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甘南州高寒天然草地生长状况遥感监测 被引量:13

Monitoring of grassland herbage accumulation by using remote sensing in Gannan Prefecture
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摘要 以甘南州高寒天然草地为研究对象,利用2016-2019年草地地上生物量实测数据和MOD13Q1植被指数产品,构建了甘南州草地地上生物量遥感反演模型,分析了近20年(2000-2019年)甘南州高寒天然草地地上生物量的时空分布特征。结果表明:1)MODIS EVI植被指数适宜于甘南州高寒天然草地地上生物量变化监测研究,模型决定系数R^2=0.5249,均方根误差RMSE=527.9 kg·hm^-2;2)20年间甘南州高寒草甸和山地草甸的地上生物量均呈增加趋势,而沼泽类草地地上生物量呈减少趋势;3)近20年来甘南州草地呈现出整体恢复、局部恶化的趋势,全州66.04%的草地呈稳定或恢复趋势,33.96%的草地地上生物量呈减少趋势,其中18.08%的草地呈持续性恶化趋势。研究结果为甘南州草地植被动态监测和高寒草地退化修复提供了数据支持。 This study focuses on grasslands in the Gannan Prefecture,we used the measured data of aboveground grassland biomass(AGB)and the MOD13 Q1 vegetation index product from 2016 to 2019,to construct a remote sensing inversion model of grassland AGB,and further analyzed the characteristics of temporal and spatial changes in the grassland AGB in the last 20 years(from 2000 to 2019).The results showed that:1)Our moderate resolution imaging spectroradiometer enhanced vegetation index(MODIS EVI)is suitable for grassland biomass inversion(R^2=0.5249,RMSE=527.9 kg·ha^-1).2)In the past 20 years,the alpine meadows and mountain meadows showed an increasing trend,while swamp AGB decreased.3)In the past 20 years,the grasslands in Gannan Prefecture showed a trend towards partial deterioration;the overall trend for grassland vegetation in Gannan Prefecture was positive,66.04%of the grassland showed a trend of stabilization or recovery,33.96%of the grassland AGB decreased,and 18.08%of the grasslands showed a continuous deterioration trend.The study results provide data support for Gannan Prefecture grassland vegetation dynamic monitoring and alpine grassland degradation restoration.
作者 陆荫 杨淑霞 李晓红 LU Yin;YANG Shuxia;LI Xiaohong(Gansu Province Environmental Monitoring Center,Lanzhou 730020,Gansu,China)
出处 《草业科学》 CAS CSCD 北大核心 2021年第1期32-43,共12页 Pratacultural Science
关键词 草地地上生物量 植被指数 变化趋势 高寒天然草地 甘南州 时空分布 遥感反演模型 aboveground biomass vegetation index variation trend alpine natural grassland Gannan Prefecture spatial and temporal distribution remote sensing inversion model
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