Southern European functional urban areas (FUAs) are increasingly subject to heatwave (HW) events, calling for anticipated climate adaptation measures. In the urban context, such adaptation strategies require a thorough understanding of the built-up response to the incoming solar radiation, i.e., the urban energy balance cycle and its implications for the Urban Heat Island (UHI) effect. Despite readily available, diurnal Land Surface Temperature (LST) data does not provide a meaningful picture of the UHI, in these midlatitudes FUAs. On the contrary, the mid-morning satellite overpass is characterized by the absence of a significant surface UHI (SUHI) signal, corresponding to the period of the day when the urban-rural air temperature difference is typically negative. Conversely, nocturnal high-resolution LST data is rarely available. In this study, an energy balance-based machine learning approach is explored, considering the Local Climate Zones (LCZ), to describe the daily cycle of the heat flux components and predict the nocturnal SUHI, during an HW event. While the urban and rural spatial outlines are not visible in the diurnal thermal image, they become apparent in the latent and storage heat flux maps – built-up infrastructures uptake heat during the day which is released back into the atmosphere, during the night, whereas vegetation land surfaces loose diurnal heat through evapotranspiration. For the LST prediction model, a random forest (RF) approach is implemented. RF results show that the model accurately predicts the LST, ensuring mean square errors inferior to 0.1 K. Both the latent and storage heat flux components, together with LCZ classification, are the most important explanatory variables for the nocturnal LST prediction, supporting the adoption of the energy balance approach. In future research, other locations and time-series data shall be trained and tested, providing an efficient local urban climate monitoring tool, where in-situ air temperature observations are not available.

A deteção remota (DR), em Portugal é um tema muito recente com possibilidade de ser analisado mais pormenorizadamente, tendo em conta o conjunto de aplicações que pode ter, em diversas áreas científicas. Por esse motivo, esta dissertação veio aprofundar esse assunto através da análise da temperatura da superfície terrestre em contextos nacionais distintos. Land Surface Temperature ou temperatura da superfície terrestre (LST), como será referida ao longo da presente dissertação, tem adquirido um reconhecimento cada vez maior ao longo do tempo, levando a um interesse cada vez significativo no desenvolvimento de várias metodologias de modo a efetuar a sua medição a partir do espaço. Assim sendo, a LST é utilizada em estudos relacionados com as variações da superfície terrestre, nomeadamente as ilhas de calor urbano (ICU), a evapotranspiração e o stress da vegetação. Estas variações da superfície permitem a criação de várias aplicações relacionadas com os diferentes processos, nomeadamente, a monitorização do clima, o ciclo hidrológico, a avaliação de modelos e a assimilação de dados. O satélite Landsat 8 (LS8) tem o potencial de fornecer estimativas LST com uma alta resolução espacial, o que torna possível realizar este estudo de forma mais eficaz. De uma forma sucinta, o estudo realizado consistiu numa primeira fase na aplicação de um algoritmo designado Statistical Mono-Window (SMW), que permitiu a criação de uma base de dados de calibração e validação, de maneira a garantir uma maior variabilidade das condições atmosféricas e de superfície. Posteriormente, através da aquisição de dados do LS8 e do sensor Advanced Spaceorne Thermal Emission and Reflection Radiometer (ASTER) procedeu-se à criação de um repositório que permitisse gerar um conjunto de imagens referente à LST, entre junho de 2016 e junho de 2017, na cidade de Coimbra. Consequentemente, relacionou-se a análise térmica da coleção de imagens obtidas anteriormente com os riscos geológicos associados e os problemas sociais que se possam manifestar, através de modelos de DR especializados. O resultado esperado deste trabalho diz respeito à possibilidade de analisar as variações da superfície da terra a partir da DR, como é o caso do estudo das ICU que corresponde à diferença da LST relacionando as zonas urbanas com as zonas não urbanas vizinhas. O desenvolvimento do método machine learning aliado ao cloud computing permitiu que esta análise térmica ocorra com uma alta resolução espacial através da aquisição de dados.

Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE.

The growth of cities, along with the disorderly formation of large metropolises around the world, results in major changes in Land Use. However, few studies relate the urban sprawl and its effects on the cities of the Northeast of Brazil (NEB). Thus, this study aimed to evaluate the urban sprawl in Maceió-Alagoas between 1985 to 2020 from orbital Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) products to detect the changes and their environmental effects. This study used orbital products acquired from the systems-sensors Landsat 5 / Thematic Mapper (TM) and 8 / Operational Land Imager (OLI). The study used four images to observe the Spatio-temporal variation of urbanization, corresponding to the years 1987, 1998, 2006, and 2020. It used the environment software R for the generation of NDVI and LST thematic maps. The results obtained pointed out substantial changes in the Land Use detected by the NDVI and increased LST over the 35 years. Such variability occurred in the neighborhoods located in the northern and northwestern portion of the city, resulting from the incentive programs of the Federal Government in 2010, mainly the Benedito Bentes Complex (CBB), with the most significant transformations in the use and occupation of the soil, mainly the largest increase in LST between 7.5-10.0 °C. The effects produced by urban sprawls have been mitigated due to the environmental protection areas.

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Climate change is a matter of considerable global importance, as evidenced by the increased urban surface temperatures in developed and undeveloped areas. Hence, this study aims to analyze the threshold and index of the urban heat island (UHI) phenomenon within the urban region of Thimphu, Bhutan. This study conducts a temporal analysis of the Normalized Difference Vegetation Index (NDVI), Normalized Different Buildup Index (NDBI) and Land Surface Temperature (LST) to detect Urban Heat Islands (UHIs) in Thimphu, Bhutan. The research focuses on assessing changes in vegetation cover and surface temperatures over time to identify and understand the patterns of UHI development in urban areas. This has potential benefits for urban planners involved in urban planning and management, as well as for increasing public awareness regarding the urban heat island effect. Advancements in thermal remote sensing, GIS, and statistical techniques have facilitated the monitoring of LST and its association with Land Use and Land Cover (LULC). To investigate this connection, supervised classification, and change detection were conducted to ascertain the spatial trends in LULC changes. Subsequently, the spatial distribution of LST was acquired using the thermal band of Landsat imagery. Regression analysis was then applied to investigate the link between surface temperature and various land surface characteristics, including both types of land use and land cover, as well as associated indices. In summary, this analysis contributes to the broader understanding of urbanization impacts on the local climate and provides valuable insights for sustainable urban planning and climate mitigation strategies in Thimphu, Bhutan.

Pest risk maps for agricultural use are usually constructed from data obtained from in-situ meteorological weather stations, which are relatively sparsely distributed and are often quite expensive to install and difficult to maintain. This leads to the creation of maps with relatively low spatial resolution, which are very much dependent on interpolation methodologies. Considering that agricultural applications typically require a more detailed scale analysis than has traditionally been available, remote sensing technology can offer better monitoring at increasing spatial and temporal resolutions, thereby improving pest management results and reducing costs. This article uses ground temperature, or land surface temperature (LST), data distributed by EUMETSAT/LSASAF (with a spatial resolution of 3 x 3 km (nadir resolution) and a revisiting time of 15 min) to generate one of the most commonly used parameters in pest modelling and monitoring: “thermal integral over air temperature (accumulated degree-days)”. The results show a clear association between the accumulated LST values over a threshold and the accumulated values computed from meteorological stations over the same threshold (specific to a particular tomato pest). The results are very promising and enable the production of risk maps for agricultural pests with a degree of spatial and temporal detail that is difficult to achieve using in-situ meteorological stations.

Kathmandu is experiencing rapid urban growth since last few decades. Cities are expanding across the countryside at the expense of productive land. Such urban sprawl has incurred adverse environmental consequences affecting quality of life of urban residents in the valley. Recently, Kathmandu has been identified to be on the verge of climate change, especially in the context of urban warming. Thus exploring the impact of urban growth on land surface temperature could be an effective means to unveil environmental issues caused by anthropogenic activities. This can be useful for the urban planners in urban planning and management as well as to raise public awareness regarding urban warming effect. Advancement in thermal Remote Sensing, GIS and statistical procedure has enabled monitoring land surface temperature and its correlation to land use and land cover. To analyze such relationship, we performed supervised classification and change detection to determine the spatial trend of land use and land cover change. After that we obtained the spatial pattern of LST using thermal band of Landsat images. Then we applied regression analysis to explore the relationship between surface temperature and land surface characteristics including both land use land cover types and land use and land cover indices. Based upon our analysis, we found that urban area has increased considerably by 259% during the period 1988-2014. The surface temperatures were found to be greater for bare soil and urban land use types. The regression analysis showed positive correlation between urban growth and LST. Finally we found LULC indices based approach better than LULC class for LST prediction.