摘要
潮间带是滨海湿地的重要组成部分,对生态和经济的发展具有重要意义。由于海水与陆地的动态交互作用,以瞬时性遥感图像为数据源的遥感信息提取方法难以准确获取潮滩范围。针对此问题,研究提出了一种基于Google Earth Engine(GEE)云平台和遥感指数的潮间带信息提取方法。该方法利用2021年的Landsat8时序影像数据,在最大光谱指数合成算法(maximum spectral index composite,MSIC)和大津算法(OTSU)形成多层自动决策树分类模型的基础之上,构建基于融合数字高程模型(digital elevation model,DEM)数据的决策树算法,并以舟山群岛海岸带为例,计算舟山群岛潮间带面积。研究结果显示2021年舟山群岛潮间带面积为35.19 km 2。通过谷歌地球的高空间分辨率影像进行精度评价,总体精度为97.7%,Kappa系数为0.95,具有较好的提取精度和实用效果。该方法能够实现自动、快速地提取潮间带信息,为海岸带资源的可持续管理和利用提供数据支撑,进一步促进海岸带区域的高质量发展。
Intertidal zones,as important parts of coastal wetlands play a significant role in ecological and economic development.However,the dynamic interaction between seawater and land makes it difficult to accurately determine the tidal flat area using the remote sensing information extraction method based on instant remote sensing images.To solve this problem,this study developed an intertidal information extraction method based on Google Earth Engine(GEE)platform and remote sensing index.This proposed method was applied to study the coastal zone of Zhoushan Islands.First,a decision tree algorithm based on the fusion of the digital elevation model(DEM)data was built using the Landsat8 time series image data in 2021.Then,a multi-layer automatic decision tree classification model was formed using the maximum spectral index composite(MSIC)and the Otsu algorithm(OTSU).Based on this,the DEM data were fused to extract and calculate the area of the intertidal zone in Zhoushan Islands.The results show that the area of the intertidal zone in Zhoushan Islands is 35.19 km 2 in 2021.The evaluation based on the Google Earth high-resolution images shows that this proposed method has a general precision of 97.7% and a Kappa coefficient of 0.95,indicating good extraction precision and practical effects.This method can provide data support for sustainable management and utilization of coastal zone resources through automatic and rapid extraction of intertidal information,thus promoting regional high-quality development.
作者
陈慧欣
陈超
张自力
汪李彦
梁锦涛
CHEN Huixin;CHEN Chao;ZHANG Zili;WANG Liyan;LIANG Jintao(Marine Science and Technology College,Zhejiang Ocean University,Zhoushan 316022,China;Zhejiang Province Ecological Environment Monitoring Centre(Zhejiang Key Laboratory of Ecological and Environmental Monitoring,Forewarning and Quality Control),Hangzhou 310012,China)
出处
《自然资源遥感》
CSCD
北大核心
2022年第4期60-67,共8页
Remote Sensing for Natural Resources
基金
国家自然科学基金项目“人类活动影响下的群岛区域海岸线时空演变机制分析”(编号:42171311)资助。
