Aboveground Biomass Inversion Based on Object-Oriented Classification and Pearson–mRMR–Machine Learning Model

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2024-05, Vol.16 (9), p.1537
• Main Authors: Chen, Xinyang, Yang, Keming, Ma, Jun, Jiang, Kegui, Gu, Xinru, Peng, Lishun
• Format: Article
• Language: English
• Subjects: aboveground biomass; Accuracy; Algorithms; Biomass; Carbon cycle; Carbon cycle (Biogeochemistry); Carbon sequestration; China; Cities; Classification; coal city ecosystem; Coal mining; Ecosystem protection; Ecosystems; Emissions (Pollution); Forests; Geospatial data; Grasses; Grasslands; Information processing; Land cover; Learning algorithms; Machine learning; Mathematical models; Modelling; Natural resources; Parameters; Physiology; PmM model; Precipitation; Regression analysis; Remote sensing; remote sensing inversion; Sustainable use; Topography; Urban areas; Urban health; Vegetation; Vegetation index; Waterways
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16091537

Cities play a crucial role in the carbon cycle. Measuring urban aboveground biomass (AGB) is essential for evaluating carbon sequestration. Satellite remote sensing enables large-scale AGB inversion. However, the apparent differences between forest and grassland biomass pose a significant challenge to the accurate estimation of urban AGB using satellite-based data. To address this limitation, this study proposed a novel AGB estimation method using the eastern part of the Zhahe mining area in Huaibei City as the study area, which integrates land cover classification, feature selection, and machine learning modelling to generate high quality biomass maps of different vegetation types in an urban area with complex feature distribution. Utilizing the GEE platform and Sentinel-2 image, we developed an object-oriented machine learning classification algorithm, combining SNIC and GLCM to extract vegetation information. Optimal feature variables for forest and crop-grass AGB inversion were selected using the Pearson–mRMR algorithm. Finally, we constructed nine machine learning models for AGB inversion and selected the model with the highest accuracy to generate the AGB map of the study area. The results of the study are as follows: (1) Compared with the pixel-based classification method, the object-oriented classification method can extract the boundaries of different vegetation types more accurately. (2) Forest AGB is strongly correlated with vegetation indices and physiological parameters, while agri-grass AGB is primarily associated with vegetation indices and vegetation physiological parameters. (3) For forest AGB modelling, the RF-R model outperforms other machine learning models with an R2 of 0.77. For agri-grass AGB modelling, the XGBoost-R model is more accurate, with an R2 of 0.86. (4) The mean forest AGB in the study area was 4.60 kg/m2, while the mean agri-grass AGB was 0.71 kg/m2. High AGB values were predominantly observed in forested areas, which were mainly distributed along roads, waterways, and mountain ranges. Overall, this study contributes to a better understanding of the health of local urban ecosystems and provides valuable insights for ecosystem protection and the sustainable use of natural resources.