Articles | Volume 14, issue 6
https://doi.org/10.5194/essd-14-2833-2022
© 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-14-2833-2022
© 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
| 
23 Jun 2022
Data description paper |
| 23 Jun 2022
| 23 Jun 2022
Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas
Jeremy Baynes
CORRESPONDING AUTHOR
Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
Anne Neale
Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
Torrin Hultgren
EPA National Geospatial Support Team, ITS-EPA III Infrastructure Support and Application Hosting Contract, Research Triangle Park, NC 27711, USA
Related subject area
Land Cover and Land Use
ChinaWheatYield30m: a 30 m annual winter wheat yield dataset from 2016 to 2021 in China
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
A new cropland area database by country circa 2020
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 GEDI data with a deep learning approach
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
LCM2021 – The UK Land Cover Map 2021
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
SinoLC-1: the first 1-meter resolution national-scale land-cover map of China created with the deep learning framework and open-access data
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)
HISDAC-ES: Historical Settlement Data Compilation for Spain (1900–2020)
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
Gridded pollen-based Holocene regional plant cover in temperate and northern subtropical China suitable for climate modelling
Location, biophysical and agronomic parameters for croplands in northern Ghana
Historical nitrogen fertilizer use in China from 1952 to 2018
SiDroForest: a comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labeled trees, synthetically generated tree crowns, and Sentinel-2 labeled image patches
History of anthropogenic Nitrogen inputs (HaNi) to the terrestrial biosphere: a 5 arcmin resolution annual dataset from 1860 to 2019
LUCAS cover photos 2006–2018 over the EU: 874 646 spatially distributed geo-tagged close-up photos with land cover and plant species label
Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries
Vectorized dataset of roadside noise barriers in China using street view imagery
A global map of local climate zones to support earth system modelling and urban-scale environmental science
Mapping 10 m global impervious surface area (GISA-10m) using multi-source geospatial data
High-resolution map of sugarcane cultivation in Brazil using a phenology-based method
GISD30: global 30 m impervious-surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform
High-resolution land use and land cover dataset for regional climate modelling: a plant functional type map for Europe 2015
A national extent map of cropland and grassland for Switzerland based on Sentinel-2 data
Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product
Harmonized in situ datasets for agricultural land use mapping and monitoring in tropical countries
NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019
Refined burned-area mapping protocol using Sentinel-2 data increases estimate of 2019 Indonesian burning
The dataset of walled cities and urban extent in late imperial China in the 15th–19th centuries
GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery
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.
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.
Francesco Nicola Tubiello, Giulia Conchedda, Leon Casse, Pengyu Hao, Giorgia De Santis, and Zhongxin Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-211, https://doi.org/10.5194/essd-2023-211, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
We describe a new dataset of cropland area circa the year 2020, with global coverageand country detial. 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.
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 Discuss., https://doi.org/10.5194/essd-2023-196, https://doi.org/10.5194/essd-2023-196, 2023
Revised manuscript accepted for ESSD
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 technologies and artificial intelligence methods, we created high-resolution tree height and biomass maps of Metropolitan France that outperform previous products available. This study, based on freely available data, brings essential information to support climate-efficient forest management policies at low-cost.
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.
Christopher G. Marston, Aneurin W. O'Neil, R. Daniel Morton, Claire M. Wood, and Clare S. Rowland
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-78, https://doi.org/10.5194/essd-2023-78, 2023
Revised manuscript accepted for ESSD
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.
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.
Zhuohong Li, Wei He, Mofan Cheng, Jingxin Hu, Guangyi Yang, and Hongyan Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-87, https://doi.org/10.5194/essd-2023-87, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Nowadays, the 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-meter resolution LC map of China, SinoLC-1, was built. The results showed SinoLC-1 had an overall accuracy of 73.61 % and conformed closely to the official survey reports. The comparison with other datasets suggests SinoLC-1 can be a better support for downstream applications and provide more accurate info to users.
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.
Johannes H. Uhl, Dominic Royé, Keith Burghardt, José Antonio Aldrey Vázquez, Manuel Borobio Sanchiz, and Stefan Leyk
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-53, https://doi.org/10.5194/essd-2023-53, 2023
Revised manuscript accepted for ESSD
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.
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.
Furong Li, Marie-José Gaillard, Xianyong Cao, Ulrike Herzschuh, Shinya Sugita, Jian Ni, Yan Zhao, Chengbang An, Xiaozhong Huang, Yu Li, Hongyan Liu, Aizhi Sun, and Yifeng Yao
Earth Syst. Sci. Data, 15, 95–112, https://doi.org/10.5194/essd-15-95-2023, https://doi.org/10.5194/essd-15-95-2023, 2023
Short summary
Short summary
The objective of this study is present the first gridded and temporally continuous quantitative plant-cover reconstruction for temperate and northern subtropical China over the last 12 millennia. The reconstructions are based on 94 pollen records and include estimates for 27 plant taxa, 10 plant functional types, and 3 land-cover types. The dataset is suitable for palaeoclimate modelling and the evaluation of simulated past vegetation cover and anthropogenic land-cover change from models.
Jose Luis Gómez-Dans, Philip Edward Lewis, Feng Yin, Kofi Asare, Patrick Lamptey, Kenneth Kobina Yedu Aidoo, Dilys Sefakor MacCarthy, Hongyuan Ma, Qingling Wu, Martin Addi, Stephen Aboagye-Ntow, Caroline Edinam Doe, Rahaman Alhassan, Isaac Kankam-Boadu, Jianxi Huang, and Xuecao Li
Earth Syst. Sci. Data, 14, 5387–5410, https://doi.org/10.5194/essd-14-5387-2022, https://doi.org/10.5194/essd-14-5387-2022, 2022
Short summary
Short summary
We provide a data set to support mapping croplands in smallholder landscapes in Ghana. The data set contains information on crop location on three agroecological zones for 2 years, temporal series of measurements of leaf area index and leaf chlorophyll concentration for maize canopies and yield. We demonstrate the use of these data to validate cropland masks, create a maize mask using satellite data and explore the relationship between satellite measurements and yield.
Zhen Yu, Jing Liu, and Giri Kattel
Earth Syst. Sci. Data, 14, 5179–5194, https://doi.org/10.5194/essd-14-5179-2022, https://doi.org/10.5194/essd-14-5179-2022, 2022
Short summary
Short summary
We developed a 5 km annual nitrogen (N) fertilizer use dataset in China, covering the period from 1952 to 2018. We found that previous FAO-data-based N fertilizer products overestimated the N use in low, but underestimated in high, cropland coverage areas in China. The new dataset has improved the spatial distribution and corrected the existing biases, which is beneficial for biogeochemical cycle simulations in China, such as the assessment of greenhouse gas emissions and food production.
Femke van Geffen, Birgit Heim, Frederic Brieger, Rongwei Geng, Iuliia A. Shevtsova, Luise Schulte, Simone M. Stuenzi, Nadine Bernhardt, Elena I. Troeva, Luidmila A. Pestryakova, Evgenii S. Zakharov, Bringfried Pflug, Ulrike Herzschuh, and Stefan Kruse
Earth Syst. Sci. Data, 14, 4967–4994, https://doi.org/10.5194/essd-14-4967-2022, https://doi.org/10.5194/essd-14-4967-2022, 2022
Short summary
Short summary
SiDroForest is an attempt to remedy data scarcity regarding vegetation data in the circumpolar region, whilst providing adjusted and labeled data for machine learning and upscaling practices. SiDroForest contains four datasets that include SfM point clouds, individually labeled trees, synthetic tree crowns and labeled Sentinel-2 patches that provide insights into the vegetation composition and forest structure of two important vegetation transition zones in Siberia, Russia.
Hanqin Tian, Zihao Bian, Hao Shi, Xiaoyu Qin, Naiqing Pan, Chaoqun Lu, Shufen Pan, Francesco N. Tubiello, Jinfeng Chang, Giulia Conchedda, Junguo Liu, Nathaniel Mueller, Kazuya Nishina, Rongting Xu, Jia Yang, Liangzhi You, and Bowen Zhang
Earth Syst. Sci. Data, 14, 4551–4568, https://doi.org/10.5194/essd-14-4551-2022, https://doi.org/10.5194/essd-14-4551-2022, 2022
Short summary
Short summary
Nitrogen is one of the critical nutrients for growth. Evaluating the change in nitrogen inputs due to human activity is necessary for nutrient management and pollution control. In this study, we generated a historical dataset of nitrogen input to land at the global scale. This dataset consists of nitrogen fertilizer, manure, and atmospheric deposition inputs to cropland, pasture, and rangeland at high resolution from 1860 to 2019.
Raphaël d'Andrimont, Momchil Yordanov, Laura Martinez-Sanchez, Peter Haub, Oliver Buck, Carsten Haub, Beatrice Eiselt, and Marijn van der Velde
Earth Syst. Sci. Data, 14, 4463–4472, https://doi.org/10.5194/essd-14-4463-2022, https://doi.org/10.5194/essd-14-4463-2022, 2022
Short summary
Short summary
Between 2006 and 2018, 875 661 LUCAS cover (i.e. close-up) photos were taken over a systematic sample of the European Union. This geo-located photo dataset has been curated and is being made available along with the surveyed label data, including land cover and plant species.
Han Su, Bárbara Willaarts, Diana Luna-Gonzalez, Maarten S. Krol, and Rick J. Hogeboom
Earth Syst. Sci. Data, 14, 4397–4418, https://doi.org/10.5194/essd-14-4397-2022, https://doi.org/10.5194/essd-14-4397-2022, 2022
Short summary
Short summary
There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps showing where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availability, and climate change affect small and large farms.
Zhen Qian, Min Chen, Yue Yang, Teng Zhong, Fan Zhang, Rui Zhu, Kai Zhang, Zhixin Zhang, Zhuo Sun, Peilong Ma, Guonian Lü, Yu Ye, and Jinyue Yan
Earth Syst. Sci. Data, 14, 4057–4076, https://doi.org/10.5194/essd-14-4057-2022, https://doi.org/10.5194/essd-14-4057-2022, 2022
Short summary
Short summary
Roadside noise barriers (RNBs) are important urban infrastructures to ensure a city is liveable. This study provides the first reliable and nationwide vectorized RNB dataset with street view imagery in China. The generated RNB dataset is evaluated in terms of two aspects, i.e., the detection accuracy and the completeness and positional accuracy. The method is based on a developed geospatial artificial intelligence framework.
Matthias Demuzere, Jonas Kittner, Alberto Martilli, Gerald Mills, Christian Moede, Iain D. Stewart, Jasper van Vliet, and Benjamin Bechtel
Earth Syst. Sci. Data, 14, 3835–3873, https://doi.org/10.5194/essd-14-3835-2022, https://doi.org/10.5194/essd-14-3835-2022, 2022
Short summary
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.
Xin Huang, Jie Yang, Wenrui Wang, and Zhengrong Liu
Earth Syst. Sci. Data, 14, 3649–3672, https://doi.org/10.5194/essd-14-3649-2022, https://doi.org/10.5194/essd-14-3649-2022, 2022
Short summary
Short summary
Using more than 2.7 million Sentinel images, we proposed a global ISA mapping method and produced the 10-m global ISA dataset (GISA-10m), with overall accuracy exceeding 86 %. The inter-comparison between different global ISA datasets showed the superiority of our results. The ISA distribution at urban and rural was discussed and compared. For the first time, courtesy of the high spatial resolution, the global road ISA was further identified, and its distribution was discussed.
Yi Zheng, Ana Cláudia dos Santos Luciano, Jie Dong, and Wenping Yuan
Earth Syst. Sci. Data, 14, 2065–2080, https://doi.org/10.5194/essd-14-2065-2022, https://doi.org/10.5194/essd-14-2065-2022, 2022
Short summary
Short summary
Brazil is the largest sugarcane producer. Sugarcane in Brazil can be harvested all year round. The flexible phenology makes it difficult to identify sugarcane in Brazil at a country scale. We developed a phenology-based method which can identify sugarcane with limited training data. The sugarcane maps for Brazil obtain high accuracy through comparison against field samples and statistical data. The maps can be used to monitor growing conditions and evaluate the feedback to climate of sugarcane.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Yuan Gao, Xidong Chen, and Jun Mi
Earth Syst. Sci. Data, 14, 1831–1856, https://doi.org/10.5194/essd-14-1831-2022, https://doi.org/10.5194/essd-14-1831-2022, 2022
Short summary
Short summary
Accurately mapping impervious-surface dynamics has great scientific significance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral-generalization method and time-series Landsat imagery on the Google Earth Engine cloud computing platform.
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.
Robert Pazúr, Nica Huber, Dominique Weber, Christian Ginzler, and Bronwyn Price
Earth Syst. Sci. Data, 14, 295–305, https://doi.org/10.5194/essd-14-295-2022, https://doi.org/10.5194/essd-14-295-2022, 2022
Short summary
Short summary
We mapped the distribution of cropland and permanent grassland across Switzerland, where the agricultural land is considerably spatially heterogeneous due to strong variability in topography and climate, thus presenting challenges to mapping. The resulting map has high accuracy in lowlands as well as in mountainous areas. Thus, we believe that the presented mapping approach and resulting map will provide a solid ground for further research in agricultural land cover and landscape structure.
George Z. Xian, Kelcy Smith, Danika Wellington, Josephine Horton, Qiang Zhou, Congcong Li, Roger Auch, Jesslyn F. Brown, Zhe Zhu, and Ryan R. Reker
Earth Syst. Sci. Data, 14, 143–162, https://doi.org/10.5194/essd-14-143-2022, https://doi.org/10.5194/essd-14-143-2022, 2022
Short summary
Short summary
Continuous change detection algorithms were implemented with time series satellite records to produce annual land surface change products for the conterminous United States. The land change products are in 30 m spatial resolution and represent land cover and change from 1985 to 2017 across the country. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems.
Audrey Jolivot, Valentine Lebourgeois, Louise Leroux, Mael Ameline, Valérie Andriamanga, Beatriz Bellón, Mathieu Castets, Arthur Crespin-Boucaud, Pierre Defourny, Santiana Diaz, Mohamadou Dieye, Stéphane Dupuy, Rodrigo Ferraz, Raffaele Gaetano, Marie Gely, Camille Jahel, Bertin Kabore, Camille Lelong, Guerric le Maire, Danny Lo Seen, Martha Muthoni, Babacar Ndao, Terry Newby, Cecília Lira Melo de Oliveira Santos, Eloise Rasoamalala, Margareth Simoes, Ibrahima Thiaw, Alice Timmermans, Annelise Tran, and Agnès Bégué
Earth Syst. Sci. Data, 13, 5951–5967, https://doi.org/10.5194/essd-13-5951-2021, https://doi.org/10.5194/essd-13-5951-2021, 2021
Short summary
Short summary
This paper presents nine standardized crop type reference datasets collected between 2013 and 2020 in seven tropical countries. It aims at participating in the difficult exercise of mapping agricultural land use through satellite image classification in those complex areas where few ground truth or census data are available. These quality-controlled datasets were collected in the framework of the international JECAM initiative and contain 27 074 polygons documented by detailed keywords.
Jichong Han, Zhao Zhang, Yuchuan Luo, Juan Cao, Liangliang Zhang, Fei Cheng, Huimin Zhuang, Jing Zhang, and Fulu Tao
Earth Syst. Sci. Data, 13, 5969–5986, https://doi.org/10.5194/essd-13-5969-2021, https://doi.org/10.5194/essd-13-5969-2021, 2021
Short summary
Short summary
The accurate planting area and spatial distribution information is the basis for ensuring food security at continental scales. We constructed a paddy rice map database in Southeast and Northeast Asia for 3 years (2017–2019) at a 10 m spatial resolution. There are fewer mixed pixels in our paddy rice map. The large-scale and high-resolution maps of paddy rice are useful for water resource management and yield monitoring.
David L. A. Gaveau, Adrià Descals, Mohammad A. Salim, Douglas Sheil, and Sean Sloan
Earth Syst. Sci. Data, 13, 5353–5368, https://doi.org/10.5194/essd-13-5353-2021, https://doi.org/10.5194/essd-13-5353-2021, 2021
Short summary
Short summary
Severe burning struck Indonesia in 2019. Drawing on new satellite imagery, we present and validate new 2019 burned-area estimates for Indonesia.
We show that > 3.11 million hectares (Mha) burned in 2019, double the official estimate from the Indonesian Ministry of Environment and Forestry. Our relatively more accurate estimates have important implications for carbon-emission calculations from forest and peatland fires in Indonesia.
Qiaofeng Xue, Xiaobin Jin, Yinong Cheng, Xuhong Yang, and Yinkang Zhou
Earth Syst. Sci. Data, 13, 5071–5085, https://doi.org/10.5194/essd-13-5071-2021, https://doi.org/10.5194/essd-13-5071-2021, 2021
Short summary
Short summary
We reconstructed the walled cities of China that extend from the 15th century to 19th century based on multiple historical documents. By restoring the extent of the city walls, it is helpful to explore the urban area in this period. The correlation and integration of the lifetime and the spatial data led to the creation of the China City Wall Areas Dataset (CCWAD). Based on the proximity to the time of most of the city walls, we produce the China Urban Extent Dataset (CUED) from CCWAD.
Miao Zhang, Bingfang Wu, Hongwei Zeng, Guojin He, Chong Liu, Shiqi Tao, Qi Zhang, Mohsen Nabil, Fuyou Tian, José Bofana, Awetahegn Niguse Beyene, Abdelrazek Elnashar, Nana Yan, Zhengdong Wang, and Yiliang Liu
Earth Syst. Sci. Data, 13, 4799–4817, https://doi.org/10.5194/essd-13-4799-2021, https://doi.org/10.5194/essd-13-4799-2021, 2021
Short summary
Short summary
Cropping intensity (CI) is essential for agricultural land use management, but fine-resolution global CI is not available. We used multiple satellite data on Google Earth Engine to develop a first 30 m resolution global CI (GCI30). GCI30 performed well, with an overall accuracy of 92 %. GCI30 not only exhibited high agreement with existing CI products but also provided many spatial details. GCI30 can facilitate research on sustained cropland intensification to improve food production.
Cited articles
Azar, D., Engstrom, R., Graesser, J., and Comenetz, J.: Generation of
fine-scale population layers using multi-resolution satellite imagery and
geospatial data, Remote Sens. Environ., 130, 219–232,
https://doi.org/10.1016/j.rse.2012.11.022, 2013.
Baynes, J., Neale, A., and Hultgren, T.: 2010 Dasymetric Population for the
Conterminous United States v3, US Environmental Protection Agency Office
of Research and Development [data set],
https://doi.org/10.23719/1522948, 2021.
Bellwood, D. R., Hoey, A. S., and Hughes, T. P.: Human activity selectively
impacts the ecosystem roles of parrotfishes on coral reefs, Proc. Biol. Sci.,
279, 1621–1629, https://doi.org/10.1098/rspb.2011.1906, 2012.
Carroll, R. J., Chen, R., George, E. I., Li, T. H., Newton, H. J.,
Schmiediche, H., and Wang, N.: Ozone Exposure and Population Density in
Harris County, Texas, J. Am. Stat. Assoc., 92,
392–404, https://doi.org/10.1080/01621459.1997.10473988, 1997.
Cinner, J. E., Graham, N. A., Huchery, C., and Macneil, M. A.: Global
effects of local human population density and distance to markets on the
condition of coral reef fisheries, Conserv. Biol., 27, 453–458,
https://doi.org/10.1111/j.1523-1739.2012.01933.x, 2013.
CoreLogic: CoreLogic Parcel, HIFLD [data set], https://hifld-geoplatform.hub.arcgis.com (last access: 15 June 2022), 2018.
Dmowska, A. and Stepinski, T. F.: A high resolution population grid for the
conterminous United States: The 2010 edition, Comput. Environ. Urban, 61, 13–23,
https://doi.org/10.1016/j.compenvurbsys.2016.08.006, 2017.
Dobson, J. E., Bright, E. A., Coleman, P. R., Durfee, R. C., and Worley, B. A.: LandScan: A Global Population Database for Estimating Populations at Risk, Photogramm. Eng. Rem. S., 66, 7, 849–857, 2000.
Fang, Y. and Jawitz, J. W.: High-resolution reconstruction of the United
States human population distribution, 1790 to 2010, Sci. Data, 5, 180067,
https://doi.org/10.1038/sdata.2018.67, 2018.
Fang, Y. and Jawitz, J. W.: The evolution of human population distance to
water in the USA from 1790 to 2010, Nat. Commun., 10, 430,
https://doi.org/10.1038/s41467-019-08366-z, 2019.
Fang, Y., Ceola, S., Paik, K., McGrath, G., Rao, P. S. C., Montanari, A.,
and Jawitz, J. W.: Globally Universal Fractal Pattern of Human Settlements
in River Networks, Earth's Future, 6, 1134–1145, https://doi.org/10.1029/2017ef000746, 2018.
Federal Railroad Administration: North American Rail Lines, Bureau of Transportation Statistics [data set], https://www.bts.gov/maps (last access: 15 June 2022), 2019.
GDAL/OGR contributors: GDAL/OGR Geospatial Data Abstraction Software
Library, Open Source Geospatial Foundation [code],
https://gdal.org (last access: 15 June 2022), 2019.
Gergely, K. J. and McKerrow, A.: PAD-US – National inventory of protected
areas (ver. 1.1, August 2016): US Geological Survey Fact Sheet 2013–3086, Report, 2, https://doi.org/10.3133/fs20133086, 2016.
Goodchild, M. F. and Lam, N. S.-N.: Areal interpolation: A variant
of the traditional spatial problem, Geo-Processing, 1, 297–312,
1980.
HERE: NAVSTREETS Streets Data, HERE [data set], https://www.here.com/ (last access: 15 June 2022), 2017.
Homer, C., Dewitz, J., Jin, S., Xian, G., Costello, C., Danielson, P., Gass,
L., Funk, M., Wickham, J., Stehman, S., Auch, R., and Riitters, K.:
Conterminous United States land cover change patterns 2001–2016 from the
2016 National Land Cover Database, ISPRS J. Photogramm., 162, 184–199,
https://doi.org/10.1016/j.isprsjprs.2020.02.019, 2020.
Jones, K. E., Patel, N. G., Levy, M. A., Storeygard, A., Balk, D.,
Gittleman, J. L., and Daszak, P.: Global trends in emerging infectious
diseases, Nature, 451, 990–993, https://doi.org/10.1038/nature06536, 2008.
Karunarathne, A. and Lee, G.: Estimating Hilly Areas Population Using a
Dasymetric Mapping Approach: A Case of Sri Lanka's Highest Mountain Range,
ISPRS Int. Geo-Inf., 8, 166, https://doi.org/10.3390/ijgi8040166,
2019.
Leyk, S., Gaughan, A. E., Adamo, S. B., de Sherbinin, A., Balk, D., Freire, S., Rose, A., Stevens, F. R., Blankespoor, B., Frye, C., Comenetz, J., Sorichetta, A., MacManus, K., Pistolesi, L., Levy, M., Tatem, A. J., and Pesaresi, M.: The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use, Earth Syst. Sci. Data, 11, 1385–1409, https://doi.org/10.5194/essd-11-1385-2019, 2019.
Liu, C., Wang, F., and Xu, Y.: Habitation environment suitability and
population density patterns in China: A regionalization approach, Growth
Change, 50, 184–200, https://doi.org/10.1111/grow.12283, 2019.
Lloyd, C. T., Chamberlain, H., Kerr, D., Yetman, G., Pistolesi, L., Stevens,
F. R., Gaughan, A. E., Nieves, J. J., Hornby, G., MacManus, K., Sinha, P.,
Bondarenko, M., Sorichetta, A., and Tatem, A. J.: Global spatio-temporally
harmonised datasets for producing high-resolution gridded population
distribution datasets, Big Earth Data, 3, 108–139,
https://doi.org/10.1080/20964471.2019.1625151, 2019.
Mennis, J. and Hultgren, T.: Intelligent Dasymetric Mapping and Its
Application to Areal Interpolation, Cartogr. Geogr. Inf.
Sc., 33, 179–194, https://doi.org/10.1559/152304006779077309, 2006.
Moos, M., Vinodrai, T., Revington, N., and Seasons, M.: Planning for Mixed
Use: Affordable for Whom?, J. Am. Plann. Assoc., 84,
7–20, https://doi.org/10.1080/01944363.2017.1406315, 2018.
Morais, R. A., Connolly, S. R., and Bellwood, D. R.: Human exploitation
shapes productivity-biomass relationships on coral reefs, Glob. Chang. Biol., 26, 1295–1305,
https://doi.org/10.1111/gcb.14941, 2019.
Nagle, N. N., Buttenfield, B. P., Leyk, S., and Spielman, S.: Dasymetric
Modeling and Uncertainty, Ann. Assoc. Am. Geogr.,
104, 80–95, https://doi.org/10.1080/00045608.2013.843439, 2014.
Nahayo, L., Ndayisaba, F., Karamage, F., Nsengiyumva, J. B., Kalisa, E.,
Mind'je, R., Mupenzi, C., and Li, L.: Estimating landslides vulnerability in
Rwanda using analytic hierarchy process and geographic information system,
Integr. Environ. Assess. Manag., 15, 364–373, https://doi.org/10.1002/ieam.4132, 2019.
Nasiri, H., Yusof, M. J. M., Ali, T. A. M., and Hussein, M. K. B.: District
flood vulnerability index: urban decision-making tool, Int. J. Environ. Sci. Te., 16, 2249–2258,
https://doi.org/10.1007/s13762-018-1797-5, 2018.
Nicholls, R. J. and Small, C.: Improved Estimates of Coastal Population and
Exposure to Hazards Released, EOS, 83, 301–305, 2002.
OpenStreetMap contributors: Planet dump, Planet OSM [data set],
https://planet.osm.org (last access: 15 June 2022), 2019.
Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., and Neale, A.:
EnviroAtlas: A new geospatial tool to foster ecosystem services science and
resource management, Ecosyst. Serv., 14, 45–55,
https://doi.org/10.1016/j.ecoser.2015.04.005, 2015.
Radeloff, V. C., Stewart, S. I., Hawbaker, T. J., Gimmi, U., Pidgeon, A. M.,
Flather, C. H., Hammer, R. B., and Helmers, D. P.: Housing growth in and
near United States protected areas limits their conservation value, Proc.
Natl. Acad. Sci. USA, 107, 940–945, https://doi.org/10.1073/pnas.0911131107, 2010.
Samoli, E., Stergiopoulou, A., Santana, P., Rodopoulou, S., Mitsakou, C.,
Dimitroulopoulou, C., Bauwelinck, M., de Hoogh, K., Costa, C.,
Mari-Dell'Olmo, M., Corman, D., Vardoulakis, S., Katsouyanni, K., and
Consortium, E.-H.: Spatial variability in air pollution exposure in relation
to socioeconomic indicators in nine European metropolitan areas: A study on
environmental inequality, Environ. Pollut., 249, 345–353,
https://doi.org/10.1016/j.envpol.2019.03.050, 2019.
Samson, J., Berteaux, D., McGill, B. J., and Humphries, M. M.: Geographic
disparities and moral hazards in the predicted impacts of climate change on
human populations, Global Ecol. Biogeogr., 20, 532–544,
https://doi.org/10.1111/j.1466-8238.2010.00632.x, 2011.
Schmidt, W. P., Suzuki, M., Thiem, V. D., White, R. G., Tsuzuki, A.,
Yoshida, L. M., Yanai, H., Haque, U., Tho le, H., Anh, D. D., and Ariyoshi,
K.: Population density, water supply, and the risk of dengue fever in
Vietnam: cohort study and spatial analysis, PLoS Med., 8, e1001082,
https://doi.org/10.1371/journal.pmed.1001082, 2011.
Smith, A., Bates, P. D., Wing, O., Sampson, C., Quinn, N., and Neal, J.: New
estimates of flood exposure in developing countries using high-resolution
population data, Nat. Commun., 10, 1814, https://doi.org/10.1038/s41467-019-09282-y, 2019.
Song, Y. and Knaap, G.-J.: Measuring Urban Form: Is Portland Winning the War on Sprawl?, J. Am. Plann. Assoc., 70, 210–225, 2004.
Sorichetta, A., Hornby, G. M., Stevens, F. R., Gaughan, A. E., Linard, C.,
and Tatem, A. J.: High-resolution gridded population datasets for Latin
America and the Caribbean in 2010, 2015, and 2020, Sci. Data, 2, 150045,
https://doi.org/10.1038/sdata.2015.45, 2015.
Stevens, F. R., Gaughan, A. E., Linard, C., and Tatem, A. J.: Disaggregating
census data for population mapping using random forests with remotely-sensed
and ancillary data, PLoS One, 10, e0107042, https://doi.org/10.1371/journal.pone.0107042,
2015.
Taubenböck, H., Weigand, M., Esch, T., Staab, J., Wurm, M., Mast, J.,
and Dech, S.: A new ranking of the world's largest cities – Do
administrative units obscure morphological realities?, Remote Sens. Environ., 232, 111353, https://doi.org/10.1016/j.rse.2019.111353, 2019.
Theobald, D. M.: Development and Applications of a Comprehensive Land Use
Classification and Map for the US, PLOS ONE, 9, e94628,
https://doi.org/10.1371/journal.pone.0094628, 2014.
Tobler, W. R.: Smooth Pycnophylactic Interpolation for Geographical Regions,
J. Am. Stat. Assoc., 74, 519–530,
https://doi.org/10.1080/01621459.1979.10481647, 1979.
US Census Bureau: Special Release – Census Blocks with Population and Housing Counts, TIGER/Line Shapefiles [data set], https://www.census.gov/geographies/mapping-files/2010/geo/tiger-line-file.html (last access: 15 June 2022), 2012.
US Census Bureau: 2010 TIGER/Line Shapefiles Technical Documentation, https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2010/TGRSHP10SF1.pdf (last access: 15 June 2022), 2012.
US Department of Agriculture (Economic Research Service): 2013
Rural-Urban Continuum Codes, USDA [data set], https://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx (last access: 15 June 2022), 2020.
US Environmental Protection Agency: The Benefits and Costs of the Clean Air Act from 1990 to 2020, Final Report – Rev. A, https://www.epa.gov/sites/default/files/2015-07/documents/fullreport_rev_a.pdf (last access: 15 June 2022), 2011.
US Environmental Protection Agency: USEPA/Dasymetric-Toolbox-ArcGISPro: v1.0.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.6645816, 2022a.
US Environmental Protection Agency: USEPA/Dasymetric-Toolbox-OpenSource: v1.0.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.6645824, 2022b.
US Geological Survey, EROS Data Center: USGS 30 Meter Resolution,
One-Sixtieth Degree National Elevation Dataset for CONUS, Alaska, Hawaii, Puerto Rico, and the U. S. Virgin Islands, US Geological Survey [data set], https://www.usgs.gov/programs/national-geospatial-program/national-map
(last access: 15 June 2022), 1999.
US Geological Survey, Gap Analysis Program: Protected Areas Database of
the United States (PAD-US), US Geological Survey [data set],
https://doi.org/10.5066/P955KPLE, 2018.
Venter, O., Sanderson, E. W., Magrach, A., Allan, J. R., Beher, J., Jones,
K. R., Possingham, H. P., Laurance, W. F., Wood, P., Fekete, B. M., Levy, M.
A., and Watson, J. E.: Sixteen years of change in the global terrestrial
human footprint and implications for biodiversity conservation, Nat. Commun.,
7, 12558, https://doi.org/10.1038/ncomms12558, 2016.
Weber, E. M., Seaman, V. Y., Stewart, R. N., Bird, T. J., Tatem, A. J.,
McKee, J. J., Bhaduri, B. L., Moehl, J. J., and Reith, A. E.:
Census-independent population mapping in northern Nigeria, Remote Sens.
Environ., 204, 786–798, https://doi.org/10.1016/j.rse.2017.09.024, 2018.
Wei, C., Taubenböck, H., and Blaschke, T.: Measuring urban agglomeration
using a city-scale dasymetric population map: A study in the Pearl River
Delta, China, Habitat Int., 59, 32–43,
https://doi.org/10.1016/j.habitatint.2016.11.007, 2017.
Wickham, J., Stehman, S. V., Sorenson, D. G., Gass, L., and Dewitz, J. A.:
Thematic accuracy assessment of the NLCD 2016 land cover for the
conterminous United States, Remote Sens. Environ., 257, 112357,
https://doi.org/10.1016/j.rse.2021.112357, 2021.
Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Bender, S. M., Case,
A., Costello, C., Dewitz, J., Fry, J., Funk, M., Granneman, B., Liknes, G.
C., Rigge, M., and Xian, G.: A new generation of the United States National
Land Cover Database: Requirements, research priorities, design, and
implementation strategies, ISPRS J. Photogramm., 146, 108–123, https://doi.org/10.1016/j.isprsjprs.2018.09.006, 2018.
Ye, T., Zhao, N., Yang, X., Ouyang, Z., Liu, X., Chen, Q., Hu, K., Yue, W.,
Qi, J., Li, Z., and Jia, P.: Improved population mapping for China using
remotely sensed and points-of-interest data within a random forests model,
Sci. Total Environ., 658, 936–946, https://doi.org/10.1016/j.scitotenv.2018.12.276, 2019.
Yuan, H., Gao, X., and Qi, W.: Fine-Scale Spatiotemporal Analysis of
Population Vulnerability to Earthquake Disasters: Theoretical Models and
Application to Cities, Sustainability, 11, 2149, https://doi.org/10.3390/su11072149, 2019.
Short summary
Census data are typically provided in irregularly shaped spatial units. To get a more refined estimate of population density, we downscaled population counts from United States (US) census blocks to a 30 m grid using intelligent dasymetric mapping. Furthermore, we improved our density estimates by using multiple spatial datasets to identify and mask uninhabited areas. Masking these uninhabited areas improved density estimates for every state in the conterminous US.
Census data are typically provided in irregularly shaped spatial units. To get a more refined...
Altmetrics
Final-revised paper
Preprint