Vegetation Classification and a Biomass Inversion Model for Wildfires in Chongli Based on Remote Sensing Data

Abstract

Vegetation classification, biomass assessment, and wildfire dynamics are interconnected wildfire-ecosystem components. The Chongli District, located in Zhangjiakou City, was the venue for skiing at the 2022 Winter Olympics. Its high mountains and dense forests create a unique environment. The establishment of alpine ski resorts highlighted the importance of comprehensive forest surveys. Understanding vegetation types and their biomass is critical to assessing the distribution of local forest resources and predicting the likelihood of forest fires. This study used satellite multispectral data from the Sentinel-2B satellite to classify the surface vegetation in the Chongli District through K-means clustering. By combining this classification with a biomass inversion model, the total biomass of the survey area can be calculated. The biomass inversion equation established based on multispectral remote sensing data and terrain information in the Chongli area have a strong correlation (shrub forest R2 = 0.811, broadleaf forest R2 = 0.356, coniferous forest R2 = 0.223). These correlation coefficients are key indicators for our understanding of the relationship between remote sensing data and actual vegetation biomass, reflecting the performance of the biomass inversion model. Taking shrubland as an example, a correlation coefficient as high as 0.811 shows the model’s ability to accurately predict the biomass of this type of vegetation. In addition, through multiple linear regression, the optimal shrub, broadleaf, and coniferous forest biomass models were obtained, with the overall accuracy reaching 93.58%, 89.56%, and 97.53%, respectively, meeting the strict requirements for survey accuracy. This study successfully conducted vegetation classification and biomass inversion in the Chongli District using remote sensing data. The research results have important implications for the prevention and control of forest fires.

Estimation of Rubber Plantation Biomass Based on Variable Optimization from Sentinel-2 Remote Sensing Imagery

“…Their results indicated that integrating VIs with TA variables yielded a higher accuracy in tree species classification and health assessment compared to using VIs alone, resulting in an overall accuracy (OA) improvement of 4.24%. These textural features coupled with optical spectral VIs could potentially play a crucial role in enhancing the estimation accuracy of forest AGB [25,26]. Lourenço et al [27] integrated spectral bands, spectral indices, and GLCM derived from high-resolution satellite imagery with a Random Forest Regression technique for forest biomass estimation, yielding a promising accuracy R² = 0.82, RMSE = 10.5 t/ha).…”