International Journal of Remote Sensing · 1988 · 40 citations · 14 references
EngineeringForest BiometricsLand UseForestryDiverse Incidence AnglesLand CoverTerrestrial SensingChange AnalysisEarth ScienceSocial SciencesGeophysicsBiogeographyAtmospheric ScienceSpatial DistributionImaging RadarRadar Signal ProcessingShuttle Radar MappingSatellite ImagingMeteorologyBiodiversitySir-b ImagesSynthetic Aperture RadarGeographyRadar ApplicationDeforestationLand Cover MapRadar ImagingRadarDigital DataRemote SensingRadar Image ProcessingForest Inventory
Abstract Shuttle Imaging Radar-B (SIR-B) images, obtained at two different incidence angles were analysed for discrimination and mapping of vegetation in the rainforest of Borneo. In an area of coastal lowland three units of forest canopy and two units of open surface cover were distinguished and mapped. The backscatter characteristics of the units were compared and analysed by ratioing digital image data of sample sites against digital data of reference sites. The ratios show that the backscatter for each surface unit is clustered in separate, mostly non-overlapping, domains at both incidence angles. Different rates of change with incidence angle indicate a strong angular dependence in the dominant backscatter mechanism of swamp that is not apparent for the other units. A corresponding digitally coregistered Landsat multispectral scanner (MSS) image helped to verify the spatial distribution of the mapped units in the coastal lowland. In the interior upland three major forest species associations that have contrasted canopy structures were not discriminated on the SIR-B images. Owing to perennial cloud cover the distribution and extent of the different canopies cannot be determined from Landsat MSS or other images obtained at optical wavelengths. The ability to discriminate and map different forest canopies in the interior of Borneo requires a wider radar response capability which may be obtainable at shorter wavelengths and with multipolariz-ation states.
14
Photogrammetric Engineering and Remote Sensing
John Nicol, B. King, Ding Xiaoli · UCL Discovery (University College London) · 2007 · 9.4K citations