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The complicated forest stand structure and associated abundant tree species in the Amazon often induce difficulty in estimating aboveground biomass (AGB) using remotely sensed data. This paper explores AGB estimation using Landsat Thematic Mapper (TM) data in the eastern and western Brazilian Amazon, and discusses the impacts of forest stand structure on AGB estimation. Estimating AGB is still a challenging task, especially for the sites with complicated biophysical environments. The TM spectral responses are more suitable for AGB estimation in the sites with relatively simple forest stand structure than for the sites with complicated forest stand structure. Conversely, textures appear more important than spectral responses in AGB estimation in the sites with complicated forest stand structure. A combination of spectral responses and textures improves AGB estimation performance. Different study areas having various biophysical conditions affect AGB estimation performance. [ABSTRACT FROM AUTHOR]

Abundant vegetation species and associated complex forest stand structures in moist tropical regions often create difficulties in accurately classifying land-use and land-cover (LULC) features. This paper examines the value of spectral mixture analysis (SMA) using Landsat Thematic Mapper (TM) data for improving LULC classification accuracy in a moist tropical area in Rondônia, Brazil. Different routines, such as constrained and unconstrained least-squares solutions, different numbers of endmembers, and minimum noise fraction transformation, were examined while implementing the SMA approach. A maximum likelihood classifier was also used to classify fraction images into seven LULC classes: mature forest, intermediate secondary succession, initial secondary succession, pasture, agricultural land, water, and bare land. The results of this study indicate that reducing correlation between image bands and using four endmembers improve classification accuracy. The overall classification accuracy was 86.6% for the seven LULC classes using the best SMA processing routine, which represents very good results for such a complex environment. The overall classification accuracy using a maximum likelihood approach was 81.4%. Another finding is that use of constrained or unconstrained solutions for unmixing the atmospherically corrected or raw Landsat TM images does not have significant influence on LULC classification performances when image endmembers are used in a SMA approach. [ABSTRACT FROM AUTHOR]

River floodplains are the dominant wetland habitat in the Amazon river basin, providing important habitation for aquatic flora and fauna, and playing a key role in sustaining regional fish production. The annual inundation pulse has been identified as the dominant environmental factor affecting aquatic biota on the floodplain, and the characteristics of this pulse, in terms of timing, duration and amplitude, vary spatially on the floodplain as a function of fluctuations in river stage height and topography. River floodplains are furthermore globally significant sources of methane (CH[sub 4]) and other trace gases essential to climate regulation. Refined information on wetland distributions and dynamics are currently needed to improve estimates of habitat availability and to calculate regional contributions of trace gases, especially CH[sub 4], to the troposphere. This paper describes how multitemporal time series of spaceborne L-band Synthetic Aperture Radar (SAR) data from the Japanese Earth Resource Satellite 1 (JERS-1) were used to generate a model of the spatial and temporal variation of inundation on the floodplain of a typical black water river in the Central Brazilian Amazon and how this model was utilized, together with in situ measurements of river stage heights and CH4 fluxes, to model regional estimates of CH[sub 4] emissions. We also demonstrate how a JERS-1 SAR time series can be used to map the spatial variation of flood duration on the floodplain, a key factor controlling local variations in plant biodiversity. For both applications, the availability of adequate time series of satellite sensor data is the prime factor affecting the reliability and accuracy of the flood models and the spatial details of the flood duration map. The availability of in situ data, especially daily river height measurements, was also critical for the development of the flooding model and for the subsequent decoupling of the model from the satellite sensor data. [ABSTRACT FROM AUTHOR]

During the Global Rain Forest Mapping (GRFM) project, the JERS-1 SAR (Synthetic Aperture Radar) satellite acquired wall-to-wall image coverage of the humid tropical forests of the world. The rationale for the project was to demonstrate the application of spaceborne L-band radar in tropical forest studies. In particular, the use of orbital radar data for mapping land cover types, estimating the area of floodplains, and monitoring deforestation and forest regeneration were of primary importance. In this paper we examine the information content of the JERS-1 SAR data for mapping land cover types in the Amazon basin. More than 1500 high-resolution (12.5 m pixel spacing) images acquired during the low flood period of the Amazon river were resampled to 100 m resolution and mosaicked into a seamless image of about 8 million km[sup 2], including the entire Amazon basin. This image was used in a classifier to generate a 1 km resolution land cover map. The inputs to the classifier were 1 km resolution mean backscatter and seven first-order texture measures derived from the 100 m data by using a 10 x 10 independent sampling window. The classification approach included two interdependent stages. First, a supervised maximum a posteriori Baysian approach classified the mean backscatter image into five general cover categories: terra firme forest (including secondary forest), savanna, inundated vegetation, open deforested areas and open water. A hierarchical decision rule based on texture measures was then applied to attempt further discrimination of known subcategories of vegetation types based on taxonomic information and woody biomass levels. True distributions of the general categories were identified from the RADAMBRASIL project vegetation maps and several field studies. Training and validation test sites were chosen from the JERS-1 image by consulting the RADAM vegetation maps. After several iterations and combining land cover types, 14 vegetation classes were successfully separated at the 1 km scale. The accuracy of the classification methodology was estimated to be 78% when using the validation sites. The results were also verified by comparison with the RADAM- and AVHRR-based 1 km resolution land cover maps. [ABSTRACT FROM AUTHOR]

This paper compares the use of biophysical indices and coefficients of variation derived from airborne synthetic aperture radar for discriminating and characterising land cover classes in the Tucurui reservoir. It assumes that the structural and dielectric properties of the land cover classes are sufficiently distinct to permit their discrimination using airborne C-band SAR data at different polarizations. Analyses of the results show that any of the proposed indices can be used effectively to discriminate among the land cover classes in the Tucurui reservoir. Certain combinations of polarizations and biophysical indices can, however, improve the saparability of the land cover classes. [ABSTRACT FROM AUTHOR]