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Abstract: Optical imagery can reveal spectral properties of forest canopy, which rarely allows for finding accurate correspondence of canopy features with soils and hydrology. In Amazonia non-floodable swampy forests can not be easily distinguished from non-floodable terra-firme forests using just bidimensional spectral data. Accurate topographic data are required for the understanding of land surface processes at finer scales. Topographic detail has now become available with the Shuttle Radar Topographic Mission (SRTM) data. This new digital elevation model (DEM) shows the feature-rich relief of lowland rain forests, adding to the ability to map rain forest environments through many quantitative terrain descriptors. In this paper we report on the development of a new quantitative topographic algorithm, called HAND (Height Above the Nearest Drainage), based on SRTM-DEM data. We tested the HAND descriptor for a groundwater, topographic and vegetation dataset from central Amazonia. The application of the HAND descriptor in terrain classification revealed strong correlation between soil water conditions, like classes of water table depth, and topography. This correlation obeys the physical principle of soil draining potential, or relative vertical distance to drainage, which can be detected remotely through the topography of the vegetation canopy found in the SRTM-DEM data. [Copyright &y& Elsevier]

The availability of a wide range of satellite measurements of environmental variables at different spatial and temporal resolutions, together with an increasing number of digitized and georeferenced species occurrences, has created the opportunity to model and monitor species geographic distribution and richness at regional to continental scales. In this paper, we examine the application of recently developed global data products from satellite observations in modeling the potential distribution of tree species and diversity in the Amazon basin. We use data from satellite sensors, including MODIS, QSCAT, SRTM, and TRMM, to develop different environmental variables related to vegetation, landscape, and climate. These variables are used in a maximum entropy method (Maxent) to model the geographical distribution of five commercial trees and to classify the patterns of tree alpha-diversity in the Amazon basin. Maxent simulations are analyzed using binomial tests of omission rates and the area under the receiver operating characteristics (ROC) curves to examine the model performance, the accuracy of geographic distributions, and the significance of environmental variables for discriminating suitable habitats. To evaluate the importance of satellite data, we used the Maxent jackknife test to quantify the training gains from data layers and to compare the results with model simulations using climate-only data. For all species and tree alpha-diversity, modeled distributions are in agreement with historical data and field observations. The results compare with climate-derived patterns, but provide better spatial resolution and detailed information on the habitat characteristics. Among satellite data products, QSCAT backscatter, representing canopy moisture and roughness, and MODIS leaf area index (LAI) are the most important variables in almost all cases. Model simulations suggest that climate and remote sensing results are complementary and that the best distribution patterns can be achieved when the two data sets are combined. [Copyright &y& Elsevier]

This paper presents the use of time series of SAR images to map the flood temporal dynamics and the spatial distribution of vegetation over a large Amazonian floodplain. The region under study (3500 km2) presents a diversity of landscape units with open lakes, bogs, large meadows, savannahs, alluvial forests and terra firma forest, covered by 21 images acquired by J-ERS between 1993 and 1997. Ground data include in situ observations of vegetation structure and flood extent as well as water level records. Image analysis demonstrates that temporal variations of the radar backscatter can be used to monitor efficiently the flood extent regardless of the landscape units. Also, analysis of the backscatter temporal variation greatly reduces the confusion between smooth surfaces (e.g. open water bodies, bare soils) inherent to L-band backscatter. The mapping method is based on decision rules over two decision variables: 1) the mean backscatter coefficient computed over the whole time series; 2) the total change computed using an “Absolute Change” estimator. The first variable provides classification into rough vegetation types while the second variable yields a direct estimate of the intensity of change that is related to flood dynamics. The classifier is first applied to the whole time series to map the maximum and minimum flood extent by defining 3 flood conditions: never flooded (NF); occasionally flooded (OF); permanently flooded (PF). It also furnishes the broad land cover type: open water/bare soils/low vegetation/forest. The accuracy of the flood extent mapping shows a kappa value of 0.82. Then, the classifier is run iteratively on the OF pixels to monitor flood stages during which the occasionally flooded areas get submerged. The mapping accuracy is assessed on one intermediate flood stage, showing a precision in excess of 90%. The importance of the time sampling for flood mapping is discussed along with the influence of SAR backscatter accuracy and the number of images. Then general guidelines for floodplain mapping are presented. By combining water level reports with maps of different flood stages the flooding pattern can be retrieved along with the vegetation succession processes. It is shown that the spatial distribution of vegetation communities is governed by flood stress and can be modelled as a function of the mean annual exposure to floods. [Copyright &y& Elsevier]