典型文献
Machine learning-based estimates of aboveground biomass of subalpine forests using Landsat 8 OLI and Sentinel-2B images in the Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau
文献摘要：
Accurate estimates of forest aboveground bio-mass (AGB) are critical for supporting strategies of eco-system conservation and climate change mitigation. The Jiuzhaigou National Nature Reserve, located in Eastern Tibet Plateau, has rich forest resources on steep slopes and is very sensitive to climate change but plays an important role in the regulation of regional carbon cycles. However, an estimation of AGB of subalpine forests in the Nature Reserve has not been carried out and whether a global bio-mass model is available has not been determined. To pro-vide this information, Landsat 8 OLI and Sentinel-2B data were combined to estimate subalpine forest AGB using lin-ear regression, and two machine learning approaches-ran-dom forest and extreme gradient boosting, with 54 inven-tory plots. Regardless of forest type, Observed AGB of the Reserve varied from 61.7 to 475.1 Mg ha ?1 with an average of 180.6 Mg ha ?1 . Results indicate that integrating the Land-sat 8 OLI and Sentinel-2B imagery significantly improved model efficiency regardless of modelling approaches. The results highlight a potential way to improve the prediction of forest AGB in mountainous regions. Modelled AGB indi-cated a strong spatial variability. However, the modelled bio-mass varied greatly with global biomass products, indicating that global biomass products should be evaluated in regional AGB estimates and more field observations are required, particularly for areas with complex terrain to improve model accuracy.
文献关键词：
中图分类号：
作者姓名：
Ke Luo;Yufeng Wei;Jie Du;Liang Liu;Xinrui Luo;Yuehong Shi;Xiangjun Pei;Ningfei Lei;Ci Song;Jingji Li;Xiaolu Tang
作者机构：
College of Earth Science,Chengdu University of Technology,Chengdu 610059,People's Republic of China;State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu University of Technology,Chengdu 610059,People's Republic of China;Jiuzhaigou Nature Reserve Administration,Aba Tibetan and Qiang Autonomous Prefecture,Jiuzhai 623402, People's Republic of China;College of Ecology and Environment,Chengdu University of Technology,Chengdu 610059,People's Republic of China;State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil&Water Pollution,Chengdu Univer-Sity of Technology,Chengdu 610059, People's Republic of China;China Railway,Eryuan Engineering Group Co.Ltd, Chengdu 610031,People's Republic of China
文献出处：
引用格式：
[1]Ke Luo;Yufeng Wei;Jie Du;Liang Liu;Xinrui Luo;Yuehong Shi;Xiangjun Pei;Ningfei Lei;Ci Song;Jingji Li;Xiaolu Tang-.Machine learning-based estimates of aboveground biomass of subalpine forests using Landsat 8 OLI and Sentinel-2B images in the Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau)[J].林业研究（英文版）,2022(04):1329-1340
A类：
inven,Modelled
B类：
Machine,learning,estimates,aboveground,biomass,subalpine,forests,using,Landsat,OLI,Sentinel,2B,images,Jiuzhaigou,National,Nature,Reserve,Eastern,Tibet,Plateau,Accurate,AGB,critical,supporting,strategies,eco,system,conservation,climate,change,mitigation,located,has,rich,resources,steep,slopes,very,sensitive,but,plays,important,role,regulation,regional,carbon,cycles,However,estimation,not,been,carried,out,whether,global,available,determined,To,vide,this,information,data,were,combined,regression,two,machine,approaches,ran,dom,extreme,gradient,boosting,tory,plots,Regardless,type,Observed,varied,from,Mg,average,Results,indicate,that,integrating,imagery,significantly,improved,efficiency,regardless,modelling,results,highlight,potential,way,prediction,mountainous,regions,strong,spatial,variability,modelled,greatly,products,indicating,should,evaluated,more,field,observations,required,particularly,areas,complex,terrain,accuracy
AB值：
0.510409
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