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Title: | HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia |
Authors: | Rennó, Camilo Daleles Nobre, Antônio Donato Cuartas, Luz Adriana Soares, Joao V. Hodnett, Martin G. Tomasella, J. Waterloo, M. J. |
Keywords: | Agricultural Engineering Correlation Methods Drainage Earth Sciences Engineering Geology Forestry Geodetic Satellites Geomorphology Groundwater Hydrogeology Rain Soil Moisture Soils Surveying Topography Underwater Soils Vegetation Water Amazonia Data Sets Descriptor Descriptors Digital Elevation Model (dem) Forest Canopies Land-surface Processes Optical Imagery Physical Principles Rain Forests Shuttle Radar Topographic Mission Soil Water Spectral Data Spectral Properties Strong Correlations Terrain Classification Topographic Data Vegetation Canopies Water Table Depths Tracking Radar Algorithm Canopy Classification Data Set Digital Elevation Model Drainage Imagery Mapping Method Optical Method Rainforest Remote Sensing Swamp Forest Terrain Agriculture Algorithms Correlation Data Drainage Forestry Groundwater Hydrology Mapping Mathematical Models Moisture Optical Instruments Plants Rain Soil Topography Amazonia South America |
Issue Date: | 2008 |
metadata.dc.publisher.journal: | Remote Sensing of Environment |
metadata.dc.relation.ispartof: | Volume 112, Número 9, Pags. 3469-3481 |
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. © 2008 Elsevier Inc. All rights reserved. |
metadata.dc.identifier.doi: | 10.1016/j.rse.2008.03.018 |
Appears in Collections: | Artigos |
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