Articles | Volume 15, issue 2
https://doi.org/10.5194/essd-15-1005-2023
© Author(s) 2023. 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-15-1005-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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)
Xiaoyong Li
State Key Laboratory of Urban and Regional Ecology, Research Center
for Eco-environmental Sciences, Chinese Academy of Sciences, Beijing 100085,
China
Schiller Institute for Integrated Science and Society, Department of
Earth and Environmental Sciences, Boston College, Chestnut Hill, MA 02467,
USA
International Center for Climate and Global Change Research, College
of Forestry, Wildlife and Environment, Auburn University, Auburn, AL 36849,
USA
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Hanqin Tian
CORRESPONDING AUTHOR
Schiller Institute for Integrated Science and Society, Department of
Earth and Environmental Sciences, Boston College, Chestnut Hill, MA 02467,
USA
Chaoqun Lu
Department of Ecology, Evolution, and Organismal Biology, Iowa State
University, Ames, IA 50011, USA
Shufen Pan
International Center for Climate and Global Change Research, College
of Forestry, Wildlife and Environment, Auburn University, Auburn, AL 36849,
USA
Schiller Institute for Integrated Science and Society, Department of
Earth and Environmental Sciences, Boston College, Chestnut Hill, MA 02467,
USA
Related authors
No articles found.
Kyle E. Hinson, Marjorie A. M. Friedrichs, Raymond G. Najjar, Maria Herrmann, Zihao Bian, Gopal Bhatt, Pierre St-Laurent, Hanqin Tian, and Gary Shenk
Biogeosciences, 20, 1937–1961, https://doi.org/10.5194/bg-20-1937-2023, https://doi.org/10.5194/bg-20-1937-2023, 2023
Short summary
Short summary
Climate impacts are essential for environmental managers to consider when implementing nutrient reduction plans designed to reduce hypoxia. This work highlights relative sources of uncertainty in modeling regional climate impacts on the Chesapeake Bay watershed and consequent declines in bay oxygen levels. The results demonstrate that planned water quality improvement goals are capable of reducing hypoxia levels by half, offsetting climate-driven impacts on terrestrial runoff.
Sian Kou-Giesbrecht, Vivek Arora, Christian Seiler, Almut Arneth, Stefanie Falk, Atul Jain, Fortunat Joos, Daniel Kennedy, Jürgen Knauer, Stephen Sitch, Michael O'Sullivan, Naiqing Pan, Qing Sun, Hanqin Tian, Nicolas Vuichard, and Sönke Zaehle
EGUsphere, https://doi.org/10.5194/egusphere-2023-167, https://doi.org/10.5194/egusphere-2023-167, 2023
Short summary
Short summary
Nitrogen (N) is an essential limiting nutrient to terrestrial carbon (C) sequestration. We evaluate N cycling in an ensemble of terrestrial biosphere models. We find that they simulate significant variability in N processes. Models tended to overestimate C storage per unit N in vegetation and soil, which could have consequences for projecting the future terrestrial C sink. However, N cycling measurements are highly uncertain and more are necessary to guide the development of N cycling in models.
Wenxiu Zhang, Di Liu, Hanqin Tian, Naiqin Pan, Ruqi Yang, Wenhan Tang, Jia Yang, Fei Lu, Buddhi Dayananda, Han Mei, Siyuan Wang, and Hao Shi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-428, https://doi.org/10.5194/essd-2022-428, 2022
Manuscript not accepted for further review
Short summary
Short summary
High temporal resolution surface ozone concentration data is still lacking in China, so we used deep learning to generate hourly surface ozone data (HrSOD) during 2005–2020 across China. HrSOD showed that surface O3 in China tended to increase from 2016 to 2019, despite a decrease in 2020. HrSOD had high spatial and temporal accuracies, long time ranges and high temporal resolution, enabling it to be easily converted to various evaluation indicators for ecosystem and human health assessments.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
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.
Zihao Bian, Hanqin Tian, Qichun Yang, Rongting Xu, Shufen Pan, and Bowen Zhang
Earth Syst. Sci. Data, 13, 515–527, https://doi.org/10.5194/essd-13-515-2021, https://doi.org/10.5194/essd-13-515-2021, 2021
Short summary
Short summary
The estimation of manure nutrient production and application is critical for the efficient use of manure nutrients. This study developed four manure nitrogen and phosphorus datasets with high spatial resolution and a long time period (1860–2017) in the US. The datasets can provide useful information for stakeholders and scientists who focus on agriculture, nutrient budget, and biogeochemical cycle.
Peiyu Cao, Chaoqun Lu, Jien Zhang, and Avani Khadilkar
Atmos. Chem. Phys., 20, 11907–11922, https://doi.org/10.5194/acp-20-11907-2020, https://doi.org/10.5194/acp-20-11907-2020, 2020
Short summary
Short summary
In this study, we estimate monthly ammonia emission from synthetic nitrogen fertilizer use across the contiguous US from 1900 to 2015. The results indicate the important role that cropland expansion and nitrogen fertilizer enrichment played in enhancing NH3 emissions. It shows such long-term human activities have dramatically changed the spatiotemporal and seasonal patterns of NH3 emission, impacting air pollution and public health in the US.
Shufen Pan, Naiqing Pan, Hanqin Tian, Pierre Friedlingstein, Stephen Sitch, Hao Shi, Vivek K. Arora, Vanessa Haverd, Atul K. Jain, Etsushi Kato, Sebastian Lienert, Danica Lombardozzi, Julia E. M. S. Nabel, Catherine Ottlé, Benjamin Poulter, Sönke Zaehle, and Steven W. Running
Hydrol. Earth Syst. Sci., 24, 1485–1509, https://doi.org/10.5194/hess-24-1485-2020, https://doi.org/10.5194/hess-24-1485-2020, 2020
Short summary
Short summary
Evapotranspiration (ET) links global water, carbon and energy cycles. We used 4 remote sensing models, 2 machine-learning algorithms and 14 land surface models to analyze the changes in global terrestrial ET. These three categories of approaches agreed well in terms of ET intensity. For 1982–2011, all models showed that Earth greening enhanced terrestrial ET. The small interannual variability of global terrestrial ET suggests it has a potential planetary boundary of around 600 mm yr-1.
Xuecao Li, Yuyu Zhou, Lin Meng, Ghassem R. Asrar, Chaoqun Lu, and Qiusheng Wu
Earth Syst. Sci. Data, 11, 881–894, https://doi.org/10.5194/essd-11-881-2019, https://doi.org/10.5194/essd-11-881-2019, 2019
Short summary
Short summary
We generated a long-term (1985–2015) and medium-resolution (30 m) product of phenology indicators in urban domains in the conterminous US using Landsat satellite observations. The derived phenology indicators agree well with in situ observations and provide more spatial details in complex urban areas compared to the existing coarse resolution phenology products (e.g., MODIS). The published data are of great use for urban phenology studies (e.g., pollen-induced respiratory allergies).
Rongting Xu, Hanqin Tian, Shufen Pan, Shree R. S. Dangal, Jian Chen, Jinfeng Chang, Yonglong Lu, Ute Maria Skiba, Francesco N. Tubiello, and Bowen Zhang
Earth Syst. Sci. Data, 11, 175–187, https://doi.org/10.5194/essd-11-175-2019, https://doi.org/10.5194/essd-11-175-2019, 2019
Short summary
Short summary
We provide three gridded datasets of synthetic nitrogen (N) fertilizer and manure N inputs in global pastures and rangelands at a resolution of 0.5° × 0.5° for the period 1860–2016 (i.e., annual manure N deposition (by grazing animals) rate, synthetic N fertilizer use rate and manure N application rate). These three datasets could fill data gaps of N inputs in global and regional grasslands and serve as input drivers for earth system models.
Peiyu Cao, Chaoqun Lu, and Zhen Yu
Earth Syst. Sci. Data, 10, 969–984, https://doi.org/10.5194/essd-10-969-2018, https://doi.org/10.5194/essd-10-969-2018, 2018
Short summary
Short summary
A long-term N fertilizer use history is important for both field investigators and modeling community to examine the cumulative impacts of N fertilizer uses. We developed a spatially explicit time-series data set of nitrogen fertilizer use in agricultural land of the continental US during 1850–2015 at a resolution of 5 km × 5 km based on multiple data sources and historical cropland maps. It contains nitrogen fertilizer use rate, application timing, and ammonium and nitrate form fertilizer use.
Bowen Zhang, Hanqin Tian, Chaoqun Lu, Shree R. S. Dangal, Jia Yang, and Shufen Pan
Earth Syst. Sci. Data, 9, 667–678, https://doi.org/10.5194/essd-9-667-2017, https://doi.org/10.5194/essd-9-667-2017, 2017
Short summary
Short summary
This work addressed how manure nitrogen (N) production and application to cropland have changed over time and space. The 5 arcmin gridded global dataset of manure nitrogen production generated from this study could be used as an input for global or regional land surface and ecosystem models to evaluate the impacts of manure nitrogen on key biogeochemical processes and water quality.
Guangsheng Chen, Shufen Pan, Daniel J. Hayes, and Hanqin Tian
Earth Syst. Sci. Data, 9, 545–556, https://doi.org/10.5194/essd-9-545-2017, https://doi.org/10.5194/essd-9-545-2017, 2017
Short summary
Short summary
Through synthesizing multiple inventory data sources, this study developed methods to spatialize the time series plantation forest and tree species distribution data for the conterminous US during 1928–2012. These time series and gridded data set can be readily applied in regional Earth system modeling frameworks for assessing the impacts of plantation management practices on forest productivity, carbon and nitrogen stocks, and greenhouse gas and water fluxes on regional or national scales.
Rongting Xu, Hanqin Tian, Chaoqun Lu, Shufen Pan, Jian Chen, Jia Yang, and Bowen Zhang
Clim. Past, 13, 977–990, https://doi.org/10.5194/cp-13-977-2017, https://doi.org/10.5194/cp-13-977-2017, 2017
Short summary
Short summary
As N2O emissions were present in preindustrial times, only the difference between current and preindustrial emissions represents net human-induced climate change. Large uncertainty exists in previous estimates of preindustrial N2O emissions from the land biosphere. Our estimate using process-based model was the first study that provided the preindustrial N2O emission at the biome, sector or country, and global level, which could be a useful reference for future climate mitigation.
Chaoqun Lu and Hanqin Tian
Earth Syst. Sci. Data, 9, 181–192, https://doi.org/10.5194/essd-9-181-2017, https://doi.org/10.5194/essd-9-181-2017, 2017
Short summary
Short summary
This work has addressed how agricultural nitrogen and phosphorous fertilizer use has changed over time and space. The final product covers global agricultural land, spanning from 1961 to 2013 at a spatial resolution of 0.5° × 0.5° latitude by longitude. It can serve as an important input driver for regional and global assessment and Earth system modeling of agricultural productivity, crop yield, greenhouse gas balance, global nutrient budget, and ecosystem feedback to climate.
C. Zhang, H. Tian, S. Pan, G. Lockaby, and A. Chappelka
Biogeosciences, 11, 7107–7124, https://doi.org/10.5194/bg-11-7107-2014, https://doi.org/10.5194/bg-11-7107-2014, 2014
Short summary
Short summary
Based on a comprehensive analysis framework including 15 factors, a factorial analysis scheme was developed to quantify a relative contribution of individual factors to carbon dynamics induced by urbanization. A case study in the southern US showed that land conversion had larger impacts than other factors, causing C loss. Urban managements & the interactive effect among factors compensated for 42% of the C loss in LC. The altered disturbance regime after urbanization enhanced the urban C sink.
H. Tian, G. Chen, C. Lu, X. Xu, W. Ren, K. Banger, B. Zhang, B. Tao, S. Pan, M. Liu, and C. Zhang
Biogeosciences Discuss., https://doi.org/10.5194/bgd-10-19811-2013, https://doi.org/10.5194/bgd-10-19811-2013, 2013
Revised manuscript has not been submitted
Related subject area
Domain: ESSD – Land | Subject: Land Cover and Land Use
Annual emissions of carbon from land use, land-use change, and forestry from 1850 to 2020
An open-source automatic survey of green roofs in London using segmentation of aerial imagery
Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data
A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models
Estimating local agricultural gross domestic product (AgGDP) across the world
Classification and mapping of European fuels using a hierarchical, multipurpose fuel classification system
Harmonising the land-use flux estimates of global models and national inventories for 2000–2020
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
An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multi-source product fusion approach
Richard A. Houghton and Andrea Castanho
Earth Syst. Sci. Data, 15, 2025–2054, https://doi.org/10.5194/essd-15-2025-2023, https://doi.org/10.5194/essd-15-2025-2023, 2023
Short summary
Short summary
We update a previous analysis of carbon emissions (annual and national) from land use, land-use change, and forestry from 1850 to 2020. We use data from the latest (2020) Global Forest Resources Assessment, incorporate shifting cultivation, and include improvements to the bookkeeping model and recent estimates of emissions from peatlands. Net global emissions declined steadily over the decade from 2011 to 2020 (mean of 0.96 Pg C yr−1), falling below 1.0 Pg C yr−1 for the first time in 30 years.
Charles H. Simpson, Oscar Brousse, Nahid Mohajeri, Michael Davies, and Clare Heaviside
Earth Syst. Sci. Data, 15, 1521–1541, https://doi.org/10.5194/essd-15-1521-2023, https://doi.org/10.5194/essd-15-1521-2023, 2023
Short summary
Short summary
Adding plants to roofs of buildings can reduce indoor and outdoor temperatures and so can reduce urban overheating, which is expected to increase due to climate change and urban growth. To better understand the effect this has on the urban environment, we need data on how many buildings have green roofs already.
We used a computer vision model to find green roofs in aerial imagery in London, producing a dataset identifying what buildings have green roofs and improving on previous methods.
Chunling Sun, Hong Zhang, Lu Xu, Ji Ge, Jingling Jiang, Lijun Zuo, and Chao Wang
Earth Syst. Sci. Data, 15, 1501–1520, https://doi.org/10.5194/essd-15-1501-2023, https://doi.org/10.5194/essd-15-1501-2023, 2023
Short summary
Short summary
Over 90 % of the world’s rice is produced in the Asia–Pacific region. In this study, a rice-mapping method based on Sentinel-1 data for mainland Southeast Asia is proposed. A combination of spatiotemporal features with strong generalization is selected and input into the U-Net model to obtain a 20 m resolution rice area map of mainland Southeast Asia in 2019. The accuracy of the proposed method is 92.20 %. The rice area map is concordant with statistics and other rice area maps.
Kandice L. Harper, Céline Lamarche, Andrew Hartley, Philippe Peylin, Catherine Ottlé, Vladislav Bastrikov, Rodrigo San Martín, Sylvia I. Bohnenstengel, Grit Kirches, Martin Boettcher, Roman Shevchuk, Carsten Brockmann, and Pierre Defourny
Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, https://doi.org/10.5194/essd-15-1465-2023, 2023
Short summary
Short summary
We built a spatially explicit annual plant-functional-type (PFT) dataset for 1992–2020 exhibiting intra-class spatial variability in PFT fractional cover at 300 m. For each year, 14 maps of percentage cover are produced: bare soil, water, permanent snow/ice, built, managed grasses, natural grasses, and trees and shrubs, each split into leaf type and seasonality. Model simulations indicate significant differences in simulated carbon, water, and energy fluxes in some regions using this new set.
Yating Ru, Brian Blankespoor, Ulrike Wood-Sichra, Timothy S. Thomas, Liangzhi You, and Erwin Kalvelagen
Earth Syst. Sci. Data, 15, 1357–1387, https://doi.org/10.5194/essd-15-1357-2023, https://doi.org/10.5194/essd-15-1357-2023, 2023
Short summary
Short summary
Economic statistics are frequently produced at an administrative level that lacks detail to examine development patterns and the exposure to natural hazards. This paper disaggregates national and subnational administrative statistics of agricultural GDP into a global dataset at the local level using satellite-derived indicators. As an illustration, the paper estimates that the exposure of areas with extreme drought to agricultural GDP is USD 432 billion, where nearly 1.2 billion people live.
Elena Aragoneses, Mariano García, Michele Salis, Luís M. Ribeiro, and Emilio Chuvieco
Earth Syst. Sci. Data, 15, 1287–1315, https://doi.org/10.5194/essd-15-1287-2023, https://doi.org/10.5194/essd-15-1287-2023, 2023
Short summary
Short summary
We present a new hierarchical fuel classification system with a total of 85 fuels that is useful for preventing fire risk at different spatial scales. Based on this, we developed a European fuel map (1 km resolution) using land cover datasets, biogeographic datasets, and bioclimatic modelling. We validated the map by comparing it to high-resolution data, obtaining high overall accuracy. Finally, we developed a crosswalk for standard fuel models as a first assignment of fuel parameters.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
Short summary
Short summary
Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
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.
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.
Cited articles
Bartholome, E. and Belward, A. S.: GLC2000: a new approach to global land
cover mapping from Earth observation data, Int. J. Remote Sens., 26,
1959–1977, https://doi.org/10.1080/01431160412331291297, 2005.
Bigelow, D. P. and Borchers, A.: Major Uses of Land in the United States
2012, U.S. Department of Agriculture, Economic Research Service, https://www.ers.usda.gov/publications/pub-details/?pubid=84879 (last access: 13 February 2023), 2017.
Billington, R. A. and Ridge, M.: Westward expansion: a history of the
American frontier, University of New Mexico Press, ISBN 9780826319814, 2001.
Boryan, C., Yang, Z., Mueller, R., and Craig, M.: Monitoring US agriculture:
the US Department of Agriculture, National Agricultural Statistics Service,
Cropland Data Layer Program, Geocarto. Int., 26, 341–358, https://doi.org/10.1080/10106049.2011.562309, 2011.
Cao, B., Yu, L., Li, X., Chen, M., Li, X., Hao, P., and Gong, P.: A 1 km global cropland dataset from 10 000 BCE to 2100 CE, Earth Syst. Sci. Data, 13, 5403–5421, https://doi.org/10.5194/essd-13-5403-2021, 2021.
Chen, G., Pan, S., Hayes, D. J., and Tian, H.: Spatial and temporal patterns of plantation forests in the United States since the 1930s: an annual and gridded data set for regional Earth system modeling, Earth Syst. Sci. Data, 9, 545–556, https://doi.org/10.5194/essd-9-545-2017, 2017.
Chen, H., Tian, H., Liu, M., Melillo, J., Pan, S., and Zhang, C.: Effect of
Land-Cover Change on Terrestrial Carbon Dynamics in the Southern United
States, J. Environ. Qual., 35, 1533–1547, https://doi.org/10.2134/jeq2005.0198, 2006.
Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G.,
Peng, S., Lu, M., Zhang, W., Tong, X., and Mills, J.: Global land cover
mapping at 30 m resolution: A POK-based operational approach, ISPRS. J.
Photogramm. Remote Sens., 103, 7–27, https://doi.org/10.1016/j.isprsjprs.2014.09.002, 2015.
Clawson, M.: Forests in the long sweep of American history, Science, 204,
1168–1174, https://doi.org/10.1126/science.204.4398.1168, 1979.
Cole, K. L., Davis, M. B., Stearns, F., Guntenspergen, G., and Walker, K.:
Historical landcover changes in the Great Lakes region, U.S. Geological Survey, Biological Resources Division, https://hdl.handle.net/11299/165997 (last access: 13 February 2023), 1998.
Coulson, D. P. and Joyce, L.: United States state-level population estimates:
Colonization to 1999, U.S. Department of Agriculture, Forest Service, Rocky
Mountain Research Station, https://doi.org/10.2737/RMRS-GTR-111, 2003.
Crossley, M. S.: County-level crop area in the USA 1840–2017,
Inter-university Consortium for Political and Social Research, Ann Arbor, MI, https://doi.org/10.3886/E115795V3, 2020.
Crossley, M. S., Burke, K. D., Schoville, S. D., and Radeloff, V. C.: Recent
collapse of crop belts and declining diversity of US agriculture since 1840,
Glob. Change Biol., 27, 151–164, https://doi.org/10.1111/gcb.15396, 2021.
Dangal, S. R. S., Felzer, B. S., and Hurteau, M. D.: Effects of agriculture and
timber harvest on carbon sequestration in the eastern US forests, J.
Geophys. Res.-Biogeo., 119, 35–54, https://doi.org/10.1002/2013JG002409, 2014.
Domke, G. M., Oswalt, S. N., Walters, B. F., and Morin, R. S.: Tree planting
has the potential to increase carbon sequestration capacity of forests in
the United States, P. Natl. Acad. Sci. USA, 117, 24649–24651,
https://doi.org/10.1073/pnas.2010840117, 2020.
Drummond, M. A. and Loveland, T. R.: Land-use pressure and a transition to
forest-cover loss in the eastern United States, BioScience, 60, 286–298,
https://doi.org/10.1525/bio.2010.60.4.7, 2010.
Ellis, E. C., Gauthier, N., Klein Goldewijk, K., Bliege Bird, R., Boivin, N., Díaz, S., Fuller, D. Q., Grill J. L., Kaplan, J. O., Kingston, N., Locke, H., McMichael, C. N. H., Ranco, D., Rick, T. C., Shaw, R. M., Stephens, L., Svenning, J., and Watson, J. E. M.: People have shaped most of terrestrial nature for at least 12,000 years, P. Natl. Acad. Sci. USA, 118, e2023483118, https://doi.org/10.1073/pnas.2023483118, 2021.
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.
Foster, D. R.: Land-Use History (1730–1990) and Vegetation Dynamics in
Central New-England, USA, J. Ecol., 80, 753–772, https://doi.org/10.2307/2260864, 1992.
Foster, D. R., Motzkin, G., and Slater, B.: Land-use history as long-term
broad-scale disturbance: regional forest dynamics in central New England,
Ecosystems, 1, 96–119, https://doi.org/10.1007/s100219900008,
1998.
Fretwell, J. D., Williams, J. S., and Redman, P. J.: National water summary
on wetland resources, U.S. Government Printing Office, https://doi.org/10.3133/wsp2425, 1996.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N.,
Sibley, A., and Huang, X.: MODIS Collection 5 global land cover: Algorithm
refinements and characterization of new datasets, Remote Sens. Environ.,
114, 168–182, https://doi.org/10.1016/j.rse.2009.08.016, 2010.
Fuchs, R., Herold, M., Verburg, P. H., and Clevers, J. G. P. W.: A high-resolution and harmonized model approach for reconstructing and analysing historic land changes in Europe, Biogeosciences, 10, 1543–1559, https://doi.org/10.5194/bg-10-1543-2013, 2013.
Garrison, C. E.: Forestry and Tree Planting in Virginia, Reforestation, Nurseries, and Genetic Resources (RNGR), https://rngr.net/publications/tpn/55-2/forestry-and-tree-planting-in-virginia (last access: 13 February 2023), 2012.
Grassi, G., House, J., Dentener, F., Federici, S., den Elzen, M., and
Penman, J.: The key role of forests in meeting climate targets requires
science for credible mitigation, Nature Clim. Change, 7, 220–226, https://doi.org/10.1038/nclimate3227, 2017.
Griscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax, G., Miteva,
D. A., Schlesinger, W. H., Shoch, D., Siikamaki, J. V., Smith, P., Woodbury,
P., Zganjar, C., Blackman, A., Campari, J., Conant, R. T., Delgado, C.,
Elias, P., Gopalakrishna, T., Hamsik, M. R., Herrero, M., Kiesecker, J.,
Landis, E., Laestadius, L., Leavitt, S. M., Minnemeyer, S., Polasky, S.,
Potapov, P., Putz, F. E., Sanderman, J., Silvius, M., Wollenberg, E., and
Fargione, J.: Natural climate solutions, P. Natl. Acad. Sci. USA,
114, 11645–11650, https://doi.org/10.1073/pnas.1710465114,
2017.
Hagenauer, J. and Helbich, M.: A geographically weighted artificial neural
network, Int. J. Geogr. Inform. Sci., 36,
215–235, https://doi.org/10.1080/13658816.2021.1871618, 2022.
Haines, M., Fishback, P., and Rhode, P.: United States Agriculture Data,
1840–2012, Inter-university Consortium for Political and Social Research,
https://doi.org/10.3886/ICPSR35206.v4, 2018.
Hall, B., Motzkin, G., Foster, D. R., Syfert, M., and Burk, J.: Three
hundred years of forest and land-use change in Massachusetts, USA, J.
Biogeogr., 29, 1319–1335, https://doi.org/10.1046/j.1365-2699.2002.00790.x, 2002.
Hanberry, B. B., Kabrick, J. M., He, H. S., and Palik, B. J.: Historical
trajectories and restoration strategies for the Mississippi River Alluvial
Valley, For. Eco. Manag., 280, 103–111, https://doi.org/10.1016/j.foreco.2012.05.033, 2012.
Heimlich, R. E. and Daugherty, A. B.: America's cropland: Where does it come from,
United States Department of Agriculture, 3–9, https://handle.nal.usda.gov/10113/IND20394000 (last access: 13 February 2023), 1991.
Hart, J. F.: Loss and abandonment of cleared farm land in the Eastern United
States, An. Assoc. Am. Geogr., 58, 417–440, 1968.
He, F., Li, S., and Zhang, X.: A spatially explicit reconstruction of forest
cover in China over 1700–2000, Glob. Planet. Change, 131, 73–81, https://doi.org/10.1016/j.gloplacha.2015.05.008, 2015.
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. Remote, 162,
184–199, https://doi.org/10.1016/j.isprsjprs.2020.02.019, 2020.
Houghton, R. A., Hackler, J. L., and Lawrence, K. T.: The US carbon budget:
Contributions from land-use change, Science, 285, 574–578, https://doi.org/10.1126/science.285.5427.574, 1999.
Hurt, R. D.: American agriculture: A brief history, Purdue University Press,
2002.
Hurtt, G. C., Frolking, S., Fearon, M. G., Moore, B., Shevliakova, E.,
Malyshev, S., Pacala, S. W., and Houghton, R. A.: The underpinnings of
land-use history: three centuries of global gridded land-use transitions,
wood-harvest activity, and resulting secondary lands, Glob. Change Biol.,
12, 1208–1229, https://doi.org/10.1111/j.1365-2486.2006.01150.x, 2006.
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and Zhang, X.: Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6, Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, 2020.
Jeon, S. B., Olofsson, P., and Woodcock, C. E.: Land use change in New
England: a reversal of the forest transition, J. Land Use Sci., 9, 105–130,
https://doi.org/10.1080/1747423X.2012.754962, 2014.
Klein Goldewijk, K., Beusen, A., Doelman, J., and Stehfest, E.: Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth Syst. Sci. Data, 9, 927–953, https://doi.org/10.5194/essd-9-927-2017, 2017.
Lark, T. J., Mueller, R. M., Johnson, D. M., and Gibbs, H. K.: Measuring
land-use and land-cover change using the US department of agriculture's
cropland data layer: Cautions and recommendations, Int. J. Appl. Earth Obs.
Geoinf., 62, 224–235, https://doi.org/10.1016/j.jag.2017.06.007, 2017.
Lark, T. J., Spawn, S. A., Bougie, M., and Gibbs, H. K.: Cropland expansion
in the United States produces marginal yields at high costs to wildlife,
Nat. Commun., 11, 1–11, https://doi.org/10.1038/s41467-020-18045-z, 2020.
Lark, T. J., Schelly, I. H., and Gibbs, H. K.: Accuracy, Bias, and
Improvements in Mapping Crops and Cropland across the United States Using
the USDA Cropland Data Layer, Remote Sens., 13, 968, https://doi.org/10.3390/rs13050968, 2021.
Leyk, S. and Uhl, J. H.: HISDAC-US, historical settlement data compilation
for the conterminous United States over 200 years, Sci. Data, 5, 1–14,
https://doi.org/10.1038/sdata.2018.175, 2018.
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.
Li, S., He, F., and Zhang, X.: A spatially explicit reconstruction of
cropland cover in China from 1661 to 1996, Regional Environmental Change,
16, 417–428, https://doi.org/10.1007/s10113-014-0751-4, 2016.
Li, X., Yu, L., Sohl, T., Clinton, N., Li, W., Zhu, Z., Liu, X., and Gong,
P.: A cellular automata downscaling based 1 km global land use datasets
(2010–2100), Sci. Bull., 61, 1651–1661,
https://doi.org/10.1007/s11434-016-1148-1, 2016.
Li, X., Tian, H., Pan, S., and Lu, C.: Land use and land cover changes in
the contiguous United States from 1630 to 2020 (v2.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7055086, 2022.
Liu, M. and Tian, H.: China's land cover and land use change from 1700 to
2005: Estimations from high-resolution satellite data and historical
archives, Global Biogeochem. Cy., 24, GB3003, https://doi.org/10.1029/2009gb003687, 2010.
Liu, X., Liang, X., Li, X., Xu, X., Ou, J., Chen, Y., Li, S., Wang, S., and
Pei, F.: A future land use simulation model (FLUS) for simulating multiple
land use scenarios by coupling human and natural effects, Landsc. Urban
Plan., 168, 94–116, https://doi.org/10.1016/j.landurbplan.2017.09.019, 2017.
MacCleery, D. W.: American forests: a history of resiliency and recovery,
U.S. Department of Agriculture, Forest History Society, ISBN 0890300488, 2011.
Meinig, D. W.: The Shaping of America: A Geographical Perspective on 500
Years of History, vol. 2: Continental America, 1800–1867, Yale University Press, https://www.jstor.org/stable/j.ctt5hk0p1 (last access: 13 February 2023), 1993.
Mergener, R., Botti, W., and Heyd, R.: Forestry and Tree Planting in
Michigan, Reforestation, Nurseries, and Genetic Resources (RNGR), https://rngr.net/publications/tpn/57-1/forestry-and-tree-planting-in-michigan (last access: 13 February 2023), 2014.
Olmstead, A. L. and Rhode, P. W.: A history of California agriculture, Giannini
Foundation of Agricultural Economics, University of California, https://giannini.ucop.edu/ (last access: 13 February 2023), 2017.
Oswalt, S. N., Smith, W. B., Miles, P. D. and Pugh, Scott, A.: Forest
Resources of the United States, 2012: a technical document supporting the
Forest Service 2010 update of the RPA Assessment, U.S. Department of
Agriculture, Forest Service, Washington Office, https://doi.org/10.2737/WO-GTR-91, 2014.
Oswalt, S. N., Miles, P. D., Pugh, S. A., and Smith, W. B.: Forest
Resources of the United States, 2017: a technical document supporting the
Forest Service 2020 RPA Assessment, U.S. Department of Agriculture, Forest
Service, Washington Office, https://doi.org/10.2737/WO-GTR-97, 2019.
Peng, S., Ciais, P., Maignan, F., Li, W., Chang, J., Wang, T., and Yue, C.:
Sensitivity of land use change emission estimates to historical land use and
land cover mapping, Global Biogeochem. Cy., 31, 626–643, https://doi.org/10.1002/2015gb005360, 2017.
Reuss, L. A., Wooten, H. H., and Marschner, F. J.: Inventory of Major Land Uses in the United States, U.S.
Department of Agriculture, https://ageconsearch.umn.edu/record/314797 (last access: 13 February 2023), 1948.
Rollins, M. G.: LANDFIRE: a nationally consistent vegetation, wildland fire,
and fuel assessment, Int. J. Wildland Fire, 18, 235–249, https://doi.org/10.1071/WF08088, 2009.
Schulman, S. A.: The Lumber Industry of the Upper Cumberland River Valley, Tennessee Historical Quarterly, 32, 255–264, https://www.jstor.org/stable/42623392 (last access: 3 February 2023), 1973.
Smith, W. B., Vissage, J. S., Darr, D. R., and Sheffield, R. M.: Forest
Resources of the United States, 1997, U.S. Department of Agriculture Forest
Service, https://doi.org/10.2737/NC-GTR-219, 2001.
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, https://doi.org/10.1080/1747423X.2016.1147619, 2016.
Sohl, T. L., Sayler, K. L., Bouchard, M. A., Reker, R. R., Friesz, A. M.,
Bennett, S. L., Sleeter, B. M., Sleeter, R. R., Wilson, T., and Soulard, C.:
Spatially explicit modeling of 1992–2100 land cover and forest stand age
for the conterminous United States, Ecol. Appl., 24, 1015–1036, https://doi.org/10.1890/13-1245.1, 2014.
Stanturf, J. A., Palik, B. J., and Dumroese, R. K.: Contemporary forest
restoration: A review emphasizing function, For. Ecol. Manag., 331, 292–323,
https://doi.org/10.1016/j.foreco.2014.07.029, 2014.
Steyaert, L. T. and Knox, R. G.: Reconstructed historical land cover and
biophysical parameters for studies of land-atmosphere interactions within
the eastern United States, J. Geophys. Res.-Atmos., 113, D02101, https://doi.org/10.1029/2006jd008277, 2008.
Thompson, J. R., Carpenter, D. N., Cogbill, C. V., and Foster, D. R.: Four
Centuries of Change in Northeastern United States Forests, Plos One, 8,
e72540, https://doi.org/10.1371/journal.pone.0072540, 2013.
Tian, H., Chen, G., Zhang, C., Liu, M., Sun, G., Chappelka, A., Ren, W., Xu,
X., Lu, C., and Pan, S.: Century-scale responses of ecosystem carbon storage
and flux to multiple environmental changes in the southern United States,
Ecosystems, 15, 674–694, https://doi.org/10.1007/s10021-012-9539-x, 2012.
Tian, H., Banger, K., Tao, B., and Dadhwal, V. K.: History of land use in India
during 1880–2010: Large-scale land transformation reconstructed from
satellite data and historical achieves, Glob. Planet. Change, 121, 76–88,
https://doi.org/10.1016/j.gloplacha.2014.07.005, 2014.
Tian, H., Xu, R., Pan, S., Yao, Y., Bian, Z., Cai, W., Hopkinson, C.,
Justic, D., Lohrenz, S., Lu, C., Ren, W., and Yang, J.: Long-term trajectory of
nitrogen loading and delivery from Mississippi River Basin to the Gulf of
Mexico, Global Biogeochem. Cy., 34, e2019GB006475, https://doi.org/10.1029/2019GB006475, 2020.
Uhl, J. H., Leyk, S., McShane, C. M., Braswell, A. E., Connor, D. S., and Balk, D.: Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States, Earth Syst. Sci. Data, 13, 119–153, https://doi.org/10.5194/essd-13-119-2021, 2021.
U.S. Department of Agriculture: Summary Report: 2017 National Resources
Inventory, Natural Resources Conservation Service Washington, D.C., and Center
for Survey Statistics and Methodology, Iowa State University, Ames, Iowa, https://www.nrcs.usda.gov/nri (last access: 13 February 2023),
2020.
U.S. Department of Agriculture and Economic Research Service: Our Land and Water Resources: Current and Prospective Supplies and Uses, U.S. Government Printing Office, https://www.govinfo.gov/app/details/CZIC-s21-a46-no-1290 (last access: 13 February 2023), 1974.
Verburg, P. H. and Overmars, K. P.: Combining top-down and bottom-up
dynamics in land use modeling: exploring the future of abandoned farmlands
in Europe with the Dyna-CLUE model, Landscape Ecol., 24, 1167–1181,
https://doi.org/10.1007/s10980-009-9355-7, 2009.
Verburg, P. H., Schulp, C. J. E., Witte, N., and Veldkamp, A.: Downscaling
of land use change scenarios to assess the dynamics of European landscapes,
Agr. Ecosyst. Environ., 114, 39–56, https://doi.org/10.1016/j.agee.2005.11.024, 2006.
Waisanen, P. J. and Bliss, N. B.: Changes in population and agricultural
land in conterminous United States counties, 1790 to 1997, Global
Biogeochem. Cy., 16, 84-81–84-19, https://doi.org/10.1029/2001gb001843, 2002.
West, T. O., Page, Y. L., Huang, M., Wolf, J., and Thomson, A. M.:
Downscaling global land cover projections from an integrated assessment
model for use in regional analyses: results and evaluation for the US from
2005 to 2095, Environ. Res. Lett., 9, 064004, https://doi.org/10.1088/1748-9326/9/6/064004, 2014.
Winkler, K., Fuchs, R., Rounsevell, M., and Herold, M.: Global land use
changes are four times greater than previously estimated, Nat. Commun., 12,
1–10, https://doi.org/10.1038/s41467-021-22702-2, 2021.
Williams, M.: Americans and Their Forests: A Historical Geography, Cambridge University Press, ISBN 9780521428378, 1992.
Yang, J., Tao, B., Shi, H., Ouyang, Y., Pan, S., Ren, W., and Lu, C.:
Integration of remote sensing, county-level census, and machine learning for
century-long regional cropland distribution data reconstruction, Int. J.
Appl. Earth Obs. Geoinf., 91, 102151, https://doi.org/10.1016/j.jag.2020.102151, 2020.
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. Remote, 146, 108–123,
https://doi.org/10.1016/j.isprsjprs.2018.09.006, 2018.
Yu, Z. and Lu, C.: Historical cropland of the continental U.S. from 1850 to
2016, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.881801, 2017.
Yu, Z. and Lu, C.: Historical cropland expansion and abandonment in the
continental U.S. during 1850 to 2016, Glob. Ecol. Biogeogr., 27, 322–333,
https://doi.org/10.1111/geb.12697, 2018.
Yu, Z., Lu, C., Tian, H., and Canadell, J. G.: Largely underestimated carbon
emission from land use and land cover change in the conterminous United
States, Glob. Change Biol., 25, 3741–3752, https://doi.org/10.1111/gcb.14768, 2019.
Zumkehr, A. and Campbell, J. E.: Historical U.S. Cropland areas and the
potential for bioenergy production on abandoned croplands, Environ. Sci.
Technol., 47, 3840–3847, https://doi.org/10.1021/es3033132,
2013.
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.
We reconstructed land use and land cover (LULC) history for the conterminous United States...