Spatial Interconnections of Land Surface Temperatures with Land Cover/Use: A Case Study of Tokyo

As one of the most populated metropolitan areas in the world, the Tokyo Metropolitan Area (TMA) has experienced severe climatic modifications and pressure due to densified human activities and urban expansion. The surface urban heat island (SUHI) phenomenon particularly constitutes a significant threat to human comfort and geo-environmental health in TMA. This study aimed to profile the spatial interconnections between land surface temperature (LST) and land cover/use in TMA from 2001 to 2015 using multi-source spatial data. To this end, the thermal gradients between the urban and non-urban fabric areas in TMA were examined by joint analysis of land cover/use and LST. The spatiotemporal aggregation patterns, variations, and movement trajectories of SUHI intensity in TMA were identified and delineated. The spatial relationship between SUHI and the potential driving forces in TMA was clarified using geographically weighted regression (GWR) analysis. The results show that the thermal environment of TMA exhibited a polynucleated spatial structure with multiple thermal island cores. Overall, the magnitude and extent of SUHI in TMA increased and expanded from 2001 to 2015. During that time, SUHIs clustered in the compact residential quarters and redevelopment/renovation areas rather than downtown. The GWR models showed better performance than ordinary least squares (OLS) models, with Adj R2 > 0.9, indicating that the magnitude of SUHI significantly depended on its neighboring geographical setting, including land cover composition and configuration, population size, and terrain. We suggest that UHI mitigation in Tokyo should be focused on alleviating the magnitude of persistent thermal cores and controlling unstable SUHI occurrence based on partitioned or location-specific landscape design. This study’s findings have immense implications for SUHI mitigation in metropolitan areas situated in bay regions.

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Measuring the Urban Land Surface Temperature Variations Under Zhengzhou City Expansion Using Landsat-Like Data

Remote Sensing ◽

10.3390/rs12050801 ◽

2020 ◽

Vol 12 (5) ◽

pp. 801

Author(s):

Haibo Yang ◽

Chaofan Xi ◽

Xincan Zhao ◽

Penglei Mao ◽

Zongmin Wang ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Driving Forces ◽

Thermal Environment ◽

Urban Expansion ◽

Strong Positive Correlation ◽

Landsat Data

Satellite-based remote sensing technologies are utilized extensively to investigate urban thermal environment under rapid urban expansion. Current Moderate Resolution Imaging Spectroradiometer (MODIS) data are, however, unable to adequately represent the spatially detailed information because of its relatively coarser spatial resolution, while Landsat data cannot explore the temporally continued analysis due to the lower temporal resolution. Combining MODIS and Landsat data, “Landsat-like” data were generated by using the Flexible Spatiotemporal Data Fusion method (FSDAF) to measure land surface temperature (LST) variations, and Landsat-like data including Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built Index (NDBI) were generated to analyze LST dynamic driving forces. Results show that (1) the estimated “Landsat-like” data are capable of measuring the LST variations; (2) with the urban expansion from 2013 to 2016, LST increases ranging from 1.80 °C to 3.92 °C were detected in areas where the impervious surface area (ISA) increased, while LST decreases ranging from −3.52 °C to −0.70 °C were detected in areas where ISA decreased; (3) LST has a significant negative correlation with the NDVI and a strong positive correlation with NDBI in summer. Our findings can provide information useful for mitigating undesirable thermal conditions and for long-term urban thermal environmental management.

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Exploring the Patterns and Drivers of Urban Expansion in the Texas Triangle Megaregion

Land ◽

10.3390/land10111244 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1244

Author(s):

Jiani Guo ◽

Ming Zhang

Keyword(s):

Land Cover ◽

Metropolitan Areas ◽

Driving Forces ◽

Urban Expansion ◽

Demographic Data ◽

Census Bureau ◽

Multiple Sources ◽

Mixed Effect ◽

Effect Regression ◽

Sustainability Initiatives

As the world becomes increasingly urbanized, it is vital for planners and policy-makers to understand the patterns of urban expansion and the underlying driving forces. This study examines the spatiotemporal patterns of urban expansion in the Texas Triangle megaregion and explores the drivers behind the expansion. The study used data from multiple sources, including land cover and imperviousness data from the National Land Cover Database (NLCD) 2001–2016, transportation data from the Texas Department of Transportation (TxDOT), and ancillary socio-demographic data from the U.S. Census Bureau. We conducted spatial cluster analysis and mixed-effect regression analysis. The results show that: (1) urban expansion in the Texas Triangle between 2001 and 2016 showed a decreasing trend, and 95% of the newly urbanized land was in metropolitan areas, especially at the periphery of the central cities; (2) urban expansion in non-metropolitan areas displayed a scattered pattern, comparing to the clustered form in metro areas; (3) the expansion process in the Texas Triangle exhibited a pattern of increased development compactness and intensity; and (4) population and economic growth played a definitive role in driving the urban expansion in the Texas Triangle while highway density also mattered. These results suggest a megaregion-wide emerging trend deviating from the sprawling development course known in Texas’ urban growth history. The changing trend can be attributed to the pro-sustainability initiatives taken by several anchor cities and metropolitan planning agencies in the Texas Triangle.

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Spatiotemporal Influence of Land Use/Land Cover Change Dynamics on Surface Urban Heat Island: A Case Study of Abuja Metropolis, Nigeria

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10050272 ◽

2021 ◽

Vol 10 (5) ◽

pp. 272

Author(s):

Auwalu Faisal Koko ◽

Wu Yue ◽

Ghali Abdullahi Abubakar ◽

Akram Ahmed Noman Alabsi ◽

Roknisadeh Hamed

Keyword(s):

Land Use ◽

Land Cover ◽

Land Surface ◽

Urban Expansion ◽

Urban Heat Islands ◽

Sustainable Land Use ◽

Land Use Land Cover ◽

Urban Centers ◽

Rapid Urbanization ◽

Urban Heat

Rapid urbanization in cities and urban centers has recently contributed to notable land use/land cover (LULC) changes, affecting both the climate and environment. Therefore, this study seeks to analyze changes in LULC and its spatiotemporal influence on the surface urban heat islands (UHI) in Abuja metropolis, Nigeria. To achieve this, we employed Multi-temporal Landsat data to monitor the study area’s LULC pattern and land surface temperature (LST) over the last 29 years. The study then analyzed the relationship between LULC, LST, and other vital spectral indices comprising NDVI and NDBI using correlation analysis. The results revealed a significant urban expansion with the transformation of 358.3 sq. km of natural surface into built-up areas. It further showed a considerable increase in the mean LST of Abuja metropolis from 30.65 °C in 1990 to 32.69 °C in 2019, with a notable increase of 2.53 °C between 2009 and 2019. The results also indicated an inverse relationship between LST and NDVI and a positive connection between LST and NDBI. This implies that urban expansion and vegetation decrease influences the development of surface UHI through increased LST. Therefore, the study’s findings will significantly help urban-planners and decision-makers implement sustainable land-use strategies and management for the city.

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Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia

Sustainability ◽

10.3390/su14010160 ◽

2021 ◽

Vol 14 (1) ◽

pp. 160

Author(s):

Najmeh Mozaffaree Pour ◽

Tõnu Oja

Keyword(s):

Land Cover ◽

Driving Forces ◽

Urban Expansion ◽

Network Models ◽

Influential Factors ◽

Residential Areas ◽

Neural Network Models ◽

Independent Variables ◽

The Core ◽

Mlp Model

Estonia mainly experienced urban expansion after regaining independence in 1991. Employing the CORINE Land Cover dataset to analyze the dynamic changes in land use/land cover (LULC) in Estonia over 28 years revealed that urban land increased by 33.96% in Harju County and by 19.50% in Tartu County. Therefore, after three decades of LULC changes, the large number of shifts from agricultural and forest land to urban ones in an unplanned manner have become of great concern. To this end, understanding how LULC change contributes to urban expansion will provide helpful information for policy-making in LULC and help make better decisions for future transitions in urban expansion orientation and plan for more sustainable cities. Many different factors govern urban expansion; however, physical and proximity factors play a significant role in explaining the spatial complexity of this phenomenon in Estonia. In this research, it was claimed that urban expansion was affected by the 12 proximity driving forces. In this regard, we applied LR and MLP neural network models to investigate the prediction power of these models and find the influential factors driving urban expansion in two Estonian counties. Using LR determined that the independent variables “distance from main roads (X7)”, “distance from the core of main cities of Tallinn and Tartu land (X2)”, and “distance from water land (X11)” had a higher negative correlation with urban expansion in both counties. Indeed, this investigation requires thinking towards constructing a balance between urban expansion and its driving forces in the long term in the way of sustainability. Using the MLP model determined that the “distance from existing residential areas (X10)” in Harju County and the “distance from the core of Tartu (X2)” in Tartu County were the most influential driving forces. The LR model showed the prediction power of these variables to be 37% for Harju County and 45% for Tartu County. In comparison, the MLP model predicted nearly 80% of variability by independent variables for Harju County and approximately 50% for Tartu County, expressing the greater power of independent variables. Therefore, applying these two models helped us better understand the causative nature of urban expansion in Harju County and Tartu County in Estonia, which requires more spatial planning regulation to ensure sustainability.

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RS and GIS Supported Urban LULC and UHI Change Simulation and Assessment

Journal of Sensors ◽

10.1155/2020/5863164 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Pei Liu ◽

Shoujun Jia ◽

Ruimei Han ◽

Yuanping Liu ◽

Xiaofeng Lu ◽

...

Keyword(s):

Land Surface ◽

Thermal Environment ◽

Urban Expansion ◽

Dynamic Change ◽

Urban Sustainability ◽

Remotely Sensed Data ◽

Rapid Urbanization ◽

Urban Heat ◽

High Level ◽

The Impact

Rapid urbanization has become a major urban sustainability concern due to environmental impacts, such as the development of urban heat island (UHI) and the reduction of urban security states. To date, most research on urban sustainability development has focused on dynamic change monitoring or UHI state characterization, while there is little literature on UHI change analysis. In addition, there has been little research on the impact of land use and land cover changes (LULCCs) on UHI, especially simulates future trends of LULCCs, UHI change, and dynamic relationship of LULCCs and UHI. The purpose of this research is to design a remote sensing-based framework that investigates and analyzes how the LULCCs in the process of urbanization affected thermal environment. In order to assess and predict the impact of LULCCs on urban heat environment, multitemporal remotely sensed data from 1986 to 2016 were selected as source data, and Geographic Information System (GIS) methods such as the CA-Markov model were employed to construct the proposed framework. The results showed that (1) there has been a substantial strength of urban expansion during the 40-year study period, (2) the farthest distance urban center of gravity moves from north-northeast (NEE) to west-southwest (WSW) direction, (3) the dominate temperature was middle level, sub-high level, and high level in the research area, (4) there was a higher changing frequency and range from east to west, and (5) there was a significant negative correlation between land surface temperature and vegetation and significant positive correlation between temperature and human settlement.

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Changes in Land Use Influenced by Anthropogenic Activity

Oxford Research Encyclopedia of Environmental Science ◽

10.1093/acrefore/9780199389414.013.37 ◽

2020 ◽

Author(s):

Lang Wang ◽

Zong-Liang Yang

Keyword(s):

Land Use ◽

Land Cover ◽

Land Management ◽

Land Surface ◽

Anthropogenic Activities ◽

Urban Expansion ◽

Management Strategies ◽

Terrestrial Ecosystems ◽

Anthropogenic Activity ◽

Management Actions

The terms “land cover” and “land use” are often used interchangeably, although they have different meanings. Land cover is the biophysical material at the surface of the Earth, whereas land use refers to how people use the land surface. Land use concerns the resources of the land, their products, and benefits, in addition to land management actions and activities. The history of changes in land use has passed through several major stages driven by developments in science and technology and demands for food, fiber, energy, and shelter. Modern changes in land use have been increasingly affected by anthropogenic activities at a scale and magnitude that have not been seen. These changes in land use are largely driven by population growth, urban expansion, increasing demands for energy and food, changes in diets and lifestyles, and changing socioeconomic conditions. About 70% of the Earth’s ice-free land surface has been altered by changes in land use, and these changes have had environmental impacts worldwide, ranging from effects on the composition of the Earth’s atmosphere and climate to the extensive modification of terrestrial ecosystems, habitats, and biodiversity. A number of different methods have been developed give a thorough understanding of these changes in land use and the multiple effects and feedbacks involved. Earth system observations and models are examples of two crucial technologies, although there are considerable uncertainties in both techniques. Cross-disciplinary collaborations are highly desirable in future studies of land use and management. The goals of mitigating climate change and maintaining sustainability should always be considered before implementing any new land management strategies.

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Analyzing the Spatial Heterogeneity of the Built Environment and Its Impact on the Urban Thermal Environment—Case Study of Downtown Shanghai

Sustainability ◽

10.3390/su132011302 ◽

2021 ◽

Vol 13 (20) ◽

pp. 11302

Author(s):

Jiejie Han ◽

Xi Zhao ◽

Hao Zhang ◽

Yu Liu

Keyword(s):

Built Environment ◽

Spatial Heterogeneity ◽

Land Surface ◽

Urban Areas ◽

Thermal Environment ◽

Urban Expansion ◽

Surface Heat ◽

Positive Contribution ◽

Heat Island ◽

Island Effect

Ongoing urban expansion has accelerated the explosive growth of urban populations and has led to a dramatic increase in the impervious surface area within urban areas. This, in turn, has exacerbated the surface heat island effect within cities. However, the importance of the surface heat island effect within urban areas, scilicet the intra-SUHI effect, has attracted less concern. The aim of this study was to quantitatively explore the relationship between the spatial heterogeneity of a built environment and the intra-urban surface heat island (intra-SUHI) effect using the thermally sharpened land surface temperature (LST) and high-resolution land-use classification products. The results show that at the land parcel scale, the parcel-based relative intensity of intra-SUHI should be attributed to the land parcels featured with differential land developmental intensity. Furthermore, the partial least squares regression (PLSR) modeling quantified the relative importance of the spatial heterogeneity indices of the built environment that exhibit a negative contribution to decreasing the parcel-based intra-SUHI effect or a positive contribution to increasing the intra-SUHI effect. Finally, based on the findings of this study, some practical countermeasures towards mitigating the adverse intra-SUHI effect and improving urban climatic adaption are discussed.

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Land Cover Change Dynamics and their Impacts on Thermal Environment of Dadri Block, Gautam Budh Nagar, India

Journal of Landscape Ecology ◽

10.2478/jlecol-2020-0007 ◽

2020 ◽

Vol 13 (2) ◽

pp. 1-13

Author(s):

Sushma Shastri ◽

Prafull Singh ◽

Pradipika Verma ◽

Praveen Kumar Rai ◽

A. P. Singh

Keyword(s):

Land Use ◽

Land Cover ◽

Land Surface ◽

Large Scale ◽

Thermal Environment ◽

Negative Impact ◽

Information Source ◽

Physical Parameters ◽

Change Analysis ◽

Landsat Satellite

AbstractLand use / land cover (LULC) has been considered as one of the important bio-physical parameters and have significant affect on local environmental change, particularly increasing anthropogenic temperature. Remote sensing images from Landsat series satellites are a major information source for LULC change analysis. In the present investigation, long term changes in LULC and its negative impact on land surface temperature (LST) were analyzed using multi-temporal Landsat satellite images between 2000 to 2016. firstly LULC of the study area has been classified and temporal changes in land use classes were quantify, and observed that in most of the land use classes such as vegetation (-1.28 %), water bodies (-1.65 %), agriculture (-3.52) and open land (-2.43 %) have shown negative change, however large scale positive changes in built-up area (+8.87 %) has been observed during the analysis, which is mainly due to continuous urbanization and growth of population in the area. The classified thermal images from the same period also show mean temperature of the area has increased by 1.60 °C since last 16 years. The observation from the present study reveals that due to the large-scale land use change practices in urban and peri-urban area witnessed for the rising temperature due to loss natural vegetation and other natural resources.

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Influence of Land Use and Land Cover Change on Land surface temperature

E3S Web of Conferences ◽

10.1051/e3sconf/202128301038 ◽

2021 ◽

Vol 283 ◽

pp. 01038

Author(s):

Jing Sun ◽

Jing He

Keyword(s):

Land Use ◽

Land Cover ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Urban Expansion ◽

Rapid Urbanization ◽

Lulc Changes ◽

The Mean ◽

The Relationship

The rapid urbanization process has recently led to significant land use and land cover (LULC) changes, thereby affecting the climate and the environment. The purpose of this study is to analyze the LULC changes in Hefei City, Anhui Province, and their relationship with land surface temperature (LST). To achieve this goal, multitemporal Landsat data were used to monitor the LULC and LST between 2005 and 2015. The study also used correlation analysis to analyze the relationship between LST, LULC, and other spectral indices (NDVI, NDBI, and NDWI). The results show that the built-up land has expanded significantly, transforming from 488.26 km2 in 2005 to 575.64 km2 in 2015. It further shows that the mean LST in Hefei city has increased from 284.0 K in 2005 to 285.86 K in 2015. The results also indicate that there is a positive correlation between LST and NDVI and NDBI, while there is a negative correlation between LST and NDWI. This means that urban expansion and reduced water bodies will lead to an increase in LST.

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A Geographically Weighted Regression Analysis of the Underlying Factors Related to the Surface Urban Heat Island Phenomenon

Remote Sensing ◽

10.3390/rs10091428 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1428 ◽

Cited By ~ 30

Author(s):

Chunhong Zhao ◽

Jennifer Jensen ◽

Qihao Weng ◽

Russell Weaver

Keyword(s):

Land Cover ◽

Geographically Weighted Regression ◽

Land Surface ◽

Metropolitan Areas ◽

San Antonio ◽

Weighted Regression ◽

Heat Island ◽

Global Regression ◽

Urban Heat ◽

Surface Urban Heat Island

This study investigated how underlying biophysical attributes affect the characterization of the Surface Urban Heat Island (SUHI) phenomenon using (and comparing) two statistical techniques: global regression and geographically weighted regression (GWR). Land surface temperature (LST) was calculated from Landsat 8 imagery for 20 July 2015 for the metropolitan areas of Austin and San Antonio, Texas. We sought to examine SUHI by relating LST to Lidar-derived terrain factors, land cover composition, and landscape pattern metrics developed using the National Land Cover Database (NLCD) 2011. The results indicate that (1) land cover composition is closely related to the SUHI effect for both metropolitan areas, as indicated by the global regression coefficients of building fraction and NDVI, with values of 0.29 and −0.74 for Austin, and 0.19 and −0.38 for San Antonio, respectively. The terrain morphology was also an indicator of the SUHI phenomenon, implied by the elevation (0.20 for Austin and 0.09 for San Antonio) and northness (0.20 for Austin and 0.09 for San Antonio); (2) the SUHI phenomenon of Austin on 20 July 2015 was affected by the spatial pattern of the land use and land cover (LULC), which was not detected for San Antonio; and (3) with a local determination coefficient higher than 0.8, GWR had higher explanatory power of the underlying factors compared to global regression. By accommodating spatial non-stationarity and allowing the model parameters to vary in space, GWR illustrated the spatial heterogeneity of the relationships between different land surface properties and the LST. The GWR analysis of SUHI phenomenon can provide unique information for site-specific land planning and policy implementation for SUHI mitigation.

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