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Remote Sens. 2016, 8(10), 865; doi:10.3390/rs8100865

Using Google Earth Surface Metrics to Predict Plant Species Richness in a Complex Landscape

1
Departamento de Ecología y Recursos Naturales, Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
2
Centro del Cambio Global y la Sustentabilidad en el Sureste, A.C., Calle Centenario del Instituto Juárez S/N, Villahermosa 86000, Tabasco, Mexico
*
Author to whom correspondence should be addressed.
Academic Editors: Lars T. Waser, Parth Sarathi Roy, Clement Atzberger and Prasad S. Thenkabail
Received: 3 September 2016 / Revised: 6 October 2016 / Accepted: 13 October 2016 / Published: 20 October 2016
View Full-Text   |   Download PDF [3736 KB, uploaded 20 October 2016]   |  

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. View Full-Text
Keywords: image surface metrics; Google Earth; SPOT; species richness estimation; vegetation attributes estimation; biodiversity estimation; oak forest image surface metrics; Google Earth; SPOT; species richness estimation; vegetation attributes estimation; biodiversity estimation; oak forest
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Block, S.; González, E.J.; Gallardo-Cruz, J.A.; Fernández, A.; Solórzano, J.V.; Meave, J.A. Using Google Earth Surface Metrics to Predict Plant Species Richness in a Complex Landscape. Remote Sens. 2016, 8, 865.

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