草业学报2017，Vol.26Issue(6)：28-36,9.DOI:10.11686/cyxb2016316
基于遥感数据的呼伦贝尔草原放牧强度研究
A study of grazing intensity in the Hulunbuir grasslands using remote sensing
摘要
Abstract
The Hulunbuir meadow steppe,located in northeast China,is an important site for animal husbandry.The potential to further develop animal husbandry in this region is closely related to the growth of grass and to sustainable utilization of grasslands more generally.As a critical component of the grassland ecosystem,the intensity of grazing has become an important issue.In this study,remote sensing data,based on Landsat images,and Net Primary Productivity (NPP) data were collected from June to July 2014 and used to estimate grazing intensity in Xeltala pastures.Multi-image Landsat optical data was used to calculate aboveground biomass and biomass increments,while the NPP data was used to analyze grass growth.Unlike previous studies,this analysis used NPP data to take account of the heterogeneity of grassland conditions.The results indicate that this method is capable of accurately estimating grazing intensity,with a R2 of 0.7996 when validated by measurements on the ground.Grazing intensity in the Xeltala pastures ranged from 1 to 2.5 Au/ha,with the latter being over-grazed.The local map suggested that heavy grazing areas were distributed around a small lake and in certain locations where abundant grass favoured concentrations of cattle.Light grazing areas,on the other hand,were located in enclosed places where the grass was reserved for winter supplies.Extending the study area to include the city of Hailaer,the grazing intensity map clearly demonstrated the variations between areas of light,moderate and over-grazing,with Hailaer in the southwest appearing as extremely lightly grazed compared to the northeastern meadows.关键词
遥感数据/呼伦贝尔草原/生物量/净初级生产力/放牧强度Key words
remote sensing data/Hulunbuir Grassland/biomass/net primary productivity/grazing intensity引用本文复制引用
王梦佳,孙睿,刘喆,辛晓平,刘刚,张蕾,乔晨..基于遥感数据的呼伦贝尔草原放牧强度研究[J].草业学报,2017,26(6):28-36,9.基金项目
国家科技支撑计划项目(2013BAC03B02),国家自然科学基金(41471349)和中央高校基本科研业务费专项(2014kJJCA02)资助. （2013BAC03B02）
