Land use classification and spatio-temporal pattern of carbon emissions based on multi-source remote sensing collaboration: a case study of Qinghai Lake basin

doi:10.13474/j.cnki.11-2246.2026.0329

Abstract

Abstract: In the context of the “Dual Carbon” strategy,precise identification of regional land use changes and their spatiotemporal patterns of carbon emissions is crucial.Taking the Qinghai Lake basin as a case study,this paper establishes a technical framework integrating “multi-source data coordination,land use classification,and spatio-temporal analysis of carbon emissions”.By combining Sentinel-1/2 satellite data,automated sampling,and multi-feature optimization techniques,an object-oriented random forest classification model is developed to achieve accurate land use classification in the Qinghai Lake basin.Building on this foundation,the study combines coefficient analysis with indirect energy consumption estimation to calculate and analyze the spatiotemporal evolution of land use-related carbon emissions from 2017 to 2022.The findings demonstrate:①The proposed framework significantly reduces manual annotation workload and improves sampling consistency,effectively compressing 37 feature variables to 27.Through the implementation of the object-oriented random forest model,both overall classification accuracy and Kappa coefficient exceeds 96%.②The carbon balance structure in Qinghai Lake basin shows construction land as the primary carbon source (accounting for over 93%of total emissions),with water bodies,forests,and grasslands serving as major carbon sinks.③From 2017 to 2022,net carbon emissions in the basin surges from 6.262×10⁴ t to 13.445×10⁴ t,representing a 114.71%increase,driven by urban expansion and declining carbon sequestration capacity of forests and wetlands.④The spatial distribution of carbon emissions exhibits a “corridor-based concentration of sources—waterside concentration of sinks” pattern.The automated classification and carbon accounting framework develop in this study provides decision-making support for regional ecological management and carbon neutrality pathways.

Key words: land use classification, carbon emissions, spatio-temporal pattern, multi-source remote sensing, feature selection, Qinghai Lake basin

CLC Number:

P237

SONG Yingbin, XU Qing, YE Fei, LUO Wei. Land use classification and spatio-temporal pattern of carbon emissions based on multi-source remote sensing collaboration: a case study of Qinghai Lake basin[J]. Bulletin of Surveying and Mapping, 2026, 0(3): 174-180.

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