Mapping regional land cover with MODIS data for biological conservation: Examples from the Greater Yellowstone Ecosystem, USA and Pará State, Brazil

The paper investigated the application of MODIS data for mapping regional land cover at moderate resolutions (250 and 500 m), for regional conservation purposes. Land cover maps were generated for two major conservation areas (Greater Yellowstone Ecosystem—GYE, USA and the Pará State, Brazil) using MODIS data and decision tree classifications. The MODIS land cover products were evaluated using existing Landsat TM land cover maps as reference data. The Landsat TM land cover maps were processed to their fractional composition at the MODIS resolution (250 and 500 m). In GYE, the MODIS land cover was very successful at mapping extensive cover types (e.g. coniferous forest and grasslands) and far less successful at mapping smaller habitats (e.g. wetlands, deciduous tree cover) that typically occur in patches that are smaller than the MODIS pixels, but are reported to be very important to biodiversity conservation. The MODIS classification for Pará State was successful at producing a regional forest/non-forest product which is useful for monitoring the extreme human impacts such as deforestation. The ability of MODIS data to map secondary forest remains to be tested, since regrowth typically harbors reduced levels of biodiversity. The two case studies showed the value of using multi-date 250 m data with only two spectral bands, as well as single day 500 m data with seven spectral bands, thus illustrating the versatile use of MODIS data in two contrasting environments. MODIS data provide new options for regional land cover mapping that are less labor-intensive than Landsat and have higher resolution than previous 1 km AVHRR or the current 1 km global land cover product. The usefulness of the MODIS data in addressing biodiversity conservation questions will ultimately depend upon the patch sizes of important habitats and the land cover transformations that threaten them.     

基于光谱归一化的变组分光谱混合分析(NMESMA)方法及其应用

混合像元问题在低、中分辨率遥感图像中尤为突出,混合像元的存在不仅会影响地物识别和图像分类精度,也是遥感科学向定量化发展的主要障碍之一。因此,遥感图像混合像元分解及其地表覆盖信息的定量提取是近年来研究的热点。针对城市土地覆盖信息的定量提取问题,利用中等分辨率遥感图像(Landsat TM),集成光谱归一化与变组分光谱混合分析(NMESMA)的方法,基于植被-非渗透表面-土壤(V\|I\|S)模型,定量提取研究区植被、土壤和非渗透表面3类土地覆盖的定量信息,并与固定组分的光谱混合分析(LSMA)分解结果进行对比分析。结果表明:基于光谱归一化的变组分光谱混合分析(NMESMA)方法获得的精度高于传统固定组分的光谱混合分析(LSMA)结果,可有效解决光谱异质性较高的城市区域的混合像元问题,为有效提取城市地表覆盖信息,研究城市生态环境变化和模拟分析,提供了有效的信息提取方法。     

Coastal wetland vegetation classification with a Landsat Thematic Mapper image

Coastal wetland vegetation classification with remotely sensed data has attracted increased attention but remains a challenge. This paper explored a hybrid approach on a Landsat Thematic Mapper (TM) image for classifying coastal wetland vegetation classes. Linear spectral mixture analysis was used to unmix the TM image into four fraction images, which were used for classifying major land covers with a thresholding technique. The spectral signatures of each land cover were extracted separately and then classified into clusters with the unsupervised classification method. Expert rules were finally used to modify the classified image. This research indicates that the hybrid approach employing sub-pixel information, an analyst's knowledge and characteristics of coastal wetland vegetation distribution shows promise in successfully distinguishing coastal vegetation classes, which are difficult to separate with a maximum likelihood classifier (MLC). The hybrid method provides significantly better classification results than MLC.     

The 250 m global land cover change product from the Moderate Resolution Imaging Spectroradiometer of NASA's Earth Observing System

One of the products to be derived from data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) will identify locations where land cover changes attributable to human activities are occurring. The product aims to serve as an alarm where rapid land cover conversion can subsequently be analysed with higher resolution sensors such as Landsat 7. This paper describes the production procedure and change detection algorithms for the 250 m MODIS land cover change product. Multiple change detection algorithms, including three spectral methods and two texture methods, are utilized to generate the product from the two 250 m MODIS bands. The change detection methods are implemented with look-up tables (LUTs) which are initially generated using data from the Advanced Very High Resolution Radiometer (AVHRR) and Landsat Thematic Mapper (TM) until MODIS data become available. The results from the five methods are combined to improve confidence in the product. We test the performance of each method against several sets of simulated MODIS data derived from Landsat TM image pairs. The test data represent tropical deforestation, agricultural expansion and urbanization. The commission errors of the five methods and the combination are approximately 10%, with reasonable omission errors less than 25%.     

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.     

Application of spectral mixture analysis for terrain evaluation studies

In this article, we describe an approach to calculate the spectral mixture within pixels and classify multispectral images. The results are compared with the classified images by traditional supervised rules such as Maximum Likelihood and appreciable results were accomplished. The method considers the number of endmembers that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. The only requirement for this method is a radiometrically corrected image because the endmembers are directly selected from the image. To make the method presented here more efficient, we propose to apply it only to the classes having low accuracy after a traditional supervised classification. Because the land cover classes in this study are related to a geomorphological terrain unit, we propose to mask the terrain units having problematic classes and decompose these units into their endmembers. A geomorphological analysis of the study area (Tonle Sap basin in Cambodia) was made to establish the relationship between land cover, landforms and soils through terrain mapping units. Then we performed a supervised classification of a Landsat Thematic Mapper (TM) image and of the same image merged with a SPOT-panchromatic (PAN) image, based on the land covers corresponding to the terrain mapping units. Then we masked a terrain unit having problematic spectral classes and applied the spectral mixture analysis which allowed an efficient separation of the land cover classes agglomerated in the preliminary classification. The result of this re-classification was re-inserted into the first classification and was compared statistically with the results obtained in the preliminary classification. We consider this procedure an efficient method to improve the results obtained from a supervised classification. The method can separate different land covers that were agglomerated in the preliminary segmentation. In our case, the classification accuracy for the terrain unit used (the fluvial terrace) increases from 62% (using only the TM bands) and 69% (using TM+ SPOT) to 83%.     

Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data

In this article, Landsat TM images acquired during the same season from both 1984 and 1997 were analysed for urban built-up land change detection in Beijing, China, where great changes have taken place during the recent decades. To reduce the spectral confusion between urban 'built-up' and rural 'non built-up' land cover categories, we propose a new structural method based on road density combined with spectral bands for change detection. The road density represents one type of structural information while the multiple Landsat TM bands represent spectral information. Road density maps for both dates were produced using a gradient direction profile analysis (GDPA) algorithm and then integrated with spectral bands. Results from the spectral-structural postclassification comparison (SSPCC) and spectral-structural image differencing (SSID) methods were evaluated and compared with spectral-only change detection methods. The proposed SSPCC method greatly reduced spectral confusion and increased the accuracy of land cover classification compared with spectral classification, which in turn improved the change detection results. This article also shows that the SSID change detection result complemented spectral band differencing by detecting areas with greater structural changes, some of which were missed, by spectral band differencing.     

Super-resolution land cover mapping with indicator geostatistics

Many satellite images have a coarser spatial resolution than the extent of land cover patterns on the ground, leading to mixed pixels whose composite spectral response consists of responses from multiple land cover classes. Spectral unmixing procedures only determine the fractions of such classes within a coarse pixel without locating them in space. Super-resolution or sub-pixel mapping aims at providing a fine resolution map of class labels, one that displays realistic spatial structure (without artifact discontinuities) and reproduces the coarse resolution fractions. In this paper, existing approaches for super-resolution mapping are placed within an inverse problem framework, and a geostatistical method is proposed for generating alternative synthetic land cover maps at the fine (target) spatial resolution; these super-resolution realizations are consistent with all the information available.More precisely, indicator coKriging is used to approximate the probability that a pixel at the fine spatial resolution belongs to a particular class, given the coarse resolution fractions and (if available) a sparse set of class labels at some informed fine pixels. Such Kriging-derived probabilities are used in sequential indicator simulation to generate synthetic maps of class labels at the fine resolution pixels. This non-iterative and fast simulation procedure yields alternative super-resolution land cover maps that reproduce: (i) the observed coarse fractions, (ii) the fine resolution class labels that might be available, and (iii) the prior structural information encapsulated in a set of indicator variogram models at the fine resolution. A case study is provided to illustrate the proposed methodology using Landsat TM data from SE China.     

Approaches to fractional land cover and continuous field mapping: A comparative assessment over the BOREAS study region

Subpixel land cover mapping involves the estimation of surface properties using sensors whose spatial sampling is coarse enough to produce mixtures of the properties within each pixel. This study evaluates five algorithms for mapping subpixel land cover fractions and continuous fields of vegetation properties within the BOREAS study area. The algorithms include a conventional “hard”, per-pixel classifier, a neural network, a clustering/look-up-table approach, multivariate regression, and linear least squares inversion. A land cover map prepared using a Landsat TM mosaic was adopted as the source of fine scale calibration and validation data. Coarse scale mixtures of five basic land cover classes and continuous vegetation fields, both corresponding to the field of view of SPOT-VEGETATION imagery (1.15-km pixel size), were synthesised from the TM mosaic using a modelled point spread function. Two measures of land cover distribution were used, fractions of fine scale land cover categories and continuous fields of vegetation structural characteristics. The subpixel algorithms were applied using both proximate (<100 km) and distant (>400 km) separation between training and validation regions. “Hard” classification performed poorly in estimating proportions or continuous fields. The neural network, look-up-table and multivariate regression algorithms produced good matches of spatial patterns and regional land cover composition for the proximate treatment. However, all three methods exhibited substantial biases with the distant treatment due to the characteristics of the training data. Linear least squares inversion offers a relatively unbiased but less precise alternative for subpixel proportion and fraction mapping as it avoids calibration to the a priori distribution of land cover in the training data. In general, a combination of multivariate regression for proximate training data and linear least squares inversion for distant training data resulted in woody fraction estimates within 20% of the Landsat TM classification-based estimates.     

Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation-impervious-soil model through remote sensing

In this study, Landsat 5-TM data were used to map urban land classes and the changes that occurred within them over a period of six years. The land classes were identified by Landsat 5-TM scenes taken in the same season in 1988 and 1994. The phenomena of land class changes were evaluated by adopting two remote sensing approaches, namely mapping and modelling, in a case study of the Bangkok Metropolitan area of Thailand. The quantitative results of changes, which were computed from a post-classification method, were used to analyse the pattern of changes in the urban land classes. The change-detection analysis indicated that 2% of agricultural land was lost, and there was a 14% increase in the commercial areas. The results demonstrated that the pattern of change in the urban land classes in Bangkok was that of agriculture lands to open lands; open lands to residential, and residential to commercial. The highest commercial land growth was observed in the high-density residential areas along main roads and the railway line. Data were generated from the two dates of TM images for the vegetationimpervious-soil (V-I-S) composition model. The trends of changes in the urban land classes and the anatomy of the study area were presented quantitatively through the V-I-S model. Good agreement was obtained between the values of changes computed for the impervious surfaces from the V-I-S model (which showed 6% changes) and the change-detection map (which showed 5.6% changes). The results of changes in the spatial pattern of commercial and residential areas (high, medium and low) emphasize that remote sensing data can be used for V-I-S modelling and mapping of urban surface features.     

Sub-pixel estimation of urban land cover components with linear mixture model analysis and Landsat Thematic Mapper imagery

We examine the utility of linear mixture modelling in the sub-pixel analysis of Landsat Enhanced Thematic Mapper (ETM) imagery to estimate the three key land cover components in an urban/suburban setting: impervious surface, managed/unmanaged lawn and tree cover. The relative effectiveness of two different endmember sets was also compared. The interior endmember set consisted of the median pixel value of the training pixels of each land cover and the exterior endmember set was the extreme pixel value. As a means of accuracy assessment, the resulting land cover estimates were compared with independent estimates obtained from the visual interpretation of digital orthophotography and classified IKONOS imagery. Impervious surface estimates from the Landsat ETM showed a high degree of similarity (RMS error (RMSE) within approximately ±10 to 15%) to that obtained using high spatial resolution digital orthophotography and IKONOS imagery. The partition of the vegetation component into tree vs grass cover was more problematic due to the greater spectral similarity between these land cover types with RMSE of approximately ±12 to 22%. The interior endmember set appeared to provide better differentiation between grass and urban tree cover than the exterior endmember set. The ability to separate the grass vs tree components in urban vegetation is of major importance to the study of the urban/suburban ecosystems as well as watershed assessment.     

基于遥感数据的城市地表温度与土地覆盖定量研究

利用Landsat TM数据,以徐州市为研究区,采用单窗算法反演地表温度,通过混合像元分解和V-I-S(植被—不透水面层—土壤)模型将土地覆盖类型分解为对城市热环境具有重要影响的植被、土壤、不透水面层3个分量,最后利用得到的3种地物比例、直接分类后的土地覆盖类型和地表温度对研究区城市热岛的空间分布特征、地表温度与土地覆盖类型以及各种影响因子之间的关系进行定量研究。研究成果能够有效地应用于城市人居环境研究和生态环境过程分析中。     

Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran

Rapid changes of land use and land cover (LULC) in urban areas have become a major environmental concern due to environmental impacts, such as the reduction of green spaces and development of urban heat islands (UHI). Monitoring and management plans are required to solve this problem effectively. The Tabriz metropolitan area in Iran, selected as a case study for this research, is an example of a fast growing city. Multi-temporal images acquired by Landsat 4, 5 TM and Landsat 7 ETM+ sensors on 30 June 1989, 18 August 1998, and 2 August 2001 respectively, were corrected for radiometric and geometric errors, and processed to extract LULC classes and land surface temperature (LST). The relationship between temporal dynamics of LST and LULC was then examined. The temperature vegetation index (TVX) space was constructed in order to study the temporal variability of thermal data and vegetation cover. Temporal trajectory of pixels in the TVX space showed that most changes due to urbanization were observable as the pixels migrated from the low temperature-dense vegetation condition to the high temperature-sparse vegetation condition in the TVX space. The uncertainty analysis revealed that the trajectory analysis in the TVX space involved a class-dependant noise component. This emphasized the need for multiple LULC control points in the TVX space. In addition, this research suggests that the use of multi-temporal satellite data together with the examination of changes in the TVX space is effective and useful in urban LULC change monitoring and analysis of urban surface temperature conditions as long as the uncertainty is addressed.     

Use of cartographic data and Landsat TM images to determine land use change in the vicinity of Mexico City

Evaluation of change in land use is important for planning further development in populated areas. Here we attempt to determine the growth of urban areas in the vicinity of Mexico City, using a 1993 Landsat Thematic Mapper (TM) image and cartographic data contained in maps published by the Instituto Nacional de Estadistica Geografia e Informatica (INEGI 1975, 1983). The area occupied by urban areas in 1975 and 1983 was quantified using raster images generated by scanning the maps. Supervised classification processes were applied to a 1993 Landsat TM image in bands 1, 2, 3, 4, 5 and 7, of the area of Chalco. The image was pre-processed and then processed to enhance the spectral response of the surface materials. The different land cover types that characterise distinct land uses in the study area were identified in the image and an overall classification accuracy of 82% was estimated using aerial photographs from the Chalco area. The resulting evaluation of the land use changes in the Chalco urban area was plotted, and a growth greater than 14% per year was estimated.     

Assessing the potential of VEGETATION sensor data for mapping snow and ice cover: A Normalized Difference Snow and Ice Index

The VEGETATION (VGT) sensor in SPOT 4 has four spectral bands that are equivalent to Landsat Thematic Mapper (TM) bands (blue, red, near-infrared and mid-infrared spectral bands) and provides daily images of the global land surface at a 1-km spatial resolution. We propose a new index for identifying and mapping of snow/ice cover, namely the Normalized Difference Snow/Ice Index (NDSII), which uses reflectance values of red and mid-infrared spectral bands of Landsat TM and VGT. For Landsat TM data, NDSII is calculated as NDSII_TM=(TM3-TM5)/(TM3+TM5); for VGT data, NDSII is calculated as NDSII_VGT=(B2-MIR)/(B2+MIR). As a case study we used a Landsat TM image that covers the eastern part of the Qilian mountain range in the Qinghai-Xizang (Tibetan) plateau of China. NDSIITM gave similar estimates of the area and spatial distribution of snow/ice cover to the Normalized Difference Snow Index (NDSI=(TM2-TM5)/(TM2+TM5)) which has been proposed by Hall et al. The results indicated that the VGT sensor might have the potential for operational monitoring and mapping of snow/ice cover from regional to global scales, when using NDSII_VGT.     

Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques

The majority of the world's population now resides in urban environments and information on the internal composition and dynamics of these environments is essential to enable preservation of certain standards of living. Remotely sensed data, especially the global coverage of moderate spatial resolution satellites such as Landsat, Indian Resource Satellite and Système Pour l'Observation de la Terre (SPOT), offer a highly useful data source for mapping the composition of these cities and examining their changes over time. The utility and range of applications for remotely sensed data in urban environments could be improved with a more appropriate conceptual model relating urban environments to the sampling resolutions of imaging sensors and processing routines. Hence, the aim of this work was to take the Vegetation-Impervious surface-Soil (VIS) model of urban composition and match it with the most appropriate image processing methodology to deliver information on VIS composition for urban environments. Several approaches were evaluated for mapping the urban composition of Brisbane city (south-east Queensland, Australia) using Landsat 5 Thematic Mapper data and 1:5000 aerial photographs. The methods evaluated were: image classification; interpretation of aerial photographs; and constrained linear mixture analysis. Over 900 reference sample points on four transects were extracted from the aerial photographs and used as a basis to check output of the classification and mixture analysis. Distinctive zonations of VIS related to urban composition were found in the per-pixel classification and aggregated air-photo interpretation; however, significant spectral confusion also resulted between classes. In contrast, the VIS fraction images produced from the mixture analysis enabled distinctive densities of commercial, industrial and residential zones within the city to be clearly defined, based on their relative amount of vegetation cover. The soil fraction image served as an index for areas being (re)developed. The logical match of a low (L)-resolution, spectral mixture analysis approach with the moderate spatial resolution image data, ensured the processing model matched the spectrally heterogeneous nature of the urban environments at the scale of Landsat Thematic Mapper data.     

Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS

We used three Landsat images together with socio‐economic data in a post‐classification analysis to map the spatial dynamics of land use/cover changes and identify the urbanization process in Nairobi city. Land use/cover statistics, extracted from Landsat Multi‐spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM+) images for 1976, 1988 and 2000 respectively, revealed that the built‐up area has expanded by about 47?km². The road network has influenced the spatial patterns and structure of urban development, so that the expansion of the built‐up areas has assumed an accretive as well as linear growth along the major roads. The urban expansion has been accompanied by loss of forests and urban sprawl. Integration of demographic and socio‐economic data with land use/cover change revealed that economic growth and proximity to transportation routes have been the major factors promoting urban expansion. Topography, geology and soils were also analysed as possible factors influencing expansion. The integration of remote sensing and Geographical Information System (GIS) was found to be effective in monitoring land use/cover changes and providing valuable information necessary for planning and research. A better understanding of the spatial and temporal dynamics of the city's growth, provided by this study, forms a basis for better planning and effective spatial organization of urban activities for future development of Nairobi city.     

Scale dependence of biophysical structure in deforested areas bordering the Tapajós National Forest, Central Amazon

Spatial variation of land-surface properties is a major challenge to ecological and biogeochemical studies in the Amazon basin. The scale dependence of biophysical variation (e.g., mixtures of vegetation cover types), as depicted in Landsat observations, was assessed for the common land-cover types bordering the Tapajós National Forest, Central Brazilian Amazon. We first collected hyperspectral signatures of vegetation and soils contributing to the optical reflectance of landscapes in a 600-km² region. We then employed a spectral mixture model AutoMCU that utilizes bundles of the field spectra with Monte Carlo analysis to estimate sub-pixel cover of green plants, senescent vegetation and soils in Landsat Thematic Mapper (TM) pixels. The method proved useful for quantifying biophysical variability within and between individual land parcels (e.g., across different pasture conditions). Image textural analysis was then performed to assess surface variability at the inter-pixel scale. We compared the results from the textural analysis (inter-pixel scale) to spectral mixture analysis (sub-pixel scale). We tested the hypothesis that very high resolution, sub-pixel estimates of surface constituents are needed to detect important differences in the biophysical structure of deforested lands. Across a range of deforestation categories common to the region, there was strong correlation between the fractional green and senescent vegetation cover values derived from spectral unmixing and texture analysis variance results (r²>0.85, p<0.05). These results support the argument that, in deforested areas, biophysical heterogeneity at the scale of individual field plots (sub-pixel) is similar to that of whole clearings when viewed from the Landsat vantage point.     

Landsat-based land cover mapping in the lower Yuna River watershed in the Dominican Republic

To aid in the environmental planning and management of Los Haitises National Park, a neotropical park in the Dominican Republic, a land cover change analysis was performed on the lower Yuna River watershed, within which a portion of the park exists and which contains a diversity of agricultural practices. Separate image classifications were performed on a 1973 Landsat MSS image and a 1985 Landsat TM image with resulting overall classification accuracies of 77.3 per cent and 81.3 per cent, respectively. In both classifications, spectral similarities between the various growth stages of rice, mangrove, orchard, and permanent grassland made separation and delineation of these classes difficult. The implications of land cover trends within the watershed for ecologic and economic management issues which affect both the watershed and the park were discussed.