Lidar-derived environmental drivers of epiphytic bryophyte biomass in tropical montane cloud forests,Remote Sensing of Environment

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Lidar-derived environmental drivers of epiphytic bryophyte biomass in tropical montane cloud forests
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.rse.2020.112166
Guan-Yu Lai , Hung-Chi Liu , Chih-Hsin Chung , Chi-Kuei Wang , Cho-ying Huang

Abstract Epiphytic bryophytes (EBs) are commonly found in tropical montane cloud forests (TMCFs), and they play significant roles in ecological functioning. Field sampling to assess the abundance of EB is challenging because of their “epiphytic” habitat, which makes large-scale quantifications impractical. The abundance of EBs is highly related to forest structure, physical environment and microclimate. These characteristics may permit landscape-scale assessments using a synoptic sensing approach. In this study, we investigated the relationship between the plot-scale EB biomass density (kg ha−1) and a comprehensive set of field and airborne light detection and ranging (lidar)-derived forest biophysical, topographic and bioclimatic attributes (factors), and assessed the feasibility of landscape-scale mapping of EB biomass in TMCFs. The study was carried out in 16,773 ha of TMCFs on Chilan Mountain in northeastern Taiwan. The relationship between EB biomass density data from 21 plots (30 × 30 m) and 39 field or 1-m gridded lidar data-derived forest structural, topographic and bioclimatic factors was investigated. We applied a partial least squares regression (PLSR) model to minimize the effects of multicollinearity among those 39 factors, and selected latent variables (LVs) explaining the majority of data variation for landscape-scale EB biomass mapping. The first four LVs explained 92% of the data variation, and the performance of the PLSR was satisfactory (R2 = 0.92, p

更新日期：2021-02-01

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