Boineelo Moyo

Land use land cover (LULC) changes are critical drivers of global environmental changes, profoundly impacting sustainability in rapidly urbanizing regions. In urban areas, where vegetation cover is reduced, LULC changes significantly affect the distribution and intensity of Land Surface Temperature (LST), which in turn influences electricity demand and consumption. This study explores the relationship between urbanization, vegetation loss, surface temperature and electricity demand in Gaborone, Botswana. The study utilized the Landsat data to conduct a 15-year (2005–2020) spatio-temporal analysis of LULC changes and LST dynamics, identified surface urban heat islands (SUHI) using the Getis-Ord Gi* hot-spot analysis and assessed the correlation between LST and key spectral indices adopted in the study. The analysis employed Google Earth Engine (GEE) cloud computing platform and the Random Forest (RF) machine learning algorithm, known for their accuracy and efficiency in classification and change detection studies. Findings reveal substantial LULC transformations in Gaborone with approximately 12 km² of land converted to Built-Up areas, driving a 2.36% annual urban growth rate and shrinking green and bare spaces. Consequently, surface temperatures rose by about 2.1 °C, impacting energy demand, particularly for cooling during hot summers. This demand surge, coupled with urban expansion, has fueled higher electricity prices, posing new challenges in climate adaptation and mitigation. The study provides critical insights into the interplay between urbanization, rising temperatures, and energy consumption in Gaborone. These insights highlight the urgent need for developing sustainable urban planning and energy strategies to address the impacts of climate change, land degradation, and energy poverty.

Land use land cover change (LULC) is a critical driver of global change, influencing environmental sustainability. The spatio-temporal changes of LULC have a significant impact on the distribution and intensity of the Land Surface Temperatures (LST), especially in urban areas with reduced vegetation cover. Understanding these effects is crucial for assessing progress towards United Nations Sustainable Development Goals (SDGs) 11 and 13, which aim to improve urban environments and address climate change. However, little is known about the impacts of LULC changes on LST in Botswana. Therefore, this study investigated the impacts of LULC change on LST dynamics in Gaborone, a rapidly urbanising city in Botswana. Gaborone has experienced increased surface temperatures over the years, resulting in land degradation, periodic heat waves that have exacerbated energy poverty and quality of life. The study utilized the Landsat data to conduct a 15-year (2005 – 2020) spatio-temporal analysis of Gaborone LULC changes and LST dynamics. The analysis employed cutting-edge remote sensing techniques, including the big data Google Earth Engine (GEE) cloud computing platform and the Random Forest (RF) machine learning algorithm, known for their accuracy and efficiency in classification and change detection studies. By leveraging the GEE cloud platform, this study enables continuous monitoring of both LULC and LST variations in Gaborone. The findings revealed that over the 15-year period, approximately 12km2 of LULC was converted to built-up areas, resulting in a 2.36% annual urban growth rate and reduced green and bare spaces. Consequently, surface temperatures have increased by approximately 2.1◦C, aggravating the energy demand for cooling in summer. This trend, along with population growth and urbanization, has contributed to rapidly increasing electricity prices. The outcomes of this study provide valuable insights into the interplay between LULC changes, LST dynamics, and urban development in Gaborone, highlighting the urgent need for sustainable strategies to address land degradation, energy poverty, and quality of life.

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.