ž1999.. Fig. 6 shows the application of the phenological correction procedure to study the landscape degradation for the period 1972–1995. This is the longest timespan covered by the available full-resolution digital data for Noril’sk and so it is of particular interest. A straightforward NDVI subtraction in this case is of no use, since the 1972 image is 70 days later phenologically than the 1995 image. Application of the AVHRR NDVI difference image from July and 11–20 September 1995 Žphenological analogues of 9
Table 1 Changes of vegetation cover around Noril’sk city Ž1961–1995. Description Areas of change Areas where green vegetation disappeared Areas where living forests were replaced by dead forests with living understorey Areas where green vegetation appeared Areas where dead vegetation was replaced by dead forest with living understorey Areas of no change Remaining areas of green vegetation Areas where vegetation remained dead or absent Excluded from analysis Snow, ice, clouds, mixed pixels Water bodies Mountain and urban areas affected by shadows Total
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Area Žkm2 .
Percent area
80 10
21 3
8 4
2 1
22 59
6 16
15 40 131 368
4 11 36 100
200
O.V. Tutubalina, W.G. Rees r Cold Regions Science and Technology 32 (2001) 191–203
Fig. 5. Differing levels of forest ecosystem disturbance. Ža. Catastrophic state, 5 km SE of Noril’sk, nearly all vegetation dead, soil layer destroyed. Žb. Severely disturbed state, 55 km SE of Noril’sk, trees are dead, shrubs are mostly dead, ground vegetation is dominated by 1–2 grass species. Žc. Moderately disturbed state, 30 km E of Noril’sk, half of the trees are dead or damaged, diversity of other plants is reduced. Žd. Slightly disturbed state, 45 km E of Noril’sk, 10–30% of trees are dead or damaged, other plants slightly inhibited. Že. Nearly background state, 110 km SE of Noril’sk, no dead trees, typical range of all species, not inhibited.
July 1995 and 13 September 1972. shows that the phenological correction has worked. It highlights areas of negative change, including several areas in the southwest part of the change image. These are areas of industrial and residential expansion in the vicinity of Noril’sk airport, in the settlements of Talnakh, Noril’sk and Kaierkan, around the
Nadezhdinsky factory and Kaierkan coal quarry, and in the river port on the Noril’skaya River, as well as in a large area about 30 km southwest of Noril’sk city and east of Alykel airport Žshown by a rectangular frame in Fig. 6b., that was not previously reported as an area of degradation. Visual examination of the original images confirms this change. The
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Fig. 6. NDVI difference images Ž1972–1995.. Ža. NDVI difference between MSS 1972 and TM 1995. Žb. AVHRR-corrected image from Ža.. The rectangular frame shows a previously unreported area of significant vegetation decrease. Water bodies, snow, ice and clouds are masked in white. The palette shows NDVI difference values. Gauss–Kruger ¨ projection on the Krasovsky ellipsoid Žcoordinates in metres..
total area of land showing negative NDVI change below y0.10 is 186 km2 . These are areas immediately around settlements and southwest of Noril’sk. 4. Discussion In this paper, we have presented two approaches to remote sensing of landscape degradation in the Noril’sk region. The first of these is a local detailed multitemporal study based on a hybrid classification approach. This approach is generally well understood, and the only innovation we report here is to adapt it to the local conditions. Concentrating on the small area around Noril’sk, and using images of roughly the same season Ž7 July 1961 and 9 July 1995. with similar amounts of snow cover, permitted this approach to be applied successfully. The small area and detailed scale of study are most favourable to study the permafrost aspects of the land degradation, particularly if thermal infrared imagery is available.
Second, we have described a regional multitemporal study based on NDVI change mapping with phenological correction. We have developed this novel technique to adequately describe the long-term degradation in the region, somewhat masked by seasonal effects in the available imagery. The technique shows good potential, although there are several possible problems. These may include, for example, bias in the bandwise linear regression towards the low- and mid-range reflectance values, typical for phenologically stable surfaces, or the absence of normalisation for directional reflectance effects in the NDVI 10-day composites Že.g. Chopping, 2000., as well as coarse resolution of the AVHRR data used for correction Žabout 1 km at best.. However, there is likely to be more satellite sensor data available for the phenological correction, including almost daily VEGETATION data from the SPOT 4 satellite Ž1-km resolution, 4 bands., and MODIS data from the Terra satellite Ž0.25–1-km resolution, 36 bands.. Daily meteorological data are also likely to be available,
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and this can greatly improve the basic phenological correction. 5. Conclusions This paper has demonstrated in general that satellite remote sensing has considerable scope as a tool for monitoring land degradation in permafrost areas, as a result of airborne contaminants, building and ground disturbance. The monitoring, which is dependent on the impact of the contaminants on vegetation, can be performed at local and regional scales. The paper has also presented specific examples to show the loss of vegetation from the city of Noril’sk over a period of 35 years, and the growth of landscape damage over a larger area during the period 1972 to 1995. It is clear that the approaches outlined in this paper can be extended, both to finer scales and to other forms of contamination or pollution not considered here, such as water pollution and thermal disturbance of permafrost. These will be areas of future research. Acknowledgements Colleagues from the Geography Faculty of Moscow State University, particularly Professors A.P. Kapitsa, V.I. Kravtsova and E.I. Golubeva, Dr. A.V. Krasnushkin and V.A. Spector made important contributions to this work. The staff of the Noril’sk Regional Nature Protection Committee, the Taimyr Centre of Hydrometeorological Service, the Scientific-Research Institute of the Agriculture of the Far North, the Hydrotechnical Service and Laboratory of Reliability of the Noril’sk Mining and Metallurgical Combine, the Noril’sk branch of the Scientific-Research Institute for Foundations and Buried Structures, the Noril’sk Complex Geological Exploration Expedition, the Taimyr Land Registry Committee and the Russian Army gave invaluable field support. AVHRR datasets and the GTOPO30 digital terrain model were provided by the United State Geological Survey. Financial support for field work and image acquisition was given by the British Council, the Know How Fund, the Jephcott Trust, the B.B. Roberts Fund and the Christ’s and Trinity Colleges, Cambridge.
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