Satellite and Ground-Based Sensors for the Urban Heat Island Analysis in the City of Rome

In this work, the trend of the Urban Heat Island (UHI) of Rome is analyzed by both ground-based weather stations and a satellite-based infrared sensor. First, we have developed a suitable algorithm employing satellite brightness temperatures for the estimation of the air temperature belonging to the layer of air closest to the surface. UHI spatial characteristics have been assessed using air temperatures measured by both weather stations and brightness temperature maps from the Advanced Along Track Scanning Radiometer (AATSR) on board ENVISAT polar-orbiting satellite. In total, 634 daytime and nighttime scenes taken between 2003 and 2006 have been processed. Analysis of the Canopy Layer Heat Island (CLHI) during summer months reveals a mean growth in magnitude of 3–4 K during nighttime and a negative or almost zero CLHI intensity during daytime, confirmed by the weather stations

Downscaling landsat land surface temperature over the urban area of Florence

A new downscaling algorithm for land surface temperature (LST) images retrieved from Landsat Thematic Mapper (TM) was developed over the city of Florence and the results assessed against a high-resolution aerial image. The Landsat TM thermal band has a spatial resolution of 120 m, resampled at 30 m by the US Geological Survey (USGS) agency, whilst the airborne ground spatial resolution was 1 m. Substantial differences between Landsat USGS and airborne thermal data were observed on a 30 m grid: therefore a new statistical downscaling method at 30 m was developed. The overall root mean square error with respect to aircraft data improved from 3.3 °C (USGS) to 3.0 °C with the new method, that also showed better results with respect to other regressive downscaling techniques frequently used in literature. Such improvements can be ascribed to the selection of independent variables capable of representing the heterogeneous urban landscape

Monitoring the impact of land cover change on surface urban heat island through google earth engine. Proposal of a global methodology, first applications and problems

All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to "Make cities inclusive, safe, resilient and sustainable". In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992-2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities

The use of satellite data, meteorology and land use data to define high resolution temperature exposure for the estimation of health effects in Italy

Introduction. Despite the mounting evidence on heat-related health risks, there is limited evidence in suburban and rural areas. The limited spatial resolution of temperature data also hinders the evidence of the differential heat effect within cities due to individual and area-based characteristics. Methods. Satellite land surface temperature (LST), observed meteorological and spatial and spatio-temporal land use data were combined in mixed-effects regression models to estimate daily mean air temperature with a 1x1km resolution for the period 2000-2010. For each day, random intercepts and slopes for LST were estimated to capture the day-to-day temporal variability of the Ta–LST relationship. The models were also nested by climate zones to better capture local climates and daily weather patterns across Italy. The daily exposure data was used to estimate the effects and impacts of heat on cause-specific mortality and hospital admissions in the Lazio region at municipal level in a time series framework. Furthermore, to address the differential effect of heat within an urban area and account for potential effect modifiers a case cross-over study was conducted in Rome. Mean temperature was attributed at the individual level to the Rome Population Cohort and the urban heat island (UHI) intensity using air temperature data was calculated for Rome. Results. Exposure model performance was very good: in the stage 1 model (only on grid cells with both LST and observed data) a mean R2 value of 0.96 and RMSPE of 1.1°C and R2 of 0.89 and 0.97 for the spatial and temporal domains respectively. The model was also validated with regional weather forecasting model data and gave excellent results (R2=0.95 RMSPE=1.8°C. The time series study showed significant effects and impacts on cause-specific mortality in suburban and rural areas of the Lazio region, with risk estimates comparable to those found in urban areas. High temperatures also had an effect on respiratory hospital admissions. Age, gender, pre-existing cardiovascular disease, marital status, education and occupation were found to be effect modifiers of the temperature-mortality association. No risk gradient was found by socio-economic position (SEP) in Rome. Considering the urban heat island (UHI) and SEP combined, differential effects of heat were observed by UHI among same SEP groupings. Impervious surfaces and high urban development were also effect modifiers of the heat-related mortality risk. Finally, the study found that high resolution gridded data provided more accurate effect estimates especially for extreme temperature intervals. Conclusions. Results will help improve heat adaptation and response measures and can be used predict the future heat-related burden under different climate change scenarios.Open Acces

Urban surface temperature time series estimation at the local scale by spatial-spectral unmixing of satellite observations

The study of urban climate requires frequent and accurate monitoring of land surface temperature (LST), at the local scale. Since currently, no space-borne sensor provides frequent thermal infrared imagery at high spatial resolution, the scientific community has focused on synergistic methods for retrieving LST that can be suitable for urban studies. Synergistic methods that combine the spatial structure of visible and near-infrared observations with the more frequent, but low-resolution surface temperature patterns derived by thermal infrared imagery provide excellent means for obtaining frequent LST estimates at the local scale in cities. In this study, a new approach based on spatial-spectral unmixing techniques was developed for improving the spatial resolution of thermal infrared observations and the subsequent LST estimation. The method was applied to an urban area in Crete, Greece, for the time period of one year. The results were evaluated against independent high-resolution LST datasets and found to be very promising, with RMSE less than 2 K in all cases. The developed approach has therefore a high potential to be operationally used in the near future, exploiting the Copernicus Sentinel (2 and 3) observations, to provide high spatio-temporal resolution LST estimates in cities

Mapping Europe into local climate zones

Cities are major drivers of environmental change at all scales and are especially at risk from the ensuing effects, which include poor air quality, flooding and heat waves. Typically, these issues are studied on a city-by-city basis owing to the spatial complexity of built landscapes, local topography and emission patterns. However, to ensure knowledge sharing and to integrate local-scale processes with regional and global scale modelling initiatives, there is a pressing need for a world-wide database on cities that is suited for environmental studies. In this paper we present a European database that has a particular focus on characterising urbanised landscapes. It has been derived using tools and techniques developed as part of the World Urban Database and Access Portal Tools (WUDAPT) project, which has the goal of acquiring and disseminating climate-relevant information on cities worldwide. The European map is the first major step toward creating a global database on cities that can be integrated with existing topographic and natural land-cover databases to support modelling initiatives

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학위논문(석사) -- 서울대학교대학원 : 환경대학원 환경계획학과, 2023. 8. Young-Sung LEE.There is direct relationship between urbanization and Land use and cover (LULC) change, there is also relationship between the land surface temperature (LST) and Albedo, Albedo and LST can be influenced by urbanization at the same time. In this study, i try to explore the impact of urbanization on surface urban heat island (SUHI). There are no researches studied by remote sensing method in North District of Hong Kong. In this study, i have classified the LULC in North District of Hong Kong, then, LST has been analyzed by using Landsat (TM/OLI) images. The inversion LST was obtained in this study's usage of the maximum likelihood classifier approach (supervised classification) to categorize pictures. There are so many methods to define urban area and non-urban area, simplified urban extend (SUE) method is used to distinguish urban area and non-urban area and then calculate the surface urban heat island (SUHI) in North District of Hong Kong, the correlation between urban heat island effect and urban green space and building land can provide important information for our urban development and environmental protection. To study the influence of urban green space and building land on urban heat island effect, I also analyzed the correlation between land surface temperature and Albedo, and the relationship between LST and NDVI, NDBI shows the influence of vegetation area on UHI is negative. Then the positive correlation between urban building land and surface temperature distribution is that urban building land has positive influence on UHI, it also shows building area can enhance urban heat island effect. The results of LULC revealed that According to the classification results, from 1987 to 2004, 71.157% of forest area remained unchanged, 26.903% of forest were changed into urban area, 9.529% of barren area were changed into urban area.From 2004 to 2021, 71.157% of forest area remained unchanged, 26.903% of forest area were changed into urban area, 9.529% of barren area were changed into urban area. From 1987 to 2021, 42.654% of forest area changed into other land area, urban area continued to increase from 1987 to 2021CHAPTER Ⅰ. INTRODUCTION 6 1.Research Objectives and Significance of Study 6 2. The influencing factors of Urban Heat Island 9 3. Research range 10 4. Problems among studies 13 CHAPTER Ⅱ. LITERATURE REVIEW 14 1. Urban heat island (UHI) and urban heat intensity (UHII) 15 2. The research methods of urban heat island and progress 17 3. Surface meteorological data observation between urban area and suburb area 16 3.1. Boundary layer numerical model simulation 17 3.2. Remote sensing monitoring 18 3.3. Temperature-Based Heat Island remote sensing Monitoring Method 21 3.3.1. A Method of Heat Island remote sensing Monitoring Based on Vegetation Index (NDVI) 22 3.3.2. A Method of Heat Island remote sensing Monitoring Based on "Heat Landscape." 23 4. Research urban heat island by remote sensing inversion 23 4.1. Radiation transfer equation method 24 4.2. Mono-window algorithm 25 4.3. Split-window algorithm 26 5. The effect of urbanization on urban heat island by remote sensing 27 CHAPTER Ⅲ. RESEARCH METHODOLOGY 28 1. Research Contents and Technical Route 28 1.1. Research Contents 28 1.2. Technical Route 29 1.2.1. Access to remote sensing data 30 1.2.2. Remote sensing data preprocessing 30 (1) Geometric correction 31 (2) Atmospheric correction 31 (3) Radiometric calibration 32 (4) Study area image clip 32 2. Supervised Classification 34 2.1 Accuracy Assessment 34 2.2 land surface temperature (LST) 34 3. Correlation between LST and EDVI, NDBI 36 4. UHII 37 CHAPTER Ⅳ. OUTCOME 39 1. Separability 41 2. Accuracy 41 3. Land use and land cover (LULC) classification result 42 4. Calculation of NDVI and FVC through ENVI 5.3 43 5. Correlation analysis between UHI and NDVI and NDBI 45 6. Relationship between LST and LULC 46 7. The Albedo and LST 50 8. Analysis of Albedo and LST in significant sub-areas 52 9. The distribution of SUHI in the North District of Hong Kong 53 10. the development of urbanization and urban heat island in the North District of Hong Kong 53 CHAPTER Ⅴ. CONCLUSION 55 Main innovations and limitations in this study 57 Research prospect of urban heat island in Hong Kong region 58 REFERENCE 59석

Analysis of urban heat island climates along the I-85/I-40 corridor in central North Carolina

Land surface temperature is a significant parameter for identifying micro-climatic changes and their spatial distributions relative to the urban environment. This paper examined and identified the urban heat islands and their spatial and temporal variability along the I-85/I-40 corridor in central North Carolina between 1990 and 2002. More specifically, the study focused on: (1) understanding the behavior of the spectral and thermal signatures of various land cover and land use types and their relationships with UHI development, and (2) applying digital remote sensing techniques to observe and measure the temporal and spatial variability of these surface heat islands. An assemblage of remotely sensed imagery (Landsat data), land surface temperature data, land cover and land use classifications, vegetation indices, and archived weather data was used to create maps, charts and statistical models to indicate and display the magnitude and spatial extent of these thermal climates. The data revealed that urbanization in the I-85/I-40 corridor region increased significantly between 1990 and 2002. Quantitative results from the satellite imagery also indicated that differences in land cover/ land use types, anthropogenic heat sources, and land surface temperature variability likely contributed to a temperature rise in the corridor study area thus thermal climate development

An overview of monitoring methods for assessing the performance of nature-based solutions against natural hazards

To bring to fruition the capability of nature-based solutions (NBS) in mitigating hydro-meteorological risks (HMRs) and facilitate their widespread uptake require a consolidated knowledge-base related to their monitoring methods, efficiency, functioning and the ecosystem services they provide. We attempt to fill this knowledge gap by reviewing and compiling the existing scientific literature on methods, including ground-based measurements (e.g. gauging stations, wireless sensor network) and remote sensing observations (e.g. from topographic LiDAR, multispectral and radar sensors) that have been used and/or can be relevant to monitor the performance of NBS against five HMRs: floods, droughts, heatwaves, landslides, and storm surges and coastal erosion. These can allow the mapping of the risks and impacts of the specific hydro-meteorological events. We found that the selection and application of monitoring methods mostly rely on the particular NBS being monitored, resource availability (e.g. time, budget, space) and type of HMRs. No standalone method currently exists that can allow monitoring the performance of NBS in its broadest view. However, equipments, tools and technologies developed for other purposes, such as for ground-based measurements and atmospheric observations, can be applied to accurately monitor the performance of NBS to mitigate HMRs. We also focused on the capabilities of passive and active remote sensing, pointing out their associated opportunities and difficulties for NBS monitoring application. We conclude that the advancement in airborne and satellite-based remote sensing technology has signified a leap in the systematic monitoring of NBS performance, as well as provided a robust way for the spatial and temporal comparison of NBS intervention versus its absence. This improved performance measurement can support the evaluation of existing uncertainty and scepticism in selecting NBS over the artificially built concrete structures or grey approaches by addressing the questions of performance precariousness. Remote sensing technical developments, however, take time to shift toward a state of operational readiness for monitoring the progress of NBS in place (e.g. green NBS growth rate, their changes and effectiveness through time). More research is required to develop a holistic approach, which could routinely and continually monitor the performance of NBS over a large scale of intervention. This performance evaluation could increase the ecological and socio-economic benefits of NBS, and also create high levels of their acceptance and confidence by overcoming potential scepticism of NBS implementations