气候变化领域集成服务门户(CCPortal): Risk Assessment of Rising Temperatures Using Landsat 4-9 LST Time Series and Meta® Population Dataset: An Application in Aosta Valley, NW Italy

CCPortal

DOI	10.3390/rs15092348
	Risk Assessment of Rising Temperatures Using Landsat 4-9 LST Time Series and Meta® Population Dataset: An Application in Aosta Valley, NW Italy
	Orusa, Tommaso; Viani, Annalisa; Moyo, Boineelo; Cammareri, Duke; Borgogno-Mondino, Enrico
发表日期	2023
EISSN	2072-4292
卷号	15 期号:9
英文摘要	Earth observation data have assumed a key role in environmental monitoring, as well as in risk assessment. Rising temperatures and consequently heat waves due to ongoing climate change represent an important risk considering the population, as well as animals, exposed. This study was focused on the Aosta Valley Region in NW Italy. To assess population exposure to these patterns, the following datasets have been considered: (1) HDX Meta population dataset refined and updated in order to map population distribution and its features; (2) Landsat collection (missions 4 to 9) from 1984 to 2022 obtained and calibrated in Google Earth Engine to model LST trends. A pixel-based analysis was performed considering Aosta Valley settlements and relative population distribution according to the Meta population dataset. From Landsat data, LST trends were modelled. The LST gains computed were used to produce risk exposure maps considering the population distribution and structure (such as ages, gender, etc.). To check the consistency and quality of the HDX population dataset, MAE was computed considering the ISTAT population dataset at the municipality level. Exposure-risk maps were finally realized adopting two different approaches. The first one considers only LST gain maximum by performing an ISODATA unsupervised classification clustering in which the separability of each class obtained and was checked by computing the Jeffries-Matusita (J-M) distances. The second one was to map the rising temperature exposure by developing and performing a risk geo-analysis. In this last case the input parameters considered were defined after performing a multivariate regression in which LST maximum was correlated and tested considering (a) Fractional Vegetation Cover (FVC), (b) Quote, (c) Slope, (d) Aspect, (e) Potential Incoming Solar Radiation (mean sunlight duration in the meteorological summer season), and (f) LST gain mean. Results show a steeper increase in LST maximum trend, especially in the bottom valley municipalities, and especially in new built-up areas, where more than 60% of the Aosta Valley population and domestic animals live and where a high exposure has been detected and mapped with both approaches performed. Maps produced may help the local planners and the civil protection services to face global warming from a One Health perspective.
英文关键词	Google Earth Engine; USGS NASA Landsat 4-9 missions; LST timeseries analysis; risk population assessment; HDX meta population; trends modeling; Aosta Valley; Italy; Alps; climate change
语种	英语
WOS研究方向	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000987672600001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281402
作者单位	University of Turin
推荐引用方式 GB/T 7714	Orusa, Tommaso,Viani, Annalisa,Moyo, Boineelo,et al. Risk Assessment of Rising Temperatures Using Landsat 4-9 LST Time Series and Meta® Population Dataset: An Application in Aosta Valley, NW Italy[J],2023,15(9).
APA	Orusa, Tommaso,Viani, Annalisa,Moyo, Boineelo,Cammareri, Duke,&Borgogno-Mondino, Enrico.(2023).Risk Assessment of Rising Temperatures Using Landsat 4-9 LST Time Series and Meta® Population Dataset: An Application in Aosta Valley, NW Italy.REMOTE SENSING,15(9).
MLA	Orusa, Tommaso,et al."Risk Assessment of Rising Temperatures Using Landsat 4-9 LST Time Series and Meta® Population Dataset: An Application in Aosta Valley, NW Italy".REMOTE SENSING 15.9(2023).