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.
Highlights
Biodiversity is an essential element from which all human populations benefit directly or indirectly [1]
Most of the selected predictors were provided by the Light Detection and Ranging (LiDAR) information, omitting the hyperspectral information except for the Normalized Difference Vegetation Index (ND705) (Figure 2)
The main objective was to predict and spatialize the vascular plant richness present in a secondary mixed deciduous forest of the Andes foothills of the Maule region, Chile. This was achieved using only structural variables derived from airborne LiDAR data, discarding the spaceborne hyperspectral information that, despite being significant, showed a lower predictive power
Summary
Biodiversity is an essential element from which all human populations benefit directly or indirectly [1]. They have a high rate of floristic diversity and endemism [6], in the transition zone between the Valdivian temperate rain forest and the sclerophyll forest in the Maule region [7,8], yet the region currently has very little area protected in the Chilean National System of Protected Wild Areas (SNASPE) or in other public protected areas such as nature sanctuaries, forest reserves or national parks, which currently comprise only 2% of the region’s total surface area [6] These ecosystems are extremely vulnerable, since they are subject to a sustained increase in anthropogenic pressure mainly related to the change in land use for agriculture and livestock and the illegal extraction of wood products from native forests [9,10,11]. Rapid and objective methods must be developed to assess and predict biodiversity spatially [16]; observation by remote sensing plays a fundamental role here in light of its capacity to extrapolate point information about biodiversity collected in situ to different spatial and temporal scales [17]
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