东南亚大都市地表温度及选定环境和社会经济特征网格数据集

IF 1 Q3 MULTIDISCIPLINARY SCIENCES
Homer Pagkalinawan , Laurence L Delina , Sharon Feliza Ann Macagba
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引用次数: 0

摘要

近年来,东南亚频频出现极端高温天气,绘制气温升高地区分布图对于监测高危人群至关重要。确定造成这些地区气候变暖趋势的因素对于制定适应和缓解战略也至关重要。该数据集包括从中分辨率成像分光仪(MODIS)仪器下载并处理的该地区三个大都市(马尼拉大都市、曼谷大都市区和大雅加达地区)的陆地表面温度(LST)。我们利用 MODIS 固有的网格系统绘制了卫星图像最细粒度的 LST 值。我们将它们与选定的环境和社会经济变量相结合,包括建筑和建成区、绿化区、工业区和水体、夜间光线(以近似经济活动区域)、网格人口、与水体的距离,以及每个网格中存在哪些城市基础设施(如道路和机场)的指标。该数据集以形状文件和逗号分隔变量文件格式提供,对这三个城市的城市研究非常有用。随着 LST 和其他变量数据的增加,该数据集可以很容易地更新。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gridded dataset on land surface temperature and selected environmental and socioeconomic features in Southeast Asian metropolises

As Southeast Asia grapples with extreme heat occurrences in recent years, mapping which areas are clustered with elevated temperatures is crucial for monitoring the at-risk population. Identifying the contributing factors to the warming trends in these areas is also vital in formulating adaptation and mitigation strategies. This dataset comprises land surface temperature (LST) in three metropolises in the region – Metropolitan Manila, Bangkok Metropolitan Area, and Greater Jakarta – downloaded and processed from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. We used MODIS’ inherent grid system to map LST values at the satellite image's most granular level. We combined them with selected environmental and socioeconomic variables, including building and built-up areas, areas of greeneries, industrial zones, and water bodies, nighttime light (to approximate areas of economic activities), gridded population, distance from water bodies, and indicators on which urban infrastructures, i.e. roads and airports, are present in each grid. Available in shapefile and comma-separate variable file format, this dataset is useful for urban studies in these three cities. The dataset can be easily updated as additional data on LST and other variables becomes available.

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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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