Abstract
Google Earth provides a freely available, global mosaic of high-resolution imagery from different sensors that has become popular in environmental and ecological studies. However, such imagery lacks the near-infrared band often used in studying vegetation, thus its potential for estimating vegetation properties remains unclear. In this study, we assess the potential of Google Earth imagery to describe and predict vegetation attributes. Further, we compare it to the potential of SPOT imagery, which has additional spectral information. We measured basal area, vegetation height, crown cover, density of individuals, and species richness in 60 plots in the oak forests of a complex volcanic landscape in central Mexico. We modelled each vegetation attribute as a function of surface metrics derived from Google Earth and SPOT images, and selected the best-supported linear models from each source. Total species richness was the best-described and predicted variable: the best Google Earth-based model explained nearly as much variation in species richness as its SPOT counterpart (R2 = 0.44 and 0.51, respectively). However, Google Earth metrics emerged as poor predictors of all remaining vegetation attributes, whilst SPOT metrics showed potential for predicting vegetation height. We conclude that Google Earth imagery can be used to estimate species richness in complex landscapes. As it is freely available, Google Earth can broaden the use of remote sensing by researchers and managers in low-income tropical countries where most biodiversity hotspots are found.
Highlights
Remote sensing is a useful tool in environmental and ecological research
We studied oak forests in six geomorphological units: Tepozteco Mountains (TM), and four lava fields, namely Chichinautzin (CH), Suchiooc (SU), Otates, which is divided into lower (LO) and upper (UO) subunits, and Oclayuca (OC)
The best models explained about half of the variation in community species richness for both Google Earth © (GE) and SPOT images (R2 = 0.44 and 0.51, respectively; Table 2)
Summary
Remote sensing is a useful tool in environmental and ecological research. This technology allows ecologists to gather information about the natural world more rapidly and cost effectively than traditional field methods, and aid in the analyses and integration of processes across spatial and temporal scales [1]. Landsat imagery is still the most frequently used source in remote sensing studies [7], some vegetation patterns cannot be captured at its resolution (30 m pixel) [8]. Other sensors such as SPOT (Satellite Pour l’Observation de la Terre), Quickbird, GeoEye-1 and RapidEye produce. Financial considerations are important when choosing an image source, the capabilities of different sources need rigorous assessment
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