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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
agricultural and biological sciences
Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests
Remote Sensing of Environment, Volume 90, No. 1, Year 2004
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Description
The goal of this research was to compare narrowband hyperspectral Hyperion data with broadband hyperspatial IKONOS data and advanced multispectral Advanced Land Imager (ALI) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data through modeling and classifying complex rainforest vegetation. For this purpose, Hyperion, ALI, IKONOS, and ETM+ data were acquired for southern Cameroon, a region considered to be a representative area for tropical moist evergreen and semi-deciduous forests. Field data, collected in near-real time to coincide with satellite sensor overpass, were used to (1) quantify and model the biomass of tree, shrub, and weed species; and (2) characterize forest land use/land cover (LULC) classes. The study established that even the most advanced broadband sensors (i.e., ETM+, IKONOS, and ALI) had serious limitations in modeling biomass and in classifying forest LULC classes. The broadband models explained only 13-60% of the variability in biomass across primary forests, secondary forests, and fallows. The overall accuracies were between 42% and 51% for classifying nine complex rainforest LULC classes using the broadband data of these sensors. Within individual vegetation types (e.g., primary or secondary forest), the overall accuracies increased slightly, but followed a similar trend. Among the broadband sensors, ALI sensor performed better than the IKONOS and ETM+ sensors. When compared to the three broadband sensors, Hyperion narrowband data produced (1) models that explained 36-83% more of the variability in rainforest biomass, and (2) LULC classifications with 45-52% higher overall accuracies. Twenty-three Hyperion narrowbands that were most sensitive in modeling forest biomass and in classifying forest LULC classes were identified and discussed. © 2004 Elsevier Inc. All rights reserved.
Authors & Co-Authors
Thenkabail, Prasad S.
Sri Lanka, Colombo
International Water Management Institute Iwmi Colombo
Enclona, E.
United States, New Haven
Yale University
Ashton, Mark S.
United States, New Haven
Yale University
Legg, Christopher
Nigeria, Ibadan
International Institute of Tropical Agriculture
De Dieu, Minko Jean
Cameroon
Onadef
Statistics
Citations: 371
Authors: 5
Affiliations: 4
Identifiers
Doi:
10.1016/j.rse.2003.11.018
ISSN:
00344257
Study Locations
Cameroon