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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Downscaling GOES Land Surface Temperature for Assessing Heat Wave Health Risks
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Downscaling GOES Land Surface Temperature for Assessing Heat Wave Health Risks

机译：降低GOES陆地表面温度以评估热浪健康风险

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摘要

Recent years have witnessed an emerging concern of the health impact of heat waves. A common approach to investigate heat waves is to resort to the geostationary thermal infrared imagery, such as those from the Geostationary Operational Environmental Satellite (GOES) and Meteosat Second Generation. However, coarse spatial resolutions of geostationary images cannot meet the need of assessing and monitoring heat waves in complex urban settings. To address the spatial and temporal variability of heat waves in urban areas, this letter presented a study of analyzing heat wave risk in Los Angeles, USA, by the synergistic use of GOES land surface temperature (LST), auxiliary geospatial, and census data within the framework of Crichton's Risk Triangle (i.e., hazard, exposure, and vulnerability). Principal component analysis and regression analysis were employed to downscale the original GOES LST imagery from 4 to 1 km. The resultant subhourly 1-km LST data was used to characterize and quantify heat hazard. The census population represented the exposure, while existing health, socioeconomic, and physical environmental conditions were used to describe the vulnerabilities. The risk map of heat wave was computed using the weighted indices of hazard, exposure, and vulnerability. The map was further overlaid with a zip-code data layer to generate statistics. The derived risk map showed that areas with high risk were identified in the central city, part of western LA County, and the desert area, based on a 10-point scale rank.
机译：近年来，人们已经越来越关注热浪对健康的影响。研究热波的一种常见方法是诉诸对地静止热红外图像，例如对地静止运行环境卫星（GOES）和Meteosat Second Generation的图像。但是，对地静止图像的粗略空间分辨率无法满足在复杂的城市环境中评估和监视热浪的需求。为了解决城市地区热浪的时空变化，这封信提出了对GOES地表温度（LST），辅助地理空间数据和人口普查数据的协同使用以分析美国洛杉矶热浪风险的研究。克里希顿风险三角框架（即危险，暴露和脆弱性）。主成分分析和回归分析用于将原始GOES LST影像从4 km缩小到1 km。所得的每小时不到1公里的LST数据用于表征和量化热危害。人口普查代表暴露，而现有的健康，社会经济和自然环境条件被用来描述脆弱性。使用危险，暴露和脆弱性的加权指数来计算热浪的风险图。该地图进一步用邮政编码数据层覆盖以生成统计信息。得出的风险图显示，根据10分制等级，在市中心，洛杉矶县西部的部分地区和沙漠地区确定了高风险区域。

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