Detecting land cover change at the Jornada Experimental Range, New Mexico with ASTER emissivities

Multispectral thermal infrared remote sensing of surface emissivities can detect and monitor long term land vegetation cover changes over arid regions. The technique is based on the link between spectral emissivities within the 8.5-9.5 μm interval and density of sparsely covered terrains. The link exists regardless of plant color, which means that it is often possible to distinguish bare soils from senescent and non-green vegetation. This capability is typically not feasible with vegetation indices. The method is demonstrated and verified using ASTER remote sensing observations between 2001 and 2003 over the Jornada Experimental Range, a semi-arid site in southern New Mexico, USA. A compilation of 27 nearly cloud-free, multispectral thermal infrared scenes revealed spatially coherent patterns of spectral emissivities decreasing at rates on the order of 3% per year with R² values of ∼ 0.82. These patterns are interpreted as regions of decreased vegetation densities, a view supported by ground-based leaf area index transect data. The multi-year trend revealed by ASTER's 90-m resolution data are independently confirmed by 1-km data from Terra MODIS. Comparable NDVI images do not detect the long-term spatially coherent changes in vegetation. These results show that multispectral thermal infrared data, used in conjunction with visible and near infrared data, could be particularly valuable for monitoring land cover changes.     

Lithologic mapping in the Oscar II Coast area,Graham Land,Antarctic Peninsula using ASTER data

The results of the first attempt to use Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for the purposes of lithologic mapping on the Antarctic Peninsula are presented for an area on the Oscar II Coast, eastern Graham Land. This study included undertaking laboratory reflectance spectroscopy of ～70 rock samples from the study area and spectral lithologic analysis of two ASTER scenes. Spectra of the granitoids, silicic volcanic/volcaniclastic and terrestrial sedimentary rocks in the study area display a limited range of absorption features associated with muscovite, smectite and chlorite that are generally present as the alteration products of regional metamorphism. ASTER data analysis was undertaken using the reflective bands of the Level 1B registered radiance at-sensor data and the standard thermal infrared (TIR) emissivity product (AST05). For both wavelength regions, standard qualitative image processing methods were employed to define image end-members that were used as reference within Matched Filter (MF) processing procedures. The results were interpreted with reference to existing field observations, and photogeologic analysis of the ASTER visible to near-infrared (VNIR)/shortwave infrared (SWIR) data was used to resolve ambiguities in the spectral mapping results. The results have enabled the discrimination of most of the major lithologic groups within the study area as well as delineation of hydrothermal alteration zones of propylitic, and argillic grades associated with the Mesozoic Mapple Formation volcanics. The results have extended the mapped coverage of the Mapple Formation into un-investigated regions further north and validated previously inferred geological observations concerning other rocks throughout the study area. The outcomes will enable important revisions to be made to the existing geological map of the Oscar II Coast and demonstrate that ASTER data offers potential for improving geological mapping coverage across the Antarctic Peninsula.     

Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan

Kazakhstan is the second largest country to emerge from the collapse of the Soviet Union. Consequent to the abrupt institutional changes surrounding the disintegration of the Soviet Union in the early 1990s, Kazakhstan has reportedly undergone extensive land cover/land use change. Were the institutional changes sufficiently great to affect land surface phenology at spatial resolutions and extents relevant to mesoscale meteorological models? To explore this question, we used the NDVI time series (1985-1988 and 1995-1999) from the Pathfinder Advanced Very High Resolution Radiometer (AVHRR) Land (PAL) dataset, which consists of 10 days maximum NDVI composites at a spatial resolution of 8 km. Daily minimum and maximum temperatures were extracted from the NCEP Reanalysis Project and 10 days composites of accumulated growing degree-days (AGDD) were produced. We selected for intensive study seven agricultural areas ranging from regions with rain-fed spring wheat cultivation in the north to regions of irrigated cotton and rice in the south. We applied three distinct but complementary statistical analyses: (1) nonparametric testing of sample distributions; (2) simple time series analysis to evaluate trends and seasonality; and (3) simple regression models describing NDVI as a quadratic function of AGDD.The irrigated areas displayed different temporal developments of NDVI between 1985-1988 and 1995-1999. As the temperature regime between the two periods was not significantly different, we conclude that observed differences in the temporal development of NDVI resulted from changes in agricultural practices.In the north, the temperature regime was also comparable for both periods. Based on extant socioeconomic studies and our model analyses, we conclude that the changes in the observed land surface phenology in the northern regions are caused by large increases in fallow land dominated by weedy species and by grasslands under reduced grazing pressure. Using multiple lines of evidence allowed us to build a case of whether differences in land surface phenology were mostly the result of anthropogenic influences or interannual climatic fluctuations.     

Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods

The recent release of the U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001, which represents the nation's land cover status based on a nominal date of 2001, is widely used as a baseline for national land cover conditions. To enable the updating of this land cover information in a consistent and continuous manner, a prototype method was developed to update land cover by an individual Landsat path and row. This method updates NLCD 2001 to a nominal date of 2006 by using both Landsat imagery and data from NLCD 2001 as the baseline. Pairs of Landsat scenes in the same season in 2001 and 2006 were acquired according to satellite paths and rows and normalized to allow calculation of change vectors between the two dates. Conservative thresholds based on Anderson Level I land cover classes were used to segregate the change vectors and determine areas of change and no-change. Once change areas had been identified, land cover classifications at the full NLCD resolution for 2006 areas of change were completed by sampling from NLCD 2001 in unchanged areas. Methods were developed and tested across five Landsat path/row study sites that contain several metropolitan areas including Seattle, Washington; San Diego, California; Sioux Falls, South Dakota; Jackson, Mississippi; and Manchester, New Hampshire. Results from the five study areas show that the vast majority of land cover change was captured and updated with overall land cover classification accuracies of 78.32%, 87.5%, 88.57%, 78.36%, and 83.33% for these areas. The method optimizes mapping efficiency and has the potential to provide users a flexible method to generate updated land cover at national and regional scales by using NLCD 2001 as the baseline.     

Land cover change detection at coarse spatial scales based on iterative estimation and previous state information

This study focuses on the use of coarse spatial resolution (CR, pixel size about 1 km²) remote sensing data for land cover change detection and qualification. Assuming the linear mixing model for CR pixels, the problem is that both the multitemporal class features and the pixel composition in terms of classes are unknown. The proposed algorithm is then based on the iterative alternate estimation of each unknown variable. At each iteration, the class features are estimated, thanks to the knowledge of the composition of some pixels, and then the pixel composition is re-estimated knowing the class features. The subset of known composition pixels is the subset of pixels where no change has occurred, i.e. the previous land cover map is still valid. It is derived automatically by removing at each iteration the pixels where the new composition estimation disagrees with the former one. Finally, for the final estimation of the pixel composition, a Markovian chain model is used to guide the solution, i.e. the previous land cover map is used as a ‘reminder’ or ‘memory’ term.This approach has been first validated using simulated data with different spatial resolution ratios. Then, the detection of forest change with SPOT/VGT-S10 has been considered as an actual application case. Finally, the method has been applied to change detection on the Val de Saône watershed between the 1980s and 2000. The results obtained from three coarse resolution series, NOAA/AVHRR, SPOT/VGT-S10 and SPOT/VGT-P, have been compared.     

Validation of the North American ASTER Land Surface Emissivity Database (NAALSED) version 2.0 using pseudo-invariant sand dune sites

Knowledge of the Land Surface Emissivity (LSE) in the Thermal Infrared (TIR: 8-12 µm) part of the electromagnetic spectrum is essential to derive accurate Land Surface Temperatures (LSTs) from spaceborne TIR measurements. This study focuses on validation of the emissivity product in the North American ASTER Land Surface Emissivity Database (NAALSED) v2.0 — a mean seasonal, gridded emissivity product produced at 100 m spatial resolution using all Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes from 2000 to 2008 over North America (http://emissivity.jpl.nasa.gov). The NAALSED emissivity product was validated over bare surfaces with laboratory measurements of sand samples collected at nine pseudo-invariant sand dune sites located in the western/southwestern USA. The nine sand dune sites cover a broad range of surface emissivities in the TIR. Results show that the absolute mean emissivity difference between NAALSED and the laboratory results for the nine validation sites and all five ASTER TIR bands was 0.016 (1.6%). This emissivity difference is equivalent to approximately a 1 K error in the land surface temperature for a material at 300 K in the TIR.     

Ten years of forest cover change in the Sierra Nevada detected using Landsat satellite image analysis

The Sierra Nevada of California is a region where large wildfires have been suppressed for over a century. A detailed geographic record of recent vegetation regrowth and disturbance patterns in forests of the Sierra Nevada remains a gap that can be filled with remote-sensing data. Landsat Thematic Mapper imagery was analysed to detect 10 years of recent changes (between 2000 and 2009) in forest vegetation cover for areas burned by wildfires between years of 1995 and 1999 in the region. Results confirmed the prevalence of regrowing forest vegetation during the period 2000 and 2009 over 17% of the combined burned areas. Classification of these regrowing forest vegetation areas by the Landsat normalized burn ratio (NBR) showed that there was a marked increase in average disturbance index (ΔDI) values in the transitions from low to moderate to high burn severity classes. Within the five combined wildfire perimeters, 45% of the high burn severity area delineated by the RdNBR analysis was covered by regrowing forest detected between 2000 and 2009. In contrast, a notable fraction (12%) of the entire 5 km (unburned) buffer area outside the 1995–1999 fires perimeters showed decline in forest cover, and not nearly as many regrowing forest areas, covering only 3% of all the 1995–1999 buffer areas combined. Based on comparison of these results to ground-based survey data and high-resolution aerial images, the Landsat difference index (ΔDI) methodology can fulfil much of the need for consistent, low-cost monitoring of changes due to climate and biological factors in western forest regrowth following stand-replacing disturbances.     

Comparison of atmospheric correction methods using ASTER data for the area of Crete,Greece

The purpose of atmospheric correction is to produce more accurate surface reflectance and to potentially improve the extraction of surface parameters from satellite images. To achieve this goal the influences of the atmosphere, solar illumination, sensor viewing geometry and terrain information have to be taken into account. Although a lot of information from satellite imagery can be extracted without atmospheric correction, the physically based approach offers advantages, especially when dealing with multitemporal data and/or when a comparison of data provided by different sensors is required. The use of atmospheric correction models is limited by the need to supply data related to the condition of the atmosphere at the time of imaging. Such data are not always available and the cost of their collection is considerable, hence atmospheric correction is performed with the use of standard atmospheric profiles. The use of these profiles results in a loss of accuracy. Therefore, site-dependent databases of atmospheric parameters are needed to calibrate and to adjust atmospheric correction methods for local level applications. In this article, the methodology and results of the project Adjustment of Atmospheric Correction Methods for Local Studies: Application in ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) (ATMOSAT) for the area of Crete are presented. ATMOSAT aimed at comparing several atmospheric correction methods for the area of Crete, as well as investigating the effects of atmospheric correction on land cover classification and change detection. Databases of spatio-temporal distributions of all required input parameters (atmospheric humidity, aerosols, spectral signatures, land cover and elevation) were developed and four atmospheric correction methods were applied and compared. The baseline for this comparison is the spatial distribution of surface reflectance, emitted radiance and brightness temperature as derived by ASTER Higher Level Products (HLPs). The comparison showed that a simple image based method, which was adjusted for the study area, provided satisfactory results for visible, near infrared and short-wave infrared spectral areas; therefore it can be used for local level applications. Finally, the effects of atmospheric correction on land cover classification and change detection were assessed using a time series of ASTER multispectral images acquired in 2000, 2002, 2004 and 2006. Results are in agreement with past studies, indicating that for this type of application, where a common radiometric scale is assumed among the multitemporal images, atmospheric correction should be taken into consideration in pre-processing.     

Land use/land cover changes in the coastal zone of Ban Don Bay,Thailand using Landsat 5 TM data

Land use/land cover of the Earth is changing dramatically because of human activities and natural disasters. Information about changes is useful for updating land use/land cover maps for planning and management of natural resources. Several methods for land use/land cover change detection using time series Landsat imagery data were employed and discussed. Landsat 5 TM colour composites of 1990, 1993, 1996 and 1999 were employed for locating training samples for supervised classification in the coastal areas of Ban Don Bay, Surat Thani, Thailand. This study illustrated an increasing trend of shrimp farms, forest/mangrove and urban areas with a decreasing trend of agricultural and wasteland areas. Land use changes from one category to others have been clearly represented by the NDVI composite images, which were found suitable for delineating the development of shrimp farms and land use changes in Ban Don Bay.     

Methodological variations in the production of CORINE land cover and consequences for long-term land cover change studies. The case of Spain.

ABSTRACT

Land cover information at national or regional scale is essential for science, monitoring, reporting, and policy making. CORINE Land Cover (CLC) is the most consistent land cover map for the entire European territory, with four repetitions during the period 1985–2012. The long-term consistency of CLC maps is the most appreciated strength and it should be guaranteed. Beyond some common general guidelines, the mapping approaches used in individual countries differ and change over time, leading to inconsistencies that should be known and reported. Through a series of metrics over the Spanish CLC most recent layers (CLC2006 and CLC2012) and comparisons with eight other countries CLC statistics, we demonstrate that the methodological changes recently implemented have introduced some discrepancies with previous CLC versions. The most affected classes in Spain were transitional woodland-shrub, complex cultivation patterns, artificial, grasslands, and forests. Users should be aware of the important implications these discrepancies may have in land use and land cover change studies, trend analysis, and reports.     

Employing crowdsourced geographic data and multi-temporal/multi-sensor satellite imagery to monitor land cover change: A case study in an urbanizing region of the Philippines

Land cover change (LCC) can have a significant impact on human and environmental well-being. LCC maps derived from historical remote sensing (RS) images are often used to evaluate the impacts of past LC changes and to construct models to predict future LC changes. Free moderate spatial resolution (~ 30 m) optical and synthetic aperture radar (SAR) RS imagery is now becoming increasingly available for this LCC monitoring. However, the classification algorithms used to extract LC information from these images typically require “training data” for classification (i.e. points or polygons with LC class labels), and acquiring this labelled training data can be difficult and time-consuming. Alternatively, crowdsourced geographic data (CGD) has become widely available from online sources like OpenStreetMap (OSM), and it may provide a useful source of training data for LCC monitoring. A major challenge with utilizing CGD for LCC mapping, however, is the presence of class labelling errors, and these errors can vary spatially (e.g. due to differing levels of CGD contributor expertise) and temporally (e.g. due to time lag between CGD creation and RS imagery acquisition). In this study, we investigated a new LCC mapping method which utilizes free Landsat (optical) and PALSAR mosaic (SAR) satellite imagery in combination with labelled LC data extracted from CGD sources (the OSM “landuse” and “natural” polygon datasets). A semi-unsupervised classification approach was employed for the LCC mapping to reduce the effects of class label noise in the CGD. The main motivation and benefit of the proposed method is that it does not require training data to be manually collected, allowing for a faster and more automated assessment of LCC. As a case study, we applied the method to map LCC in the Laguna de Bay area of the Philippines over the 2007–2015 period. The LCC map produced using our proposed approach achieved an overall classification accuracy of 90.2%, providing evidence that CGD and multi-temporal/multi-sensor satellite imagery, when combined, have a great potential for LCC monitoring.     

基于国产卫星数据的深圳城市化进程中土地利用/覆盖变化探讨

土地利用/覆盖变化是全球变化中的重要组成部分,城市化进程将导致大规模的土地利用/覆盖变化.文中首先分别对1999年、2006年、2010年的CBERS和HJ-1B数据进行几何校正、拼接裁剪、分类等处理,生成土地利用/覆盖分类图,然后分别计算求得深圳市1999年到2006年和2006年到2010年的土地利用/覆盖变化转移矩阵.在此基础上,研究深圳市从1999年到2010年期间土地利用/覆盖变化的空间过程.结果表明:深圳市在快速城市化进程中发生了大规模的土地利用/覆盖变化,大量的草地、耕地、未利用土地转化为城镇用地,草地和林地之间部分结构相互转化调整.同时,10年来深圳市土地利用/覆盖变化区域差异明显,伴随着宝安和龙岗两区城市化进程加快,关外土地利用/覆盖变化强度逐渐加强,而关内逐渐减弱.在深圳城市化进程中,城镇用地重心呈现出向北部扩展的趋势.     

Land cover inventory in the Netherlands using remote sensing; application in a soil and groundwater vulnerability assessment system

Abstract

Information on actual land cover is necessary for various applications, such as soil and groundwater protection studies and hydrological studies. Therefore, a decision to produce a national land cover data base of the Netherlands, using satellite images, was made. The first version of the data base is now available for the whole of the Netherlands. Prior to the supervised classification the area was stratified in more or less homogeneous areas. Because cost, time and logistics required for a random sampling of the entire country were prohibitive, a mixed quantitative/qualitative classification accuracy assessment procedure was proposed. Classification performances were quantitatively assessed by comparing the classification results with digitized ground reference maps using a Geographical Information System (GIS). This offers a flexible method for locating the incorrectly labelled pixels and determining the possible reasons thereof. Classification accuracy of the land cover types which do not change much in time was also qualitatively assessed, using aerial photographs and topographical maps. The land cover data derived from remote sensing images can be readily combined with other digitized geographical data bases (e.g. soil maps).

The results of the proposed classification and validation procedure are presented for a test site situated in a stratum in the south of the Netherlands. It is shown how the land cover data are applied in a soil and groundwater vulnerability assessment system.     

Spatiotemporal variability of snow cover on Tibet,China using MODIS remote-sensing data

ABSTRACT

Snow cover is an important component of the cryosphere, and the study on spatial and temporal variations of snow cover is essential for understanding the consequences and impacts of climate change and water resources management. In this study, the temporal variation of snow-covered area (SCA) and spatial variability of snow-cover frequency (SCF) on Tibet is analysed based on the Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra snow cover product (MOD10A2) from 2000 to 2015, and relationships with main climate variables are investigated. Results are as follows: (1) there is a very weak decreasing trend in annual mean SCA, and a slight increasing trend in autumn and winter and a slight decreasing trend in spring and more robust decreasing trend in summer for SCA are found. (2) The temporal variation of SCA is negatively correlated with temperature, whereas it is little correlated with corresponding precipitation. (3) The general trend of spatial SCF variation on Tibet, predominated by snow-cover variations in spring and autumn, tends to decrease in spring while it tends to increase in autumn. (4) The spatial variability of SCF is attributed to snow-cover variations in autumn and spring, which is more obvious in higher latitudes in autumn while it is more noticeable in lower-latitude southeastern plateau in spring. (5) The regions with higher variability of snow cover are main pastoral land and more prone to snow-related disaster in Tibet, becoming key zone of snow-cover monitoring and disaster prevention and mitigation.     

Characterizing multi-decadal,annual land cover change dynamics in Houston,TX based on automated classification of Landsat imagery

In 2017, Hurricane Harvey caused substantial loss of life and property in the swiftly urbanizing region of Houston, TX. Now in its wake, researchers are tasked with investigating how to plan for and mitigate the impact of similar events in the future, despite expectations of increased storm intensity and frequency as well as accelerating urbanization trends. Critical to this task is the development of automated workflows for producing accurate and consistent land cover maps of sufficiently fine spatio-temporal resolution over large areas and long timespans. In this study, we developed an innovative automated classification algorithm that overcomes some of the traditional trade-offs between fine spatio-temporal resolution and extent – to produce a multi-scene, 30m annual land cover time series characterizing 21 years of land cover dynamics in the 35,000 km2 Greater Houston area. The ensemble algorithm takes advantage of the synergistic value of employing all acceptable Landsat imagery in a given year, using aggregate votes from the posterior predictive distributions of multiple image composites to mitigate against misclassifications in any one image, and fill gaps due to missing and contaminated data, such as those from clouds and cloud shadows. The procedure is fully automated, combining adaptive signature generalization and spatio-temporal stabilization for consistency across sensors and scenes. The land cover time series is validated using independent, multi-temporal fine-resolution imagery, achieving crisp overall accuracies between 78–86% and fuzzy overall accuracies between 91–94%. Validated maps and corresponding areal cover estimates corroborate what census and economic data from the Greater Houston area likewise indicate: rapid growth from 1997–2017, demonstrated by the conversion of 2,040 km² (± 400 km²) to developed land cover, 14% of which resulted from the conversion of wetlands. Beyond its implications for urbanization trends in Greater Houston, this study demonstrates the potential for automated approaches to quantifying large extent, fine resolution land cover change, as well as the added value of temporally-dense time series for characterizing higher-order spatio-temporal dynamics of land cover, including periodicity, abrupt transitions, and time lags from underlying demographic and socio-economic trends.     

The impact of imperfect ground reference data on the accuracy of land cover change estimation

Error in the ground reference data set used in studies of land cover change can be a source of bias in the estimation of land cover change and of change detection accuracy. The magnitude of the bias introduced may be very large even if the ground reference data set is of a high accuracy. Sometimes the bias is of a predictable systematic nature and so may be reduced or even removed. The impacts of ground reference data error on the accuracy of estimates of the extent of change and on change detection accuracy were explored with simulated data. In one scenario illustrated, the producer's accuracy of change detection was estimated to be ～61% when in reality it was 80%, the substantial underestimation of accuracy arising through the use of a ground reference data set with an accuracy of 90%. In the same scenario, the extent of change was also substantially overestimated at 26%, when in reality a change of only 20% had occurred. Reducing the effect of error in ground reference data will enable more accurate estimation of land cover change and a more realistic appraisal of the quality of remote sensing as a source of data on land cover change.     

High-resolution urban change modeling and flood exposure estimation at a national scale using open geospatial data: A case study of the Philippines

Many developing countries in Asia are experiencing rapid urban expansion in climate hazard prone areas. To support climate resilient urban planning efforts, here we present an approach for simulating future urban land-use changes and evaluating potential flood exposure at a high spatial resolution (30 m) and national scale. As a case study, we applied this model to the Philippines – a country frequently affected by extreme rainfall events. Urban land-use changes were simulated to the year 2050 using a trend-based logistic regression cellular automata model, considering three different scenarios of urban expansion (assuming low/medium/high population growth). Flood exposure assessment was then conducted by overlaying the land-use simulation results onto a global floodplain map. We found that approximately 6040–13,850 ha of urban land conversion is likely to be located in flood prone regions between 2019 and 2050 (depending on the scenario), affecting approximately 2.5–5.8 million additional urban residents. In locations with high rates of future urban development in flood prone areas (Mindanao Island, in particular), climate resilient land-use plans should be developed/enforced, and flood mitigation infrastructure protected (in the case of “nature-based” infrastructure) or constructed. The data selected for our land-use change modeling and flood exposure assessment were all openly and (near-)globally available, with the intention that our methodology can potentially be applied in other countries where rapid urban expansion is occurring. The 2050 urban land-use maps generated in this study are available for download at https://www.iges.or.jp/en/pub/ph-urban2050/en to allow for their use in future works.     

Analysis of rain fall and the temperature of Coimbatore District using land use and land cover change detection by image segmentation

Image segmentation is a process has done for the classification of high resolution remote sensing images in the present research work. The segmentation results are capable of influencing the subsequent process effects. An image can be partitioned into a number of disjoint segments which is used to represent the image structures. It is found that it is more compact to represent an image and the low level and high structures can be combined. There are different types of methods to segment an image namely, threshold-based, edge-based and region-based. Region growing approach is image segmentation methods in which the neighboring pixels are examined and merged with the class region in case of no edges are detected. The iteration is done for every pixel boundary. Unlike gradient and Laplacian methods, the edges of the region are found by the region growing and it is perfectly their region. The images are determined by the LANDSAT TM satellite data. The remote sensing technique is used for collecting information about the Coimbatore district. The sensed data is a key to many diverse applications. The contribution of this work for Coimbatore district is to find the change of the Land used and Land covered in the entire region and also to find the changes in the green lands, vegetation and Land surface utilized for urban area. The neighboring regions are taken into account and the similarities are checked in the growing process. No single region is allowed to dominate the entire proceedings. A certain number of regions are allowed to grow at a time. Comparable regions will gradually combine into expanding regions. The Control of these methods may be quite complicated but efficient methods have been developed. The directions of growing pixels are easy and efficient to implement on parallel computers. The threshold-based segmentation is completely depending on the gray level images which regards the reflectivity of the featured images. It determines a threshold based on brightness of the ground objects. It is purely from the image background. But it is rapid and its uncertainty is significant. It is not convenient to process multi-spectral images.     

Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data

Evaluation of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image of the Mountain Pass, California area indicates that several important lithologic groups can be mapped in areas with good exposure by using spectral-matching techniques. The three visible and six near-infrared bands, which have 15-m and 30-m resolution, respectively, were calibrated by using in situ measurements of spectral reflectance. Calcitic rocks were distinguished from dolomitic rocks by using matched-filter processing in which image spectra were used as references for selected spectral categories. Skarn deposits and associated bright coarse marble were mapped in contact metamorphic zones related to intrusion of Mesozoic and Tertiary granodioritic rocks. Fe-muscovite, which is common in these intrusive rocks, was distinguished from Al-muscovite present in granitic gneisses and Mesozoic granite.Quartzose rocks were readily discriminated, and carbonate rocks were mapped as a single broad unit through analysis of the 90-m resolution, five-band surface emissivity data, which is produced as a standard product at the EROS Data Center. Three additional classes resulting from spectral-angle mapper processing ranged from (1) a broad granitic rock class (2) to predominately granodioritic rocks and (3) a more mafic class consisting mainly of mafic gneiss, amphibolite and variable mixtures of carbonate rocks and silicate rocks.     

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.