摘要
植被覆盖是衡量生态环境质量和人居环境的重要指标,然而目前还缺乏城市内部长时间序列的NDVI数据集。作者基于Terre-MODIS NDVI、GIMMS NDVI产品数据,运用深度学习超分辨率的算法制作了1990、2000、2010、2020年中国250-m分辨率NDVI数据集。然后,通过叠加不同时期城市的行政范围和实体地域范围,提取不同城市范围内的NDVI平均值,得到中国十年度及361个城市平均NDVI值统计数据集(250-m,1990–2020)。该数据集显示1990–2020年中国全境和城市范围的NDVI均呈先降低后升高的趋势,但存在显著的空间异质性。该数据集可为城市生态环境治理、城市绿地规划建设、生态环境政策制定和政府绩效考核等提供基础数据支撑,也可作为城市化与气候变化驱动下生态系统演化研究的基础数据。数据集存储为.tif和.xlsx格式,空间分辨率为250 m,由5个文件组成,数据量为6.51 GB(压缩为5个文件,1.83 GB)。
Vegetation cover is an important indicator used to measure the quality of the ecological environment and human settlements.However,there is still a lack of long-term NDVI datasets within urban areas.Based on Terre-MODIS NDVI and GIMMS NDVI product data,we used deep learning superresolution algorithms to produce 250-m resolution NDVI datasets for China in 1990,2000,2010 and 2020.Then,by superimposing the administrative scope and urban physical area in various periods,we extracted the average value of NDVI in different urban boundaries and obtained a statistical dataset of the average NDVI value of China’s ten years and 361 cities(250-m,1990–2020).This dataset indicates an initially decreasing and subsequently increasing trend of NDVI for both all of China and the urban physical areas from 1990 to 2020,yet the NDVI trend has significant spatial heterogeneity.The dataset can support urban ecological and environmental governance,urban green space planning and construction,ecological and environmental policy formulation and government performance assessment,as well as ecosystem evolution research driven by urbanization and climate change.The dataset is archived in.tif and.xlsx formats with a spatial resolution of 250 m and consists of 5 files with data size of 6.51 GB(compressed into 5 files,1.83 GB).
作者
刘海猛
周天宇
勾鹏
Liu H.M.;Zhou T.Y.;Gou P.(Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;Research Centra of Big Data Technology,Nanhu Laboratory,Jiaxing 314002,China)
出处
《全球变化数据学报(中英文)》
CSCD
2023年第1期65-74,V0065-V0074,共20页
Journal of Global Change Data & Discovery
基金
国家自然科学基金(42171210)
中华人民共和国教育部(22JJD790015)。