An Evaluation of Surface Urban Heat Islands in Two Contrasting Cities

This thesis presents a comparative study on surface urban heat islands effects in Baghdad and Perth. The first part evaluates expansion of built-up areas and quantifies its effects on land surface temperature patterns. The second part examines the extent to which the urban thermal environment is influenced by spatial patterns of land use and land cover (LULC) categories. The final part investigates the thermophysical behaviour of various urban LULC categories using albedo and LST parameters

Ecosystem Service and Land-Use Changes in Asia

This book highlights the role of research in Ecosystem Services and Land Use Changes in Asia. The contributions include case studies that explore the impacts of direct and indirect drivers affecting provision of ecosystem services in Asian countries, including China, India, Mongolia, Sri Lanka, and Vietnam. Findings from these empirical studies contribute to developing sustainability in Asia at both local and regional scales

Quantifying the spatio-temporal temperature dynamics of Greater London using thermal Earth observation

PhD ThesisUrban areas are highly sensitive to extreme events such as heatwaves. In order to understand how cities will respond to thermal stress it is critical to quantify not only their temporal temperature dynamics but also their spatial temperature variability. However, many cities lack weather station networks with a sufficient spatial distribution to characterise spatio-temporal intraurban temperature dynamics. One means by which spatially complete measurements of urban temperature may be derived is to employ satellite thermal Earth observed data. While some success has been achieved in understanding the temperature characteristics of cities using such data, relatively little work has been undertaken on establishing the use of long time-series Earth observed data as a supplement or alternative to screen-level air temperatures frequently utilised in urban climatological studies. In this thesis a software framework, centred around the use of a spatial database, is developed which can be used to gain an improved understanding of how satellite thermal Earth observed data can be used in the long timeseries analysis of urban temperature dynamics. The utility of the system is demonstrated by processing a 23 year time series (1985-2008) of 1,141 Advanced Very High Resolution Radiometer (AVHRR) images and hourly United Kingdom (UK) Met Office weather station measurements for the Greater London area. London was selected as the region of interest as it is the UK’s only megacity, and has been shown to exhibit both a significant urban heat island and a severe increase in population mortality during previous heatwave events. The software framework was employed to conduct two inter-related sets of analysis. First, the relationship over time between AVHRR estimated surface temperature (EST) and screen-level air temperature records is investigated and quantified. The resulting relationships are then used to produce an empirical model that can predict spatially complete summer-season air temperi atures for London. Cross-validation testing of the model at selected London weather stations showed model root mean square error (RMSE) ranging from 2.70 to 2.94°C and absolute errors in air temperature estimation of 0.45 to 1.67°C. A key finding of the thesis is that the minimal variation in prediction error between the different stations indicate a level of spatial robustness in the model across the urban surface, that is within the limits of the AVHRR EST precision. In addition, the model was used to estimate spatially averaged air temperatures over the Greater London area for selected summers, and showed a maximum error in air temperature prediction of 1.44°C. Furthermore, the prediction error for the heatwave summer of 2003 was 0.51°C, suggesting that such a model can successfully be used to estimate air temperatures for extreme heatwave summers. Such predictions are directly relevant to future assessments of urban population exposure to heatwaves, and it is envisaged that they could be used in conjunction with a population vulnerability index to create a spatially complete heatwave risk map for London. This work is then extended to investigate the utility of satellite estimated surface temperature measurements to characterise temporally and spatially intra-urban heatwave dynamics using the commonly employed urban heat island intensity metric (UHII). Analysis of the AVHRR EST found that the data are highly sensitive to local meteorological conditions, and that temporal aggregation at the monthly scale is required to provide robust data-sets for inter-year analysis of summer temperatures and generation of the UHII metric. Statistical testing of EST and air-temperature derived UHII for the heatwave summer of 2003 against other non-heatwave summers showed no significant increase in intensity at the 95% confidence level. This raises questions as to the applicability of the UHII metric to capture increases in urban temperatures during a heatwave event.Engineering and Physical Sciences Research Council and the School of Civil Engineering and Geoscience

CHARACTERIZING RICE RESIDUE BURNING AND ASSOCIATED EMISSIONS IN VIETNAM USING A REMOTE SENSING AND FIELD-BASED APPROACH

Agricultural residue burning, practiced in croplands throughout the world, adversely impacts public health and regional air quality. Monitoring and quantifying agricultural residue burning with remote sensing alone is difficult due to lack of field data, hazy conditions obstructing satellite remote sensing imagery, small field sizes, and active field management. This dissertation highlights the uncertainties, discrepancies, and underestimation of agricultural residue burning emissions in a small-holder agriculturalist region, while also developing methods for improved bottom-up quantification of residue burning and associated emissions impacts, by employing a field and remote sensing-based approach. The underestimation in biomass burning emissions from rice residue, the fibrous plant material left in the field after harvest and subjected to burning, represents the starting point for this research, which is conducted in a small-holder agricultural landscape of Vietnam. This dissertation quantifies improved bottom-up air pollution emissions estimates through refinements to each component of the fine-particulate matter emissions equation, including the use of synthetic aperture radar timeseries to explore rice land area variation between different datasets and for date of burn estimates, development of a new field method to estimate both rice straw and stubble biomass, and also improvements to emissions quantification through the use of burning practice specific emission factors and combustion factors. Moreover, the relative contribution of residue burning emissions to combustion sources was quantified, demonstrating emissions are higher than previously estimated, increasing the importance for mitigation. The dissertation further explored air pollution impacts from rice residue burning in Hanoi, Vietnam through trajectory modelling and synoptic meteorology patterns, as well as timeseries of satellite air pollution and reanalysis datasets. The results highlight the inherent difficulty to capture air pollution impacts in the region, especially attributed to cloud cover obstructing optical satellite observations of episodic biomass burning. Overall, this dissertation found that a prominent satellite-based emissions dataset vastly underestimates emissions from rice residue burning. Recommendations for future work highlight the importance for these datasets to account for crop and burning practice specific emission factors for improved emissions estimates, which are useful to more accurately highlight the importance of reducing emissions from residue burning to alleviate air quality issues

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

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 Expansion, Land Use Land Cover Change and Human Impacts: A Case Study of Rawalpindi

Urbanization in Pakistan has increased rapidly from 25% in 1972 to 42% in 2012. Peripheral zones are being pushed by urbanization much beyond their previous extents. Moreover, dispersed developments along the highways/motorways and unplanned expansion of existing urban centres is instigating a substantial loss of vegetation and open spaces. This research is an effort to analyse the relationship between urban expansion and land use/cover change using a combination of remote sensing, census and field data. Rawalpindi has been chosen as a study area because of its rapidly changing population density and land cover over the last few decades, and availability of satellite and census data. Landsat MSS and TM images of 1972, 1979, 1998 and 2010 which are compatible with the 1972, 1981, 1998 and 2012 Census of Pakistan dates were classified using the Maximum Likelihood classifier. The results of the assessment of classification accuracy yielded an overall accuracy of 75.16%, 72.5%, for Landsat MSS 1972, 1979 images and 84.5% and 87.1% for Landsat TM 1998 and 2010 images. Results reveal that the built up area of the study area has been increased from 7,017 hectares to 36,220 hectares during the 1972 -2012 period. This expansion has been accompanied by the loss of agricultural and forest land. There has been a decrease of approximately 10,000 hectares in cropped area and 2,000 hectares in forest land of the study area during the 1998-2012 inter-censal period. Corroboration of official census data, remote sensing results and field based qualitative data supports the view that high population growth rate, industrialization, better educational and transportation facilities and proximity of the study area to the capital (Islamabad) are the major factors of urban expansion and resulting land cover changes The present research is expected to have significant implications for other rapidly urbanizing areas of Pakistan in particular, and the Global South in general, in delivering baseline information about long term land use/cover changes

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

The Combined Use of Optical and SAR Data for Large Area Impervious Surface Mapping

One of the megatrends marking our societies today is the rapid growth of urban agglomerations which is accompanied by a continuous increase of impervious surface (IS) cover. In light of this, accurate measurement of urban IS cover as an indicator for both, urban growth and environmental quality is essential for a wide range of urban ecosystems studies. The aim of this work is to present an approach based on both optical and SAR data in order to quantify urban impervious surface as a continuous variable on regional scales. The method starts with the identification of relevant areas by a semi automated detection of settlement areas on the basis of single-polarized TerraSAR-X data. Thereby the distinct texture and the high density of dihedral corner reflectors prevailing in build-up areas are utilized to automatically delineate settlement areas by the use of an object-based image classification method. The settlement footprints then serve as reference area for the impervious surface estimation based on a Support Vector Regression (SVR) model which relates percent IS to spectral reflectance values. The training procedure is based on IS values derived from high resolution QuickBird data. The developed method is applied to SPOT HRG data from 2005 and 2009 covering almost the whole are of Can Tho Province in the Mekong Delta, Vietnam. In addition, a change detection analysis was applied in order to test the suitability of the modelled IS results for the automated detection of constructional developments within urban environments. Overall accuracies between 84 % and 91% for the derived settlement footprints and absolute mean errors below 15% for the predicted versus training percent IS values prove the suitability of the approach for an area-wide mapping of impervious surfaces thereby exclusively focusing on settlement areas on the basis of remotely sensed image data

Land cover change from national to global scales:A spatiotemporal assessment of trajectories, transitions and drivers

Changes in global land cover (LC) have significant consequences for global environmental change, impacting the sustainability of biogeochemical cycles, ecosystem services, biodiversity, and food security. Different forms of LC change have taken place across the world in recent decades due to a combination of natural and anthropogenic drivers, however, the types of change and rates of change have traditionally been hard to quantify. This thesis exploits the properties of the recently released ESA-CCI-LC product – an internally consistent, high-resolution annual time-series of global LC extending from 1992 to 2018. Specifically, this thesis uses a combination of trajectories and transition maps to quantify LC changes over time at national, continental and global scales, in order to develop a deeper understanding of what, where and when significant changes in LC have taken place and relates these to natural and anthropogenic drivers. This thesis presents three analytical chapters that contribute to achieving the objectives and the overarching aim of the thesis. The first analytical chapter initially focuses on the Nile Delta region of Egypt, one of the most densely populated and rapidly urbanising regions globally, to quantify historic rates of urbanisation across the fertile agricultural land, before modelling a series of alternative futures in which these lands are largely protected from future urban expansion. The results show that 74,600 hectares of fertile agricultural land in the Nile Delta (Old Lands) was lost to urban expansion between 1992 and 2015. Furthermore, a scenario that encouraged urban expansion into the desert and adjacent to areas of existing high population density could be achieved, hence preserving large areas of fertile agricultural land within the Nile Delta. The second analytical chapter goes on to examine LC changes across sub-Saharan Africa (SSA), a complex and diverse environment, through the joint lenses of political regions and ecoregions, differentiating between natural and anthropogenic signals of change and relating to likely drivers. The results reveal key LC change processes at a range of spatial scales, and identify hotspots of LC change. The major five key LC change processes were: (i) “gain of dry forests” covered the largest extent and was distributed across the whole of SSA; (ii) “greening of deserts” found adjacent to desert areas (e.g., the Sahel belt); (iii) “loss of tree-dominated savanna” extending mainly across South-eastern Africa; (iv) “loss of shrub-dominated savanna” stretching across West Africa, and “loss of tropical rainforests” unexpectedly covering the smallest extent, mainly in the DRC, West Africa and Madagascar. The final analytical chapter considers LC change at the global scale, providing a comprehensive assessment of LC gains and losses, trajectories and transitions, including a complete assessment of associated uncertainties. This chapter highlights variability between continents and identifies locations of high LC dynamism, recognising global hotspots for sustainability challenges. At the national scale, the chapter identifies the top 10 countries with the largest percentages of forest loss and urban expansion globally. The results show that the majority of these countries have stabilised their forest losses, however, urban expansion was consistently on the rise in all countries. The thesis concludes with recommendations for future research as global LC products become more refined (spatially, temporally and thematically) allowing deeper insights into the causes and consequences of global LC change to be determined