High-precision estimation of plant alpha diversity in different ecosystems based on Sentinel-2 data

作者：Jiaxun Xin, Jinning Li, Qingqiu Zeng, Yu Peng, Yan Wang, Xiaoyi Teng, Qianru Bao, Linyan Yang, Huining Tang, Yuqi Liu, Jiayao Xie, Yue Qi, Guanchen Liu, Xuyao Li, Ning Tang, Zhenyao Sun, Weiying Zeng, Ziyu Wei, Heyuan Chen, Lizheng He, Chenxi Song, Linmin Zhang, J. F. Qiu, Xianfei Wang, Xinyao Xu, Chonghao Chen · 发表于：Ecological Indicators · 年份：2024 · DOI：10.1016/j.ecolind.2024.112527 · 被引用次数：12 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and Land Use、Land Use and Ecosystem Services

• Satellite high-precision estimation for plant diversity is extremely needed. • Simple linear regression models were the worst. • Partial linear regression model was moderate. • Random forest model performed best. • NDVI and its standard deviation are the best predictors for plant alpha diversity. At present, the accuracy of remote sensing estimation models of plant alpha diversity is generally low, and high-precision estimation models in deciduous broadleaved forest (DBF), deciduous coniferous forest (DCF) and evergreen coniferous forest (ECF) are still lacking. The main purpose of this study is to construct high-precision remote sensing models for plant alpha diversity in multiple ecosystems at global scale. Normalized Difference Vegetation Index (NDVI) were derived from Sentinel-2 data. NDVI and NDVI based spectral diversity/heterogeneity indices were selected as predictive variables, and alpha diversity indices were selected as response variables. Simple linear regression (SLR), partial linear regression (PLSR), and random forest (RF) models were used to evaluate the predictive ability of the predictive variables against the response variables under six ecosystems (evergreen broadleaved forest (EBF), DBF, ECF, DCF, shrub, and grassland), and to compare the estimated robustness of various spectral diversity indices. In terms of prediction accuracy, the SLR models were the worst, and the PLSR model were average. RF performed best, outperforming most current models. Especia...