Impact of land use change on urban surface temperature and urban green space planning; case study of the island of Bali, Indonesia

Land use and surface temperature were monitored from 1995 to 2013 to examine green space development in Bali using Landsat and ASTER imageries. Urban areas were formed by conversion of vegetation and paddy fields. Heat islands with surface temperature of over 29 ºC were found and influenced by urban area types. High priority, low priority and not a priority zones for green space were resulted by weighted overlay of LST, NDVI and urban area types

Extraction and Analysis of Impervious Surfaces Based on a Spectral Un-Mixing Method Using Pearl River Delta of China Landsat TM/ETM+ Imagery from 1998 to 2008

Impervious surface area (ISA) is considered as an indicator of environment change and is regarded as an important input parameter for hydrological cycle simulation, water management and area pollution assessment. The Pearl River Delta (PRD), the 3rd most important economic district of China, is chosen in this paper to extract the ISA information based on Landsat images of 1998, 2003 and 2008 by using a linear spectral un-mixing method and to monitor impervious surface change by analyzing the multi-temporal Landsat-derived fractional impervious surface. Results of this study were as follows: (1) the area of ISA in the PRD increased 79.09% from 1998 to 2003 and 26.88% from 2003 to 2008 separately; (2) the spatial distribution of ISA was described according to the 1998/2003 percentage respectively. Most of middle and high percentage ISA was located in northwestern and southeastern of the whole delta, and middle percentage ISA was mainly located in the city interior, high percentage ISA was mainly located in the suburban around the city accordingly; (3) the expanding direction and trend of high percentage ISA was discussed in order to understand the change of urban in this delta; High percentage ISA moved from inner city to edge of urban area during 1998–2003 and moved to the suburban area that far from the urban area mixed with jumpily and gradually during 2003–2008. According to the discussion of high percentage ISA spatial expanded direction, it could be found out that high percentage ISA moved outward from the centre line of Pearl River of the whole delta while a high ISA percentage in both shores of the Pearl River Estuary moved toward the Pearl River; (4) combining the change of ISA with social conditions, the driving relationship was analyzed in detail. It was evident that ISA percentage change had a deep relationship with the economic development of this region in the past ten years. Contemporaneous major sport events (16th Asia Games of Guangzhou, 26th Summer Universidad of Shenzhen) and the government policies also promoted the development of the ISA. Meanwhile, topographical features like the National Nature Reserve of China restricted and affected the expansion of the ISA. Above all, this paper attempted to extract ISA in a major region of the PRD; the temporal and spatial analyses to PRD ISA demonstrated the drastic changes in developed areas of China. These results were important and valuable for land use management, ecological protection and policy establishment

Mapping Local Climate Zones for a Worldwide Database of the Form and Function of Cities

Progress in urban climate science is severely restricted by the lack of useful information that describes aspects of the form and function of cities at a detailed spatial resolution. To overcome this shortcoming we are initiating an international effort to develop the World Urban Database and Access Portal Tools (WUDAPT) to gather and disseminate this information in a consistent manner for urban areas worldwide. The first step in developing WUDAPT is a description of cities based on the Local Climate Zone (LCZ) scheme, which classifies natural and urban landscapes into categories based on climate-relevant surface properties. This methodology provides a culturally-neutral framework for collecting information about the internal physical structure of cities. Moreover, studies have shown that remote sensing data can be used for supervised LCZ mapping. Mapping of LCZs is complicated because similar LCZs in different regions have dissimilar spectral properties due to differences in vegetation, building materials and other variations in cultural and physical environmental factors. The WUDAPT protocol developed here provides an easy to understand workflow; uses freely available data and software; and can be applied by someone without specialist knowledge in spatial analysis or urban climate science. The paper also provides an example use of the WUDAPT project results

An integrative approach using remote sensing and social analysis to identify different settlement types and the specific living conditions of its inhabitants

Someday in 2007, the world population reached a historical landmark: for the first time in human history, more than half of the world´s population was urban. A stagnation of this urbanization process is not in sight, so that by 2050, already 70 percent of humankind is projected to live in urban settlements. Over the last few decades, enormous migrations from rural hinterlands to steadily growing cities could be witnessed coming along with a dramatic growth of the world’s urban population. The speed and the scale of this growth, particularly in the so called less developed regions, are posing tremendous challenges to the countries concerned as well as to the world community. Within mega cities the strongest trends and the most extreme dimensions of the urbanization process can be observed. Their rapid growth results in uncontrolled processes of fragmentation which is often associated with pronounced poverty, social inequality, socio-spatial and political fragmentation, environmental degradation as well as population demands that outstrip environmental service capacity. For the majority of the mega cities a tremendous increase of informal structures and processes has to be observed. Consequentially informal settlements are growing, which represent those characteristic municipal areas being subject to particularly high population density, dynamics as well as marginalization. They have quickly become the most visible expression of urban poverty in developing world cities. Due to the extreme dynamics, the high complexity and huge spatial dimension of mega cities, urban administrations often only have an obsolete or not even existing data basis available to be at all informed about developments, trends and dimensions of urban growth and change. The knowledge about the living conditions of the residents is correspondingly very limited, incomplete and not up to date. Traditional methods such as statistical and regional analyses or fieldwork are no longer capable to capture such urban process. New data sources and monitoring methodologies are required in order to provide an up to date information basis as well as planning strate¬gies to enable sustainable developments and to simplify planning processes in complex urban structures. This research shall seize the described problem and aims to make a contribution to the requirements of monitoring fast developing mega cities. Against this background a methodology is developed to compensate the lack of socio-economic data and to deduce meaningful information on the living conditions of the inhabitants of mega cities. Neither social science methods alone nor the exclusive analysis of remote sensing data can solve the problem of the poor quality and outdated data base. Conventional social science methods cannot cope with the enormous developments and the tremendous growth as they are too labor-, as well as too time- and too cost-intensive. On the other hand, the physical discipline of remote sensing does not allow for direct conclusions on social parameters out of remote sensing images. The prime objective of this research is therefore the development of an integrative approach − bridging remote sensing and social analysis – in order to derive useful information about the living conditions in this specific case of the mega city Delhi and its inhabitants. Hence, this work is established in the overlapping range of the research topics remote sensing, urban areas and social science. Delhi, as India’s fast growing capital, meanwhile with almost 25 million residents the second largest city of the world, represents a prime example of a mega city. Since the second half of the 20th century, Delhi has been transformed from a modest town with mainly administrative and trade-related functions to a complex metropolis with a steep socio-economic gradient. The quality and amount of administrative and socio-economic data are poor and the knowledge about the circumstances of Delhi’s residents is correspondingly insufficient and outdated. Delhi represents therefore a perfectly suited study area for this research. In order to gather information about the living conditions within the different settlement types a methodology was developed and conducted to analyze the urban environment of the mega city Delhi. To identify different settlement types within the urban area, regarding the complex and heterogeneous appearance of the Delhi area, a semi-automated, object-oriented classification approach, based on segmentation derived image objects, was implemented. As the complete conceptual framework of this research, the classification methodology was developed based on a smaller representative training area at first and applied to larger test sites within Delhi afterwards. The object-oriented classification of VHR satellite imagery of the QuickBird sensor allowed for the identification of five different urban land cover classes within the municipal area of Delhi. In the focus of the image analysis is yet the identification of different settlement types and amongst these of informal settlements in particular. The results presented within this study demonstrate, that, based on density classes, the developed methodology is suitable to identify different settlement types and to detect informal settlements which are mega urban risk areas and thus potential residential zones of vulnerable population groups. The remote sensing derived land cover maps form the foundation for the integrative analysis concept and deliver there¬fore the general basis for the derivation of social attributes out of remote sensing data. For this purpose settlement characteristics (e.g., area of the settlement, average building size, and number of houses) are estimated from the classified QuickBird data and used to derive spatial information about the population distribution. In a next step, the derived information is combined with in-situ information on socio-economic conditions (e.g., family size, mean water consumption per capita/family) extracted from georeferenced questionnaires conducted during two field trips in Delhi. This combined data is used to characterize a given settlement type in terms of specific population and water related variables (e.g., population density, total water consumption). With this integrative methodology a catalogue can be compiled, comprising the living conditions of Delhi’s inhabitants living in specific settlement structures – and this in a quick, large-scaled, cost effective, by random or regularly repeatable way with a relatively small required data basis.The combined application of remotely sensed imagery and socio-economic data allows for the mapping, capturing and characterizing the socio-economic structures and dynamics within the mega city of Delhi, as well as it establishes a basis for the monitoring of the mega city of Delhi or certain areas within the city respectively by remote sensing. The opportunity to capture the condition of a mega city and to monitor its development in general enables the persons in charge to identify unbeneficial trends and to intervene accordingly from an urban planning perspective and to countersteer against a non-adequate supply of the inhabitants of different urban districts, primarily of those of informal settlements. This study is understood to be a first step to the development of methods which will help to identify and understand the different forms, actors and processes of urbanization in mega cities. It could support a more proactive and sustainable urban planning and land management – which in turn will increase the importance of urban remote sensing techniques. In this regard, the most obvious and direct beneficiaries are on the one hand the governmental agencies and urban planners and on the other hand, and which is possibly the most important goal, the inhabitants of the affected areas, whose living conditions can be monitored and improved as required. Only if the urban monitoring is quickly, inexpensively and easily available, it will be accepted and applied by the authorities, which in turn enables for the poorest to get the support they need. All in all, the listed benefits are very convincing and corroborate the combined use of remotely sensed and socio-economic data in mega city research

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

Linking thermal variability and change to urban growth in Harare Metropolitan City using remotely sensed data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal. Pietermaritzburg, 2017.Urban growth, which involves Land Use and Land Cover Changes (LULCC), alters land surface thermal properties. Within the framework of rapid urban growth and global warming, land surface temperature (LST) and its elevation have potential significant socio-economic and environmental implications. Hence the main objectives of this study were to (i) map urban growth, (ii) link urban growth with indoor and outdoor thermal conditions and (iii) estimate implications of thermal trends on household energy consumption as well as predict future urban growth and temperature patterns in Harare Metropolitan, Zimbabwe. To achieve these objectives, broadband multi-spectral Landsat 5, 7 and 8, in-situ LULC observations, air temperature (Ta) and humidity data were integrated. LULC maps were obtained from multi-spectral remote sensing data and derived indices using the Support Vector Machine Algorithm, while LST were derived by applying single channel and split window algorithms. To improve remote sensing based urban growth mapping, a method of combining multi-spectral reflective data with thermal data and vegetation indices was tested. Vegetation indices were also combined with socio-demographic data to map the spatial distribution of heat vulnerability in Harare. Changes in outdoor human thermal discomfort in response to seasonal LULCC were evaluated, using the Discomfort Index (DI) derived parsimoniously from LST retrieved from Landsat 8 data. Responses of LST to long term urban growth were analysed for the period from 1984 to 2015. The implications of urban growth induced temperature changes on household air-conditioning energy demand were analysed using Landsat derived land surface temperature based Degree Days. Finally, the Cellular Automata Markov Chain (CAMC) analysis was used to predict future landscape transformation at 10-year time steps from 2015 to 2045. Results showed high overall accuracy of 89.33% and kappa index above 0.86 obtained, using Landsat 8 bands and indices. Similar results were observed when indices were used as stand-alone dataset (above 80%). Landsat 8 derived bio-physical surface properties and socio-demographic factors, showed that heat vulnerability was high in over 40% in densely built-up areas with low-income when compared to “leafy” suburbs. A strong spatial correlation (α = 0.61) between heat vulnerability and surface temperatures in the hot season was obtained, implying that LST is a good indicator of heat vulnerability in the area. LST based discomfort assessment approach retrieved DI with high accuracy as indicated by mean percentage error of less than 20% for each sub-season. Outdoor thermal discomfort was high in hot dry season (mean DI of 31oC), while the post rainy season was the most comfortable (mean DI of 19.9oC). During the hot season, thermal discomfort was very low in low density residential areas, which are characterised by forests and well maintained parks (DI ≤27oC). Long term changes results showed that high density residential areas increased by 92% between 1984 and 2016 at the expense of cooler green-spaces, which decreased by 75.5%, translating to a 1.98oC mean surface temperature increase. Due to surface alterations from urban growth between 1984 and 2015, LST increased by an average of 2.26oC and 4.10oC in the cool and hot season, respectively. This decreased potential indoor heating energy needed in the cool season by 1 degree day and increased indoor cooling energy during the hot season by 3 degree days. Spatial analysis showed that during the hot season, actual energy consumption was low in high temperature zones. This coincided with areas occupied by low income strata indicating that they do not afford as much energy and air conditioning facilities as expected. Besides quantifying and strongly relating with energy requirement, degree days provided a quantitative measure of heat vulnerability in Harare. Testing vegetation indices for predictive power showed that the Urban Index (UI) was comparatively the best predictor of future urban surface temperature (r = 0.98). The mean absolute percentage error of the UI derived temperature was 5.27% when tested against temperature derived from thermal band in October 2015. Using UI as predictor variable in CAMC analysis, we predicted that the low surface temperature class (18-28oC) will decrease in coverage, while the high temperature category (36-45oC) will increase in proportion covered from 42.5 to 58% of city, indicating further warming as the city continues to grow between 2015 and 2040. Overall, the findings of this study showed that LST, human thermal comfort and air-conditioning energy demand are strongly affected by seasonal and urban growth induced land cover changes. It can be observed that urban greenery and wetlands play a significant role of reducing LST and heat transfer between the surface and lower atmosphere and LST may continue unless effective mitigation strategies, such as effective vegetation cover spatial configuration are adopted. Limitations to the study included inadequate spatial and low temporal resolution of Landsat data, few in-situ observations of temperature and LULC classification which was area specific thus difficult for global comparison. Recommendations for future studies included data merging to improve spatial and temporal representation of remote sensing data, resource mobilization to increase urban weather station density and image classification into local climate zones which are of easy global interpretation and comparison

Remotely Sensed Index of Deforestation/Urbanization for use in Climate Models

The purpose of this investigation is to use a new method for deriving land surface parameters from a combination of thermal infrared and vegetation index measurements from satellites (Landsat-TM, and NOAA-AVHRR) and to integrate these parameters with more conventional data bases. We have completed an investigation of urbanization in the State College, PA area and have begun work in Chester County, PA, and Costa Rica. Our basic hypothesis is that changes in land use, including deforestation, exert a profound influence on local microclimates whose effects may greatly exceed in importance those occurring on larger scales

The ecological roles of golf courses in urban landscapes

The proliferation of urban golf courses accounts for a growing proportion of the urban land area in Australia and other countries. While many suggest that golf courses have an environmentally negative impact, others believe they are important nodes in the network of urban green space and can provide refugial habitat for wildlife. However, research on golf course ecology is in its infancy and this limits development of explicit guidelines for ecologically sound development. Therefore, this PhD research used remote sensing technology to investigate the ecological roles of golf courses in maintenance of vegetation at the urban landscape scale. The thesis explores temporal and spatial landscape data as well as the possible cooling effects golf course can provide in the Perth Metropolitan Region. The multifunctional aspects of green spaces in golf courses are highlighted in this study. Firstly, by using moderate resolution satellite imagery (Landsat) time series data for three decades from 1988 to 2018 to assess temporal changes in vegetation cover, the study found that vegetation clearance was significant and vegetation cover has become increasingly fragmented. It was concluded that golf courses contribute to urban conservation through the maintenance of vegetation cover and by increasing habitat connectivity during the long period of urbanisation. Secondly, high resolution satellite imagery (PlanetScope (PS) Level 3B) was then used to compare spatially the characteristics of vegetation within golf courses with other urban land-use. It found that golf courses have less conservation values than conservation land, but their role in preservation of native vegetation, vegetation health and habitat connectivity is more significant than other highly intensive urban land-uses. Thirdly, analysis of multispectral high resolution airborne imagery for assessing the capacity of golf courses in mitigating the urban temperature revealed that urban golf courses can provide cooling effects in the urban environment through the provision of tree coverage and other green areas. Despite limitations of the research being carried out at the landscape scale, the findings of the thesis can enhance the future integration of golf courses into urban biodiversity conservation and ecosystem service improvement

Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data.

This paper presents an innovative multisensor, multitemporal machine-learning approach using remote sensing big data for the detection of archaeological mounds in Cholistan (Pakistan). The Cholistan Desert presents one of the largest concentrations of Indus Civilization sites (from ca 3300 to 1500 BC). Cholistan has figured prominently in theories about changes in water availability, the rise and decline of the Indus Civilization, and the transformation of fertile monsoonal alluvial plains into an extremely arid margin. This paper implements a multisensor, multitemporal machine-learning approach for the remote detection of archaeological mounds. A classifier algorithm that employs a large-scale collection of synthetic-aperture radar and multispectral images has been implemented in Google Earth Engine, resulting in an accurate probability map for mound-like signatures across an area that covers ca 36,000 km2 The results show that the area presents many more archaeological mounds than previously recorded, extending south and east into the desert, which has major implications for understanding the archaeological significance of the region. The detection of small (30 ha) suggests that there were continuous shifts in settlement location. These shifts are likely to reflect responses to a dynamic and changing hydrological network and the influence of the progressive northward advance of the desert in a long-term process that culminated in the abandonment of much of the settled area during the Late Harappan period.ER