Research on remote sensing classification and modeling methods for aboveground biomass of grassland resources in Yunnan
[Objective]Above-Ground Biomass (AGB) is an important indicator for measuring grassland produc-tivity and ecosystem health. Accurately assessing grassland AGB is crucial for scientifically guiding the development and utilization of grassland resources,as well as for maintaining and restoring ecological functions.Yunnan has a com-plex terrain and diverse climate,with abundant and varied grassland resources. Exploring the use of satellite remote sensing data to develop an AGB classification modeling method for Yunnan Province.[Method]Based on the field data of four grassland types collected from grassland resource surveys,a "NDVI-VFC-AGB" classification and modeling system was developed for the entire Yunnan region. First,the grassland resources were classified into four types using aspect,elevation,and latitude factors.Second,an "NDVI-VFC" inversion model was established by cor-relating the NDVI of image pixels with the VFC from the sample plots for the four grassland types. Next,a "VFC-AGB" fitting model was constructed using field-measured data from the sample plots for the four grassland types. Fi-nally,spatial statistics were performed by overlaying the inverted AGB onto the grassland resource pattern across the entire region,yielding AGB data for each statistical unit.[Results]A simple four-class classification based on sample statistics achieved a classification accuracy of approximately 82％. Utilizing this classification,remote sensing models for VFC and AGB were developed and inverted. Sampling inspection of the model results showed biases of 17.21％ and 18.87％ for VFC and AGB,respectively.The models provided estimates of the quantity and distribution of AGB for grassland resources across the entire province,with the statistical results being largely consistent with the average values obtained from pure field plot surveys.[Conclusion]Satellite remote sensing NDVI reflects vegetation cover-age and growth and can be effectively used for "NDVI-VFC" modeling with high accuracy.Compared to single model-ing methods,the "VFC-AGB" classification modeling can significantly improve the accuracy of satellite remote sens-ing inversion for AGB.
万方数据
维普
