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La tesis doctoral presenta un avance innovador en el análisis de entornos urbanos, profundizando en elementos fundamentales que regulan sus cambios y procesos. Se centra en los desafíos del análisis urbano de zonas extensas y complejas y del estudio a medida en escalas locales, con la implementación de conjuntos de datos de múltiples fuentes (datos abiertos y teledetección) para identificar áreas de interés para su intervención de forma remota. Este enfoque integra características biofísicas existentes del entorno construido, la vegetación (prestando atención a la escala espacio-temporal y su impacto en servicios ecosistémicos), el albedo de banda ancha, y la temperatura superficial del suelo (LST). El desarrollo de la tesis se estructura en cuatro secciones, que comienzan con una revisión exhaustiva de los métodos de evaluación urbana en remoto, seguida por la caracterización detallada de la vegetación. Posteriormente, se propone un marco de clasificación de las características biofísicas, se diagnostica el entorno urbano según el comportamiento de las características existentes y, finalmente, se definen patrones morfológicos. Las secciones concluyen con el diseño de un método para caracterizar áreas de interés y su aplicación en los procesos de toma de decisiones. El diseño del método se fundamenta en la extracción de características y cálculo de variables biofísicas a distancia, fusionando datos del Catastro (una base de datos de edificaciones en España), imágenes satelitales de alta y muy alta resolución, y datos infrarrojos térmicos (TIR). Para validar el método, se lleva a cabo en un caso de estudio que cumple con criterios establecidos previamente en la metodología, ubicado en el barrio de Canillas, Madrid, España. Los desarrollados en la tesis priorizan la integración de componentes biofísicos urbanos en su estado actual, con detalles intrincados y escalas espacio-temporales esenciales para la planificación urbana. Los resultados abordan los desafíos en los, The PhD thesis represents an innovative advance in the analysis of urban environments, delving into fundamental elements that drive their changes and processes. It focuses on the challenges of urban analysis of extensive and complex areas and tailored studies at local scales, using multisouce datasets (open data and remote sensing) to remotly identify areas of interest for intervention. This approach integrates existing biophysical characteristics of the built environment, vegetation (with attention to the spatio-temporal scale and its impact on ecosystem services), broadband albedo, and land surface temperature (LST). The thesis is structured in four sections, starting with an exhaustive review of remote urban assessment methods, followed by a detailed characterisation of vegetation. Subsequently, a classifi cation framework for biophysical features is proposed, the urban context in diagnosed according to the behaviour of the existing features and, fi nally, morphological patterns are defi ned. The sections conclude with the design of a method to characterise areas of interest to be applied in decision-making processes. The design of the method relies on the retrieval of the characteristics and calculation of biophysical variables by fusing data from the Cadastre (a database of buildings in Spain), high and very-high-resolution satellite images, and thermal infrared (TIR) data. To validate the method, it is conducted in a case study that meets the criteria previously established in the methodology, located in the neighbourhood of Canillas, Madrid, Spain. The processes carried out in the thesis prioritise the integration of urban biophysical components in their current state, with intricate details and spatiotemporal scales essential for urban planning. The results address challenges in current urban analyses and have been disseminated through papers published in scientifi c journals, books, congresses and international conferences proceedings, recognised for their

Daily maximum, TX, and minimum, TN, temperatures recorded at Fabra Observatory, for the period 1914–2020 (from June to September), have permitted to identify the daytime and night time heatwaves, HWs, at the Barcelona Metropolitan Region, BMR, along 107 years. Four heatwaves indices have been computed both for maximum and minimum tempera- tures: the number of events per season, HWN; their maximum duration, HWD; their frequency of occurrence, HWF; and the amplitude of the hottest day event, HWA. Trends in these indices have been obtained and their statistical significance quantified by means of the Mann-Kendall test. For the whole period (1914–2020), positive significant trends have been detected for the four indices, with a marked positive trend since year 2000 for TX, and since 1985 for TN. Nevertheless, when 31-year moving windows have been analysed, both positive and negative significant trends are obtained, being remarkable the change in trend signs around 1960s. The convenience of using the four HW indices is evaluated applying a Principal Component Analysis, PCA, obtaining two principal components. Additionally, the spatial distributions of the MODIS land surface temperatures, LST, corresponding to some extreme heatwaves, permits the detection of two hotspots in the BMR, one for TX and the other for TN. It is also worth mentioning that correlations between the Western Mediter- ranean Oscillation index, WeMOi, and HW episodes are detected, being notable that, at the beginning of these episodes, WEMOi values are usually lower. These correlations could improve the HWs forecasting., Rainfall data have been supplied by Servei Mete- orològic de Catalunya (Generalitat de Catalunya), Fabra Observa- tory (RACA, Barcelona), Agencia Estatal de Meteorología (AEMET, Spanish Government), MODIS satellite data (NASA), and Wester Mediterranean Oscillation indices (WeMO) supplied by the Climatol- ogy Group of UB (2017SGR1362). This research was supported by the Spanish Ministry of Science, Innovation and Universities: grant number PID2019-105976RB-I00]. The authors also thank the editor and the anonymous reviewers for their valuable comments and sugges- tions to improve the manuscript., Postprint (published version)

Increasing population and rapid development of built-up land can cause an increase in land surface temperature. These changes are often associated with many factors, including changes in land use, and surface parameters, economic development, and vegetation. This research aims to analyze the temperature factor as an indicator of indoor thermal comfort to support the learning process in schools in the Banten Province area. This research uses a spatial temporal approach with descriptive statistical analysis. Based on the results, show that the land surface temperature at each school location in Banten province shows conditions that are not constant. The number of schools with uncomfortable conditions varies from 2019 (89.07%), 2020 (42.65%), 2021 (45.66%), 2022 (82.66%), and 2023 (52.44%). This shows that there has been a decrease in uncomfortable conditions for studying by 36.63% in the last 5 years., Meningkatnya jumlah populasi dan pesatnya perkembangan lahan terbangun dapat menyebabkan terjadinya peningkatan Suhu Permukaan Daratan. Perubahan ini sering dikaitkan dengan banyak faktor, termasuk perubahan penggunaan lahan, dan parameter permukaan, perkembangan ekonomi, dan vegetasi. Penelitian ini bertujuan menganalisis faktor suhu sebagai indikator kenyaman termal dalam ruangan untuk mendukung proses pembelajaran disekolah di wilayah Provinsi Banten. Penelitian ini menggunakan pendekatan spasial temporal dengan analisis statistik deskriptif. Berdasarkan hasil menunjukan suhu permukaan daratan di setiap lokasi sekolah di provinsi Banten menunjukan kondisi yang tidak konstan. Jumlah sekolah dengan kondisi kurang nyaman bervariasi dari 2019 (89.07%), 2020 (42.65%), 2021 (45.66%), 2022 (82.66%), dan 2023 (52.44%). Hal ini menunjukkan terdapat penurunan kondisi kurang nyaman untuk belajar sebesar 36.63% dalam 5 tahun terakhir.

Increasing population and rapid development of built-up land can cause an increase in land surface temperature. These changes are often associated with many factors, including changes in land use, and surface parameters, economic development, and vegetation. This research aims to analyze the temperature factor as an indicator of indoor thermal comfort to support the learning process in schools in the Banten Province area. This research uses a spatial temporal approach with descriptive statistical analysis. Based on the results, show that the land surface temperature at each school location in Banten province shows conditions that are not constant. The number of schools with uncomfortable conditions varies from 2019 (89.07%), 2020 (42.65%), 2021 (45.66%), 2022 (82.66%), and 2023 (52.44%). This shows that there has been a decrease in uncomfortable conditions for studying by 36.63% in the last 5 years., Meningkatnya jumlah populasi dan pesatnya perkembangan lahan terbangun dapat menyebabkan terjadinya peningkatan Suhu Permukaan Daratan. Perubahan ini sering dikaitkan dengan banyak faktor, termasuk perubahan penggunaan lahan, dan parameter permukaan, perkembangan ekonomi, dan vegetasi. Penelitian ini bertujuan menganalisis faktor suhu sebagai indikator kenyaman termal dalam ruangan untuk mendukung proses pembelajaran disekolah di wilayah Provinsi Banten. Penelitian ini menggunakan pendekatan spasial temporal dengan analisis statistik deskriptif. Berdasarkan hasil menunjukan suhu permukaan daratan di setiap lokasi sekolah di provinsi Banten menunjukan kondisi yang tidak konstan. Jumlah sekolah dengan kondisi kurang nyaman bervariasi dari 2019 (89.07%), 2020 (42.65%), 2021 (45.66%), 2022 (82.66%), dan 2023 (52.44%). Hal ini menunjukkan terdapat penurunan kondisi kurang nyaman untuk belajar sebesar 36.63% dalam 5 tahun terakhir.

Annual irrigation maps and frequency data for the Rhine basin derived from hydrological modeling and land surface temperature. Data underlying the research of: Identifying irrigated areas using land surface temperature and hydrological modelling: Application to Rhine basin

Long-term remotely sensed soil moisture (SM) data is essential for understanding the land-atmosphere hydrological and energy interactions at both local and global scales. Passive microwave SM retrieval remains challenging at the global scale, especially in areas with complex terrain conditions, due to the difficulties in acquiring accurate land surface parameters (e.g., vegetation and surface roughness) across large extents and the uncertainties caused by the assumptions associated with current retrieval algorithms. This study addresses these challenges by providing a comprehensive evaluation of satellite SM products and introducing Soil Moisture Index (SMI)-based indicators derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature (TB). It is noteworthy that SMI, recently proposed and showing promising results in the low-frequency L-band, lacks validation for high-frequency microwave observations. This research fills this critical gap by evaluating the emissivity-based soil moisture index (ESMI) utilizing C- and X-band TB. To mitigate potential uncertainties in land surface temperature (LST) data used in emissivity-based methods, we propose an alternative brightness temperature-based soil moisture indicator (TBSMI). Simulation experiments demonstrated the effectiveness of TBSMI for capturing SM dynamics with a strong correlation coefficient of 0.95. TBSMI was then evaluated against in situ measurements from a total of 553 ground stations in 12 dense and 4 sparse SM networks worldwide under different climatic and environmental conditions from 1 April 2015 to 31 December 2017. Inter-comparisons were also made with two widely used AMSR2 SM products [i.e., the land parameter retrieval model (LPRM) product, and the Japan Aerospace Exploration Agency (JAXA) product], as well as with the ESMI. The results suggested that TBSMI exhibited the best performance with a mean R of 0.65 against in situ observations, followed by ESMI (mean R of 0.58), whi

Rapid urbanization has led to an extreme environment, especially in cities like Lahore that is overpopulated and overuse of land resulting in accelerating temperature. The research aims to investigate the commercialization impact on the microclimate of Karim Block, Lahore, which is being over-commercialized, and to propose a strategy to mitigate rising temperature from land use conversion. Theoretical review, policy analysis, ENVI Erdas Imagine, and GIS satellite imagery analysis are conducted to assess Land Surface Temperature from 2016 to 2023. It was revealed that the temperature has increased significantly due to land use change policies, elimination of green infrastructure, and traffic congestion. It highlights the importance of reclassifying land use, incorporating green infrastructure, and mitigating climate change impact. It is recommended to incorporate green roofing to reduce temperature significantly, reclassify Karim Block’s land use by prioritizing green infrastructure, and develop future commercialization policies that cater to environmental susceptibility and promote pedestrianization.

The response of vegetation canopy conductance (gc) to changes in moisture availability ((Formula presented.)) is a major source of uncertainty in climate projections. While vegetation typically reduces stomatal conductance during drought, accurately modeling how and to what degree stomata respond to changes in moisture availability at global scales is particularly challenging, because no global scale gc observations exist. Here, we leverage a collection of satellite, reanalysis and station-based near-surface air and surface temperature estimates, which are physically and statistically linked to (Formula presented.) due to the local cooling effect of gc through transpiration, to develop a novel emergent constraint of (Formula presented.) in an ensemble of Earth System Models (ESMs). We find that ESMs systematically underestimate (Formula presented.) by ∼33%, particularly in grasslands, croplands, and savannas in semi-arid and bordering regions of the Central United States, Central Europe, Southeastern South America, Southern Africa, Eastern Australia, and parts of East Asia. We show that this underestimation occurs because ESMs inadequately reduce gc when soil moisture decreases. As gc controls carbon, water and energy fluxes, the misrepresentation of modeled (Formula presented.) contributes to biases in ESM projections of gross primary production, transpiration, and temperature during droughts. Our results suggest that the severity and duration of droughts may be misrepresented in ESMs due to the impact of sustained gc on both soil moisture dynamics and the biosphere-atmosphere feedbacks that affect local temperatures and regional weather patterns.

Rapid urbanization has led to an extreme environment, especially in cities like Lahore that is overpopulated and overuse of land resulting in accelerating temperature. The research aims to investigate the commercialization impact on the microclimate of Karim Block, Lahore, which is being over-commercialized, and to propose a strategy to mitigate rising temperature from land use conversion. Theoretical review, policy analysis, ENVI Erdas Imagine, and GIS satellite imagery analysis are conducted to assess Land Surface Temperature from 2016 to 2023. It was revealed that the temperature has increased significantly due to land use change policies, elimination of green infrastructure, and traffic congestion. It highlights the importance of reclassifying land use, incorporating green infrastructure, and mitigating climate change impact. It is recommended to incorporate green roofing to reduce temperature significantly, reclassify Karim Block’s land use by prioritizing green infrastructure, and develop future commercialization policies that cater to environmental susceptibility and promote pedestrianization.

Current research analyzes the efficacy of several green infrastructure components in reducing the urban heat island effect in Peshawar city, KPK. It focuses on three public squares that have different forms of GI. Lack of planning for urban open and green spaces have negatively impacted cities and resulted in poor life quality, hence these must be evaluated for intervention planning. By integrating Land Surface Temperature information with on-site observations, simulations, and vegetation maps, overall research direction was established. Research highlighted intricate interactions among many GI characteristics. The study enlightened that while grass alone provides only marginal cooling effects (0.4–0.8°C), strategically combining trees with creative fountain design may dramatically lower urban temperatures (up to 6°C) relative to neighboring regions. GI's contribution to UHI mitigation is enhanced by these research findings, which offer useful resources for urban planners and designers. Simulations and further evaluations are proposed.

La tesis doctoral presenta un avance innovador en el análisis de entornos urbanos, profundizando en elementos fundamentales que regulan sus cambios y procesos. Se centra en los desafíos del análisis urbano de zonas extensas y complejas y del estudio a medida en escalas locales, con la implementación de conjuntos de datos de múltiples fuentes (datos abiertos y teledetección) para identificar áreas de interés para su intervención de forma remota. Este enfoque integra características biofísicas existentes del entorno construido, la vegetación (prestando atención a la escala espacio-temporal y su impacto en servicios ecosistémicos), el albedo de banda ancha, y la temperatura superficial del suelo (LST). El desarrollo de la tesis se estructura en cuatro secciones, que comienzan con una revisión exhaustiva de los métodos de evaluación urbana en remoto, seguida por la caracterización detallada de la vegetación. Posteriormente, se propone un marco de clasificación de las características biofísicas, se diagnostica el entorno urbano según el comportamiento de las características existentes y, finalmente, se definen patrones morfológicos. Las secciones concluyen con el diseño de un método para caracterizar áreas de interés y su aplicación en los procesos de toma de decisiones. El diseño del método se fundamenta en la extracción de características y cálculo de variables biofísicas a distancia, fusionando datos del Catastro (una base de datos de edificaciones en España), imágenes satelitales de alta y muy alta resolución, y datos infrarrojos térmicos (TIR). Para validar el método, se lleva a cabo en un caso de estudio que cumple con criterios establecidos previamente en la metodología, ubicado en el barrio de Canillas, Madrid, España. Los desarrollados en la tesis priorizan la integración de componentes biofísicos urbanos en su estado actual, con detalles intrincados y escalas espacio-temporales esenciales para la planificación urbana. Los resultados abordan los desafíos en los, The PhD thesis represents an innovative advance in the analysis of urban environments, delving into fundamental elements that drive their changes and processes. It focuses on the challenges of urban analysis of extensive and complex areas and tailored studies at local scales, using multisouce datasets (open data and remote sensing) to remotly identify areas of interest for intervention. This approach integrates existing biophysical characteristics of the built environment, vegetation (with attention to the spatio-temporal scale and its impact on ecosystem services), broadband albedo, and land surface temperature (LST). The thesis is structured in four sections, starting with an exhaustive review of remote urban assessment methods, followed by a detailed characterisation of vegetation. Subsequently, a classifi cation framework for biophysical features is proposed, the urban context in diagnosed according to the behaviour of the existing features and, fi nally, morphological patterns are defi ned. The sections conclude with the design of a method to characterise areas of interest to be applied in decision-making processes. The design of the method relies on the retrieval of the characteristics and calculation of biophysical variables by fusing data from the Cadastre (a database of buildings in Spain), high and very-high-resolution satellite images, and thermal infrared (TIR) data. To validate the method, it is conducted in a case study that meets the criteria previously established in the methodology, located in the neighbourhood of Canillas, Madrid, Spain. The processes carried out in the thesis prioritise the integration of urban biophysical components in their current state, with intricate details and spatiotemporal scales essential for urban planning. The results address challenges in current urban analyses and have been disseminated through papers published in scientifi c journals, books, congresses and international conferences proceedings, recognised for their

Aim: Earth observation-based estimates of land–atmosphere exchange of carbon are essential for understanding the response of the terrestrial biosphere to climatic change and other anthropogenic forcing. Temperature, soil water content and gross primary production are the main drivers of ecosystem respiration (Reco), and the main aims of this study are to develop an Reco model driven by long-term global-scale Earth observations and to study Reco spatiotemporal dynamics 1982–2015. Location: Global scale. Time Period: 1982–2015. Major Taxa Studied: Terrestrial ecosystems. Methods: We parameterized and applied a global Reco model for 1982–2015 using novel Earth observation-based data. We studied the relationships between Reco measured at field sites globally and land surface temperature, gross primary production and soil water content. Trends 1982–2015 were quantified, and the contributions from terrestrial regions to the spatiotemporal variability were evaluated. Results: The Reco model (LGS-Reco) captured the between-site and intra- and interannual variability in field-observed Reco and soil respiration well in comparison with other Earth observation-based products. The global annual Reco was on average 105.6 ± 2.3 Pg C for 1982–2015, which is close to 105 Pg C according to residuals of the carbon exchange processes within the global carbon budgets. The trend in global terrestrial Reco 1982–2015 was 0.19 ± 0.02 Pg C y−1, with the strongest positive trends found in cropland areas, whereas negative trends were primarily observed for savannah/shrublands of Southern Africa and South America. Trends were especially strong during the eighties and nineties, but substantially smaller 1998–2015. Main Conclusions: The LGS-Reco model revealed a substantial increase in global Reco during recent decades. However, the growth rates of glob

La tesis doctoral presenta un avance innovador en el análisis de entornos urbanos, profundizando en elementos fundamentales que regulan sus cambios y procesos. Se centra en los desafíos del análisis urbano de zonas extensas y complejas y del estudio a medida en escalas locales, con la implementación de conjuntos de datos de múltiples fuentes (datos abiertos y teledetección) para identificar áreas de interés para su intervención de forma remota. Este enfoque integra características biofísicas existentes del entorno construido, la vegetación (prestando atención a la escala espacio-temporal y su impacto en servicios ecosistémicos), el albedo de banda ancha, y la temperatura superficial del suelo (LST). El desarrollo de la tesis se estructura en cuatro secciones, que comienzan con una revisión exhaustiva de los métodos de evaluación urbana en remoto, seguida por la caracterización detallada de la vegetación. Posteriormente, se propone un marco de clasificación de las características biofísicas, se diagnostica el entorno urbano según el comportamiento de las características existentes y, finalmente, se definen patrones morfológicos. Las secciones concluyen con el diseño de un método para caracterizar áreas de interés y su aplicación en los procesos de toma de decisiones. El diseño del método se fundamenta en la extracción de características y cálculo de variables biofísicas a distancia, fusionando datos del Catastro (una base de datos de edificaciones en España), imágenes satelitales de alta y muy alta resolución, y datos infrarrojos térmicos (TIR). Para validar el método, se lleva a cabo en un caso de estudio que cumple con criterios establecidos previamente en la metodología, ubicado en el barrio de Canillas, Madrid, España. Los desarrollados en la tesis priorizan la integración de componentes biofísicos urbanos en su estado actual, con detalles intrincados y escalas espacio-temporales esenciales para la planificación urbana. Los resultados abordan los desafíos en los, The PhD thesis represents an innovative advance in the analysis of urban environments, delving into fundamental elements that drive their changes and processes. It focuses on the challenges of urban analysis of extensive and complex areas and tailored studies at local scales, using multisouce datasets (open data and remote sensing) to remotly identify areas of interest for intervention. This approach integrates existing biophysical characteristics of the built environment, vegetation (with attention to the spatio-temporal scale and its impact on ecosystem services), broadband albedo, and land surface temperature (LST). The thesis is structured in four sections, starting with an exhaustive review of remote urban assessment methods, followed by a detailed characterisation of vegetation. Subsequently, a classifi cation framework for biophysical features is proposed, the urban context in diagnosed according to the behaviour of the existing features and, fi nally, morphological patterns are defi ned. The sections conclude with the design of a method to characterise areas of interest to be applied in decision-making processes. The design of the method relies on the retrieval of the characteristics and calculation of biophysical variables by fusing data from the Cadastre (a database of buildings in Spain), high and very-high-resolution satellite images, and thermal infrared (TIR) data. To validate the method, it is conducted in a case study that meets the criteria previously established in the methodology, located in the neighbourhood of Canillas, Madrid, Spain. The processes carried out in the thesis prioritise the integration of urban biophysical components in their current state, with intricate details and spatiotemporal scales essential for urban planning. The results address challenges in current urban analyses and have been disseminated through papers published in scientifi c journals, books, congresses and international conferences proceedings, recognised for their

Aim: Earth observation-based estimates of land–atmosphere exchange of carbon are essential for understanding the response of the terrestrial biosphere to climatic change and other anthropogenic forcing. Temperature, soil water content and gross primary production are the main drivers of ecosystem respiration (Reco), and the main aims of this study are to develop an Reco model driven by long-term global-scale Earth observations and to study Reco spatiotemporal dynamics 1982–2015. Location: Global scale. Time Period: 1982–2015. Major Taxa Studied: Terrestrial ecosystems. Methods: We parameterized and applied a global Reco model for 1982–2015 using novel Earth observation-based data. We studied the relationships between Reco measured at field sites globally and land surface temperature, gross primary production and soil water content. Trends 1982–2015 were quantified, and the contributions from terrestrial regions to the spatiotemporal variability were evaluated. Results: The Reco model (LGS-Reco) captured the between-site and intra- and interannual variability in field-observed Reco and soil respiration well in comparison with other Earth observation-based products. The global annual Reco was on average 105.6 ± 2.3 Pg C for 1982–2015, which is close to 105 Pg C according to residuals of the carbon exchange processes within the global carbon budgets. The trend in global terrestrial Reco 1982–2015 was 0.19 ± 0.02 Pg C y−1, with the strongest positive trends found in cropland areas, whereas negative trends were primarily observed for savannah/shrublands of Southern Africa and South America. Trends were especially strong during the eighties and nineties, but substantially smaller 1998–2015. Main Conclusions: The LGS-Reco model revealed a substantial increase in global Reco during recent decades. However, the growth rates of glob

Land surface temperature (LST) captures fundamental information on the spatiotemporal variation of energy balance at the surface. The trade-off between spatial and temporal resolutions of remote sensing images (retrieved LSTs), however, restricts fine-scale thermal environmental investigations. In this context, a novel dual-layer composite framework (DCF) for LST downscaling coupling spatial autocorrelation and spatial heterogeneity was developed based on the two fundamental laws of geography and used to improve existing kernel-driven methods. Besides, a new non-parametric kernel-driven LST downscaling method (N-DLST) was also proposed under the DCF, in which Bayesian non-parametric general regression (BNGR) was applied to predict the high-resolution LSTs with auto-selected kernels. In the experiment of downscaling Landsat 8 LST from 300 m to 30 m over the highly heterogeneous urban area, the N-DLST method significantly outperformed the original kernel-driven methods, with the highest coefficient of determination (R2 = 0.93) and lowest root mean square error (RMSE = 0.85). Moreover, the enhanced effects of DCF in downscaling LST were demonstrated by comparing the accuracy of the disaggregation of radiometric surface temperature (DisTrad), the geographically weighted regression-based method (GWR), and random forest (RF) method before and after their improvements. Visual interpretation and quantitative assessments revealed that the DCF could improve the accuracy of DisTrad, GWR, and RF methods with an increase in R2 by approximately 0.09 and a decrease in RMSE by more than 0.4 °C. In the cases of LST downscaling over highly heterogeneous contexts and water bodies, N-DLST effectively preserved the textures and large-scale variations, yielding the most consistent spatial pattern with the reference LST. Given the simplicity of the modelling process and absence of auxiliary data, the DCF could strengthen the p

Current research analyzes the efficacy of several green infrastructure components in reducing the urban heat island effect in Peshawar city, KPK. It focuses on three public squares that have different forms of GI. Lack of planning for urban open and green spaces have negatively impacted cities and resulted in poor life quality, hence these must be evaluated for intervention planning. By integrating Land Surface Temperature information with on-site observations, simulations, and vegetation maps, overall research direction was established. Research highlighted intricate interactions among many GI characteristics. The study enlightened that while grass alone provides only marginal cooling effects (0.4–0.8°C), strategically combining trees with creative fountain design may dramatically lower urban temperatures (up to 6°C) relative to neighboring regions. GI's contribution to UHI mitigation is enhanced by these research findings, which offer useful resources for urban planners and designers. Simulations and further evaluations are proposed.

Although Scandinavian countries, including Sweden, are relatively less vulnerable to climate change, Swedish residential dwellings specifically those constructed before the 1975s are likely to be impacted by current extreme weather events (EWE) such as urban heat islands (UHI). This EWE can worsen air quality, increase heat-related illnesses (particularly among vulnerable populations), and raise maintenance costs and energy demand for cooling in buildings.This study aimed to map UHI risk in Sweden's residential sector using a scenario-based analysis approach. Moderate Resolution Imaging Spectroradiometer (MODIS) Remote sensing (RS) imagery data (land surface temperature (LST)) and some statistical data (including the number of houses, building typologies, and characteristics) collected from the SCB (Statistics Sweden) and TABULA database are used. The overall UHI risk maps for Sweden are developed following the risk matrix approach, by weighting and aggregating the created maps for UHI hazard, building exposure, and vulnerability. Here, the geographical information system ArcGIS pro 3.1 was used to carry out the different spatial analysis tasks, including pre-processing of spatial data, developing required maps, and performing raster calculations.The outcomes reveal a range of areas posing risks, with most high-risk zones situated in the southern and southeastern regions. Moreover, there is a discernible impact of the UHI on most of the buildings across Sweden constructed prior to the 1960s. Nevertheless, for structures built between 1961 to 1975, only those in the southern regions display potential susceptibility to the UHI. Furthermore, the western areas exhibit a low UHI risk.Despite the limitation of data used, the findings of this study have practical implications, as they can help homeowners, renovation companies, and policymakers implement appropriate adaptation strategies. The approach used is comprehensive, easily applicable, scalable, and can be replicated

Urban ecosystems are defined by unique relationships between biological diversity and distribution, socio-demographics, and climate. These relationships occur in the context of a changing world: increasing temperatures, a shifting composition of urban residents, and rapid urbanization. It is likely that the relationships which structure the urban environment, such as that between temperature and income, or race-based exposure to heat and greenness, are not temporally stable. As urban temperatures and greenness have well known associations with morbidity, mortality, and mental health, non-stationarity of urban ecological relationships may pose significant challenges to the well-being of urban residents. I tested assumptions about non-stationarity by assessing the change through time of urban ecological relationships at three distinct spatial scales: the urban (single city) scale (chapter 1), at a continental scale (chapter 2), and at a global scale (chapter 3). At each spatial scale I used a multidecadal time series of satellite imagery, coupled with landcover and socio-demographic information, to assess how the dynamics of urban greenness, temperature, and vegetative cooling evolved through time and what factors mediated any change. In chapter 1, where I used Los Angeles, CA as a case study, I found large intra-urban variability in how greenness, temperature, and vegetative cooling changed over time. The consequence of these changes was a weakening of the effectiveness of income as a mediator of urban greenness and temperature (the luxury effect). I corroborated the multidecadal decline in the luxury effect in chapter 2, a continental-scale assessment where I looked at 52 cities from the conterminous United States. In this chapter I additionally compared the biophysical dynamics in every city with a nearby non-urban reference site. Urbanization weakened the relationship between the weather and landcover and the biophysical environment, where the most arid cities wer

Planners often regard streets as targets for mitigating urban heat across cities by virtue of being abundant, publicly-owned, low-albedo, low-vegetation surfaces. Few studies, however, have assessed the role streets play in contributing to urban heat, and the scale of their effect relative to the built environment around them. We examine the relationship between road area and land surface temperature across a variety of biophysical regions through the urban areas of Los Angeles and San Bernardino Counties in Southern California. Our results show that wide streets have no consistent, detectable effect on urban heat. Rather, vegetation is the primary cooling mechanism for urban areas. In the absence of trees, concrete highways are the coolest surfaces, though particular hot or cool pockets (e.g., airports, industrial centers, parks) can dominate neighborhood temperature signatures. In considering LST mitigation strategies, these hotspots might outweigh the cumulative effects of road surface changes.

Die räumliche Verteilung der Vegetation in einer hochalpinen Landschaft wird in hohem Maße von den mikroklimatischen Standortfaktoren geprägt. Der Oberflächentemperatur kommt in diesem Kontext als Indikator für die mikroklimatische Variabilität der Bedingungen an Vegetationsstandorten aufgrund von komplexer Topographie und heterogenen Oberflächeneigenschaften eine Schlüsselrolle zu. Infrarot-Thermographie mittels einer tragbaren Kamera kann hier Informationen in hoher zeitlicher und räumlicher Auflösung bei akzeptabler räumlicher Abdeckung liefern. Um die räumlich-zeitliche Variabilität der Oberflächentemperatur zu untersuchen, wurden mithilfe einer Thermalkamera zwei 24 h-Zeitreihen am 12.08.2021 und 27.10.2021 von der Südwestflanke des Schrankogel aufgenommen. Zusammen mit den im Kontext des MICROCLIM-Projektes aufgenommenen RGB-Webcambildern wurden die Thermographiezeitreihen mittels eines Monoplotting-Verfahrens auf ein Digitales Oberflächenmodell (DTM) projiziert. Die anschließende atmosphärische und Emissionsgradkorrektur der projizierten Temperaturbilder erwies sich als notwendiger Schritt zur Interpretation der Zeitreihen. Tests zeigten, dass das Korrekturmodell die Abweichung zwischen gemessener und realer Oberflächentemperatur mit guten Ergebnissen korrigieren kann. Um die Oberflächentemperaturen in einem mikroklimatischen Rahmen untersuchen zu können, wurden aus den RGB-Bildern, dem DTM und Lufttemperaturmessungen ein Vegetationsindex (ExGreen), die potentielle tägliche Solarstrahlungssumme, die mittlere Lufttemperatur und der Sky View Factor für die jeweiligen Messtage berechnet. Es zeigt sich eine große Variabilität der räumlichen Verteilung der Oberflächentemperaturen zwischen Sommer und Herbst aufgrund phänologischer Veränderungen der Vegetationsbedeckung, die sich auch im ExGreen widerspiegeln. Die Tagestemperaturen der Oberfläche korrelieren im Sommer stark mit der Vegetationsbedeckung und im Herbst, durch das Fehlen aktiver Vegetation, mehr mit der, The spatial distribution of vegetation in a high alpine landscape is highly influenced by microclimatic site location factors. In this context, surface temperature plays a key role as an indicator for the microclimatic variability of conditions at vegetation sites due to complex topography and heterogeneous surface properties. Here, Infrared thermography using a portable thermal camera can provide information at high temporal and spatial resolution with acceptable spatial coverage. To investigate the spatio-temporal variability of surface temperature, two 24 h time series were acquired from the southwestern flank of Mt. Schrankogel on August 12, 2021 and October 27, 2021 using a thermal camera. Together with the RGB webcam images taken within the MICROCLIM project, the thermal time series were projected onto a digital surface model (DSM) using a monoplotting procedure. Subsequent atmospheric and emissivity correction of the projected temperature images proved to be a necessary step in interpreting the time series. Tests showed, that the correction model was able to minimize the difference between measured and real surface temperature with good results. In order to examine surface temperatures in a microclimatic framework, a vegetation index (ExGreen), potential incoming solar radiation, mean air temperature, and sky view factor were calculated from the RGB images, DSM, and air temperature measurements for each measurement day. A large variability in the spatial distribution of surface temperatures between summer and fall was detected due to phenological changes in vegetation cover, which are also reflected in the ExGreen. Daytime surface temperatures strongly correlate with vegetation cover in summer. In autumn daytime surface temperatures correlate more with solar radiation and thus topographic conditions, due to the absence of active vegetation. In general, nighttime temperatures are tied to air temperature and sky view factor. Regression analysis using a Random F, Masterarbeit Universität Innsbruck 2023

Detecting change-points in multivariate settings is usually carried out by analyzing all marginals either independently, via univariate methods, or jointly, through multivariate approaches. The former discards any inherent dependencies between different marginals and the latter may suffer from domination/masking among different change-points of distinct marginals. As a remedy, we propose an approach which groups marginals with similar temporal behaviors, and then performs group-wise multivariate change-point detection. Our approach groups marginals based on hierarchical clustering using distances which adjust for inherent dependencies. Through a simulation study we show that our approach, by preventing domination/masking, significantly enhances the general performance of the employed multivariate change-point detection method. Finally, we apply our approach to two datasets: (i) Land Surface Temperature in Spain, during the years 2000–2021, and (ii) The WikiLeaks Afghan War Diary data.

The Brazilian Amazon has been a focus of land development with large swaths of forests converted to agriculture. Forest degradation by selective logging and fires has accompanied the agricultural frontier and has resulted in significant impacts on Amazonian ecosystems. Changes in forest structure resulting from forest disturbances have large impacts on the surface energy balance, including on land surface temperature (LST) and evapotranspiration (ET). This study's objective is to assess the effects of forest disturbances on water fluxes and forest structure in a transitional forest site in the Southern Amazon. We used ET and LST products from MODIS and Landsat 8 and GEDI-derived forest structure data to address our research questions. We found that disturbances induced seasonal water stress, more pronounced in croplands/pastures than in forests (differences up to 20% in the dry season), and more pronounced in second-growth and recently burned areas than in logged and intact forests (differences up to 12% in the dry season). Moreover, ET and LST were negatively related, with more consistent relationships across disturbance classes in the dry season (R2: 0.41–0.87) than in the wet season (R2: 0.18–0.49). Forest and cropland or pasture classes showed contrasting relationships in the dry season. Finally, we found that forest structure exhibited stronger relationships with ET and LST in the most disturbed forests (R2: 0.01–0.43) than in the least disturbed forests (R2 < 0.05). Our findings help to elucidate degraded forests functioning under a changing climate and to improve estimates of water and energy fluxes in Amazonian degraded forests.

The development of urbanization and the transformation of green lands into impermeable land increase temperature and create urban heat islands (UHIs). Our observations with remote sensing instruments of Landsat platforms show considerable changes in land use types in Beijing city with the shrinking of green lands, expansion of built envi-ronments, and a slight increase in the temperature during the recent four decades. Using remote sensing instruments of Landsat platforms and registered data from two meteorological stations in Beijing, this study finds the relationship between land surface temperature (LST) and the increasing conversion of cultivated lands into built-up areas. This article presents innovative research that shows the mutual correlation well and recommends revisions in the land use policies for better weather. The geographically weighted regression model (GWR) with a Gaussian weighting kernel function analyzes the impact of various urban land use types on the LST and the increase UHIs. In Beijing city, green lands show fewer standard deviations (SD) in the average temperatures equal to 0.109, while the industrial spaces exhibit a high SD equal to 0.212. The outcomes of this paper contribute to finding optimal land use policies everywhere in the world with the increasing urbanization through simulating its model for a more comfortable life., QC 20230918

Detecting change-points in multivariate settings is usually carried out by analyzing all marginals either independently, via univariate methods, or jointly, through multivariate approaches. The former discards any inherent dependencies between different marginals and the latter may suffer from domination/masking among different change-points of distinct marginals. As a remedy, we propose an approach which groups marginals with similar temporal behaviors, and then performs group-wise multivariate change-point detection. Our approach groups marginals based on hierarchical clustering using distances which adjust for inherent dependencies. Through a simulation study we show that our approach, by preventing domination/masking, significantly enhances the general performance of the employed multivariate change-point detection method. Finally, we apply our approach to two datasets: (i) Land Surface Temperature in Spain, during the years 2000–2021, and (ii) The WikiLeaks Afghan War Diary data.

Detecting change-points in multivariate settings is usually carried out by analyzing all marginals either independently, via univariate methods, or jointly, through multivariate approaches. The former discards any inherent dependencies between different marginals and the latter may suffer from domination/masking among different change-points of distinct marginals. As a remedy, we propose an approach which groups marginals with similar temporal behaviors, and then performs group-wise multivariate change-point detection. Our approach groups marginals based on hierarchical clustering using distances which adjust for inherent dependencies. Through a simulation study we show that our approach, by preventing domination/masking, significantly enhances the general performance of the employed multivariate change-point detection method. Finally, we apply our approach to two datasets: (i) Land Surface Temperature in Spain, during the years 2000–2021, and (ii) The WikiLeaks Afghan War Diary data.

Although Scandinavian countries, including Sweden, are relatively less vulnerable to climate change, Swedish residential dwellings specifically those constructed before the 1975s are likely to be impacted by current extreme weather events (EWE) such as urban heat islands (UHI). This EWE can worsen air quality, increase heat-related illnesses (particularly among vulnerable populations), and raise maintenance costs and energy demand for cooling in buildings.This study aimed to map UHI risk in Sweden's residential sector using a scenario-based analysis approach. Moderate Resolution Imaging Spectroradiometer (MODIS) Remote sensing (RS) imagery data (land surface temperature (LST)) and some statistical data (including the number of houses, building typologies, and characteristics) collected from the SCB (Statistics Sweden) and TABULA database are used. The overall UHI risk maps for Sweden are developed following the risk matrix approach, by weighting and aggregating the created maps for UHI hazard, building exposure, and vulnerability. Here, the geographical information system ArcGIS pro 3.1 was used to carry out the different spatial analysis tasks, including pre-processing of spatial data, developing required maps, and performing raster calculations.The outcomes reveal a range of areas posing risks, with most high-risk zones situated in the southern and southeastern regions. Moreover, there is a discernible impact of the UHI on most of the buildings across Sweden constructed prior to the 1960s. Nevertheless, for structures built between 1961 to 1975, only those in the southern regions display potential susceptibility to the UHI. Furthermore, the western areas exhibit a low UHI risk.Despite the limitation of data used, the findings of this study have practical implications, as they can help homeowners, renovation companies, and policymakers implement appropriate adaptation strategies. The approach used is comprehensive, easily applicable, scalable, and can be replicated

Although Scandinavian countries, including Sweden, are relatively less vulnerable to climate change, Swedish residential dwellings specifically those constructed before the 1975s are likely to be impacted by current extreme weather events (EWE) such as urban heat islands (UHI). This EWE can worsen air quality, increase heat-related illnesses (particularly among vulnerable populations), and raise maintenance costs and energy demand for cooling in buildings.This study aimed to map UHI risk in Sweden's residential sector using a scenario-based analysis approach. Moderate Resolution Imaging Spectroradiometer (MODIS) Remote sensing (RS) imagery data (land surface temperature (LST)) and some statistical data (including the number of houses, building typologies, and characteristics) collected from the SCB (Statistics Sweden) and TABULA database are used. The overall UHI risk maps for Sweden are developed following the risk matrix approach, by weighting and aggregating the created maps for UHI hazard, building exposure, and vulnerability. Here, the geographical information system ArcGIS pro 3.1 was used to carry out the different spatial analysis tasks, including pre-processing of spatial data, developing required maps, and performing raster calculations.The outcomes reveal a range of areas posing risks, with most high-risk zones situated in the southern and southeastern regions. Moreover, there is a discernible impact of the UHI on most of the buildings across Sweden constructed prior to the 1960s. Nevertheless, for structures built between 1961 to 1975, only those in the southern regions display potential susceptibility to the UHI. Furthermore, the western areas exhibit a low UHI risk.Despite the limitation of data used, the findings of this study have practical implications, as they can help homeowners, renovation companies, and policymakers implement appropriate adaptation strategies. The approach used is comprehensive, easily applicable, scalable, and can be replicated

Although Scandinavian countries, including Sweden, are relatively less vulnerable to climate change, Swedish residential dwellings specifically those constructed before the 1975s are likely to be impacted by current extreme weather events (EWE) such as urban heat islands (UHI). This EWE can worsen air quality, increase heat-related illnesses (particularly among vulnerable populations), and raise maintenance costs and energy demand for cooling in buildings.This study aimed to map UHI risk in Sweden's residential sector using a scenario-based analysis approach. Moderate Resolution Imaging Spectroradiometer (MODIS) Remote sensing (RS) imagery data (land surface temperature (LST)) and some statistical data (including the number of houses, building typologies, and characteristics) collected from the SCB (Statistics Sweden) and TABULA database are used. The overall UHI risk maps for Sweden are developed following the risk matrix approach, by weighting and aggregating the created maps for UHI hazard, building exposure, and vulnerability. Here, the geographical information system ArcGIS pro 3.1 was used to carry out the different spatial analysis tasks, including pre-processing of spatial data, developing required maps, and performing raster calculations.The outcomes reveal a range of areas posing risks, with most high-risk zones situated in the southern and southeastern regions. Moreover, there is a discernible impact of the UHI on most of the buildings across Sweden constructed prior to the 1960s. Nevertheless, for structures built between 1961 to 1975, only those in the southern regions display potential susceptibility to the UHI. Furthermore, the western areas exhibit a low UHI risk.Despite the limitation of data used, the findings of this study have practical implications, as they can help homeowners, renovation companies, and policymakers implement appropriate adaptation strategies. The approach used is comprehensive, easily applicable, scalable, and can be replicated

Die räumliche Verteilung der Vegetation in einer hochalpinen Landschaft wird in hohem Maße von den mikroklimatischen Standortfaktoren geprägt. Der Oberflächentemperatur kommt in diesem Kontext als Indikator für die mikroklimatische Variabilität der Bedingungen an Vegetationsstandorten aufgrund von komplexer Topographie und heterogenen Oberflächeneigenschaften eine Schlüsselrolle zu. Infrarot-Thermographie mittels einer tragbaren Kamera kann hier Informationen in hoher zeitlicher und räumlicher Auflösung bei akzeptabler räumlicher Abdeckung liefern. Um die räumlich-zeitliche Variabilität der Oberflächentemperatur zu untersuchen, wurden mithilfe einer Thermalkamera zwei 24 h-Zeitreihen am 12.08.2021 und 27.10.2021 von der Südwestflanke des Schrankogel aufgenommen. Zusammen mit den im Kontext des MICROCLIM-Projektes aufgenommenen RGB-Webcambildern wurden die Thermographiezeitreihen mittels eines Monoplotting-Verfahrens auf ein Digitales Oberflächenmodell (DTM) projiziert. Die anschließende atmosphärische und Emissionsgradkorrektur der projizierten Temperaturbilder erwies sich als notwendiger Schritt zur Interpretation der Zeitreihen. Tests zeigten, dass das Korrekturmodell die Abweichung zwischen gemessener und realer Oberflächentemperatur mit guten Ergebnissen korrigieren kann. Um die Oberflächentemperaturen in einem mikroklimatischen Rahmen untersuchen zu können, wurden aus den RGB-Bildern, dem DTM und Lufttemperaturmessungen ein Vegetationsindex (ExGreen), die potentielle tägliche Solarstrahlungssumme, die mittlere Lufttemperatur und der Sky View Factor für die jeweiligen Messtage berechnet. Es zeigt sich eine große Variabilität der räumlichen Verteilung der Oberflächentemperaturen zwischen Sommer und Herbst aufgrund phänologischer Veränderungen der Vegetationsbedeckung, die sich auch im ExGreen widerspiegeln. Die Tagestemperaturen der Oberfläche korrelieren im Sommer stark mit der Vegetationsbedeckung und im Herbst, durch das Fehlen aktiver Vegetation, mehr mit der, The spatial distribution of vegetation in a high alpine landscape is highly influenced by microclimatic site location factors. In this context, surface temperature plays a key role as an indicator for the microclimatic variability of conditions at vegetation sites due to complex topography and heterogeneous surface properties. Here, Infrared thermography using a portable thermal camera can provide information at high temporal and spatial resolution with acceptable spatial coverage. To investigate the spatio-temporal variability of surface temperature, two 24 h time series were acquired from the southwestern flank of Mt. Schrankogel on August 12, 2021 and October 27, 2021 using a thermal camera. Together with the RGB webcam images taken within the MICROCLIM project, the thermal time series were projected onto a digital surface model (DSM) using a monoplotting procedure. Subsequent atmospheric and emissivity correction of the projected temperature images proved to be a necessary step in interpreting the time series. Tests showed, that the correction model was able to minimize the difference between measured and real surface temperature with good results. In order to examine surface temperatures in a microclimatic framework, a vegetation index (ExGreen), potential incoming solar radiation, mean air temperature, and sky view factor were calculated from the RGB images, DSM, and air temperature measurements for each measurement day. A large variability in the spatial distribution of surface temperatures between summer and fall was detected due to phenological changes in vegetation cover, which are also reflected in the ExGreen. Daytime surface temperatures strongly correlate with vegetation cover in summer. In autumn daytime surface temperatures correlate more with solar radiation and thus topographic conditions, due to the absence of active vegetation. In general, nighttime temperatures are tied to air temperature and sky view factor. Regression analysis using a Random F, eingereicht von Benedikt Hiebl, in englischer Sprache, Masterarbeit Universität Innsbruck 2023

The validated influence of urban biophysical structure on environmental processes within urban areas has heightened the emphasis on studies examining morphological patterns to determine precise locations and underlying causes of urban climate conditions. The present study aims to characterise morphological patterns describing the distribution of Land Surface Temperature (LST) based on a prior classification of biophysical variables, including urban density (building intensity and average height), surface characteristics, shortwave solar radiation (broadband albedo), and seasonal variations in vegetation cover (high, medium, and low levels), retrieved from multisource datasets. To describe the distribution of LST, the variables were calculated, classified, and subsequently, analysed individually and collectively concerning winter and summer LST values applied in an urban neighbourhood in Madrid, Spain. The results from the analytical approaches (observation, correlations, and multiple regressions) were compared to define the morphological patterns. The selection of areas resulting from the morphological patterns with the most unfavourable LST values showed agreement of up to 89% in summer and up to 70% for winter, demonstrating the feasibility of the methods applied to identify priority areas for intervention by season. Notably, low and high vegetation levels emerged as pivotal biophysical characteristics influencing LST distribution compared to the other characteristics, emphasising the significance of integrating detailed seasonal vegetation variations in urban analyses.

Although Scandinavian countries, including Sweden, are relatively less vulnerable to climate change, Swedish residential dwellings specifically those constructed before the 1975s are likely to be impacted by current extreme weather events (EWE) such as urban heat islands (UHI). This EWE can worsen air quality, increase heat-related illnesses (particularly among vulnerable populations), and raise maintenance costs and energy demand for cooling in buildings.This study aimed to map UHI risk in Sweden's residential sector using a scenario-based analysis approach. Moderate Resolution Imaging Spectroradiometer (MODIS) Remote sensing (RS) imagery data (land surface temperature (LST)) and some statistical data (including the number of houses, building typologies, and characteristics) collected from the SCB (Statistics Sweden) and TABULA database are used. The overall UHI risk maps for Sweden are developed following the risk matrix approach, by weighting and aggregating the created maps for UHI hazard, building exposure, and vulnerability. Here, the geographical information system ArcGIS pro 3.1 was used to carry out the different spatial analysis tasks, including pre-processing of spatial data, developing required maps, and performing raster calculations.The outcomes reveal a range of areas posing risks, with most high-risk zones situated in the southern and southeastern regions. Moreover, there is a discernible impact of the UHI on most of the buildings across Sweden constructed prior to the 1960s. Nevertheless, for structures built between 1961 to 1975, only those in the southern regions display potential susceptibility to the UHI. Furthermore, the western areas exhibit a low UHI risk.Despite the limitation of data used, the findings of this study have practical implications, as they can help homeowners, renovation companies, and policymakers implement appropriate adaptation strategies. The approach used is comprehensive, easily applicable, scalable, and can be replicated

Understanding tropical secondary forest canopy greenness and responses to climatic conditions is important for climate change mitigation, particularly in the tropics where secondary forest growth is a substantial carbon sink and a promoted natural climate solution. We here test three hypotheses: (a) forest canopy greenness is higher in younger, secondary forests than in older, primary or mature forests, (b) secondary forests are more vulnerable to climatic pressures and (c) there are significant differences between forest types regarding primary-secondary canopy greenness and their differential responses to drought anomalies. To explore these relationships, we monitored wet and dry seasonal greenness from 2001 to 2020, estimated through the enhanced vegetation index (EVI), of Peruvian tropical dry, montane and lowland secondary forests and compared it to nearby primary forests. We developed predictive models of seasonal EVI using remotely sensed variables, including land surface temperature (LST), evapotranspiration (ET), potential evapotranspiration (PET), ratio of ET and PET (ETn), and the standard precipitation index (SPI). Overall, there was a higher change in annual and seasonal EVI for secondary forests compared to primary forests. However, primary forests maintained relatively stable EVI levels during the wet season despite drought anomalies. When decoupling forest type canopy greenness and drought response, primary forest greenness in dry and lowland ecosystems were temporally more stable. Secondary montane had a lower increase in greenness when drought anomalies held during different seasons. Stepwise multiple linear regression models indicated that LST and ETn, a plant water use index, were the most significant factors to predict greening fluctuations in dry and montane forest types. ET and SPI mostly drove wet season mean EVI across all forest types. Predictors of dry season mean EVI varied, but mostly including water availability. Our results suggest tha

Tourism both contributes to environmental change and is, in turn, affected by such change. One area that is of considerable significance is tourism's contribution to land use change and its subsequent impacts. Land use change, such as tourism urbanization, together with global heating leads to an increase in surface temperature and deterioration in environmental quality and human comfort levels. Understanding the implications of tourism-related land use change is therefore of considerable importance for climate adaptation. Using Landsat satellite images, the evolution of land users/covers and the temperature of the land surface between 1985 and 2020 were studied in Cyprus. The analysis of the data confirms that tourism development activities have influenced the increase in urban hotspots. This destination has experienced a 6.2 & DEG;C increase in average temperature and 168.8% in the urban hotspot. Based on the findings the study highlights the implications for sustainable tourism development and potential means of mitigation., Bibliografiskt granskad

The daily surface temperature variability, characterizing the dispersion of day-to-day temperature anomalies, is a fundamental aspect of the climate. It can be represented by the temperature standard deviation in a week. Studies reveal that daily temperature variability is a critical determinant of societal and natural outcomes, such as public health, crop yield, economic growth, etc. Although the overall warming trend is now well established in the scientific community, previous studies have shown little consensus about changes in daily temperature variability over the globe in recent decades; this is due to limited simulation accuracy and in-situ measurement distribution. Therefore, it is urgently needed to generate a reliable, global, long-term, observation-derived, daily temperature dataset in order to analyze variability changes and potential driving factors. The Advanced Very High-Resolution Radiometer (AVHRR) data provide an exceptional chance to record long-term land surface temperature (LST) over the entire globe. However, the AVHRR LST suffers from two restrictions: cloud contamination and orbital drift. Accordingly, we develop a surface energy balance (SEB)-based algorithm to recover the LST under clouds, and a two-step method to correct the artificial spurious temperature variation due to orbital drift. In the SEB method, 1) the hypothetical LST of missing pixels is first reconstructed by assimilating dispersed clear-sky retrievals into a continuous LST time-evolving model built by reanalysis data, and 2) the reconstructed LST is then corrected by superposing the cloud effect, estimated by satellite radiation products based on SEB theory. The two-step correction includes 1) calibrating the systematic bias of diurnal temperature cycles (DTCs) simulated from reanalysis data using satellite product climatology, 2) correcting the calibrated DTCs in detail by historical AVHRR LSTs during the years 1981-2021, and averaging the corrected DTCs to get daily mean

Tremendous progress has been made in estimating soil moisture from satellite remote sensing data. Several global-scale coarse-resolution products have also been generated and released for various applications in the Earth system. However, high-resolution soil moisture estimation is still in its infancy. Currently, two main methods are used for this purpose: downscaling approaches and direct retrieval from microwave and optical/thermal data. Several studies have attempted to comprehensively evaluate the performance of these approaches and have found that each method has its own strengths and weaknesses, with no single method outperforming the others. In this study, we aim to investigate the advantages of integrating optical, thermal, and microwave data to estimate soil moisture by leveraging an intensive soil moisture network and triple collocation method. Firstly, we determined the best-performing coarse-resolution microwave soil moisture product through the triple collocation approach. Secondly, we generated 1-km soil moisture using a downscaling approach based on land surface temperature and vegetation index, utilizing the best-performing SMAP L3 descending product. Thirdly, we evaluated the high-resolution downscaled soil moisture, Sentinel 1 soil moisture, and SMAP/Sentinel 1 combined soil moisture products using soil moisture measurements from the REMEDHUS station network, ETOPO1 elevation, CHIRPS precipitation, and the ESA CCI land cover map. Finally, we investigated and demonstrated the advantages of merging these products through point-scale evaluation and large-scale spatial pattern comparison.

Urbanization has caused significant changes in urban morphology in three-dimensional (3D) space. Although previous studies found a close association between 2D/3D urban morphology and land surface temperature (LST), the conclusions are various and the reasons lie in three fundamental components of urban morphology: physical form, resolution and time. Beijing's old city includes massive vernacular and modern architecture, which provides an ideal laboratory for the studies. In a new approach, 3D landscape metrics were applied to analyze built forms from the aspect of composition and configuration, and to explain their contributions to LSTs together with tree height data across scales and seasons. Tree height contributed most during summer. Compared to composition, configuration metrics were less efficient for cross-scale LST changes. The relative importance of building features is scale-dependent. At small scales (under 105 m), 3D composition features contributed more to LST, while 2D composition features were dominant at larger scales (over 180 m). At intermediate scales, 2D and 3D composition metrics together affect the LSTs. The scale and seasonal dependencies indicated in this study are derived from the application of our concept to a particular city. More experiments to other cities should be carried out for an in-depth understanding of the associations.

Detecting change-points in multivariate settings is usually carried out by analyzing all marginals either independently, via univariate methods, or jointly, through multivariate approaches. The former discards any inherent dependencies between different marginals and the latter may suffer from domination/masking among different change-points of distinct marginals. As a remedy, we propose an approach which groups marginals with similar temporal behaviors, and then performs group-wise multivariate change-point detection. Our approach groups marginals based on hierarchical clustering using distances which adjust for inherent dependencies. Through a simulation study we show that our approach, by preventing domination/masking, significantly enhances the general performance of the employed multivariate change-point detection method. Finally, we apply our approach to two datasets: (i) Land Surface Temperature in Spain, during the years 2000–2021, and (ii) The WikiLeaks Afghan War Diary data.

Urban morphology varies within and among cities. Previous literature well discussed how 2D/3D morphology affects land surface temperature (LST) from physical form, scale and time. However, they focused on ranking the contributions. How quantifying the heating/cooling efficiency and conducting multi-cities comparisons is challenging. This study proposed a new framework to process this issue by integrating cities, scales, and seasons. Among 24 cities close to China's Eastern Seaboard, building coverage ratio (BCR), building height (BH), surface developed ratio (SDR), and tree height (TH) were dominant contributors to LST over seasons. Among them, TH had the highest cooling efficiency, although it varied from cities. For every 1 m increase, LST decreased about 0.3 °C in northern cities during summer and tiny during winter, while LST decreased about 0.2 in southern cities during both summer and winter. Economic activities significantly affected the heating/cooling efficiency of dominant building features. High economic levels and population aggregation cause a significant increase in the heating effect of BCR, but a decrease in the cooling effect of BH and SDR, which collectively leads to a warmer environment. According to the results, this study offered several recommendations for better local adaptation strategies of urban transformation, with a focus on heat management.

Urbanization has adverse environmental effects, such as rising surface temperatures. This study analyzes the relationship between the urban heat island (UHI) intensity and Tianjin city's land cover characteristics. The land use cover change (LUCC) effects on the green areas and the land surface temperature (LST) were also studied. The land cover characteristics were divided into five categories: a built-up area, an agricultural area, a bare area, a forest, and water. The LST was calculated using the thermal bands of spatial images taken from 2005 to 2020. The increase in the built-up area was mainly caused by the agricultural area decreasing by 11.90%. The average land surface temperature of the study area increased from 23.50 to 36.51 °C, and the region moved to a high temperature that the built-up area's temperature increased by 1.5%. Still, the increase in vegetation cover was negative. From 2020 to 2050, the land surface temperature is expected to increase by 9.5 °C. The high-temperature areas moved into an aerial distribution, and the direction of urbanization determined their path. Urban heat island mitigation is best achieved through forests and water, and managers of urban areas should avoid developing bare land since they may suffer from degradation. The increase in the land surface temperature caused by the land cover change proves that the site is becoming more urbanized. The findings of this study provide valuable information on the various aspects of urbanization in Tianjin and other regions. In addition, future research should look into the public health issues associated with rapid urbanization.

Detecting change-points in multivariate settings is usually carried out by analyzing all marginals either independently, via univariate methods, or jointly, through multivariate approaches. The former discards any inherent dependencies between different marginals and the latter may suffer from domination/masking among different change-points of distinct marginals. As a remedy, we propose an approach which groups marginals with similar temporal behaviors, and then performs group-wise multivariate change-point detection. Our approach groups marginals based on hierarchical clustering using distances which adjust for inherent dependencies. Through a simulation study we show that our approach, by preventing domination/masking, significantly enhances the general performance of the employed multivariate change-point detection method. Finally, we apply our approach to two datasets: (i) Land Surface Temperature in Spain, during the years 2000–2021, and (ii) The WikiLeaks Afghan War Diary data.

Soil moisture is a key parameter in hydrological research and drought management. The inversion of soil moisture based on land surface temperature (LST) and NDVI triangular feature spaces has been widely used in various studies. Remote sensing provides regional LST data with coarse spatial resolutions which are insufficient for field scale (tens of meters). In this study, we bridged the data gap by adopting a Data Mining Sharpener algorithm to downscale MODIS thermal data with Vis-NIR imagery from Sentinel-2. To evaluate the downscaling algorithm, an unmanned aerial system (UAS) equipped with a thermal sensor was used to capture the ultra-fine resolution LST at three sites in the Tang River Basin in China. The obtained fine-resolution LST data were then used to calculate the Temperature Vegetation Dryness Index (TVDI) for soil moisture monitoring. Results indicated that downscaled LST data from satellites showed spatial patterns similar to UAS-measured LST, although discrepancies still existed. Based on the fine-resolution LST data, a 10-m resolution TVDI map was generated. Significant negative correlations were observed between the TVDI and in-situ soil moisture measurements (Pearson’s r of −0.67 and −0.71). Overall, the fine-resolution TVDI derived from the downscaled LST has a high potential for capturing spatial soil moisture variation.

One of the primary applications of satellite Land Surface Temperature (LST) observations lies in their utilization for modeling of actual evapotranspiration (ET) in agricultural crops, with the primary goals of monitoring and enhancing irrigation practices and improving crop water use productivity, as stipulated by Sustainable Development Goal (SDG) indicator 6.4.1. Evapotranspiration is a complex and dynamic process, both temporally and spatially, necessitating LST observations with high spatio-temporal resolution. Presently, none of the existing spaceborne thermal sensors can provide quasi-daily field-scale LST observations, prompting the development of methods for data fusion (thermal sharpening) of observations from various shortwave and thermal sensors to meet this spatio-temporal requirement. Previous research has demonstrated the effectiveness of combining shortwave-multispectral Sentinel-2 observations with thermal-infrared Sentinel-3 observations to derive daily, field-scale LST and ET estimates. However, these studies also highlighted limitations in capturing the distinct thermal contrast between cooler LST in irrigated agricultural areas and the hotter, adjacent dry regions. In this study, we aim to address this limitation by incorporating information on thermal spatial variability observed by Landsat satellites into the data fusion process, without being constrained by infrequent or cloudy Landsat thermal observations and while retaining the longwave radiance emission captured by the Sentinel-3 thermal sensor at its native resolution. Two approaches are evaluated, both individually and as a complementary combination, and validated against in situ LST measurements. The best performing approach, which leads to reduction in root mean square error of up to 1.5 K when compared to previous research, is subsequently used to estimate parcel-level actual evapotranspiration. The ET modeling process has also undergone various improvements regarding the gap-filling

Land urbanization not only efficiently met the requirements of social development but also brought severe heating effects, especially the changes in Land Surface Temperature (LST). The effects of building density on LST and driving relation of the configuration of buildings remain poorly understood over large areas. Using Landsat 8 satellite imagery acquired from the summer of 2019, this study quantified the heating effects (k) of building density on LST across 35 cities in China, and further analyzed the driving relation of the configuration of buildings such as the size, shape, and spatial distribution on k. Here the regression analysis results showed that the building density had a significant relationship with LST, and the k varied from 1.10 to 7.27 amount of the different cities. The size and aggregation distribution of buildings were the main positive drivers for the effects of building density on LST, and the shape of buildings was not obviously related to the k-value. The results of thermal environment simulation showed that the major reason might be that the close spatial relationship reduced the heat exchange between buildings and the atmosphere, resulting in higher LST. These conclusions will provide an important reference for urban planning and design.

[EN] Rapid urbanization contributes to the development of phenomena such as climate variability, especially in tropical countries, which negatively impact ecosystems and humans, factors that influence urban sustainability. Additionally, the increase of building construction prevents the flow of wind streams contributing to the retention of pollutants and hot air masses, causing events such as urban heat islands (UHI). This study aimed to analyze from the micro-territorial level, the influence of urban growth on the UHI phenomenon over the last two decades (2000¿2020) in the locality of Kennedy, in Bogotá, Colombia. For this purpose, environmental and socio-economic factors were evaluated. For the former, Landsat satellite images and spectral indices were used to evaluate the spatial¿temporal variation in the quantity and quality of vegetation, bodies of water, urbanized areas, impervious surfaces, as well as to calculate the land surface temperature and its distribution in the study area. With regard to the socio-economic factors, the variables considered for analysis were population density and energy consumption. Lastly, a principal component analysis was carried out to identify possible associations between the variables and to identify the contribution of each micro-territory to the UHI phenomenon in the study area. The spatio-temporal variations reveal a growing trend over time, especially in impermeable areas where several economic activities, vehicular traffic, and population density converge, which require certain actions to be prioritized in territorial planning and the addition of public green spaces in urban zones

The land surface temperature (LST) pattern is regarded as one of the most important indicators of the environmental consequences of land use/land cover change. The possible contribution of land surface to the warming phenomenon is being investigated by scientists across the world. This research focuses on variations in surface temperature and urban heat islands (UHIs) over the course of two seasons, i.e., winter and summer. Using remotely sensed datasets and geospatial techniques, an attempt was made to analyze the spatiotemporal variation in urban heat islands (UHIs) and its association with LULC over Chandigarh from 2000 to 2020. The Enhanced Built-up and Bareness Index (EBBI), Dry Built-up Index (DBI), and Dry Bare-Soil Index (DBSI) were used to identify built-up areas in the city. The results revealed an increase of 10.08% in BA, whereas the vegetation decreased by 4.5% over the study period, which is in close agreement with the EBBI, DBI, and DBSI assessments. From 2000 to 2020, the UHI intensities increased steadily in both the summer and winter seasons. Dense built-up areas such as the industrial unit of the city possessed the highest UHIindex (>0.7) values., Validerad;2022;Nivå 2;2022-09-22 (sofila)

The land surface temperature (LST) pattern is regarded as one of the most important indicators of the environmental consequences of land use/land cover change. The possible contribution of land surface to the warming phenomenon is being investigated by scientists across the world. This research focuses on variations in surface temperature and urban heat islands (UHIs) over the course of two seasons, i.e., winter and summer. Using remotely sensed datasets and geospatial techniques, an attempt was made to analyze the spatiotemporal variation in urban heat islands (UHIs) and its association with LULC over Chandigarh from 2000 to 2020. The Enhanced Built-up and Bareness Index (EBBI), Dry Built-up Index (DBI), and Dry Bare-Soil Index (DBSI) were used to identify built-up areas in the city. The results revealed an increase of 10.08% in BA, whereas the vegetation decreased by 4.5% over the study period, which is in close agreement with the EBBI, DBI, and DBSI assessments. From 2000 to 2020, the UHI intensities increased steadily in both the summer and winter seasons. Dense built-up areas such as the industrial unit of the city possessed the highest UHIindex (>0.7) values., Validerad;2022;Nivå 2;2022-09-22 (sofila)

Pistachio is an important economic crop in arid and semi-arid regions of Iran. A major problem leading to a reduction in crop quality and reduced marketability is extreme air temperature in summer, which causes sunburn of pistachio leaves and fruit. A solution proposed to deal with the negative effects of high temperatures and increase water consumption efficiency in pistachio orchards is use of light-reflecting compounds. This study investigated the effect of foliar application of gypsum, sulfur, and NAX-95 (calcium-based suspension coating) to trees in a pistachio orchard (150 ha) in central Iran. The effect of these foliar products is assessed at plot scale, using control plots sprayed with calcium sulfate, based on temperature and evapotranspiration changes analyzed through remote sensing. Landsat 8 sensor images and RGB images collected by UAVs (spatial resolution of 30 m and 20 cm, respectively), on the same dates, before and after foliar spray application, were merged using the PCA method and bilinear interpolation re-sampling. Land surface temperature (LST) was then estimated using the split-window algorithm, and daily evapotranspiration using the surface energy balance algorithm for land (SEBAL) algorithm. A land use map was prepared and used to isolate pistachio trees in the field and assess weed cover, whose effect was not accounted. The results showed that temperature remained constant in the control plot between the spraying dates, indicating no environmental changes. In the main plots, gypsum had the greatest effect in reducing the temperature of pistachio trees. The plots with foliar spraying with gypsum displayed a mean tree temperature (47-48 degrees C) decrease of 3.3 degrees C in comparison with the control plots (>49 degrees C), leading to an average decline in evapotranspiration of 0.18 mm/day. NAX-95 and sulfur reduced tree temperature by on average 1.3 degrees C and 0.6 degrees C, respectively. Thus, gypsum is the most suitable foliar-spray, QC 20230109

Pistachio is an important economic crop in arid and semi-arid regions of Iran. A major problem leading to a reduction in crop quality and reduced marketability is extreme air temperature in summer, which causes sunburn of pistachio leaves and fruit. A solution proposed to deal with the negative effects of high temperatures and increase water consumption efficiency in pistachio orchards is use of light-reflecting compounds. This study investigated the effect of foliar application of gypsum, sulfur, and NAX-95 (calcium-based suspension coating) to trees in a pistachio orchard (150 ha) in central Iran. The effect of these foliar products is assessed at plot scale, using control plots sprayed with calcium sulfate, based on temperature and evapotranspiration changes analyzed through remote sensing. Landsat 8 sensor images and RGB images collected by UAVs (spatial resolution of 30 m and 20 cm, respectively), on the same dates, before and after foliar spray application, were merged using the PCA method and bilinear interpolation re-sampling. Land surface temperature (LST) was then estimated using the split-window algorithm, and daily evapotranspiration using the surface energy balance algorithm for land (SEBAL) algorithm. A land use map was prepared and used to isolate pistachio trees in the field and assess weed cover, whose effect was not accounted. The results showed that temperature remained constant in the control plot between the spraying dates, indicating no environmental changes. In the main plots, gypsum had the greatest effect in reducing the temperature of pistachio trees. The plots with foliar spraying with gypsum displayed a mean tree temperature (47–48 °C) decrease of 3.3 °C in comparison with the control plots (>49 °C), leading to an average decline in evapotranspiration of 0.18 mm/day. NAX-95 and sulfur reduced tree temperature by on average 1.3 °C and 0.6 °C, respectively. Thus, gypsum is the most suitable foliar-spraying compound to lower the temperatu

Land surface temperature (LST) and land surface albedo (LSA) are the two key regional and global climate-controlling parameters; assessing their behavior would likely result in a better understanding of the appropriate adaptation strategies to mitigate the consequences of climate change. This study was conducted to explore the spatiotemporal variability in LST and LSA across different land use/cover (LULC) classes in northwest Iran. To do so, we first applied an object-oriented algorithm to the 10 m resolution Sentinel-2 images of summer 2019 to generate a LULC map of a 3284 km2 region in northwest Iran. Then, we computed the LST and LSA of each LULC class using the SEBAL algorithm, which was applied to the Landsat-8 images from the summer of 2019 and winter of 2020. The results showed that during the summer season, the maximum and minimum LSA values were associated with barren land (0.33) and water bodies (0.11), respectively; during the winter season, the maximum LSA value was observed for farmland and snow cover, and the minimum value was observed in forest areas (0.21). The maximum and minimum LST values in summer were acquired from rangeland (37 °C) and water bodies (24 °C), respectively; the maximum and minimum values of winter values were detected in forests (4.14 °C) and snow cover (−21.36 °C), respectively. Our results revealed that barren land and residential areas, having the maximum LSA in summer, were able to reduce the heating effects to some extent. Forest areas, due to their low LSA and high LST, particularly in winter, had a greater effect on regional warming compared with other LULC classes. Our study suggests that forests might not always mitigate the effects of global warming as much as we expect., Validerad;2023;Nivå 2;2023-01-01 (joosat);Licens full text: CC BY license