{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T04:08:12Z","timestamp":1774498092776,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,29]],"date-time":"2023-04-29T00:00:00Z","timestamp":1682726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2013Matusita (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.<\/jats:p>","DOI":"10.3390\/rs15092348","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T12:10:03Z","timestamp":1682943003000},"page":"2348","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Risk Assessment of Rising Temperatures Using Landsat 4\u20139 LST Time Series and Meta\u00ae Population Dataset: An Application in Aosta Valley, NW Italy"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6750-0438","authenticated-orcid":false,"given":"Tommaso","family":"Orusa","sequence":"first","affiliation":[{"name":"Department of Agricultural, Forest and Food Sciences (DISAFA), GEO4Agri DISAFA Lab, Universit\u00e0 degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco, Italy"},{"name":"Earth Observation Valle d\u2019Aosta\u2014eoVdA, Localit\u00e0 L\u2019\u00cele-Blonde, 5, 11020 Brissogne, Italy"},{"name":"IN.VA spa, Localit\u00e0 L\u2019\u00cele-Blonde, 5, 11020 Brissogne, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1441-7678","authenticated-orcid":false,"given":"Annalisa","family":"Viani","sequence":"additional","affiliation":[{"name":"Istituto Zooprofilattico Sperimentale Piemonte, Liguria e Valle d\u2019Aosta (IZS PLV) S.C Valle d\u2019Aosta\u2014CeRMAS (National Reference Center for Wildlife Diseases), Localit\u00e0 Amerique, 7\/G, 11020 Quart, Italy"}]},{"given":"Boineelo","family":"Moyo","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics, Stuttgart University of Applied Sciences, Schellingstra\u00dfe 24, 70174 Stuttgart, Germany"},{"name":"HRSL, Core Science, Meta Research, 1 Hacker Way, Menlo Park, CA 94025, USA"}]},{"given":"Duke","family":"Cammareri","sequence":"additional","affiliation":[{"name":"Earth Observation Valle d\u2019Aosta\u2014eoVdA, Localit\u00e0 L\u2019\u00cele-Blonde, 5, 11020 Brissogne, Italy"},{"name":"IN.VA spa, Localit\u00e0 L\u2019\u00cele-Blonde, 5, 11020 Brissogne, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4570-8013","authenticated-orcid":false,"given":"Enrico","family":"Borgogno-Mondino","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Forest and Food Sciences (DISAFA), GEO4Agri DISAFA Lab, Universit\u00e0 degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.scitotenv.2018.02.268","article-title":"Heatwave and Health Events: A Systematic Evaluation of Different Temperature Indicators, Heatwave Intensities and Durations","volume":"630","author":"Xu","year":"2018","journal-title":"Sci. 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