摘要
滨海湿地具有重要的经济价值与生态价值,快速准确地监测其现状对滨海湿地资源的保护和管理具有重要意义。由于潮汐动态变化、植被光谱相似性以及云覆盖等因素的影响,滨海湿地的遥感监测具有较大挑战。本文提出了一个综合考虑潮位变化及植被物候特征的滨海湿地遥感分类方法,基于GEE(Google Earth Engine)平台,首先引入Fmask(Function of mask)算法进行云检测与去云处理,然后利用S-G(Savitzky-Golay)滤波算法重构NDVI(Normalized Difference Vegetation Index)时间序列数据,提取植被物候特征参数,采用随机森林算法实现互花米草(Spartina alterniflora)、芦苇(Phragmites australis)、碱蓬(Suaeda salsa)与茅草(Imperata cylindrica)4种湿地植被类型的提取;最后利用最大光谱指数合成算法(Maximum Spectral Index Composite,MSIC)生成最高与最低潮位合成影像,结合大津算法(Otsu)提取光滩与海水,实现滨海湿地的精细化遥感分类。研究结果表明,生长季开始时间、生长季结束时间、生长季时长、基准值、振幅、小季节积分是区分滨海湿地植被的重要植被物候特征参数。利用本方法对盐城滨海湿地进行分类,湿地总体分类精度达96.50%,Kappa系数为0.957 1,湿地植被中互花米草的使用者精度最高,为96.59%;其次是芦苇与碱蓬;茅草最低,为93.55%。与面向对象分类相比,本方法不仅能够提取完整的光滩范围,而且将总体精度提高了10.25%,体现出植被物候特征在滨海湿地动态变化遥感监测中的应用潜力。
Coastal wetlands have important economic and ecological value.Rapid and accurate monitoring of the status of coastal wetlands is of great significance for the protection and management of coastal wetland resources.Due to factors such as the variability of the tide-level changes,similarity of vegetation spectra,and frequent cloud cover,remote sensing monitoring of coastal wetlands faced certain challenges.In this paper,we proposed a multi-technology coupled remote sensing classification method of coastal wetlands that considers tide-level changes and vegetation phenological characteristics.Based on the Google Earth Engine(GEE) platform,the Fmask(Function of mask) algorithm was first performed for cloud testing and cloud removal processing.Then,the S-G(SavitzkyGolay) filtering algorithm was used to reconstruct NDVI time series data and extract vegetation phenological characteristic parameters.In this phase,the random forest algorithm was applied for the classification of four vegetation types namely Phragmites australi,Suaeda salsa,Spartina alterniflora,and Imperata cylindrical.Finally,the Maximum Spectral Index Composite(MSIC) algorithm was used to generate composite images of the highest and lowest tide levels.The tidal flats and seawater were precisely extracted using the Otsu algorithm based on these two composite images.Combining these feature types,the refined remote sensing classification of coastal wetlands was ideally obtained.The results showed that start-of-season time,end-of-season time,length of season,base value,amplitude,and small seasonal integral were the six key vegetation phenological characteristic parameters for distinguishing different types of coastal wetland vegetation.Applying this method to classify coastal wetlands on the Yancheng coast,the overall classification accuracy was 96.50%,and the Kappa coefficient reached 0.957 1.Among the wetland vegetation,the highest user accuracy was 96.59% for Spartina alterniflora,followed by P.australi and Suaeda salsa,and the lowest was 93.55% for Imper
作者
顾容
张东
钱林峰
吕林
陈艳艳
于凌程
Gu Rong;Zhang Dong;Qian Linfeng;Lv Lin;Chen Yanyan;Yu Lingcheng(College of Marine Science and Engineering,Nanjing Normal University,Nanjing 210023,China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China;Sea Area Use Dynamic Surveillant and Monitoring Center of Jiangsu Province,Nanjing 210017,China)
出处
《海洋学报》
CAS
CSCD
北大核心
2024年第5期103-115,共13页
基金
国家自然科学基金项目(41771447)
江苏省海洋科技创新项目(JSZRHYKJ202307)
事业单位研究项目(WSW5310DY2022LJ)。
关键词
GEE平台
潮位
植被物候特征
云检测
S-G滤波算法
最大光谱指数合成
Google Earth Engine
tide-level
vegetation phenological characteristics
cloud testing
Savitzky-Golay filtering algorithm
Maximum Spectral Index Composite
