29 October 2007 Hyperion and CBERS satellite image classification intercomparison for Cerrado and agricultural mapping
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Abstract
The Cerrado is a savanna ecoregion with grassland and woodland subtypes covering ~one-quarter of Brazil and is considered to be a biodiversity hotspot, threatened by land-use conversion. Hyperspectral remote sensing enables spatio-temporal monitoring, while providing the possibility of vegetation-mapping at a high level of specificity. However, because imaging spectrometer data availability/coverage is currently limited, a need exists for effective exploitation of multispectral satellite imagery with broad-area spatial coverage. The objective was to assess the utility of hyperspectral Hyperion and multispectral CBERS-2 satellite imagery in discriminating among Cerrado subtypes and agricultural classes. Temporally-coincident field-transect data for Cerrado physiognomies and agricultural sites were collected, including biophysical metrics. Nonmetric multidimensional scaling and hierarchical cluster analysis were used to identify potential environmental gradients of biophysical groupings. Four Cerrado subclasses were identified: Campo Limpo (Open Cerrado Grassland), Campo Sujo (Shrub Savanna), Cerrado Típico (Wooded Cerrado), and Cerrado Denso (Cerrado Woodland). Subclasses were then merged, forming two Cerrado subclasses. To facilitate sensor intercomparison, image classification involved PCA transformations, followed by unsupervised clustering of the component images. Results indicate that both dimensionality-reduced Hyperion and CBERS datasets were sufficient in distinguishing between the two more general Cerrado subclasses and agriculture, but the Hyperion-derived classification was more accurate.
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Anthony M. Filippi, Anthony M. Filippi, Christian Brannstrom, Christian Brannstrom, David M. Cairns, David M. Cairns, Daehyun Kim, Daehyun Kim, } "Hyperion and CBERS satellite image classification intercomparison for Cerrado and agricultural mapping", Proc. SPIE 6749, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VII, 674913 (29 October 2007); doi: 10.1117/12.738184; https://doi.org/10.1117/12.738184
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