Articles | Volume 15, issue 2
https://doi.org/10.5194/essd-15-681-2023
© 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-15-681-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
08 Feb 2023
Data description paper |
| 08 Feb 2023
| 08 Feb 2023
TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing
Steve Ahlswede
CORRESPONDING AUTHOR
Remote Sensing Image Analysis Group, Technische Universität Berlin, Einsteinufer 17, 10587 Berlin, Germany
Christian Schulz
CORRESPONDING AUTHOR
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Straße des 17. Juni 145, 10623 Berlin, Germany
Christiano Gava
Smart Data and Knowledge Services, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI), Trippstadter Str. 122, 67663 Kaiserslautern, Germany
Patrick Helber
Vision Impulse GmbH, Trippstadter Str. 122, 67663 Kaiserslautern, Germany
Benjamin Bischke
Vision Impulse GmbH, Trippstadter Str. 122, 67663 Kaiserslautern, Germany
Michael Förster
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Straße des 17. Juni 145, 10623 Berlin, Germany
Florencia Arias
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Straße des 17. Juni 145, 10623 Berlin, Germany
Jörn Hees
Smart Data and Knowledge Services, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI), Trippstadter Str. 122, 67663 Kaiserslautern, Germany
Fachbereich Informatik, Hochschule Bonn-Rhein-Sieg, Grantham-Allee 20, 53757 Sankt Augustin, Germany
Begüm Demir
Remote Sensing Image Analysis Group, Technische Universität Berlin, Einsteinufer 17, 10587 Berlin, Germany
Birgit Kleinschmit
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Straße des 17. Juni 145, 10623 Berlin, Germany
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Till Francke, Cosimo Brogi, Alby Duarte Rocha, Michael Förster, Maik Heistermann, Markus Köhli, Daniel Rasche, Marvin Reich, Paul Schattan, Lena Scheiffele, and Martin Schrön
Geosci. Model Dev., 18, 819–842, https://doi.org/10.5194/gmd-18-819-2025, https://doi.org/10.5194/gmd-18-819-2025, 2025
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Multiple methods for measuring soil moisture beyond the point scale exist. Their validation is generally hindered by not knowing the truth. We propose a virtual framework in which this truth is fully known and the sensor observations for cosmic ray neutron sensing, remote sensing, and hydrogravimetry are simulated. This allows for the rigorous testing of these virtual sensors to understand their effectiveness and limitations.
Katharina H. Horn, Stenka Vulova, Hanyu Li, and Birgit Kleinschmit
Nat. Hazards Earth Syst. Sci., 25, 383–401, https://doi.org/10.5194/nhess-25-383-2025, https://doi.org/10.5194/nhess-25-383-2025, 2025
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In this study we applied a random forest machine learning algorithm to model current and future forest fire susceptibility (FFS) in north-eastern Germany using anthropogenic, climatic, topographic, soil, and vegetation variables. Model accuracy ranged between 69 % and 71 %, showing moderately high model reliability for predicting FFS. The model results underline the importance of anthropogenic and vegetation parameters. This study will support regional forest fire prevention and management.
Maik Heistermann, Till Francke, Lena Scheiffele, Katya Dimitrova Petrova, Christian Budach, Martin Schrön, Benjamin Trost, Daniel Rasche, Andreas Güntner, Veronika Döpper, Michael Förster, Markus Köhli, Lisa Angermann, Nikolaos Antonoglou, Manuela Zude-Sasse, and Sascha E. Oswald
Earth Syst. Sci. Data, 15, 3243–3262, https://doi.org/10.5194/essd-15-3243-2023, https://doi.org/10.5194/essd-15-3243-2023, 2023
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Cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of root-zone soil water content (SWC). The signal observed by a single CRNS sensor is influenced by the SWC in a radius of around 150 m (the footprint). Here, we have put together a cluster of eight CRNS sensors with overlapping footprints at an agricultural research site in north-east Germany. That way, we hope to represent spatial SWC heterogeneity instead of retrieving just one average SWC estimate from a single sensor.
Alby Duarte Rocha, Stenka Vulova, Christiaan van der Tol, Michael Förster, and Birgit Kleinschmit
Hydrol. Earth Syst. Sci., 26, 1111–1129, https://doi.org/10.5194/hess-26-1111-2022, https://doi.org/10.5194/hess-26-1111-2022, 2022
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Evapotranspiration (ET) is a sum of soil evaporation and plant transpiration. ET produces a cooling effect to mitigate heat waves in urban areas. Our method uses a physical model with remote sensing and meteorological data to predict hourly ET. Designed for uniform vegetation, it overestimated urban ET. To correct it, we create a factor using vegetation fraction that proved efficient for reducing bias and improving accuracy. This approach was tested on two Berlin sites and can be used to map ET.
Lena-Marie Kuhlemann, Doerthe Tetzlaff, Aaron Smith, Birgit Kleinschmit, and Chris Soulsby
Hydrol. Earth Syst. Sci., 25, 927–943, https://doi.org/10.5194/hess-25-927-2021, https://doi.org/10.5194/hess-25-927-2021, 2021
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We studied water partitioning under urban grassland, shrub and trees during a warm and dry growing season in Berlin, Germany. Soil evaporation was highest under grass, but total green water fluxes and turnover time of soil water were greater under trees. Lowest evapotranspiration losses under shrub indicate potential higher drought resilience. Knowledge of water partitioning and requirements of urban green will be essential for better adaptive management of urban water and irrigation strategies.
Benjamin Fersch, Till Francke, Maik Heistermann, Martin Schrön, Veronika Döpper, Jannis Jakobi, Gabriele Baroni, Theresa Blume, Heye Bogena, Christian Budach, Tobias Gränzig, Michael Förster, Andreas Güntner, Harrie-Jan Hendricks Franssen, Mandy Kasner, Markus Köhli, Birgit Kleinschmit, Harald Kunstmann, Amol Patil, Daniel Rasche, Lena Scheiffele, Ulrich Schmidt, Sandra Szulc-Seyfried, Jannis Weimar, Steffen Zacharias, Marek Zreda, Bernd Heber, Ralf Kiese, Vladimir Mares, Hannes Mollenhauer, Ingo Völksch, and Sascha Oswald
Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, https://doi.org/10.5194/essd-12-2289-2020, 2020
C. Rumbaur, N. Thevs, M. Disse, M. Ahlheim, A. Brieden, B. Cyffka, D. Duethmann, T. Feike, O. Frör, P. Gärtner, Ü. Halik, J. Hill, M. Hinnenthal, P. Keilholz, B. Kleinschmit, V. Krysanova, M. Kuba, S. Mader, C. Menz, H. Othmanli, S. Pelz, M. Schroeder, T. F. Siew, V. Stender, K. Stahr, F. M. Thomas, M. Welp, M. Wortmann, X. Zhao, X. Chen, T. Jiang, J. Luo, H. Yimit, R. Yu, X. Zhang, and C. Zhao
Earth Syst. Dynam., 6, 83–107, https://doi.org/10.5194/esd-6-83-2015, https://doi.org/10.5194/esd-6-83-2015, 2015
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Domain: ESSD – Land | Subject: Land Cover and Land Use
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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.
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The inconsistent coverage of Landsat data due to its long revisit intervals and frequent cloud cover poses challenges to large-scale land monitoring. We developed a global 30 m 23-year (2000–2022) daily seamless data cube (SDC) of surface reflectance based on Landsat 5, 7, 8, and 9 and MODIS products. The SDC exhibits enhanced capabilities for monitoring land cover changes and robust consistency in both spatial and temporal dimensions, which are important for global environmental monitoring.
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Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, https://doi.org/10.5194/essd-16-5357-2024, 2024
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Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.
Yuling Chen, Haitao Yang, Zekun Yang, Qiuli Yang, Weiyan Liu, Guoran Huang, Yu Ren, Kai Cheng, Tianyu Xiang, Mengxi Chen, Danyang Lin, Zhiyong Qi, Jiachen Xu, Yixuan Zhang, Guangcai Xu, and Qinghua Guo
Earth Syst. Sci. Data, 16, 5267–5285, https://doi.org/10.5194/essd-16-5267-2024, https://doi.org/10.5194/essd-16-5267-2024, 2024
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The national-scale continuous maps of arithmetic mean height and weighted mean height across China address the challenges of accurately estimating forest stand mean height using a tree-based approach. These maps produced in this study provide critical datasets for forest sustainable management in China, including climate change mitigation (e.g., terrestrial carbon estimation), forest ecosystem assessment, and forest inventory practices.
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Earth Syst. Sci. Data, 16, 5171–5189, https://doi.org/10.5194/essd-16-5171-2024, https://doi.org/10.5194/essd-16-5171-2024, 2024
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The study provided an annual 100 m 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.
Adrià Descals, David L. A. Gaveau, Serge Wich, Zoltan Szantoi, and Erik Meijaard
Earth Syst. Sci. Data, 16, 5111–5129, https://doi.org/10.5194/essd-16-5111-2024, https://doi.org/10.5194/essd-16-5111-2024, 2024
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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.
Ran Jia, Xiuqi Fang, Yundi Yang, Masayuki Yokozawa, and Yu Ye
Earth Syst. Sci. Data, 16, 4971–4994, https://doi.org/10.5194/essd-16-4971-2024, https://doi.org/10.5194/essd-16-4971-2024, 2024
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We reconstructed a cropland area change dataset in Northeast China over the past millennium by integrating multisource data with a unified standard using the historical and archaeological record, statistical yearbook, and national land survey. Cropland in Northeast China exhibited phases of expansion–reduction–expansion over the past millennium. This dataset can be used for improving the land use and land cover change (LUCC) dataset and assessing LUCC-induced carbon emission and climate change.
Stefania Di Tommaso, Sherrie Wang, Rob Strey, and David B. Lobell
Earth Syst. Sci. Data, 16, 4931–4947, https://doi.org/10.5194/essd-16-4931-2024, https://doi.org/10.5194/essd-16-4931-2024, 2024
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Sugarcane plays a vital role in food, biofuel, and farmer income globally, yet its cultivation faces numerous social and environmental challenges. Despite its significance, accurate mapping remains limited. Our study addresses this gap by introducing a novel 10 m global dataset of sugarcane maps spanning 2019–2022. Comparisons with field data, pre-existing maps, and official government statistics all indicate the high precision and high recall of our maps.
Jie Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Geli Zhang, Xuebin Yang, Xiaocui Wu, Chandrashekhar Biradar, and Yang Hu
Earth Syst. Sci. Data, 16, 4619–4639, https://doi.org/10.5194/essd-16-4619-2024, https://doi.org/10.5194/essd-16-4619-2024, 2024
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Existing satellite-based forest maps have large uncertainties due to different forest definitions and mapping algorithms. To effectively manage forest resources, timely and accurate annual forest maps at a high spatial resolution are needed. This study improved forest maps by integrating PALSAR-2 and Landsat images. Annual evergreen and non-evergreen forest-type maps were also generated. This critical information supports the Global Forest Resources Assessment.
David A. Gibbs, Melissa Rose, Giacomo Grassi, Joana Melo, Simone Rossi, Viola Heinrich, and Nancy L. Harris
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-397, https://doi.org/10.5194/essd-2024-397, 2024
Revised manuscript accepted for ESSD
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Updated global maps of greenhouse gas emissions and sequestration by forests from 2001 onwards using satellite-derived data show that forests are strong net carbon sinks, capturing about as much CO2 each year on average as the United States emits from fossil fuels. After reclassifying fluxes to countries’ reporting categories for national greenhouse gas inventories, we found that roughly two-thirds of the total net flux from forests is anthropogenic and one-third is non-anthropogenic.
Xuemeng Tian, Davide Consoli, Martijn Witjes, Florian Schneider, Leandro Parente, Murat Şahin, Yu-Feng Ho, Robert Minařík, and Tomislav Hengl
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-266, https://doi.org/10.5194/essd-2024-266, 2024
Revised manuscript accepted for ESSD
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Our study introduces a Landsat-based data cube simplifying access to detailed environmental data across Europe from 2000 to 2022, covering vegetation, water, soil, and crops. Our experiments demonstrate its effectiveness in developing environmental models and maps. Tailored feature selection is crucial for its effective use in environmental modeling. It aims to support comprehensive environmental monitoring and analysis, helping researchers and policymakers in managing environmental resources.
Xin Zhao, Kazuya Nishina, Haruka Izumisawa, Yuji Masutomi, Seima Osako, and Shuhei Yamamoto
Earth Syst. Sci. Data, 16, 3893–3911, https://doi.org/10.5194/essd-16-3893-2024, https://doi.org/10.5194/essd-16-3893-2024, 2024
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Mapping a rice calendar in a spatially explicit manner with a consistent framework remains challenging at a global or continental scale. We successfully developed a new gridded rice calendar for monsoon Asia based on Sentinel-1 and Sentinel-2 images, which characterize transplanting and harvesting dates and the number of rice croppings in a comprehensive framework. Our rice calendar will be beneficial for rice management, production prediction, and the estimation of greenhouse gas emissions.
Yuehong Chen, Congcong Xu, Yong Ge, Xiaoxiang Zhang, and Ya'nan Zhou
Earth Syst. Sci. Data, 16, 3705–3718, https://doi.org/10.5194/essd-16-3705-2024, https://doi.org/10.5194/essd-16-3705-2024, 2024
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Population data is crucial for human–nature interactions. Gridded population data can address limitations of census data in irregular units. In China, rapid urbanization necessitates timely and accurate population grids. However, existing datasets for China are either outdated or lack recent census data. Hence, a novel approach was developed to disaggregate China’s seventh census data into 100 m population grids. The resulting dataset outperformed the existing LandScan and WorldPop datasets.
Michael MacFerrin, Christopher Amante, Kelly Carignan, Matthew Love, and Elliot Lim
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-250, https://doi.org/10.5194/essd-2024-250, 2024
Revised manuscript accepted for ESSD
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Here we present Earth TOPOgraphy (ETOPO) 2022, the latest iteration of NOAA’s global, seamless topographic-bathymetric dataset. ETOPO 2022 is a significant upgrade in resolution and accuracy from previous ETOPO releases, freely available in multiple data formats and resolutions for all uses (public or private), excepting navigation.
Shuchao Ye, Peiyu Cao, and Chaoqun Lu
Earth Syst. Sci. Data, 16, 3453–3470, https://doi.org/10.5194/essd-16-3453-2024, https://doi.org/10.5194/essd-16-3453-2024, 2024
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We reconstructed annual cropland density and crop type maps, including nine major crop types (corn, soybean, winter wheat, spring wheat, durum wheat, cotton, sorghum, barley, and rice), from 1850 to 2021 at 1 km × 1 km resolution. We found that the US total crop acreage has increased by 118 × 106 ha (118 Mha), mainly driven by corn (30 Mha) and soybean (35 Mha). Additionally, the US cropping diversity experienced an increase in the 1850s–1960s, followed by a decline over the past 6 decades.
Fang Chen, Lei Wang, Yu Wang, Haiying Zhang, Ning Wang, Pengfei Ma, and Bo Yu
Earth Syst. Sci. Data, 16, 3369–3382, https://doi.org/10.5194/essd-16-3369-2024, https://doi.org/10.5194/essd-16-3369-2024, 2024
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Storage tanks are responsible for approximately 25 % of CH4 emissions in the atmosphere, exacerbating climate warming. Currently there is no publicly accessible storage tank inventory. We generated the first high-spatial-resolution (1–2 m) storage tank dataset (STD) over 92 typical cities in China in 2021, totaling 14 461 storage tanks with the construction year from 2000–2021. It shows significant agreement with CH4 emission spatially and temporally, promoting the CH4 control strategy proposal.
Xingyi Huang, Yuwei Yin, Luwei Feng, Xiaoye Tong, Xiaoxin Zhang, Jiangrong Li, and Feng Tian
Earth Syst. Sci. Data, 16, 3307–3332, https://doi.org/10.5194/essd-16-3307-2024, https://doi.org/10.5194/essd-16-3307-2024, 2024
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The Tibetan Plateau, with its diverse vegetation ranging from forests to alpine grasslands, plays a key role in understanding climate change impacts. Existing maps lack detail or miss unique ecosystems. Our research, using advanced satellite technology and machine learning, produced the map TP_LC10-2022. Comparisons with other maps revealed TP_LC10-2022's excellence in capturing local variations. Our map is significant for in-depth ecological studies.
Simon Boitard, Arnaud Mialon, Stéphane Mermoz, Nemesio J. Rodríguez-Fernández, Philippe Richaume, Julio César Salazar-Neira, Stéphane Tarot, and Yann H. Kerr
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-184, https://doi.org/10.5194/essd-2024-184, 2024
Revised manuscript accepted for ESSD
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Above Ground Biomass (AGB) is a critical component of the Earth carbon cycle. The presented dataset aims to help monitoring this essential climate variable with AGB time series from 2011 onward, derived with a carefully calibrated spatial relationship between the measurements of the Soil Moisture and Ocean Salinity (SMOS) mission and pre-existing AGB maps. The produced dataset has been extensively compared with other available AGB time series and can be used in AGB studies.
Qinghang Mei, Zhao Zhang, Jichong Han, Jie Song, Jinwei Dong, Huaqing Wu, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 16, 3213–3231, https://doi.org/10.5194/essd-16-3213-2024, https://doi.org/10.5194/essd-16-3213-2024, 2024
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In order to make up for the lack of long-term soybean planting area maps in China, we firstly generated a dataset of soybean planting area with a spatial resolution of 10 m for major producing areas in China from 2017 to 2021 (ChinaSoyArea10m). Compared with existing datasets, ChinaSoyArea10m has higher consistency with census data and further improvement in spatial details. The dataset can provide reliable support for subsequent studies on yield monitoring and food security.
Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, and Mengzi Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-193, https://doi.org/10.5194/essd-2024-193, 2024
Revised manuscript accepted for ESSD
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This study developed a new approach to long-time continuous annual vegetation mapping from remote sensing imagery, and mapped the vegetation of the Qinghai-Tibet Plateau (QTP) from 2000 to 2022 through the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping reached 80.9%, with the reference annual 2020 reaching an accuracy of 86.5% and a Kappa coefficient of 0.85. The study supports the use of remote sensing data to mapping a long-time continuous annual vegetation.
Yavar Pourmohamad, John T. Abatzoglou, Erin J. Belval, Erica Fleishman, Karen Short, Matthew C. Reeves, Nicholas Nauslar, Philip E. Higuera, Eric Henderson, Sawyer Ball, Amir AghaKouchak, Jeffrey P. Prestemon, Julia Olszewski, and Mojtaba Sadegh
Earth Syst. Sci. Data, 16, 3045–3060, https://doi.org/10.5194/essd-16-3045-2024, https://doi.org/10.5194/essd-16-3045-2024, 2024
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The FPA FOD-Attributes dataset provides > 300 biological, physical, social, and administrative attributes associated with > 2.3×106 wildfire incidents across the US from 1992 to 2020. The dataset can be used to (1) answer numerous questions about the covariates associated with human- and lightning-caused wildfires and (2) support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.
Zhenghang Yuan, Zhitong Xiong, Lichao Mou, and Xiao Xiang Zhu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-140, https://doi.org/10.5194/essd-2024-140, 2024
Revised manuscript accepted for ESSD
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ChatEarthNet is an image-text dataset that provides high-quality, detailed natural language descriptions for global-scale satellite data. It consists of 163,488 image-text pairs with captions generated by ChatGPT-3.5, and an additional 10,000 image-text pairs with captions generated by ChatGPT-4V(ision). This dataset has significant potential for training and evaluating vision-language geo-foundation models in remote sensing.
Ewa Grabska-Szwagrzyk, Dirk Tiede, Martin Sudmanns, and Jacek Kozak
Earth Syst. Sci. Data, 16, 2877–2891, https://doi.org/10.5194/essd-16-2877-2024, https://doi.org/10.5194/essd-16-2877-2024, 2024
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We accurately mapped 16 dominant tree species and genera in Poland using Sentinel-2 observations from short periods in spring, summer, and autumn (2018–2021). The classification achieved more than 80% accuracy in country-wide forest species mapping, with variation based on species, region, and observation frequency. Freely accessible resources, including the forest tree species map and training and test data, can be found at https://doi.org/10.5281/zenodo.10180469.
Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data, 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024, https://doi.org/10.5194/essd-16-2605-2024, 2024
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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.
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).
Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-536, https://doi.org/10.5194/essd-2023-536, 2024
Revised manuscript accepted for ESSD
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Our team has developed an irrigation map with 100 m resolution, which is more detailed than existing one. We used satellite images and focused on the crop status during the dry conditions. We found that 23.4 % of global cropland is irrigated, with the most extensive areas in India, China, the US, and Pakistan. We also explored the distribution of central pivot systems, which are commonly used in the US and Saudi Arabia. This new map can better support water management and food security globally.
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).
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.
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.
Cited articles
Ahlswede, S., Thekke-Madam, N., Schulz, C., Kleinschmit, B., and Demir, B.:
Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree
Species Classification Based on Explanation Methods, in: IEEE International
Geoscience and Remote Sensing Symposium, 17–22 July 2022, Kuala Lumpur, Malaysia, https://doi.org/10.48550/arXiv.2201.07495, 2022. a
Ansari, M., Homayouni, S., Safari, A., and Niazmardi, S.: A New Convolutional
Kernel Classifier for Hyperspectral Image Classification, IEEE J.
Sel. Top. Appl., 14,
11240–11256, 2021. a
Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., and Nemani,
R.: Deepsat: a learning framework for satellite imagery, in: Proceedings of
the 23rd SIGSPATIAL international conference on advances in geographic
information systems, 3–6 November 2015, Seattle, Washington, USA, 1–10, https://doi.org/10.48550/arXiv.1509.03602, 2015. a
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and
Wood, E. F.: Present and future Köppen-Geiger climate classification maps
at 1-km resolution, Scientific Data, 5, 1–12, 2018. a
Böckmann, T.: Warum sind Betriebsinventuren für die forstliche Praxis
wichtig (Why is two-phase sampling for stratification so important for
forestry enterprises?), Forstarchiv, 87, 31–37, 2016. a
Boser, B. E., Guyon, I. M., and Vapnik, V. N.: A training algorithm for optimal
margin classifiers, in: Proceedings of the fifth annual workshop on
Computational learning theory, 27–29 July 1992, New York, United States, 144–152, https://doi.org/10.1145/130385.130401, 1992. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H.:
Encoder-decoder with atrous separable convolution for semantic image
segmentation, in: Proceedings of the European conference on computer vision
(ECCV), 8–14 September 2018, Munich, Germany, 801–818, https://doi.org/10.48550/arXiv.1802.02611, 2018. a
Cherrington, E., Flores-Anderson, A., Thapa, R. B., Herndon, K. E., Wahome, A.,
Oduor, P., Mubea, K., Ouko, E., and Hanh, N.: Perspectives on the Future
Application of SAR in Forest and Environmental Monitoring, in: The SAR
handbook: comprehensive methodologies for forest monitoring and biomass
estimation, edited by: Flores-Anderson, A. I., Herndon, K. E., Thapa, R. B.,
and Cherrington, E., chap. 8, NASA, https://doi.org/10.25966/nr2c-s697, 2019. a
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L.: Imagenet: A
large-scale hierarchical image database, in: 2009 IEEE conference on computer
vision and pattern recognition, IEEE, 20–25 June 2009, Miami, Florida, USA, 248–255, https://doi.org/10.1109/CVPR.2009.5206848, 2009. a
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X.,
Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N.:
An Image is Worth 16 × 16 Words: Transformers for Image Recognition at Scale,
in: International Conference on Learning Representations, 26 April–1 May 2020, Addis Ababa, Ethiopia, https://doi.org/10.48550/arXiv.2010.11929, 2020. a
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.: Sentinel-2:
ESA's optical high-resolution mission for GMES operational services, Remote
Sens. Environ., 120, 25–36, 2012. a
Egli, S. and Höpke, M.: CNN-Based Tree Species Classification Using High
Resolution RGB Image Data from Automated UAV Observations, Remote Sens.,
12, 3892, https://doi.org/10.3390/rs12233892, 2020. a
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A.: The
pascal visual object classes (voc) challenge, Int. J.
Comput. Vision, 88, 303–338, 2010. a
Fassnacht, F. E., Latifi, H., Stereńczak, K., Modzelewska, A., Lefsky, M.,
Waser, L. T., Straub, C., and Ghosh, A.: Review of studies on tree species
classification from remotely sensed data, Remote Sens. Environ., 186,
64–87, 2016. a
Fricker, G. A., Ventura, J. D., Wolf, J. A., North, M. P., Davis, F. W., and
Franklin, J.: A Convolutional Neural Network Classifier Identifies Tree
Species in Mixed-Conifer Forest from Hyperspectral Imagery, Remote Sensing,
11, 2326, https://doi.org/10.3390/rs11192326, 2019. a
Ganz, S., Adler, P., and Kändler, G.: Forest Cover Mapping Based on a
Combination of Aerial Images and Sentinel-2 Satellite Data Compared to
National Forest Inventory Data, Forests, 11, 1322, https://doi.org/10.3390/f11121322, 2020. a, b
Goutte, C. and Gaussier, E.: A probabilistic interpretation of precision,
recall and F-score, with implication for evaluation, in: Advances in
Information Retrieval. Lecture Notes in Computer Science, Vol. 3408, edited by: Losada,
D. E. and Fernández-Luna, J. M., Springer, 345–359, https://doi.org/10.1007/978-3-540-31865-1_25, 2005. a
Grabska, E., Hostert, P., Pflugmacher, D., and Ostapowicz, K.: Forest stand
species mapping using the Sentinel-2 time series, Remote Sens., 11, 1197, https://doi.org/10.3390/rs11101197,
2019. a, b
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C.,
Turakhia, M. P., and Ng, A. Y.: Cardiologist-level arrhythmia detection and
classification in ambulatory electrocardiograms using a deep neural network,
Nat. Med., 25, 65–69, 2019. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learning for image
recognition, in: Proceedings of the IEEE conference on computer vision and
pattern recognition, 27–30 June 2016, Las Vegas, Nevada, USA, 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016. a
Helber, P., Bischke, B., Dengel, A., and Borth, D.: EuroSAT: A Novel Dataset
and Deep Learning Benchmark for Land Use and Land Cover Classification, IEEE
J. Sel. Top. Appl.,
12, 2217–2226, 2019. a
Hemmerling, J., Pflugmacher, D., and Hostert, P.: Mapping temperate forest tree
species using dense Sentinel-2 time series, Remote Sens. Environ.,
267, 112743, https://doi.org/10.1016/j.rse.2021.112743, 2021. a
Hlásny, T., Barka, I., Roessiger, J., Kulla, L., Trombik, J.,
Sarvašová, Z., Bucha, T., Kovalčík, M., and
Čihák, T.: Conversion of Norway spruce forests in the face of
climate change: a case study in Central Europe, Eur. J. For.
Res., 136, 1013–1028, 2017. a
Hollaus, M. and Vreugdenhil, M.: Radar satellite imagery for detecting bark
beetle outbreaks in forests, Current Forestry Reports, 5, 240–250, 2019. a
Holzwarth, S., Thonfeld, F., Abdullahi, S., Asam, S., Da Ponte Canova, E.,
Gessner, U., Huth, J., Kraus, T., Leutner, B., and Kuenzer, C.: Earth
observation based monitoring of forests in Germany: A review, Remote Sens.,
12, 3570, https://doi.org/10.3390/rs12213570, 2020. a, b, c, d
Hong, D., Gao, L., Yokoya, N., Yao, J., Chanussot, J., Du, Q., and Zhang, B.:
More diverse means better: Multimodal deep learning meets remote-sensing
imagery classification, IEEE T. Geosci. Remote Sens.,
59, 4340–4354, 2020. a
Immitzer, M., Neuwirth, M., Böck, S., Brenner, H., Vuolo, F., and
Atzberger, C.: Optimal input features for tree species classification in
Central Europe based on multi-temporal Sentinel-2 data, Remote Sens., 11,
2599, https://doi.org/10.3390/rs11222599, 2019. a, b
IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I,
II and III to the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change, IPCC, Geneva, Switzerland, https://www.ipcc.ch/report/ar5/syr/ (last access: 31 January 2023), 2014. a
Karlson, M., Ostwald, M., Reese, H., Sanou, J., Tankoano, B., and Mattsson, E.:
Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian
woodlands using Landsat 8 and random forest, Remote Sens., 7,
10017–10041, 2015. a
Kattenborn, T., Eichel, J., Wiser, S., Burrows, L., Fassnacht, F. E., and
Schmidtlein, S.: Convolutional Neural Networks accurately predict cover
fractions of plant species and communities in Unmanned Aerial Vehicle
imagery, Remote Sensing in Ecology and Conservation, 6, 472–486, 2020. a
Kattenborn, T., Leitloff, J., Schiefer, F., and Hinz, S.: Review on
Convolutional Neural Networks (CNN) in vegetation remote sensing, ISPRS
J. Photogramm., 173, 24–49, 2021. a
Kowalski, K., Senf, C., Hostert, P., and Pflugmacher, D.: Characterizing spring
phenology of temperate broadleaf forests using Landsat and Sentinel-2 time
series, Int. J. Appl. Earth Obs., 92, 102172, https://doi.org/10.1016/j.jag.2020.102172, 2020. a, b
Krzystek, P., Serebryanyk, A., Schnörr, C., Červenka, J., and
Heurich, M.: Large-Scale Mapping of Tree Species and Dead Trees in
Šumava National Park and Bavarian Forest National Park Using Lidar and
Multispectral Imagery, Remote Sensing, 12, 661, https://doi.org/10.3390/rs12040661, 2020. a
LGLN: Geodatenportal Niedersachsen (GDI-NI), Landesamt für Geoinformation und
Landesvermessung Niedersachsen,
https://www.geodaten.niedersachsen.de/, last access: 31 December 2020. a
MacDicken, K., Jonsson, Ö., Piña, L., Maulo, S., Contessa, V., Adikari,
Y., Garzuglia, M., Lindquist, E., Reams, G., and D'Annunzio, R.: Global
forest resources assessment 2015: how are the world's forests changing?, FAO, https://agris.fao.org/agris-search/search.do?recordID=XF2017001127 (last access: 31 January 2023),
2016. a
Martins, G. B., La Rosa, L. E. C., Happ, P. N., Coelho Filho, L. C. T., Santos,
C. J. F., Feitosa, R. Q., and Ferreira, M. P.: Deep learning-based tree
species mapping in a highly diverse tropical urban setting, Urban For. Urban Gree., 64, 127241, https://doi.org/10.1016/j.ufug.2021.127241, 2021. a
Mäyrä, J., Keski-Saari, S., Kivinen, S., Tanhuanpää, T.,
Hurskainen, P., Kullberg, P., Poikolainen, L., Viinikka, A., Tuominen, S.,
Kumpula, T., and Vihervaara, P.: Tree species classification from airborne hyperspectral
and LiDAR data using 3D convolutional neural networks, Remote Sens.
Environ., 256, 112322, https://doi.org/10.1016/j.rse.2021.112322, 2021. a
NW-FVA: Waldzustandsbericht 2021 für Niedersachsen, Nordwestdeutsche
Forstliche Versuchsanstalt, Niedersächsisches Ministerium für
Ernährung, Landwirtschaft und Verbraucherschutz, Zenodo,
https://doi.org/10.5281/zenodo.5615008, 2021. a
Ottosen, T.-B., Petch, G., Hanson, M., and Skjøth, C. A.: Tree cover mapping
based on Sentinel-2 images demonstrate high thematic accuracy in Europe,
Int. J. Appl. Earth Obs., 84,
101947, https://doi.org/10.1016/j.jag.2019.101947, 2020. a
Pasquarella, V. J., Holden, C. E., and Woodcock, C. E.: Improved mapping of
forest type using spectral-temporal Landsat features, Remote Sens.
Environ., 210, 193–207, 2018. a
Saborowski, J., Marx, A., Nagel, J., and Böckmann, T.: Double sampling for
stratification in periodic inventories—Infinite population approach, Forest
Ecol. Manag., 260, 1886–1895, 2010. a
Schmitt, M., Hughes, L. H., Qiu, C., and Zhu, X. X.: SEN12MS–A Curated Dataset
of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and
Data Fusion, arXiv [preprint], https://doi.org/10.48550/arXiv.1906.07789, 2019. a
Schuldt, B., Buras, A., Arend, M., Vitasse, Y., Beierkuhnlein, C., Damm, A.,
Gharun, M., Grams, T. E., Hauck, M., Hajek, P., Hartmann, H., Hiltbrunner, E., Hoch, G., Holloway-Phillips, M., Körner, C., Larysch, E., Lübbe, T., Nelson, D., Rammig, A., Rigling, A., Rose, L., Ruehr, N., Schumann, K., Weiser, F., Werner, C., Wohlgemuth, T., Zang, C., and Kahmen, A.: A first assessment of
the impact of the extreme 2018 summer drought on Central European forests,
Basic Appl. Ecol., 45, 86–103, 2020. a
Scott, G. J., England, M. R., Starms, W. A., Marcum, R. A., and Davis, C. H.:
Training deep convolutional neural networks for land–cover classification of
high-resolution imagery, IEEE Geosci. Remote S., 14,
549–553, 2017. a
Senf, C., Buras, A., Zang, C. S., Rammig, A., and Seidl, R.: Excess forest
mortality is consistently linked to drought across Europe, Nat.
Commun., 11, 1–8, 2020. a
Sesnie, S. E., Finegan, B., Gessler, P. E., Thessler, S., Ramos Bendana, Z.,
and Smith, A. M.: The multispectral separability of Costa Rican rainforest
types with support vector machines and Random Forest decision trees,
Int. J. Remote Sens., 31, 2885–2909, 2010. a
Smith, L. N.: Cyclical learning rates for training neural networks, in: 2017
IEEE winter conference on applications of computer vision (WACV), IEEE, 24–31 March 2017, Santa Rosa, CA, USA,
464–472, https://doi.org/10.1109/WACV.2017.58, 2017. a
Sumbul, G., Charfuelan, M., Demir, B., and Markl, V.: Bigearthnet: A
Large-Scale Benchmark Archive for Remote Sensing Image Understanding, in:
IEEE International Geoscience and Remote Sensing Symposium, 28 July–2 August 2019, Yokohama, Japan, 5901–5904, https://doi.org/10.1109/IGARSS.2019.8900532,
2019. a, b
Sumbul, G., De Wall, A., Kreuziger, T., Marcelino, F., Costa, H., Benevides,
P., Caetano, M., Demir, B., and Markl, V.: BigEarthNet-MM: A Large-Scale,
Multimodal, Multilabel Benchmark Archive for Remote Sensing Image
Classification and Retrieval, IEEE Geoscience and Remote Sensing Magazine, 9,
174–180, 2021. a
Takahashi, K., Yamamoto, K., Kuchiba, A., and Koyama, T.: Confidence interval
for micro-averaged F1 and macro-averaged F1 scores, Appl. Intell., 52,
4961–4972, 2022. a
Tanase, M. A., Aponte, C., Mermoz, S., Bouvet, A., Le Toan, T., and Heurich,
M.: Detection of windthrows and insect outbreaks by L-band SAR: A case study
in the Bavarian Forest National Park, Remote Sens. Environ., 209,
700–711, 2018. a
Thonfeld, F., Gessner, U., Holzwarth, S., Kriese, J., da Ponte, E., Huth, J.,
and Kuenzer, C.: A First Assessment of Canopy Cover Loss in Germany's
Forests after the 2018–2020 Drought Years, Remote Sens., 14, 562, https://doi.org/10.3390/rs14030562, 2022. a
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E.,
Potin, P., Rommen, B., Floury, N., Brown, M., Traver, I. N., 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. a
Tucker, C. J.: Red and photographic infrared linear combinations for monitoring
vegetation, Remote Sens. Environ., 8, 127–150, 1979. a
Waser, L. T., Rüetschi, M., Psomas, A., Small, D., and Rehush, N.: Mapping
dominant leaf type based on combined Sentinel-1/-2 data–Challenges for
mountainous countries, ISPRS J. Photogramm.,
180, 209–226, 2021. a
Weinstein, B. G., Marconi, S., Bohlman, S., Zare, A., and White, E.: Individual
tree-crown detection in RGB imagery using semi-supervised deep learning
neural networks, Remote Sensing, 11, 1309, https://doi.org/10.3390/rs11111309, 2019a. a, b
Weinstein, B. G., Marconi, S., Bohlman, S. A., Zare, A., and White, E. P.:
Geographic generalization in airborne RGB deep learning tree detection,
bioRxiv [preprint], https://doi.org/10.1101/790071, 2019b. a
Weinstein, B. G., Marconi, S., Bohlman, S., Zare, A., Singh, A., Graves, S. J.,
and White, E.: A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network,
eLife, 10, e62922, https://doi.org/10.7554/eLife.62922, 2021. a, b
Welle, T., Aschenbrenner, L., Kuonath, K., Kirmaier, S., and Franke, J.:
Mapping Dominant Tree Species of German Forests, Remote Sens., 14, 3330, https://doi.org/10.3390/rs14143330,
2022. a
Wurm, M., Stark, T., Zhu, X. X., Weigand, M., and Taubenböck, H.: Semantic
segmentation of slums in satellite images using transfer learning on fully
convolutional neural networks, ISPRS J. Photogramm., 150, 59–69, 2019. a
Xu, K., Zhang, Z., Yu, W., Zhao, P., Yue, J., Deng, Y., and Geng, J.: How
Spatial Resolution Affects Forest Phenology and Tree-Species Classification
Based on Satellite and Up-Scaled Time-Series Images, Remote Sens., 13,
2716, https://doi.org/10.3390/rs13142716, 2021. a
Yang, Y. and Newsam, S.: Bag-Of-Visual-Words and Spatial Extensions for
Land-Use Classification, ACM SIGSPATIAL International Conference on Advances
in Geographic Information Systems, 2–5 November 2005, San Jose, California, USA, https://doi.org/10.1145/1869790.1869829, 2010.
a
Zhang, C., Xia, K., Feng, H., Yang, Y., and Du, X.: Tree species classification
using deep learning and RGB optical images obtained by an unmanned aerial
vehicle, J. Forestry Res., 32, 1879–1888, 2021. a
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.
Imagery from air and space is the primary source of large-scale forest mapping. Our study...
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