9 documents found, page 1 of 1

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 ref...

Mappig and monitoring land degradation risks in the western Brazilian Amazon us...

Mapping and monitoring land degradation in areas under human-induced stresses have urgent tasks in remote sensing whose importance has not yet been fully appreciated. In this study, a surface cover index (SCI) is developed to evaluate and map potential land degradation risks associated with deforestation and accompanying soil erosion in a Western Brazilian Amazon rural settlement study area. The relationships b...

Land-cover classification in the Brazilian Amazon with the integration of Lands...

Land-cover classification with remotely sensed data in moist tropical regions in a challenge due to the complex biophysical conditions. This paper explores techniques to improve land-cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM +) and Radarsat data. A wavelet-merging technique was used t...

Multitemporal spectral mixture analysis for Amazonian land-cover change detection.

The complex landscape and environmental conditions in the moist tropical region often result in poor land-cover change detection accuracy using traditional change detection methods. This paper explores linear spectral mixture analysis (LSMA) of multitemporal thematic mapper (TM) images to detect land-cover change in Rondônia, Brazilian Amazon basin. Three image endmembers (shade, green vegetation, and soil) wer...

The roles of textural images in improving land-cover classification in the Braz...

Texture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite...

Aboveground forest biomass estimation with Landsat and LiDAR data and uncertain...

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimatio...

Land use/cover classification in the Brazilian Amazon using satellite images.

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Throu...

Comparison of land-cover classification methods in the Brazilian Amazon Basin.

Four distinctly different classifiers were used to analyze multispectral data. Which of these classifiers is most suitable for a specific study area is not always clear. This paper provides a comparison of minimum-distance classifier (MDC), maximumlikelihood classifier (MLC), extraction and classification of homogeneous objects (ECHO), and decision-tree classifier based on linear spectral mixture analysis (DTC-...

Linear mixture model applied to Amazonian vegetation classification.