Vegetation Fraction Images Research Articles

Mapping Burned Areas of Mato Grosso State Brazilian Amazon Using Multisensor Datasets

Quantifying forest fires remain a challenging task for the implementation of public policies aimed to mitigate climate change. In this paper, we propose a new method to provide an annual burned area map of Mato Grosso State located in the Brazilian Amazon region, taking advantage of the high spatial and temporal resolution sensors. The method consists of generating the vegetation, soil, and shade fraction images by applying the Linear Spectral Mixing Model (LSMM) to the Landsat-8 OLI (Operational Land Imager), PROBA-V (Project for On-Board Autonomy–Vegetation), and Suomi NPP-VIIRS (National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite) datasets. The shade fraction images highlight the burned areas, in which values are represented by low reflectance of ground targets, and the mapping was performed using an unsupervised classifier. Burned areas were evaluated in terms of land use and land cover classes over the Amazon, Cerrado and Pantanal biomes in the Mato Grosso State. Our results showed that most of the burned areas occurred in non-forested areas (66.57%) and old deforestation (21.54%). However, burned areas over forestlands (11.03%), causing forest degradation, reached more than double compared with burned areas identified in consolidated croplands (5.32%). The results obtained were validated using the Sentinel-2 data and compared with active fire data and existing global burned areas products, such as the MODIS (Moderate Resolution Imaging Spectroradiometer product) MCD64A1 and MCD45A1, and Fire CCI (ESA Climate Change Initiative) products. Although there is a good visual agreement among the analyzed products, the areas estimated were quite different. Our results presented correlation of 51% with Sentinel-2 and agreement of r2 = 0.31, r2 = 0.29, and r2 = 0.43 with MCD64A1, MCD45A1, and Fire CCI products, respectively. However, considering the active fire data, it was achieved the better performance between active fire presence and burn mapping (92%). The proposed method provided a general perspective about the patterns of fire in various biomes of Mato Grosso State, Brazil, that are important for the environmental studies, specially related to fire severity, regeneration, and greenhouse gas emissions.

Vegetation Fraction Images Derived from PROBA-V Data for Rapid Assessment of Annual Croplands in Brazil

This paper presents a new method for rapid assessment of the extent of annual croplands in Brazil. The proposed method applies a linear spectral mixing model (LSMM) to PROBA-V time series images to derive vegetation, soil, and shade fraction images for regional analysis. We used S10-TOC (10 days synthesis, 1 km spatial resolution, and top-of-canopy) products for Brazil and S5-TOC (five days synthesis, 100 m spatial resolution, and top-of-canopy) products for Mato Grosso State (Brazilian Legal Amazon). Using the time series of the vegetation fraction images of the whole year (2015 in this case), only one mosaic composed with maximum values of vegetation fraction was generated, allowing detecting and mapping semi-automatically the areas occupied by annual crops during the year. The results (100 m spatial resolution map) for the Mato Grosso State were compared with existing global datasets (Finer Resolution Observation and Monitoring—Global Land Cover (FROM-GLC) and Global Food Security—Support Analyses Data (GFSAD30)). Visually those maps present a good agreement, but the area estimated are not comparable since the agricultural class definition are different for those maps. In addition, we found 11.8 million ha of agricultural areas in the entire Brazilian territory. The area estimation for the Mato Grosso State was 3.4 million ha for 1 km dataset and 5.3 million ha for 100 m dataset. This difference is due to the spatial resolution of the PROBA-V datasets used. A coefficient of determination of 0.82 was found between PROBA-V 100 m and Landsat-8 OLI area estimations for the Mato Grosso State. Therefore, the proposed method is suitable for detecting and mapping annual croplands distribution operationally using PROBA-V datasets for regional analysis.

Discriminating Land Use and Land Cover Classes in Brazil Based on the Annual PROBA-V 100 m Time Series

Brazil, with more than 8 million km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , presents six different biomes, ranging from natural grasslands (Pampa biome) to tropical rainfall forests (Amazônia biome), with different land-use types (mostly pasturelands and croplands) and pressures (mainly in the Cerrado biome). The objective of this article is to present a new method to discriminate the most representative land use and land cover (LULC) classes of Brazil, based on the PROBA-V images. The images were converted into vegetation, soil, and shade fraction images by applying the linear spectral mixing model. Then, the pixel-based, highest proportion, annual mosaics of the fraction images, and their corresponding standard deviation images were derived and classified using the random forest algorithm. The following LULC classes were considered: forestlands, shrublands, grasslands, croplands, pasturelands, water bodies, and others. An agreement analysis was conducted with two available LULC maps derived from the Landsat satellite, the MapBiomas, and the Finer Resolution Observation and Monitoring-Global Land Cover (FROM-GLC) projects. Forestlands (412 million ha) and pasturelands (242 million ha) were the two most representative LULC classes; and croplands accounted for 30 million ha. The results presented an overall agreement of 69% and 58% with the MapBiomas and FROM-GLC projects, respectively. The proposed method is a good alternative to support operational projects of LULC map production that are important for planning biodiversity conservation or environmentally sustainable land occupation.

Monitoring deforestation and forest degradation using multi-temporal fraction images derived from Landsat sensor data in the Brazilian Amazon

ABSTRACT Deforestation is the replacement of forest by other land use while degradation is a reduction of long-term canopy cover and/or forest stock. Forest degradation in the Brazilian Amazon is mainly due to selective logging of intact/un-managed forests and to uncontrolled fires. The deforestation contribution to carbon emission is already known but determining the contribution of forest degradation remains a challenge. Discrimination of logging from fires, both of which produce different levels of forest damage, is important for the UNFCCC (United Nations Framework Convention on Climate Change) REDD+ (Reducing Emissions from Deforestation and Forest Degradation) program. This work presents a semi-automated procedure for monitoring deforestation and forest degradation in the Brazilian Amazon using fraction images derived from Linear Spectral Mixing Model (LSMM). Part of a Landsat Thematic Mapper (TM) scene (path/row 226/068) covering part of Mato Grosso State in the Brazilian Amazon, was selected to develop the proposed method. First, the approach consisted of mapping deforested areas and mapping forest degraded by fires using image segmentation. Next, degraded areas due to selective logging activities were mapped using a pixel-based classifier. The results showed that the vegetation, soil, and shade fraction images allowed deforested areas to be mapped and monitored and to separate degraded forest areas caused by selective logging and by fires. The comparison of Landsat Operational Land Imager (OLI) and RapidEye results for the year 2013 showed an overall accuracy of 94%. We concluded that spatial resolution plays an important role for mapping selective logging features due to their characteristics. Therefore, when compared to Landsat data, the current availability of higher spatial and temporal resolution data, such as provided by Sentinel-2, is expected to improve the assessment of deforestation and forest degradation, especially caused by selective logging. This will facilitate the implementation of actions for forest protection.

Seasonality of vegetation types of South America depicted by moderate resolution imaging spectroradiometer (MODIS) time series

The development, implementation and enforcement of policies involving the rational use of the land and the conservation of natural resources depend on an adequate characterization and understanding of the land cover, including its dynamics. This paper presents an approach for monitoring vegetation dynamics using high-quality time series of MODIS surface reflectance data by generating fraction images using Linear Spectral Mixing Model (LSMM) over South America continent. The approach uses physically-based fraction images, which highlight target information and reduce data dimensionality. Further dimensionality was also reduced by using the vegetation fraction images as input to a Principal Component Analysis (PCA). The RGB composite of the first three PCA components, accounting for 92.9% of the dataset variability, showed good agreement with the main ecological regions of South America continent. The analysis of 21 temporal profiles of vegetation fraction values and precipitation data over South America showed the ability of vegetation fractions to represent phenological cycles over a variety of environments. Comparisons between vegetation fractions and precipitation data indicated the close relationship between water availability and leaf mass/chlorophyll content for several vegetation types. In addition, phenological changes and disturbance resulting from anthropogenic pressure were identified, particularly those associated with agricultural practices and forest removal. Therefore the proposed method supports the management of natural and non-natural ecosystems, and can contribute to the understanding of key conservation issues in South America, including deforestation, disturbance and fire occurrence and management.

Relationship between remote sensing data and field-observed interril erosion

Abstract: The objective of this work was to evaluate the relationship between different remote sensing data, derived from satellite images, and interrill soil losses obtained in the field by using a portable rainfall simulator. The study was carried out in an area of a hydrographic basin, located in Médio Paraíba do Sul, in the state of Rio de Janeiro - one of the regions most affected by water erosion in Brazil. Evaluations were performed for different vegetation indices (NDVI, Savi, EVI, and EVI2) and fraction images (FI), derived from linear spectral mixture analysis (LSMA), obtained from RapidEye, Sentinel2A, and Landsat 8 OLI images. Vegetation indices are more adequate to predict soil loss than FI, highlighting EVI2, whose exponential model showed R2 of 0.74. The best prediction models are generated from the RapidEye image, which shows the highest spatial resolution among the sensors evaluated.

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Abstract Mediterranean ecosystems are adapted to recurrent forest fires by having regeneration mechanisms that overcome the immediate effects of fire. However, the increasing frequency of fires in most European Mediterranean countries is challenging the natural regrowth capability of these ecosystems. In this context, monitoring post-fire vegetation recovery is a priority for forest management and soil erosion control. In this work, a 13-year series (1999–2011) of Landsat 5 Thematic Mapper (TM)/Landsat 7 Enhanced Thematic Mapper (ETM +) data was used to model post-fire vegetation recovery as a function of burn severity and to quantify post-fire resilience as a measure of vegetation cover regrowth. We evaluated a large forest fire located in Spain that burned approximately 30 km 2 of Pinus pinaster Ait. in August 1998. 88 field plots of four burn severity levels (unburned, low, moderate and high) were measured in the field a year after the fire. As a variable representative of vegetation, we chose the shade normalized green vegetation fraction image (SGV) obtained by applying Multiple Endmember Spectral Mixture Analysis (MESMA) to the original Landsat TM/ETM + images. The SGV values were extracted for the 88 field plots and, after performing a one-way analysis of variance (ANOVA), a Fisher's Least Significant Difference (LSD) test allowed us to estimate resilience of vegetation cover as the number of post-fire years exhibiting a statistically significant difference between burned and unburned areas. Next, SGV values were referenced to unburned control plots values and the vegetation recovery index (VRI) was defined. The evolution in time curve of VRI for low, moderate and highly fire affected vegetation was fit using trend models (specifically, an exponential trend for VRI in high and moderate burn severity levels; a linear trend for low burn severity level, Root Mean Square Error, RMSE = 0.18, 0.13, and 0.09, respectively). We observed that vegetation cover affected by low severity fire recovered to its original state after 7 years, and vegetation cover affected by moderate severity recovered after 13 years. Vegetation affected by high severity fire was estimated to recover after 20 years. We conclude that VRI time series based on multi-temporal MESMA fractions from Landsat data can be considered a valuable indicator of the post-fire vegetation cover recovery. Its temporal evolution represented post-fire vegetation cover regrowth adequately and facilitated the estimate of vegetation cover resilience in Mediterranean forests.

Soil erosion risk assessment and spatial mapping using LANDSAT-7 ETM+, RUSLE, and GIS—a case study

This paper discusses the application of the Revised Universal Soil Loss Equation (RUSLE) in conjunction with LANDSAT-7 ETM+ remote sensing data, and geographical information system (GIS) to the spatial mapping of soil erosion risk in Jhagrabaria Watershed Allahabad, U.P., India. Soil map and topographical data were used to develop the soil erodibility factor (K), and a digital elevation model (DEM) image was used to generate the topographic factor (LS). The cover-management factor (C) was developed based on vegetation, shade, and soil fraction images derived from spectral mixture analysis of a LANDSAT Enhanced Thematic Mapper Plus (LANDSAT-7 ETM+) image, and support practice factor (P) was developed by crossing operation between land use/land cover classification map and slope map. Assuming the same climatic conditions in the study area, the rainfall-runoff erosivity (R) factor was not used. The value of K for the study area lies between 0.25 and 0.485, LS values were less than 1.4, and C and P values were less than 1. A soil erosion risk map with five classes (very low, low, medium, medium high, and high) was produced based on the simplified RUSLE within the GIS environment and was linked to land use/land cover (LULC) image to explore the relationships between soil erosion risk (SER) and LULC distribution. The results indicated that the land use/land cover (LULC) having the most succession and mature vegetation are in low-erosion-risk areas, while the barren and fallow lands are usually associated with high- to medium-erosion-risk areas. The spatial maps of the SER has been generated that can be utilized in the policy matter and planning for the watershed studied. This research also implied that the remote sensing and GIS tools and techniques provide the highly promising and important tools for evaluating and mapping soil erosion risk in the Jhagrabaria Watershed.

Modelo de Estabilidade dos Solos a Erosão

The development of new techniques that facilitate environmental planning are needed, so this work presents a new methodological approach : the Model of Stability of Soil Science (MES) , which uses data obtained by remote sensing in order to support environmental planning . The technique was applied in the city of Costa Rica - MS with Landsat generating an environmental zoning proposal for the study area. The letter slope was drawn through the SRTM digital elevation model. The Charter Fitness of Lands (agricultural capability of the soil) was obtained from the Multi Referral Atlas of the State of Mato Grosso do Sul linear spectral mixture model to derive vegetation fraction image ( letter of vegetation cover ) was used . With this data set we applied the MES through map algebra, which uses the overlay of information layers slope, agricultural soil suitability and land cover. Data obtained via MES were compared with those observed in the field. Thus it can be seen that the MES was efficient in assessing the areas most vulnerable to the erosive action, moreover, proved to be an efficient tool for decision making.

Soil Erosion Risk Assessment in the Sincanlı Sub-Watershed of the Akarçay Basin (Afyonkarahisar, Turkey) Using the Universal Soil Loss Equation (USLE)

This paper contains research in which the authors applied the Universal Soil Loss Equation (USLE), Geographical Information Systems (GIS), and remote sensing to the mapping of the soil erosion risk in the Sincanli sub-watershed area of the Akarcay Basin, Afyonkarahisar, Turkey. The rainfall-runoff erosivity factor (R) was developed from annual precipitation data and previous studies, soil map and soil survey data was used to develop the soil erodibility factor (K), and a digital elevation model image was used to generate the topographic factor (LS). The cover-management factor (C) was developed based on vegetation, shade, and soil fraction images derived from spectral mixture analysis of a Landsat Thematic Mapper image. A soil erosion risk map with four classes (low, slight, moderate, and high) was produced based on the simplified USLE within the GIS environment. The low erosion category is very common (83%), while, to only a limited extent, the watershed falls within the slight erosion risk category (2%), mostly seen in the central section of the study area. The low and slight erosion risk areas are situated in the flat plain and low slope areas where there is no soil erosion by water. Moderate risk of erosion occurs in a small area (4%) of the watershed, but about 11% of the watershed is under high erosion risk, found mostly in the south-western, southern and eastern, and partly in the north-eastern sections of the watershed where there are mountains consisting of resistant rocks such as limestone, tuff-agglomerate, andesite, and trachyte. It is believed that the results of this study can be used to advise the local authorities/government in prioritizing the areas of immediate erosion mitigation. This research implies that GIS and remote sensing provide promising tools

Using Landsat Imagery and Census Data for Urban Population Density Modeling in Port-au-Prince, Haiti

This research uses the most recent (2003) census data and a Landsat ETM+ image to build a population estimation model for Port-au-Prince, Haiti. The purpose of the study is to establish the linkage of population density with remotely sensed surface reflectance signals of an urban area, and use that to estimate population when census data are not available in a timely fashion. The research begins with deriving subpixel vegetation-impervious surface-soil (VIS) fractions derived from the Landsat ETM+ multispectral bands, and then uses the geographically weighted regression (GWR) model to examine how the variation of population density can be explained by the VIS variables and their derivatives. With comparison to the ordinary least square (OLS) model, the GWR model accounts for spatial non-stationarity in the relationship between population patterns and land characteristics in the study area. The study reveals that three VIS variables are significant in explaining population density: the mean value of houses fraction image, the mean value of vegetation fraction image, and the standard deviation of vegetation fraction image.

Fraction images for monitoring intra-annual phenology of different vegetation physiognomies in Amazonia

In this study we investigate the potential of fraction images derived from a linear spectral mixture model to detect vegetation phenology in Amazonia, and evaluate their relationships with the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices. Time series of MODIS 250-m data over three contrasting land cover types in the Amazon were used in conjunction with rainfall data, a land cover map and a forest inventory survey to support the interpretation of our findings. Each vegetation physiognomy was characterized by a particular intra-annual variability detected by a combination of the fraction images. Both vegetation and shade fractions were important to evaluate the seasonality of the open tropical forest (OTF). The association of these results with forest inventory data and the literature suggests that Enhanced Vegetation Index (EVI) and vegetation fraction images are sensitive to structural changes in the canopy of OTF. In cerrado grassland (CG) the phenology was better characterized by combined soil and vegetation fractions. Soybean (SB) areas were characterized by the highest ranges in the vegetation and soil fraction images. Vegetation fraction and vegetation indices for the OTF showed a significant positive relationship with EVI but not with Normalized Difference Vegetation Index (NDVI). Significant relationships for vegetation fraction and vegetation indices were also found for the CG and soybean areas. In contrast to vegetation index approaches to monitoring phenology, fraction images provide additional information that allows a more comprehensive exploration of the spectral and structural changes in vegetation formations.

Monitoring land cover in Acre State, western Brazilian Amazonia, using multitemporal remote sensing data

This article presents the use of multitemporal remote sensing data for monitoring land cover changes in Acre State, western Brazilian Amazonia. For this study, the 2000 Landsat ETM+ (ETM+, Enhanced Thematic Mapper Plus), the 1990 Landsat TM (TM, Thematic Mapper) and 1980 Landsat MSS (MSS, Multispectral Scanner System) were used. The 2005 and 2007 Terra MODIS (MODIS, Moderate Resolution Imaging Spectroradiometer) images were also used to map deforestation that had occurred during the recent years and to map burned areas that occurred in the 2005 dry year. The ETM+, TM, MSS and MODIS images were converted to vegetation, soil and shade fraction images, using linear spectral mixing model, in order to reduce the data volume for digital processing. Then land cover maps were produced by digital classification of these fraction images. The results showed that deforestation increased by 7100 km2 from 1980 to 1990, by 5100 km2 from 1990 to 2000 and by 3300 km2 from 2000 to 2007. About 2100 km2 of vegetation regrowth areas were observed in the 2000 ETM+ images. The analysis of 2005 MODIS images showed that 3700 km2 of deforested areas and 2800 km2 of forested areas were burned in Acre State in 2005. This kind of information is important for regional and global environmental studies and for efforts to control such burning and deforestation activities in the future.

Evaluation of a linear spectral mixture model and vegetation indices (NDVI and EVI) in a study of schistosomiasis mansoni and Biomphalaria glabrata distribution in the state of Minas Gerais, Brazil

This paper analyses the associations between Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) on the prevalence of schistosomiasis and the presence of Biomphalaria glabrata in the state of Minas Gerais (MG), Brazil. Additionally, vegetation, soil and shade fraction images were created using a Linear Spectral Mixture Model (LSMM) from the blue, red and infrared channels of the Moderate Resolution Imaging Spectroradiometer spaceborne sensor and the relationship between these images and the prevalence of schistosomiasis and the presence of B. glabrata was analysed. First, we found a high correlation between the vegetation fraction image and EVI and second, a high correlation between soil fraction image and NDVI. The results also indicate that there was a positive correlation between prevalence and the vegetation fraction image (July 2002), a negative correlation between prevalence and the soil fraction image (July 2002) and a positive correlation between B. glabrata and the shade fraction image (July 2002). This paper demonstrates that the LSMM variables can be used as a substitute for the standard vegetation indices (EVI and NDVI) to determine and delimit risk areas for B. glabrata and schistosomiasis in MG, which can be used to improve the allocation of resources for disease control.

Estimating impervious surfaces using linear spectral mixture analysis with multitemporal ASTER images

Impervious surface is a key indicator of urban environmental quality and degree of urbanization. Therefore, estimation and mapping of impervious surfaces by using remote sensing digital images has attracted increasing attention recently. For mid-latitude cities, seasonal vegetation phenology has a significant effect on the spectral response of terrestrial features, and image analysis must take into account this environmental characteristic. In this paper, three Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, acquired on 3 October 2000, 16 June 2001 and 5 April 2004, respectively, were used to test the seasonal sensitivity of impervious surface estimation. The study area was the city of Indianapolis (Marion County), Indiana, USA. Linear spectral mixture analysis (LSMA) was applied to generate high-albedo, low-albedo, vegetation and soil fraction images (endmembers), and impervious surfaces were then estimated by adding high- and low-albedo fraction images. In addition, land use/land cover (LULC) and land surface temperature (LST) maps were generated and used to create image masks to remove non-impervious pixels. The accuracy of the impervious surface maps was checked against Digital Orthophoto Quarter Quadrangle (DOQQ) images. Three accuracy indicators, the root mean square error (RMSE), mean average error (MAE) and correlation coefficient (R 2), were calculated and compared to analyse the seasonal sensitivity of impervious surface estimation. Our results indicate that vegetation phenology has a fundamental impact on impervious surface estimation. The summer (June) image was better for impervious surface estimation than the spring (April) and autumn (October) images. The LULC and LST image masks can significantly increase the accuracy of impervious surface estimation. The mean LST was found appropriate to be set as the threshold for the various image masks. A summer image was most appropriate because there was full growth of vegetation, and mapping of impervious surfaces was more effective with a contrasting spectral response from green vegetation. The mixing space, based on the four endmembers, was perfectly three-dimensional. By contrast, there was significant amount of bare soil and ground and non-photosynthetic vegetation in the spring and autumn images. Plant phenology caused changes in the variance partitioning and impacted the mixing space characterization, leading to a less accurate estimation of the impervious surfaces.

Combining wavelets and linear spectral mixture model for MODIS satellite sensor time-series analysis

This work presents a methodology that uses digital fraction images derived from Linear Spectral Mixture Model and wavelets transform from MODIS satellite sensor time-series for land cover change analysis. Our approach uses MODIS surface reflectance images acquired from 2000 to 2006 time period. For this study, a test site was selected in the Mato Grosso State, Brazilian Amazonia. This site has shown high deforestation rates in the last years. The samples of land cover classes were collected during four field campaigns (2003, 2004, 2005 and 2006) to be used as ground truth. The linear spectral mixture model was applied to the MODIS surface reflectance images of red surface reflectance band (620-670 nm bandwidth), near infrared surface reflectance band (NIR, 841-876 nm bandwidth) and medium infrared surface reflectance band (MIR, 2105-2155 nm bandwidth). This model generated the vegetation, shade, and soil fraction images. In the next step, the Meyer orthogonal Discrete Wavelets Transform was used for filtering the time-series of MODIS fraction images. The filtered signal was reconstructed excluding high frequencies for each pixel in the fraction images (soil, vegetation, and shade) of the time-series. This computational procedure allows to observe the original signal without clouds and other noises. The results show that wavelets transform can provide a gain in multitemporal analysis and visualization on inter-annual fraction images variability patterns.

Satellite Estimation of Aboveground Biomass and Impacts of Forest Stand Structure

Heterogeneous Amazonian landscapes and complex forest stand structure often make aboveground biomass (AGB) estimation difficult. In this study, spectral mixture analysis was used to convert a Landsat Thematic Mapper (TM) image into green vegetation, shade, and soil fraction images. Entropy was used to analyze the complexity of forest stand structure and to examine impacts of different stand structures on TM reflectance data. The relationships between AGB and fraction images or TM spectral signatures were investigated based on successional and primary forests, respectively, and AGB estimation models were developed for both types of forests. Our findings indicate that the AGB estimation models using fraction images perform better for successional forest biomass estimation than using TM spectral signatures. However, both models based on TM spectral signatures and fractions provided poor performance for primary forest biomass estimation. The complex stand structure and associated canopy shadow greatly reduced relationships between AGB and TM reflectance or fraction images.

Mapping soil erosion risk in Rondônia, Brazilian Amazonia: using RUSLE, remote sensing and GIS

AbstractThis article discusses research in which the authors applied the Revised Universal Soil Loss Equation (RUSLE), remote sensing, and geographical information system (GIS) to the maping of soil erosion risk in Brazilian Amazonia. Soil map and soil survey data were used to develop the soil erodibility factor (K), and a digital elevation model image was used to generate the topographic factor (LS). The cover‐management factor (C) was developed based on vegetation, shade, and soil fraction images derived from spectral mixture analysis of a Landsat Enhanced Thematic Mapper Plus image. Assuming the same climatic conditions and no support practice in the study area, the rainfall–runoff erosivity (R) and the support practice (P) factors were not used. The majority of the study area has K values of less than 0·2, LS values of less than 2·5, and C values of less than 0·25. A soil erosion risk map with five classes (very low, low, medium, medium‐high, and high) was produced based on the simplified RUSLE within the GIS environment, and was linked to land use and land cover (LULC) image to explore relationships between soil erosion risk and LULC distribution. The results indicate that most successional and mature forests are in very low and low erosion risk areas, while agroforestry and pasture are usually associated with medium to high risk areas. This research implies that remote sensing and GIS provide promising tools for evaluating and mapping soil erosion risk in Amazonia. Copyright © 2004 John Wiley &amp; Sons, Ltd.

Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies

Remote sensing of urban heat islands (UHIs) has traditionally used the Normalized Difference Vegetation Index (NDVI) as the indicator of vegetation abundance to estimate the land surface temperature (LST)–vegetation relationship. This study investigates the applicability of vegetation fraction derived from a spectral mixture model as an alternative indicator of vegetation abundance. This is based on examination of a Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City, IN, USA, acquired on June 22, 2002. The transformed ETM+ image was unmixed into three fraction images (green vegetation, dry soil, and shade) with a constrained least-square solution. These fraction images were then used for land cover classification based on a hybrid classification procedure that combined maximum likelihood and decision tree algorithms. Results demonstrate that LST possessed a slightly stronger negative correlation with the unmixed vegetation fraction than with NDVI for all land cover types across the spatial resolution (30 to 960 m). Correlations reached their strongest at the 120-m resolution, which is believed to be the operational scale of LST, NDVI, and vegetation fraction images. Fractal analysis of image texture shows that the complexity of these images increased initially with pixel aggregation and peaked around 120 m, but decreased with further aggregation. The spatial variability of texture in LST was positively correlated with those in NDVI and in vegetation fraction. The interplay between thermal and vegetation dynamics in the context of different land cover types leads to the variations in spectral radiance and texture in LST. These variations are also present in the other imagery, and are responsible for the spatial patterns of urban heat islands. It is suggested that the areal measure of vegetation abundance by unmixed vegetation fraction has a more direct correspondence with the radiative, thermal, and moisture properties of the Earth's surface that determine LST.

Coupling spectral unmixing and trend analysis for monitoring of long-term vegetation dynamics in Mediterranean rangelands

The development of vegetation cover is one of the primary indicators for land degradation, stability, or regeneration in regions threatened by overgrazing. This paper addresses the problem how spatially explicit information about degradation processes in European Mediterranean rangelands can be derived from long time series of satellite data. The selected test site in central Crete, Greece, is considered to be representative for the highly heterogeneous character of such landscapes. The monitoring approach comprises the time period between 1977 and 1996, covered by nine Landsat TM and four Landsat MSS images. Special emphasis has hence been put on the evaluation of potentials and drawbacks when coupling Landsat TM and MSS based results. The data sets were geometrically and radiometrically pre-processed in a rigorous fashion, followed by a linear spectral unmixing approach and a time series analysis of vegetation fraction images. Based on the resulting map, the spatio-temporal patterns of vegetation cover changes are explained. Even a test site such as central Crete, with its limited spatial extend, exhibits heterogeneous patterns of change, supporting the hypothesis that long time series of EOS data from Landsat-like sensors are mandatory to identify the relevant changes at landscape level.