Mapping tropical rainforest canopies using multi-temporal spaceborne imaging spectroscopy

Ben Somers; Gregory P. Asner

doi:10.1117/12.2028508

16 October 2013 Mapping tropical rainforest canopies using multi-temporal spaceborne imaging spectroscopy

Ben Somers, Gregory P. Asner

Proceedings Volume 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV; 888704 (2013) https://doi.org/10.1117/12.2028508
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

The use of imaging spectroscopy for florisic mapping of forests is complicated by the spectral similarity among coexisting species. Here we evaluated an alternative spectral unmixing strategy combining a time series of EO-1 Hyperion images and an automated feature selection strategy in MESMA. Instead of using the same spectral subset to unmix each image pixel, our modified approach allowed the spectral subsets to vary on a per pixel basis such that each pixel is evaluated using a spectral subset tuned towards maximal separability of its specific endmember class combination or species mixture. The potential of the new approach for floristic mapping of tree species in Hawaiian rainforests was quantitatively demonstrated using both simulated and actual hyperspectral image time-series. With a Cohen’s Kappa coefficient of 0.65, our approach provided a more accurate tree species map compared to MESMA (Kappa = 0.54). In addition, by the selection of spectral subsets our approach was about 90% faster than MESMA. The flexible or adaptive use of band sets in spectral unmixing as such provides an interesting avenue to address spectral similarities in complex vegetation canopies.

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Ben Somers and Gregory P. Asner "Mapping tropical rainforest canopies using multi-temporal spaceborne imaging spectroscopy", Proc. SPIE 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, 888704 (16 October 2013); https://doi.org/10.1117/12.2028508

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KEYWORDS

Feature selection

Imaging spectroscopy

Reflectivity

Hyperspectral simulation

Remote sensing

Sensors

Signal to noise ratio

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