Estimation of NPP in Huangshan District Based on Deep Learning and CASA Model

A novel framework combining deep learning and the CASA model (HRNet-CASA) achieves highly accurate estimation of net primary productivity (NPP) in Huangshan District, offering a precise tool for assessing forest ecosystem health. The study leverages high-resolution remote sensing images to classify six forest types using HRNet, then integrates climate data with the CASA model to simulate NPP. Validation against field data confirms the framework's reliability, demonstrating its potential to advance ecological research and environmental conservation efforts in complex forest landscapes.

Wang, Z., Zhou, Y., Sun, X., & Xu, Y. (2024). Estimation of NPP in Huangshan District Based on Deep Learning and CASA Model. Forests, 15(8), 1467. https://doi.org/10.3390/f15081467