Articles | Volume 13, issue 3
https://doi.org/10.5194/essd-13-1211-2021
© 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-13-1211-2021
© 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
| 
24 Mar 2021
Data description paper |
| 24 Mar 2021
| 24 Mar 2021
High-resolution global map of smallholder and industrial closed-canopy oil palm plantations
Adrià Descals
CREAF, Cerdanyola del Vallès, 08193 Barcelona, Spain
Serge Wich
School of Biological and Environmental Sciences, Liverpool John Moores
University, James Parsons Building, 3 Byrom Street, Liverpool L3 3AF, UK
Institute for Biodiversity and Ecosystem Dynamics, University of
Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands
Erik Meijaard
Borneo Futures, Bandar Seri Begawan BA 2711, Brunei Darussalam
Durrell Institute of Conservation and Ecology, University of Kent,
Canterbury CT2 7NR, UK
School of Biological Sciences, University of Queensland, Queensland
4072, Australia
David L. A. Gaveau
Center for International Forestry Research, P.O. Box 0113 BOCBD,
Bogor, Indonesia
TheTreeMap, Bagadou Bas, 46600 Martel, France
Stephen Peedell
European Commission, Joint Research Centre, 20127 Ispra, Italy
Zoltan Szantoi
CORRESPONDING AUTHOR
European Commission, Joint Research Centre, 20127 Ispra, Italy
Department of Geography and Environmental Studies, Stellenbosch University, Stellenbosch 7602, South Africa
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This study provides a 10 m global oil palm extent layer for 2021 and a 30 m oil palm planting-year layer from 1990 to 2021. The oil palm extent layer was produced using a convolutional neural network that identified industrial and smallholder plantations using Sentinel-1 data. The oil palm planting year was developed using a methodology specifically designed to detect the early stages of oil palm development in the Landsat time series.
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The spatial extent of coconut palm is understudied despite its increasing demand and associated impacts. We present the first global coconut palm layer at 20 m resolution. The layer was produced using deep learning and remotely sensed data. The global coconut area estimate is 12.31 Mha for dense coconut palm, but the estimate is 3 times larger when sparse coconut palm is considered. This means that coconut production can likely increase on the lands currently allocated to coconut palm.
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
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Severe burning struck Indonesia in 2019. Drawing on new satellite imagery, we present and validate new 2019 burned-area estimates for Indonesia.
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Adrià Descals, David L. A. Gaveau, Serge Wich, Zoltan Szantoi, and Erik Meijaard
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This study provides a 10 m global oil palm extent layer for 2021 and a 30 m oil palm planting-year layer from 1990 to 2021. The oil palm extent layer was produced using a convolutional neural network that identified industrial and smallholder plantations using Sentinel-1 data. The oil palm planting year was developed using a methodology specifically designed to detect the early stages of oil palm development in the Landsat time series.
Kristof Van Tricht, Jeroen Degerickx, Sven Gilliams, Daniele Zanaga, Marjorie Battude, Alex Grosu, Joost Brombacher, Myroslava Lesiv, Juan Carlos Laso Bayas, Santosh Karanam, Steffen Fritz, Inbal Becker-Reshef, Belén Franch, Bertran Mollà-Bononad, Hendrik Boogaard, Arun Kumar Pratihast, Benjamin Koetz, and Zoltan Szantoi
Earth Syst. Sci. Data, 15, 5491–5515, https://doi.org/10.5194/essd-15-5491-2023, https://doi.org/10.5194/essd-15-5491-2023, 2023
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WorldCereal is a global mapping system that addresses food security challenges. It provides seasonal updates on crop areas and irrigation practices, enabling informed decision-making for sustainable agriculture. Our global products offer insights into temporary crop extent, seasonal crop type maps, and seasonal irrigation patterns. WorldCereal is an open-source tool that utilizes space-based technologies, revolutionizing global agricultural mapping.
Adrià Descals, Serge Wich, Zoltan Szantoi, Matthew J. Struebig, Rona Dennis, Zoe Hatton, Thina Ariffin, Nabillah Unus, David L. A. Gaveau, and Erik Meijaard
Earth Syst. Sci. Data, 15, 3991–4010, https://doi.org/10.5194/essd-15-3991-2023, https://doi.org/10.5194/essd-15-3991-2023, 2023
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The spatial extent of coconut palm is understudied despite its increasing demand and associated impacts. We present the first global coconut palm layer at 20 m resolution. The layer was produced using deep learning and remotely sensed data. The global coconut area estimate is 12.31 Mha for dense coconut palm, but the estimate is 3 times larger when sparse coconut palm is considered. This means that coconut production can likely increase on the lands currently allocated to coconut palm.
G. Mosomtai, J. L. Kasiiti, R. M. Murithi, P. Sandström, T. Landmann, O. W. Lwande, O. A. Hassan, C. Ahlm, R. Sang, M. Evander, Z. Szantoi, and G. Ottavianelli
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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
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Severe burning struck Indonesia in 2019. Drawing on new satellite imagery, we present and validate new 2019 burned-area estimates for Indonesia.
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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
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Larger ecological zones and wildlife corridors in sub-Saharan Africa require monitoring, as social and economic demands put high pressure on them. Copernicus’ Hot-Spot Monitoring service developed a satellite-imagery-based monitoring workflow to map such areas. Here, we present a total of 560 442 km2 from which 153 665 km2 is mapped with eight land cover classes while 406 776 km2 is mapped with up to 32 classes. Besides presenting the thematic products, we also present our validation datasets.
Robert J. Parker, Hartmut Boesch, Martin J. Wooster, David P. Moore, Alex J. Webb, David Gaveau, and Daniel Murdiyarso
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The current El Niño event has had a dramatic impact on the amount of Indonesian biomass burning and subsequent greenhouse gas emission. We have used satellite observations of CH4 and CO2 of these fires to probe aspects of their chemical composition. We show large enhancements in the amount of these species, due to the fire emissions. The ability to determine large-scale emission ratios from space allows the combustion behaviour of very large regions of burning to be characterised and understood.
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-178, https://doi.org/10.5194/essd-2024-178, 2024
Revised manuscript accepted for ESSD
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Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data, 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024, https://doi.org/10.5194/essd-16-2605-2024, 2024
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NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 120 000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. This paper summarizes those results and provides links to details on ~ 80 individual flight lines. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
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We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, https://doi.org/10.5194/essd-16-2007-2024, 2024
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This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-77, https://doi.org/10.5194/essd-2024-77, 2024
Preprint under review for ESSD
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Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Hui Li, Xiaobo Wang, Shaoqiang Wang, Jinyuan Liu, Yuanyuan Liu, Zhenhai Liu, Shiliang Chen, Qinyi Wang, Tongtong Zhu, Lunche Wang, and Lizhe Wang
Earth Syst. Sci. Data, 16, 1689–1701, https://doi.org/10.5194/essd-16-1689-2024, https://doi.org/10.5194/essd-16-1689-2024, 2024
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Utilizing satellite remote sensing data, we established a multi-season rice calendar dataset named ChinaRiceCalendar. It exhibits strong alignment with field observations collected by agricultural meteorological stations across China. ChinaRiceCalendar stands as a reliable dataset for investigating and optimizing the spatiotemporal dynamics of rice phenology in China, particularly in the context of climate and land use changes.
Giulia Ronchetti, Luigi Nisini Scacchiafichi, Lorenzo Seguini, Iacopo Cerrani, and Marijn van der Velde
Earth Syst. Sci. Data, 16, 1623–1649, https://doi.org/10.5194/essd-16-1623-2024, https://doi.org/10.5194/essd-16-1623-2024, 2024
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We present a dataset of EU-wide harmonized subnational crop area, production, and yield statistics with information on data sources, processing steps, missing and derived data, and quality checks. Statistical records (344 282) collected from 1975 to 2020 for soft and durum wheat, winter and spring barley, grain maize, sunflower, and sugar beet were aligned with the EUROSTAT crop legend and the 2016 territorial classification for 961 regions. Time series have a median length of 21 years.
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
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This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Qiangqiang Sun, Ping Zhang, Xin Jiao, Xin Lin, Wenkai Duan, Su Ma, Qidi Pan, Lu Chen, Yongxiang Zhang, Shucheng You, Shunxi Liu, Jinmin Hao, Hong Li, and Danfeng Sun
Earth Syst. Sci. Data, 16, 1333–1351, https://doi.org/10.5194/essd-16-1333-2024, https://doi.org/10.5194/essd-16-1333-2024, 2024
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To provide multifaceted changes under climate change and anthropogenic impacts, we estimated monthly vegetation and soil fractions in 2001–2022, providing an accurate estimate of surface heterogeneous composition, better than vegetation index and vegetation continuous-field products. We find a greening trend on Earth except for the tropics. A combination of interactive changes in vegetation and soil can be adopted as a valuable measurement of climate change and anthropogenic impacts.
Kai Cheng, Yuling Chen, Tianyu Xiang, Haitao Yang, Weiyan Liu, Yu Ren, Hongcan Guan, Tianyu Hu, Qin Ma, and Qinghua Guo
Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024, https://doi.org/10.5194/essd-16-803-2024, 2024
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To quantify forest carbon stock and its future potential accurately, we generated a 30 m resolution forest age map for China in 2020 using multisource remote sensing datasets based on machine learning and time series analysis approaches. Validation with independent field samples indicated that the mapped forest age had an R2 of 0.51--0.63. Nationally, the average forest age is 56.1 years (standard deviation of 32.7 years).
Wolfgang Alexander Obermeier, Clemens Schwingshackl, Ana Bastos, Giulia Conchedda, Thomas Gasser, Giacomo Grassi, Richard A. Houghton, Francesco Nicola Tubiello, Stephen Sitch, and Julia Pongratz
Earth Syst. Sci. Data, 16, 605–645, https://doi.org/10.5194/essd-16-605-2024, https://doi.org/10.5194/essd-16-605-2024, 2024
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We provide and compare country-level estimates of land-use CO2 fluxes from a variety and large number of models, bottom-up estimates, and country reports for the period 1950–2021. Although net fluxes are small in many countries, they are often composed of large compensating emissions and removals. In many countries, the estimates agree well once their individual characteristics are accounted for, but in other countries, including some of the largest emitters, substantial uncertainties exist.
Cameron I. Ludemann, Nathan Wanner, Pauline Chivenge, Achim Dobermann, Rasmus Einarsson, Patricio Grassini, Armelle Gruere, Kevin Jackson, Luis Lassaletta, Federico Maggi, Griffiths Obli-Laryea, Martin K. van Ittersum, Srishti Vishwakarma, Xin Zhang, and Francesco N. Tubiello
Earth Syst. Sci. Data, 16, 525–541, https://doi.org/10.5194/essd-16-525-2024, https://doi.org/10.5194/essd-16-525-2024, 2024
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Nutrient budgets help identify the excess or insufficient use of fertilizers and other nutrient sources in agriculture. They allow the calculation of indicators, such as the nutrient balance (surplus or deficit) and nutrient use efficiency, that help to monitor agricultural productivity and sustainability. This article describes a global cropland nutrient budget that provides data on 205 countries and territories from 1961 to 2020 (data available at https://www.fao.org/faostat/en/#data/ESB).
Raphaël d'Andrimont, Momchil Yordanov, Fernando Sedano, Astrid Verhegghen, Peter Strobl, Savvas Zachariadis, Flavia Camilleri, Alessandra Palmieri, Beatrice Eiselt, Jose Miguel Rubio Iglesias, and Marijn van der Velde
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-494, https://doi.org/10.5194/essd-2023-494, 2024
Revised manuscript accepted for ESSD
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LUCAS 2022 Copernicus is a large an systematic in-situ field survey of 137,966 polygons over the EU in 2022. The data holds 82 land cover classes and 40 land use classes.
Yuanwei Qin, Xiangming Xiao, Hao Tang, Ralph Dubayah, Russell Doughty, Diyou Liu, Fang Liu, Yosio Shimabukuro, Egidio Arai, Xinxin Wang, and Berrien Moore III
Earth Syst. Sci. Data, 16, 321–336, https://doi.org/10.5194/essd-16-321-2024, https://doi.org/10.5194/essd-16-321-2024, 2024
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Forest definition has two major biophysical parameters, i.e., canopy height and canopy coverage. However, few studies have assessed forest cover maps in terms of these two parameters at a large scale. Here, we assessed the annual forest cover maps in the Brazilian Amazon using 1.1 million footprints of canopy height and canopy coverage. Over 93 % of our forest cover maps are consistent with the FAO forest definition, showing the high accuracy of these forest cover maps in the Brazilian Amazon.
Xiangan Liang, Qiang Liu, Jie Wang, Shuang Chen, and Peng Gong
Earth Syst. Sci. Data, 16, 177–200, https://doi.org/10.5194/essd-16-177-2024, https://doi.org/10.5194/essd-16-177-2024, 2024
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The state-of-the-art MODIS surface reflectance products suffer from temporal and spatial gaps, which make it difficult to characterize the continuous variation of the terrestrial surface. We proposed a framework for generating the first global 500 m daily seamless data cubes (SDC500), covering the period from 2000 to 2022. We believe that the SDC500 dataset can interest other researchers who study land cover mapping, quantitative remote sensing, and ecological science.
Rémy Ballot, Nicolas Guilpart, and Marie-Hélène Jeuffroy
Earth Syst. Sci. Data, 15, 5651–5666, https://doi.org/10.5194/essd-15-5651-2023, https://doi.org/10.5194/essd-15-5651-2023, 2023
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Assessing the benefits of crop diversification – a key element of agroecological transition – on a large scale requires a description of current crop sequences as a baseline, which is lacking at the scale of Europe. To fill this gap, we used a dataset that provides temporally and spatially incomplete land cover information to create a map of dominant crop sequence types for Europe over 2012–2018. This map is a useful baseline for assessing the benefits of future crop diversification.
Kristof Van Tricht, Jeroen Degerickx, Sven Gilliams, Daniele Zanaga, Marjorie Battude, Alex Grosu, Joost Brombacher, Myroslava Lesiv, Juan Carlos Laso Bayas, Santosh Karanam, Steffen Fritz, Inbal Becker-Reshef, Belén Franch, Bertran Mollà-Bononad, Hendrik Boogaard, Arun Kumar Pratihast, Benjamin Koetz, and Zoltan Szantoi
Earth Syst. Sci. Data, 15, 5491–5515, https://doi.org/10.5194/essd-15-5491-2023, https://doi.org/10.5194/essd-15-5491-2023, 2023
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WorldCereal is a global mapping system that addresses food security challenges. It provides seasonal updates on crop areas and irrigation practices, enabling informed decision-making for sustainable agriculture. Our global products offer insights into temporary crop extent, seasonal crop type maps, and seasonal irrigation patterns. WorldCereal is an open-source tool that utilizes space-based technologies, revolutionizing global agricultural mapping.
Yangyang Fu, Xiuzhi Chen, Chaoqing Song, Xiaojuan Huang, Jie Dong, Qiongyan Peng, and Wenping Yuan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-432, https://doi.org/10.5194/essd-2023-432, 2023
Revised manuscript accepted for ESSD
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This study proposed the Winter-Triticeae Crops Index (WTCI)，which had great performance and stable spatiotemporal transferability in identifying winter-triticeae crops in 65 countries worldwide, with an overall accuracy of 87.7 %. The first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022 were further produced based on the WTCI method. The product can serve as an important basis for agricultural applications.
Jia Zhou, Jin Niu, Ning Wu, and Tao Lu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-403, https://doi.org/10.5194/essd-2023-403, 2023
Revised manuscript accepted for ESSD
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The study provided an annual 100-meter resolution glimpse into the grazing activities across the Qinghai-Tibet Plateau. The newly minted Gridded Dataset of Grazing Intensity (GDGI) not only boasts exceptional accuracy but also acts as a pivotal resource for further research and strategic planning, with the potential to shape sustainable grazing practices, guide informed environmental stewardship, and ensure the longevity of the region’s precious ecosystems.
Gerardo E. Soto, Steven Wilcox, Patrick E. Clark, Francesco P. Fava, Nathan M. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher Barrett
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-217, https://doi.org/10.5194/essd-2023-217, 2023
Revised manuscript accepted for ESSD
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Using machine learning classification and linear unmixing, this paper produced Landsat-based time series of land cover classes and vegetation fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and bare ground. This dataset represents a first multi-decadal high-resolution dataset specifically designed for mapping and monitoring rangelands health in East Africa including Kenya, Ethiopia, and Somalia, which are dominated by arid and semi-arid extensive rangeland systems.
Francesco N. Tubiello, Giulia Conchedda, Leon Casse, Pengyu Hao, Giorgia De Santis, and Zhongxin Chen
Earth Syst. Sci. Data, 15, 4997–5015, https://doi.org/10.5194/essd-15-4997-2023, https://doi.org/10.5194/essd-15-4997-2023, 2023
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We describe a new dataset of cropland area circa the year 2020, with global coverage and country detail. Data are generated from geospatial information on the agreement characteristics of six high-resolution cropland maps. By helping to highlight features of cropland characteristics and underlying causes for agreement across land cover products, the dataset can be used as a tool to help guide future mapping efforts towards improved agricultural monitoring.
Martin Schwartz, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cedric Vega, Jean-Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, and Ibrahim Fayad
Earth Syst. Sci. Data, 15, 4927–4945, https://doi.org/10.5194/essd-15-4927-2023, https://doi.org/10.5194/essd-15-4927-2023, 2023
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As forests play a key role in climate-related issues, their accurate monitoring is critical to reduce global carbon emissions effectively. Based on open-access remote-sensing sensors, and artificial intelligence methods, we created high-resolution tree height, wood volume, and biomass maps of metropolitan France that outperform previous products. This study, based on freely available data, provides essential information to support climate-efficient forest management policies at a low cost.
Zhuohong Li, Wei He, Mofan Cheng, Jingxin Hu, Guangyi Yang, and Hongyan Zhang
Earth Syst. Sci. Data, 15, 4749–4780, https://doi.org/10.5194/essd-15-4749-2023, https://doi.org/10.5194/essd-15-4749-2023, 2023
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Nowadays, a very-high-resolution land-cover (LC) map with national coverage is still unavailable in China, hindering efficient resource allocation. To fill this gap, the first 1 m resolution LC map of China, SinoLC-1, was built. The results showed that SinoLC-1 had an overall accuracy of 73.61 % and conformed to the official survey reports. Comparison with other datasets suggests that SinoLC-1 can be a better support for downstream applications and provide more accurate LC information to users.
Johannes H. Uhl, Dominic Royé, Keith Burghardt, José A. Aldrey Vázquez, Manuel Borobio Sanchiz, and Stefan Leyk
Earth Syst. Sci. Data, 15, 4713–4747, https://doi.org/10.5194/essd-15-4713-2023, https://doi.org/10.5194/essd-15-4713-2023, 2023
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Historical, fine-grained geospatial datasets on built-up areas are rarely available, constraining studies of urbanization, settlement evolution, or the dynamics of human–environment interactions to recent decades. In order to provide such historical data, we used publicly available cadastral building data for Spain and created a series of gridded surfaces, measuring age, physical, and land-use-related features of the built environment in Spain and the evolution of settlements from 1900 to 2020.
Christopher G. Marston, Aneurin W. O'Neil, R. Daniel Morton, Claire M. Wood, and Clare S. Rowland
Earth Syst. Sci. Data, 15, 4631–4649, https://doi.org/10.5194/essd-15-4631-2023, https://doi.org/10.5194/essd-15-4631-2023, 2023
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The UK Land Cover Map 2021 (LCM2021) is a UK-wide land cover data set, with 21- and 10-class versions. It is intended to support a broad range of UK environmental research, including ecological and hydrological research. LCM2021 was produced by classifying Sentinel-2 satellite imagery. LCM2021 is distributed as a suite of products to facilitate easy use for a range of applications. To support research at different spatial scales it includes 10 m, 25 m and 1 km resolution products.
Yu Zhao, Shaoyu Han, Jie Zheng, Hanyu Xue, Zhenhai Li, Yang Meng, Xuguang Li, Xiaodong Yang, Zhenhong Li, Shuhong Cai, and Guijun Yang
Earth Syst. Sci. Data, 15, 4047–4063, https://doi.org/10.5194/essd-15-4047-2023, https://doi.org/10.5194/essd-15-4047-2023, 2023
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In the present study, we generated a 30 m Chinese winter wheat yield dataset from 2016 to 2021, called ChinaWheatYield30m. The dataset has high spatial resolution and great accuracy. It is the highest-resolution yield dataset known. Such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling or regional climate evaluation.
Feng Yang and Zhenzhong Zeng
Earth Syst. Sci. Data, 15, 4011–4021, https://doi.org/10.5194/essd-15-4011-2023, https://doi.org/10.5194/essd-15-4011-2023, 2023
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We generated a 4.77 m resolution annual tree cover map product for Southeast Asia (SEA) for 2016–2021 using Planet-NICFI and Sentinel-1 imagery. Maps were created with good accuracy and high consistency during 2016–2021. The baseline maps at 4.77 m can be converted to forest cover maps for SEA at various resolutions to meet different users’ needs. Our products can help resolve rounding errors in forest cover mapping by counting isolated trees and monitoring long, narrow forest cover removal.
Adrià Descals, Serge Wich, Zoltan Szantoi, Matthew J. Struebig, Rona Dennis, Zoe Hatton, Thina Ariffin, Nabillah Unus, David L. A. Gaveau, and Erik Meijaard
Earth Syst. Sci. Data, 15, 3991–4010, https://doi.org/10.5194/essd-15-3991-2023, https://doi.org/10.5194/essd-15-3991-2023, 2023
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The spatial extent of coconut palm is understudied despite its increasing demand and associated impacts. We present the first global coconut palm layer at 20 m resolution. The layer was produced using deep learning and remotely sensed data. The global coconut area estimate is 12.31 Mha for dense coconut palm, but the estimate is 3 times larger when sparse coconut palm is considered. This means that coconut production can likely increase on the lands currently allocated to coconut palm.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data, 15, 3819–3852, https://doi.org/10.5194/essd-15-3819-2023, https://doi.org/10.5194/essd-15-3819-2023, 2023
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This paper introduces the new high-resolution land use and land cover change dataset LUCAS LUC for Europe (version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Wanru He, Xuecao Li, Yuyu Zhou, Zitong Shi, Guojiang Yu, Tengyun Hu, Yixuan Wang, Jianxi Huang, Tiecheng Bai, Zhongchang Sun, Xiaoping Liu, and Peng Gong
Earth Syst. Sci. Data, 15, 3623–3639, https://doi.org/10.5194/essd-15-3623-2023, https://doi.org/10.5194/essd-15-3623-2023, 2023
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Most existing global urban products with future projections were developed in urban and non-urban categories, which ignores the gradual change of urban development at the local scale. Using annual global urban extent data from 1985 to 2015, we forecasted global urban fractional changes under eight scenarios throughout 2100. The developed dataset can provide spatially explicit information on urban fractions at 1 km resolution, which helps support various urban studies (e.g., urban heat island).
Zeping Liu, Hong Tang, Lin Feng, and Siqing Lyu
Earth Syst. Sci. Data, 15, 3547–3572, https://doi.org/10.5194/essd-15-3547-2023, https://doi.org/10.5194/essd-15-3547-2023, 2023
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Large-scale maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In this paper, we propose a deep-learning method for high-resolution BRA mapping (2.5 m) from Sentinel-2 imagery (10 m). The resulting China building rooftop area dataset (CBRA) is the first multi-annual (2016–2021) and high-resolution (2.5 m) BRA dataset in China. Cross-comparisons show that the CBRA achieves the best performance in capturing the spatiotemporal information.
Ruoque Shen, Baihong Pan, Qiongyan Peng, Jie Dong, Xuebing Chen, Xi Zhang, Tao Ye, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, https://doi.org/10.5194/essd-15-3203-2023, 2023
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Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. This study developed a new rice-mapping method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data, and we found that the distribution maps have good accuracy.
Charles R. Lane, Ellen D'Amico, Jay R. Christensen, Heather E. Golden, Qiusheng Wu, and Adnan Rajib
Earth Syst. Sci. Data, 15, 2927–2955, https://doi.org/10.5194/essd-15-2927-2023, https://doi.org/10.5194/essd-15-2927-2023, 2023
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Non-floodplain wetlands (NFWs) – wetlands located outside floodplains – confer watershed-scale resilience to hydrological, biogeochemical, and biotic disturbances. Although they are frequently unmapped, we identified ~ 33 million NFWs covering > 16 × 10 km2 across the globe. NFWs constitute the majority of the world's wetlands (53 %). Despite their small size (median 0.039 km2), these imperiled systems have an outsized impact on watershed functions and sustainability and require protection.
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data, 15, 2347–2373, https://doi.org/10.5194/essd-15-2347-2023, https://doi.org/10.5194/essd-15-2347-2023, 2023
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A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.
Richard A. Houghton and Andrea Castanho
Earth Syst. Sci. Data, 15, 2025–2054, https://doi.org/10.5194/essd-15-2025-2023, https://doi.org/10.5194/essd-15-2025-2023, 2023
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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
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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.
Cited articles
Austin, K. G., Schwantes, A., Gu, Y., and Kasibhatla, P. S.: What causes
deforestation in Indonesia?, Environ. Res. Lett., 14, 024007, https://doi.org/10.1088/1748-9326/aaf6db, 2019.
Bronkhorst, E., Cavallo, E., van Dorth tot Medler, M., Klinghammer, S.,
Smit, H. H., Gijsenbergh, A., and van der Laan, C.: Current practices and
innovations in smallholder palm oil finance in Indonesia and Malaysia:
Long-term financing solutions to promote sustainable supply chains, Center for International Forestry Research (CIFOR), Bogor, Indonesia, https://doi.org/10.17528/cifor/006612, 2017.
Byerlee, D., Falcon, W. P., and Naylor, R.: The tropical oil crop revolution:
food, feed, fuel, and forests, Oxford University Press, Oxford, UK, 2017.
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L.:
Deeplab: Semantic image segmentation with deep convolutional nets, atrous
convolution, and fully connected
crfs, IEEE T. Pattern Anal., 40, 834–848, 2017.
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H.: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, in: Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11211, edited by: Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y., Springer, Cham, https://doi.org/10.1007/978-3-030-01234-2_49, 2018.
Descals, A.: oil_palm_global v1.0 (Version 1.0), Zenodo, https://doi.org/10.5281/zenodo.4617748, 2021.
Descals, A., Szantoi, Z., Meijaard, E., Sutikno, H., Rindanata, G., and Wich,
S.: Oil Palm (Elaeis guineensis) Mapping with Details: Smallholder versus
Industrial Plantations and their Extent in Riau, Sumatra, Remote Sens.-Basel,
11, 2590, https://doi.org/10.3390/rs11212590, 2019.
Descals, A., Wich, S., Meijaard, E., Gaveau, D., Peedell, S., and Szantoi,
Z.: High resolution global industrial and smallholder oil palm map for
2019 (Version v0) [Data set], Zenodo, https://doi.org/10.5281/zenodo.4473715, 2021.
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon,
F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A.,
Spoto, F., Sy, O., Marchese, F., and Bargellini, P. G.: Sentinel-2: ESA's
optical high-resolution mission for GMES operational services, Remote Sens.
Environ., 120, 25–36, 2012.
Earth Engine Data Catalog: Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected, log scaling, available at: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD (last access: 17 March 2021), 2014.
Earth Engine Data Catalog: Sentinel-2 MSI: MultiSpectral Instrument, Level-2A, available at: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR (last access: 17 March 2021), 2017.
FAO: FAOSTAT statistical database, available at: http://www.fao.org/faostat/en/, last access: 10 June 2020.
Fitzherbert, E. B., Struebig, M. J., Morel, A., Danielsen, F., Brühl, C.
A., Donald, P. F., and Phalan, B.: How will oil palm expansion affect
biodiversity?, Trends Ecol. Evol., 23, 538–545, 2008.
Gaveau, D., Pirard, R., Salim, M. A., Tonoto, P., Yaen, H., Parks, S. A., and
Carmenta, R.: Overlapping land claims limit the use of satellites to monitor
no-deforestation commitments and no-burning compliance, Conserv. Lett.,
10, 257–264, 2017.
Gaveau, D., Locatelli, B., Salim, M. A., Yaen, H., Pacheco, P., and Sheil,
D.: Rise and fall of forest loss and industrial plantations in Borneo
(2000–2017), Conserv. Lett., 12, e12622, https://doi.org/10.1111/conl.12622, 2019.
Gaveau, D., Locatelli, B., Salim, M., Husnayaen, H., Manurung, T., Descals, A., Angelsen, A., Meijaard, E., and Sheil D.: Slowing deforestation in Indonesia follows declining oil palm expansion and lower oil prices, 15 January 2021, PREPRINT (Version 1), https://doi.org/10.21203/rs.3.rs-143515/v1, 2021.
GitHub: DeepLab: Deep Labelling for Semantic Image Segmentation, available at: https://github.com/tensorflow/models/tree/master/research/deeplab, last access: 18 March 2021.
Glenday, S. and Gary, P.: Indonesian oil palm smallholder farmers: A
typology of organizational models, needs, and investment opportunities, Daemeter Consult., Bogor, Indonesia, 22 pp., 2015.
Google: Industrial oil palm in Sumatra, available at: https://www.google.com/maps/@-3.969372,105.048514,782m/data=!3m1!1e3, last access: 18 March 2021.
Google Earth Engine: DigitalGlobe imagery, available at:
https://code.earthengine.google.com, last access: 1 August 2020.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore,
R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone,
Remote Sens. Environ., 202, 18–27, 2017.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A.:
Very high resolution interpolated climate surfaces for global land areas,
Int. J. Climatol., 25, 1965–1978, https://doi.org/10.1002/joc.1276, 2005 (data available at: https://developers.google.com/earth-engine/datasets/catalog/WORLDCLIM_V1_BIO, last access: 18 March 2021).
International Food Policy Research Institute: Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0, Harvard Dataverse, V4, Washington, D.C., USA, https://doi.org/10.7910/DVN/PRFF8V, 2019.
Jelsma, I., Schoneveld, G. C., Zoomers, A., and Van Westen, A.: Unpacking
Indonesia's independent oil palm smallholders: An actor-disaggregated
approach to identifying environmental and social performance challenges,
Land Use Policy, 69, 281–297, 2017.
Koh, L. P., Miettinen, J., Liew, S. C., and Ghazoul, J.: Remotely sensed
evidence of tropical peatland conversion to oil
palm, P. Natl. Acad. Sci. USA, 108, 5127–5132, https://doi.org/10.1073/pnas.1018776108, 2011.
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444, 2015.
Lee, J. S. H., Wich, S., Widayati, A., and Koh, L. P.: Detecting industrial
oil palm plantations on Landsat images with Google Earth
Engine, Remote Sens. Appl. Soc. Environ., 4, 219–224, 2016.
Li, W., Fu, H., Yu, L., and Cracknell, A.: Deep learning based oil palm tree
detection and counting for high-resolution remote sensing images, Remote Sens.-Basel, 9, 22, https://doi.org/10.3390/rs9010022, 2017.
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., and Johnson, B. A.: Deep
learning in remote sensing applications: A meta-analysis and
review, ISPRS J. Photogramm., 152, 166–177, 2019.
McAlpine, C. A., Johnson, A., Salazar, A., Syktus, J., Wilson, K., Meijaard,
E., Seabrook, L., Dargusch, P., Nordin, H., and Sheil, D.: Forest loss and
Borneo's climate, Environ. Res. Lett., 13, 044009, https://doi.org/10.1088/1748-9326/aaa4ff, 2018.
Meijaard, E. and Sheil, D.: The moral minefield of ethical oil palm and
sustainable development, Frontiers in Forests and Global Change, 2, 22, https://doi.org/10.3389/ffgc.2019.00022, 2019.
Meijaard, E. and Gaveau, D.: Global oil palm map, Dryad, Dataset, https://doi.org/10.5061/dryad.ghx3ffbn9, 2021.
Meijaard, E., Garcia-Ulloa, J., Sheil, D., Wich, S., Carlson, K. M., Juffe-Bignoli, D., and Brooks, T. M.: Oil palm and biodiversity: a situation analysis by the IUCN Oil Palm Task Force, IUCN, Gland, Switzerland, 116, ISBN 9782831719108, 2018.
Meijaard, E., Abrams, J., Juffe-Bignoli, D., Voigt, M., and Sheil, D.:
Coconut oil, conservation and the conscientious consumer, Curr. Biol., 20, R757–R758, https://doi.org/10.1016/j.cub.2020.05.059, 2020a.
Meijaard, E., Brooks, T. M., Carlson, K. M., Slade, E. M., Garcia-Ulloa, J.,
Gaveau, D. L., Lee, J. S. H., Santika, T., Juffe-Bignoli, D., Struebig, M.
J., Wich, S., Ancrenaz, M., Pin Koh, L., Zamira, N., Abrams, J., Prins, H.
H. T., Sendashonga, C. N., Murdiyarso, D., Furumo, P. R., Macfarlane, N.,
Hoffmann, R., Persio, M., Descals, A., Szantoi, Z., and Sheil, D.: The
environmental impacts of palm oil in context, Nat. Plants, 6, 1418–1426, https://doi.org/10.1038/s41477-020-00813-w, 2020b.
Miettinen, J. and Liew, S. C.: Separability of insular Southeast Asian woody
plantation species in the 50 m resolution ALOS PALSAR mosaic product, Remote
Sens. Lett., 2, 299–307, 2011.
Nomura, K., Mitchard, E. T., Patenaude, G., Bastide, J., Oswald, P., and Nwe,
T.: Oil palm concessions in southern Myanmar consist mostly of unconverted
forest, Sci. Rep.-UK, 9, 1–9, 2019.
OECD: OECD-FAO Agricultural Outlook 2018–2027, OECD Publishing, Paris/FAO, Rome, available at: https://doi.org/10.1787/agr_outlook-2018-en (last access: 18 March 2021), 2018.
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and
Wulder, M. A.: Good practices for estimating area and assessing accuracy of
land change, Remote Sens. Environ., 148, 42–57, 2014.
Oon, A., Ngo, K. D., Azhar, R., Ashton-Butt, A., Lechner, A. M., and Azhar,
B.: Assessment of ALOS-2 PALSAR-2L-band and Sentinel-1 C-band SAR
backscatter for discriminating between large-scale oil palm plantations and
smallholdings on tropical peatlands, Remote Sens. Appl. Soc. Environ., 13,
183–190, 2019.
Paoli, G. D., Gillespie, P., Wells, P. L., Hovani, L., Sileuw, A., Franklin,
N., and Schweithelm, J.: Oil palm in Indonesia: governance, decision making
and implications for sustainable development, The Nature Conservancy Indonesia Program, Jakarta, 2013.
Pirker, J., Mosnier, A., Kraxner, F., Havlík, P., and Obersteiner, M.:
What are the limits to oil palm expansion?, Global Environ. Chang., 40,
73–81, 2016.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C.:
Mobilenetv2: Inverted residuals and linear bottlenecks, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, 4510–4520, https://doi.org/10.1109/CVPR.2018.00474, 2018.
Santika, T., Wilson, K. A., Budiharta, S., Kusworo, A., Meijaard, E., Law,
E. A., Friedman, R., Hutabarat, J. A., Indrawan, T. P., St. John, F. A., and Struebig, M. J.: Heterogeneous impacts of community forestry on forest
conservation and poverty alleviation: Evidence from Indonesia, People Nat.,
1, 204–219, 2019.
Shaharum, N. S. N., Shafri, H. Z. M., Ghani, W. A. W. A. K., Samsatli, S.,
Al-Habshi, M. M. A., and Yusuf, B.: Oil palm mapping over Peninsular Malaysia
using Google Earth Engine and machine learning algorithms, Remote Sens.
Appl. Soc. Environ., 17, 100287, https://doi.org/10.1016/j.rsase.2020.100287, 2020.
Shorten, C. and Khoshgoftaar, T. M.: A survey on image data augmentation for
deep learning, J. Big Data, 6, 60, https://doi.org/10.1186/s40537-019-0197-0, 2019.
Strona, G., Stringer, S. D., Vieilledent, G., Szantoi, Z., Garcia-Ulloa, J.,
and Wich, S. A.: Small room for compromise between oil palm cultivation and
primate conservation in Africa, P. Natl. Acad. Sci. USA, 115, 8811–8816,
2018.
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E.,
Potin, P., Rommen, B., Floury, N., Brown, M., Navas Traver, I., Deghaye, P.,
Duesmann, B., Rosich, B., Miranda, N., Bruno, C., L'Abbate, M., Croci, R.,
Pietropaolo, A., Huchler, M., and Rostan, F.: GMES Sentinel-1 mission, Remote
Sens. Environ., 120, 9–24, 2012.
Wardeh, M., Sharkey, K. J., and Baylis, M.: Integration of shared-pathogen
networks and machine learning reveals the key aspects of zoonoses and
predicts mammalian reservoirs, P. R. Soc. B, 287, 20192882, https://doi.org/10.1098/rspb.2019.2882, 2020.
Wich, S. A., Garcia-Ulloa, J., Kühl, H. S., Humle, T., Lee, J. S., and
Koh, L. P.: Will oil palm's homecoming spell doom for Africa's great apes?,
Curr. Biol., 24, 1659–1663, 2014.
Woittiez, L. S., van Wijk, M. T., Slingerland, M., van Noordwijk, M., and
Giller, K. E.: Yield gaps in oil palm: A quantitative review of contributing
factors, Eur. J. Agron., 83, 57–77, 2017.
Xu, Y., Yu, L., Li, W., Ciais, P., Cheng, Y., and Gong, P.: Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016 [Data set], Zenodo, https://doi.org/10.5281/zenodo.3467071, 2019.
Xu, Y., Yu, L., Li, W., Ciais, P., Cheng, Y., and Gong, P.: Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016, Earth Syst. Sci. Data, 12, 847–867, https://doi.org/10.5194/essd-12-847-2020, 2020.
Yu, X., Wu, X., Luo, C., and Ren, P.: Deep learning in remote sensing scene
classification: a data augmentation enhanced convolutional neural network
framework, Gisci. Remote Sens.,, 54, 741–758, https://doi.org/10.1080/15481603.2017.1323377, 2017.
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.
Decision-making for sustainable vegetable oil production requires accurate global oil crop maps....
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