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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

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

Monitoring urban heat island through google earth engine. Potentialities and difficulties in different cities of the United States

The aim of this work is to exploit the large-scale analysis capabilities of the innovative Google Earth Engine platform in order to investigate the temporal variations of the Urban Heat Island phenomenon as a whole. A intuitive methodology implementing a large-scale correlation analysis between the Land Surface Temperature and Land Cover alterations was thus developed. The results obtained for the Phoenix MA are promising and show how the urbanization heavily affects the magnitude of the UHI effects with significant increases in LST. The proposed methodology is therefore able to efficiently monitor the UHI phenomenon

Trend Analysis of Las Vegas Land Cover and Temperature Using Remote Sensing

The Las Vegas urban area expanded rapidly during the last two decades. In order to understand the impacts on the environment, it is imperative that the rate and type of urban expansion is determined. Remote sensing is an efficient and effective way to study spatial change in urban areas and Spectral Mixture Analysis (SMA) is a valuable technique to retrieve subpixel landcover information from remote sensing images. In this research, urban growth trends in Las Vegas are studied over the 1990 to 2010 period using images from Landsat 5 Thematic Mapper (TM) and National Agricultural Imagery Program (NAIP). The SMA model of TM pixels is calibrated using high resolution NAIP classified image. The trends of land cover change are related to the land surface temperature trends derived from TM thermal infrared images. The results show that the rate of change of various land covers followed a linear trend in Las Vegas. The largest increase occurred in residential buildings followed by roads and commercial buildings. Some increase in vegetation cover in the form of tree cover and open spaces (grass) is also seen and there is a gradual decrease in barren land and bladed ground. Trend analysis of temperature shows a reduction over the new development areas with increased vegetation cover especially, in the form of golf courses and parks. This research provides a useful insight about the role of vegetation in ameliorating temperature rise in arid urban areas

Combined Effects of Impervious Surface and Vegetation Cover on Air Temperature Variations in a Rapidly Expanding Desert City

The goal of this study is to improve our understanding of the interac- tive function of impervious and vegetation covers at different levels of the local and intra-urban spatial scales in relation to air temperatures in an urban environment. A multiple regression model was developed using impervious and vegetation frac- tions at different scales to predict maximum air temperature for the entire Phoenix metropolitan area in Arizona, USA. This study demonstrates that a small amount of impervious cover in a desert city can still increase maximum air temperature despite abundant vegetation cover.

Urban energy exchanges monitoring from space

One important challenge facing the urbanization and global environmental change community is to understand the relation between urban form, energy use and carbon emissions. Missing from the current literature are scientific assessments that evaluate the impacts of different urban spatial units on energy fluxes; yet, this type of analysis is needed by urban planners, who recognize that local scale zoning affects energy consumption and local climate. However, satellite-based estimation of urban energy fluxes at neighbourhood scale is still a challenge. Here we show the potential of the current satellite missions to retrieve urban energy budget, supported by meteorological observations and evaluated by direct flux measurements. We found an agreement within 5% between satellite and in-situ derived net all-wave radiation; and identified that wall facet fraction and urban materials type are the most important parameters for estimating heat storage of the urban canopy. The satellite approaches were found to underestimate measured turbulent heat fluxes, with sensible heat flux being most sensitive to surface temperature variation (-64.1, +69.3 W m-2 for ±2 K perturbation); and also underestimate anthropogenic heat flux. However, reasonable spatial patterns are obtained for the latter allowing hot-spots to be identified, therefore supporting both urban planning and urban climate modelling

Urban imperviousness effects on summer surface temperatures nearby residential buildings in different urban zones of Parma

Rapid and unplanned urban growth is responsible for the continuous conversion of green or generally natural spaces into artificial surfaces. The high degree of imperviousness modifies the urban microclimate and no studies have quantified its influence on the surface temperature (ST) nearby residential building. This topic represents the aim of this study carried out during summer in different urban zones (densely urbanized or park/rural areas) of Parma (Northern Italy). Daytime and nighttime ASTER images, the local urban cartography and the Italian imperviousness databases were used. A reproducible/replicable framework was implemented named "Building Thermal Functional Area" (BTFA) useful to lead building-proxy thermal analyses by using remote sensing data. For each residential building (n = 8898), the BTFA was assessed and the correspondent ASTER-LST value (ST_BTFA) and the imperviousness density were calculated. Both daytime and nighttime ST_BTFA significantly (p < 0.001) increased when high levels of imperviousness density surrounded the residential buildings. These relationships were mostly consistent during daytime and in densely urbanized areas. ST_BTFA differences between urban and park/rural areas were higher during nighttime (above 1 °C) than daytime (about 0.5 °C). These results could help to identify "urban thermal Hot-Spots" that would benefit most from mitigation actions

Evaluating the Variability of Urban Land Surface Temperatures Using Drone Observations

Urbanization and climate change are driving increases in urban land surface temperatures that pose a threat to human and environmental health. To address this challenge, we must be able to observe land surface temperatures within spatially complex urban environments. However, many existing remote sensing studies are based upon satellite or aerial imagery that capture temperature at coarse resolutions that fail to capture the spatial complexities of urban land surfaces that can change at a sub-meter resolution. This study seeks to fill this gap by evaluating the spatial variability of land surface temperatures through drone thermal imagery captured at high-resolutions (13 cm). In this study, flights were conducted using a quadcopter drone and thermal camera at two case study locations in Milwaukee, Wisconsin and El Paso, Texas. Results indicate that land use types exhibit significant variability in their surface temperatures (3.9–15.8 °C) and that this variability is influenced by surface material properties, traffic, weather and urban geometry. Air temperature and solar radiation were statistically significant predictors of land surface temperature (R2 0.37–0.84) but the predictive power of the models was lower for land use types that were heavily impacted by pedestrian or vehicular traffic. The findings from this study ultimately elucidate factors that contribute to land surface temperature variability in the urban environment, which can be applied to develop better temperature mitigation practices to protect human and environmental health

Modeling Land-Cover Types Using Multiple Endmember Spectral Mixture Analysis in a Desert City

Spectral mixture analysis is probably the most commonly used approach among sub-pixel analysis techniques. This method models pixel spectra as a linear combination of spectral signatures from two or more ground components. However, spectral mixture analysis does not account for the absence of one of the surface features or spectral variation within pure materials since it utilizes an invariable set of surface features. Multiple endmember spectral mixture analysis (MESMA), which addresses these issues by allowing endmembers to vary on a per pixel basis, was employed in this study to model Landsat ETM+ reflectance in the Phoenix metropolitan area. Image endmember spectra of vegetation, soils, and impervious surfaces were collected with the use of a fine resolution Quickbird image and the pixel purity index. This study employed 204 (=3x17x4) total four-endmember models for the urban subset and 96 (=6x6x2x4) total five-endmember models for the non-urban subset to identify fractions of soil, impervious surface, vegetation, and shade. The Pearson correlation between the fraction outputs from MESMA and reference data from Quickbird 60 cm resolution data for soil, impervious, and vegetation were 0.8030, 0.8632, and 0.8496 respectively. Results from this study suggest that the MESMA approach is effective in mapping urban land covers in desert cities at sub- pixel level.