Predicting tropical dry forest successional attributes from space: is the key hidden in image texture?

PloS One
J Alberto Gallardo-CruzCarlos Martorell

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 accu...Continue Reading

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Citations

Mar 17, 2018·International Journal of Biometeorology·Sarah Freitas MagalhãesGerardo Arturo Sánchez Azofeifa
Feb 19, 2021·Frontiers in Plant Science·Esther GrünerMichael Wachendorf

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Software Mentioned

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IR MEAN
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