Articles | Volume 14, issue 8
https://doi.org/10.5194/essd-14-3835-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-3835-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
29 Aug 2022
Data description paper |
| 29 Aug 2022
| 29 Aug 2022
A global map of local climate zones to support earth system modelling and urban-scale environmental science
Matthias Demuzere
CORRESPONDING AUTHOR
Urban Climatology Group, Department of Geography, Ruhr-University Bochum, Bochum, Germany
Jonas Kittner
Urban Climatology Group, Department of Geography, Ruhr-University Bochum, Bochum, Germany
Alberto Martilli
Environmental Department, CIEMAT, Madrid, Spain
Gerald Mills
School of Geography, University College Dublin, Dublin, Ireland
Christian Moede
Urban Climatology Group, Department of Geography, Ruhr-University Bochum, Bochum, Germany
Iain D. Stewart
Global Cities Institute, University of Toronto, Toronto, Ontario, Canada
Jasper van Vliet
Institute for Environmental Studies, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081, HV, Amsterdam, the Netherlands
Benjamin Bechtel
Urban Climatology Group, Department of Geography, Ruhr-University Bochum, Bochum, Germany
Related authors
Kazeem Ishola, Gerald Mills, Ankur Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-304, https://doi.org/10.5194/hess-2023-304, 2024
Preprint under review for HESS
Short summary
Short summary
The global soil information contributes to uncertainty in many models that monitor soil hydrothermal changes. Using the NOAH-MP model with two different global soil information, we show under-represented soil properties in wet loam soil, leading to dry bias in soil moisture. The dry bias is higher and drought categories are more severe in SOILGRIDS. We conclude that models should consider using detailed regionally-derived soil information, to reduce model uncertainties.
Jorn Van de Velde, Matthias Demuzere, Bernard De Baets, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 26, 2319–2344, https://doi.org/10.5194/hess-26-2319-2022, https://doi.org/10.5194/hess-26-2319-2022, 2022
Short summary
Short summary
An important step in projecting future climate is the bias adjustment of the climatological and hydrological variables. In this paper, we illustrate how bias adjustment can be impaired by bias nonstationarity. Two univariate and four multivariate methods are compared, and for both types bias nonstationarity can be linked with less robust adjustment.
Jorn Van de Velde, Bernard De Baets, Matthias Demuzere, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-83, https://doi.org/10.5194/hess-2020-83, 2020
Revised manuscript not accepted
Short summary
Short summary
Though climate models have different types of biases in comparison to the observations, most research is focused on adjusting the intensity. Yet, variables like precipitation are also biased in the occurrence: there are too many days with rainfall. We compared four methods for adjusting the occurrence, with the goal of improving flood representation. From this comparison, we concluded that more advanced methods do not necessarily add value, especially in multivariate settings.
Jeroen Claessen, Annalisa Molini, Brecht Martens, Matteo Detto, Matthias Demuzere, and Diego G. Miralles
Biogeosciences, 16, 4851–4874, https://doi.org/10.5194/bg-16-4851-2019, https://doi.org/10.5194/bg-16-4851-2019, 2019
Short summary
Short summary
Bidirectional interactions between vegetation and climate are unraveled over short (monthly) and long (inter-annual) temporal scales. Analyses use a novel causal inference method based on wavelet theory. The performance of climate models at representing these interactions is benchmarked against satellite data. Climate models can reproduce the overall climate controls on vegetation at all temporal scales, while their performance at representing biophysical feedbacks on climate is less adequate.
Zongbo Shi, Tuan Vu, Simone Kotthaus, Roy M. Harrison, Sue Grimmond, Siyao Yue, Tong Zhu, James Lee, Yiqun Han, Matthias Demuzere, Rachel E. Dunmore, Lujie Ren, Di Liu, Yuanlin Wang, Oliver Wild, James Allan, W. Joe Acton, Janet Barlow, Benjamin Barratt, David Beddows, William J. Bloss, Giulia Calzolai, David Carruthers, David C. Carslaw, Queenie Chan, Lia Chatzidiakou, Yang Chen, Leigh Crilley, Hugh Coe, Tie Dai, Ruth Doherty, Fengkui Duan, Pingqing Fu, Baozhu Ge, Maofa Ge, Daobo Guan, Jacqueline F. Hamilton, Kebin He, Mathew Heal, Dwayne Heard, C. Nicholas Hewitt, Michael Hollaway, Min Hu, Dongsheng Ji, Xujiang Jiang, Rod Jones, Markus Kalberer, Frank J. Kelly, Louisa Kramer, Ben Langford, Chun Lin, Alastair C. Lewis, Jie Li, Weijun Li, Huan Liu, Junfeng Liu, Miranda Loh, Keding Lu, Franco Lucarelli, Graham Mann, Gordon McFiggans, Mark R. Miller, Graham Mills, Paul Monk, Eiko Nemitz, Fionna O'Connor, Bin Ouyang, Paul I. Palmer, Carl Percival, Olalekan Popoola, Claire Reeves, Andrew R. Rickard, Longyi Shao, Guangyu Shi, Dominick Spracklen, David Stevenson, Yele Sun, Zhiwei Sun, Shu Tao, Shengrui Tong, Qingqing Wang, Wenhua Wang, Xinming Wang, Xuejun Wang, Zifang Wang, Lianfang Wei, Lisa Whalley, Xuefang Wu, Zhijun Wu, Pinhua Xie, Fumo Yang, Qiang Zhang, Yanli Zhang, Yuanhang Zhang, and Mei Zheng
Atmos. Chem. Phys., 19, 7519–7546, https://doi.org/10.5194/acp-19-7519-2019, https://doi.org/10.5194/acp-19-7519-2019, 2019
Short summary
Short summary
APHH-Beijing is a collaborative international research programme to study the sources, processes and health effects of air pollution in Beijing. This introduction to the special issue provides an overview of (i) the APHH-Beijing programme, (ii) the measurement and modelling activities performed as part of it and (iii) the air quality and meteorological conditions during joint intensive field campaigns as a core activity within APHH-Beijing.
Ashley M. Broadbent, Andrew M. Coutts, Kerry A. Nice, Matthias Demuzere, E. Scott Krayenhoff, Nigel J. Tapper, and Hendrik Wouters
Geosci. Model Dev., 12, 785–803, https://doi.org/10.5194/gmd-12-785-2019, https://doi.org/10.5194/gmd-12-785-2019, 2019
Short summary
Short summary
We present a simple model for assessing the cooling impacts of vegetation and water features (green and blue infrastructure) in urban environments. This model is designed to be computationally efficient so that those without technical knowledge or access to high-performance computers can use it. TARGET can be used to model average street-level air temperature at canyon to block scales (e.g. 100 m resolution). The model is carefully designed to provide reliable and accurate cooling estimates.
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153, https://doi.org/10.5194/gmd-11-4139-2018, https://doi.org/10.5194/gmd-11-4139-2018, 2018
Short summary
Short summary
Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global
hydro-climatic biomescorrespond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.
Christina Papagiannopoulou, Diego G. Miralles, Stijn Decubber, Matthias Demuzere, Niko E. C. Verhoest, Wouter A. Dorigo, and Willem Waegeman
Geosci. Model Dev., 10, 1945–1960, https://doi.org/10.5194/gmd-10-1945-2017, https://doi.org/10.5194/gmd-10-1945-2017, 2017
Short summary
Short summary
Global satellite observations provide a means to unravel the influence of climate on vegetation. Common statistical methods used to study the relationships between climate and vegetation are often too simplistic to capture the complexity of these relationships. Here, we present a novel causality framework that includes data fusion from various databases, time series decomposition, and machine learning techniques. Results highlight the highly non-linear nature of climate–vegetation interactions.
Hendrik Wouters, Matthias Demuzere, Ulrich Blahak, Krzysztof Fortuniak, Bino Maiheu, Johan Camps, Daniël Tielemans, and Nicole P. M. van Lipzig
Geosci. Model Dev., 9, 3027–3054, https://doi.org/10.5194/gmd-9-3027-2016, https://doi.org/10.5194/gmd-9-3027-2016, 2016
Short summary
Short summary
A methodology is presented for translating three-dimensional information of urban areas into land-surface parameters that can be easily employed in atmospheric modelling. As demonstrated with the COSMO-CLM model for a Belgian summer, it enables them to represent urban heat islands and their dependency on urban design with a low computational cost. It allows for efficiently incorporating urban information systems (e.g., WUDAPT) into climate change assessment and numerical weather prediction.
H. Wouters, K. De Ridder, M. Demuzere, D. Lauwaet, and N. P. M. van Lipzig
Atmos. Chem. Phys., 13, 8525–8541, https://doi.org/10.5194/acp-13-8525-2013, https://doi.org/10.5194/acp-13-8525-2013, 2013
Alberto Martilli, Negin Nazarian, E. Scott Krayenhoff, Jacob Lachapelle, Jiachen Lu, Esther Rivas, Alejandro Rodriguez-Sanchez, Beatriz Sanchez, and José Luis Santiago
Geosci. Model Dev., 17, 5023–5039, https://doi.org/10.5194/gmd-17-5023-2024, https://doi.org/10.5194/gmd-17-5023-2024, 2024
Short summary
Short summary
Here, we present a model that quantifies the thermal stress and its microscale variability at a city scale with a mesoscale model. This tool can have multiple applications, from early warnings of extreme heat to the vulnerable population to the evaluation of the effectiveness of heat mitigation strategies. It is the first model that includes information on microscale variability in a mesoscale model, something that is essential for fully evaluating heat stress.
Kazeem Ishola, Gerald Mills, Ankur Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-304, https://doi.org/10.5194/hess-2023-304, 2024
Preprint under review for HESS
Short summary
Short summary
The global soil information contributes to uncertainty in many models that monitor soil hydrothermal changes. Using the NOAH-MP model with two different global soil information, we show under-represented soil properties in wet loam soil, leading to dry bias in soil moisture. The dry bias is higher and drought categories are more severe in SOILGRIDS. We conclude that models should consider using detailed regionally-derived soil information, to reduce model uncertainties.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
Short summary
Short summary
This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data, 15, 3819–3852, https://doi.org/10.5194/essd-15-3819-2023, https://doi.org/10.5194/essd-15-3819-2023, 2023
Short summary
Short summary
This paper introduces the new high-resolution land use and land cover change dataset LUCAS LUC for Europe (version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Jorn Van de Velde, Matthias Demuzere, Bernard De Baets, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 26, 2319–2344, https://doi.org/10.5194/hess-26-2319-2022, https://doi.org/10.5194/hess-26-2319-2022, 2022
Short summary
Short summary
An important step in projecting future climate is the bias adjustment of the climatological and hydrological variables. In this paper, we illustrate how bias adjustment can be impaired by bias nonstationarity. Two univariate and four multivariate methods are compared, and for both types bias nonstationarity can be linked with less robust adjustment.
Vanessa Reinhart, Peter Hoffmann, Diana Rechid, Jürgen Böhner, and Benjamin Bechtel
Earth Syst. Sci. Data, 14, 1735–1794, https://doi.org/10.5194/essd-14-1735-2022, https://doi.org/10.5194/essd-14-1735-2022, 2022
Short summary
Short summary
The LANDMATE plant functional type (PFT) land cover dataset for Europe 2015 (Version 1.0) is a gridded, high-resolution dataset for use in regional climate models. LANDMATE PFT is prepared using the expertise of regional climate modellers all over Europe and is easily adjustable to fit into different climate model families. We provide comprehensive spatial quality information for LANDMATE PFT, which can be used to reduce uncertainty in regional climate model simulations.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-252, https://doi.org/10.5194/essd-2021-252, 2021
Manuscript not accepted for further review
Short summary
Short summary
This paper introduces the new high-resolution land-use land-cover change dataset LUCAS LUC historical and future land use and land cover change dataset (Version 1.0), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Jorn Van de Velde, Bernard De Baets, Matthias Demuzere, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-83, https://doi.org/10.5194/hess-2020-83, 2020
Revised manuscript not accepted
Short summary
Short summary
Though climate models have different types of biases in comparison to the observations, most research is focused on adjusting the intensity. Yet, variables like precipitation are also biased in the occurrence: there are too many days with rainfall. We compared four methods for adjusting the occurrence, with the goal of improving flood representation. From this comparison, we concluded that more advanced methods do not necessarily add value, especially in multivariate settings.
Negin Nazarian, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 13, 937–953, https://doi.org/10.5194/gmd-13-937-2020, https://doi.org/10.5194/gmd-13-937-2020, 2020
Short summary
Short summary
We present an update to the Multi-Layer Urban Canopy Model by revisiting the parameterization of length scales based on high-resolution and validated large-eddy simulations. Additionally, the inclusion of dispersive fluxes in the parameterization schemes are also discussed. The results demonstrate that updated parameterizations improve the accuracy of the vertical exchange of momentum in the street canyon.
Jeroen Claessen, Annalisa Molini, Brecht Martens, Matteo Detto, Matthias Demuzere, and Diego G. Miralles
Biogeosciences, 16, 4851–4874, https://doi.org/10.5194/bg-16-4851-2019, https://doi.org/10.5194/bg-16-4851-2019, 2019
Short summary
Short summary
Bidirectional interactions between vegetation and climate are unraveled over short (monthly) and long (inter-annual) temporal scales. Analyses use a novel causal inference method based on wavelet theory. The performance of climate models at representing these interactions is benchmarked against satellite data. Climate models can reproduce the overall climate controls on vegetation at all temporal scales, while their performance at representing biophysical feedbacks on climate is less adequate.
Zongbo Shi, Tuan Vu, Simone Kotthaus, Roy M. Harrison, Sue Grimmond, Siyao Yue, Tong Zhu, James Lee, Yiqun Han, Matthias Demuzere, Rachel E. Dunmore, Lujie Ren, Di Liu, Yuanlin Wang, Oliver Wild, James Allan, W. Joe Acton, Janet Barlow, Benjamin Barratt, David Beddows, William J. Bloss, Giulia Calzolai, David Carruthers, David C. Carslaw, Queenie Chan, Lia Chatzidiakou, Yang Chen, Leigh Crilley, Hugh Coe, Tie Dai, Ruth Doherty, Fengkui Duan, Pingqing Fu, Baozhu Ge, Maofa Ge, Daobo Guan, Jacqueline F. Hamilton, Kebin He, Mathew Heal, Dwayne Heard, C. Nicholas Hewitt, Michael Hollaway, Min Hu, Dongsheng Ji, Xujiang Jiang, Rod Jones, Markus Kalberer, Frank J. Kelly, Louisa Kramer, Ben Langford, Chun Lin, Alastair C. Lewis, Jie Li, Weijun Li, Huan Liu, Junfeng Liu, Miranda Loh, Keding Lu, Franco Lucarelli, Graham Mann, Gordon McFiggans, Mark R. Miller, Graham Mills, Paul Monk, Eiko Nemitz, Fionna O'Connor, Bin Ouyang, Paul I. Palmer, Carl Percival, Olalekan Popoola, Claire Reeves, Andrew R. Rickard, Longyi Shao, Guangyu Shi, Dominick Spracklen, David Stevenson, Yele Sun, Zhiwei Sun, Shu Tao, Shengrui Tong, Qingqing Wang, Wenhua Wang, Xinming Wang, Xuejun Wang, Zifang Wang, Lianfang Wei, Lisa Whalley, Xuefang Wu, Zhijun Wu, Pinhua Xie, Fumo Yang, Qiang Zhang, Yanli Zhang, Yuanhang Zhang, and Mei Zheng
Atmos. Chem. Phys., 19, 7519–7546, https://doi.org/10.5194/acp-19-7519-2019, https://doi.org/10.5194/acp-19-7519-2019, 2019
Short summary
Short summary
APHH-Beijing is a collaborative international research programme to study the sources, processes and health effects of air pollution in Beijing. This introduction to the special issue provides an overview of (i) the APHH-Beijing programme, (ii) the measurement and modelling activities performed as part of it and (iii) the air quality and meteorological conditions during joint intensive field campaigns as a core activity within APHH-Beijing.
Ashley M. Broadbent, Andrew M. Coutts, Kerry A. Nice, Matthias Demuzere, E. Scott Krayenhoff, Nigel J. Tapper, and Hendrik Wouters
Geosci. Model Dev., 12, 785–803, https://doi.org/10.5194/gmd-12-785-2019, https://doi.org/10.5194/gmd-12-785-2019, 2019
Short summary
Short summary
We present a simple model for assessing the cooling impacts of vegetation and water features (green and blue infrastructure) in urban environments. This model is designed to be computationally efficient so that those without technical knowledge or access to high-performance computers can use it. TARGET can be used to model average street-level air temperature at canyon to block scales (e.g. 100 m resolution). The model is carefully designed to provide reliable and accurate cooling estimates.
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153, https://doi.org/10.5194/gmd-11-4139-2018, https://doi.org/10.5194/gmd-11-4139-2018, 2018
Short summary
Short summary
Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global
hydro-climatic biomescorrespond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.
Christina Papagiannopoulou, Diego G. Miralles, Stijn Decubber, Matthias Demuzere, Niko E. C. Verhoest, Wouter A. Dorigo, and Willem Waegeman
Geosci. Model Dev., 10, 1945–1960, https://doi.org/10.5194/gmd-10-1945-2017, https://doi.org/10.5194/gmd-10-1945-2017, 2017
Short summary
Short summary
Global satellite observations provide a means to unravel the influence of climate on vegetation. Common statistical methods used to study the relationships between climate and vegetation are often too simplistic to capture the complexity of these relationships. Here, we present a novel causality framework that includes data fusion from various databases, time series decomposition, and machine learning techniques. Results highlight the highly non-linear nature of climate–vegetation interactions.
Beatriz Sanchez, Jose-Luis Santiago, Alberto Martilli, Magdalena Palacios, and Frank Kirchner
Atmos. Chem. Phys., 16, 12143–12157, https://doi.org/10.5194/acp-16-12143-2016, https://doi.org/10.5194/acp-16-12143-2016, 2016
Short summary
Short summary
This paper is focused on analyzing the coupled behavior between dispersion of reactive pollutants and atmospheric dynamics in different atmospheric conditions using a computational fluid dynamics model. It allows one to provide the selection of the chemical reactions needed that gives the best compromise between accuracy in modeling NO and NO2 dispersion in the streets and the computational time required. The conclusions can be applied to future studies about modeling air quality in cities.
Hendrik Wouters, Matthias Demuzere, Ulrich Blahak, Krzysztof Fortuniak, Bino Maiheu, Johan Camps, Daniël Tielemans, and Nicole P. M. van Lipzig
Geosci. Model Dev., 9, 3027–3054, https://doi.org/10.5194/gmd-9-3027-2016, https://doi.org/10.5194/gmd-9-3027-2016, 2016
Short summary
Short summary
A methodology is presented for translating three-dimensional information of urban areas into land-surface parameters that can be easily employed in atmospheric modelling. As demonstrated with the COSMO-CLM model for a Belgian summer, it enables them to represent urban heat islands and their dependency on urban design with a low computational cost. It allows for efficiently incorporating urban information systems (e.g., WUDAPT) into climate change assessment and numerical weather prediction.
B. Bechtel, M. Pesaresi, L. See, G. Mills, J. Ching, P. J. Alexander, J. J. Feddema, A. J. Florczyk, and I. Stewart
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 1371–1378, https://doi.org/10.5194/isprs-archives-XLI-B8-1371-2016, https://doi.org/10.5194/isprs-archives-XLI-B8-1371-2016, 2016
B. Bechtel
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 243–250, https://doi.org/10.5194/isprs-archives-XLI-B8-243-2016, https://doi.org/10.5194/isprs-archives-XLI-B8-243-2016, 2016
O. Conrad, B. Bechtel, M. Bock, H. Dietrich, E. Fischer, L. Gerlitz, J. Wehberg, V. Wichmann, and J. Böhner
Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, https://doi.org/10.5194/gmd-8-1991-2015, 2015
Short summary
Short summary
The System for Automated Geoscientific Analyses (SAGA) is a comprehensive and globally established open source geographic information system (GIS) for scientific analysis and modeling. The current version 2.1.4 offers more than 700 tools that represent the broad scopes of SAGA in numerous fields of geoscientific endeavor. In this paper, we inform about the system’s architecture and functionality and highlight the wide spectrum of scientific applications of SAGA in a review of published studies.
H. Wouters, K. De Ridder, M. Demuzere, D. Lauwaet, and N. P. M. van Lipzig
Atmos. Chem. Phys., 13, 8525–8541, https://doi.org/10.5194/acp-13-8525-2013, https://doi.org/10.5194/acp-13-8525-2013, 2013
Related subject area
Domain: ESSD – Land | Subject: Land Cover and Land Use
Map of forest tree species for Poland based on Sentinel-2 data
The ABoVE L-band and P-band airborne synthetic aperture radar surveys
A 30 m annual cropland dataset of China from 1986 to 2021
Global 1 km land surface parameters for kilometer-scale Earth system modeling
ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China
Harmonized European Union subnational crop statistics can reveal climate impacts and crop cultivation shifts
GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method
A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022
Retrieval of dominant methane (CH4) emission sources, the first high resolution (1–2m) dataset of storage tanks of China in 2000–2021
A 2020 forest age map for China with 30 m resolution
Country-level estimates of gross and net carbon fluxes from land use, land-use change and forestry
A global FAOSTAT reference database of cropland nutrient budgets and nutrient use efficiency (1961–2020): nitrogen, phosphorus and potassium
Annual maps of forest cover in the Brazilian Amazon from analyses of PALSAR and MODIS images
Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products
ChinaSoyArea10m: a dataset of soybean planting areas with a spatial resolution of 10 m across China from 2017 to 2021
The first map of crop sequence types in Europe over 2012–2018
WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping
A new cropland area database by country circa 2020
Physical, Social, and Biological Attributes for Improved Understanding and Prediction of Wildfires: FPA FOD-Attributes Dataset
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach
SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data
HISDAC-ES: historical settlement data compilation for Spain (1900–2020)
LCM2021 – the UK Land Cover Map 2021
A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types
ChinaWheatYield30m: a 30 m annual winter wheat yield dataset from 2016 to 2021 in China
Annual time-series 1-km maps of crop area and types in the conterminous US (CropAT-US): cropping diversity changes during 1850–2021
Refined fine-scale mapping of tree cover using time series of Planet-NICFI and Sentinel-1 imagery for Southeast Asia (2016–2021)
High-resolution global map of closed-canopy coconut palm
High-resolution land use and land cover dataset for regional climate modelling: historical and future changes in Europe
Global urban fractional changes at a 1 km resolution throughout 2100 under eight scenarios of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs)
China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery
High-resolution distribution maps of single-season rice in China from 2017 to 2022
Mapping global non-floodplain wetlands
An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multisource product-fusion approach
Annual emissions of carbon from land use, land-use change, and forestry from 1850 to 2020
An open-source automatic survey of green roofs in London using segmentation of aerial imagery
Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data
A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models
Estimating local agricultural gross domestic product (AgGDP) across the world
Classification and mapping of European fuels using a hierarchical, multipurpose fuel classification system
Harmonising the land-use flux estimates of global models and national inventories for 2000–2020
Four-century history of land transformation by humans in the United States (1630–2020): annual and 1 km grid data for the HIStory of LAND changes (HISLAND-US)
A 250 m annual alpine grassland AGB dataset over the Qinghai–Tibet Plateau (2000–2019) in China based on in situ measurements, UAV photos, and MODIS data
AsiaRiceYield4km: seasonal rice yield in Asia from 1995 to 2015
TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing
UGS-1m: fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework
AI4Boundaries: an open AI-ready dataset to map field boundaries with Sentinel-2 and aerial photography
GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020
CALC-2020: a new baseline land cover map at 10 m resolution for the circumpolar Arctic
MDAS: a new multimodal benchmark dataset for remote sensing
Ewa Grabska-Szwagrzyk, Dirk Tiede, Martin Sudmanns, and Jacek Kozak
Earth Syst. Sci. Data, 16, 2877–2891, https://doi.org/10.5194/essd-16-2877-2024, https://doi.org/10.5194/essd-16-2877-2024, 2024
Short summary
Short summary
We accurately mapped 16 dominant tree species and genera in Poland using Sentinel-2 observations from short periods in spring, summer, and autumn (2018–2021). The classification achieved more than 80% accuracy in country-wide forest species mapping, with variation based on species, region, and observation frequency. Freely accessible resources, including the forest tree species map and training and test data, can be found at https://doi.org/10.5281/zenodo.10180469.
Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data, 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024, https://doi.org/10.5194/essd-16-2605-2024, 2024
Short summary
Short summary
NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 120 000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. This paper summarizes those results and provides links to details on ~ 80 individual flight lines. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
Short summary
Short summary
We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, https://doi.org/10.5194/essd-16-2007-2024, 2024
Short summary
Short summary
This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
Hui Li, Xiaobo Wang, Shaoqiang Wang, Jinyuan Liu, Yuanyuan Liu, Zhenhai Liu, Shiliang Chen, Qinyi Wang, Tongtong Zhu, Lunche Wang, and Lizhe Wang
Earth Syst. Sci. Data, 16, 1689–1701, https://doi.org/10.5194/essd-16-1689-2024, https://doi.org/10.5194/essd-16-1689-2024, 2024
Short summary
Short summary
Utilizing satellite remote sensing data, we established a multi-season rice calendar dataset named ChinaRiceCalendar. It exhibits strong alignment with field observations collected by agricultural meteorological stations across China. ChinaRiceCalendar stands as a reliable dataset for investigating and optimizing the spatiotemporal dynamics of rice phenology in China, particularly in the context of climate and land use changes.
Giulia Ronchetti, Luigi Nisini Scacchiafichi, Lorenzo Seguini, Iacopo Cerrani, and Marijn van der Velde
Earth Syst. Sci. Data, 16, 1623–1649, https://doi.org/10.5194/essd-16-1623-2024, https://doi.org/10.5194/essd-16-1623-2024, 2024
Short summary
Short summary
We present a dataset of EU-wide harmonized subnational crop area, production, and yield statistics with information on data sources, processing steps, missing and derived data, and quality checks. Statistical records (344 282) collected from 1975 to 2020 for soft and durum wheat, winter and spring barley, grain maize, sunflower, and sugar beet were aligned with the EUROSTAT crop legend and the 2016 territorial classification for 961 regions. Time series have a median length of 21 years.
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
Short summary
Short summary
This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Qiangqiang Sun, Ping Zhang, Xin Jiao, Xin Lin, Wenkai Duan, Su Ma, Qidi Pan, Lu Chen, Yongxiang Zhang, Shucheng You, Shunxi Liu, Jinmin Hao, Hong Li, and Danfeng Sun
Earth Syst. Sci. Data, 16, 1333–1351, https://doi.org/10.5194/essd-16-1333-2024, https://doi.org/10.5194/essd-16-1333-2024, 2024
Short summary
Short summary
To provide multifaceted changes under climate change and anthropogenic impacts, we estimated monthly vegetation and soil fractions in 2001–2022, providing an accurate estimate of surface heterogeneous composition, better than vegetation index and vegetation continuous-field products. We find a greening trend on Earth except for the tropics. A combination of interactive changes in vegetation and soil can be adopted as a valuable measurement of climate change and anthropogenic impacts.
Fang Chen, Lei Wang, Yu Wang, Haiying Zhang, Ning Wang, Pengfei Ma, and Bo Yu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-10, https://doi.org/10.5194/essd-2024-10, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Storage tanks are responsible for approximately 25 % of CH4 emissions in the atmosphere, exacerbating climate warming. Currently there is no publicly accessible storage tank inventory. We generated the first high spatial resolution (1–2m) storage tank dataset (STD) over 92 typical cities in China in 2021, totaling 14,461 storage tanks with construction year of 2000–2021. It shows significant agreement with CH4 emission spatially and temporally, promoting the CH4 control strategy proposal.
Kai Cheng, Yuling Chen, Tianyu Xiang, Haitao Yang, Weiyan Liu, Yu Ren, Hongcan Guan, Tianyu Hu, Qin Ma, and Qinghua Guo
Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024, https://doi.org/10.5194/essd-16-803-2024, 2024
Short summary
Short summary
To quantify forest carbon stock and its future potential accurately, we generated a 30 m resolution forest age map for China in 2020 using multisource remote sensing datasets based on machine learning and time series analysis approaches. Validation with independent field samples indicated that the mapped forest age had an R2 of 0.51--0.63. Nationally, the average forest age is 56.1 years (standard deviation of 32.7 years).
Wolfgang Alexander Obermeier, Clemens Schwingshackl, Ana Bastos, Giulia Conchedda, Thomas Gasser, Giacomo Grassi, Richard A. Houghton, Francesco Nicola Tubiello, Stephen Sitch, and Julia Pongratz
Earth Syst. Sci. Data, 16, 605–645, https://doi.org/10.5194/essd-16-605-2024, https://doi.org/10.5194/essd-16-605-2024, 2024
Short summary
Short summary
We provide and compare country-level estimates of land-use CO2 fluxes from a variety and large number of models, bottom-up estimates, and country reports for the period 1950–2021. Although net fluxes are small in many countries, they are often composed of large compensating emissions and removals. In many countries, the estimates agree well once their individual characteristics are accounted for, but in other countries, including some of the largest emitters, substantial uncertainties exist.
Cameron I. Ludemann, Nathan Wanner, Pauline Chivenge, Achim Dobermann, Rasmus Einarsson, Patricio Grassini, Armelle Gruere, Kevin Jackson, Luis Lassaletta, Federico Maggi, Griffiths Obli-Laryea, Martin K. van Ittersum, Srishti Vishwakarma, Xin Zhang, and Francesco N. Tubiello
Earth Syst. Sci. Data, 16, 525–541, https://doi.org/10.5194/essd-16-525-2024, https://doi.org/10.5194/essd-16-525-2024, 2024
Short summary
Short summary
Nutrient budgets help identify the excess or insufficient use of fertilizers and other nutrient sources in agriculture. They allow the calculation of indicators, such as the nutrient balance (surplus or deficit) and nutrient use efficiency, that help to monitor agricultural productivity and sustainability. This article describes a global cropland nutrient budget that provides data on 205 countries and territories from 1961 to 2020 (data available at https://www.fao.org/faostat/en/#data/ESB).
Yuanwei Qin, Xiangming Xiao, Hao Tang, Ralph Dubayah, Russell Doughty, Diyou Liu, Fang Liu, Yosio Shimabukuro, Egidio Arai, Xinxin Wang, and Berrien Moore III
Earth Syst. Sci. Data, 16, 321–336, https://doi.org/10.5194/essd-16-321-2024, https://doi.org/10.5194/essd-16-321-2024, 2024
Short summary
Short summary
Forest definition has two major biophysical parameters, i.e., canopy height and canopy coverage. However, few studies have assessed forest cover maps in terms of these two parameters at a large scale. Here, we assessed the annual forest cover maps in the Brazilian Amazon using 1.1 million footprints of canopy height and canopy coverage. Over 93 % of our forest cover maps are consistent with the FAO forest definition, showing the high accuracy of these forest cover maps in the Brazilian Amazon.
Xiangan Liang, Qiang Liu, Jie Wang, Shuang Chen, and Peng Gong
Earth Syst. Sci. Data, 16, 177–200, https://doi.org/10.5194/essd-16-177-2024, https://doi.org/10.5194/essd-16-177-2024, 2024
Short summary
Short summary
The state-of-the-art MODIS surface reflectance products suffer from temporal and spatial gaps, which make it difficult to characterize the continuous variation of the terrestrial surface. We proposed a framework for generating the first global 500 m daily seamless data cubes (SDC500), covering the period from 2000 to 2022. We believe that the SDC500 dataset can interest other researchers who study land cover mapping, quantitative remote sensing, and ecological science.
Qinghang Mei, Zhao Zhang, Jichong Han, Jie Song, Jinwei Dong, Huaqing Wu, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-467, https://doi.org/10.5194/essd-2023-467, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
In order to make up for the lack of long-term soybean planting area maps in China, we firstly generated a dataset of soybean planting area with a spatial resolution of 10 m for major producing areas in China from 2017 to 2021 (ChinaSoyArea10m). Compared with existing data sets, ChinaSoyArea10m has higher consistency with census data and further improvement in spatial details. The dataset can provide reliable support for subsequent studies on yield monitoring and food security.
Rémy Ballot, Nicolas Guilpart, and Marie-Hélène Jeuffroy
Earth Syst. Sci. Data, 15, 5651–5666, https://doi.org/10.5194/essd-15-5651-2023, https://doi.org/10.5194/essd-15-5651-2023, 2023
Short summary
Short summary
Assessing the benefits of crop diversification – a key element of agroecological transition – on a large scale requires a description of current crop sequences as a baseline, which is lacking at the scale of Europe. To fill this gap, we used a dataset that provides temporally and spatially incomplete land cover information to create a map of dominant crop sequence types for Europe over 2012–2018. This map is a useful baseline for assessing the benefits of future crop diversification.
Kristof Van Tricht, Jeroen Degerickx, Sven Gilliams, Daniele Zanaga, Marjorie Battude, Alex Grosu, Joost Brombacher, Myroslava Lesiv, Juan Carlos Laso Bayas, Santosh Karanam, Steffen Fritz, Inbal Becker-Reshef, Belén Franch, Bertran Mollà-Bononad, Hendrik Boogaard, Arun Kumar Pratihast, Benjamin Koetz, and Zoltan Szantoi
Earth Syst. Sci. Data, 15, 5491–5515, https://doi.org/10.5194/essd-15-5491-2023, https://doi.org/10.5194/essd-15-5491-2023, 2023
Short summary
Short summary
WorldCereal is a global mapping system that addresses food security challenges. It provides seasonal updates on crop areas and irrigation practices, enabling informed decision-making for sustainable agriculture. Our global products offer insights into temporary crop extent, seasonal crop type maps, and seasonal irrigation patterns. WorldCereal is an open-source tool that utilizes space-based technologies, revolutionizing global agricultural mapping.
Francesco N. Tubiello, Giulia Conchedda, Leon Casse, Pengyu Hao, Giorgia De Santis, and Zhongxin Chen
Earth Syst. Sci. Data, 15, 4997–5015, https://doi.org/10.5194/essd-15-4997-2023, https://doi.org/10.5194/essd-15-4997-2023, 2023
Short summary
Short summary
We describe a new dataset of cropland area circa the year 2020, with global coverage and country detail. Data are generated from geospatial information on the agreement characteristics of six high-resolution cropland maps. By helping to highlight features of cropland characteristics and underlying causes for agreement across land cover products, the dataset can be used as a tool to help guide future mapping efforts towards improved agricultural monitoring.
Yavar Pourmohamad, John T. Abatzoglou, Erin J. Belval, Erica Fleishman, Karen Short, Matthew C. Reeves, Nicholas Nauslar, Philip E. Higuera, Eric Henderson, Sawyer Ball, Amir AghaKouchak, Jeffrey P. Prestemon, Julia Olszewski, and Mojtaba Sadegh
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-430, https://doi.org/10.5194/essd-2023-430, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
This dataset provides >300 biological, physical, social and administrative attributes associated with >2.3 million wildfire incidents across the US from 1992–2020.
Martin Schwartz, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cedric Vega, Jean-Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, and Ibrahim Fayad
Earth Syst. Sci. Data, 15, 4927–4945, https://doi.org/10.5194/essd-15-4927-2023, https://doi.org/10.5194/essd-15-4927-2023, 2023
Short summary
Short summary
As forests play a key role in climate-related issues, their accurate monitoring is critical to reduce global carbon emissions effectively. Based on open-access remote-sensing sensors, and artificial intelligence methods, we created high-resolution tree height, wood volume, and biomass maps of metropolitan France that outperform previous products. This study, based on freely available data, provides essential information to support climate-efficient forest management policies at a low cost.
Zhuohong Li, Wei He, Mofan Cheng, Jingxin Hu, Guangyi Yang, and Hongyan Zhang
Earth Syst. Sci. Data, 15, 4749–4780, https://doi.org/10.5194/essd-15-4749-2023, https://doi.org/10.5194/essd-15-4749-2023, 2023
Short summary
Short summary
Nowadays, a very-high-resolution land-cover (LC) map with national coverage is still unavailable in China, hindering efficient resource allocation. To fill this gap, the first 1 m resolution LC map of China, SinoLC-1, was built. The results showed that SinoLC-1 had an overall accuracy of 73.61 % and conformed to the official survey reports. Comparison with other datasets suggests that SinoLC-1 can be a better support for downstream applications and provide more accurate LC information to users.
Johannes H. Uhl, Dominic Royé, Keith Burghardt, José A. Aldrey Vázquez, Manuel Borobio Sanchiz, and Stefan Leyk
Earth Syst. Sci. Data, 15, 4713–4747, https://doi.org/10.5194/essd-15-4713-2023, https://doi.org/10.5194/essd-15-4713-2023, 2023
Short summary
Short summary
Historical, fine-grained geospatial datasets on built-up areas are rarely available, constraining studies of urbanization, settlement evolution, or the dynamics of human–environment interactions to recent decades. In order to provide such historical data, we used publicly available cadastral building data for Spain and created a series of gridded surfaces, measuring age, physical, and land-use-related features of the built environment in Spain and the evolution of settlements from 1900 to 2020.
Christopher G. Marston, Aneurin W. O'Neil, R. Daniel Morton, Claire M. Wood, and Clare S. Rowland
Earth Syst. Sci. Data, 15, 4631–4649, https://doi.org/10.5194/essd-15-4631-2023, https://doi.org/10.5194/essd-15-4631-2023, 2023
Short summary
Short summary
The UK Land Cover Map 2021 (LCM2021) is a UK-wide land cover data set, with 21- and 10-class versions. It is intended to support a broad range of UK environmental research, including ecological and hydrological research. LCM2021 was produced by classifying Sentinel-2 satellite imagery. LCM2021 is distributed as a suite of products to facilitate easy use for a range of applications. To support research at different spatial scales it includes 10 m, 25 m and 1 km resolution products.
Xingyi Huang, Yuwei Yin, Luwei Feng, Xiaoye Tong, Xiaoxin Zhang, Jiangrong Li, and Feng Tian
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-327, https://doi.org/10.5194/essd-2023-327, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
The Tibetan Plateau, with its diverse vegetation from forests to alpine grasslands, plays a key role in understanding climate change impacts. Existing maps lack detail or miss unique ecosystems. Our research, using advanced satellite technology and machine learning, produced the TP_LC10-2022 map. Comparisons with other maps revealed TP_LC10-2022's excellence in capturing local variations. Our map is significant for in-depth ecological studies.
Yu Zhao, Shaoyu Han, Jie Zheng, Hanyu Xue, Zhenhai Li, Yang Meng, Xuguang Li, Xiaodong Yang, Zhenhong Li, Shuhong Cai, and Guijun Yang
Earth Syst. Sci. Data, 15, 4047–4063, https://doi.org/10.5194/essd-15-4047-2023, https://doi.org/10.5194/essd-15-4047-2023, 2023
Short summary
Short summary
In the present study, we generated a 30 m Chinese winter wheat yield dataset from 2016 to 2021, called ChinaWheatYield30m. The dataset has high spatial resolution and great accuracy. It is the highest-resolution yield dataset known. Such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling or regional climate evaluation.
Shuchao Ye, Peiyu Cao, and Chaoqun Lu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-195, https://doi.org/10.5194/essd-2023-195, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
In this study, we reconstructed the annual cropland density and crop type map from 1850 to 2021 at 1 km by 1 km resolution. The cropland density map presents the distribution and percentage of planted area in each 1 km by 1 km pixel. The crop type map displays the distribution of nine major crop types (corn, soybean, winter wheat, spring wheat, durum wheat, cotton, sorghum, barley, and rice) and one type of “others” (all remaining crop types excluding idle/fallow farm land, and cropland pasture).
Feng Yang and Zhenzhong Zeng
Earth Syst. Sci. Data, 15, 4011–4021, https://doi.org/10.5194/essd-15-4011-2023, https://doi.org/10.5194/essd-15-4011-2023, 2023
Short summary
Short summary
We generated a 4.77 m resolution annual tree cover map product for Southeast Asia (SEA) for 2016–2021 using Planet-NICFI and Sentinel-1 imagery. Maps were created with good accuracy and high consistency during 2016–2021. The baseline maps at 4.77 m can be converted to forest cover maps for SEA at various resolutions to meet different users’ needs. Our products can help resolve rounding errors in forest cover mapping by counting isolated trees and monitoring long, narrow forest cover removal.
Adrià Descals, Serge Wich, Zoltan Szantoi, Matthew J. Struebig, Rona Dennis, Zoe Hatton, Thina Ariffin, Nabillah Unus, David L. A. Gaveau, and Erik Meijaard
Earth Syst. Sci. Data, 15, 3991–4010, https://doi.org/10.5194/essd-15-3991-2023, https://doi.org/10.5194/essd-15-3991-2023, 2023
Short summary
Short summary
The spatial extent of coconut palm is understudied despite its increasing demand and associated impacts. We present the first global coconut palm layer at 20 m resolution. The layer was produced using deep learning and remotely sensed data. The global coconut area estimate is 12.31 Mha for dense coconut palm, but the estimate is 3 times larger when sparse coconut palm is considered. This means that coconut production can likely increase on the lands currently allocated to coconut palm.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data, 15, 3819–3852, https://doi.org/10.5194/essd-15-3819-2023, https://doi.org/10.5194/essd-15-3819-2023, 2023
Short summary
Short summary
This paper introduces the new high-resolution land use and land cover change dataset LUCAS LUC for Europe (version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Wanru He, Xuecao Li, Yuyu Zhou, Zitong Shi, Guojiang Yu, Tengyun Hu, Yixuan Wang, Jianxi Huang, Tiecheng Bai, Zhongchang Sun, Xiaoping Liu, and Peng Gong
Earth Syst. Sci. Data, 15, 3623–3639, https://doi.org/10.5194/essd-15-3623-2023, https://doi.org/10.5194/essd-15-3623-2023, 2023
Short summary
Short summary
Most existing global urban products with future projections were developed in urban and non-urban categories, which ignores the gradual change of urban development at the local scale. Using annual global urban extent data from 1985 to 2015, we forecasted global urban fractional changes under eight scenarios throughout 2100. The developed dataset can provide spatially explicit information on urban fractions at 1 km resolution, which helps support various urban studies (e.g., urban heat island).
Zeping Liu, Hong Tang, Lin Feng, and Siqing Lyu
Earth Syst. Sci. Data, 15, 3547–3572, https://doi.org/10.5194/essd-15-3547-2023, https://doi.org/10.5194/essd-15-3547-2023, 2023
Short summary
Short summary
Large-scale maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In this paper, we propose a deep-learning method for high-resolution BRA mapping (2.5 m) from Sentinel-2 imagery (10 m). The resulting China building rooftop area dataset (CBRA) is the first multi-annual (2016–2021) and high-resolution (2.5 m) BRA dataset in China. Cross-comparisons show that the CBRA achieves the best performance in capturing the spatiotemporal information.
Ruoque Shen, Baihong Pan, Qiongyan Peng, Jie Dong, Xuebing Chen, Xi Zhang, Tao Ye, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, https://doi.org/10.5194/essd-15-3203-2023, 2023
Short summary
Short summary
Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. This study developed a new rice-mapping method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data, and we found that the distribution maps have good accuracy.
Charles R. Lane, Ellen D'Amico, Jay R. Christensen, Heather E. Golden, Qiusheng Wu, and Adnan Rajib
Earth Syst. Sci. Data, 15, 2927–2955, https://doi.org/10.5194/essd-15-2927-2023, https://doi.org/10.5194/essd-15-2927-2023, 2023
Short summary
Short summary
Non-floodplain wetlands (NFWs) – wetlands located outside floodplains – confer watershed-scale resilience to hydrological, biogeochemical, and biotic disturbances. Although they are frequently unmapped, we identified ~ 33 million NFWs covering > 16 × 10 km2 across the globe. NFWs constitute the majority of the world's wetlands (53 %). Despite their small size (median 0.039 km2), these imperiled systems have an outsized impact on watershed functions and sustainability and require protection.
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data, 15, 2347–2373, https://doi.org/10.5194/essd-15-2347-2023, https://doi.org/10.5194/essd-15-2347-2023, 2023
Short summary
Short summary
A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.
Richard A. Houghton and Andrea Castanho
Earth Syst. Sci. Data, 15, 2025–2054, https://doi.org/10.5194/essd-15-2025-2023, https://doi.org/10.5194/essd-15-2025-2023, 2023
Short summary
Short summary
We update a previous analysis of carbon emissions (annual and national) from land use, land-use change, and forestry from 1850 to 2020. We use data from the latest (2020) Global Forest Resources Assessment, incorporate shifting cultivation, and include improvements to the bookkeeping model and recent estimates of emissions from peatlands. Net global emissions declined steadily over the decade from 2011 to 2020 (mean of 0.96 Pg C yr−1), falling below 1.0 Pg C yr−1 for the first time in 30 years.
Charles H. Simpson, Oscar Brousse, Nahid Mohajeri, Michael Davies, and Clare Heaviside
Earth Syst. Sci. Data, 15, 1521–1541, https://doi.org/10.5194/essd-15-1521-2023, https://doi.org/10.5194/essd-15-1521-2023, 2023
Short summary
Short summary
Adding plants to roofs of buildings can reduce indoor and outdoor temperatures and so can reduce urban overheating, which is expected to increase due to climate change and urban growth. To better understand the effect this has on the urban environment, we need data on how many buildings have green roofs already.
We used a computer vision model to find green roofs in aerial imagery in London, producing a dataset identifying what buildings have green roofs and improving on previous methods.
Chunling Sun, Hong Zhang, Lu Xu, Ji Ge, Jingling Jiang, Lijun Zuo, and Chao Wang
Earth Syst. Sci. Data, 15, 1501–1520, https://doi.org/10.5194/essd-15-1501-2023, https://doi.org/10.5194/essd-15-1501-2023, 2023
Short summary
Short summary
Over 90 % of the world’s rice is produced in the Asia–Pacific region. In this study, a rice-mapping method based on Sentinel-1 data for mainland Southeast Asia is proposed. A combination of spatiotemporal features with strong generalization is selected and input into the U-Net model to obtain a 20 m resolution rice area map of mainland Southeast Asia in 2019. The accuracy of the proposed method is 92.20 %. The rice area map is concordant with statistics and other rice area maps.
Kandice L. Harper, Céline Lamarche, Andrew Hartley, Philippe Peylin, Catherine Ottlé, Vladislav Bastrikov, Rodrigo San Martín, Sylvia I. Bohnenstengel, Grit Kirches, Martin Boettcher, Roman Shevchuk, Carsten Brockmann, and Pierre Defourny
Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, https://doi.org/10.5194/essd-15-1465-2023, 2023
Short summary
Short summary
We built a spatially explicit annual plant-functional-type (PFT) dataset for 1992–2020 exhibiting intra-class spatial variability in PFT fractional cover at 300 m. For each year, 14 maps of percentage cover are produced: bare soil, water, permanent snow/ice, built, managed grasses, natural grasses, and trees and shrubs, each split into leaf type and seasonality. Model simulations indicate significant differences in simulated carbon, water, and energy fluxes in some regions using this new set.
Yating Ru, Brian Blankespoor, Ulrike Wood-Sichra, Timothy S. Thomas, Liangzhi You, and Erwin Kalvelagen
Earth Syst. Sci. Data, 15, 1357–1387, https://doi.org/10.5194/essd-15-1357-2023, https://doi.org/10.5194/essd-15-1357-2023, 2023
Short summary
Short summary
Economic statistics are frequently produced at an administrative level that lacks detail to examine development patterns and the exposure to natural hazards. This paper disaggregates national and subnational administrative statistics of agricultural GDP into a global dataset at the local level using satellite-derived indicators. As an illustration, the paper estimates that the exposure of areas with extreme drought to agricultural GDP is USD 432 billion, where nearly 1.2 billion people live.
Elena Aragoneses, Mariano García, Michele Salis, Luís M. Ribeiro, and Emilio Chuvieco
Earth Syst. Sci. Data, 15, 1287–1315, https://doi.org/10.5194/essd-15-1287-2023, https://doi.org/10.5194/essd-15-1287-2023, 2023
Short summary
Short summary
We present a new hierarchical fuel classification system with a total of 85 fuels that is useful for preventing fire risk at different spatial scales. Based on this, we developed a European fuel map (1 km resolution) using land cover datasets, biogeographic datasets, and bioclimatic modelling. We validated the map by comparing it to high-resolution data, obtaining high overall accuracy. Finally, we developed a crosswalk for standard fuel models as a first assignment of fuel parameters.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
Short summary
Short summary
Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Xiaoyong Li, Hanqin Tian, Chaoqun Lu, and Shufen Pan
Earth Syst. Sci. Data, 15, 1005–1035, https://doi.org/10.5194/essd-15-1005-2023, https://doi.org/10.5194/essd-15-1005-2023, 2023
Short summary
Short summary
We reconstructed land use and land cover (LULC) history for the conterminous United States during 1630–2020 by integrating multi-source data. The results show the widespread expansion of cropland and urban land and the shrinking of natural vegetation in the past four centuries. Forest planting and regeneration accelerated forest recovery since the 1920s. The datasets can be used to assess the LULC impacts on the ecosystem's carbon, nitrogen, and water cycles.
Huifang Zhang, Zhonggang Tang, Binyao Wang, Hongcheng Kan, Yi Sun, Yu Qin, Baoping Meng, Meng Li, Jianjun Chen, Yanyan Lv, Jianguo Zhang, Shuli Niu, and Shuhua Yi
Earth Syst. Sci. Data, 15, 821–846, https://doi.org/10.5194/essd-15-821-2023, https://doi.org/10.5194/essd-15-821-2023, 2023
Short summary
Short summary
The accuracy of regional grassland aboveground biomass (AGB) is always limited by insufficient ground measurements and large spatial gaps with satellite pixels. This paper used more than 37 000 UAV images as bridges to successfully obtain AGB values matching MODIS pixels. The new AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m2. Our new dataset provides important input parameters for understanding the Qinghai–Tibet Plateau during global climate change.
Huaqing Wu, Jing Zhang, Zhao Zhang, Jichong Han, Juan Cao, Liangliang Zhang, Yuchuan Luo, Qinghang Mei, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 15, 791–808, https://doi.org/10.5194/essd-15-791-2023, https://doi.org/10.5194/essd-15-791-2023, 2023
Short summary
Short summary
High-spatiotemporal-resolution rice yield datasets are limited over a large region. We proposed an explicit method to predict rice yield based on machine learning methods and generated a seasonal 4 km resolution rice yield dataset across Asia (AsiaRiceYield4km) for 1995–2015. The seasonal rice yield accuracy of AsiaRiceYield4km is high and much improved compared with previous datasets. AsiaRiceYield4km will fill the current data gap and better support agricultural monitoring systems.
Steve Ahlswede, Christian Schulz, Christiano Gava, Patrick Helber, Benjamin Bischke, Michael Förster, Florencia Arias, Jörn Hees, Begüm Demir, and Birgit Kleinschmit
Earth Syst. Sci. Data, 15, 681–695, https://doi.org/10.5194/essd-15-681-2023, https://doi.org/10.5194/essd-15-681-2023, 2023
Short summary
Short summary
Imagery from air and space is the primary source of large-scale forest mapping. Our study introduces a new dataset with over 50000 image patches prepared for deep learning tasks. We show how the information for 20 European tree species can be extracted from different remote sensing sensors. Our algorithms can detect single species with precision scores up to 88 %. With a pixel size of 20×20 cm, forestry administration can now derive large-scale tree species maps at a very high resolution.
Qian Shi, Mengxi Liu, Andrea Marinoni, and Xiaoping Liu
Earth Syst. Sci. Data, 15, 555–577, https://doi.org/10.5194/essd-15-555-2023, https://doi.org/10.5194/essd-15-555-2023, 2023
Short summary
Short summary
A large-scale and high-resolution urban green space (UGS) product with 1 m of 31 major cities in China (UGS-1m) is generated based on a deep learning framework to provide basic UGS information for relevant UGS research, such as distribution, area, and UGS rate. Moreover, an urban green space dataset (UGSet) with a total of 4454 samples of 512 × 512 in size are also supplied as the benchmark to support model training and algorithm comparison.
Raphaël d'Andrimont, Martin Claverie, Pieter Kempeneers, Davide Muraro, Momchil Yordanov, Devis Peressutti, Matej Batič, and François Waldner
Earth Syst. Sci. Data, 15, 317–329, https://doi.org/10.5194/essd-15-317-2023, https://doi.org/10.5194/essd-15-317-2023, 2023
Short summary
Short summary
AI4boundaries is an open AI-ready data set to map field boundaries with Sentinel-2 and aerial photography provided with harmonised labels covering seven countries and 2.5 M parcels in Europe.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Xidong Chen, Shangrong Lin, Jinqing Wang, Jun Mi, and Wendi Liu
Earth Syst. Sci. Data, 15, 265–293, https://doi.org/10.5194/essd-15-265-2023, https://doi.org/10.5194/essd-15-265-2023, 2023
Short summary
Short summary
An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping and generates the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal wetland sub-categories (mangrove, salt marsh and tidal flats).
Chong Liu, Xiaoqing Xu, Xuejie Feng, Xiao Cheng, Caixia Liu, and Huabing Huang
Earth Syst. Sci. Data, 15, 133–153, https://doi.org/10.5194/essd-15-133-2023, https://doi.org/10.5194/essd-15-133-2023, 2023
Short summary
Short summary
Rapid Arctic changes are increasingly influencing human society, both locally and globally. Land cover offers a basis for characterizing the terrestrial world, yet spatially detailed information on Arctic land cover is lacking. We employ multi-source data to develop a new land cover map for the circumpolar Arctic. Our product reveals regionally contrasting biome distributions not fully documented in existing studies and thus enhances our understanding of the Arctic’s terrestrial system.
Jingliang Hu, Rong Liu, Danfeng Hong, Andrés Camero, Jing Yao, Mathias Schneider, Franz Kurz, Karl Segl, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 15, 113–131, https://doi.org/10.5194/essd-15-113-2023, https://doi.org/10.5194/essd-15-113-2023, 2023
Short summary
Short summary
Multimodal data fusion is an intuitive strategy to break the limitation of individual data in Earth observation. Here, we present a multimodal data set, named MDAS, consisting of synthetic aperture radar (SAR), multispectral, hyperspectral, digital surface model (DSM), and geographic information system (GIS) data for the city of Augsburg, Germany, along with baseline models for resolution enhancement, spectral unmixing, and land cover classification, three typical remote sensing applications.
Cited articles
Abascal, A., Rothwell, N., Shonowo, A., Thomson, D. R., Elias, P., Elsey, H.,
Yeboah, G., and Kuffer, M.: “Domains of deprivation framework” for
mapping slums, informal settlements, and other deprived areas in LMICs to
improve urban planning and policy: A scoping review, Comput. Enviro.
Urban, 93, 101770, https://doi.org/10.1016/j.compenvurbsys.2022.101770,
2022. a
Alexander, P., Bechtel, B., Chow, W., Fealy, R., and Mills, G.: Linking urban
climate classification with an urban energy and water budget model:
Multi-site and multi-seasonal evaluation, Urban Climate, 17, 196–215,
https://doi.org/10.1016/j.uclim.2016.08.003, 2016. a
Alexander, P. J., Mills, G., and Fealy, R.: Using LCZ data to run an urban
energy balance model, Urban Climate, 13, 14–37,
https://doi.org/10.1016/j.uclim.2015.05.001, 2015. a
Aminipouri, M., Knudby, A. J., Krayenhoff, E. S., Zickfeld, K., and Middel, A.:
Modelling the impact of increased street tree cover on mean radiant
temperature across Vancouver's local climate zones, Urban For. Urban
Gree., 39, 9–17, https://doi.org/10.1016/j.ufug.2019.01.016, 2019. a
Assarkhaniki, Z., Sabri, S., and Rajabifard, A.: Using open data to detect the
structure and pattern of informal settlements: an outset to support inclusive
SDGs' achievement, Big Earth Data, 5, 497–526,
https://doi.org/10.1080/20964471.2021.1948178, 2021. a
Bai, X., Dawson, R. J., Ürge-Vorsatz, D., Delgado, G. C., Barau, A. S.,
Dhakal, S., Dodman, D., Leonardsen, L., Masson-Delmotte, V., Roberts, D., and
Schultz, S.: Six research priorities for cities, Nature, 555, 23–25,
https://doi.org/10.1038/d41586-018-02409-z, 2018. a, b
Baklanov, A., Grimmond, C., Carlson, D., Terblanche, D., Tang, X., Bouchet, V.,
Lee, B., Langendijk, G., Kolli, R., and Hovsepyan, A.: From urban
meteorology, climate and environment research to integrated city services,
Urban Climate, 23, 330–341, https://doi.org/10.1016/j.uclim.2017.05.004, 2018. a
Bande, L., Manandhar, P., Ghazal, R., and Marpu, P.: Characterization of Local
Climate Zones Using ENVI-met and Site Data in the City of Al-Ain, UAE,
International Journal of Sustainable Development and Planning, 15, 751–760,
https://doi.org/10.18280/ijsdp.150517, 2020. a
Barlow, J. F.: Progress in observing and modelling the urban boundary layer,
Urban Climate, 10, 216–240, https://doi.org/10.1016/j.uclim.2014.03.011, 2014. a
Bauer, P., Stevens, B., and Hazeleger, W.: A digital twin of Earth for the
green transition, Nat. Clim. Change, 11,
https://doi.org/10.1038/s41558-021-00986-y, 2021. a
Bechtel, B. and Daneke, C.: Classification of local climate zones based on
multiple earth observation data, IEEE J. Sel. Top. Appl., 5, 1191–1202,
https://doi.org/10.1109/JSTARS.2012.2189873, 2012. a
Bechtel, B., Alexander, P., Böhner, J., Ching, J., Conrad, O., Feddema,
J., Mills, G., See, L., and Stewart, I.: Mapping Local Climate Zones for a
Worldwide Database of the Form and Function of Cities, ISPRS International
Journal of Geo-Information, 4, 199–219, https://doi.org/10.3390/ijgi4010199, 2015. a, b, c, d, e
Bechtel, B., Demuzere, M., Sismanidis, P., Fenner, D., Brousse, O., Beck, C.,
Van Coillie, F., Conrad, O., Keramitsoglou, I., Middel, A., Mills, G.,
Niyogi, D., Otto, M., See, L., and Verdonck, M.-L.: Quality of Crowdsourced
Data on Urban Morphology—The Human Influence Experiment (HUMINEX), Urban
Science, 1, 15, https://doi.org/10.3390/urbansci1020015, 2017. a, b, c, d
Bechtel, B., Alexander, P. J., Beck, C., Böhner, J., Brousse, O., Ching,
J., Demuzere, M., Fonte, C., Gál, T., Hidalgo, J., Hoffmann, P.,
Middel, A., Mills, G., Ren, C., See, L., Sismanidis, P., Verdonck, M.-L., Xu,
G., and Xu, Y.: Generating WUDAPT Level 0 data – Current status of
production and evaluation, Urban Climate, 27, 24–45,
https://doi.org/10.1016/j.uclim.2018.10.001, 2019a. a
Bechtel, B., Demuzere, M., Mills, G., Zhan, W., Sismanidis, P., Small, C., and
Voogt, J.: SUHI analysis using Local Climate Zones – A comparison of 50
cities, Urban Climate, 28, 100451, https://doi.org/10.1016/j.uclim.2019.01.005,
2019b. a
Bechtel, B., Demuzere, M., and Stewart, I. D.: A Weighted Accuracy Measure for
Land Cover Mapping: Comment on Johnson et al. Local Climate Zone (LCZ) Map
Accuracy Assessments Should Account for Land Cover Physical Characteristics
that Affect the Local Thermal Environment,
Remote Sens., 12, 1769, https://doi.org/10.3390/rs12111769, 2020. a, b
Beck, C., Straub, A., Breitner, S., Cyrys, J., Philipp, A., Rathmann, J.,
Schneider, A., Wolf, K., and Jacobeit, J.: Air temperature characteristics
of local climate zones in the Augsburg urban area (Bavaria, southern Germany)
under varying synoptic conditions, Urban Climate, 25, 152–166,
https://doi.org/10.1016/j.uclim.2018.04.007, 2018. a
Benjamin, K., Luo, Z., and Wang, X.: Crowdsourcing Urban Air Temperature Data
for Estimating Urban Heat Island and Building Heating/Cooling Load in
London, Energies, 14, 5208, https://doi.org/10.3390/en14165208, 2021. a
Bettencourt, L. and West, G.: A unified theory of urban living, Nature, 467,
912–913, https://doi.org/10.1038/467912a, 2010. a
Bettencourt, L. M., Lobo, J., Helbing, D., Kühnert, C., and West, G. B.:
Growth, innovation, scaling, and the pace of life in cities, P. Natl. Acad. Sci. USA, 104,
7301–7306, https://doi.org/10.1073/pnas.0610172104, 2007. a
Bettencourt, L. M., Yang, V. C., Lobo, J., Kempes, C. P., Rybski, D., and
Hamilton, M. J.: The interpretation of urban scaling analysis in time,
J. R. Soc. Interface, 17, 1–9, https://doi.org/10.1098/rsif.2019.0846,
2020. a
Biljecki, F., Chew, L. Z. X., Milojevic-Dupont, N., and Creutzig, F.: Open
government geospatial data on buildings for planning sustainable and
resilient cities, arXiv [preprint], https://doi.org/10.48550/arXiv.2107.04023, 2021. a
Bokwa, A., Geletič, J., Lehnert, M., Žuvela-Aloise, M.,
Hollósi, B., Gál, T., Skarbit, N., Dobrovolný, P., Hajto,
M. J., Kielar, R., Walawender, J. P., Štastný, P., Holec, J.,
Ostapowicz, K., Burianová, J., and Garaj, M.: Heat load assessment in
Central European cities using an urban climate model and observational
monitoring data, Energ. Buildings, 201, 53–69,
https://doi.org/10.1016/j.enbuild.2019.07.023, 2019. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a, b
Brousse, O., Martilli, A., Foley, M., Mills, G., and Bechtel, B.: WUDAPT, an
efficient land use producing data tool for mesoscale models? Integration of
urban LCZ in WRF over Madrid, Urban Climate, 17, 116–134,
https://doi.org/10.1016/j.uclim.2016.04.001, 2016. a
Brousse, O., Georganos, S., Demuzere, M., Vanhuysse, S., Wouters, H., Wolff,
E., Linard, C., and Lipzig, N. P. V.: Urban Climate Using Local Climate
Zones in Sub-Saharan Africa to tackle urban health issues, Urban Climate,
27, 227–242, https://doi.org/10.1016/j.uclim.2018.12.004, 2019. a, b, c
Brousse, O., Georganos, S., Demuzere, M., Dujardin, S., Lennert, M., Linard,
C., Snow, R. W., Thiery, W., and van Lipzig, N. P. M.: Can we use local
climate zones for predicting malaria prevalence across sub-Saharan African
cities?, Environ. Res. Lett., 15, 124051,
https://doi.org/10.1088/1748-9326/abc996, 2020a. a, b, c, d, e
Brousse, O., Wouters, H., Demuzere, M., Thiery, W., Van de Walle, J., and
Lipzig, N. P. M.: The local climate impact of an African city during
clear‐sky conditions – Implications of the recent urbanization in Kampala
(Uganda), Int. J. Climatol., 40, 4586–4608,
https://doi.org/10.1002/joc.6477, 2020b. a, b
Brousse, O., Simpson, C., Walker, N., Fenner, D., Meier, F., Taylor, J., and
Heaviside, C.: Evidence of horizontal urban heat advection in London using
six years of data from a citizen weather station network, Environ.
Res. Lett., 17, 044041, https://doi.org/10.1088/1748-9326/ac5c0f, 2022. a
Buchhorn, M., Lesiv, M., Tsendbazar, N. E., Herold, M., Bertels, L., and Smets,
B.: Copernicus global land cover layers-collection 2, Remote Sens., 12,
1–14, https://doi.org/10.3390/rs12061044, 2020a. a, b
Buchhorn, M., Smets, B., Bertels, L., Roo, B. D., Lesiv, M., Tsendbazar, N.-E.,
Herold, M., and Fritz, S.: Copernicus Global Land Service: Land Cover 100m:
collection 3: epoch 2018: Globe, Zenodo [data set], https://doi.org/10.5281/zenodo.3518038,
2020b. a, b
Chen, C., Bagan, H., Xie, X., La, Y., and Yamagata, Y.: Combination of
sentinel-2 and palsar-2 for local climate zone classification: A case study
of nanchang, China, Remote Sens., 13, 1–21, https://doi.org/10.3390/rs13101902,
2021a. a, b
Chen, G., Xie, J., Li, W., Li, X., Hay Chung, L. C., Ren, C., and Liu, X.:
Future “local climate zon” spatial change simulation in Greater Bay
Area under the shared socioeconomic pathways and ecological control line,
Build. Environ., 203, 108077,
https://doi.org/10.1016/j.buildenv.2021.108077, 2021b. a
Chieppa, J., Bush, A., and Mitra, C.: Using `Local Climate Zones” to
detect urban heat island on two small cities in Alabama, Earth Interact.,
22, 1–22, https://doi.org/10.1175/EI-D-17-0020.1, 2018. a
Chinchor, N.: MUC-4 evaluation metrics, in: Proceedings of the 4th conference
on Message understanding – MUC4 '92, Association for Computational
Linguistics, Morristown, NJ, USA, p. 22, https://doi.org/10.3115/1072064.1072067, 1992. a, b
Ching, J., Mills, G., Bechtel, B., See, L., Feddema, J., Wang, X., Ren, C.,
Brousse, O., Martilli, A., Neophytou, M., Mouzourides, P., Stewart, I.,
Hanna, A., Ng, E., Foley, M., Alexander, P., Aliaga, D., Niyogi, D.,
Shreevastava, A., Bhalachandran, P., Masson, V., Hidalgo, J., Fung, J.,
Andrade, M., Baklanov, A., Dai, W., Milcinski, G., Demuzere, M., Brunsell,
N., Pesaresi, M., Miao, S., Mu, Q., Chen, F., and Theeuwes, N.: WUDAPT: An
Urban Weather, Climate, and Environmental Modeling Infrastructure for the
Anthropocene, B. Am. Meteorol. Soc., 99,
1907–1924, https://doi.org/10.1175/BAMS-D-16-0236.1, 2018. a, b, c, d, e, f, g, h
Ching, J., Aliaga, D., Mills, G., Masson, V., See, L., Neophytou, M., Middel,
A., Baklanov, A., Ren, C., Ng, E., Fung, J., Wong, M., Huang, Y., Martilli,
A., Brousse, O., Stewart, I., Zhang, X., Shehata, A., Miao, S., Wang, X.,
Wang, W., Yamagata, Y., Duarte, D., Li, Y., Feddema, J., Bechtel, B.,
Hidalgo, J., Roustan, Y., Kim, Y., Simon, H., Kropp, T., Bruse, M., Lindberg,
F., Grimmond, S., Demuzure, M., Chen, F., Li, C., Gonzales-Cruz, J.,
Bornstein, B., He, Q., Tzu-Ping, Hanna, A., Erell, E., Tapper, N., Mall, R.,
and Niyogi, D.: Pathway using WUDAPT's Digital Synthetic City tool towards
generating urban canopy parameters for multi-scale urban atmospheric
modeling, Urban Climate, 28, 100459, https://doi.org/10.1016/j.uclim.2019.100459,
2019. a, b, c, d
Conners, R. W., Trivedi, M. M., and Harlow, C. A.: Segmentation of a
high-resolution urban scene using texture operators (Sunnyvale,
California), Comput. Vision Graph., 25, 273–310,
https://doi.org/10.1016/0734-189X(84)90197-X, 1984. a
Coops, N. C. and Wulder, M. A.: Breaking the Habit(at), Trend. Ecol.
Evol., 34, 585–587, https://doi.org/10.1016/j.tree.2019.04.013, 2019. a
Corbane, C., Pesaresi, M., Politis, P., Syrris, V., Florczyk, A. J., Soille,
P., Maffenini, L., Burger, A., Vasilev, V., Rodriguez, D., Sabo, F.,
Dijkstra, L., and Kemper, T.: Big earth data analytics on Sentinel-1 and
Landsat imagery in support to global human settlements mapping, Big Earth
Data, 1, 118–144, https://doi.org/10.1080/20964471.2017.1397899, 2017. a
Corbane, C., Pesaresi, M., Kemper, T., Politis, P., Florczyk, A. J., Syrris,
V., Melchiorri, M., Sabo, F., and Soille, P.: Automated global delineation
of human settlements from 40 years of Landsat satellite data archives, Big
Earth Data, 3, 140–169, https://doi.org/10.1080/20964471.2019.1625528, 2019. a
Corbane, C., Politis, P., Kempeneers, P., Simonetti, D., Soille, P., Burger,
A., Pesaresi, M., Sabo, F., Syrris, V., and Kemper, T.: A global cloud free
pixel- based image composite from Sentinel-2 data, Data in Brief, 31,
105737, https://doi.org/10.1016/j.dib.2020.105737, 2020. a
Corbane, C., Syrris, V., Sabo, F., Politis, P., Melchiorri, M., Pesaresi, M.,
Soille, P., and Kemper, T.: Convolutional neural networks for global human
settlements mapping from Sentinel-2 satellite imagery, Neural Comput.
Appl., 33, 6697–6720, https://doi.org/10.1007/s00521-020-05449-7, 2021. a, b, c, d, e, f, g
Costello, A., Abbas, M., Allen, A., Ball, S., Bell, S., Bellamy, R., Friel, S.,
Groce, N., Johnson, A., Kett, M., Lee, M., Levy, C., Maslin, M., McCoy, D.,
McGuire, B., Montgomery, H., Napier, D., Pagel, C., Patel, J., de Oliveira,
J. A. P., Redclift, N., Rees, H., Rogger, D., Scott, J., Stephenson, J.,
Twigg, J., Wolff, J., and Patterson, C.: Managing the health effects of
climate change, Lancet, 373, 1693–1733, https://doi.org/10.1016/s0140-6736(09)60935-1, 2009. a
Creutzig, F., Baiocchi, G., Bierkandt, R., Pichler, P.-P., and Seto, K. C.:
Global typology of urban energy use and potentials for an urbanization
mitigation wedge, P. Natl. Acad. Sci. USA, 112, 6283–6288,
201315545, https://doi.org/10.1073/pnas.1315545112, 2015. a
Creutzig, F., Agoston, P., Minx, J. C., Canadell, J. G., Andrew, R. M.,
Quéré, C. L., Peters, G. P., Sharifi, A., Yamagata, Y., and
Dhakal, S.: Urban infrastructure choices structure climate solutions,
Nat. Clim. Change, 6, 1054, https://doi.org/10.1038/nclimate3169, 2016. a
Creutzig, F., Lohrey, S., Bai, X., Baklanov, A., Dawson, R., Dhakal, S., Lamb,
W. F., McPhearson, T., Minx, J., Munoz, E., and Walsh, B.: Upscaling urban
data science for global climate solutions, Global Sustain., 2, 1–25,
https://doi.org/10.1017/sus.2018.16, 2019. a
D'Amour, C. B., Reitsma, F., Baiocchi, G., Barthel, S., Güneralp, B.,
Erb, K. H., Haberl, H., Creutzig, F., and Seto, K. C.: Future urban land
expansion and implications for global croplands, P. Natl. Acad. Sci. USA, 114, 8939–8944,
https://doi.org/10.1073/pnas.1606036114, 2017. a
Demuzere, M., Orru, K., Heidrich, O., Olazabal, E., Geneletti, D., Orru, H.,
Bhave, A. G., Mittal, N., Feliu, E., and Faehnle, M.: Mitigating and
adapting to climate change: Multi-functional and multi-scale assessment of
green urban infrastructure, J. Environ. Manage., 146,
107–115, https://doi.org/10.1016/j.jenvman.2014.07.025, 2014. a
Demuzere, M., Hankey, S., Mills, G., Zhang, W., Lu, T., and Bechtel, B.:
Combining expert and crowd-sourced training data to map urban form and
functions for the continental US, Nature Scientific Data, 7, 1–13,
https://doi.org/10.1038/s41597-020-00605-z, 2020a. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Demuzere, M., Mihara, T., Redivo, C. P., Feddema, J., and Setton, E.:
Multi-temporal LCZ maps for Canadian functional urban areas, OSF Preprints,
https://doi.org/10.31219/osf.io/h5tm6, 2020b. a
Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D.,
van Vliet, J., and Bechtel, B.: Global map of Local Climate Zones, Zenodo [data set],
https://doi.org/10.5281/zenodo.6364594, 2022a. a, b
Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D.,
van Vliet, J., and Bechtel, B.: Teaser Local Climate Zone maps extracted
from the global map of Local Climate Zones, Zenodo [data set], https://doi.org/10.5281/zenodo.6364705,
2022b. a
Demuzere, M., Argüeso, D., Zonato, A., and Kittner, J.: W2W: A Python package that injects WUDAPT's Local Climate Zone information in WRF (v0.4.1), Zenodo [code], https://doi.org/10.5281/zenodo.7016607, 2022c. a
Di Gregorio, A. and Leonardi, U.: Land cover classification system – user manual software version 3, https://www.fao.org/3/i5428e/i5428e.pdf (last access: 23 August 2022), 2016. a
Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John,
J. G., Krasting, J. P., Malyshev, S., Naik, V., Paulot, F., Shevliakova, E.,
Stock, C. A., Zadeh, N., Balaji, V., Blanton, C., Dunne, K. A., Dupuis, C.,
Durachta, J., Dussin, R., Gauthier, P. P., Griffies, S. M., Guo, H.,
Hallberg, R. W., Harrison, M., He, J., Hurlin, W., McHugh, C., Menzel, R.,
Milly, P. C., Nikonov, S., Paynter, D. J., Ploshay, J., Radhakrishnan, A.,
Rand, K., Reichl, B. G., Robinson, T., Schwarzkopf, D. M., Sentman, L. T.,
Underwood, S., Vahlenkamp, H., Winton, M., Wittenberg, A. T., Wyman, B.,
Zeng, Y., and Zhao, M.: The GFDL Earth System Model Version 4.1 (GFDL-ESM
4.1): Overall Coupled Model Description and Simulation Characteristics,
J. Adv. Model. Earth Sy., 12, 1–56,
https://doi.org/10.1029/2019MS002015, 2020. a
Eldesoky, A. H., Gil, J., and Pont, M. B.: The suitability of the urban local
climate zone classification scheme for surface temperature studies in
distinct macroclimate regions, Urban Climate, 37, 100823,
https://doi.org/10.1016/j.uclim.2021.100823, 2021. a
ESA: Land Cover CCI Product User Guide Version 2.0, Tech. rep., European
Space Agency, http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: 23 August 2022), 2017. a
Esch, T., Heldens, W., Hirne, A., Keil, M., Marconcini, M., Roth, A., Zeidler,
J., Dech, S., and Strano, E.: Breaking new ground in mapping human
settlements from space – The Global Urban Footprint, ISPRS J.
Photogramm., 134, 30–42,
https://doi.org/10.1016/j.isprsjprs.2017.10.012, 2017. a
Esch, T., Brzoska, E., Dech, S., Leutner, B., Palacios-Lopez, D.,
Metz-Marconcini, A., Marconcini, M., Roth, A., and Zeidler, J.: World
Settlement Footprint 3D – A first three-dimensional survey of the global
building stock, Remote Sens. Environ., 270, 112877,
https://doi.org/10.1016/j.rse.2021.112877, 2022. a
Fenner, D., Meier, F., Bechtel, B., Otto, M., and Scherer, D.: Intra and inter
“local climate zone” variability of air temperature as observed by
crowdsourced citizen weather stations in Berlin, Germany, Meteorol.
Z., 26, 525–547, https://doi.org/10.1127/metz/2017/0861, 2017. a
Fenner, D., Bechtel, B., Demuzere, M., Kittner, J., and Meier, F.:
CrowdQC+ – A Quality-Control for Crowdsourced Air-Temperature Observations
Enabling World-Wide Urban Climate Applications, Front. Environ.
Sci., 9, 1–21, https://doi.org/10.3389/fenvs.2021.720747, 2021. a
Florczyk, A., Melchiorri, M., Corban, C., Schiavina, M., Maffenini, L.,
Pesaresi, M., Politis, P., Sabo, F., Carneiro Freire, S., Ehrlich, D.,
Kemper, T., Tommasi, P., Airaghi, D., and Zanchetta, L.: Description of the
GHS Urban Centre Database 2015, public release 2019, version 1.0, Publications Office, https://doi.org/10.2760/037310, 2019. a, b
Forget, Y., Shimoni, M., and Gilbert, M.: Complementarity Between Sentinel-1
and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa, Preprints 2018, 2018100695,
https://doi.org/10.20944/preprints201810.0695.v1, 2018. a
Fuhrer, O., Chadha, T., Hoefler, T., Kwasniewski, G., Lapillonne, X., Leutwyler, D., Lüthi, D., Osuna, C., Schär, C., Schulthess, T. C., and Vogt, H.: Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0, Geosci. Model Dev., 11, 1665–1681, https://doi.org/10.5194/gmd-11-1665-2018, 2018. a
Gál, T., Mahó, S. I., Skarbit, N., and Unger, J.: Numerical
modelling for analysis of the effect of different urban green spaces on urban
heat load patterns in the present and in the future, Comput. Environ.
Urban, 87, 101600, https://doi.org/10.1016/j.compenvurbsys.2021.101600, 2021. a
Georgescu, M., Chow, W. T. L., Wang, Z. H., Brazel, A., Trapido-Lurie, B., Roth, M., and Benson-Lira, V.: Prioritizing urban sustainability solutions:
coordinated approaches must incorporate scale-dependent built environment
induced effects, Environ. Res. Lett., 10, 061001,
https://doi.org/10.1088/1748-9326/10/6/061001, 2015. a
Gilabert, J., Deluca, A., Lauwaet, D., Ballester, J., Corbera, J., and Llasat, M. C.: Assessing heat exposure to extreme temperatures in urban areas using the Local Climate Zone classification, Nat. Hazards Earth Syst. Sci., 21, 375–391, https://doi.org/10.5194/nhess-21-375-2021, 2021. a
Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., Liu, X., Xu, B., Yang,
J., Zhang, W., and Zhou, Y.: Annual maps of global artificial impervious
area (GAIA) between 1985 and 2018, Remote Sens. Environ., 236,
111510, https://doi.org/10.1016/j.rse.2019.111510, 2020. a, b
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore,
R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone,
Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031,
2017. a
Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X.,
and Briggs, J. M.: Global change and the ecology of cities, Science, 319, 756–760, https://doi.org/10.1126/science.1150195, 2008. a
Grimmond, S., Bouchet, V., Molina, L. T., Baklanov, A., Tan, J.,
Schlünzen, K. H., Mills, G., Golding, B., Masson, V., Ren, C., Voogt,
J., Miao, S., Lean, H., Heusinkveld, B., Hovespyan, A., Teruggi, G., Parrish,
P., and Joe, P.: Integrated urban hydrometeorological, climate and
environmental services: Concept, methodology and key messages, Urban
Climate, 33, 100623, https://doi.org/10.1016/j.uclim.2020.100623, 2020. a
Güneralp, B., Zhou, Y., Ürge-Vorsatz, D., Gupta, M., Yu, S., Patel,
P. L., Fragkias, M., Li, X., and Seto, K. C.: Global scenarios of urban
density and its impacts on building energy use through 2050, P.
Natl. Acad. Sci. USA, 114,
8945–8950, https://doi.org/10.1073/pnas.1606035114, 2017. a
Gutowski, W. J., Ullrich, P. A., Hall, A., Leung, L. R., O'Brien, T. A.,
Patricola, C. M., Arritt, R. W., Bukovsky, M. S., Calvin, K. V., Feng, Z.,
Jones, A. D., Kooperman, G. J., Monier, E., Pritchard, M. S., Pryor, S. C.,
Qian, Y., Rhoades, A. M., Roberts, A. F., Sakaguchi, K., Urban, N., and
Zarzycki, C.: The Ongoing Need for High-Resolution Regional Climate Models:
Process Understanding and Stakeholder Information, B. Am.
Meteorol. Soc., 101, E664–E683, https://doi.org/10.1175/BAMS-D-19-0113.1,
2020. a
Hammerberg, K., Brousse, O., Martilli, A., and Mahdavi, A.: Implications of
employing detailed urban canopy parameters for mesoscale climate modelling: a
comparison between WUDAPT and GIS databases over Vienna, Austria,
Int. J. Climatol., 38, e1241–e1257,
https://doi.org/10.1002/joc.5447, 2018. a
Haralick, R. M., Dinstein, I., and Shanmugam, K.: Textural Features for Image
Classification, IEEE T. Syst. Man Cyb., SMC-3, 610–621,
https://doi.org/10.1109/TSMC.1973.4309314, 1973. a
Hay Chung, L. C., Xie, J., and Ren, C.: Improved machine-learning mapping of
local climate zones in metropolitan areas using composite Earth observation
data in Google Earth Engine, Build. Environ., 199, 107879,
https://doi.org/10.1016/j.buildenv.2021.107879, 2021. a, b, c
Hertwig, D., Ng, M., Grimmond, S., Vidale, P. L., and McGuire, P. C.:
High‐resolution global climate simulations: Representation of cities,
Int. J. Climatol., 41, 3266–3285, https://doi.org/10.1002/joc.7018,
2021. a, b
Hidalgo, J., Lemonsu, A., and Masson, V.: Between progress and obstacles in
urban climate interdisciplinary studies and knowledge transfer to society,
Ann. NY Acad. Sci., 1436, 5–18,
https://doi.org/10.1111/nyas.13986, 2018. a
Hirsch, A. L., Evans, J. P., Thomas, C., Conroy, B., Hart, M. A., Lipson, M.,
and Ertler, W.: Resolving the influence of local flows on urban heat
amplification during heatwaves, Environ. Res. Lett., 16,
064066, https://doi.org/10.1088/1748-9326/ac0377, 2021. a
Hu, J., Ghamisi, P., and Zhu, X.: Feature Extraction and Selection of
Sentinel-1 Dual-Pol Data for Global-Scale Local Climate Zone Classification,
ISPRS Int. Geo-Inf., 7, 379,
https://doi.org/10.3390/ijgi7090379, 2018. a
IPCC: Climate Change 2022: Impacts, Adaptation, and Vulnerability, Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., and Rama, B., Cambridge University Press, in Press, 2022. a
Jiang, S., Huang, F., Zhan, W., Bechtel, B., Liu, Z., Demuzere, M., Huang, Y.,
Xu, Y., Xia, W., Quan, J., Hong, F., Jiang, L., Lai, J., Wang, C., Kong, F.,
Du, H., Miao, S., Chen, Y., Zhang, X., Planning, R., and Development, U.:
Mapping Local Climate Zones : A Bibliometric Meta- Analysis and Systematic
Review, OSF preprints, 1–106, https://doi.org/10.31219/osf.io/c2bez, 2021. a, b
Jung, M., Dahal, P. R., Butchart, S. H., Donald, P. F., De Lamo, X., Lesiv,
M., Kapos, V., Rondinini, C., and Visconti, P.: A global map of terrestrial
habitat types, Sci. Data, 7, 1–8, https://doi.org/10.1038/s41597-020-00599-8,
2020. a
Kabano, P., Lindley, S., and Harris, A.: Evidence of urban heat island impacts
on the vegetation growing season length in a tropical city, Landscape
Urban Plan., 206, 103989, https://doi.org/10.1016/j.landurbplan.2020.103989, 2021. a
Kamath, H. G., Singh, M., Magruder, L. A., Yang, Z.-l., and Niyogi, D.:
GLOBUS: GLObal Building heights for Urban Studies, ArXiv [preprint],
https://doi.org/10.48550/arXiv.2205.12224, 2022. a
Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C. S. B., Oleson, K. W.,
Kouznetsov, R., Masson, V., and Järvi, L.: Seasonal surface urban
energy balance and wintertime stability simulated using three land-surface
models in the high-latitude city Helsinki, Q. J. Roy.
Meteor. Soc., 142, 401–417, https://doi.org/10.1002/qj.2659, 2016. a
Kotharkar, R., Ghosh, A., Kapoor, S., and Reddy, D. G. K.: Approach to local
climate zone based energy consumption assessment in an Indian city, Energ.
Buildings, 259, 111835, https://doi.org/10.1016/j.enbuild.2022.111835, 2022. a
Kuffer, M., Thomson, D. R., Boo, G., Mahabir, R., Grippa, T., Vanhuysse, S.,
Engstrom, R., Ndugwa, R., Makau, J., Darin, E., de Albuquerque, J. P., and
Kabaria, C.: The role of earth observation in an integrated deprived area
mapping “system” for low-to-middle income countries, Remote Sensing, 12, 982,
https://doi.org/10.3390/rs12060982, 2020. a
Lai, D., Liu, W., Gan, T., Liu, K., and Chen, Q.: A review of mitigating
strategies to improve the thermal environment and thermal comfort in urban
outdoor spaces, Sci. Total Environ., 661, 337–353,
https://doi.org/10.1016/j.scitotenv.2019.01.062, 2019. a
Leconte, F., Bouyer, J., and Claverie, R.: Nocturnal cooling in Local Climate
Zone: Statistical approach using mobile measurements, Urban Climate, 33,
100629, https://doi.org/10.1016/j.uclim.2020.100629, 2020. a
Lehnert, M., Savić, S., Milošević, D., Dunjić, J., and
Geletič, J.: Mapping Local Climate Zones and Their Applications in
European Urban Environments: A Systematic Literature Review and Future
Development Trends, ISPRS Int. Geo-Inf., 10, 260,
https://doi.org/10.3390/ijgi10040260, 2021. a
Li, M., Koks, E., Taubenböck, H., and van Vliet, J.: Continental-scale
mapping and analysis of 3D building structure, Remote Sens.
Environ., 245, 111859, https://doi.org/10.1016/j.rse.2020.111859,
2020. a, b
Li, X., Gong, P., Zhou, Y., Wang, J., Bai, Y., Chen, B., Hu, T., Xiao, Y., Xu,
B., Yang, J., Liu, X., Cai, W., Huang, H., Wu, T., Wang, X., Lin, P., Li, X.,
Chen, J., He, C., Li, X., Yu, L., Clinton, N., and Zhu, Z.: Mapping global
urban boundaries from the global artificial impervious area (GAIA) data,
Environ. Res. Lett., 15, 094044, https://doi.org/10.1088/1748-9326/ab9be3,
2020. a
Lindberg, F., Grimmond, C., Gabey, A., Huang, B., Kent, C. W., Sun, T.,
Theeuwes, N. E., Järvi, L., Ward, H. C., Capel-Timms, I., Chang, Y.,
Jonsson, P., Krave, N., Liu, D., Meyer, D., Olofson, K. F. G., Tan, J.,
Wästberg, D., Xue, L., and Zhang, Z.: Urban Multi-scale Environmental
Predictor (UMEP): An integrated tool for city-based climate services,
Environ. Model. Softw., 99, 70–87,
https://doi.org/10.1016/j.envsoft.2017.09.020, 2018. a
Liu, S., Qi, Z., Li, X., and Yeh, A. G.-O.: Integration of Convolutional
Neural Networks and Object-Based Post-Classification Refinement for Land Use
and Land Cover Mapping with Optical and SAR Data, Remote Sensing, 11, 690,
https://doi.org/10.3390/rs11060690, 2019. a
Lu, T., Marshall, J. D., Zhang, W., Hystad, P., Kim, S.-Y., Bechle, M. J.,
Demuzere, M., and Hankey, S.: National Empirical Models of Air Pollution
Using Microscale Measures of the Urban Environment, Environ. Sci.
Technol., 55, 15519–15530, https://doi.org/10.1021/acs.est.1c04047, 2021. a
Lu, Y., Yang, J., and Ma, S.: Dynamic Changes of Local Climate Zones in the
Guangdong–Hong Kong–Macao Greater Bay Area and Their Spatio-Temporal
Impacts on the Surface Urban Heat Island Effect between 2005 and 2015,
Sustainability, 13, 6374, https://doi.org/10.3390/su13116374, 2021. a
Lyu, T., Buccolieri, R., and Gao, Z.: A numerical study on the correlation
between sky view factor and summer microclimate of local climate zones,
Atmosphere, 10, 438, https://doi.org/10.3390/atmos10080438, 2019. a
MacKenzie, A. R., Whyatt, J. D., Barnes, M. J., Davies, G., and Hewitt, C. N.:
Urban form strongly mediates the allometric scaling of airshed pollution
concentrations, Environ. Res. Lett., 14, 124078,
https://doi.org/10.1088/1748-9326/ab50e3, 2019. a
Maharoof, N., Emmanuel, R., and Thomson, C.: Compatibility of local climate
zone parameters for climate sensitive street design: Influence of openness
and surface properties on local climate, Urban Climate, 33, 100642,
https://doi.org/10.1016/j.uclim.2020.100642, 2020. a
Manoli, G., Fatichi, S., Schläpfer, M., Yu, K., Crowther, T. W., Meili,
N., Burlando, P., Katul, G. G., and Bou-Zeid, E.: Magnitude of urban heat
islands largely explained by climate and population, Nature, 573, 55–60,
https://doi.org/10.1038/s41586-019-1512-9, 2019. a, b
Martilli, A.: An idealized study of city structure, urban climate, energy
consumption, and air quality, Urban Climate, 10, 430–446,
https://doi.org/10.1016/j.uclim.2014.03.003, 2014. a
Martilli, A., Roth, M., Winston, T. L., Demuzere, M., Lipson, M., Krayenhoff,
S., Sailor, D., Nazarian, N., Voogt, J., Wouters, H., Middel, A., Stewart,
I. D., Bechtel, B., Christen, A., and Hart, M.: Summer average urban-rural
surface temperature differences do not indicate the need for urban heat
reduction, OSF preprints, https://doi.org/10.31219/osf.io/8gnbf, 2020. a, b
Masson, V., Heldens, W., Bocher, E., Bonhomme, M., Bucher, B., Burmeister, C.,
de Munck, C., Esch, T., Hidalgo, J., Kanani-Sühring, F., Kwok, Y. T.,
Lemonsu, A., Lévy, J. P., Maronga, B., Pavlik, D., Petit, G., See, L.,
Schoetter, R., Tornay, N., Votsis, A., and Zeidler, J.: City-descriptive
input data for urban climate models: Model requirements, data sources and
challenges, Urban Climate, 31, 100536, https://doi.org/10.1016/j.uclim.2019.100536,
2020. a
Matsaba, E. O., Langer, I., Adimo, A. O., Mukundi, J. B., and Wesonga, J. M.:
Spatio-Temporal Variability of Simulated 2 m Air Temperature for Nairobi
City, Kenya, Current Urban Studies, 8, 205–221,
https://doi.org/10.4236/cus.2020.82011, 2020. a
McDonough, L. K., Santos, I. R., Andersen, M. S., O'Carroll, D. M., Rutlidge,
H., Meredith, K., Oudone, P., Bridgeman, J., Gooddy, D. C., Sorensen, J. P.,
Lapworth, D. J., MacDonald, A. M., Ward, J., and Baker, A.: Changes in
global groundwater organic carbon driven by climate change and urbanization,
Nat. Commun., 11, 1–10, https://doi.org/10.1038/s41467-020-14946-1, 2020. a, b
Middel, A., Häb, K., Brazel, A. J., Martin, C. a., and Guhathakurta, S.:
Impact of urban form and design on mid-afternoon microclimate in Phoenix
Local Climate Zones, Landscape Urban Plan., 122, 16–28,
https://doi.org/10.1016/j.landurbplan.2013.11.004, 2014. a
Mills, S., Weiss, S., and Liang, C.: VIIRS day/night band (DNB) stray light
characterization and correction, in: Earth Observing Systems XVIII, edited
by: Butler, J. J., Xiong, X. J., and Gu, X.,
International Society for Optics and Photonics, SPIE, 8866, 549–566,
https://doi.org/10.1117/12.2023107, 2013. a
Milošević, D., Savić, S., Kresoja, M., Lužanin, Z.,
Šećerov, I., Arsenović, D., Dunjić, J., and
Matzarakis, A.: Analysis of air temperature dynamics in the “local climate
zones” of Novi Sad (Serbia) based on long-term database from an urban
meteorological network, Int. J. Biometeorol., 66, 371–384,
https://doi.org/10.1007/s00484-020-02058-w, 2021. a
Molnár, G., Gyöngyösi, A. Z., and Gál, T.: Integration
of an LCZ-based classification into WRF to assess the intra-urban temperature
pattern under a heatwave period in Szeged, Hungary, Theor. Appl.
Climatol., 138, 1139–1158, https://doi.org/10.1007/s00704-019-02881-1, 2019. a
Moradi, M., Krayenhoff, E. S., and Aliabadi, A. A.: A comprehensive
indoor–outdoor urban climate model with hydrology: The Vertical City
Weather Generator (VCWG v2.0.0), Build. Environ., 207, 108406,
https://doi.org/10.1016/j.buildenv.2021.108406, 2022. a
Mu, Q., Miao, S., Wang, Y., Li, Y., He, X., and Yan, C.: Evaluation of
employing local climate zone classification for mesoscale modelling over
Beijing metropolitan area, Meteorol. Atmos. Phys., 132,
315–326, https://doi.org/10.1007/s00703-019-00692-7, 2020. a
Nagendra, H., Bai, X., Brondizio, E. S., and Lwasa, S.: The urban south and
the predicament of global sustainability, Nature Sustainability, 1,
341–349, https://doi.org/10.1038/s41893-018-0101-5, 2018. a
Oke, T. R., Mills, G., Christen, A., and Voogt, J. A.: Urban Climates,
Cambridge University Press, Cambridge, https://doi.org/10.1017/9781139016476, 2017. a, b, c
Oleson, K. W. and Feddema, J.: Parameterization and Surface Data Improvements
and New Capabilities for the Community Land Model Urban (CLMU), J.
Adv. Model. Earth Sy., 12, 1–30, https://doi.org/10.1029/2018ms001586,
2020. a, b
Owusu, M., Kuffer, M., Belgiu, M., Grippa, T., Lennert, M., Georganos, S., and
Vanhuysse, S.: Towards user-driven earth observation-based slum mapping,
Comput. Environ. Urban, 89, 101681,
https://doi.org/10.1016/j.compenvurbsys.2021.101681, 2021. a
Pan, Z.: WUDAPT Level 0 training data for HUAICHU (China, People's Republic of), LCZ Generator, submitted,
https://lcz-generator.rub.de/factsheets/f81f240993b6ca50180232c878755c18736e308a/f81f240993b6ca50180232c878755c18736e308a_factsheet.html (last access: 22 August 2022), 2021. a
Patel, P., Karmakar, S., Ghosh, S., and Niyogi, D.: Improved simulation of
very heavy rainfall events by incorporating WUDAPT urban land use/land cover
in WRF, Urban Climate, 32, 100616, https://doi.org/10.1016/j.uclim.2020.100616,
2020. a
Patel, P., Jamshidi, S., Nadimpalli, R., Aliaga, D. G., Mills, G., Chen, F.,
Demuzere, M., and Niyogi, D.: Modeling Large‐Scale Heatwave by
Incorporating Enhanced Urban Representation, J. Geophys.
Res.-Atmos., 127, 1–33, https://doi.org/10.1029/2021JD035316, 2022. a, b
Patella, V., Florio, G., Magliacane, D., Giuliano, A., Crivellaro, M. A.,
Bartolomeo, D., Genovese, A., Palmieri, M., Postiglione, A., Ridolo, E.,
Scaletti, C., Ventura, M. T., and Zollo, A.: Urban air pollution and climate
change: “The Decalogue: Allergy Safe Tree” for allergic and respiratory
diseases care, Clinical and Molecular Allergy, 16, 20,
https://doi.org/10.1186/s12948-018-0098-3, 2018. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in {P}ython, J. Mach.
Learn. Res., 12, 2825–2830, 2011. a, b
Pellegatti Franco, D. M., Andrade, M. d. F., Ynoue, R. Y., and Ching, J.:
Effect of Local Climate Zone (LCZ) classification on ozone chemical
transport model simulations in Sao Paulo, Brazil, Urban Climate, 27,
293–313, https://doi.org/10.1016/j.uclim.2018.12.007, 2019. a
Perera, N. and Emmanuel, R.: A “Local Climate Zone” based approach to
urban planning in Colombo, Sri Lanka, Urban Climate, 23, 188–203,
https://doi.org/10.1016/j.uclim.2016.11.006, 2018. a
Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C.,
Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C. E., Armston,
J., Dubayah, R., Blair, J. B., and Hofton, M.: Mapping global forest canopy
height through integration of GEDI and Landsat data, Remote Sens.
Environ., 253, 112165, https://doi.org/10.1016/j.rse.2020.112165, 2021. a
Potgieter, J., Nazarian, N., Lipson, M. J., Hart, M. A., Ulpiani, G., Morrison,
W., and Benjamin, K.: Combining High-Resolution Land Use Data With
Crowdsourced Air Temperature to Investigate Intra-Urban Microclimate,
Front. Environ. Sci., 9, 1–19,
https://doi.org/10.3389/fenvs.2021.720323, 2021. a
Quah, A. K. and Roth, M.: Diurnal and weekly variation of anthropogenic heat
emissions in a tropical city, Singapore, Atmos. Environ., 46,
92–103, https://doi.org/10.1016/j.atmosenv.2011.10.015, 2012. a
Raven, J., Stone, B., Mills, G., Towers, J., Katzschner, L., Leone, M. F.,
Gaborit, P., Georgescu, M., Hariri, M., Lee, J., LeJava, J., Sharifi, A.,
Visconti, C., and Rudd, A.: Urban Planning and Urban Design, in: Climate Change
and Cities, Cambridge University Press, 139–172, https://doi.org/10.1017/9781316563878.012, 2018. a
Reba, M. and Seto, K. C.: A systematic review and assessment of algorithms to
detect, characterize, and monitor urban land change, Remote Sens.
Environ., 242, 111739, https://doi.org/10.1016/j.rse.2020.111739, 2020. a, b, c
Ribeiro, H. V., Rybski, D., and Kropp, J. P.: Effects of changing population
or density on urban carbon dioxide emissions, Nat. Commun., 10,
1–9, https://doi.org/10.1038/s41467-019-11184-y, 2019. a
Rosentreter, J., Hagensieker, R., and Waske, B.: Towards large-scale mapping
of local climate zones using multitemporal Sentinel 2 data and convolutional
neural networks, Remote Sens. Environ., 237, 111472,
https://doi.org/10.1016/j.rse.2019.111472, 2020. a
Rosenzweig, C., Solecki, W., Hammer, S. A., and Mehrotra, S.: Cities lead the
way in climate-change action, Nature, 467, 909–911, https://doi.org/10.1038/467909a,
2010. a
Santos, L. G. R., Singh, V. K., Mughal, M. O., Nevat, I., Norford, L. K., and
Fonseca, J. A.: Estimating building's anthropogenic heat: a joint local
climate zone and land use classification method, eSIM 2021 conference,
12–19, https://doi.org/10.3929/ethz-b-000445814, 2020. a
Sapena, M., Wurm, M., Taubenböck, H., Tuia, D., and Ruiz, L. A.:
Estimating quality of life dimensions from urban spatial pattern metrics,
Comput. Environ. Urban, 85, 101549,
https://doi.org/10.1016/j.compenvurbsys.2020.101549, 2021. a
Schär, C., Fuhrer, O., Arteaga, A., Ban, N., Charpilloz, C., Di
Girolamo, S., Hentgen, L., Hoefler, T., Lapillonne, X., Leutwyler, D.,
Osterried, K., Panosetti, D., Rüdisühli, S., Schlemmer, L.,
Schulthess, T. C., Sprenger, M., Ubbiali, S., and Wernli, H.:
Kilometer-Scale Climate Models: Prospects and Challenges, B.
Am. Meteorol. Soc., 101, E567–E587,
https://doi.org/10.1175/bams-d-18-0167.1, 2020. a
Schiavina, M., Moreno-Monroy, A., Maffenini, L., and Veneri, P.: GHSL-OECD
Functional Urban Areas 2019, https://doi.org/10.2760/67415, 2019. a
Schläpfer, M., Lee, J., and Bettencourt, L. M. A.: Urban Skylines:
building heights and shapes as measures of city size, ArXiv [preprint], 1–17,
https://doi.org/10.48550/arXiv.1512.00946, 2015. a, b
Schneider, A., Friedl, M. A., and Potere, D.: Mapping global urban areas using
MODIS 500-m data: New methods and datasets based on “urban ecoregions”,
Remote Sens. Environ., 114, 1733–1746,
https://doi.org/10.1016/j.rse.2010.03.003, 2010. a, b, c
Seto, K. C., Güneralp, B., and Hutyra, L. R.: Global forecasts of urban
expansion to 2030 and direct impacts on biodiversity and carbon pools.,
P. Natl. Acad. Sci. USA, 109, 16 083–8, https://doi.org/10.1073/pnas.1211658109, 2012. a
Sharma, R., Hooyberghs, H., Lauwaet, D., and De Ridder, K.: Urban Heat
Island and Future Climate Change – Implications for Delhi's Heat, J.
Urban Health, 96, 235–251, https://doi.org/10.1007/s11524-018-0322-y, 2019. a
Shimada, M., Itoh, T., Motooka, T., Watanabe, M., Shiraishi, T., Thapa, R., and
Lucas, R.: New global forest/non-forest maps from ALOS PALSAR data
(2007–2010), Remote Sens. Environm., 155, 13–31,
https://doi.org/10.1016/j.rse.2014.04.014, 2014. a
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy
height globally with spaceborne lidar, J. Geophys. Res., 116,
G04021, https://doi.org/10.1029/2011JG001708, 2011. a
Skarbit, N., Stewart, I. D., Unger, J., and Gál, T.: Employing an urban
meteorological network to monitor air temperature conditions in the “local
climate zones” of Szeged, Hungary, Int. J. Climatol., 37,
582–596, https://doi.org/10.1002/joc.5023, 2017. a
Steeneveld, G.-J., Klompmaker, J. O., Groen, R. J., and Holtslag, A. A.: An
urban climate assessment and management tool for combined heat and air
quality judgements at neighbourhood scales, Resour. Conserv.
Recy., 132, 204–217, https://doi.org/10.1016/j.resconrec.2016.12.002, 2018. a
Stewart, I. D.: A systematic review and scientific critique of methodology in
modern urban heat island literature, Int. J. Climatol.,
31, 200–217, https://doi.org/10.1002/joc.2141, 2011a. a
Stewart, I. D.: Redefining the Urban Heat Island, Electronic Theses and Dissertations (ETDs),
University of British Columbia, 1–352,
https://circle.ubc.ca/handle/2429/38069, 2011b. a
Stewart, I. D.: Developing a field guide to identify “local climate zones” in
cities, in: 10th International Conference on Urban Climate/14th Symposium
on the Urban Environment, New York City, USA, https://ams.confex.com/ams/ICUC10/meetingapp.cgi/Paper/342085
(last access: 22 August 2022), 2018. a
Stewart, I. D. and Mills, G.: The Urban Heat Island – A Guidebook, Elsevier,
https://doi.org/10.1016/C2017-0-02872-0, 2021. a
Stewart, I. D., Krayenhoff, E. S., Voogt, J. A., Lachapelle, J. A., Allen,
M. A., and Broadbent, A. M.: Time Evolution of the Surface Urban Heat
Island, Earth's Future, 9, e2021EF002178, https://doi.org/10.1029/2021EF002178, 2021. a
Stone, B., Mednick, A. C., Holloway, T., and Spak, S. N.: Is Compact Growth
Good for Air Quality?, J. Am. Plann. Assoc., 73,
404–418, https://doi.org/10.1080/01944360708978521, 2007. a
Tadono, T., Nagai, H., Ishida, H., Oda, F., Naito, S., Minakawa, K., and
Iwamoto, H.: Generation of the 30 M-MESH global digital surface model by
alos prism, in: International Archives of the Photogrammetry, Remote Sensing
and Spatial Information Sciences – ISPRS Archives, 157–162,
https://doi.org/10.5194/isprsarchives-XLI-B4-157-2016, 2016. a
Takane, Y., Kikegawa, Y., Hara, M., and Grimmond, C. S. B.: Urban warming and
future air-conditioning use in an Asian megacity: importance of positive
feedback, npj Clim. Atmos. Sci., 2, 1–11,
https://doi.org/10.1038/s41612-019-0096-2, 2019. a
Taubenböck, H., Debray, H., Qiu, C., Schmitt, M., Wang, Y., and Zhu,
X. X.: Seven city types representing morphologic configurations of cities
across the globe, Cities, 105, 102814, https://doi.org/10.1016/j.cities.2020.102814,
2020. a
UN: World Urbanization Prospects: The 2018 Revision, in: Population Division of
the United Nations Department of Economic and Social Affairs, 123 pp.,
https://doi.org/10.18356/b9e995fe-en, 2019. a
Van de Walle, J., Brousse, O., Arnalsteen, L., Byarugaba, D., Ddumba, D. S.,
Demuzere, M., Lwasa, S., Nsangi, G., Sseviiri, H., Thiery, W., Vanhaeren, R.,
Wouters, H., and P.M. van Lipzig, N.: Can local fieldwork help to
represent intra-urban variability of canopy parameters relevant for tropical
African climate studies?, Theor. Appl. Climatol., 146, 457–474,
https://doi.org/10.1007/s00704-021-03733-7, 2021. a, b
Van de Walle, J., Brousse, O., Arnalsteen, L., Brimicombe, C., Byarugaba, D.,
Demuzere, M., Jjemba, E., Lwasa, S., Misiani, H., Nsangi, G., Soetewey, F.,
Sseviiri, H., Thiery, W., Vanhaeren, R., Zaitchik, B. F., and van Lipzig, N.
P. M.: Lack of vegetation exacerbates exposure to dangerous heat in dense
settlements in a tropical African city, Environ. Res. Lett., 17,
024004, https://doi.org/10.1088/1748-9326/ac47c3, 2022. a, b
van Vliet, J.: Direct and indirect loss of natural area from urban expansion,
Nature Sustainability, 2, 755–763, https://doi.org/10.1038/s41893-019-0340-0, 2019. a
Vandamme, S., Demuzere, M., Verdonck, M.-L., Zhang, Z., and Coillie, F. V.:
Revealing Kunming's (China) Historical Urban Planning Policies Through Local
Climate Zones, Remote Sensing, 11, 1731, https://doi.org/10.3390/rs11141731, 2019. a, b, c
Varentsov, M., Samsonov, T., and Demuzere, M.: Impact of Urban Canopy
Parameters on a Megacity's Modelled Thermal Environment, Atmosphere, 11,
1349, https://doi.org/10.3390/atmos11121349, 2020. a, b, c
Varentsov, M., Fenner, D., Meier, F., Samsonov, T., and Demuzere, M.:
Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow
with Reference and Crowdsourced Observations, Front. Environ.
Sci., 9, 1–21, https://doi.org/10.3389/fenvs.2021.716968, 2021. a, b
Varquez, A. C., Shota, K., Khanh, D. N., and Kanda, M.: Global 1-km present
and future hourly anthropogenic heat flux, Figshare [data set],
https://doi.org/10.6084/m9.figshare.12612458.v6, 2020. a
Varquez, A. C. G., Kiyomoto, S., Khanh, D. N., and Kanda, M.: Global 1-km
present and future hourly anthropogenic heat flux, Sci. Data, 8, 64,
https://doi.org/10.1038/s41597-021-00850-w, 2021. a, b
Verdonck, M.-L., Okujeni, A., van der Linden, S., Demuzere, M., De Wulf, R.,
and Van Coillie, F.: Influence of neighbourhood information on “Local
Climate Zone” mapping in heterogeneous cities, Int. J.
Appl. Earth Obs., 62, 102–113,
https://doi.org/10.1016/j.jag.2017.05.017, 2017. a, b
Verdonck, M.-l., Demuzere, M., Hooyberghs, H., Beck, C., Cyrys, J., Schneider,
A., Dewulf, R., and Van Coillie, F.: The potential of local climate zones
maps as a heat stress assessment tool, supported by simulated air temperature
data, Landscape Urban Plan., 178, 183–197,
https://doi.org/10.1016/j.landurbplan.2018.06.004, 2018. a, b
Verdonck, M.-l., Demuzere, M., Bechtel, B., Beck, C., Brousse, O., Droste, A.,
Fenner, D., Leconte, F., and Van Coillie, F.: The Human Influence
Experiment (Part 2): Guidelines for Improved Mapping of Local Climate Zones
Using a Supervised Classification, Urban Science, 3, 27,
https://doi.org/10.3390/urbansci3010027, 2019a. a
Verdonck, M.-L., Demuzere, M., Hooyberghs, H., Priem, F., and Van Coillie,
F.: Heat risk assessment for the Brussels capital region under different
urban planning and greenhouse gas emission scenarios, J.
Environ. Manage., 249, 109210, https://doi.org/10.1016/j.jenvman.2019.06.111,
2019b. a, b
Wang, J. and Ouyang, W.: Attenuating the surface Urban Heat Island within the
Local Thermal Zones through land surface modification, J.
Environ. Manage., 187, 239–252, https://doi.org/10.1016/j.jenvman.2016.11.059,
2017. a
Wang, J., Chen, Y., Liao, W., He, G., Tett, S. F. B., Yan, Z., Zhai, P., Feng,
J., Ma, W., Huang, C., and Hu, Y.: Anthropogenic emissions and urbanization
increase risk of compound hot extremes in cities, Nat. Clim. Change, 11,
1084–1089, https://doi.org/10.1038/s41558-021-01196-2, 2021. a
Wang, R., Cai, M., Ren, C., Bechtel, B., Xu, Y., and Ng, E.: Detecting
multi-temporal land cover change and land surface temperature in Pearl River
Delta by adopting local climate zone, Urban Climate, 28, 100455,
https://doi.org/10.1016/j.uclim.2019.100455, 2019. a
Williams, K., Joynt, J. L., and Hopkins, D.: Adapting to Climate Change in the
Compact City: The Suburban Challenge, Built Environ., 36, 105–115,
https://doi.org/10.2148/benv.36.1.105, 2010. a
Wong, M. M. F., Fung, J. C. H., Ching, J., Yeung, P. P. S., Tse, J. W. P., Ren,
C., Wang, R., and Cai, M.: Evaluation of uWRF performance and modeling
guidance based on WUDAPT and NUDAPT UCP datasets for Hong Kong, Urban
Climate, 28, 100460, https://doi.org/10.1016/j.uclim.2019.100460, 2019. a
Wouters, H., Demuzere, M., Blahak, U., Fortuniak, K., Maiheu, B., Camps, J., Tielemans, D., and van Lipzig, N. P. M.: The efficient urban canopy dependency parametrization (SURY) v1.0 for atmospheric modelling: description and application with the COSMO-CLM model for a Belgian summer, Geosci. Model Dev., 9, 3027–3054, https://doi.org/10.5194/gmd-9-3027-2016, 2016. a
Wu, Y., Sharifi, A., Yang, P., Borjigin, H., Murakami, D., and Yamagata, Y.:
Mapping building carbon emissions within local climate zones in Shanghai,
Enrgy. Proced., 152, 815–822, https://doi.org/10.1016/j.egypro.2018.09.195, 2018. a
Xu, C., Hystad, P., Chen, R., Van Den Hoek, J., Hutchinson, R. A., Hankey,
S., and Kennedy, R.: Application of training data affects success in
broad-scale local climate zone mapping, Int. J. Appl.
Earth Obs., 103, 102482,
https://doi.org/10.1016/j.jag.2021.102482, 2021. a, b, c, d
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal,
J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of
global terrain elevations, Geophysical Research Letters, 44, 5844–5853,
https://doi.org/10.1002/2017GL072874, 2017. a
Yang, X., Yao, L., Jin, T., Peng, L. L., Jiang, Z., Hu, Z., and Ye, Y.:
Assessing the thermal behavior of different local climate zones in the
Nanjing metropolis, China, Build. Environ., 137, 171–184,
https://doi.org/10.1016/j.buildenv.2018.04.009, 2018. a
Yang, X., Peng, L. L., Jiang, Z., Chen, Y., Yao, L., He, Y., and Xu, T.:
Impact of urban heat island on energy demand in buildings: Local climate
zones in Nanjing, Appl. Energ., 260, 114279,
https://doi.org/10.1016/j.apenergy.2019.114279, 2020. a
Yokoya, N., Ghamisi, P., Xia, J., Sukhanov, S., Heremans, R., Tankoyeu, I.,
Bechtel, B., Le Saux, B., Moser, G., and Tuia, D.: Open Data for Global
Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion
Contest, IEEE J. Sel. Top. Appl., 11, 1363–1377, https://doi.org/10.1109/JSTARS.2018.2799698, 2018. a
Yoo, C., Lee, Y., Cho, D., Im, J., and Han, D.: Improving local climate zone
classification using incomplete building data and sentinel 2 images based on
convolutional neural networks, Remote Sensing, 12, 1–22,
https://doi.org/10.3390/rs12213552, 2020. a
Zhang, X., Liu, L., Wu, C., Chen, X., Gao, Y., Xie, S., and Zhang, B.: Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform, Earth Syst. Sci. Data, 12, 1625–1648, https://doi.org/10.5194/essd-12-1625-2020, 2020. a, b, c, d
Zhang, Y., Kong, D., Gan, R., Chiew, F. H., Mcvicar, T. R., Zhang, Q., and
Yang, Y.: Coupled estimation of 500 m and 8-day resolution global
evapotranspiration and gross primary production in 2002–2017, Remote
Sens. Environ., 222, 165–182, https://doi.org/S003442571830590X, 2019. a
Zhang, Y., Zhang, J., Zhang, X., Zhou, D., and Gu, Z.: Analyzing the
Characteristics of UHI (Urban Heat Island) in Summer Daytime Based on
Observations on 50 Sites in 11 LCZ (Local Climate Zone) Types in Xi'an,
China, Sustainability, 13, 83, https://doi.org/10.3390/su13010083, 2020. a
Zhao, C., Weng, Q., and Hersperger, A. M.: Characterizing the 3-D urban
morphology transformation to understand urban-form dynamics: A case study of
Austin, Texas, USA, Landscape Urban Plan., 203, 103881,
https://doi.org/10.1016/j.landurbplan.2020.103881, 2020. a
Zhao, C., Weng, Q., Wang, Y., Hu, Z., and Wu, C.: Use of local climate zones
to assess the spatiotemporal variations of urban vegetation phenology in
Austin, Texas, USA, GIScience Remote Sens., 59, 393–409,
https://doi.org/10.1080/15481603.2022.2033485, 2022. a, b
Zhao, L., Oleson, K., Bou-Zeid, E., Krayenhoff, E. S., Bray, A., Zhu, Q.,
Zheng, Z., Chen, C., and Oppenheimer, M.: Global multi-model projections of
local urban climates, Nat. Clim. Change, 11, 152–157,
https://doi.org/10.1038/s41558-020-00958-8, 2021. a, b, c, d
Zhao, Z., Shen, L., Li, L., Wang, H., and He, B.-J.: Local Climate Zone
Classification Scheme Can Also Indicate Local-Scale Urban Ventilation
Performance: An Evidence-Based Study, Atmosphere, 11, 776,
https://doi.org/10.3390/atmos11080776, 2020.
a
Zhi, C., Tang, Y., Chang, L., and Demuzere, M.: The evolution of
three-dimensional urban spatial form and its planning response to the surface
heat island effect: Taking Beijing as an example, International Urban
Planning, 36, 61–68, https://doi.org/10.19830/j.upi.2021.407, 2021. a, b
Zhou, Y., Smith, S. J., Zhao, K., Imhoff, M., Thomson, A., Bond-Lamberty, B.,
Asrar, G. R., Zhang, X., He, C., and Elvidge, C. D.: A global map of urban
extent from nightlights, Environ. Res. Lett., 10, 054011,
https://doi.org/10.1088/1748-9326/10/5/054011, 2015. a
Zhu, X. X., Qiu, C., Hu, J., Shi, Y., Wang, Y., Schmitt, M., and
Taubenböck, H.: The urban morphology on our planet – Global
perspectives from space, Remote Sens. Environ., 269, 112794,
https://doi.org/10.1016/j.rse.2021.112794, 2022. a
Zhu, Z., Zhou, Y., Seto, K. C., Stokes, E. C., Deng, C., Pickett, S. T., and
Taubenböck, H.: Understanding an urbanizing planet: Strategic
directions for remote sensing, Remote Sens. Environ., 228, 164–182,
https://doi.org/10.1016/j.rse.2019.04.020, 2019. a
Zhuo, L., Zheng, J., Zhang, X., Li, J., and Liu, L.: An improved method of
night-time light saturation reduction based on EVI, Int. J.
Remote Sens., 36, 4114–4130, https://doi.org/10.1080/01431161.2015.1073861, 2015. a, b
Zhuo, L., Shi, Q., Tao, H., Zheng, J., and Li, Q.: An improved temporal
mixture analysis unmixing method for estimating impervious surface area based
on MODIS and DMSP-OLS data, ISPRS J. Photogramm., 142, 64–77, https://doi.org/10.1016/j.isprsjprs.2018.05.016, 2018. a, b
Zonato, A., Martilli, A., Di Sabatino, S., Zardi, D., and Giovannini, L.:
Evaluating the performance of a novel WUDAPT averaging technique to define
urban morphology with mesoscale models, Urban Climate, 31, 100584,
https://doi.org/10.1016/j.uclim.2020.100584, 2020. a
Zong, L., Liu, S., Yang, Y., Ren, G., Yu, M., Zhang, Y., and Li, Y.:
Synergistic Influence of Local Climate Zones and Wind Speeds on the Urban
Heat Island and Heat Waves in the Megacity of Beijing, China, Front.
Earth Sci., 9, 673786, https://doi.org/10.3389/feart.2021.673786, 2021. a
Short summary
Because urban areas are key contributors to climate change but are also susceptible to multiple hazards, one needs spatially detailed information on urban landscapes to support environmental services. This global local climate zone map describes this much-needed intra-urban heterogeneity across the whole surface of the earth in a universal language and can serve as a basic infrastructure to study e.g. environmental hazards, energy demand, and climate adaptation and mitigation solutions.
Because urban areas are key contributors to climate change but are also susceptible to multiple...
Altmetrics
Final-revised paper
Preprint