基于Worldview-3与Sentinel-1 SAR数据的草原矿区复垦植被生物量反演方法研究

利用多源遥感数据定量反演矿区复垦植被生物量是高效、动态、大面积监测土地复垦和生态恢复效果的必要手段之一.本文以内蒙古草原露天煤矿为研究区,联合遥感光学与雷达数据各自的优势,探索基于Worldview-3(WV-3)与Sentinel-1 SAR数据的矿区复垦植被生物量反演方法,选择主成分-小波变换(W-PCA)算法对W...

Estimating biomass of reclaimed vegetation in prairie mining area: Inversion method based on Worldview-3 and Sentinel-1 SAR data

Quantitative inversion of reclaimed vegetation biomass in prairie mining area based on the remote sensing technology is the basis of dynamic monitoring and evaluation of mining ecological environment. In this research, focussing on the reclaimed vegetation in the grassland open-pit coal mine in Inner Mongolia, we combine the advantages of optical and radar remote sensing to explore the inversion method for biomass estimation based on Worldview-3 and Sentinel-1 SAR data. The principal component-wavelet transform algorithm was selected for data fusion. We revealed the correlation between parameters such as band reflectivity, vegetation index, backscatter coefficient or texture feature and biomass, established multivariate biomass inversion models, and analyzed the spatial uncertainty of different biomass models. The results are as follows: (1) After image fusion using W-PCA method, both data entropy and average gradient of the fusion data were significantly improved, and the fused 8th band (NIR2) had the highest correlation coefficient, the lowest spectral distortion, and the highest spectral fidelity. (2) Correlation analysis revealed a significant positive correlation between biomass and EVI, NDVI, VH polarization scattering coefficient, VH mean texture or the 8th band after fusion. Compared with a single variable, using the joint variables, NDVI of WV-3 and VH mean texture of Sentinel-1, it achieved the highest model accuracy (R²=0.8340, RMSE=16.4646 g/m², Ac=81.52%), while the 8th band after fusion gave the highest verification accuracy (R²=0.7983, RMSE=22.8283 g/m², Ac=74.64%). (3) According to the residual uncertainty analysis of different models, the Sentinel-1 variables are more prone to overestimation and saturation, whereas the joint variables can achieve complementary advantages. Using fusion data significantly improved the biomass overestimation for biomass below 40 g/m² and saturation for biomass greater than 100 g/m², reducing the model uncertainty by 2.42-9.68 g/m² on average. It can be seen that the combination of optical and microwave cooperative remote sensing can effectively improve the estimation accuracy of vegetation biomass, thereby providing effective data support for fine monitoring of reclaimed vegetation in mining areas.