Risk Assessment of Rising Temperatures Using Landsat 4–9 LST Time Series and Meta® Population Dataset: An Application in Aosta Valley, NW Italy

Abstract

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.

Environmental Influence on the Occurrence of Multi-Organ Cystic Echinococcosis Infection in a Patient from Sardinia, Italy

“…Rising temperatures and subsequent heat waves due to ongoing climate change are essential exposure factors when considering social lifestyle modifications and interpenetration with the animal sphere. The Geographical Information System (GIS) and other technologies could improve patient and environmental risk assessment in zonal and interzonal CE by interlacing history in animal and environmental spheres [ 40 ]. Thus, it would be interesting to conduct future studies utilizing a One Health approach by using new and innovative field support technologies, such as GIS or other technologies, to better contextualize the case report, the risk assessment context, and the environmental sphere, to be cross-referenced along with the animal and environmental fields.…”

Predicting Suitable Areas for African Swine Fever Outbreaks in Wild Boars in South Korea and Their Implications for Managing High-Risk Pig Farms

“…Integration with GIS and remote sensing (RS) has great potential to predict the spread of wildlife-borne diseases [ 13 , 88 , 89 , 90 , 91 ]. In order to further improve the results of the present study, additional environmental information obtained from RS is required for GIS analysis [ 91 , 92 ].…”

Satellite Time-Series Analysis for Thermal Anomaly Detection in the Naples Urban Area, Italy

“…The use of nighttime data allows us to avoid both direct solar heating and the topographic effect, minimizing their influence, as well as rendering the reflectance component negligible. Another advantage of nighttime images is that surface temperature is more constant than in daytime due to the absence of solar radiation, and temperature values are dependent only on the thermal energy remitted by buildings [45]. The TOA brightness temperature is provided by L8 band 10, queried through the GEE capabilities, and is common for daytime and nighttime procedures.…”