CHARACTERIZING BIOPHYSICAL ATTRIBUTES IN TROPICAL SECONDARY FOREST ENVIRONMENTS WITH MULTITEMPORAL HYPERSPECTRAL IMAGES

Author(s)

Liesenberg, Veraldo ; IEEE

• Document type: Journal article
• Source: 2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022); v. N/A, p. 4-pg., 2022-01-01.

Abstract

Multitemporal Hyperion/EO-1 images acquired at both nadir and off-nadir configurations were evaluated for characterization of above-ground biomass (AGB) and plant area index (PAI). Field measurements were conducted in areas of primary forest and three successional forest stages (e.g., initial, intermediate, and advanced) in Eastern Amazon (Brazil). Support vector regression (SVR) was applied using surface reflectance values as input variables. Results showed that vegetation anisotropy influenced correlations values. Narrow and broadband vegetation indices were strongly affected according to the sun-view angle configuration. Improvements of up to 30Mg.ha(-1) are found for the prediction of AGB according to the selection of the data acquisition. The best results for the biomass characterization were found in the scenes acquired in the backscattering direction and at nadir under a lower sun zenith configuration. The results reveal therefore the importance of a proper geometry configuration selection for the forthcoming Hyperspectral missions. (AU)

• FAPESP's process: 13/05081-9 - Characterizing secondary forest stages in different tropical rain forest environments under the recent REDD+ protocols: an attempt with multiple sources of remote sensing data
• Grantee: Veraldo Liesenberg
• Support Opportunities: Scholarships in Brazil - Post-Doctoral