| Grassland is an important part of terrestrial ecosystems and plays an important role in maintaining ecosystem balance.The global grassland area accounts for about half of the land area and is widely distributed and renewable.There are differences in the role played by grasslands in different regions.Grassland in karst regions plays an important role in water conservation,soil conservation,rock desertification prevention,and so on.Grassland biomass is an important index for evaluating ecological sensitivity and vulnerability.Accurately obtaining grassland biomass has guiding significance for reasonable determination of livestock carrying capacity and rocky desertification degree of grasslands.At present,the related research on grassland biomass in China is mainly concentrated in the northern region,while in the southern region,there are many types of grass species,scattered distribution,and mixed cultivation of shrubs and grasses.There is little research on their biomass.Therefore,carrying out remote sensing inversion of grassland biomass in karst mountainous areas is of great significance to grassland ecological monitoring and effective utilization of grassland resources in karst regions.This paper uses Guizhou Province as a research area,combined with field measurements of grassland biomass,MODIS and Landsat 8 OLITIRS images from July to September,mean temperature,average rainfall,and average sunshine hours from May to September,and established the five planting index（The monitoring models for NDVI,DVI,RVI,EVI,and SAVI）and above-ground biomass were designed to explore a methodology for grassland biomass inversion suitable for karst mountain areas.The main findings are as follows:（1）The optimal inversion model constructed based on Landsat 8 vegetation index under the same biomass type and model type is superior to the optimal inversion model constructed based on MODIS vegetation index,indicating that Landsat8 image is more suitable for grassland in Guizhou Province than MODIS imagery.Ground-based biomass remote sensing inversion.Biomass inversion based on support vector machine regression for superiority and inferiority of each model type under the same biomass type and imagery>（better）biomass inversion combined with estimated grass height>（better）Biometrics based on vegetation index method Volume inversion.（2）Using the optimal inversion model,the support vector regression model constructed by Landsat 8-RVI and meteorological data,the grassland aboveground biomass in Guizhou Province was estimated by remote sensing.The fresh weight model determines the coefficient R2=0.3740,the root mean square error RMSE=529.33;the dry weight model determines the coefficient R2=0.3401,the root mean square error RMSE=52.24.Biomass estimation results showed that the grassland fresh weight and dry weight biomass in Guizhou Province in 2017 were 2.36×1010 kg and 6.54×109 kg respectively,and the average fresh and dry weight biomass were1656.07 g/m2 and 433.21 g/m2,respectively.（3）The relationship between grassland aboveground biomass and related factors was analyzed.The highest average areas of grassland aboveground biomass were observed at temperatures of 18.90-20.40°,rainfall of 1000-1060mm,non-carbonate rocks,Zunyi City,and elevations of 500-1000 m.Slope 25-35°area. |
