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Remote Sens. 2015, 7(5), 5660-5696; doi:10.3390/rs70505660

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

1
Biospheric Sciences Laboratory, Goddard Space Flight Center, Greenbelt, MD 20771, USA
2
Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY 10027, USA
3
US Fish and Wildlife Service, Southwest Regional Office, Albuquerque, NM 87102, USA
4
Department of Geography, McGill University, Montreal, QC H3A 2K6, Canada
5
Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
6
Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT 06269, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Xiangming Xiao, Jinwei Dong, Josef Kellndorfer and Prasad S. Thenkabail
Received: 14 February 2015 / Accepted: 14 April 2015 / Published: 5 May 2015
View Full-Text   |   Download PDF [3495 KB, uploaded 5 May 2015]   |  

Abstract

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p < 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer’s accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes. View Full-Text
Keywords: hyperspectral fusion; Landsat; Costa Rica; reforestation; secondary forests; payments for environmental services (PES); tree plantations; remote sensing hyperspectral fusion; Landsat; Costa Rica; reforestation; secondary forests; payments for environmental services (PES); tree plantations; remote sensing
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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).

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MDPI and ACS Style

Fagan, M.E.; DeFries, R.S.; Sesnie, S.E.; Arroyo-Mora, J.P.; Soto, C.; Singh, A.; Townsend, P.A.; Chazdon, R.L. Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery. Remote Sens. 2015, 7, 5660-5696.

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