Use este identificador para citar ou linkar para este item: https://repositorio.inpa.gov.br/handle/1/18530
Título: HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia
Autor: Rennó, Camilo Daleles
Nobre, Antônio Donato
Cuartas, Luz Adriana
Soares, Joao V.
Hodnett, Martin G.
Tomasella, J.
Waterloo, M. J.
Palavras-chave: 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
Data do documento: 2008
Revista: Remote Sensing of Environment
É parte de: 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.
DOI: 10.1016/j.rse.2008.03.018
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