A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

编号 030029904

推送时间 20210712

研究领域 森林经理 

年份 2021 

类型 期刊 

语种 英语

标题 A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

来源期刊 Forests

期 第299期

发表时间 20210630

关键词 biodiversity indices;  Sentinel-2;  Landsat-8;  RapidEye;  machine learning;  Mediterranean forest habitats;  WorldView-2; 

摘要 Earth observation data can provide important information for tree species diversity mapping and monitoring. The relatively recent advances in remote sensing data characteristics and processing systems elevate the potential of satellite imagery for providing accurate, timely, consistent, and robust spatially explicit estimates of tree species diversity over forest ecosystems. This study was conducted in Northern Pindos National Park, the largest terrestrial park in Greece and aimed to assess the potential of four satellite sensors with different instrumental characteristics, for the estimation of tree diversity. Through field measurements, we originally quantified two diversity indices, namely the Shannon diversity index (H’) and Simpson’s diversity (D1). Random forest regression models were developed for associating remotely sensed spectral signal with tree species diversity within the area. The models generated from the use of the WorldView-2 image were the most accurate with a coefficient of determination of up to 0.44 for?H’?and 0.37 for?D1. The Sentinel-2 -based models of tree species diversity performed slightly worse, but were better than the Landsat-8 and RapidEye models. The coefficient of variation quantifying internal variability of spectral values within each plot provided little or no usage for improving the modelling accuracy. Our results suggest that very-high-spatial-resolution imagery provides the most important information for the assessment of tree species diversity in heterogeneous Mediterranean ecosystems.

服务人员 付贺龙

服务院士 唐守正

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