Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 441387
Title Predicting Tropical Dry Forest Successional Attributes from Space: Is the Key Hidden in Image Texture?
Author(s) Gallardo-Cruz, J.A.; Meave, J.A.; Gonzalez, E.J.; Lebrija Trejos, E.E.; Romero-Romero, M.A.; Perez-Garcia, E.A.; Gallardo-Cruz, R.; Hernandez-Stefanoni, J.L.; Martorell, C.
Source PLoS ONE 7 (2012)2. - ISSN 1932-6203
Department(s) Forest Ecology and Forest Management
Publication type Refereed Article in a scientific journal
Publication year 2012
Keyword(s) greenhouse-gas emissions - thematic mapper imagery - remotely-sensed data - landsat tm data - secondary forest - biomass estimation - cross-validation - climate-change - rain-forest - countryside biogeography
Abstract Biodiversity conservation and ecosystem-service provision will increasingly depend on the existence of secondary vegetation. Our success in achieving these goals will be determined by our ability to accurately estimate the structure and diversity of such communities at broad geographic scales. We examined whether the texture (the spatial variation of the image elements) of very high-resolution satellite imagery can be used for this purpose. In 14 fallows of different ages and one mature forest stand in a seasonally dry tropical forest landscape, we estimated basal area, canopy cover, stem density, species richness, Shannon index, Simpson index, and canopy height. The first six attributes were also estimated for a subset comprising the tallest plants. We calculated 40 texture variables based on the red and the near infrared bands, and EVI and NDVI, and selected the best-fit linear models describing each vegetation attribute based on them. Basal area (R-2 = 0.93), vegetation height and cover (0.89), species richness (0.87), and stand age (0.85) were the best-described attributes by two-variable models. Cross validation showed that these models had a high predictive power, and most estimated vegetation attributes were highly accurate. The success of this simple method (a single image was used and the models were linear and included very few variables) rests on the principle that image texture reflects the internal heterogeneity of successional vegetation at the proper scale. The vegetation attributes best predicted by texture are relevant in the face of two of the gravest threats to biosphere integrity: climate change and biodiversity loss. By providing reliable basal area and fallow-age estimates, image-texture analysis allows for the assessment of carbon sequestration and diversity loss rates. New and exciting research avenues open by simplifying the analysis of the extent and complexity of successional vegetation through the spatial variation of its spectral information.
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