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
Tài liệu tham khảo
Apollonio, 2016, Land use change impact on flooding areas: the case study of Cervaro Basin (Italy), Sustainability, 8, 996, 10.3390/su8100996
Bechtel, 2015, Mapping local climate zones for a worldwide database of the form and function of cities, ISPRS Int. J. Geo Inf., 4, 199, 10.3390/ijgi4010199
Bechtel, 2019, Generating WUDAPT Level 0 data--current status of production and evaluation, Urban Clim., 27, 24, 10.1016/j.uclim.2018.10.001
Brousse, 2019, Using local climate zones in Sub-Saharan Africa to tackle urban health issues, Urban Clim., 27, 227, 10.1016/j.uclim.2018.12.004
Cai, 2016, Local climate zone study for sustainable megacities development by using improved WUDAPT methodology--a case study in Guangzhou, Procedia Environ. Sci., 36, 82, 10.1016/j.proenv.2016.09.017
Cai, 2018, Investigating the relationship between local climate zone and land surface temperature using an improved WUDAPT methodology–a case study of Yangtze River Delta, China, Urban Clim., 24, 485, 10.1016/j.uclim.2017.05.010
Chen, 2011, Estimating the relationship between urban forms and energy consumption: a case study in the Pearl River Delta, 2005-2008, Landsc. Urban Plan., 102, 33, 10.1016/j.landurbplan.2011.03.007
Chen, 2020, Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: a semantic segmentation solution, Remote Sens. Environ., 251, 10.1016/j.rse.2020.112096
Chen, 2020, Numerical simulation of local climate zone cooling achieved through modification of trees, albedo and green roofs—a case study of Changsha, China, Sustainability, 12, 2752, 10.3390/su12072752
Chen, 2021, Future “local climate zone” spatial change simulation in Greater Bay Area under the shared socioeconomic pathways and ecological control line, Build. Environ., 203, 10.1016/j.buildenv.2021.108077
Chung, 2021, Improved machine-learning mapping of local climate zones in metropolitan areas using composite earth observation data in Google Earth Engine, Build. Environ., 199
Cohen, 2010, Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation, Remote Sens. Environ., 114, 2911, 10.1016/j.rse.2010.07.010
Demuzere, 2019, Mapping Europe into local climate zones, PLoS One, 14, 10.1371/journal.pone.0214474
Demuzere, 2019, Global transferability of local climate zone models, Urban Clim., 27, 46, 10.1016/j.uclim.2018.11.001
Drusch, 2012, Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services, Remote Sens. Environ., 120, 25, 10.1016/j.rse.2011.11.026
Feng, 2018, A multiple dataset approach for 30-m resolution land cover mapping: a case study of continental Africa, Int. J. Remote Sens., 39, 3926, 10.1080/01431161.2018.1452073
Friedmann, 2006, Four theses in the study of China’s urbanization, Int. J. Urban Reg. Res., 30, 440, 10.1111/j.1468-2427.2006.00671.x
Gong, 2019, 40-Year (1978–2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing, Sci. Bull., 64, 756, 10.1016/j.scib.2019.04.024
Gong, 2020, Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018, Sci. Bull., 65, 182, 10.1016/j.scib.2019.12.007
Gong, 2020, Annual maps of global artificial impervious area (GAIA) between 1985 and 2018, Remote Sens. Environ., 236, 10.1016/j.rse.2019.111510
Gorelick, 2017, Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18, 10.1016/j.rse.2017.06.031
Huang, 2019, 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, ISPRS J. Photogramm. Remote Sens., 152, 119, 10.1016/j.isprsjprs.2019.04.010
Huang, 2021, Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery, ISPRS J. Photogramm. Remote Sens., 175, 403, 10.1016/j.isprsjprs.2021.03.019
Ke, 2018, Direct and indirect loss of natural habitat due to built-up area expansion: a model-based analysis for the city of Wuhan, China, Land Use Policy, 74, 231, 10.1016/j.landusepol.2017.12.048
Kotharkar, 2022, Approach to local climate zone based energy consumption assessment in an Indian city, Energy Build., 111835
Kuang, 2020, Investigating the patterns and dynamics of urban green space in China’s 70 major cities using satellite remote sensing, Remote Sens., 12, 10.3390/rs12121929
La, 2020, Urban land cover mapping under the Local Climate Zone scheme using Sentinel-2 and PALSAR-2 data, Urban Clim., 33, 10.1016/j.uclim.2020.100661
Lau, 2019, Outdoor thermal comfort in different urban settings of sub-tropical high-density cities: an approach of adopting local climate zone (LCZ) classification, Build. Environ., 154, 227, 10.1016/j.buildenv.2019.03.005
Lehnert, 2021, Mapping local climate zones and their applications in European urban environments: a systematic literature review and future development trends, ISPRS Int. J. Geo Inf., 10, 260, 10.3390/ijgi10040260
Li, 2018, Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992--2015), Earth Syst. Sci. Data, 10, 219, 10.5194/essd-10-219-2018
Li, 2020, Mapping global urban boundaries from the global artificial impervious area (GAIA) data, Environ. Res. Lett., 15, 10.1088/1748-9326/ab9be3
Liu, 2020, Local climate zone mapping as remote sensing scene classification using deep learning: a case study of metropolitan China, ISPRS J. Photogramm. Remote Sens., 164, 229, 10.1016/j.isprsjprs.2020.04.008
Liu, 2014, Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s, J. Geogr. Sci., 24, 195, 10.1007/s11442-014-1082-6
Liu, 2018, Quantitative effects of urban spatial characteristics on outdoor thermal comfort based on the LCZ scheme, Build. Environ., 143, 443, 10.1016/j.buildenv.2018.07.019
Liu, 2018, High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine platform, Remote Sens. Environ., 209, 227, 10.1016/j.rse.2018.02.055
Liu, 2018, Identifying patterns and hotspots of global land cover transitions using the ESA CCI land cover dataset, Remote Sens. Lett., 9, 972, 10.1080/2150704X.2018.1500070
Liu, 2020, High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015, Nat. Sustain., 3, 564, 10.1038/s41893-020-0521-x
Loveland, 2012, Landsat: building a strong future, Remote Sens. Environ., 122, 22, 10.1016/j.rse.2011.09.022
Mayaux, 2008, Remote sensing of land-cover and land-use dynamics, 85
McClure, 2015, Simulating the dynamic effect of land use and transport policies on the health of populations, Am. J. Public Health, 105, S223, 10.2105/AJPH.2014.302303
Meiyappan, 2012, Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years, Front. Earth Sci., 6, 122, 10.1007/s11707-012-0314-2
Milošević, 2016, Outdoor human thermal comfort in local climate zones of Novi Sad (Serbia) during heat wave period, Hungarian Geograph. Bull., 65, 129, 10.15201/hungeobull.65.2.4
Ning, 2018, Spatiotemporal patterns and characteristics of land-use change in China during 2010–2015, J. Geogr. Sci., 28, 547, 10.1007/s11442-018-1490-0
Papadomanolaki, 2021, A deep multitask learning framework coupling semantic segmentation and fully convolutional LSTM networks for urban change detection, IEEE Trans. Geosci. Remote Sens., 59, 7651, 10.1109/TGRS.2021.3055584
Peng, 2016, Urban thermal environment dynamics and associated landscape pattern factors: a case study in the Beijing metropolitan region, Remote Sens. Environ., 173, 145, 10.1016/j.rse.2015.11.027
Poursanidis, 2015, Landsat 8 vs. Landsat 5: a comparison based on urban and peri-urban land cover mapping, Int. J. Appl. Earth Obs. Geoinf., 35, 259
Qiao, 2020, The impact of urban renewal on land surface temperature changes: a case study in the main city of Guangzhou, China, Remote Sens., 12, 794, 10.3390/rs12050794
Qiu, 2019, Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network, ISPRS J. Photogramm. Remote Sens., 154, 151, 10.1016/j.isprsjprs.2019.05.004
Qiu, 2020, Multilevel feature fusion-based CNN for local climate zone classification from sentinel-2 images: benchmark results on the So2Sat LCZ42 dataset, IEEE J. Select. Top. Appl. Earth Observ. Remote Sens., 13, 2793, 10.1109/JSTARS.2020.2995711
Rosentreter, 2020, Towards large-scale mapping of local climate zones using multitemporal Sentinel 2 data and convolutional neural networks, Remote Sens. Environ., 237, 10.1016/j.rse.2019.111472
See, 2015, Developing a community-based worldwide urban morphology and materials database (WUDAPT) using remote sensing and crowdsourcing for improved urban climate modelling, Joint Urban Remote Sensing Event (JURSE), 2015, 1
Sefrin, 2020, Deep learning for land cover change detection, Remote Sens., 13, 10.3390/rs13010078
Sida, 2020, Urban heat island studies based on local climate zones: a systematic overview, Acta Geograph. Sin., 75, 09001860
Stewart, 2012, Local climate zones for urban temperature studies, Bull. Am. Meteorol. Soc., 93, 1879, 10.1175/BAMS-D-11-00019.1
Sun, 2016, Contribution of urbanization to warming in China, Nat. Clim. Chang., 6, 706, 10.1038/nclimate2956
Torres, 2012, GMES Sentinel-1 mission, Remote Sens. Environ., 120, 9, 10.1016/j.rse.2011.05.028
Verdonck, 2018, The potential of local climate zones maps as a heat stress assessment tool, supported by simulated air temperature data, Landsc. Urban Plan., 178, 183, 10.1016/j.landurbplan.2018.06.004
Wang, 2017, Fusion of Landsat 8 OLI and Sentinel-2 MSI data, IEEE Trans. Geosci. Remote Sens., 55, 3885, 10.1109/TGRS.2017.2683444
Wang, 2019, Detecting multi-temporal land cover change and land surface temperature in Pearl River Delta by adopting local climate zone, Urban Clim., 28, 10.1016/j.uclim.2019.100455
Wu, 2018, Mapping building carbon emissions within local climate zones in Shanghai, Energy Procedia, 152, 815, 10.1016/j.egypro.2018.09.195
Xu, 2017, Classification of local climate zones using ASTER and Landsat data for high-density cities, IEEE J. Select. Top. Appl. Earth Observ. Remote Sens., 10, 3397, 10.1109/JSTARS.2017.2683484
Xu, 2020, 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, Sci. China Earth Sci., 63, 1390, 10.1007/s11430-019-9606-4
Yang, 2020, Impact of urban heat island on energy demand in buildings: Local climate zones in Nanjing, Appl. Energy, 260, 114279, 10.1016/j.apenergy.2019.114279
Yang, 2022, Urban vertical profiles of three most urbanized Chinese cities and the spatial coupling with horizontal urban expansion, Land Use Policy, 113, 10.1016/j.landusepol.2021.105919
Yoo, 2019, Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images, ISPRS J. Photogramm. Remote Sens., 157, 155, 10.1016/j.isprsjprs.2019.09.009
Zhao, 2019, Application of airborne remote sensing data on mapping local climate zones: cases of three metropolitan areas of Texas, US, Comput. Environ. Urban. Syst., 74, 175, 10.1016/j.compenvurbsys.2018.11.002
Zhao, 2020, Exploring difference in land surface temperature between the city centres and urban expansion areas of China’s major cities, Int. J. Remote Sens., 41, 8965, 10.1080/01431161.2020.1797216
Zheng, 2018, GIS-based mapping of local climate zone in the high-density city of Hong Kong, Urban Clim., 24, 419, 10.1016/j.uclim.2017.05.008
Zhu, 2019, Understanding an urbanizing planet: strategic directions for remote sensing, Remote Sens. Environ., 228, 164, 10.1016/j.rse.2019.04.020
Zhu, 2022, The urban morphology on our planet–global perspectives from space, Remote Sens. Environ., 269, 10.1016/j.rse.2021.112794
Zhuang, 2020, An analysis of urban renewal decision-making in China from the perspective of transaction costs theory: the case of Chongqing, J. Housing Built Environ., 35, 1177, 10.1007/s10901-020-09733-9