A Novel Vegetation Index Approach Using Sentinel-2 Data and Random Forest Algorithm for Estimating Forest Stock Volume in the Helan Mountains, Ningxia, China

Forest stock volume (FSV) is a major indicator of forest ecosystem health and it also plays an important part in understanding the worldwide carbon cycle. A precise comprehension distribution patterns variations FSV crucial assessment sequestration potential optimization management programs sink. In this study, novel vegetation index based on Sentinel-2 data for modeling with random (RF) algorithm Helan Mountains, China has been developed. Among all other variables correlation coefficient r = 0.778, (NDVIRE) developed red-edge bands was most significant. Meanwhile, model that combined indices (bands + VIs-based model, BVBM) performed best training phase (R2 0.93, RMSE 10.82 m3ha−1) testing 0.60, 27.05 m3ha−1). Using Mountains first mapped accuracy 80.46% obtained. The RF thus effective method to assess FSV. addition, can provide new estimate areas, especially sequestration.