Refined wetland classification of international wetland cities based on the random forest algorithm and knowledge-driven rules： A case study of Changde city， China

被引：0
|
作者：
Deng Y. [1 ,2 ]
Jiang W. [1 ,2 ]
Wang X. [1 ,2 ]
Peng K. [1 ,2 ]
机构：
[1] State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing
[2] Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing
来源：
National Remote Sensing Bulletin | 2023年 / 27卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Changde city; international wetland cities; knowledge-based rules; random forest; Sentinel-1/2; wetland classification;
D O I：
10.11834/jrs.20232293
中图分类号：
学科分类号：
摘要：
Obtaining refined wetland resource information is important for the restoration, protection, management, and utilization of wetlands in international wetland cities and for regional sustainable development. At present, refined wetland classification research for international wetland cities is lacking, especially for the detailed classification of water bodies in wetlands. Refined wetland classification results could provide vital information support for the nomination and assessment of potential or existing international wetland cities. This study takes Changde City, a typical international wetland city, as the case study area. On the basis of the Google Earth Engine (GEE) cloud computing platform and Sentinel 1/2 time series remote sensing data and terrain data in 2020, the minimum redundancy-maximum correlation algorithm and the recursive gradient boosting tree algorithm are first used to optimize the wetland classification feature set. Then, an intelligent model for refined urban wetland classification integrating pixel-based random forest and object-oriented knowledge rule decision model is constructed to realize the refined classification of wetland resources in Changde City. Using multisource remote sensing data, the GEE cloud computing platform, a machine learning algorithm, and knowledge-driven rule-based model, this study can accurately and efficiently extract refine wetland information of international wetland cities. The methodology developed in this study could be operationally transferred to other urban wetland mapping and has great application potential in the nomination and management of international wetland cities as well as the restoration, protection, and sustainable development and utilization of wetland resources. The results are as follows (1) The number of features before and after the optimization of wetland classification features is reduced from 63 to 16, and the overall accuracy is reduced by 0.9%. The characteristics of water index, vegetation frequency, antd built-up area index in the dry period are of great importance, and feature optimization can reduce the redundancy of feature data and improve the classification efficiency. (2) The results of the fine classification of wetlands in Changde City include eight wetland types: rivers, lakes, reservoirs, aquaculture ponds/pits, canals, mudflats, sedge and reed with an overall accuracy of 91.53% and a kappa coefficient of 0.89. These results meet the requirements for the fine wetland classification of international wetland cities, indicating that the precision of the urban wetland refine classification method framework is high. (3) Wetlands in Changde City are mainly distributed in the eastern and western parts of the Dongting Lake Plain, showing a spatial pattern of more in the east and less in the west. © 2023 National Remote Sensing Bulletin. All rights reserved.
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页码：6 / 20
页数：14
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