A Novel Forest EcoSpatial Network for Carbon Stocking Using Complex Network Theory in the Yellow River Basin

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-05, Vol.15 (10), p.2612
• Main Authors: Zhang, Huiqing, Lin, Simei, Yu, Qiang, Gao, Ge, Xu, Chenglong, Huang, Huaguo
• Format: Article
• Language: English
• Subjects: Analysis; Carbon; Carbon neutrality; Carbon sequestration; Climate change; Corridors; coupled coordination model; Datasets; Deforestation; ecospatial network; Ecosystem services; Ecosystems; Emission standards; Environmental impact; forest area calibration; Forests; Optimization; Pattern analysis; Provinces; Remote sensing; River basins; River ecology; Rivers; Spatial analysis; Spatial distribution; spatial distribution of carbon stock; the yellow river basin; Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15102612

The Yellow River Basin serves as a crucial ecological barrier in China, emphasizing the importance of accurately examining the spatial distribution of forest carbon stocks and enhancing carbon sequestration in order to attain “carbon peaking and carbon neutrality”. Forest patches have complex interactions that impact ecosystem services. To our knowledge, very few studies have explored the connection between these interactions and carbon stock. This study addressed this gap by utilizing complex network theory to establish a forest ecospatial network (ForEcoNet) in the Yellow River Basin in which forest patches are represented as nodes (sources) and their interactions as edges (corridors). Our objective was to optimize the ForEcoNet’s structure and enhance forest carbon stocks. First, we employed downscaling technology to allocate the forest carbon stocks of the 69 cities in the study area to grid cells, generating a spatial distribution map of forest carbon density in the Yellow River Basin. Next, we conducted morphological spatial pattern analysis (MSPA) and used the minimum cumulative resistance model (MCR) to extract the ForEcoNet in the basin. Finally, we proposed optimization of the ForEcoNet based on the coupling coordination between the node carbon stock and topological structure. The results showed that: (1) the forest carbon stocks of the upper, middle, and lower reaches accounted for 42.35%, 54.28%, and 3.37% of the total, respectively, (2) the ForEcoNet exhibited characteristics of both a random network and a scale-free network and demonstrated poor network stability, and (3) through the introduction of 51 sources and 46 corridors, we optimized the network and significantly improved its robustness. These findings provide scientific recommendations for the optimization of forest allocation in the Yellow River Basin and achieving the goal of increasing the forest carbon stock.