摘要
黄河三角洲是我国最年轻、最完整的河口湿地生态系统,湿地演变剧烈使其成为我国生态较脆弱的地区之一,亟需监测其生态环境的时空演变。然而,由于单一传感器难以兼顾遥感数据的空间分辨率与时间分辨率,其很难获得高时空分辨率的遥感数据。因此,本研究基于Landsat和MODIS数据集,通过使用灵活的时空数据融合方法(Flexible Spatiotemporal Data Fusion,FSDAF)和时间序列线性拟合模型(Time Series Linear Fitting Model,TSLFM),首次获得了2000–2020年黄河三角洲地区8 d时间分辨率、30 m空间分辨率的增强植被指数(Enhanced vegetation index,EVI)数据集。为保证数据集的准确性和可靠度,本数据集从数据源的质控,融合模型的选择,以及模型独立验证进行质量控制,且获得了较好的验证结果(R2>0.92)及空间格局。本数据集能很好反映黄河三角洲植被长期动态变化的空间格局,为研究黄河三角洲复杂植被动态、植被生产力等提供数据支撑。
The Yellow River Delta is the youngest and most intact estuarine wetland ecosystem in China.Its rapid wetland evolution has made it one of the country's most ecologically vulnerable regions,creating an urgent need to monitor the spatiotemporal evolution of its ecological environmental.However,due to the tradeoff between spatial and temporal resolution in remote sensing data,it is challenging for a single sensor to capture high spatiotemporal resolution remote sensing data.Therefore,this study leverages Landsat and MODIS datasets and employs a flexible spatiotemporal data fusion method(Flexible Spatiotemporal Data Fusion,FSDAF)and a time series linear fitting model(Time Series Linear Fitting Model,TSLFM)to produce—for the first time—an Enhanced Vegetation Index(EVI)dataset for the Yellow River Delta region with an 8-day temporal resolution and 30-meter spatial resolution from 2000 to 2020.To ensure the accuracy and reliability of the dataset,quality control was conducted on data-source quality,model selection for data fusion,and independent model validation,resulting in strong validation performance(R²>0.92)and robust spatial patterns.This dataset effectively reflects the spatial patterns of long-term vegetation dynamics in the Yellow River Delta and provides valuable data support for studying the region’s complex vegetation dynamics and vegetation productivity.
作者
麦子琪
牛忠恩
赵英
李琳琳
李茜雨
王彬
MAI Ziqi;NIU Zhongen;ZHAO Ying;LI Linlin;LI Xiyu;WANG Bin(School of Resources and Environmental Engineering,Ludong University,Yantai 264025,P.R.China;School of Hydraulic and Civil Engineering,Ludong University,Yantai 264025,P.R.China;College of Resource Environment and Tourism,Capital Normal University,Beijing 100048,P.R.China;College of Geography and Environment,Shandong Normal University,Shandong Normal University,Jinan 250014,P.R.China)
基金
山东省自然科学基金(ZR2022QD118)
国家自然科学基金(42320104006、42201312)。
关键词
增强植被指数(EVI)
黄河三角洲
高时空分辨率
长时间序列
30
m
时空融合
Enhanced Vegetation Index(EVI)
Yellow River Delta
high spatiotemporal resolution
long-term time series
30 m
spatiotemporal data fusion
