Wetlands Mapping and Monitoring with Long-Term Time Series Satellite Data Based on Google Earth Engine, Random Forest, and Feature Optimization: A Case Study in Gansu Province, China

Jian Zhang (), Xiaoqian Liu, Yao Qin, Yaoyuan Fan and Shuqian Cheng
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Jian Zhang: College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
Xiaoqian Liu: College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
Yao Qin: College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
Yaoyuan Fan: College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
Shuqian Cheng: College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China

Land, 2024, vol. 13, issue 9, 1-25

Abstract: Given global climate change and rapid land cover changes due to human activities, accurately identifying, extracting, and monitoring the long-term evolution of wetland resources is profoundly significant, particularly in areas with fragile ecological conditions. Gansu Province, located in northwest China, contains all wetland types except coastal wetlands. The complexity of its wetland types has resulted in a lack of accurate and comprehensive information on wetland changes. Using Gansu Province as a case study, we employed the GEE platform and Landsat time-series satellite data, combining high-quality sample datasets with feature-optimized multi-source feature sets. The random forest algorithm was utilized to create wetland classification maps for Gansu Province across eight periods from 1987 to 2020 at a 30 m resolution and to quantify changes in wetland area and type. The results showed that the wetland mapping method achieved robust classification results, with an average overall accuracy (OA) of 96.0% and a kappa coefficient of 0.954 across all years. The marsh type exhibited the highest average user accuracy (UA) and producer accuracy (PA), at 96.4% and 95.2%, respectively. Multi-source feature aggregation and feature optimization effectively improve classification accuracy. Topographic and seasonal features were identified as the most important for wetland extraction, while textural features were the least important. By 2020, the total wetland area in Gansu Province was 10,575.49 km 2 , a decrease of 4536.86 km 2 compared to 1987. The area of marshes decreased the most, primarily converting into grasslands and forests. River, lake, and constructed wetland types generally exhibited an increasing trend with fluctuations. This study provides technical support for wetland ecological protection in Gansu Province and offers a reference for wetland mapping, monitoring, and sustainable development in arid and semi-arid regions.

Keywords: wetland mapping; random forests; feature optimization; long time series; wetland dynamics (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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