摘要
海岸带湿地具有重要的生态价值和经济开发价值,明确其时空变化特征与影响因素对于维持区域生态系统平衡和可持续发展具有重要意义。以Landsat TM/ETM+/OLI影像为基本数据源,综合利用面向对象与深度学习分类方法对1985~2015年闽东南低海拔海岸带地区的湿地信息进行提取,以揭示其时空演变特征与驱动力因素。结果表明:基于面向对象—深度学习分类方法对湿地进行信息提取,整体分类精度可达93%以上,分类结果整体性好;1985~2015年自然湿地面积呈减少趋势,人工湿地面积呈增加趋势,分别减少和增加250.31 km^(2)和251.36 km^(2);湿地二级类型中,30 a间河口/浅海水域和淤泥质海滩面积减少最大,盐田/水产养殖场面积增加最大;1985~2015年湿地变化类型多样,且2000~2015年较1985~2000年湿地变化更为剧烈;湿地变化是人类活动和自然环境变化共同作用的结果,其中人类活动是影响湿地变化的主要原因。该方法及研究结果可为海岸带湿地监测与保护管理提供技术支持和决策参考。
Wetlands located in coastal zone have important ecological and economic development value.It is of great significance to understand spatiotemporal characteristics and influencing factors of wetland change for maintaining regional ecosystem balance and sustainable development.Taking Landsat TM/ETM+/OLI imag⁃es as the basic data source,wetland information extraction of low-elevation coastal zone of Southeast Fujian in 1985,2000,and 2015 were carried out combing with object-oriented and deep learning classification methods.The spatiotemporal evolution characteristics and driving factors of wetland change were revealed.The results showed that:using object-oriented deep learning classification method,the overall classification accuracy of wetlands was more than 93%,and the classification results were desirable.During 1985~2015,the natural wet⁃lands showed a decreasing trend,and the human-made wetlands showed an increasing trend,with-250.31 km^(2)and 251.36 km^(2),respectively.Among the second-class wetland types,the estuary/shallow sea water and mud⁃flats decreased the most area in 30 years,and the salt pans/aquaculture ponds increased the most area.The types of wetland change were diverse from 1985 to 2015,and the wetland changes from 2000 to 2015 were more drastic than those from 1985 to 2000.The wetland dynamics attributed to natural environment change and the influence of human activities,in which human activities were the critical causes.This study can provide tech⁃nical support and decision-making references for the monitoring,conservation,and management of coastal zone wetlands.
作者
路春燕
雷依凡
苏颖
黄雨菲
刘明月
贾明明
Lu Chunyan;Lei Yifan;Su Ying;Huang Yufei;Liu Mingyue;Jia Mingming(College of Computer and Information Sciences,Fujian Agriculture and Forestry University,Fuzhou 350002,China;Key Laboratory of Wetland Ecology and Environment,Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences,Changchun 130102,China;College of Mining Engineering,North China University of Science and Technology,Tangshan 063210,China)
出处
《遥感技术与应用》
CSCD
北大核心
2021年第4期713-727,共15页
Remote Sensing Technology and Application
基金
国家自然科学基金青年基金项目(41901375)
福建农林大学杰出青年研究人才计划项目(XJQ201920)
福建农林大学科技创新专项基金项目(CXZX2020106A)
吉林省科技发展计划项目(20200301014RQ)。
关键词
低海拔海岸带地区
湿地
面向对象分类
深度学习
闽东南
Low-elevation coastal zone
Wetlands
Object-oriented classification
Deep learning
Southeast Fujian
