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遥感技术与应用  2021, Vol. 36 Issue (4): 728-741    DOI: 10.11873/j.issn.1004-0323.2021.4.0728
湿地遥感专栏     
遥感监测东北地区典型湖泊湿地变化的方法研究
李晓东1,2(),闫守刚3(),宋开山2
1.山东省黄河三角洲生态环境重点实验室,滨州学院,山东 滨州 256603
2.中国科学院湿地生态与环境重点实验室,中国科学院东北地理与农业生态应用研究所,吉林 长春 130102
3.枣庄学院旅游与资源环境学院,山东 枣庄 277160
Remote Sensing of Lake Wetlands Change in Northeast of China Using the Modified Detection Method
Xiaodong Li1,2(),Shougang Yan3(),Kaishan Song2
1.Shandong Key Laboratory of Eco-Environmental Science for Yellow River Delta,Binzhou University,Binzhou 256603,China
2.Key Laboratory of Wetland Ecology and Environment,Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences,Changchun 130102,China
3.College of Tourism,Resources and Environment,Zaozhuang UniversityShandong Jinan 250014,China
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摘要:

湖泊湿地生态脆弱,易受环境因子的影响。近十年来,东北地区湖泊湿地的时空格局发生了显著变化。如何简单、有效地提取湿地变化范围进而确定其变化类型是湿地变化检测中最需要解决的问题。基于2006~2016年30 m空间分辨率的Landsat-TM/OLI影像数据,水体、植被和土壤等生态因子的动态变化率被用于提取东北地区湖泊湿地变化范围;多维特征数据集的湿地分类方案确定湿地的变化类型。另外,湿地变化检测类型分为转出类型(湿地减少),转入类型(湿地增加)和湿地间转换类型(湿地相对稳定)。最终基于动态变化率计算方法,松嫩平原湿地、兴凯湖湿地和呼伦湖湖泊湿地变化结果的正检率均高于90%。同时,利用年内多时相数据和综合多维生态指数共同表征地表的状态变化,实验区的湖泊湿地分类结果的整体分类精度和 Kappa 系数分别达到 84.31%和0.788。湖泊湿地变化检测方法具有很好的检测精度,可以代表研究区湖泊湿地类型的实际变化,是湿地资源调查与遥感监测技术研究的有益补充,为进一步深化与拓宽地表生态质量评价及其动态变化检测的方法研究提供理论基础。

关键词: 湿地变化检测松嫩平原湖泊湿地兴凯湖湿地呼伦湖湿地中国东北    
Abstract:

Lake Wetlands have larger ecological function such as climate regulation and biodiversity, and economic effects-flood storage and shipping. In recent decades, the spatiotemporal variation ofLake Wetlands in Northeast China is different from the global change feature. Based on the landsat-5/8 image data with 30m spatial resolution from 2006 to 2016, thedetection method based on the Dynamic Ratio algorithm is used for extracting the change ecological information, and determining the change area of Lake Wetlands; the classification scheme based on the multidimensional-indexes is constructed to extract the change types of Lake Wetlands. In addition, the change types of Lake Wetlands are divided into the transfer-off (this is, the decrease of wetlands), the transfer-in (the increase of wetlands), and the conversion of wetlands (the relatively unchanged wetlands). The finalresults showed that: from 2006 to 2016, based on the dynamic change results of the DRM method, the correct detection ratio of wetland change in Songnen Plain, Xingkai Lake and Hulun Lake are 90.48, 90.2 and 93.81%, respectively. Meanwhile, the overall accuracy and Kappa coefficient of the land-cover classification results in the experimental area reached 84.31% and 0.788 respectively. The Lake Wetlands in Northeast China have the change trend characterized by the improvement feature, which can represent the actual fluctuation of wetland types in the study area. This method also has higher detection accuracy under complex surface types, which is a beneficial supplement to the resource’s investigation of Lake Wetlands and remote sensing monitoring on the wetland change.

Key words: The change detection of wetland    Songnen Plain Lake Wetlands    Xingkai Lake Wetlands    Hulun Lake Wetlands    Northeast China
收稿日期: 2020-05-13 出版日期: 2021-09-26
ZTFLH:  P237  
基金资助: 博士科研启动基金项目(801002020107);吉林省教育厅“十三五”科学技术研究项目“向海自然保护区沙丘榆林的土壤种子库的时空格局研究”(JJKH20170005KJ);国家重点研发计划项目“全球多时空尺度遥感动态监测与模拟预测”(2016YFB0501502)
通讯作者: 闫守刚     E-mail: xiaodonglee@126.com;yanshougang@126.com
作者简介: 李晓东(1977-),男,山东东营人,博士,主要从事遥感应用研究。E?mail:xiaodonglee@126.com
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引用本文:

李晓东,闫守刚,宋开山. 遥感监测东北地区典型湖泊湿地变化的方法研究[J]. 遥感技术与应用, 2021, 36(4): 728-741.

Xiaodong Li,Shougang Yan,Kaishan Song. Remote Sensing of Lake Wetlands Change in Northeast of China Using the Modified Detection Method. Remote Sensing Technology and Application, 2021, 36(4): 728-741.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.4.0728        http://www.rsta.ac.cn/CN/Y2021/V36/I4/728

图1  研究区位置图
实验区传感器/轨道号数据收集日期
松嫩平原TM/11902820060817、20060902、20061004
湖泊湿地OLI/11902820160714、20160828、20160919
呼伦湖TM/12502620050621、20050723、20050808
湿地OLI/12502620170530、20170724、20160825
兴凯湖TM/11402820050614、20051001、20060830
湿地OLI/11402820170609、20170918、20171015
表1  Landsat 影像的采集日期
图2  方法实现流程
图3  基于DRM,ENVI_DIFF和ENVI_SPAD算法的湖泊湿地变化检测结果
ENVI_DIFFENVI_SPAD
实验区平均值标准差阈值平均值标准差阈值范围N
松嫩平原湿地-0.0340.115(-∞,-0.206),(0.138,+∞)0.1230.081(0.244,+∞)1.5
兴凯湖湿地0.2220.041(-∞,-0.283),(-0.161,+∞)0.1400.077(0.255,+∞)1.5
呼伦湖湿地0.0120.065(-∞,-0.087),(0.110,+∞)0.1000.112(0.266,+∞)1.5
表2  实验区湿地变化检测的阈值范围
研究区类型DRMENVI_DIFFENVI_SPAD
样点百分比/%样点百分比/%样点百分比/%
正检率9590.488883.818278.10
松嫩平原漏检率76.671110.481817.14
湖泊湿地误检率32.8665.7154.76
总计105105105
正检率9290.29088.248785.3
兴凯湖漏检率76.8654.976.86
湿地误检率32.9476.8687.84
总计102102102
正检率9193.818587.637880.41
呼伦湖漏检率22.0688.251717.53
湿地误检率44.1244.1222.06
总计979797
表3  不同方法的精度分析
图4  湿地变化检测结果对比分析
图5  湖泊湿地分类结果
实验区松嫩平原湖泊湿地兴凯湖湿地呼伦湖湿地
OA/%Kappa系数OA/%Kappa系数OA/%Kappa系数
2006年82.040.79386.30.82082.50.735
2016年87.730.85780.60.72586.70.799
表4  实验区湖泊湿地类型分类的准确性评估
图6  2005~2016年湖泊湿地变化检测结果
图7  不同组合的比较分析
类型分类方案波段数
方案-1B2-B5+DEM+SLP6
方案-2B2-B5+MNDWI’s AVG/SD +DEM+SLP8
方案-3B2-B5+NDVI’s AVG/SD +DEM+SLP8
方案-4B2-B5+NDBI’s AVG/SD +DEM+SLP8
方案-5B2-B5+MNDWI/NDVI/NDBI’s AVG +DEM+SLP9
方案-6B2-B5+MNDWI/NDBI’s AVG/SD +DEM+SLP10
方案-7B2-B5+MNDWI/NDVI’s AVG/SD +DEM+SLP10
方案-8NDVI/NDBI/MNDWI’s ACC/AVG/SD +DEM+SLP11
方案-9B2-B5+NDVI/NDBI/MNDWI’s AVG/SD +DEM+SLP12
方案-10B2-B5+NDVI/NDBI/MNDWI’s ACC/AVG/SD +DEM+SLP15
表5  湖泊湿地类型可分性的验证
图8  不同方案的总体精度和Kappa系数
图9  误检、漏检像素点与湿地类型分布图
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