Articles | Volume 13, issue 1
https://doi.org/10.5194/essd-13-119-2021
© Author(s) 2021. 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-13-119-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
27 Jan 2021
Data description paper |
| 27 Jan 2021
Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States
Johannes H. Uhl
CORRESPONDING AUTHOR
Department of Geography, University of Colorado Boulder, Boulder, CO 80309, USA
Institute of Behavioral Science, University of Colorado Boulder, Boulder, CO 80309, USA
Stefan Leyk
Department of Geography, University of Colorado Boulder, Boulder, CO 80309, USA
Institute of Behavioral Science, University of Colorado Boulder, Boulder, CO 80309, USA
Caitlin M. McShane
Department of Geography, University of Colorado Boulder, Boulder, CO 80309, USA
Anna E. Braswell
Earth Lab, Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, CO 80303, USA
Dylan S. Connor
School of Geographical Sciences & Urban Planning, Arizona State University, Tempe, AZ 85281, USA
Deborah Balk
CUNY Institute for Demographic Research and Marxe School of Public and International Affairs, Baruch College, City University of New York, New York City, NY 10017, USA
Related authors
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
Preprint under review 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.
Johannes H. Uhl, Stefan Leyk, Weiwei Duan, Zekun Li, Basel Shbita, Yao-Yi Chiang, and Craig A. Knoblock
Abstr. Int. Cartogr. Assoc., 3, 297, https://doi.org/10.5194/ica-abs-3-297-2021, https://doi.org/10.5194/ica-abs-3-297-2021, 2021
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
Preprint under review 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.
Kytt MacManus, Deborah Balk, Hasim Engin, Gordon McGranahan, and Rya Inman
Earth Syst. Sci. Data, 13, 5747–5801, https://doi.org/10.5194/essd-13-5747-2021, https://doi.org/10.5194/essd-13-5747-2021, 2021
Short summary
Short summary
New estimates of population and land area by settlement types within low-elevation coastal zones (LECZs) based on four sources of population data, four sources of settlement data and four sources of elevation data for the years 1990, 2000 and 2015. The paper describes the sensitivity of these estimates and discusses the fitness of use guiding user decisions. Data choices impact the number of people estimated within LECZs, but across all sources the LECZs are predominantly urban and growing.
Izabela Karsznia, Albert Adolf, and Stefan Leyk
Abstr. Int. Cartogr. Assoc., 3, 139, https://doi.org/10.5194/ica-abs-3-139-2021, https://doi.org/10.5194/ica-abs-3-139-2021, 2021
Johannes H. Uhl, Stefan Leyk, Weiwei Duan, Zekun Li, Basel Shbita, Yao-Yi Chiang, and Craig A. Knoblock
Abstr. Int. Cartogr. Assoc., 3, 297, https://doi.org/10.5194/ica-abs-3-297-2021, https://doi.org/10.5194/ica-abs-3-297-2021, 2021
Stefan Leyk, Andrea E. Gaughan, Susana B. Adamo, Alex de Sherbinin, Deborah Balk, Sergio Freire, Amy Rose, Forrest R. Stevens, Brian Blankespoor, Charlie Frye, Joshua Comenetz, Alessandro Sorichetta, Kytt MacManus, Linda Pistolesi, Marc Levy, Andrew J. Tatem, and Martino Pesaresi
Earth Syst. Sci. Data, 11, 1385–1409, https://doi.org/10.5194/essd-11-1385-2019, https://doi.org/10.5194/essd-11-1385-2019, 2019
Short summary
Short summary
Population data are essential for studies on human–nature relationships, disaster or environmental health. Several global and continental gridded population data have been produced but have never been systematically compared. This article fills this gap and critically compares these gridded population datasets. Through the lens of the
fitness for useconcept it provides users with the knowledge needed to make informed decisions about appropriate data use in relation to the target application.
Hamidreza Zoraghein and Stefan Leyk
Proc. Int. Cartogr. Assoc., 1, 130, https://doi.org/10.5194/ica-proc-1-130-2018, https://doi.org/10.5194/ica-proc-1-130-2018, 2018
Related subject area
Land Cover and Land Use
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
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
Annual emissions of carbon from land use, land-use change, and forestry 1850–2020
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
An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multi-source product fusion approach
Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas
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
Land-use harmonization datasets for annual global carbon budgets
An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific regions
A historical reconstruction of cropland in China from 1900 to 2016
Dataset of 1 km cropland cover from 1690 to 1999 in Scandinavia
The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data
GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery
A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine
Mid-19th-century building structure locations in Galicia and Austrian Silesia under the Habsburg Monarchy
High-resolution global map of smallholder and industrial closed-canopy oil palm plantations
A 30 m resolution dataset of China's urban impervious surface area and green space, 2000–2018
A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps
Early-season mapping of winter wheat in China based on Landsat and Sentinel images
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.
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.
Richard A. Houghton and Andrea Castanho
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-351, https://doi.org/10.5194/essd-2022-351, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
The paper updates a previous analysis of carbon emissions (annual and national) from land use, land-use change, and forestry, 1850–2020. It used data from the latest (2020) Forest Resource Assessment from the U.N., incorporated shifting cultivation, and included improvements to the bookkeeping model and recent estimates of emissions from peatlands. Net global emissions declined steadily over the decade 2011–2020 (average 0.96 PgC yr-1), falling below 1.0 PgC yr-1 for the first time in 30 years.
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.
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-142, https://doi.org/10.5194/essd-2022-142, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
A global land cover map with fine spatial resolution (e.g., 30 m) is important for climate and environmental studies, food security, biodiversity conservation, carbon cycling, etc. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing multiple existing land cover products based on the Dempster-Shafer theory of evidence on the Google Earth Engine platform.
Jeremy Baynes, Anne Neale, and Torrin Hultgren
Earth Syst. Sci. Data, 14, 2833–2849, https://doi.org/10.5194/essd-14-2833-2022, https://doi.org/10.5194/essd-14-2833-2022, 2022
Short summary
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.
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.
Louise Chini, George Hurtt, Ritvik Sahajpal, Steve Frolking, Kees Klein Goldewijk, Stephen Sitch, Raphael Ganzenmüller, Lei Ma, Lesley Ott, Julia Pongratz, and Benjamin Poulter
Earth Syst. Sci. Data, 13, 4175–4189, https://doi.org/10.5194/essd-13-4175-2021, https://doi.org/10.5194/essd-13-4175-2021, 2021
Short summary
Short summary
Carbon emissions from land-use change are a large and uncertain component of the global carbon cycle. The Land-Use Harmonization 2 (LUH2) dataset was developed as an input to carbon and climate simulations and has been updated annually for the Global Carbon Budget (GCB) assessments. Here we discuss the methodology for producing these annual LUH2 updates and describe the 2019 version which used new cropland and grazing land data inputs for the globally important region of Brazil.
Zoltan Szantoi, Andreas Brink, and Andrea Lupi
Earth Syst. Sci. Data, 13, 3767–3789, https://doi.org/10.5194/essd-13-3767-2021, https://doi.org/10.5194/essd-13-3767-2021, 2021
Short summary
Short summary
The ever-evolving landscapes in the African, Caribbean and Pacific regions should be monitored for land cover changes. The Global Land Monitoring Service of the Copernicus Programme, and in particular the Hot Spot Monitoring activity, developed a satellite-imagery-based workflow to monitor such areas. Here, we present a total of 852 025 km2 of areas mapped with up to 32 land cover classes. Thematic land cover and land cover change maps, as well as validation datasets, are presented.
Zhen Yu, Xiaobin Jin, Lijuan Miao, and Xuhong Yang
Earth Syst. Sci. Data, 13, 3203–3218, https://doi.org/10.5194/essd-13-3203-2021, https://doi.org/10.5194/essd-13-3203-2021, 2021
Short summary
Short summary
We reconstructed the annual, 5 km × 5 km resolution cropland percentage map that covers mainland China and spans from 1900 to 2016. Our results are advantageous, as they reconcile accuracy, temporal coverage, and spatial resolutions. We further examined the cropland shift pattern and its driving factors in China using the reconstructed maps. This work will greatly contribute to the field of global ecology and land surface modeling.
Xueqiong Wei, Mats Widgren, Beibei Li, Yu Ye, Xiuqi Fang, Chengpeng Zhang, and Tiexi Chen
Earth Syst. Sci. Data, 13, 3035–3056, https://doi.org/10.5194/essd-13-3035-2021, https://doi.org/10.5194/essd-13-3035-2021, 2021
Short summary
Short summary
The cropland area of each administrative unit based on statistics in Scandinavia from 1690 to 1999 is allocated into 1 km grid cells. The cropland area increased from 1690 to 1950 and then decreasd in the following years, especially in southeastern Scandinavia. Comparing global datasets with this study, the spatial patterns show considerable differences. Our dataset is validated using satellite-based cropland cover data and results in previous studies.
Jichong Han, Zhao Zhang, Yuchuan Luo, Juan Cao, Liangliang Zhang, Jing Zhang, and Ziyue Li
Earth Syst. Sci. Data, 13, 2857–2874, https://doi.org/10.5194/essd-13-2857-2021, https://doi.org/10.5194/essd-13-2857-2021, 2021
Short summary
Short summary
Large-scale and high-resolution maps of rapeseed are important for ensuring global energy security. We generated a new database for the rapeseed planting area (2017–2019) at 10 m spatial resolution based on multiple data. Also, we analyzed the rapeseed rotation patterns in 25 representative areas from different countries. The derived rapeseed maps are useful for many purposes including crop growth monitoring and production and optimizing planting structure.
Xiao Zhang, Liangyun Liu, Xidong Chen, Yuan Gao, Shuai Xie, and Jun Mi
Earth Syst. Sci. Data, 13, 2753–2776, https://doi.org/10.5194/essd-13-2753-2021, https://doi.org/10.5194/essd-13-2753-2021, 2021
Short summary
Short summary
Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m landcover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib on the Google Earth Engine computing platform.
Bowen Cao, Le Yu, Victoria Naipal, Philippe Ciais, Wei Li, Yuanyuan Zhao, Wei Wei, Die Chen, Zhuang Liu, and Peng Gong
Earth Syst. Sci. Data, 13, 2437–2456, https://doi.org/10.5194/essd-13-2437-2021, https://doi.org/10.5194/essd-13-2437-2021, 2021
Short summary
Short summary
In this study, the first 30 m resolution terrace map of China was developed through supervised pixel-based classification using multisource, multi-temporal data based on the Google Earth Engine platform. The classification performed well with an overall accuracy of 94 %. The terrace mapping algorithm can be used to map large-scale terraces in other regions globally, and the terrace map will be valuable for studies on soil erosion, carbon cycle, and ecosystem service assessments.
Dominik Kaim, Marcin Szwagrzyk, Monika Dobosz, Mateusz Troll, and Krzysztof Ostafin
Earth Syst. Sci. Data, 13, 1693–1709, https://doi.org/10.5194/essd-13-1693-2021, https://doi.org/10.5194/essd-13-1693-2021, 2021
Short summary
Short summary
We present a dataset of mid-19th-century building structure locations in former Galicia and Austrian Silesia (parts of the Habsburg Monarchy), located in present-day Czechia, Poland, and Ukraine. It consists of two kinds of building structures: residential and farm-related buildings. The dataset may serve as an important input in studying long-term socio-economic processes and human–environmental interactions or as a valuable reference for continental settlement reconstructions.
Adrià Descals, Serge Wich, Erik Meijaard, David L. A. Gaveau, Stephen Peedell, and Zoltan Szantoi
Earth Syst. Sci. Data, 13, 1211–1231, https://doi.org/10.5194/essd-13-1211-2021, https://doi.org/10.5194/essd-13-1211-2021, 2021
Short summary
Short summary
Decision-making for sustainable vegetable oil production requires accurate global oil crop maps. We used high-resolution satellite data to train a deep learning model that accurately classified industrial and smallholder oil palm, the main oil-producing crop. Our results outperformed previous studies and proved the suitability of deep learning for land use mapping. The global oil palm area was 21±0.42 Mha for 2019; however, young and sparse plantations were not included in this estimate.
Wenhui Kuang, Shu Zhang, Xiaoyong Li, and Dengsheng Lu
Earth Syst. Sci. Data, 13, 63–82, https://doi.org/10.5194/essd-13-63-2021, https://doi.org/10.5194/essd-13-63-2021, 2021
Short summary
Short summary
We propose a hierarchical principle for remotely sensed urban land use and land cover change for mapping intra-urban structure and component dynamics. China’s Land Use/cover Dataset (CLUD) is updated, delineating the imperviousness and green surface conditions in cities from 2000 to 2018. The newly developed datasets can be used to enhance our understanding of urbanization impacts on ecological and regional climatic conditions and on urban dwellers' environments.
Qiangyi Yu, Liangzhi You, Ulrike Wood-Sichra, Yating Ru, Alison K. B. Joglekar, Steffen Fritz, Wei Xiong, Miao Lu, Wenbin Wu, and Peng Yang
Earth Syst. Sci. Data, 12, 3545–3572, https://doi.org/10.5194/essd-12-3545-2020, https://doi.org/10.5194/essd-12-3545-2020, 2020
Short summary
Short summary
SPAM makes plausible estimates of crop distribution within disaggregated units. It moves the data from coarser units such as countries and provinces to finer units such as grid cells and creates a global gridscape at the confluence between earth and agricultural-production systems. It improves spatial understanding of crop production systems and allows policymakers to better target agricultural- and rural-development policies for increasing food security with minimal environmental impacts.
Jie Dong, Yangyang Fu, Jingjing Wang, Haifeng Tian, Shan Fu, Zheng Niu, Wei Han, Yi Zheng, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 3081–3095, https://doi.org/10.5194/essd-12-3081-2020, https://doi.org/10.5194/essd-12-3081-2020, 2020
Short summary
Short summary
For the first time, we produced a 30 m winter wheat distribution map in China for 3 years during 2016–2018. Validated with 33 776 survey samples, the map had perfect performance with an overall accuracy of 89.88 %. Moreover, the method can identify planting areas of winter wheat 3 months prior to harvest; that is valuable information for production predictions and is urgently necessary for policymakers to reduce economic loss and assess food security.
Cited articles
Balch, J. K., Iglesias, V., Braswell, A. E., Rossi, M. W., Joseph, M. B., Mahood, A. L., Shrum, T. R., White, C. T., Scholl, V. M., McGuire, B., and Karban, C.: Social-Environmental Extremes: Rethinking Extraordinary Events as
Outcomes of Interacting Biophysical and Social Systems, Earth's Future, 8,
e2019EF001319, https://doi.org/10.1029/2019EF001319, 2020. a
Baldauf, M., Garlappi, L., and Yannelis, C.: Does climate change affect real
estate prices? Only if you believe in it, The Review of Financial Studies,
33, 1256–1295, 2020. a
Balk, D. and Yetman, G.: The global distribution of population: evaluating the
gains in resolution refinement, Center for International Earth Science
Information Network (CIESIN), Columbia University, New York, 2004. a
Bechard, A.: Gone with the Wind: Declines in Property Values as Harmful Algal
Blooms Are Blown Towards the Shore, J. Real Estate Financ., 1–16, https://doi.org/10.1007/s11146-020-09749-6, 2020. a
Bernstein, A., Gustafson, M. T., and Lewis, R.: Disaster on the horizon: The
price effect of sea level rise, J. Financ. Econ., 134,
253–272, 2019. a
Boeing, G.: Off the Grid… and Back Again? The Recent Evolution of American
Street Network Planning and Design, J. Am. Plann.
Assoc., 87,
123–137,
https://doi.org/10.1080/01944363.2020.1819382, 2020. a
Boslett, A. and Hill, E.: Shale gas transmission and housing prices, Resour. Energy Econ., 57, 36–50, 2019. a
Buchanan, M. K., Kulp, S., Cushing, L., Morello-Frosch, R., Nedwick, T., and
Strauss, B.: Sea level rise and coastal flooding threaten affordable housing,
Environ. Res. Lett., 15, 124020, https://doi.org/10.1088/1748-9326/abb266, 2020. a
Butler, M. A.: Rural-urban continuum codes for metro and nonmetro counties,
9028, US Department of Agriculture, Economic Research Service, Agriculture
and …, Washington D.C., USA, 1990. a
CIESIN: Global Rural-Urban Mapping Project.(GRUMP): Urban/rural population
grids., 2004. a
City of Los Angeles Controller's Office: Affordable Housing: Incentives
and Oversight, available at:
https://lacontroller.org/data-stories-and-maps/affordable-housing-incentives-and-oversight/ (last access: 25 January 2021),
2017. a
Clarke, W. and Freedman, M.: The rise and effects of homeowners associations,
J. Urban Econ., 112, 1–15, 2019. a
Connor, D. S., Gutmann, M. P., Cunningham, A. R., Clement, K. K., and Leyk, S.:
How Entrenched Is the Spatial Structure of Inequality in Cities? Evidence
from the Integration of Census and Housing Data for Denver from 1940 to 2016,
Ann. Am. Assoc. Geogr., 110, 1022–1039, 2020. a
D'Lima, W. and Schultz, P.: Residential Real Estate Investments and Investor
Characteristics, J. Real Estate Financ., 1–40, https://doi.org/10.1007/s11146-020-09771-8,
2020. a
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, 849–857, 2000. a
ESRI: ArcPy, available at: https://pro.arcgis.com/en/pro-app/arcpy/ (last access: 25 January 2021), 2019. a
Financial Times: Mapping how railroads built America, available at:
https://www.ft.com/video/28ef4d98-ac46-4b68-84da-3ef5cc70e730, last access: 15 July 2020. a
Freire, S., Doxsey-Whitfield, E., MacManus, K., Mills, J., and Pesaresi, M.: Development of new open and free multi-temporal global population grids at 250 m resolution,
Proceedings of the 19th AGILE International Conference on Geographic Information Science, AGILE 2016, Helsinki, Finland, 14–17 June 2016. a
GDAL/OGR contributors: GDAL/OGR Geospatial Data Abstraction software
Library, Open Source Geospatial Foundation, available at: https://gdal.org (last access: 25 January 2021),
2020. a
Gindelsky, M., Moulton, J., and Wentland, S. A.: Valuing housing services in
the era of big data: A user cost approach leveraging Zillow microdata, in:
Big Data for 21st Century Economic Statistics, University of Chicago Press, Chicago, USA,
2019. a
Hedefalk, F., Svensson, P., and Harrie, L.: Spatiotemporal historical datasets
at micro-level for geocoded individuals in five Swedish parishes, 1813–1914,
Sci. Data, 4, 1–13, 2017. a
Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., Herold, N., McKerrow, A., VanDriel, J. N., and Wickham, J.: Completion of the 2001
national land cover database for the counterminous United States,
Photogramm. Eng. Rem. S., 73, 337–341, 2007. a
Huber, P. J.: Robust regression: asymptotics, conjectures and Monte
Carlo, Ann. Stat., 1, 799–821, 1973. a
Iglesias, V. and Travis, W.: Escalating risk and pronounced inter-regional differences in exposure to natural hazards due to development patterns in the United States, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21287, https://doi.org/10.5194/egusphere-egu2020-21287, 2020 a
Jordahl, K., den Bossche, J. V., Wasserman, J., McBride, J., Fleischmann, M.,
Gerard, J., Tratner, J., Perry, M., Farmer, C., Hjelle, G. A., Gillies, S.,
Cochran, M., Bartos, M., Culbertson, L., Eubank, N., Bilogur, A., and
Albert, M.: geopandas/geopandas: v0.7.0, Zenodo, https://doi.org/10.5281/zenodo.3669853, 2020. a
Kaim, D., Kozak, J., Kolecka, N., Ziółkowska, E., Ostafin, K.,
Ostapowicz, K., Gimmi, U., Munteanu, C., and Radeloff, V. C.: Broad scale
forest cover reconstruction from historical topographic maps, Appl.
Geogr., 67, 39–48, 2016. a
Kim, M., Norwood, B., O'Connor, S., and Shen, L.: I am Jane. Do I pay more
in the housing market?, Econ. Bull., 39, 1612–1620, 2019. a
Kitchin, R.: The data revolution: Big data, open data, data infrastructures and
their consequences, Sage, London, UK, 2014. a
Klein Goldewijk, K., Beusen, A., Van Drecht, G., and De Vos, M.: The HYDE 3.1
spatially explicit database of human-induced global land-use change over the
past 12,000 years, Global Ecol. Biogeogr., 20, 73–86, 2011. a
Kugler, T. A., Grace, K., Wrathall, D. J., de Sherbinin, A., Van Riper, D., Aubrecht, C., Comer, D., Adamo, S. B., Cervone, G., Engstrom, R., and Hultquist, C.:
People and Pixels 20 years later: the current data landscape and research
trends blending population and environmental data, Popul.
Environ., 41, 209–234, 2019. a
Leyk, S. and Uhl, J. H.: Historical settlement composite layer for the U.S.
1810–2015, https://doi.org/10.7910/DVN/PKJ90M, 2018c. a, b, c, d
Leyk, S. and Uhl, J. H.: County-level uncertainty statistics accompanying the
historical settlement layers for the U.S. 1810–2015,
https://doi.org/10.7910/DVN/CXD9BW, 2018d. a, b, c, d
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. a, b
Leyk, S., Uhl, J. H., Connor, D. S., Braswell, A. E., Mietkiewicz, N., Balch,
J. K., and Gutmann, M.: Two centuries of settlement and urban development in
the United States, Sci. Adv., 6, eaba2937, https://doi.org/10.1126/sciadv.aba2937, 2020. a, b, c, d
Lieskovský, J., Kaim, D., Balázs, P., Boltižiar, M., Chmiel, M., Grabska, E., Király, G., Konkoly-Gyuró, É., Kozak, J., Antalová, K., and Kuchma, T.: Historical land use dataset of the Carpathian
region (1819–1980), J. Maps, 14, 644–651, 2018. a
Marconcini, M., Gorelick, N., Metz-Marconcini, A., and Esch, T.: Accurately
monitoring urbanization at global scale – the world settlement footprint, IOP
Conference Series: Earth and Environmental Science, 509, 012036, https://doi.org/10.1088/1755-1315/509/1/012036, 2020. a
McShane, C., Uhl, J. H., and Leyk, S.: Gridded annual land use data for the
conterminous United States 1945–2016, in preparation, 2021. a
Mietkiewicz, N., Balch, J. K., Schoennagel, T., Leyk, S., St Denis, L. A., and
Bradley, B. A.: In the Line of Fire: Consequences of Human-Ignited Wildfires
to Homes in the US (1992–2015), Fire, 3, 1–20, 2020. a
Millhouser, P.: Evaluating Landscape Connectivity and Habitat Fragmentation
Effects on Elk in the Roaring Fork and Eagle Valleys, Master's Thesis,
Pennsylvania State University, University Park, Pennsylvania, 2019. a
NYC Housing Authority: Map of NYCHA Developments, available at:
https://data.cityofnewyork.us/Housing-Development/Map-of-NYCHA-Developments/i9rv-hdr5 (last access: 25 January 2021),
2020. a
Oliphant, T. E.: A guide to NumPy, vol. 1, Trelgol Publishing, USA, 2006. a
Onda, K., Branham, J., BenDor, T. K., Kaza, N., and Salvesen, D.: Does removal
of federal subsidies discourage urban development? An evaluation of the US
Coastal Barrier Resources Act, PloS one, 15, e0233888, https://doi.org/10.1371/journal.pone.0233888, 2020. a
OpenStreetMap contributors: Planet dump retrieved from
https://planet.osm.org, available at: https://www.openstreetmap.org (last access: 1 December 2020), 2020. a
Ostafin, K., Kaim, D., Siwek, T., and Miklar, A.: Historical dataset of
administrative units with social-economic attributes for Austrian Silesia
1837–1910, Sci. Data, 7, 1–14, 2020. a
Peng, L. and Zhang, L.: House Prices and Systematic Risk: Evidence from
Microdata, Real Estate Econom., 1–24, https://doi.org/10.1111/1540-6229.12277, 2019. a
Pesaresi, M., Huadong, G., Blaes, X., Ehrlich, D., Ferri, S., Gueguen, L., Halkia, M., Kauffmann, M., Kemper, T., Lu, L., and Marin-Herrera, M. A.: A global human
settlement layer from optical HR/VHR RS data: concept and first results, IEEE J. Sel. Top. Appl.,
6, 2102–2131, 2013. a
Reba, M., Reitsma, F., and Seto, K. C.: Spatializing 6,000 years of global
urbanization from 3700 BC to AD 2000, Sci. Data, 3, 1–16, 2016. a
Safe Software Inc.: Feature Manipulation Engine, available at:
https://www.safe.com/fme/fme-desktop/, last access: 1 July 2020. a
Shen, X., Liu, P., Qiu, Y. L., Patwardhan, A., and Vaishnav, P.: Estimation of
change in house sales prices in the United States after heat pump adoption,
Nature Energy, 6, 30–37, 2021. a
Smith, J. H., Wickham, J. D., Stehman, S. V., and Yang, L.: Impacts of patch
size and land-cover heterogeneity on thematic image classification accuracy,
Photogramm. Eng. Rem. S., 68, 65–70, 2002. a
Sohl, T., Reker, R., Bouchard, M., Sayler, K., Dornbierer, J., Wika, S.,
Quenzer, R., and Friesz, A.: Modeled historical land use and land cover for
the conterminous United States, J. Land Use Sci., 11, 476–499,
2016. a
SQLite: SQLite SQL database engine, available at: https://www.sqlite.org, last access: 1 July 2020. a
Stern, M. and Lester, T. W.: Does Local Ownership of Vacant Land Reduce Crime?
An Assessment of Chicago's Large Lots Program, J. Am.
Plan. Assoc., 87, 73–84, https://doi.org/10.1080/01944363.2020.1792334, 2020. a
Tarafdar, S., Rimjha, M., Hinze, N., Hotle, S., and Trani, A. A.: Urban air
Mobility Regional Landing Site Feasibility and Fare Model Analysis in the
Greater Northern California Region, in: 2019 Integrated Communications,
Navigation and Surveillance Conference (ICNS), pp. 1–11, IEEE, 9–11 April 2019,
Herndon, VA, USA, 2019. a
Tatem, A. J.: WorldPop, open data for spatial demography, Sci. Data, 4,
1–4, 2017. a
Uhl, J. H. and Leyk, S.: Uncertainty surfaces accompanying the BUPR, BUPL, and
BUA gridded surface series, https://doi.org/10.7910/DVN/T8H5KF, 2020d. a, b, c
Uhl, J. H. and Leyk, S.: Towards a novel backdating strategy for creating
built-up land time series data using contemporary spatial constraints, Remote
Sens. Environ., 238, 111197, https://doi.org/10.1016/j.rse.2019.05.016, 2020e. a
Uhl, J. H., Leyk, S., Florczyk, A. J., Pesaresi, M., and Balk, D.: Assessing
spatiotemporal agreement between multi-temporal built-up land layers and
integrated cadastral and building data, in: International Conference on
GIScience Short Paper Proceedings, vol. 1, September 2016,
Montreal, Canada, 2016. a, b
Uhl, J. H., Zoraghein, H., Leyk, S., Balk, D., Corbane, C., Syrris, V., and
Florczyk, A. J.: Exposing the urban continuum: Implications and
cross-comparison from an interdisciplinary perspective, International Journal
of Digital Earth, 13,
22–44,
2020. a
Uhl, J. H., Connor, D. S., Leyk, S., and Braswell, A. E.: A century of decoupling size and structure of urban spaces in the United States, Commun. Earth Environ.,
1–14,
https://doi.org/10.1038/s43247-020-00082-7, 2021. a, b, c
US Census Bureau: Table 4: Population from 1790 to 1990, available at:
https://www.census.gov/population/censusdata/table-4.pdf (last access: 15 July 2020), 1993. a
US Census Bureau: American Community Survey: 2011–2015,
https://www.census.gov/newsroom/press-releases/2016/cb16-210.html (last access: 25 January 2021),
2016. a
US Census Bureau, Department of Commerce: TIGER/Line Shapefile, 2016, Series
Information for the Current Place State-based Shapefile, available at:
https://catalog.data.gov/dataset/tiger-line-shapefile-2016-series-information-for-the-current-place-state-based-shapefile (last access: 1 December 2020),
2016. a
USDA Economic Research Service: Rural-Urban Continuum Codes 2013,
Documentation, available at:
https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/documentation/ (last access: 1 December 2020),
2019. a
U.S. Department of Housing and Urban Development: Assisted housing: national
and local, county-level summaries 2019, available at:
https://www.huduser.gov/portal/datasets/assthsg.html (last access: 1 December 2020), 2019. a
USGS National Geospatial Technical Operations Center: USGS National
Structures Dataset – USGS National Map Downloadable Data Collection, available at:
https://catalog.data.gov/dataset/usgs-national-structures-dataset-nsd-downloadable-data-collection-national-geospatial-data-ass (last access: 1 December 2020),
2016. a
Wentland, S. A., Ancona, Z. H., Bagstad, K. J., Boyd, J., Hass, J. L.,
Gindelsky, M., and Moulton, J. G.: Accounting for land in the United States:
Integrating physical land cover, land use, and monetary valuation, Ecosystem
Services, 46, 101178, https://doi.org/10.1016/j.ecoser.2020.101178, 2020.
a
Wickham, J. D., Stehman, S. V., Gass, L., Dewitz, J., Fry, J. A., and Wade,
T. G.: Accuracy assessment of NLCD 2006 land cover and impervious surface,
Remote Sens. Environ., 130, 294–304, 2013. a
Zoraghein, H. and Leyk, S.: Data-enriched interpolation for temporally
consistent population compositions, GIScience Remote Sens., 56,
430–461, 2019. a
Short summary
Fine-grained geospatial data on the spatial distribution of human settlements are scarce prior to the era of remote-sensing-based Earth observation. In this paper, we present datasets derived from a large, novel building stock database, enabling the spatially explicit analysis of 200 years of land development in the United States at an unprecedented spatial and temporal resolution. These datasets greatly facilitate long-term studies of socio-environmental systems in the conterminous USA.
Fine-grained geospatial data on the spatial distribution of human settlements are scarce prior...
