Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka

Spatial urban growth and its impact on land surface temperature (LST) is a high priority environmental issue for urban policy. Although the impact of horizontal spatial growth of cities on LST is well studied, the impact of the vertical spatial distribution of buildings on LST is under-investigated. This is particularly true for cities in sub-tropical developing countries. In this study, TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-XDEM), Advanced Spaceborne Thermal Emission and Reflection (ASTER)-Global Digital Elevation Model (GDEM), and ALOS World 3D-30m (AW3D30) based Digital Surface Model (DSM) data were used to investigate the vertical growth of the Dhaka Metropolitan Area (DMA) in Bangladesh. Thermal Infrared (TIR) data (10.6-11.2μm) of Landsat-8 were used to investigate the seasonal variations in LST. Thereafter, the impact of horizontal and vertical spatial growth on LST was studied. The result showed that: (a) TanDEM-X DSM derived building height had a higher accuracy as compared to other existing DSM that reveals mean building height of the Dhaka city is approximately 10 m, (b) built-up areas were estimated to cover approximately 94%, 88%, and 44% in Dhaka South City Corporation (DSCC), Dhaka North City Corporation (DNCC), and Fringe areas, respectively, of DMA using a Support Vector Machine (SVM) classification method, (c) the built-up showed a strong relationship with LST (Kendall tau coefficient of 0.625 in summer Remote Sens. 2020, 12, 1191; doi:10.3390/rs12071191 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 1191 2 of 27 and 0.483 in winter) in comparison to vertical growth (Kendall tau coefficient of 0.156 in the summer and 0.059 in the winter), and (d) the ‘low height-high density’ areas showed high LST in both seasons. This study suggests that vertical development is better than horizontal development for providing enough open spaces, green spaces, and preserving natural features. This study provides city planners with a better understating of sustainable urban planning and can promote the formulation of action plans for appropriate urban development policies.

[1]  R. Avtar,et al.  Urban Green Spaces and Their Need in Cities of Rapidly Urbanizing India: A Review , 2019, Urban Science.

[2]  Yan Qin,et al.  Extracting and analyzing urban built-up area based on impervious surface and gravity model , 2013, Joint Urban Remote Sensing Event 2013.

[3]  K. N. Tahar.  AN EVALUATION ON DIFFERENT NUMBER OF GROUND CONTROL POINTS IN UNMANNED AERIAL VEHICLE PHOTOGRAMMETRIC BLOCK , 2013 .

[4]  Achim Roth,et al.  Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS data , 2018 .

[5]  Debasish Roy Raja.  Spatial Analysis of Land Surface Temperature in Dhaka Metropolitan Area , 2012 .

[6]  Stefania Bonafoni,et al.  Land Surface Temperature and Urban Density: Multiyear Modeling and Relationship Analysis Using MODIS and Landsat Data , 2018, Remote. Sens..

[7]  Tonni Agustiono Kurniawan,et al.  Utilizing geospatial information to implement SDGs and monitor their Progress , 2019, Environmental Monitoring and Assessment.

[8]  H. Taha.  Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat , 1997 .

[9]  Ali P. Yunus,et al.  Evaluation of DEM generation based on Interferometric SAR using TanDEM-X data in Tokyo , 2015 .

[10]  Weiqi Zhou,et al.  The higher, the cooler? Effects of building height on land surface temperatures in residential areas of Beijing , 2019, Physics and Chemistry of the Earth, Parts A/B/C.

[11]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[12]  Justin Morgenroth,et al.  Developments in Landsat Land Cover Classification Methods: A Review , 2017, Remote. Sens..

[13]  Ran Wang,et al.  Investigating the relationship between local climate zone and land surface temperature using an improved WUDAPT methodology – A case study of Yangtze River Delta, China , 2017, Urban Climate.

[14]  K. Gallo,et al.  Evaluation of the Relationship between Air and Land Surface Temperature under Clear- and Cloudy-Sky Conditions , 2011 .

[15]  Seema Jalan,et al.  SPATIO-TEMPORAL ASSESSMENT OF LAND USE/ LAND COVER DYNAMICS AND URBAN HEAT ISLAND OF JAIPUR CITY USING SATELLITE DATA , 2014 .

[16]  E. Ebinne,et al.  Accuracy Assessment of Alos W3d30, Aster Gdem and Srtm30 Dem: A Case Study of Nigeria, West Africa , 2019, Journal of Geographic Information System.

[17]  A. Bhargava,et al.  Urban Heat Island Effect: ItâÂÂs Relevance in Urban Planning , 2017 .

[18]  Prakhar Misra,et al.  Comparison of Digital Building Height Models Extracted from AW3D, TanDEM-X, ASTER, and SRTM Digital Surface Models over Yangon City , 2018, Remote. Sens..

[19]  Cem Ünsalan,et al.  Ground filtering and DTM generation from DSM data using probabilistic voting and segmentation , 2018 .

[20]  J. Beringer,et al.  Temperature and human thermal comfort effects of street trees across three contrasting street canyon environments , 2016, Theoretical and Applied Climatology.

[21]  Liping Di,et al.  The state of the art of spaceborne remote sensing in flood management , 2016, Natural Hazards.

[22]  D. Ruffieux,et al.  The Effect of Building Shadows on the Vertical Temperature Structure of the Lower Atmosphere in Downtown Denver , 1990 .

[23]  Xiaoqing Zhou,et al.  Characterizing the impact of urban morphology heterogeneity on land surface temperature in Guangzhou, China , 2016, Environ. Model. Softw..

[24]  Ke-Sheng Cheng,et al.  COMPARING LANDCOVER PATTERNS IN TOKYO, KYOTO, AND TAIPEI USING ALOS MULTISPECTRAL IMAGES , 2010 .

[25]  A. Arnfield.  Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island , 2003 .

[26]  Jianguo Wu,et al.  Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns , 2009, Landscape Ecology.

[27]  Ran Wang,et al.  Detecting multi-temporal land cover change and land surface temperature in Pearl River Delta by adopting local climate zone , 2019, Urban Climate.

[28]  Shuwen Zhang,et al.  The Effect of Urban Green Spaces on the Urban Thermal Environment and Its Seasonal Variations , 2017 .

[29]  Keechoo Choi,et al.  Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh , 2013, Remote. Sens..

[30]  K. Moffett,et al.  Remote Sens , 2015 .

[31]  Xiaolei Yu,et al.  Land Surface Temperature Retrieval from Landsat 8 TIRS - Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method , 2014, Remote. Sens..

[32]  Aliihsan Sekertekin,et al.  Pixel-Based Classification Analysis of Land Use Land Cover Using SENTINEL-2 and LANDSAT-8 Data , 2017 .

[33]  Shamsuddin Shahid.  Recent Trends in the Climate of Bangladesh , 2010 .

[34]  W. Leal Filho,et al.  The Urban Heat Island in an Urban Context: A Case Study of Mashhad, Iran , 2019, International journal of environmental research and public health.

[35]  D. Asimakopoulos.  Energy and Climate in the Urban Built Environment , 2001 .

[36]  Patrice G. Mestayer,et al.  Urban Morphology Influence on Urban Albedo: A Revisit with the Solene Model , 2013, Boundary-Layer Meteorology.

[37]  Volker Schwieger,et al.  Global Digital Elevation Model from TanDEM-X and the Calibration/Validation with worldwide kinematic GPS-Tracks , 2010 .

[38]  Takeo Tadono,et al.  Synthesis of L-Band SAR and Forest Heights Derived from TanDEM-X DEM and 3 Digital Terrain Models for Biomass Mapping , 2020, Remote. Sens..

[39]  M. Cheng,et al.  Assess the effect of different degrees of urbanization on land surface temperature using remote sensing images , 2012 .

[40]  Jaroslav Hofierka,et al.  Physically-based land surface temperature modeling in urban areas using a 3-D city model and multispectral satellite data , 2020 .

[41]  Jojene R. Santillan,et al.  Vertical Accuracy Assessment of 30-M Resolution Alos, Aster, and Srtm Global Dems Over Northeastern Mindanao, Philippines , 2016 .

[42]  A. Pullin,et al.  Urban greening to cool towns and cities: a systematic review of the empirical evidence. , 2010 .

[43]  Jianhua Xu,et al.  Land Surface Phenology and Land Surface Temperature Changes Along an Urban–Rural Gradient in Yangtze River Delta, China , 2013, Environmental Management.

[44]  George Vosselman,et al.  A deep learning approach to DTM extraction from imagery using rule-based training labels , 2018 .

[45]  Yasushi Yamaguchi,et al.  Geologic Remote Sensing in the Thermal Infrared , 1996 .

[46]  Ugur Avdan,et al.  Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data , 2016, J. Sensors.

[47]  E. Gage,et al.  Relationships between landscape pattern metrics, vertical structure and surface urban Heat Island formation in a Colorado suburb , 2017, Urban Ecosystems.

[48]  Helmut Rott,et al.  Mass changes of outlet glaciers along the Nordensjköld Coast, northern Antarctic Peninsula, based on TanDEM‐X satellite measurements , 2014 .

[49]  Feng Chen,et al.  Challenges to quantitative applications of Landsat observations for the urban thermal environment. , 2017, Journal of environmental sciences.

[50]  Hung Chak Ho,et al.  Retrieval of Urban Surface Temperature Using Remote Sensing Satellite Imagery , 2019 .

[51]  Zhi-feng Wu,et al.  Impacts of urban biophysical composition on land surface temperature in urban heat island clusters , 2015 .

[52]  S. Hossain.  Rapid Urban Growth and Poverty in Dhaka City , 2008 .

[53]  Hanqiu Xu,et al.  Analysis of Impervious Surface and its Impact on Urban Heat Environment using the Normalized Difference Impervious Surface Index (NDISI) , 2010 .

[54]  A Rajeshwari,et al.  ESTIMATION OF LAND SURFACE TEMPERATURE OF DINDIGUL DISTRICT USING LANDSAT 8 DATA , 2014 .

[55]  Laurent Guillaume Courty,et al.  Evaluation of open‐access global digital elevation models (AW3D30, SRTM, and ASTER) for flood modelling purposes , 2019, Journal of Flood Risk Management.

[56]  John R. Schott,et al.  Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration , 2014, Remote. Sens..

[57]  Haruo Sawada,et al.  Use of DEM data to monitor height changes due to deforestation , 2013, Arabian Journal of Geosciences.

[58]  T. Tadono,et al.  PRISM geometric validation and DSM generation status , 2007 .

[59]  José A. Sobrino,et al.  Satellite-derived land surface temperature: Current status and perspectives , 2013 .

[60]  Ü. Halik,et al.  Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation , 2014 .

[61]  K. Jacobsen,et al.  Comparison of Matching Algorithms for DSM Generation in Urban Areas from Ikonos Imagery , 2010 .

[62]  Ashwani Kumar Aggarwal,et al.  Population–Urbanization–Energy Nexus: A Review , 2019, Resources.

[63]  Aakriti Grover,et al.  Inter-Seasonal Variations of Surface Temperature in the Urbanized Environment of Delhi Using Landsat Thermal Data , 2014 .

[64]  Tao Zhang,et al.  A Comprehensive Evaluation of Approaches for Built-Up Area Extraction from Landsat OLI Images Using Massive Samples , 2018, Remote. Sens..

[65]  Jianya Gong,et al.  IMPACT OF BUILDING HEIGHTS ON 3D URBAN DENSITY ESTIMATION FROM SPACEBORNE STEREO IMAGERY , 2016 .

[66]  Sheeba Abraham.  The relevance of wetland conservation in Kerala , 2015 .

[67]  Manjula Ranagalage,et al.  An Urban Heat Island Study of the Colombo Metropolitan Area, Sri Lanka, Based on Landsat Data (1997-2017) , 2017, ISPRS Int. J. Geo Inf..

[68]  Sisi Zlatanova,et al.  Innovations in 3D Geo Information Systems, First International Workshop on 3D Geoinformation, 7-8 August, 2006, Kuala Lumpur, Malaysia , 2006, 3D-GIS.

[69]  Yang Shao,et al.  Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points , 2012 .

[70]  A. Woobaidullah,et al.  Correlation and Monitoring of Land Surface Temperature, Urban Heat Island with Land Use-Land Cover of Dhaka City Using Satellite Imageries , 2017 .

[71]  Qiaofeng Zhang,et al.  Urban Expansion Pattern and Land Use Dynamics in Dhaka, 1989–2014 , 2017 .

[72]  B. Dousseta,et al.  Satellite multi-sensor data analysis of urban surface temperatures and landcover , 2003 .

[73]  Aakriti Grover,et al.  Analysis of Urban Heat Island (UHI) in Relation to Normalized Difference Vegetation Index (NDVI): A Comparative Study of Delhi and Mumbai , 2015 .

[74]  N. Wong,et al.  Study of green areas and urban heat island in a tropical city , 2005 .

[75]  Vera A. Akristiniy,et al.  Vertical cities - the new form of high-rise construction evolution , 2018 .

[76]  Michael J. Oimoen,et al.  Validation of the ASTER Global Digital Elevation Model Version 2 over the conterminous United States , 2012 .

[77]  Jihui Yuan,et al.  Is urban albedo or urban green covering more effective for urban microclimate improvement?: A simulation for Osaka , 2017 .

[78]  Neveen Y. Azmy,et al.  The effect of unplanned growth of urban areas on heat island phenomena , 2018, Ain Shams Engineering Journal.

[79]  Chuanrong Zhang,et al.  Detecting horizontal and vertical urban growth from medium resolution imagery and its relationships with major socioeconomic factors , 2017 .

[80]  Michael J. Oimoen,et al.  ASTER Global Digital Elevation Model Version 2 - summary of validation results , 2011 .

[81]  Chitresh Saraswat,et al.  Potential application of remote sensing in monitoring ecosystem services of forests, mangroves and urban areas , 2017 .