2026-10-14T17:16:03Z https://www.scidb.cn/oai

10.57760/sciencedb.02144 2022-08-09T13:45:29Z

2022-08-09 Canopy height structure classification of tropical forest under driving effects of topography and historical disturbance in Menglun, Xishuangbanna-Supplementary Material doi:10.57760/sciencedb.02144 en Structure diversity is tightly related to biodiversity and ecosystem functioning. In this study, we try to classify and describe canopy height structure of different plant communities with quantified methods, and find key factors that driving the variation of canopy height structure in tropical seasonal rainforest. Canopy height and terrain factors were surveyed by a drone-based LiDAR in at Menglun sub-Natural Reserve, Xishuangbanna, and panchromatic imageries at 1981 and 2021 in this area were also compared to assess the historical and current disturbance. Multiple regression tree (MRT) was used to classify canopy height structure types, and find the effect of environment factors to canopy height structure classification. 7 classes were classified by MRT, which indicate different vegetation types include tropical seasonal rainforest, monsoon evergreen broadleaf forest, secondary forest and canopy gaps. The potential solar radiation (PSR), elevation, terrain profile curvature, slope and the brightness value of imageries at 1981 and 2021 were main factors that drive the classification. Not only terrain factors, historical human disturbance that already passed for nearly 40 years still lead the canopy height to different structure at present day. The recovery of canopy structure needs decades or even hundreds of years, the history and intensity of both climate change and human disturbance need be more concerned in biodiversity and long-term ecology studies. tropical seasonal rainforest; canopy height; swidden agriculture; LiDAR; multiple regression tree Yuan Hu Yun Deng Bo Wang Ronghua Zhou Guoshan Shi Shengdong Yuan Junsong Li Huazheng Lu Luxiang Lin PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank