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Inter-sensor relationships between IKONOS and Landsat-7 ETM+ NDVI data in three ecoregions of Africa
Authors:P. S. Thenkabail
Affiliation:Center for Earth Observation (CEO) , Department of Geology and Geophysics , 210 Whitney Avenue, Yale University, New Haven, Connecticut 06520-8109, USA E-mail: prasad.thenkabail@yale.edu
Abstract:The goal of this research was to establish inter-sensor relationships between IKONOS and Landsat-7 ETM+ data. Dry and wet season images were acquired on the same date or about the same date from IKONOS and ETM+ sensors to enable direct comparison between the two distinctly different data types. The images were from three distinct ecoregions located in African rainforests and savannas that encompass a wide range of land use/land cover classes and ecological units. The IKONOS NDVI had a high degree of correlation with ETM+ NDVI with R 2 values between 0.67 and 0.72. Inter-sensor model equations relating IKONOS NDVI with ETM+ NDVI were determined. The characteristics that contribute to the increased sensitivity in dynamic ranges of IKONOS NDVI relative to ETM+ NDVI were attributed to: (1) radiometric resolution that adds more bits per data point (11-bit IKONOS data as opposed to 8-bit ETM+); and (2) spatial resolution that helped in resolving spectral characteristics at micro landscape units. Spectral bandwidths of the two sensors had no effect on the dynamic ranges of NDVIs. Overall, the IKONOS data showed greater sensitivity to landscape units and ecological characteristics when compared with Landsat-7 ETM+ data. Across ecoregions and land use/land cover classes, the IKONOS NDVI dynamic range (?0.07 to 0.71) was considerably greater than the ETM+ NDVI dynamic range (?0.24 to 0.46). IKONOS data explained greater variability (R 2=0.73) in agroforest biomass when compared with ETM+ data (R 2=0.66). The inter-sensor relationships presented in this paper are expected to facilitate better understanding and proper interpretation of terrestrial characteristics studied using multiple sensors over time periods.
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