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邓雅文，蒋卫国，王晓雅，彭凯锋.2023.基于随机森林算法和知识规则的国际湿地城市精细湿地分类——以常德市为例.遥感学报，27（6）： 1426-1440
Deng Y W， Jiang W G， Wang X Y and Peng K F. 2023. Refined wetland classification of international wetland cities based on the random forest algorithm and knowledge-driven rules： A case study of Changde city， China. National Remote Sensing Bulletin， 27（6）：1426-1440
获取精细湿地资源信息对国际湿地城市湿地修复保护、管理利用以及区域可持续发展至关重要，目前面向国际湿地城市的湿地精细分类研究较为缺乏，尤其是针对湿地中水体的详细类别划分较少。本研究以全球典型国际湿地城市常德市为案例研究区，基于Google Earth Engine（GEE）云计算平台和2020年Sentinel-1/2时序遥感影像以及地形数据，首先采用最小冗余最大相关算法和递归梯度提升树算法进行湿地分类特征集优选，进而构建集成基于像元的随机森林和面向对象的知识规则决策模型的城市湿地精细分类智能模型，以实现常德市湿地资源精细分类。结果表明：（1）湿地分类特征优选前后特征数由63减少为16，总体精度下降0.9%，其中旱期的水体指数、植被频率和建成区指数的特征重要性较大，特征优选可以减少特征数据信息冗余、提高分类效率；（2）常德市湿地精细分类结果包括河流、湖泊、水库、养殖池/坑塘、运河/水渠、泥滩地、草滩地和芦苇湿地8种湿地类型，总体精度达91.53%，Kappa系数为0.89，可以满足国际湿地城市精细湿地分类的需求；（3）常德市湿地主要分布在东部西洞庭湖平原区域，呈现出东多西少的空间格局。本研究综合利用多源遥感数据、GEE云计算平台、机器学习算法以及知识规则模型能够准确高效地提取国际湿地城市精细湿地信息，有望迁移至其他城市湿地制图应用，在服务国际湿地城市创建及优选、湿地资源修复保护与可持续开发利用等方面具有较大应用潜力。
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
2
(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.
湿地精细分类随机森林知识规则Sentinel国际湿地城市常德市
wetland classificationrandom forestknowledge-based rulesSentinel-1/2international wetland citiesChangde city
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