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Quantifying Changes on Forest Succession in a Dry Tropical Forest Using Angular-Hyperspectral Remote Sensing

Centre for Earth Observation Sciences (CEOS), Earth and Atmospheric Sciences Department, University of Alberta, Edmonton, AB T6G 2E3, Canada
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(12), 1865;
Received: 3 October 2018 / Revised: 17 November 2018 / Accepted: 19 November 2018 / Published: 22 November 2018
(This article belongs to the Special Issue Advances in Remote Sensing of Forest Structure and Applications)
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The tropical dry forest (TDF) is one the most threatened ecosystems in South America, existing on a landscape with different levels of ecological succession. Among satellites dedicated to Earth observation and monitoring ecosystem succession, CHRIS/PROBA is the only satellite that presents quasi-simultaneous multi-angular pointing and hyperspectral imaging. These two characteristics permit the study of structural and compositional differences of TDFs with different levels of succession. In this paper, we use 2008 and 2014 CHRIS/PROBA images from a TDF in Minas Gerais, Brazil to study ecosystem succession after abandonment. Using a −55° angle of observation; several classifiers including spectral angle mapper (SAM), support vector machine (SVM), and decision trees (DT) were used to test how well they discriminate between different successional stages. Our findings suggest that the SAM is the most appropriate method to classify TDFs as a function of succession (accuracies ~80 for % for late stage, ~85% for the intermediate stage, ~70% for early stage, and ~50% for other classes). Although CHRIS/PROBA cannot be used for large-scale/long-term monitoring of tropical forests because of its experimental nature; our results support the potential of using multi-angle hyperspectral data to characterize the structure and composition of TDFs in the near future. View Full-Text
Keywords: dry forests; ecological succession; multi-angle remote sensing dry forests; ecological succession; multi-angle remote sensing

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Garcia Millan, V.; Sanchez-Azofeifa, A. Quantifying Changes on Forest Succession in a Dry Tropical Forest Using Angular-Hyperspectral Remote Sensing. Remote Sens. 2018, 10, 1865.

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