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自然资源遥感  2025, Vol. 37 Issue (1): 195-203    DOI: 10.6046/zrzyyg.2023293
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
辽河口盐地碱蓬时空动态遥感监测及其识别机理研究
李钰彬1,2(), 王宗明2, 赵传朋2(), 贾明明2, 任春颖2, 毛德华2, 于皓1
1.吉林建筑大学测绘与勘查工程学院,长春 130118
2.中国科学院东北地理与农业生态研究所,长春 130102
Remote sensing-based monitoring and identification mechanisms of the spatiotemporal dynamics of Suaeda salsa in the Liaohe estuary, China
LI Yubin1,2(), WANG Zongming2, ZHAO Chuanpeng2(), JIA Mingming2, REN Chunying2, MAO Dehua2, YU Hao1
1. School of Surveying, Mapping and Exploration Engineering, Jilin Jianzhu University, Changchun 130118, China
2. Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
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摘要 

作为全球面积最大的红海滩景观,监测辽河口盐地碱蓬的时空动态变化对揭示其“退养还湿”等保护措施成效具有重要意义。目前,卫星遥感技术已广泛应用于包括盐地碱蓬在内的滨海植被识别与制图,但现有分类方法依赖于难以解释的黑箱模型,忽视了对识别机理的探究,制约了相关方法的改进和发展。可解释人工智能的发展为黑箱算法的解析指出了新的方向。考虑到构成随机森林的决策规则具有可解释性,本研究构建了一套从已训练随机森林模型中抽取最优决策规则的新方法,最终重构得到识别盐地碱蓬的最优决策规则,即B3/B4<0.90且B5/B3≥1.46,数据整体精度优于90%; 以2017—2022年的Sentinel-2影像为数据源,实现了对辽河口盐地碱蓬的逐年动态提取,并结合质心迁移法,分析了“退养还湿”工程实施后盐地碱蓬时空变化,揭示了该区域盐地碱蓬呈现快速恢复的现状。

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李钰彬
王宗明
赵传朋
贾明明
任春颖
毛德华
于皓
关键词 退养还湿碱蓬可解释人工智能随机森林决策规则    
Abstract

The Liaohe estuary of China boasts the largest red beach landscape in the world. Monitoring the spatiotemporal dynamics of Suaeda salsa in this region is of great significance for revealing the performance of conservation measures such as returning aquaculture to wetlands. Currently, satellite remote sensing technology has been widely applied to the mapping and identification of coastal vegetation including Suaeda salsa. However, existing classification methods rely on black-box models, which are difficult to interpret, while overlooking exploring identification mechanisms. This has hindered the improvement and development of related methods. Fortunately, the advancement in explainable artificial intelligence (XAI) has provided new directions for analyzing the black-box models. Considering that the decision rules in random forests are interpretable, this study developed a new method to extract the optimal decision rules from trained random forest models. Using this method, this study ultimately reconstructed the optimal decision rules used to identify Suaeda salsa, i.e., B3/B4<0.90 & B5/B3≥1.46, with an overall data accuracy exceeding 90%. Using annual Sentinel-2 images from 2017 to 2022 as a data source, the study successfully extracted the annual dynamics of Suaeda salsa in the Liaohe Estuary. Accordingly, by combining the centroid migration method, this study analyzed the spatiotemporal changes in the Suaeda salsa following the implementation of returning aquaculture to wetlands, revealing the current status that the Suaeda salsa in this region is undergoing rapid restoration.

Key wordsreturning aquaculture to wetlands    Suaeda salsa    explainable artificial intelligence (XAI)    random forests    decision rule
收稿日期: 2023-09-22      出版日期: 2025-02-17
ZTFLH:  TP79  
基金资助:国家重点研发计划青年科学家项目“地上植被生物量广域精细多模观测技术与装备”(2022YFF1302000);国家自然科学基金青年基金项目“基于样本扩增方法与多源卫星影像的无瓣海桑扩散进程监测方法研究”(42201422);“基于Sentinel光学和雷达遥感影像的泥炭沼泽识别方法研究”(42101399);第五批吉林省青年科技人才托举工程“泥炭沼泽的信息提取研究”(QT202101)
通讯作者: 赵传朋(1991-),男,副研究员,研究方向为滨海湿地遥感。Email: zhaochuanpeng@iga.ac.cn
作者简介: 李钰彬(1999-),男,硕士研究生,研究方向为滨海湿地遥感。Email: 18636183172@163.com
引用本文:   
李钰彬, 王宗明, 赵传朋, 贾明明, 任春颖, 毛德华, 于皓. 辽河口盐地碱蓬时空动态遥感监测及其识别机理研究[J]. 自然资源遥感, 2025, 37(1): 195-203.
LI Yubin, WANG Zongming, ZHAO Chuanpeng, JIA Mingming, REN Chunying, MAO Dehua, YU Hao. Remote sensing-based monitoring and identification mechanisms of the spatiotemporal dynamics of Suaeda salsa in the Liaohe estuary, China. Remote Sensing for Natural Resources, 2025, 37(1): 195-203.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2023293      或      https://www.gtzyyg.com/CN/Y2025/V37/I1/195
Fig.1  研究区地理位置及样点分布
时间 产品类型 云量/%
2017-09-29 Level-1C 0
2018-09-04 Level-1C 1.08
2019-09-14 Level-2A 0.51
2020-09-18 Level-2A 0.01
2021-09-23 Level-2A 0
2022-09-18 Level-2A 0
Tab.1  选中的2017—2022年卫星影像
特征类型 特征
光谱
特征
B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12,VV,VH
波段比
值特征
B2/B4,B3/B4,B3/B8,B4/B2,B4/B3,B4/B5,B4/B8,B5/B3,B5/B4,B6/B3,B6/B5,B7/B3,B7/B4,B8/B2,B8/B3,B8/B4,B8/B5,B8/B11,B8/B12,B8A/B5,B11/B8,B11/B12,B12/B4,B12/B8,B12/B11,VV/VH,VH/VV
指数特
征组
归一化植被指数(normalized difference vegetation index, NDVI) N D V I = B 8 - B 4 B 8 + B 4
增强植被指数(enhanced vegetation index, EVI) E V I = B 8 - B 4 B 8 + 6 B 4 - 7.5 B 2 + 1
植被衰减指数(plant senescence reflectance index, PSRI) P S R I = B 4 - B 2 B 6
归一化差异红色变异指数(normalized difference red edge, NDRE) N D R E = B 8 - R E B 8 + R E
地表水分指数(land surface water index, LSWI) L S W I = B 8 - S W I R B 8 + S W I R
归一化水分指数(normalized difference water index, NDWI) N D W I = B 3 - B 8 B 3 + B 8
修正的归一化差异水体指数(modified normalized difference water index, mNDWI) m N D W I = B 3 - S W I R B 3 + S W I R
植被近红外反射指数(near-infrared reflectance of vegetation, NIRv) N I R v = B 8 B 8 - B 4 B 8 + B 4
Tab.2  Sentinel-2影像特征介绍
Fig.2  从已训练随机森林模型中提取决策规则识别盐地碱蓬工作流程
类别 碱蓬 非碱蓬
碱蓬 199 23
非碱蓬 1 177
UA/% 89.6 99.4
PA/% 99.5 89.0
OA/% 94.0
Tab.3  混淆矩阵与精度分析
时间 阈值信息 OA/%
2017-09-29 B3/B4<0.98 & B5/B3≥1.16 86.0
2018-09-04 B3/B4<1.0 & B5/B3≥1.20 92.5
2020-09-18 B3/B4<0.88 & B5/B3≥1.46 96.8
2021-09-23 B3/B4<0.83 & B5/B3≥1.46 97.3
2022-09-18 B3/B4<0.96 & B5/B3≥1.26 98.5
Tab.4  不同年份对应决策规则的分类精度结果
Fig.3  2017—2022年辽河口盐地碱蓬时空变化
年份 2017年 2018年 2019年 2020年 2021年 2022年
面积 1 154.69 732.43 658.15 1 380.20 3 048.38 4 221.75
Tab.5  2017—2022年辽河口盐地盐地碱蓬面积
Fig.4  决策规则内部各特征分类结果
类别 碱蓬 非碱蓬
碱蓬 139 5
非碱蓬 61 195
UA/% 96.5 75.9
PA/% 69.5 97.5
OA/% 83.3
Tab.6  SSVI提取盐地碱蓬结果的混淆矩阵
时间 阈值信息 OA/%
2017-09-20 SSVI > 4 73.0
2018-09-27 SSVI > 0.3 84.0
2020-09-30 SSVI > 1 71.5
2021-09-01 SSVI > 3 66.8
2022-09-01 SSVI > 5 79.5
Tab.7  2017—2022年SSVI提取盐地碱蓬的阈值与精度
Fig.5  2017—2022年辽河口国家自然保护区碱蓬的质心迁移情况
Fig.6  辽河口东岸盐地碱蓬转化示意图
Fig.7  辽河口西岸盐地碱蓬转化示意图
Fig.7-2  辽河口西岸盐地碱蓬转化示意图
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