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.
Highlights
In the dawn of the 21st century the magnitude of the human footprint on the planet’s ecological systems has become undeniable [1,2,3,4]
The goal of this study was to examine the potential of textural properties of a very-high resolution imagery (VHR) Quickbird image to model secondary vegetation attributes measured in the field, in a seasonally dry tropical region
Predictive potential of satellite image texture In this study we demonstrate the large potential of image texture for predicting vegetation attributes during tropical forest succession
Summary
In the dawn of the 21st century the magnitude of the human footprint on the planet’s ecological systems has become undeniable [1,2,3,4]. More critical is the difficulty in differentiating the various successional stages that secondary forests normally comprise and measure their extent [20,33,34,35,36,37]. As their structure and functions depend on their succesional status, there is a strong need to efficiently evaluate the extent and complexity of secondary vegetation existing in any region and to discern its attributes
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