Mapping Dryland Ecosystems Using Google Earth Engine and Random Forest: A Case Study of an Ecologically Critical Area in Northern China

Shuai Li, Pu Guo, Fei Sun, Jinlei Zhu, Xiaoming Cao, Xue Dong and Qi Lu ()
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Shuai Li: Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China
Pu Guo: Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China
Fei Sun: Inner Mongolia Dengkou Desert Ecosystem National Observation Research Station, Experimental Center of Desert Forestry, Chinese Academy of Forestry, Bayannur 015200, China
Jinlei Zhu: Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China
Xiaoming Cao: Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China
Xue Dong: Inner Mongolia Dengkou Desert Ecosystem National Observation Research Station, Experimental Center of Desert Forestry, Chinese Academy of Forestry, Bayannur 015200, China
Qi Lu: Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China

Land, 2024, vol. 13, issue 6, 1-20

Abstract: Drylands are characterized by unique ecosystem types, sparse vegetation, fragile environments, and vital ecosystem services. The accurate mapping of dryland ecosystems is essential for their protection and restoration, but previous approaches primarily relied on modifying land use data derived from remote sensing, lacking the direct utilization of latest remote sensing technologies and methods to map ecosystems, especially failing to effectively identify key ecosystems with sparse vegetation. This study attempts to integrate Google Earth Engine (GEE), random forest (RF) algorithm, multi-source remote sensing data (spectral, radar, terrain, texture), feature optimization, and image segmentation to develop a fine-scale mapping method for an ecologically critical area in northern China. The results showed the following: (1) Incorporating multi-source remote sensing data significantly improved the overall classification accuracy of dryland ecosystems, with radar features contributing the most, followed by terrain and texture features. (2) Optimizing the features set can enhance the classification accuracy, with overall accuracy reaching 91.34% and kappa coefficient 0.90. (3) User’s accuracies exceeded 90% for forest, cropland, and water, and were slightly lower for steppe and shrub-steppe but were still above 85%, demonstrating the efficacy of the GEE and RF algorithm to map sparse vegetation and other dryland ecosystems. Accurate dryland ecosystems mapping requires accounting for regional heterogeneity and optimizing sample data and feature selection based on field surveys to precisely depict ecosystem patterns in complex regions. This study precisely mapped dryland ecosystems in a typical dryland region, and provides baseline data for ecological protection and restoration policies in this region, as well as a methodological reference for ecosystem mapping in similar regions.

Keywords: dryland ecosystem mapping; Google Earth Engine; random forest algorithm; Landsat 8 OLI; object-based segmentation; feature optimization (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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