Resource Details

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

Literature: Journal Articles

Allen, E.B., Allen, M.F., Egerton-Warburton, L., Corkidi, L., and Gómez-Pompa, A., 2003, Impacts of early- and late-seral mycorrhizae during restoration in seasonal tropical forest, Mexico. Ecological Applications, vol. 13, no. 6, pp. 1701-1717. Fagan, M. E., DeFries, R.S., Sesnie, S.E., Arroyo-Mora, J.P., Soto, C., Singh A., Townsend, P.A., Chazdon, R.L., 2015, Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery, USA, Remote Sensing.

Contact Info

E-Mail: matthew.e.fagan@nasa.gov
Tel.: +1-301-614-6628
Fax: +1-301-614-6695

Affiliations

- Biospheric Sciences Laboratory, Goddard Space Flight Center, Greenbelt, MD 20771, USA
- Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY 10027, USA
- US Fish and Wildlife Service, Southwest Regional Office, Albuquerque, NM 87102, USA
- Department of Geography, McGill University, Montreal, QC H3A 2K6, Canada
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT 06269, USA
 

Link(s)

http://www.mdpi.com/2072-4292/7/5/5660/htm

Description

  • The 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 to accurately classify general forest types and tree plantations by species composition.
  • The researchers took a closer look at the 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 three combined.
  • Overall the results of the study indicates that the combination of all the methods improves the mapping and monitoring of reforestation with an accuracy of 88.5%  
 

Country

  • Costa Rica
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