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

Abstract

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 m 2 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 R 2 = 0.651; RSE = 3.69). An independent validation survey was performed with significant results (Adjusted R 2 = 0.571, RMSE = 5.05). Selected variables were statistically significant: OPEN ACCESSRemote Sens. 2015, 7 2693 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.

Light detection and ranging explains diversity of plants, fungi, lichens, and bryophytes across multiple habitats and large geographic extent

“…, Ceballos et al. ). Therefore, we incite researchers to carefully design studies giving insight into how LIDAR and spectral remote sensing can be amalgamated to effectively complement each other and consequently improve the prediction of local biodiversity.…”

“…Light Detection and Ranging is increasingly used as a tool for exploring, explaining, and predicting biodiversity (Ceballos et al. , Peura et al. , Zellweger et al.…”

“…Light Detection and Ranging is increasingly used as a tool for exploring, explaining, and predicting biodiversity (Ceballos et al 2015, Peura et al 2016, Guo et al 2017, Vihervaara et al 2017). An airborne LIDAR scanner records a three-dimensional set of points at sampling densities of typically 0.1-100 points/m 2 using a multi-sensor system combining laser ranging, systematic scanning, high accuracy positioning, and attitude recording (Wehr and Lohr 1999).…”

Earth Observation for Ecosystems Monitoring in Space and Time: A Special Issue in Remote Sensing

“…Research scientists and other subject matter experts submitted innovative and challenging papers that showed advances in several topics: estimating the spatial distribution of plant species richness by Light Detection and Ranging (LiDAR) and hyperspectral data [1], assessing habitat quality of forest corridor based on NDVI [2], applying remote sensing to study (marine) coral ecosystems [3], identifying ecosystem functional types [4], distinguishing between different forest trunk size classes from remote sensing [5], detecting changes in forest patterns [6], applying light use efficiency models to estimate vegetation productivity [7], classifying grassland successional stages by airborne hyperspectral images [8], proposing monitoring programs of grasslands based on multi-temporal optical and radar satellite images [9], estimating the potential of remote sensing to capture field-based plants phenology [10].…”

Evaluating the Potential of Satellite Hyperspectral Resurs-P Data for Forest Species Classification

“…Several studies revealed that forest application results from satellite hyperspectral data had a good match with results from airborne hyperspectral data (Townsend & Foster, 2002;Smith 2003). However, some authors stressed that hyperspectral satellite data demonstrated poor performance related to airborne data in such tasks as forest species mapping and canopy water content (Ustin et al, 2002;Ceballos et al, 2015).…”