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标题：A COMPARISON OF DECISION TREE-BASED MODELS FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING A COMBINATION OF AIRBORNE LIDAR AND LANDSAT DATA
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作者：H. Tamiminia ; B. Salehi ; M. Mahdianpari 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2021
卷号：V-3-2021
页码：235-241
DOI：10.5194/isprs-annals-V-3-2021-235-2021
语种：English
出版社：Copernicus Publications
摘要：Forest is one of the most crucial Earth’s resources. Forest above-ground biomass (AGB) mapping has been research endeavors for a long time in many applications since it provides valuable information for carbon cycle monitoring, deforestation, and forest degradation monitoring. A methodology to rapidly and accurately estimate AGB is essential for forest monitoring purposes. Thus, the main objective of this paper was to investigate the performance of decision tree-based models to predict AGB at a site in Huntington Wild Forest (HWF) in Essex County, NY using continuous forest inventory (CFI) plots. The results of decision tree, random forest, and deep forest regression models were compared using light detection and ranging (LiDAR), Landsat 5 TM, and a combination of them. The results illustrated the importance of integration of Landsat 5 TM and LiDAR data, which benefits from both vertical forest structure and spectral information reflected by canopy cover. In addition, the deep forest model with a root mean square error (RMSE) of 51.63 Mg/ha and R-squared (Rsup2/sup) of 0.45 outperformed other regression tree-based models, regardless of the dataset.