Integrative plant area index retrieval and spatiotemporal analysis in Taihu Lake Basin via synergistic active-passive remote sensing techniques

The Taihu Lake basin is one of the fastest-growing regions in China, where the natural environment has been seriously affected by humans. The plant area index (PAI) is an important parameter reflecting the change in vegetation growth, which plays a crucial role in studying vegetation growth and protecting the ecological environment. Advancements in remote sensing technology, complemented by machine learning techniques, have facilitated the accurate and efficient acquisition of PAI over large areas. In this study, the Taihu Lake Basin vegetation area was taken as the research object. Global Ecosystem Dynamics Investigation (GEDI) point cloud data and Landsat-8 remote sensing images were the primary information sources. MODIS land cover types were utilized to classify the vegetation into six categories. Three classical machine learning models, namely, Random Forest (RF), Support Vector Regression (SVR), and Back Propagation Neural Network (BPNN), were used to estimate the PAI in the Taihu Lake Basin. It was found that the RF model showed the best performance. The determination coefficients (R2) for grassland, evergreen forest, mixed forest, deciduous forest, farmland, and wetland were 0.71, 0.67, 0.69, 0.66, 0.65, and 0.69, respectively. Over 2000-2022, the PAI exhibited an absolute change rate of 0.035, with an overall increasing trend. The area of improved and degraded vegetation accounted for 58.33% and 41.67% of the total area, respectively. The study also revealed that PAI was positively correlated with precipitation (R = 0.64, P < 0.05) and negatively correlated with temperature (R = -0.58, P < 0.05). Different land types' effects on PAI were also analyzed, with wetland PAI having the smallest mean value and evergreen forest PAI having the most considerable mean value. This research underscores the effectiveness of integrating GEDI data and Landsat-8 imagery in PAI assessment, providing valuable insights for environmental monitoring and analysis in the Taihu Lake Basin.