Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery
This article discusses the improvement in accuracy of remote sensing to monitor and evaluate reforestation projects by combining moderate-resolution and hyperspectral imagery with multi temporal, multispectral data. The combination of these technological monitoring methods allows researchers to accurately classify general forest types and tree plantations by species composition.
Research Goals & Methods
The researchers examined recent tree plantation expansion in northeastern Costa Rica and compared four Random Forest classification models: Hyperspectral data (HD), HD and inter-annual spectral metrics, HD plus a multi-temporal forest growth classification and all of these combined.
Conclusions & Takeaways
The results of the study indicate that the combination of all the methods improves the mapping and monitoring of reforestation with an accuracy of 88.5%
IMPACTS OF EARLY- AND LATE-SERAL MYCORRHIZAE DURING RESTORATION IN SEASONAL TROPICAL FOREST, MEXICO. Ecological Applications. 2003;13:1701–1717. doi:10.1890/02-5309..
- Biospheric Sciences Laboratory, Goddard Space Flight Center, Greenbelt, MD, USA
- Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY, USA
- US Fish and Wildlife Service, Southwest Regional Office, Albuquerque, NM, USA
- Department of Geography, McGill University, Montreal, QC, Canada
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, USA
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA