4.7 Article

Lidar-derived environmental drivers of epiphytic bryophyte biomass in tropical montane cloud forests

期刊

REMOTE SENSING OF ENVIRONMENT
卷 253, 期 -, 页码 -

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2020.112166

关键词

Allometry; Forest stand; Synoptic sensing; Temperature; Topography; Tropics

资金

  1. Ministry of Science and Technology (MOST) [105-2633-M-002-003-]
  2. National Taiwan University (NTU) EcoNTU project [106R104516]
  3. NTU Research Center for Future Earth from the Featured Areas Research Center Program by the Ministry of Education in Taiwan

向作者/读者索取更多资源

Epiphytic bryophytes (EBs) are commonly found in tropical montane cloud forests (TMCFs) and play significant roles in ecological functioning. Assessing the abundance of EB is challenging due to their epiphytic habitat, but a landscape-scale mapping approach using a partial least squares regression (PLSR) model showed promising results in relating EB biomass density to various forest biophysical, topographic, and bioclimatic factors. This approach may advance our understanding of the role of EBs in TMCFs and guide ecological management in future climate scenarios.
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 x 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 multi-collinearity 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 (R-2 = 0.92, p < 0.001). The majority (35 out of 39) of the selected forest structural, topographic and bioclimatic factors were significantly related to one or more LVs, and most (37 out of 39) could be directly derived or were indirectly related to lidar metrics, thereby permitting the landscape-scale mapping of EB biomass density. We estimated that the mean (+/- standard deviation) EB biomass density was 296.5 +/- 373.1 kg ha(-1) and that the total EB biomass of the TMCF of Chilan Mountain was 4973.9 Mg. This study demonstrates that the proposed approach may be feasible for landscape-scale EB biomass mapping, thereby advancing our understanding of the role of EBs in the hydrological and nutrient cycles of TMCFs. The outcomes of the PLSR may elucidate the physiological mechanisms underpinning EB abundance in TMCFs and guide ecological management under future climate scenarios.

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