Land Cover Change Mapping Research Articles

Impact of land cover uncertainties on estimates of biospheric carbon fluxes

Large‐scale bottom‐up estimates of terrestrial carbon fluxes, whether based on models or inventory, are highly dependent on the assumed land cover. Most current land cover and land cover change maps are based on satellite data and are likely to be so for the foreseeable future. However, these maps show large differences, both at the class level and when transformed into Plant Functional Types (PFTs), and these can lead to large differences in terrestrial CO2 fluxes estimated by Dynamic Vegetation Models. In this study the Sheffield Dynamic Global Vegetation Model is used. We compare PFT maps and the resulting fluxes arising from the use of widely available moderate (1 km) resolution satellite‐derived land cover maps (the Global Land Cover 2000 and several MODIS classification schemes), with fluxes calculated using a reference high (25 m) resolution land cover map specific to Great Britain (the Land Cover Map 2000). We demonstrate that uncertainty is introduced into carbon flux calculations by (1) incorrect or uncertain assignment of land cover classes to PFTs; (2) information loss at coarser resolutions; (3) difficulty in discriminating some vegetation types from satellite data. When averaged over Great Britain, modeled CO2 fluxes derived using the different 1 km resolution maps differ from estimates made using the reference map. The ranges of these differences are 254 gC m−2 a−1 in Gross Primary Production (GPP); 133 gC m−2 a−1 in Net Primary Production (NPP); and 43 gC m−2 a−1 in Net Ecosystem Production (NEP). In GPP this accounts for differences of −15.8% to 8.8%. Results for living biomass exhibit a range of 1109 gC m−2. The types of uncertainties due to land cover confusion are likely to be representative of many parts of the world, especially heterogeneous landscapes such as those found in western Europe.

Operational land cover change detection using change vector analysis

This research study introduces the use of a change detection and classification algorithm that relies on the change vector analysis (CVA) method. Its implementation aims to ensure adequate response to operational production needs and allow optimized data processing over extended and environmentally complex areas. Automatic change class labelling relies on the use of a (3n+2)‐dimensional feature space, where n denotes the number of sensor bands. Such enhanced feature space allows for a finer and more accurate definition of change classes of the ‘from‐to’ type. Moreover, and to efficiently address the problem of change area overestimation, the proposed method takes into account specific evidence derived from the pixel's geographic neighbourhood, the latter defined as a 3×3 pixel kernel. The performance of this integrated algorithmic approach has been tested and validated in the framework of the CORINE Land Cover‐Greece 2000 and the ESA/GSE Forest Monitoring projects in three test sites located in the outskirts of the city of Ptolemais, Thasos island and the suburbs of Athens in Greece. Its implementation in such highly fragmented and dynamically changing landscape environments has resulted in qualified and accurate land cover change maps, achieving an overall level of classification accuracy of 88–96%. Compared to visual image interpretation, the method requires half the effort. In conclusion, the proposed method has proved effective and can be recommended for use in the framework of operational projects.

Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments

Remotely sensed images and processing techniques are a primary tool for mapping changes in tropical forest types important to biodiversity and environmental assessment. Detailed land cover data are lacking for most wet tropical areas that present special challenges for data collection. For this study, we utilize decision tree (DT) classifiers to map 32 land cover types of varying ecological and economic importance over an 8000 km2 study area and biological corridor in Costa Rica. We assess multivariate QUEST DTs with unbiased classification rules and linear discriminant node models for integrated vegetation mapping and change detection. Predictor variables essential to accurate land cover classification were selected using importance indices statistically derived with classification trees. A set of 35 variables from SRTM-DEM terrain variables, WorldClim grids, and Landsat TM bands were assessed.Of the techniques examined, QUEST trees were most accurate by integrating a set of 12 spectral and geospatial predictor variables for image subsets with an overall cross-validation accuracy of 93%±3.3%. Accuracy with spectral variables alone was low (69%±3.3%). A random selection of training and test set pixels for the entire landscape yielded lower classification accuracy (81%) demonstrating a positive effect of image subsets on accuracy. A post-classification change comparison between 1986 and 2001 reveals that two lowland forest types of differing tree species composition are vulnerable to agricultural conversion. Tree plantations and successional vegetation added forest cover over the 15-year time period, but sometimes replaced native forest types, reducing floristic diversity. Decision tree classifiers, capable of combining data from multiple sources, are highly adaptable for mapping and monitoring land cover changes important to biodiversity and other ecosystem services in complex wet tropical environments.

Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960–2005

This paper illustrates the result of land use/cover change in Dhaka Metropolitan of Bangladesh using topographic maps and multi-temporal remotely sensed data from 1960 to 2005. The Maximum likelihood supervised classification technique was used to extract information from satellite data, and post-classification change detection method was employed to detect and monitor land use/cover change. Derived land use/cover maps were further validated by using high resolution images such as SPOT, IRS, IKONOS and field data. The overall accuracy of land cover change maps, generated from Landsat and IRS-1D data, ranged from 85% to 90%. The analysis indicated that the urban expansion of Dhaka Metropolitan resulted in the considerable reduction of wetlands, cultivated land, vegetation and water bodies. The maps showed that between 1960 and 2005 built-up areas increased approximately 15,924 ha, while agricultural land decreased 7,614 ha, vegetation decreased 2,336 ha, wetland/lowland decreased 6,385 ha, and water bodies decreased about 864 ha. The amount of urban land increased from 11% (in 1960) to 344% in 2005. Similarly, the growth of landfill/bare soils category was about 256% in the same period. Much of the city's rapid growth in population has been accommodated in informal settlements with little attempt being made to limit the risk of environmental impairments. The study quantified the patterns of land use/cover change for the last 45 years for Dhaka Metropolitan that forms valuable resources for urban planners and decision makers to devise sustainable land use and environmental planning.

A method to detect and correct single-band missing pixels in Landsat TM and ETM+ data

Human land use is the main driver of terrestrial ecosystems change, and remote sensing is an important tool to monitor these changes. Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) images have been the most important data source to map land cover change, but image artifacts often hinder or even prohibit digital change detection. This paper addresses a group of image distortions that display erroneous values in a single band while leaving the other bands of a spectrum undisturbed. Such artifacts may be due to different phenomena, for instance transmission and ground-processing problems or single event upsets. Automated artifact detection for those phenomena is often difficult, because erroneous band values often lie well within the range of naturally occurring radiance values. We developed IDL-based software that uses edge operators to detect and label affected pixels. Using a least-squares spectral-matching algorithm, the distorted spectrum is compared with undisturbed spectra in the local neighborhood and the undisturbed spectrum of best fit is determined. The erroneous band value is then replaced with the corresponding undisturbed value. This method was tested on seven Landsat TM images and on artificial data. Our results show that the distorted areas are precisely detected and that the correction procedure leads to meaningful spectra. This approach may be useful to minimize the effect of single-band distortions and allows for subsequent image analysis without the need to mask out distorted areas. The software tool includes a user interface and is available online.

Quantifying optical and SAR image relationships for tropical landscape features in the Amazônia

This paper discusses the relationship between SAR and optical data for an Amazonian test‐site with different land cover types. L‐band HH JERS‐1 SAR and Landsat TM images acquired few days apart from each other in 1994 and 1997 were analyzed. Landsat TM fraction images (vegetation, soil, and shade) were used to characterize the terrain features in the study area. Based on 220 samples randomly distributed over different land cover types in the fraction and SAR bands, a regression analysis was performed. Consistent results between SAR data and fraction images suggest that L‐band SAR data may be a complementary source of information for mapping land cover changes in Amazônia, especially to monitor deforestation in areas frequently blurred by cloud cover in optical images.

Spatial analysis of residential fuelwood supply and demand patterns in Mexico using the WISDOM approach

Abstract A WISDOM analysis was conducted in Mexico in order to: (1) identify fuelwood (FW) hot spots in terms of residential FW use and availability of FW resources for the year 2000, and (2) estimate net CO2 emissions from the non-renewable use of FW. WISDOM (woodfuel integrated supply/demand overview mapping) is a spatially explicit method, based on geographic information system (GIS) technology, which ranks a set of spatial units according to a group of indicators, in order to identify woodfuel priority areas or woodfuel hot spots. A comprehensive analysis was conducted, integrating full coverage national data on land cover classes, land cover change maps (1993–2000), geo-referenced population censuses (1990 and 2000), and a meticulous review of the international literature and Mexican case studies. Following a spatial multi-criteria analysis, 2395 counties (out of a country total of 2424 in year 2000) were ranked based on the number, density and annual growth rate of FW users; the percentage of households that use FW; the resilience of FW consumption, and the magnitude and likely trends of FW forest resources. The WISDOM analysis allowed the identification of 304 high priority counties (HPC), which showed a spatially aggregated pattern into 16 clusters. HPC cover 4% of Mexican territory and represent 27% of total FW consumption. We estimated that 1.3 Tg CO 2 y - 1 are released to the atmosphere by non-renewable FW burning, a value that represents less than 1% of Mexican total annual CO2 emissions in 2002. The results of the analysis show that WISDOM is a useful tool for both focusing resources to critical areas where action is more needed and to obtain more accurate estimates of the impacts associated to FW use.

A Novel Method for Mapping Land Cover Changes: Incorporating Time and Space With Geostatistics

Landsat data are now available for more than 30 years, providing the longest high-resolution record of Earth monitoring. This unprecedented time series of satellite imagery allows for extensive temporal observation of terrestrial processes such as land cover and land use change. However, despite this unique opportunity, most existing change detection techniques do not fully capitalize on this long time series. In this paper, a method that exploits both the temporal and spatial domains of time series satellite data to map land cover changes is presented. The time series of each pixel in the image is modeled with a combination of: 1) pixel-specific remotely sensed data; 2) neighboring pixels derived from ground observation data; and 3) time series transition probabilities. The spatial information is modeled with variograms and integrated using indicator kriging; time series transition probabilities are combined using an information-based cascade approach. This results in a map that is significantly more accurate in identifying when, where, and what land cover changes occurred. For the six images used in this paper, the prediction accuracy of the time series improves significantly, increasing from 31% to 61%, when both space and time are considered with the maximum likelihood. The consideration of spatial continuity also reduced unwanted speckles in the classified images, removing the need for any postprocessing. These results indicate that combining space and time domains significantly improves the accuracy of temporal change detection analyses and can produce high-quality time series land cover maps

Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt

In this study, maximum likelihood supervised classification and post-classification change detection techniques were applied to Landsat images acquired in 1987 and 2001, respectively, to map land cover changes in the Northwestern coast of Egypt. A supervised classification was carried out on the six reflective bands for the two images individually with the aid of ground truth data. Ground truth information collected during six field trips conducted between 1998 and 2002 and land cover map of 1987 were used to assess the accuracy of the classification results. Using ancillary data, visual interpretation and expert knowledge of the area through GIS further refined the classification results. Post-classification change detection technique was used to produce change image through cross-tabulation. Changes among different land cover classes were assessed. During the study period, a very severe land cover change has taken place as a result of agricultural and tourist development projects. These changes in land cover led to vegetation degradation and water logging in part of the study area.

Scene selection and the use of NASA's global orthorectified Landsat dataset for land cover and land use change monitoring

This study examines the utility of NASA's circa 1990 and circa 2000 global orthorectified Landsat dataset for land cover and land use change mapping and monitoring across Africa. This is achieved by comparing the temporal and spatial variation of NDVI, measured independently by the NOAA‐AVHRR at the time of Landsat scene acquisition, against the seasonal mean for each Landsat scene extent. Decadal sequences of drift‐corrected NOAA‐AVHRR imagery were used to calculate NDVI means and standard deviations for the periods covered by the scenes composing the c.1990 and c.2000 Landsat datasets. The specific NOAA‐AVHRR NDVI values at the acquisition date of each individual Landsat scene were also calculated and the differences, both from the mean and scaled by standard deviation, were mapped for the Landsat scene footprints in the c.1990 and c.2000 datasets. The resulting maps show the temporal position of each Landsat scene within the seasonal NDVI cycle, and provide a valuable guide to assist in quantifying uncertainty and interpreting land cover and land use changes inferred from these Landsat data.

A Satellite Remote Sensing Evaluation of Urban Land Cover Changes and Its Associated Impacts on Water Resources in Karachi, Pakistan

Metropolitan cities grow beyond imaginable proportions, particularly in the developing world. Urban issues therefore warrant - and receive - increasing attention. This paper summarizes the general pattern of urban land-cover changes in Karachi, Pakistan between the years 1992 and 2003 and explores the impacts of these urban land cover changes on water resources in the study area. Subsets of two Landsat TM images acquired in March 1992 and May 1998 and a subset of Landsat ETM+ image acquired in March 2003 were used for mapping land cover changes. An unsupervised classification approach, which uses a minimum spectral distance to assign pixels to clusters, was used with the overall accuracy ranging from 89 percent to 91 percent. Land cover statistics quantify that substantial land cover changes have taken place in Karachi metropolis and that the urban land has expanded by 203.1 km2 while barren and vegetated lands have decreased by 191.9 km2 and 13.5 km2, respectively over the study period (1992-2003) . The results demonstrate that land cover changes due to urbanization has not only contaminated the water quality but also aggravated the water shortfall in the study area, making difficulties in equal distribution of supply of water to all the areas of large city.

Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing

The importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing, especially in rapidly growing metropolitan areas. We have developed a methodology to map and monitor land cover change using multitemporal Landsat Thematic Mapper (TM) data in the seven-county Twin Cities Metropolitan Area of Minnesota for 1986, 1991, 1998, and 2002. The overall seven-class classification accuracies averaged 94% for the four years. The overall accuracy of land cover change maps, generated from post-classification change detection methods and evaluated using several approaches, ranged from 80% to 90%. The maps showed that between 1986 and 2002 the amount of urban or developed land increased from 23.7% to 32.8% of the total area, while rural cover types of agriculture, forest and wetland decreased from 69.6% to 60.5%. The results quantify the land cover change patterns in the metropolitan area and demonstrate the potential of multitemporal Landsat data to provide an accurate, economical means to map and analyze changes in land cover over time that can be used as inputs to land management and policy decisions.

Comparing estimators of gross change derived from complete coverage mapping versus statistical sampling of remotely sensed data

Area of gross change in land cover can be derived from a complete coverage land cover change map of a region of interest or estimated from a statistical sample of the region. Sampling may produce significant cost savings and more timely results because change is determined over a smaller total area than required by complete coverage mapping. Mean square error (MSE) defined in the context of a survey sampling measurement model is used to compare gross change estimators obtained from the two approaches. Measurement error bias attributable to error in classifying land cover change may occur with either the sampling or complete coverage mapping approach. An additional contribution to MSE attributable to sampling variability exists for the sampling-based estimator, but not the complete coverage estimator. If this sampling variability is small, the classification error bias of the sampling approach need not be reduced very far relative to the classification error bias of complete coverage to achieve similar MSE. Data from several published change accuracy error matrices are used to provide MSE comparisons for specific applications.

A fast environmental change detection approach based on unsupervised multiscale texture clustering

In this paper, an analytic model for mapping land cover change that uses a novel multiresolution analysis in concert with image differencing to identify environmental change/no‐change areas is presented. The method adaptively chooses thresholds to segment targets from background, by using multiscale decomposition of the image difference. Changes due to atmospheric influences are automatically suppressed and resolution heterogeneity problems minimized. Examples that demonstrate the efficiency of the technique on medium spatial resolution Landsat multitemporal imagery are presented. Empirical evaluation supports the suitability of this technique for fast identification of changed scenes. As opposed to the traditional change detection methods, we directly detect land cover changes rather than simply pixels. This is evident from the less‐pixelated appearance of the change maps. The technique is recommended for regional/local environmental vulnerability and risk assessment as well for automated updating of GIS databases.

Impact of short‐term rainfall fluctuation on interannual land cover change in sub‐Saharan Africa

ABSTRACTAim Interannual land cover change plays a significant role in food security, ecosystem processes, and regional and global climate modelling. Measuring the magnitude and location and understanding the driving factors of interannual land cover change are therefore of utmost importance to improve our understanding and prediction of these impacts and to better differentiate between natural and human causes of land cover change. Despite advances in quantifying the magnitude of land cover change, the interpretation of the observed land cover change in terms of climatic, ecological and anthropogenic processes still remains a complex issue. In this paper, we map land cover change across sub‐Saharan Africa and examine the influences of rainfall fluctuations on interannual change.Location The analysis was applied to sub‐Saharan Africa.Methods Ten‐day rainfall estimates (RFE) obtained from National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Center (CPC) were used to extract information on inter and intra‐annual rainfall fluctuations. The magnitude of land cover change was quantified based on the multitemporal change vector method measuring year‐to‐year differences in bidirectional reflectance distribution function (BRDF) corrected 16‐day enhanced vegetation index (EVI) data from the Moderate Resolution Imaging Spectro‐radiometer (MODIS). Statistical models were used to estimate the relationship between short‐term rainfall variability and the magnitude of land cover change. The analysis was stratified first by physiognomic vegetation type and second by chorological data on species distribution to gain insights into spatial variations in response to short‐term rainfall fluctuations.Results The magnitude of land cover change was significantly related to rainfall variability at the 5% level. Stratification considerably strengthened the relationship between the magnitude of change and rainfall variability. Explanatory power of the models ranged from R2 = 0.22 for the unstratified model to 0.40–0.96 for the individual models stratified by patterns of species distribution. The total variability explained by the combined models including the influence of rainfall and differences in vegetation response ranged from 22% for the model not stratified by vegetation to 76% when stratified by chorological data.Main conclusions Using this methodology, we were able to measure the contribution of natural variation in precipitation to land cover change. Several ecosystems across sub‐Saharan Africa are highly sensitive to short‐term rainfall variability.

MODELING BIOMASS BURNING EMISSIONS FOR AMAZON FOREST AND PASTURES IN RONDÔNIA, BRAZIL

As a source of atmospheric carbon, biomass burning emissions associated with deforestation in the Amazon are globally significant. Once deforested, these lands continue to be sources of substantial burning emissions for many years due to frequent pasture burning. The objective of this research was to quantify biomass‐burning emissions at a local scale. We estimated carbon emissions from three sources: fires associated with primary forest slash, regenerating forest slash, and pastures. The study was conducted on a 94 370‐ha section of land surrounding Jamari, Rondônia, Brazil. In the emissions computation, we integrated site‐specific, ground‐based data (biomass, emission factors for flaming and smoldering combustion, and combustion factors for land‐cover types) with a Landsat TM land cover change map of the study area for 1984–1992. This map was used to ascertain changes in land cover based on TM image dates during a period of early colonization and rapid deforestation in Rondônia. Emissions of CO, CO2, CH4, and other hydrocarbon trace gases were calculated. Between 1984 and 1992, we found CO2 emissions generated by primary forest slash burning were 694 379 Mg C (920 kg C·ha−1·yr−1) and regenerating forest slash burning contributed 23 436 Mg C (31 kg C·ha−1·yr−1), whereas cumulative pasture burning produced 238 180 Mg C (316 kg C·ha−1·yr−1). CO and CH4 followed the same trends by land cover type. Primary forest slash burns contributed 73% of the total C emissions whereas burning regenerating forest slash and pasture produced 2% and 25%, respectively. The major finding of this work is the identification of pasture burning as a potentially important source of pyrogenic emissions, but better data on biomass and combustion efficiency could confirm this finding. Further we demonstrated a novel method for estimating area burned that links land cover type and change to burn frequency.

Multiscale advanced raster map analysis system: Definition, design and development

This paper brings together a multidisciplinary initiative to develop advanced statistical and computational techniques for analyzing, assessing, and extracting information from raster maps. This information will provide a rigorous foundation to address a wide range of applications including disease mapping, emerging infectious diseases, landscape ecological assessment, land cover trends and change detection, watershed assessment, and map accuracy assessment. It will develop an advanced map analysis system that integrates these techniques with an advanced visualization toolbox, and use the system to conduct large case studies using rich sets of raster data, primarily from remotely sensed imagery. As a result, it will be possible to study and evaluate raster maps of societal, ecological, and environmental variables to facilitate quantitative characterization and comparative analysis of geospatial trends, patterns, and phenomena. In addition to environmental and ecological studies, these techniques and tools can be used for policy decisions at national, state, and local levels, crisis management, and protection of infrastructure. Geospatial data form the foundation of an information-based society. Remote sensing has been a vastly under-utilized resource involving a multi-million dollar investment at the national levels. Even when utilized, the credibility has been at stake, largely because of lack of tools that can assess, visualize, and communicate accuracy and reliability in timely manner and at desired confidence levels. Consider an imminent 21st century scenario: What message does a multi-categorical map have about the large landscape it represents? And at what scale, and at what level of detail? Does the spatial pattern of the map reveal any societal, ecological, environmental condition of the landscape? And therefore can it be an indicator of change? How do you automate the assessment of the spatial structure and behavior of change to discover critical areas, hot spots, and their corridors? Is the map accurate? How accurate is it? How do you assess the accuracy of the map? How do we evaluate a temporal change map for change detection? What are the implications of the kind and amount of change and accuracy on what matters, whether climate change, carbon emission, water resources, urban sprawl, biodiversity, indicator species, human health, or early warning? And with what confidence? The proposed research initiative is expected to find answers to these questions and a few more that involve multi-categorical raster maps based on remote sensing and other geospatial data. It includes the development of techniques for map modeling and analysis using Markov Random Fields, geospatial statistics, accuracy assessment and change detection, upper echelons of surfaces, advanced computational techniques for geospatial data mining, and advanced visualization techniques.

Detection of deforestation and land conversion in Rondônia, Brazil using change detection techniques

Fires associated with tropical deforestation, land conversion and land use greatly contribute to emissions as well as the depletion of carbon and nutrient pools. The objective of this research was to compare change detection techniques for identifying deforestation and cattle pasture formation during a period of early colonization and agricultural expansion in the vicinity of Jamari, Rondônia. Multi-date Landsat Thematic Mapper (TM) data between 1984 and 1992 were examined in a 94 370 ha area of active deforestation to map land cover change. The tasselled cap (TC) transformation was used to enhance the contrast between forest, cleared areas and regrowth. TC images were stacked into a composite multi-date TC and used in a principal components (PC) transformation to identify change components. In addition, consecutive TC image pairs were differenced and stacked into a composite multi-date differenced image. A maximum likelihood classification of each image composite was compared for identification of land cover change. The multi-date TC composite classification had the best accuracy of 0.78 (kappa). By 1984, only 5% of the study area had been cleared, but by 1992, 11% of the area had been deforested, primarily for pasture, and 7% lost due to hydroelectric dam flooding. Finally, discrimination of pasture versus cultivation was improved due to the ability to detect land under sustained clearing opposed to land exhibiting regrowth with infrequent clearing.

Land-Cover Change Monitoring with Classification Trees Using Landsat TM and Ancillary Data

We monitored land-cover change in San Diego County (1990‐1996) using multitemporal Landsat TM data. Change vectors of Kauth Thomas features were combined with stable multitemporal Kauth Thomas features and a suite of ancillary variables within a classification tree classifier. A combination of aerial photointerpretation and field measurements yielded training and validation data. Maps of land-cover change were generated for three hierarchical levels of change classification of increasing detail: change vs. no-change; four classes representing broad increase and decrease classes; and nine classes distinguishing increases or decreases in tree canopy cover, shrub cover, and urban change. The multitemporal Kauth Thomas (both stable and change features representing brightness, greenness, and wetness) provided information for magnitude and direction of land-cover change. Overall accuracies of the land-cover change maps were high (72 to 92 percent). Ancillary variables representing elevation, fire history, and slope were most significant in mapping the most complicated level of land-cover change, contributing 15 percent to overall accuracy. Classification trees have not previously been used operationally with remotely sensed and ancillary data to map land-cover change at this level of thematic detail.

Mapping land cover change in a reindeer herding area of the Russian Arctic using Landsat TM and ETM+ imagery and indigenous knowledge

Much of Russia north of the treeline is grazed by reindeer, and this grazing has materially altered the vegetation cover in many places. Monitoring vegetation change in these remote but ecologically sensitive regions is an important task for which satellite remote sensing is well suited. Further difficulties are imposed by the highly dynamic nature of arctic phenology, and by the difficulty of obtaining accurate official data on land cover in arctic Russia even where such data exist. We have approached the problem in a novel fashion by combining a conventional multispectral analysis of satellite imagery with data on current and historical land use gathered by the techniques of social anthropology, using a study site in the Nenets Autonomous Okrug (NAO). A Landsat-7 ETM+ image from the year 2000 was used to generate a current land cover classification. A Landsat-5 TM image was used to generate a land-cover classification for 1988, taking due account of phenological differences and between the two dates. A cautious comparison of these two classifications, again taking account of possible effects of phenological differences, shows that much of the study area has already undergone a notable transformation to grass-dominated tundra, almost certainly as a result of heavy grazing by reindeer. The grazing pattern is quite heterogeneous, and may have reached unsustainable levels in some areas. Finally, we suggest that this situation is unlikely to be unique to our study area and may well be widespread throughout the Eurasian tundra zone, particularly in the west.