Integrating Cloud Computing and Landscape Metrics to Enhance Land Use/Land Cover Mapping and Dynamic Analysis in the Shandong Peninsula Urban Agglomeration

Jue Xiao, Longqian Chen (), Ting Zhang, Gan Teng and Linyu Ma
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Jue Xiao: School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
Longqian Chen: School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
Ting Zhang: School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
Gan Teng: School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
Linyu Ma: School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China

Land, 2025, vol. 14, issue 10, 1-23

Abstract: Accurate land use/land cover (LULC) maps generated through cloud computing can support large-scale land management. Leveraging the rich resources of Google Earth Engine (GEE) is essential for developing historical maps that facilitate the analysis of regional LULC dynamics. We implemented the best-performing scheme on GEE to produce 30 m LULC maps for the Shandong Peninsula urban agglomeration (SPUA) and to detect LULC changes, while closely observing the spatio-temporal trends of landscape patterns during 2004–2024 using the Shannon Diversity Index, Patch Density, and other metrics. The results indicate that (a) Gradient Tree Boost (GTB) marginally outperformed Random Forest (RF) under identical feature combinations, with overall accuracies consistently exceeding 90.30%; (b) integrating topographic features, remote sensing indices, spectral bands, land surface temperature, and nighttime light data into the GTB classifier yielded the highest accuracy (OA = 93.68%, Kappa = 0.92); (c) over the 20-year period, cultivated land experienced the most substantial reduction (11,128.09 km 2 ), accompanied by impressive growth in built-up land (9677.21 km 2 ); and (d) landscape patterns in central and eastern SPUA changed most noticeably, with diversity, fragmentation, and complexity increasing, and connectivity decreasing. These results underscore the strong potential of GEE for LULC mapping at the urban agglomeration scale, providing a robust basis for long-term dynamic process analysis.

Keywords: land use/land cover (LULC); cloud computing; machine learning; landscape pattern; Shandong Peninsula urban agglomeration (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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