摘要
季节牧场(冷季牧场和暖季牧场)面积是载畜量计算的重要指标,对于草畜平衡季节动态管理具有重要指导意义。本研究基于Landsat 8影像构建5种对植被扰动敏感的植被指数(NBR、CSI、NBR2、NDMI和NDVI),通过5种植被指数对青藏高原地区两类最主要的高寒草地(高寒草甸和高寒草原)的季节牧场可利用面积进行识别,并通过区分度(M)和分类精度定量评价各指数识别季节牧场的能力。结果表明:1)受到放牧家畜的啃食和践踏等干扰,在暖季放牧末期,冷季牧场和暖季牧场光谱差异明显;2)5种植被指数均能很好地区分高寒草地的季节牧场,M值均高于2.38,总体精度均高于87.33%;3)基于近红外(NIR)和短波红外(SWIR2)波段构建的NBR指数对高寒草甸和高寒草原的季节牧场分类效果最好,总体精度均高于93.33%,Kappa系数均大于0.83。
The usable area of seasonal pasture(cold and warm)is an important parameter for the calculation of livestock carrying capacity,which has instructive significance for seasonal dynamic management of forage-livestock balance.In this study,five vegetation indices(NBR,CSI,NBR2,NDMI and NDVI)which are sensitive to vegeta⁃tion disturbance were constructed based on Landsat 8 images to identify the seasonal usable area of the two main al⁃pine grasslands(alpine meadow and alpine steppe)in the Qinghai⁃Tibet Plateau.The abilities of the five vegetation indices for the identification of seasonal pasture were quantitatively evaluated by separability index M and classifica⁃tion accuracy.The results showed that the spectral differences between cool and warm season pastures were obvious at the end of warm season grazing due to disturbances from gnawing and trampling of grazing livestock.Five vegeta⁃tion indices distinguished the seasonal pasture of alpine grassland.The M values were all higher than 2.38,and the overall accuracy was higher than 87.33%.The NBR index based on NIR and SWIR2 band was the best vegetation index for the classification of season pastures in alpine grasslands,with the overall accuracy was higher than 93.33%.The Kappa coefficients were all higher than 0.83.
作者
方攀飞
王雷光
于龙
苏世琪
徐伟恒
周华坤
FANG Pan-fei;WANG Lei-guang;YU Long;SU Shi-qi;XU Wei-heng;ZHOU Hua-kun(Faculty of Forestry,Southwest Forestry University,Kunming 650233,China;College of Culture and Tourism,Qujing Normal University,Qujing 655011,China;Institute of Big Data and Artificial Intelligence,Southwest Forestry University,Kunming 650233,China;Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data,Kunming 650233,China;Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Regions,Northwest Institute of Plateau Biology,Xining 810008,China)
出处
《草原与草坪》
CAS
CSCD
2022年第4期1-8,共8页
Grassland and Turf
基金
国家自然科学基金项目(31660680)
青海省创新平台建设专项(2022-ZJ-Y02)。