Next Article in Journal
Exploring the Potential of High Resolution WorldView-3 Imagery for Estimating Yield of Mango
Next Article in Special Issue
Exploring Bamboo Forest Aboveground Biomass Estimation Using Sentinel-2 Data
Previous Article in Journal
Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature
Previous Article in Special Issue
Semi-Automated Delineation of Stands in an Even-Age Dominated Forest: A LiDAR-GEOBIA Two-Stage Evaluation Strategy
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessFeature PaperArticle

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; https://doi.org/10.3390/rs10121865
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)
  |  
PDF [2624 KB, uploaded 22 November 2018]
  |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top