In:
Frontiers in Ecology and Evolution, Frontiers Media SA, Vol. 11 ( 2023-3-27)
Abstract:
Monitoring forest species diversity is essential for biodiversity conservation and ecological management. Currently, unmanned aerial vehicle (UAV) remote sensing technology has been increasingly used in biodiversity monitoring due to its flexibility and low cost. In this study, we compared two methods for estimating forest species diversity indices, namely the spectral angle mapper (SAM) classification approach based on the established species-spectral library, and the self-adaptive Fuzzy C-Means (FCM) clustering algorithm by selected biochemical and structural features. We conducted this study in two complex subtropical forest areas, Mazongling (MZL) and Gonggashan (GGS) National Nature Forest Reserves using UAV-borne hyperspectral and LiDAR data. The results showed that the classification method performed better with higher values of R 2 than the clustering algorithm for predicting both species richness (0.62 & gt; 0.46 for MZL and 0.55 & gt; 0.46 for GGS) and Shannon-Wiener index (0.64 & gt; 0.58 for MZL, 0.52 & gt; 0.47 for GGS). However, the Simpson index estimated by the classification method correlated less with the field measurements than the clustering algorithm ( R 2 = 0.44 and 0.83 for MZL and R 2 = 0.44 and 0.62 for GGS). Our study demonstrated that the classification method could provide more accurate monitoring of forest diversity indices but requires spectral information of all dominant tree species at individual canopy scale. By comparison, the clustering method might introduce uncertainties due to the amounts of biochemical and structural inputs derived from the hyperspectral and LiDAR data, but it could acquire forest diversity patterns rapidly without distinguishing the specific tree species. Our findings underlined the advantages of UAV remote sensing for monitoring the species diversity in complex forest ecosystems and discussed the applicability of classification and clustering methods for estimating different individual tree-based species diversity indices.
Type of Medium:
Online Resource
ISSN:
2296-701X
DOI:
10.3389/fevo.2023.1139458
DOI:
10.3389/fevo.2023.1139458.s001
DOI:
10.3389/fevo.2023.1139458.s002
DOI:
10.3389/fevo.2023.1139458.s003
DOI:
10.3389/fevo.2023.1139458.s004
DOI:
10.3389/fevo.2023.1139458.s005
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2023
detail.hit.zdb_id:
2745634-1
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