Mapping urban morphology changes in the last two decades based on local climate zone scheme: A case study of three major urban agglomerations in China

[1]  Xiao Xiang Zhu,et al.  The urban morphology on our planet – Global perspectives from space , 2022, Remote sensing of environment.

[2]  Devireddy Vinod Kumar Reddy,et al.  Approach to local climate zone based energy consumption assessment in an Indian city , 2022, Energy and Buildings.

[3]  C. Ren,et al.  Future “local climate zone” spatial change simulation in Greater Bay Area under the shared socioeconomic pathways and ecological control line , 2021 .

[4]  Chao Ren,et al.  Improved machine-learning mapping of local climate zones in metropolitan areas using composite Earth observation data in Google Earth Engine , 2021, Building and Environment.

[5]  Xin Huang,et al.  Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery , 2021 .

[6]  Jan Geletič,et al.  Mapping Local Climate Zones and Their Applications in European Urban Environments: A Systematic Literature Review and Future Development Trends , 2021, ISPRS Int. J. Geo Inf..

[7]  Konstantinos Karantzalos,et al.  A Deep Multitask Learning Framework Coupling Semantic Segmentation and Fully Convolutional LSTM Networks for Urban Change Detection , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Sina Keller,et al.  Deep Learning for Land Cover Change Detection , 2020, Remote. Sens..

[9]  Yuyu Zhou,et al.  Exploring difference in land surface temperature between the city centres and urban expansion areas of China’s major cities , 2020 .

[10]  Xiao Xiang Zhu,et al.  Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: A semantic segmentation solution , 2020, 2009.06992.

[11]  Yoshiki Yamagata,et al.  Urban land cover mapping under the Local Climate Zone scheme using Sentinel-2 and PALSAR-2 data , 2020 .

[12]  Wenhui Kuang,et al.  Investigating the Patterns and Dynamics of Urban Green Space in China's 70 Major Cities Using Satellite Remote Sensing , 2020, Remote. Sens..

[13]  Le Yu,et al.  Mapping global urban boundaries from the global artificial impervious area (GAIA) data , 2020, Environmental Research Letters.

[14]  Qian Shi,et al.  Local climate zone mapping as remote sensing scene classification using deep learning: A case study of metropolitan China , 2020 .

[15]  Le Yu,et al.  Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm , 2020, Science China Earth Sciences.

[16]  Xiao Xiang Zhu,et al.  Multilevel Feature Fusion-Based CNN for Local Climate Zone Classification From Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  Xia Li,et al.  High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015 , 2020, Nature Sustainability.

[18]  Bohong Zheng,et al.  Numerical Simulation of Local Climate Zone Cooling Achieved through Modification of Trees, Albedo and Green Roofs—A Case Study of Changsha, China , 2020, Sustainability.

[19]  Zhenjie Liu,et al.  The Impact of Urban Renewal on Land Surface Temperature Changes: A Case Study in the Main City of Guangzhou, China , 2020, Remote. Sens..

[20]  Queena K. Qian,et al.  An analysis of urban renewal decision-making in China from the perspective of transaction costs theory: the case of Chongqing , 2020 .

[21]  Zhidian Jiang,et al.  Impact of urban heat island on energy demand in buildings: Local climate zones in Nanjing , 2020 .

[22]  Yuqi Bai,et al.  Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018. , 2020, Science bulletin.

[23]  Johannes Rosentreter,et al.  Towards large-scale mapping of local climate zones using multitemporal Sentinel 2 data and convolutional neural networks , 2020 .

[24]  Yuyu Zhou,et al.  Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 , 2020 .

[25]  Cheolhee Yoo,et al.  Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[26]  Xiao Xiang Zhu,et al.  Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network , 2019, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[27]  Zhe Zhu,et al.  Understanding an urbanizing planet: Strategic directions for remote sensing , 2019, Remote Sensing of Environment.

[28]  Xin Huang,et al.  Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: A case study of Wuhan, Central China , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[29]  Wei Zhang,et al.  40-Year (1978-2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing. , 2019, Science bulletin.

[30]  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.

[31]  Chao Ren,et al.  Outdoor thermal comfort in different urban settings of sub-tropical high-density cities: An approach of adopting local climate zone (LCZ) classification , 2019, Building and Environment.

[32]  Benjamin Bechtel,et al.  Mapping Europe into local climate zones , 2019, PloS one.

[33]  Catherine Linard,et al.  Using Local Climate Zones in Sub-Saharan Africa to tackle urban health issues , 2019, Urban Climate.

[34]  Linda See,et al.  Generating WUDAPT Level 0 data – Current status of production and evaluation , 2019, Urban Climate.

[35]  Chunhong Zhao,et al.  Application of airborne remote sensing data on mapping local climate zones: Cases of three metropolitan areas of Texas, U.S , 2019, Comput. Environ. Urban Syst..

[36]  Benjamin Bechtel,et al.  Global transferability of local climate zone models , 2019, Urban Climate.

[37]  Ayyoob Sharifi,et al.  Mapping building carbon emissions within local climate zones in Shanghai , 2018, Energy Procedia.

[38]  Lin Liu,et al.  Quantitative effects of urban spatial characteristics on outdoor thermal comfort based on the LCZ scheme , 2018, Building and Environment.

[39]  A. Schneider,et al.  The potential of local climate zones maps as a heat stress assessment tool, supported by simulated air temperature data , 2018, Landscape and Urban Planning.

[40]  Chaoqing Yu,et al.  Identifying patterns and hotspots of global land cover transitions using the ESA CCI Land Cover dataset , 2018, Remote Sensing Letters.

[41]  Jasper van Vliet,et al.  Direct and indirect loss of natural habitat due to built-up area expansion: a model-based analysis for the city of Wuhan, China. , 2018 .

[42]  Xiaoping Liu,et al.  High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform , 2018 .

[43]  Jiyuan Liu,et al.  Spatiotemporal patterns and characteristics of land-use change in China during 2010–2015 , 2018, Journal of Geographical Sciences.

[44]  Le Yu,et al.  A multiple dataset approach for 30-m resolution land cover mapping: a case study of continental Africa , 2018 .

[45]  Philippe Ciais,et al.  Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015) , 2018 .

[46]  Edward Ng,et al.  GIS-based mapping of Local Climate Zone in the high-density city of Hong Kong , 2017, Urban Climate.

[47]  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.

[48]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[49]  Peter M. Atkinson,et al.  Fusion of Landsat 8 OLI and Sentinel-2 MSI Data , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[50]  Meng Cai,et al.  Classification of Local Climate Zones Using ASTER and Landsat Data for High-Density Cities , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[51]  Eufemia Tarantino,et al.  Land Use Change Impact on Flooding Areas: The Case Study of Cervaro Basin (Italy) , 2016 .

[52]  Francis W. Zwiers,et al.  Contribution of urbanization to warming in China , 2016 .

[53]  S. Savić,et al.  Outdoor human thermal comfort in local climate zones of Novi Sad (Serbia) during heat wave period , 2016 .

[54]  Jian Peng,et al.  Urban thermal environment dynamics and associated landscape pattern factors: A case study in the Beijing metropolitan region , 2016 .

[55]  Wei Dai,et al.  Local Climate Zone Study for Sustainable Megacities Development by Using Improved WUDAPT Methodology – A Case Study in Guangzhou , 2016 .

[56]  Steffen Fritz,et al.  Developing a community-based worldwide urban morphology and materials database (WUDAPT) using remote sensing and crowdsourcing for improved urban climate modelling , 2015, 2015 Joint Urban Remote Sensing Event (JURSE).

[57]  Paul M. Salmon,et al.  Simulating the dynamic effect of land use and transport policies on the health of populations. , 2015, American journal of public health.

[58]  Nektarios Chrysoulakis,et al.  Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[59]  Iain Stewart,et al.  Mapping Local Climate Zones for a Worldwide Database of the Form and Function of Cities , 2015, ISPRS Int. J. Geo Inf..

[60]  Jiyuan Liu,et al.  Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s , 2014, Journal of Geographical Sciences.

[61]  T. Oke,et al.  Local Climate Zones for Urban Temperature Studies , 2012 .

[62]  Atul K. Jain,et al.  Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years , 2012, Frontiers of Earth Science.

[63]  John L. Dwyer,et al.  Landsat: building a strong future , 2012 .

[64]  Malcolm Davidson,et al.  GMES Sentinel-1 mission , 2012 .

[65]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[66]  Xia Li,et al.  Estimating the relationship between urban forms and energy consumption: A case study in the Pearl River Delta, 2005–2008 , 2011 .

[67]  Warren B. Cohen,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation , 2010 .

[68]  Frédéric Achard,et al.  Remote Sensing of Land-Cover and Land-Use Dynamics , 2008 .

[69]  J. Friedmann.  Four Theses in the Study of China’s Urbanization , 2006 .