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Mapping Successional Stages in a Wet Tropical Forest Using Landsat ETM+ and Forest Inventory DataIn this study, we test whether an existing classification technique based on the integration of Landsat ETM+ and forest inventory data enables detailed characterization of successional stages in a wet tropical forest site. The specific objectives were: (1) to map forest age classes across the La Selva Biological Station in Costa Rica; and (2) to quantify uncertainties in the proposed approach in relation to field data and existing vegetation maps. Although significant relationships between vegetation height entropy (a surrogate for forest age) and ETM+ data were detected, the classification scheme tested in this study was not suitable for characterizing spatial variation in age at La Selva, as evidenced by the error matrix and the low Kappa coefficient (12.9%). Factors affecting the performance of the classification at this particular study site include the smooth transition in vegetation structure between intermediate and advanced successional stages, and the low sensitivity of NDVI to variations in vertical structure at high biomass levels.
Document ID
20150008525
Acquisition Source
Jet Propulsion Laboratory
Document Type
Conference Paper
External Source(s)
Authors
Goncalves, Fabio G.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Yatskov, Mikhail
(Oregon State Univ. Corvallis, OR, United States)
dos Santos, Joao Roberto
(Instituto de Pesquisas Espaciais Cachoeira Paulista, Brazil)
Treuhaft, Robert N.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Law, Beverly E.
(Oregon State Univ. Corvallis, OR, United States)
Date Acquired
May 20, 2015
Publication Date
October 4, 2010
Subject Category
Earth Resources And Remote Sensing
Meeting Information
Meeting: Seminario de Atualizagco em SR & SIG Aplicados `Engenharia Florestal
Location: Curitiba
Country: Brazil
Start Date: October 4, 2010
End Date: October 8, 2010
Distribution Limits
Public
Copyright
Other
Keywords
forest structure
monitoring
NDVI
entropy
Remote sensing

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