摘要
三江源区位于青藏高原腹地,作为长江、黄河、澜沧江三大河流的发源地,是我国重要的生态安全屏障。基于三江源区域草地AGB的野外调查数据,本研究采用多种机器学习算法集成分析的方式构建模型,实现了高精度的三江源国家公园草地AGB时空估算。基于AGB时空模拟结果,分析了近19年(2000—2018年)三江源国家公园区域草地AGB的时空动态变化。研究结果显示:(1)通过多种机器学习结合贝叶斯平均模型,草地AGB模拟值与实测值的r为0.88,RMSE为71.60g/m^(2),表明多模型集成分析的方式对草地AGB估算获得了较好的模拟效果。(2)三江源国家公园区域草地AGB的空间分布具有明显的空间异质性,呈从东南向西北递减的趋势。(3)2000—2018年长江源国家公园、黄河源国家公园和澜沧江国家公园区域草地AGB多年平均值分别为82.96 g/m^(2)、117.54g/m^(2)和168.39 g/m^(2)。(4)近19年间,在黄河和长江源园区受到温度上升的影响草地AGB呈现出非显著性上升趋势;澜沧江区域,由于2015和2016年的降水量偏低,年际动态统计结果表现为非显著性下降趋势。
The Three River Head-water Region(TRHR)is located in the hinterland of the Tibetan Plateau,is the source of the Yangtze River,Yellow River and Lancang River.As an important water source and ecological function conservation area of China,accurate monitoring of the spatio-temporal variation in the grassland aboveground biomass(AGB)is important in the TRHR.In this study,based on field observation,remote sensing,meteorological and topographical data,we estimated the grassland AGB in the Three River Source National Park(TRSNP)and analyzed its spatiotemporal change and response to climatic factors.Four machine learning(ML)models(random forest(RF),cubist,artificial neural network(ANN)and support vector regression(SVR)models)were constructed and compared for AGB simulation.The AGB results estimated with the four ML models were then applied in the integrated analysis via Bayesian model averaging(BMA)to obtain more accurate and stable estimates(r=0.88;RMSE=71.60g/m^(2)).The results showed that the spatial distribution of grassland AGB in the TRSNP had obviously spatial heterogeneity,showing a decreasing trend from southeast to northwest.The grassland AGB in the Yangtze River Source National Park,Yellow River Source National Park and Lancang River National Park were 82.96g/m^(2),117.54g/m^(2),and 168.39g/m^(2),respectively.In the Yellow River and Yangtze River Source Parks,from 2000 to 2018 grassland AGB showed a non-significant increasing trend due to the influence of temperature increase;in the Lancang River region,the interannual dynamics statistics showed a non-significant downward trend due to the low annual precipitation in 2015 and 2016.
作者
曾纳
任小丽
何洪林
张黎
徐茜
张梦宇
陈秀芝
高超
刘畅
ZENG Na;REN Xiaoli;HE Honglin;ZHANG Li;XU Qian;ZHANG Mengyu;CHEN Xiuzhi;GAO Chao;LIU Chang(Key Laboratory of Ecosystem Network Observation and Modeling,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;School of Environment and Resources,Zhejiang Agriculture and Forestry University,Hangzhou 311300,China;National Ecology Science Data Center,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China;College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100190,China)
出处
《生态学报》
CAS
CSCD
北大核心
2023年第3期1175-1184,共10页
Acta Ecologica Sinica
基金
国家科技基础资源调查专项资助(2021FY100705)
国家自然科学基金项目(42030509)
国家重点研发计划项目(2019YFE0126503)
关键词
三江源国家公园
草地地上生物量
时空变化
降水敏感性
the Three River Source National Park
grassland aboveground biomass
temporal and spatial variation
precipitation sensitivity
