THAI NGUYEN UNIVERSITY UNIVERSITY OF AGRICULTURAL AND FORESTRY NGUYEN TRUNG ANH UTILIZING REMOTE SENSING DATA FOR ASSESSING THE EFFECT OF LAND USE CHANGES ON URBAN HEAT ISLAND IN TAIPEI CITY BACHELOR THESIS Study Mode : Full - Time Major : Environmental Science and Management Faculty : International Training and Development Center Batch : K43 – AEP THAI NGUYEN - 30/09/2015 i Thai Nguyen University of Agriculture and Forestry Degree program Bachelor of Environmental Science and Management Full name NGUYEN TRUNG ANH Student ID DTN1153110166 Thesis title Supervisor Utilizing remote sensing data for assessing the effect of land use changes on Urban Heat Island in Taipei City Assoc Prof Tang-Huang Lin and Assoc Prof Nguyen The Hung ABSTRACT Land surface temperature (LST) is an important parameter to human living environment and the pattern of regional weather The changes of LST might be caused from seasonal variation, weather patterns as well as land cover and land use (LCLU) alterations For the urban area, the type of LCLU according to urban development could increase LST thus enhanced the urban heat island (UHI) effect Taipei City has urbanized rapidly since 1967, and urban warming appeared from 1980s The effects of urbanization on local weather and climate change resulted in a remarkable increase in mean and minimum temperatures However, urbanization resulted in little change in maximum temperature in Taipei City The increase in minimum temperature in summer is significant in Taipei City The frame work of national land surface temperature is presented with remote sensing data The proposed system uses the features of existing widely used classification approaches that are amenable to data derived from remote sensing sources Keywords Taipei, Taiwan, Land surface temperature, Urbanization, Land cover change, Remote sensing; Number of pages 47 Date of submission September 30, 2015 i ACKNOWLEDGEMENT First and foremost, we wish to express our sincere thanks to Center for Space and Remote Sensing Research (CSRSR) of National Central University (NCU) for providing us all the necessary facilities and all the students who help me the scientific knowledge to complete this thesis In particular, we would like to thank our principal research adviser Assoc Prof Tang-Huang Lin and Assoc Prof Nguyen The Hung guided me wholeheartedly when we implement this thesis With qualifications, experience and time is limited to topics inevitable shortcomings I would like to receive the only protection, input of teachers so that I can complete your project I also want to say thank to International Training and Development Center – Thai Nguyen University of Agriculture and Forestry which has facilitated me the chance to come here to study and get more knowledge exchange Finally yet important, i take this opportunity to express our deepest appreciation to our families, relatives, friends who encouraged and supported us unceasingly and all who directly or indirectly, have lent their helping hand in this venture Thank you very much! Thai Nguyen September 30, 2015 Author Nguyen Trung Anh ii TABLE OF CONTENTS LIST OF FIGURES LIST OF TABLES LIST OF ABBREVIATIONS PART I INTRODUCTION 1.1 Background 1.2 The purpose of thesis 1.3 The significant of thesis PART II LITERATURE REVIEW 2.1.Theoretical basis 2.1.1 Satellite data (images) 2.1.2 Definitions of land surface temperature 2.1.3 The land cover types 11 2.2 Geographic information systems 12 2.2.1 Definition 12 2.2.2 GIS Application Areas 13 2.3.1 Definition 14 2.3.2 Basics of EMR/Atmospheric Affects Foundations of Remote Sensing 14 2.3.3 Applications of Remote Sensing Technology 15 2.4 Practical basis 16 2.4.1 The research on land surface temperature in the world 16 2.4.2 The research on land surface temperature in Viet Nam 18 PART III METHODOLOGY 20 iii 3.1 The objects of research 20 3.2 Location and research time 20 3.3 Research content 20 3.4 Methods 20 3.4.1 Collecting and selecting data 20 3.4.2 Describes methods of calculation FAR, BCR, UHI intensity 21 PART IV RESULTS 24 4.1 The natural conditions and socioeconomic in study area 24 4.1.1 Natural conditions 24 4.1.2 Probabilistic risk analyses 29 4.1.3 Economic 30 4.1.4 Education 30 4.2 Process of determining land surface temperature, land cover and land use and their interaction 33 4.2.1 Pre-Processing 33 4.3 Results from determining land surface temperature, land cover and land use and their interaction 34 4.3.1 Brightness temperature map 34 4.3.2 Floor Area Ratio (FAR) and Building coverage ratio 35 PART V DISCUSSION AND CONCLUSION 41 5.1 Discussion 41 5.2 Conclusion 42 iv LIST OF FIGURES Figure 2.1 Process of landsat data Figure 2.2 Land cover mapping of the global 12 Figure 3.1 The land surface temperature process 21 Figure 3.2 Flow chart of land surface temperature 23 Firgure 4.1 The map of Taipei City 25 Figure 4.2 Map of location and geographic environment of Taipei City 26 Figure 4.3 Estimate of affected population under various rainfall intensities in Shilin District of Taipei City 29 Figure 4.4 Population growth of Taipei City and neighboring areas, and floor area increase of newly constructed houses (1945–2009) Source: Taiwan City Statistical Year Book 2010 and Banciao City Household Registration Office, Taipei County 31 Figure 4.5 Data pre – Processing 33 Figure 4.6 Process of landsat data into indicators of land surface temperature (UHII) and land cover and land use (BCR and FAR) 34 Figure 4.7 Brightness temperature map 34 Figure 4.8 Floor Area Ratio (FAR) 35 Figure 4.9 Building Coverage Ratio (BCR) 36 Figure 4.10 Relationship between BCR and UHII .38 Figure 4.11 Relationship between FAR and UHII .38 LIST OF TABLES Table 4.1 Satellite image Table 4.2 Statistics on Land and Climate for Taipei City 32 LIST OF ABBREVIATIONS BCR Building Coverage Ratio FAR Floor Area Ratio GIS Geographic Information Systems LCCS Land Cover Classification System LST Land surface temperature RS Remote sensing TIR Thermal infrared UHI Urban Heat Island UHII Urban Heat Island Intensity TOA Top of Atmospheric TB Brightness temperature OLI Operational Land Imager NDVI Normalized Difference Vegetation Index FVC Fracting of Vegetation LCLU Land Cover and Land use PART I INTRODUCTION 1.1 Background Land surface temperature is how hot the “surface” of the Earth would feel to the touch in a particular location From a satellite’s point of view, the “surface” is whatever it sees when it looks through the atmosphere to the ground It could be snow and ice, the grass on a lawn, the roof of a building, or the leaves in the canopy of a forest Thus, land surface temperature is not the same as the spatial uniformity of air temperature that is included in the daily weather report It becomes more important to monitor land surface temperature because the warmth rising off Earth’s landscapes influences (and is influenced by) our world’s weather and climate patterns Scientists want to understand how increasing atmospheric greenhouse gases affect land surface temperature, and how rising land surface temperatures affect glaciers, ice sheets, permafrost, and the vegetation in Earth’s ecosystems Commercial farmers may also use land surface temperature maps like these to evaluate water requirements for their crops during the summer, when they are prone to heat stress Conversely, in winter, these maps can help citrus farmers to determine where and when orange groves could have been exposed to damaging frost Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local through global scales The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from space However, retrieving LST is still a challenging task since the LST retrieval problem is ill-posed This paper reviews the current status of selected remote sensing algorithms for estimating LST from thermal infrared (TIR) data A brief theoretical background of the subject is presented along with a survey of the algorithms employed for obtaining LST from space-based TIR measurements The discussion focuses on TIR data acquired from polar-orbiting satellites because of their widespread use, global applicability and higher spatial resolution compared to geostationary satellites The theoretical framework and methodologies used to derive the LST from the data are reviewed followed by the methodologies for validating satellite-derived LST Directions for future research to improve the accuracy of satellite-derived LST are then suggested Taipei City is located in a subtropical basin Because of the unique landforms of the geological basin in this typhoon area, the typhoon-fed floods are enormous in these areas In a recent study, Wang et al (2008) documented that a strong warming trend in the Taipei basin (two times higher than the world average) was observed in the period from 1897 to 2006, which accelerated after 1980 The UHI intensity of the Taipei basin reveals an increasing trend with a monthly average of 0.011°C during 1994– 2006, and during 2002–2006, the UHI anomalies show the most significant increases However, the nocturnal and diurnal UHI phenomenon were not described in those previous studies, due to the lack of detailed record from an adequate network of observations in the city So i want to about:'' Utilizing Remote Sensing data for assessing the effect of land surface temperature changes on Urban Heat Island in Taipei City'' as a suburb of Taipei, following the completion of the Taipei Rapid Transit System’s Danshui Line Table 4.1: Statistics on Land and Climate for Taipei City Item (Unit) Land Area (km²) 2007 2008 9,674 9,650 Climate 2009 2010 Mean Temperature (°C ) 23.4 Mean Relative Humidity (%) 271.7997 0.0000 9,635 +42 2009 271.7997 271.7997 271.7997 Population Density (person/km²) 2010 Comparison between 2010 and 2009 9,593 Jan Feb March April May 23.3 16.9 17.6 20.0 20.7 25.5 74 76 78 84 72 78 75 Duration of Sunshine (hour) 1,631.8 1,506.6 112.6 79.9 152.9 74.0 115.3 Precipitation (mm) 1,669.2 2,278.3 105.3 232.6 66.5 112.5 183.9 Precipitation Days (day) 152 168 15 13 Climate 18 14 2010 June July Aug Sep Oct Nov Dec Mean Temperature (°C ) 26.2 30.3 30.0 28.8 24.4 21.1 17.5 Mean Relative Humidity (%) 82 69 74 72 79 75 70 Duration of Sunshine (hour) 77.8 176.4 212.4 201.5 68.4 Precipitation (mm) 419.6 89.1 388.5 144.2 345.4 127.3 63.4 Precipitation Days (day) 23 12 13 15 19 72.4 163.0 14 Sources: Department of Land, Taipei City Government; Department of Civil Affairs, Taipei City Government; Central Weather Bureau Taipei City became a centrally administered municipality under the Executive Yuan, R.O.C., in July 1967 After several boundary readjustments, the land area of Taipei City by the end of 2010 was 271.7997 square kilometers The population density of the city is 9,635 persons per square kilometer, ranking second in the country, only 326 persons per square kilometer fewer than the population density of Kaohsiung City Taipei City is located in the subtropical zone with year-round rain and no obvious dry seasons The climate is usually damp and cold during the wintertime 32 due to the influence of the northeast monsoon The temperature rises during the summertime, since Taipei City is located in a basin that does not allow for regular ventilation The mean temperature in Taipei City in 2010 was 23.3°C, which was 0.1°C lower than that of 2009 The highest and lowest monthly mean temperatures were 30.3°C in July and 16.9°C in January, respectively, showing a gap of 13.4°C The annual number of precipitation days was 168 days and overall precipitation was 2,278.3 millimeters in 2010, which was a 36.49% increase compared to that of 2009 4.2 Process of determining land surface temperature, land cover and land use and their interaction 4.2.1 Pre-Processing Visible spectral bands: TM Bands Single band images multi – bands images (TM/ETM+) ETM + Bands Layer stacking (Bands Combination) Landsat TM Image Geometric correction Landsat ETM + Image ( Latitude/Longitude geographical coordinates) Geometric Correction Clip sudy area (Taipei city) Resample Images Images Subset Study Area Clip Figure 4.5 Data pre – Processing 33 4.3 Results from determining land surface temperature, land cover and land use and their interaction 4.3.1 Brightness temperature map Data from LANDSAT IN 1991, 1992 and 2005 was converted in to land surface temperature and presented in Figure 4.7 Figure 4.7 Brightness temperature map Figure 4.7 shows that the brightness temperature is a measurement of the radiance of the microwave radiation traveling upward from the top of the atmosphere to the satellite The remaining corrections performed by comparing the actual temperatures with those simulated by our radioactive transfer model So you can see in 34 1991 the average temperature in the form of 16 to 30 degrees The effect of urbanization on local weather and climate change in a remarkable increase in mean and minimum temperature in 1991 Through many years of urbanization accompanied by a huge increase in population led to the change of climate which is particularly temperature changes dramatically the temperature up to 39 degrees in 2002 but this did not have in 1991 ranges from 16 to 30 degrees And culminating in the high temperature spreads have fallen by 2005 as temperatures rising in many places in about 36 to 39 degrees 4.3.2 Floor Area Ratio (FAR) and Building coverage ratio The floor area ratio between the building constructed as compared to the size of the plot of land is presented in Figure 4.8 Figure 4.8 Floor Area Ratio 35 Figure 4.8 shows floor area ratio (FAR) are used to estimate the building intensity of a city from three aspects, the buildings stretching on the surface and growing along the third dimension (E.g dimensions of the buildings and the spaces between them, street widths and spacing) and urban cover (e.g vegetated, bare soil and water) within the urban canopy layer significantly affects the physics of urban climatic environment On the other hand, it is a major parameter shows development intensity and the intensity of activities taking place within a specified land area and obviously has implications on urban climate that reflects the number of prominent obstacles that affects air flow Figure 4.9 shows the spatial distribution of BCR in Taipei City area The BCR is the means percentage ratio of the total standing area of all buildings (or building footprint) to the total area of the interest area Fir\\ Figure 4.9 Building Coverage Ratio (BCR) 36 Figure 4.9 shows that a general rule, city centers have a higher BCR as compared to outer lying residential areas it was assessed the relationship with thermal environment and if there is, It is thus crucial to identify the spatial characteristic factors amongst the many indicators available to investigate FAR, BCR and were used as independent variables to motivate the average surface temperature which was a dependent variable rising on the diurnal range These simple relationships between climate and spatial characteristic indicators could help decision makers and planners to take climate adaptation into account, to ensure climate neutral development from the beginning of a planning process In fact, our results showed that the average surface temperature can be significantly increased or decreased by different spatial compositions and configurations of those features This is because the spatial characteristics including urban structure and urban cover (e.g fractions of built-up, paved, vegetated, and open water features) Therefore, it is our recommendation that urban planners should try to control for the effects of their composition Vegetation management, particularly increasing tree canopy, has been considered an effective means to mitigate excess urban heat and to alleviate the thermal discomfort in the outdoor environment for both highly urbanized areas and areas where urbanization is still in process Land surface temperature (LST) presenting urban heat island intensity (UHII) was correlated with building cover ratio (Figure 4.10) and floor area ratio (Figure 4.11) 37 Figure 4.10 Relationship between BCR and UHII Figure 4.11 Relationship between FAR and UHII 38 Figure 4.10 and 4.11 show that with the above result, we can see high correlation between UHII and building parameters, but some of them has no relationship It shows that different characteristic of building forms may affect the UHI intensity with different levels of contribution However, the lack of ground stations make it difficult for us to identify if there has the trend or not So, it’s important for us to get the temperature information in the other area of Taipei city And with more data of BCR & FAR distribution, we may group them into more groups for more detail The data of FAR and BCR values comes from the 3D structure model, and we rebuilt it with different buffers, and the center of the buffer are the location of the ground stations With different year’s structure data, we can found the changing of the coverage distributing in different years.(Fig 4.10 and Fig 4.11) After calculating the BCR and FAR values, which the BCR and FAR values have no obviously changing in these years So we assumed that the contributing of UHI intensity in this station is caused by the factors except BCR and FAR The urban heat island (UHI) effect has been a common phenomenon due to many researches which have been published However, the causes of UHI effect includes in many factors, for example, the structure of the city, population, building materials, heat convection intensity etc This research is focus on two of the factors: Floor Area Ratio (FAR) and Building Coverage Ratio (BCR), which are two important indicator of urban growth With the air temperature data, we can define the UHI intensity as the major indicator to represent the UHI effect And the first step is combining with ground weather station data and doing some basic statistical analysis, 39 and discuss the correlation with UHI intensity with different districts in Taipei city The result shows that in some districts both of the correlation coefficient of UHII&FAR and UHII&BCR However, there still a few of the value shows low correlation The causes may due to some reasons, the major one is the lack of FAR and BCR data set, most of the districts have only three points to represent three different years In the future work, the first goal is to enrich the FAR and BCR data set, the second one is using the satellite imagery to build a map of surface temperature, in order to enlarge the study area of this research The term surface urban heat island for a UHI that is measured with LST changes in urbanization were accompanied by changes in UHI From 1991, 2002 and 2005, the urban or built-up surface temperature increased in whole area of Taipei city, and continued to increase Moreover, temperature differences between the urban area and the surrounding rural areas significantly widened, especially in the core area of Taipei city This could lead to an intensified urban heat island effect in the urban areas This study, the spatial characteristics were defined as the configuration and composition of urban morphology features also significantly affects the surface temperature 40 PART V DISCUSSION AND CONCLUSION 5.1 Discussion Analyses from the map results show that the distribution of land surface temperature is different Taipei city is a big industrial zone but also a commercial center It has many aspects of active development under urbanization Living standard is relatively high with more and more comfortable and convenient conditions However, that high economic growth rate brings many impacts of high pollution levels on the urban area The land surface temperature is strongly increasing due to land cover change Building coverage ratio (BCR) and floor area ratio (FAR) are used to estimate the building intensity of a city from three aspects, the buildings stretching on the surface and growing along the third dimension The BCR is the means percentage ratio of the total standing area of all buildings (or building footprint) to the total area of the interest area, while the FAR is the ratio of the gross floor area of all buildings to the total area of the interest area It is a building density parameter used in urban planning and design disciplines It captures the impact of vertical frictional surfaces in urban land due to high-rise built surfaces and used in urban canopy parameterization of drag and turbulence production On the other hand, it is a major parameter showing development intensity and refers to the intensity of activities taking place within a specified land area and obviously has implications on urban climate that reflects the number of prominent obstacles that affects air flow A model in this study was built to determine specific contribution of FAR and BCR were used as independent variables to motivate the average surface temperature 41 (presenting by UHII) which was a dependent variable rising on the diurnal range These simple relationships between climate and spatial characteristic indicators could help decision makers and planners to take climate adaptation into account, to ensure climate neutral development from the beginning of a planning process In fact, the average surface temperature can be significantly increased or decreased by different spatial compositions and configurations of those features This is because the spatial characteristics including urban structure and urban cover (e.g fractions of built-up, paved, vegetated, and open water features) within the urban canopy layer which influences obstruct urban wind flow and increase thermal mass of urban fabric that could heat up the local climate zone Therefore, it is our recommendation that urban planners should try to control for the effects of their composition Vegetation management, particularly increasing tree canopy, has been considered an effective means to mitigate excess urban heat and to alleviate the thermal discomfort in the outdoor environment for both highly urbanized areas and areas where urbanization is still in process 5.2 Conclusion The research provides the methodology to determine Land surface temperature from remote sensing data By using ArcGIS and ENVI software, the raw thermal band data of Landsat satellite images can be rapidly are converted into land surface temperature in degree Celsius The data is used for detection of surface temperature changes during 1991, 2002 and 2015 and estimation of the increasing rate of temperature rise to understand the intensity of global warming in the present and previous periods 42 Using satellite data in calculating surface temperature is relatively simple and fast by simply using a single band temperature; however, there are still have some limitations, particularly results verifying because of the lack of equipment and times From 1991 to 2005, the urban or built-up surface temperature increased in whole area of FAR and BCR continued to increase Moreover, temperature differences between the urban area and the surrounding rural areas significantly widened, especially types; comparison of UHIs estimated for cities of different sizes under different climatic conditions; and multi-temporal studies of UHIs of a single city over four seasons’ using different satellite data All the analyses in this paper were based on the interpretation of remote sensing images and the results showed that remote sensing images are ideal for analyzing UHI, by which we analyzed not only the phenomenon of UHI but the impact factors of UHI from the regional level to the local level This result shows that in some districts both of the correlation coefficient of UHII and FAR, UHII and BCR, most of value are larger than 0.7 and some of them are even greater than 0.95 However, there still a few of the value shows low 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and GIS software for assessing the effect of land surface temperature changes on Urban Heat Island in Taipei City 3.2 Location and... Utilizing remote sensing data for assessing the effect of land use changes on Urban Heat Island in Taipei City Assoc Prof Tang-Huang Lin and Assoc Prof Nguyen The Hung ABSTRACT Land surface temperature