Risk Assessment of Rising Temperatures Using Landsat 4–9 LST Time Series and Meta<sup>®</sup> Population Dataset: An Application in Aosta Valley, NW Italy
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/9/2348 |
| _version_ | 1797601744063037440 |
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| author | Tommaso Orusa Annalisa Viani Boineelo Moyo Duke Cammareri Enrico Borgogno-Mondino |
| author_facet | Tommaso Orusa Annalisa Viani Boineelo Moyo Duke Cammareri Enrico Borgogno-Mondino |
| author_sort | Tommaso Orusa |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T04:07:58Z |
| format | Article |
| id | doaj.art-74bb5b8335734250979fb22006d9004f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T04:07:58Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-74bb5b8335734250979fb22006d9004f2023-11-17T23:38:54ZengMDPI AGRemote Sensing2072-42922023-04-01159234810.3390/rs15092348Risk Assessment of Rising Temperatures Using Landsat 4–9 LST Time Series and Meta<sup>®</sup> Population Dataset: An Application in Aosta Valley, NW ItalyTommaso Orusa0Annalisa Viani1Boineelo Moyo2Duke Cammareri3Enrico Borgogno-Mondino4Department of Agricultural, Forest and Food Sciences (DISAFA), GEO4Agri DISAFA Lab, Università degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco, ItalyIstituto Zooprofilattico Sperimentale Piemonte, Liguria e Valle d’Aosta (IZS PLV) S.C Valle d’Aosta—CeRMAS (National Reference Center for Wildlife Diseases), Località Amerique, 7/G, 11020 Quart, ItalyDepartment of Geoinformatics, Stuttgart University of Applied Sciences, Schellingstraße 24, 70174 Stuttgart, GermanyEarth Observation Valle d’Aosta—eoVdA, Località L’Île-Blonde, 5, 11020 Brissogne, ItalyDepartment of Agricultural, Forest and Food Sciences (DISAFA), GEO4Agri DISAFA Lab, Università degli Studi di Torino, Largo Paolo Braccini 2, 10095 Grugliasco, ItalyEarth 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.https://www.mdpi.com/2072-4292/15/9/2348Google Earth EngineUSGS NASA Landsat 4–9 missionsLST timeseries analysisrisk population assessmentHDX meta populationtrends modeling |
| spellingShingle | Tommaso Orusa Annalisa Viani Boineelo Moyo Duke Cammareri Enrico Borgogno-Mondino Risk Assessment of Rising Temperatures Using Landsat 4–9 LST Time Series and Meta<sup>®</sup> Population Dataset: An Application in Aosta Valley, NW Italy Remote Sensing Google Earth Engine USGS NASA Landsat 4–9 missions LST timeseries analysis risk population assessment HDX meta population trends modeling |
| title | Risk Assessment of Rising Temperatures Using Landsat 4–9 LST Time Series and Meta<sup>®</sup> Population Dataset: An Application in Aosta Valley, NW Italy |
| title_full | Risk Assessment of Rising Temperatures Using Landsat 4–9 LST Time Series and Meta<sup>®</sup> Population Dataset: An Application in Aosta Valley, NW Italy |
| title_fullStr | Risk Assessment of Rising Temperatures Using Landsat 4–9 LST Time Series and Meta<sup>®</sup> Population Dataset: An Application in Aosta Valley, NW Italy |
| title_full_unstemmed | Risk Assessment of Rising Temperatures Using Landsat 4–9 LST Time Series and Meta<sup>®</sup> Population Dataset: An Application in Aosta Valley, NW Italy |
| title_short | Risk Assessment of Rising Temperatures Using Landsat 4–9 LST Time Series and Meta<sup>®</sup> Population Dataset: An Application in Aosta Valley, NW Italy |
| title_sort | risk assessment of rising temperatures using landsat 4 9 lst time series and meta sup r sup population dataset an application in aosta valley nw italy |
| topic | Google Earth Engine USGS NASA Landsat 4–9 missions LST timeseries analysis risk population assessment HDX meta population trends modeling |
| url | https://www.mdpi.com/2072-4292/15/9/2348 |
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