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Remote Sens. 2015, 7(3), 2692-2714; doi:10.3390/rs70302692

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

1
Laboratory of Geomatics and Landscape Ecology, Forestry and Nature Conservation Faculty, University of Chile, Av. Santa Rosa 11315, La Pintana, Santiago, Chile
2
Department of Environmental Sciences, Faculty of Agronomic Sciences, University of Chile, Av. Santa Rosa 11315, La Pintana, Santiago, Chile
*
Author to whom correspondence should be addressed.
Academic Editors: Duccio Rocchini and Prasad S. Thenkabail
Received: 1 September 2014 / Revised: 18 February 2015 / Accepted: 27 February 2015 / Published: 9 March 2015
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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Abstract

The Andes foothills of central Chile are characterized by high levels of floristic diversity in a scenario, which offers little protection by public protected areas. Knowledge of the spatial distribution of this diversity must be gained in order to aid in conservation management. Heterogeneous environmental conditions involve an important number of niches closely related to species richness. Remote sensing information derived from satellite hyperspectral and airborne Light Detection and Ranging (LiDAR) data can be used as proxies to generate a spatial prediction of vascular plant richness. This study aimed to estimate the spatial distribution of plant species richness using remote sensing in the Andes foothills of the Maule Region, Chile. This region has a secondary deciduous forest dominated by Nothofagus obliqua mixed with sclerophyll species. Floristic measurements were performed using a nested plot design with 60 plots of 225 m2 each. Multiple predictors were evaluated: 30 topographical and vegetation structure indexes from LiDAR data, and 32 spectral indexes and band transformations from the EO1-Hyperion sensor. A random forest algorithm was used to identify relevant variables in richness prediction, and these variables were used in turn to obtain a final multiple linear regression predictive model (Adjusted R2 = 0.651; RSE = 3.69). An independent validation survey was performed with significant results (Adjusted R2 = 0.571, RMSE = 5.05). Selected variables were statistically significant: catchment slope, altitude, standard deviation of slope, average slope, Multiresolution Ridge Top Flatness index (MrRTF) and Digital Crown Height Model (DCM). The information provided by LiDAR delivered the best predictors, whereas hyperspectral data were discarded due to their low predictive power. View Full-Text
Keywords: flora; richness; EO-1 Hyperion; airborne LiDAR; prediction; Andes foothills flora; richness; EO-1 Hyperion; airborne LiDAR; prediction; Andes foothills
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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

Ceballos, A.; Hernández, J.; Corvalán, P.; Galleguillos, M. Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile. Remote Sens. 2015, 7, 2692-2714.

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