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

Abstract

Net primary productivity (NPP) is a key indicator of the health of forest ecosystems that offers important information about the net carbon sequestration capacity of these systems. Precise assessment of NPP is crucial for measuring carbon fixation and assessing the general well-being of forest ecosystems. Due to the distinct ecological characteristics of various forest types, accurately understanding and delineating the distribution of these types is crucial for studying NPP. Therefore, an accurate forest-type classification is necessary prior to NPP calculation to ensure the accuracy and reliability of the research findings. This study introduced deep learning technology and constructed an HRNet-CASA framework that integrates the HRNet deep learning model and the CASA model to achieve accurate estimation of forest NPP in Huangshan District, Huangshan City, Anhui Province. Firstly, based on VHR remote sensing images, we utilized the HRNet to classify the study area into six forest types and obtained the forest type distribution map of the study area. Then, combined with climate data and forest type distribution data, the CASA model was used to estimate the NPP of forest types in the study area, and the comparison with the field data proved that the HRNet-CASA framework simulated the NPP of the study area well. The experimental findings show that the HRNet-CASA framework offers a novel approach to precise forest NPP estimation. Introducing deep learning technology not only enables precise classification of forest types but also allows for accurate estimation of NPP for different types of forests. This provides a more effective tool for forest ecological research and environmental protection.