Articles | Volume 16, issue 11
https://doi.org/10.5194/essd-16-5267-2024
© Author(s) 2024. 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-16-5267-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
14 Nov 2024
Data description paper |
| 14 Nov 2024
| 14 Nov 2024
Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data
Yuling Chen
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Haitao Yang
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Zekun Yang
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Qiuli Yang
College of Geography and Remote Sensing Science, Xinjiang University, Ürümqi 800017, China
Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Ürümqi 830017, China
Weiyan Liu
State Forestry and Grassland Administration Key Laboratory of Forest Resources & Environmental Management, Beijing Forestry University, Beijing 100083, China
Guoran Huang
College of Forestry, Southwest Forestry University, Kunming 650224, China
Yu Ren
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Kai Cheng
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Tianyu Xiang
College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Mengxi Chen
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Danyang Lin
State Forestry and Grassland Administration Key Laboratory of Forest Resources & Environmental Management, Beijing Forestry University, Beijing 100083, China
Zhiyong Qi
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Jiachen Xu
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Yixuan Zhang
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Guangcai Xu
Beijing GreenValley Technology Co., Ltd., Haidian, Beijing 100091, China
Qinghua Guo
CORRESPONDING AUTHOR
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Institute of Ecology, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
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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.
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.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-217, https://doi.org/10.5194/essd-2024-217, 2024
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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.
Shuang Chen, Jie Wang, Qiang Liu, Xiangan Liang, Rui Liu, Peng Qin, Jincheng Yuan, Junbo Wei, Shuai Yuan, Huabing Huang, and Peng Gong
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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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), and daily Seamless Data Cube (SDC) of surface reflectance based on Landsat 5,7,8,9 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.
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
Revised manuscript accepted 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.
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.
Chunling Sun, Hong Zhang, Lu Xu, Ji Ge, Jingling Jiang, Lijun Zuo, and Chao Wang
Earth Syst. Sci. Data, 15, 1501–1520, https://doi.org/10.5194/essd-15-1501-2023, https://doi.org/10.5194/essd-15-1501-2023, 2023
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Over 90 % of the world’s rice is produced in the Asia–Pacific region. In this study, a rice-mapping method based on Sentinel-1 data for mainland Southeast Asia is proposed. A combination of spatiotemporal features with strong generalization is selected and input into the U-Net model to obtain a 20 m resolution rice area map of mainland Southeast Asia in 2019. The accuracy of the proposed method is 92.20 %. The rice area map is concordant with statistics and other rice area maps.
Cited articles
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.: Optuna: A Next-generation Hyperparameter Optimization Framework, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, https://doi.org/10.1145/3292500.3330701, 2019.
Allard, D.: J.-P. Chilès, P. Delfiner: Geostatistics: Modeling Spatial Uncertainty, Math. Geosci., 45, 377–380, https://doi.org/10.1007/s11004-012-9429-y, 2013.
Bouvier, M., Durrieu, S., Fournier, R. A., and Renaud, J.-P.: Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data, Remote Sens. Environ., 156, 322–334, https://doi.org/10.1016/j.rse.2014.10.004, 2015.
Cheng, K., Chen, Y., Xiang, T., Yang, H., Liu, W., Ren, Y., Guan, H., Hu, T., Ma, Q., and Guo, Q.: A 2020 forest age map for China with 30 m resolution, Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024, 2024a.
Cheng, K., Yang, H., Guan, H., Ren, Y., Chen, Y., Chen, M., Yang, Z., Lin, D., Liu, W., Xu, J., Xu, G., Ma, K., and Guo, Q.: Unveiling China's natural and planted forest spatial–temporal dynamics from 1990 to 2020, ISPRS J. Photogramm., 209, 37–50, https://doi.org/10.1016/j.isprsjprs.2024.01.024, 2024b.
Chen, Y., Yang, H., Yang, Z., Yang, Q., Liu, W., Huang, G., Ren, Y., Cheng, K., Xiang, T., Chen, M., Lin, D., Qi, Z., Xu, J., Zhang, Y., Xu, G., and Guo, Q.: Enhancing High-Resolution Forest Stand Mean Height Mapping in China through an Individual Tree-Based Approach with Close-Range LiDAR Data (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.12697784, 2024.
Choi, S., McMaster, K. L., Kohli, N., Shanahan, E., Birinci, S., An, J., Duesenberg-Marshall, M., and Lembke, E. S.: Longitudinal effects of data-based instructional changes for students with intensive learning needs: A piecewise linear–linear mixed-effects modeling approach, J. Educ. Psychol., 116, 608–628, https://doi.org/10.1037/edu0000853, 2024.
Coops, N. C., Tompalski, P., Goodbody, T. R. H., Queinnec, M., Luther, J. E., Bolton, D. K., White, J. C., Wulder, M. A., van Lier, O. R., and Hermosilla, T.: Modelling lidar-derived estimates of forest attributes over space and time: A review of approaches and future trends, Remote Sens. Environ., 260, 112477, https://doi.org/10.1016/j.rse.2021.112477, 2021.
Davies, A. B., Ancrenaz, M., Oram, F., and Asner, G. P.: Canopy structure drives orangutan habitat selection in disturbed Bornean forests, P. Natl. Acad. Sci. USA, 114, 8307–8312, https://doi.org/10.1073/pnas.1706780114, 2017.
Demidenko, E.: Mixed models: theory and applications with R, 2nd Edn., John Wiley & Sons, ISBN 978-1-118-09157-9, 2013.
Donoghue, D. N. M. and Watt, P. J.: Using LiDAR to compare forest height estimates from IKONOS and Landsat ETM+ data in Sitka spruce plantation forests, Int. J. Remote Sens., 27, 2161–2175, https://doi.org/10.1080/01431160500396493, 2006.
Duncanson, L., Kellner, J. R., Armston, J., Dubayah, R., Minor, D. M., Hancock, S., Healey, S. P., Patterson, P. L., Saarela, S., Marselis, S., Silva, C. E., Bruening, J., Goetz, S. J., Tang, H., Hofton, M., Blair, B., Luthcke, S., Fatoyinbo, L., Abernethy, K., Alonso, A., Andersen, H.-E., Aplin, P., Baker, T. R., Barbier, N., Bastin, J. F., Biber, P., Boeckx, P., Bogaert, J., Boschetti, L., Boucher, P. B., Boyd, D. S., Burslem, D. F. R. P., Calvo-Rodriguez, S., Chave, J., Chazdon, R. L., Clark, D. B., Clark, D. A., Cohen, W. B., Coomes, D. A., Corona, P., Cushman, K. C., Cutler, M. E. J., Dalling, J. W., Dalponte, M., Dash, J., de-Miguel, S., Deng, S., Ellis, P. W., Erasmus, B., Fekety, P. A., Fernandez-Landa, A., Ferraz, A., Fischer, R., Fisher, A. G., García-Abril, A., Gobakken, T., Hacker, J. M., Heurich, M., Hill, R. A., Hopkinson, C., Huang, H., Hubbell, S. P., Hudak, A. T., Huth, A., Imbach, B., Jeffery, K. J., Katoh, M., Kearsley, E., Kenfack, D., Kljun, N., Knapp, N., Král, K., Krůček, M., Labrière, N., Lewis, S. L., Longo, M., Lucas, R. M., Main, R., Manzanera, J. A., Martínez, R. V., Mathieu, R., Memiaghe, H., Meyer, V., Mendoza, A. M., Monerris, A., Montesano, P., Morsdorf, F., Næsset, E., Naidoo, L., Nilus, R., O'Brien, M., Orwig, D. A., Papathanassiou, K., Parker, G., Philipson, C., Phillips, O. L., Pisek, J., Poulsen, J. R., Pretzsch, H., Rüdiger, C., Saatchi, S., Sanchez-Azofeifa, A., Sanchez-Lopez, N., Scholes, R., Silva, C. A., Simard, M., Skidmore, A., Stereńczak, K., Tanase, M., Torresan, C., Valbuena, R., Verbeeck, H., Vrska, T., Wessels, K., White, J. C., White, L. J. T., Zahabu, E., and Zgraggen, C.: Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission, Remote Sens. Environ., 270, 112845, https://doi.org/10.1016/j.rse.2021.112845, 2022.
Fang, J., Chen, A., Peng, C., Zhao, S., and Ci, L.: Changes in Forest Biomass Carbon Storage in China Between 1949 and 1998, Science, 292, 2320–2322, https://doi.org/10.1126/science.1058629, 2001.
Fang, J., Brown, S., Tang, Y., Nabuurs, G.-J., Wang, X., and Shen, H.: Overestimated Biomass Carbon Pools of the Northern mid- and High Latitude Forests, Climatic Change, 74, 355–368, https://doi.org/10.1007/s10584-005-9028-8, 2006.
Fayad, I., Ciais, P., Schwartz, M., Wigneron, J.-P., Baghdadi, N., de Truchis, A., d'Aspremont, A., Frappart, F., Saatchi, S., Sean, E., Pellissier-Tanon, A., and Bazzi, H.: Hy-TeC: a hybrid vision transformer model for high-resolution and large-scale mapping of canopy height, Remote Sens. Environ., 302, 113945, https://doi.org/10.1016/j.rse.2023.113945, 2024.
Guo, Q., Su, Y., Hu, T., Guan, H., Jin, S., Zhang, J., Zhao, X., Xu, K., Wei, D., Kelly, M., and Coops, N. C.: Lidar Boosts 3D Ecological Observations and Modelings: A Review and Perspective, IEEE Geosci. Remote, 9, 232–257, https://doi.org/10.1109/MGRS.2020.3032713, 2021.
Guo, Q., Su, Y., Hu, T., Zhao, X., Wu, F., Li, Y., Liu, J., Chen, L., Xu, G., Lin, G., Zheng, Y., Lin, Y., Mi, X., Fei, L., and Wang, X.: An integrated UAV-borne lidar system for 3D habitat mapping in three forest ecosystems across China, Int. J. Remote Sens., 38, 2954–2972, https://doi.org/10.1080/01431161.2017.1285083, 2017.
Hall, R. J., Skakun, R. S., Arsenault, E. J., and Case, B. S.: Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume, Forest Ecol. Manag., 225, 378–390, https://doi.org/10.1016/j.foreco.2006.01.014, 2006.
Hu, J. and Szymczak, S.: A review on longitudinal data analysis with random forest, Brief Bioinform., 24, bbad002, https://doi.org/10.1093/bib/bbad002, 2023.
Hu, T., Sun, X., Su, Y., Guan, H., Sun, Q., Kelly, M., and Guo, Q.: Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications, Remote Sens., 13, 77, https://doi.org/10.3390/rs13010077, 2021.
Huang, H., Liu, C., Wang, X., Biging, G. S., Chen, Y., Yang, J., and Gong, P.: Mapping vegetation heights in China using slope correction ICESat data, SRTM, MODIS-derived and climate data, ISPRS J Photogramm., 129, 189–199, https://doi.org/10.1016/j.isprsjprs.2017.04.020, 2017.
Huo, L., Lindberg, E., and Holmgren, J.: Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD), Remote Sens. Environ., 270, 112857, https://doi.org/10.1016/j.rse.2021.112857, 2022.
Jensen, J. L. R. and Mathews, A. J.: Assessment of Image-Based Point Cloud Products to Generate a Bare Earth Surface and Estimate Canopy Heights in a Woodland Ecosystem, Remote Sens., 8, 50, https://doi.org/10.3390/rs8010050, 2016.
Jucker, T., Hardwick, S. R., Both, S., Elias, D. M. O., Ewers, R. M., Milodowski, D. T., Swinfield, T., and Coomes, D. A.: Canopy structure and topography jointly constrain the microclimate of human-modified tropical landscapes, Glob. Chang Biol., 24, 5243–5258, https://doi.org/10.1111/gcb.14415, 2018.
Jurjević, L., Liang, X., Gašparović, M., and Balenović, I.: Is field-measured tree height as reliable as believed – Part II, A comparison study of tree height estimates from conventional field measurement and low-cost close-range remote sensing in a deciduous forest, ISPRS J. Photogramm., 169, 227–241, https://doi.org/10.1016/j.isprsjprs.2020.09.014, 2020.
Kwong, I. H. Y. and Fung, T.: Tree height mapping and crown delineation using LiDAR, large format aerial photographs, and unmanned aerial vehicle photogrammetry in subtropical urban forest, Int. J. Remote Sens., 41, 5228–5256, https://doi.org/10.1080/01431161.2020.1731002, 2020.
Laar, A. v. and Akça, A. (Eds.): Measurement Of Stands, in: Forest Mensuration, Springer Netherlands, Dordrecht, 95–147, https://doi.org/10.1007/978-1-4020-5991-9_5, 2007.
Lang, N., Jetz, W., Schindler, K., and Wegner, J. D.: A high-resolution canopy height model of the Earth, Nat. Ecol. Evol., 7, 1778–1789, https://doi.org/10.1038/s41559-023-02206-6, 2023.
Lefsky, M. A.: A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System, Geophys. Res. Lett., 37, L15401, https://doi.org/10.1029/2010GL043622, 2010.
Lefsky, M. A., Harding, D. J., Keller, M., Cohen, W. B., Carabajal, C. C., Del Bom Espirito-Santo, F., Hunter, M. O., and de Oliveira Jr, R.: Estimates of forest canopy height and aboveground biomass using ICESat, Geophys. Res. Lett., 32, L22S02, https://doi.org/10.1029/2005GL023971, 2005.
Li, C., Chen, Z., Zhou, X., Zhou, M., and Li, Z.: Generalized models for subtropical forest inventory attribute estimations using a rule-based exhaustive combination approach with airborne LiDAR-derived metrics, GIsci. Remote Sens., 60, 2194601, https://doi.org/10.1080/15481603.2023.2194601, 2023.
Li, M., Liu, Q., Feng, Y., and Li, Z.: Analysis of estimation models of plantation stand heights using UAV LiDAR, National Remote Sensing Bulletin, 26, 2665–2678, https://doi.org/10.11834/jrs.20210246, 2022.
Li, W., Guo, Q., Jakubowski, M. K., and Kelly, M.: A new method for segmenting individual trees from the lidar point cloud, Photogramm Eng Remote Sens., 78, 75–84, https://doi.org/10.14358/PERS.78.1.75, 2012.
Li, W., Niu, Z., Shang, R., Qin, Y., Wang, L., and Chen, H.: High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data, Int. J. Appl. Earth Obs. Geoinf., 92, 102163, https://doi.org/10.1016/j.jag.2020.102163, 2020.
Liang, X., Kukko, A., Balenović, I., Saarinen, N., Junttila, S., Kankare, V., Holopainen, M., Mokroš, M., Surový, P., Kaartinen, H., Jurjević, L., Honkavaara, E., Näsi, R., Liu, J., Hollaus, M., Tian, J., Yu, X., Pan, J., Cai, S., Virtanen, J. P., Wang, Y., and Hyyppä, J.: Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions, IEEE Geosci. Remote Sens. Mag., 10, 32–71, https://doi.org/10.1109/MGRS.2022.3168135, 2022.
Liu, H., Zhang, Z., and Cao, L.: Estimating forest stand characteristics in a coastal plain forest plantation based on vertical structure profile parameters derived from ALS data, J. Remote Sens., 22, 872–888, 10.11834/jrs.20187465, 2018.
Liu, X., Su, Y., Hu, T., Yang, Q., Liu, B., Deng, Y., Tang, H., Tang, Z., Fang, J., and Guo, Q.: Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data, Remote Sens. Environ., 269, 112844, https://doi.org/10.1016/j.rse.2021.112844, 2022.
Lorey, T.: Die mittlere bestandeshöhe, J. Allgemeine Forst- und Jagdzeitung, 54, 149–155, 1878.
Lou, M., Zhang, H., Lei, X., Li, C., and Zang, H.: Spatial Autoregressive Models for Stand Top and Stand Mean Height Relationship in Mixed Quercus mongolica Broadleaved Natural Stands of Northeast China, Forests, 7,43, https://doi.org/10.3390/f7020043, 2016.
Lu, D., Mausel, P., Brondıìzio, E., and Moran, E.: Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin, Forest Ecol. Manag., 198, 149–167, https://doi.org/10.1016/j.foreco.2004.03.048, 2004.
Ma, Q., Su, Y., Niu, C., Ma, Q., Hu, T., Luo, X., Tai, X., Qiu, T., Zhang, Y., Bales, R. C., Liu, L., Kelly, M., and Guo, Q.: Tree mortality during long-term droughts is lower in structurally complex forest stands, Nat. Commun., 14, 7467, 10.1038/s41467-023-43083-8, 2023.
Masaka, K., Sato, H., Torita, H., Kon, H., and Fukuchi, M.: Thinning effect on height and radial growth of Pinus thunbergii Parlat. trees with special reference to trunk slenderness in a matured coastal forest in Hokkaido, Japan, J. Forest Res., 18, 475–481, https://doi.org/10.1007/s10310-012-0373-y, 2013.
Matasci, G., Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., Hobart, G. W., and Zald, H. S. J.: Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots, Remote Sens. Environ., 209, 90–106, https://doi.org/10.1016/j.rse.2017.12.020, 2018.
McGregor, I. R., Helcoski, R., Kunert, N., Tepley, A. J., Gonzalez-Akre, E. B., Herrmann, V., Zailaa, J., Stovall, A. E. L., Bourg, N. A., McShea, W. J., Pederson, N., Sack, L., and Anderson-Teixeira, K. J.: Tree height and leaf drought tolerance traits shape growth responses across droughts in a temperate broadleaf forest, New Phytol., 231, 601–616, https://doi.org/10.1111/nph.16996, 2021.
Mekruksavanich, S., Jantawong, P., Hnoohom, N., and Jitpattanakul, A.: Hyperparameter Tuning in Convolutional Neural Network for Face Touching Activity Recognition using Accelerometer Data, 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C), Bangkok, Thailand, 4–5 August 2022, 101–105, https://doi.org/10.1109/RI2C56397.2022.9910262, 2022.
Migliavacca, M., Musavi, T., Mahecha, M. D., Nelson, J. A., Knauer, J., Baldocchi, D. D., Perez-Priego, O., Christiansen, R., Peters, J., Anderson, K., Bahn, M., Black, T. A., Blanken, P. D., Bonal, D., Buchmann, N., Caldararu, S., Carrara, A., Carvalhais, N., Cescatti, A., Chen, J., Cleverly, J., Cremonese, E., Desai, A. R., El-Madany, T. S., Farella, M. M., Fernández-Martínez, M., Filippa, G., Forkel, M., Galvagno, M., Gomarasca, U., Gough, C. M., Göckede, M., Ibrom, A., Ikawa, H., Janssens, I. A., Jung, M., Kattge, J., Keenan, T. F., Knohl, A., Kobayashi, H., Kraemer, G., Law, B. E., Liddell, M. J., Ma, X., Mammarella, I., Martini, D., Macfarlane, C., Matteucci, G., Montagnani, L., Pabon-Moreno, D. E., Panigada, C., Papale, D., Pendall, E., Penuelas, J., Phillips, R. P., Reich, P. B., Rossini, M., Rotenberg, E., Scott, R. L., Stahl, C., Weber, U., Wohlfahrt, G., Wolf, S., Wright, I. J., Yakir, D., Zaehle, S., and Reichstein, M.: The three major axes of terrestrial ecosystem function, Nature, 598, 468–472, https://doi.org/10.1038/s41586-021-03939-9, 2021.
Nakai, T., Sumida, A., Kodama, Y., Hara, T., and Ohta, T.: A comparison between various definitions of forest stand height and aerodynamic canopy height, Agr. Forest Meteorol., 150 1225–1233, 2010.
Næsset, E.: Determination of mean tree height of forest stands using airborne laser scanner data, ISPRS J. Photogramm., 52, 49–56, https://doi.org/10.1016/S0924-2716(97)83000-6, 1997.
Næsset, E. and Økland, T.: Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve, Remote Sens. Environ., 79, 105–115, https://doi.org/10.1016/S0034-4257(01)00243-7, 2002.
Ni, X., Zhou, Y., Cao, C., Wang, X., Shi, Y., Park, T., Choi, S., and Myneni, R. B.: Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data, Remote Sens., 7, 8436–8452, https://doi.org/10.3390/rs70708436, 2015.
Ørka, H. O., Næsset, E., and Bollandsås, O. M.: Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data, Remote Sens. Environ., 113, 1163–1174, https://doi.org/10.1016/j.rse.2009.02.002, 2009.
Pang, Y., Zhao, F., and Li, Z.: Forest height inversion using airborne Lidar technology, J. Remote Sens., 12, 158, https://doi.org/10.11834/jrs.20080120, 2008.
Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C., Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C. E., Armston, J., Dubayah, R., Blair, J. B., and Hofton, M.: Mapping global forest canopy height through integration of GEDI and Landsat data, Remote Sens. Environ., 253, 112165, https://doi.org/10.1016/j.rse.2020.112165, 2021.
Qin, H., Zhou, W., Yao, Y., and Wang, W.: Individual tree segmentation and tree species classification in subtropical broadleaf forests using UAV-based LiDAR, hyperspectral, and ultrahigh-resolution RGB data, Remote Sens. Environ., 280, 113143, https://doi.org/10.1016/j.rse.2022.113143, 2022.
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A., Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., Petrova, S., White, L., Silman, M., and Morel, A.: Benchmark map of forest carbon stocks in tropical regions across three continents, P. Natl. Acad. Sci. USA, 108, 9899–9904, https://doi.org/10.1073/pnas.1019576108, 2011.
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy height globally with spaceborne lidar, J. Geophys. Res.-Biogeo., 116, G04021, https://doi.org/10.1029/2011JG001708, 2011.
Su, Y., Ma, Q., and Guo, Q.: Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery, Int. J. Digit Earth, 10, 307–323, https://doi.org/10.1080/17538947.2016.1227380, 2017.
Su, Y., Guo, Q., Ma, Q., and Li, W.: SRTM DEM Correction in Vegetated Mountain Areas through the Integration of Spaceborne LiDAR, Airborne LiDAR, and Optical Imagery, Remote Sens., 7, 11202–11225, https://doi.org/10.3390/rs70911202, 2015.
Swayze, N. C., Tinkham, W. T., Vogeler, J. C., and Hudak, A. T.: Influence of flight parameters on UAS-based monitoring of tree height, diameter, and density, Remote Sens. Environ., 263, 112540, https://doi.org/10.1016/j.rse.2021.112540, 2021.
Tang, J., Luyssaert, S., Richardson, A. D., Kutsch, W., and Janssens, I. A.: Steeper declines in forest photosynthesis than respiration explain age-driven decreases in forest growth, P. Natl. Acad. Sci. USA, 111, 8856–8860, https://doi.org/10.1073/pnas.1320761111, 2014.
Tao, S., Wu, F., Guo, Q., Wang, Y., Li, W., Xue, B., Hu, X., Li, P., Tian, D., Li, C., Yao, H., Li, Y., Xu, G., and Fang, J.: Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories, ISPRS J Photogramm., 110, 66–76, https://doi.org/10.1016/j.isprsjprs.2015.10.007, 2015.
Travers-Smith, H., Coops, N. C., Mulverhill, C., Wulder, M. A., Ignace, D., and Lantz, T. C.: Mapping vegetation height and identifying the northern forest limit across Canada using ICESat-2, Landsat time series and topographic data, Remote Sens. Environ., 305, 114097, https://doi.org/10.1016/j.rse.2024.114097, 2024.
Vaglio Laurin, G., Ding, J., Disney, M., Bartholomeus, H., Herold, M., Papale, D., and Valentini, R.: Tree height in tropical forest as measured by different ground, proximal, and remote sensing instruments, and impacts on above ground biomass estimates, Int. J. Appl. Earth Obs., 82, 101899, https://doi.org/10.1016/j.jag.2019.101899, 2019.
Vanclay, J. K.: Assessing site productivity in tropical moist forests: a review, Forest Ecol. Manag., 54, 257–287, https://doi.org/10.1016/0378-1127(92)90017-4, 1992.
Vatandaslar, C., Narin, O. G., and Abdikan, S.: Retrieval of forest height information using spaceborne LiDAR data: a comparison of GEDI and ICESat-2 missions for Crimean pine (Pinus nigra) stands, Trees, 37, 717–731, https://doi.org/10.1007/s00468-022-02378-x, 2023.
Wang, M., Kane, M. B., and Zhao, D.: Correlation-Regression Analysis for Understanding Dominant Height Projection Accuracy, Forest Sci., 69, e1–e10, https://doi.org/10.5849/fs-2016-092, 2023.
Wang, Y., Pyörälä, J., Liang, X., Lehtomäki, M., Kukko, A., Yu, X., Kaartinen, H., and Hyyppä, J.: In situ biomass estimation at tree and plot levels: What did data record and what did algorithms derive from terrestrial and aerial point clouds in boreal forest, Remote Sens. Environ., 232, 111309, https://doi.org/10.1016/j.rse.2019.111309, 2019a.
Wang, Y., Lehtomäki, M., Liang, X., Pyörälä, J., Kukko, A., Jaakkola, A., Liu, J., Feng, Z., Chen, R., and Hyyppä, J.: Is field-measured tree height as reliable as believed – A comparison study of tree height estimates from field measurement, airborne laser scanning and terrestrial laser scanning in a boreal forest, ISPRS J. Photogramm., 147, 132–145, https://doi.org/10.1016/j.isprsjprs.2018.11.008, 2019b.
Woods, M., Pitt, D., Penner, M., Lim, K., Nesbitt, D., Etheridge, D., and Treitz, P.: Operational implementation of a LiDAR inventory in Boreal Ontario, Forest. Chron., 87, 512–528, https://doi.org/10.5558/tfc2011-050, 2011.
Wu, Z. and Shi, F.: Mapping Forest Canopy Height at Large Scales Using ICESat-2 and Landsat: An Ecological Zoning Random Forest Approach, IEEE T. Geosci. Remote, 61, 1–16, https://doi.org/10.1109/TGRS.2022.3231926, 2023.
Xu, H., Yue, C., Zhang, Y., Liu, D., and Piao, S.: Forestation at the right time with the right species can generate persistent carbon benefits in China, P. Natl. Acad. Sci. USA, 120, e2304988120, https://doi.org/10.1073/pnas.2304988120, 2023.
Xu, Y., Li, C., Sun, Z., Jiang, L., and Fang, J.: Tree height explains stand volume of closed-canopy stands: Evidence from forest inventory data of China, Forest Ecol. Manag., 438, 51–56, https://doi.org/10.1016/j.foreco.2019.01.054, 2019.
Yang, J., Kang, Z., Cheng, S., Yang, Z., and Akwensi, P. H.: An Individual Tree Segmentation Method Based on Watershed Algorithm and Three-Dimensional Spatial Distribution Analysis From Airborne LiDAR Point Clouds, IEEE J. Sel. Top. Appl. Earth Obs., 13, 1055–1067, https://doi.org/10.1109/JSTARS.2020.2979369, 2020.
Yang, Q., Niu, C., Liu, X., Feng, Y., Ma, Q., Wang, X., Tang, H., and Guo, Q.: Mapping high-resolution forest aboveground biomass of China using multisource remote sensing data, GIsci. Remote Sens., 60, 2203303, https://doi.org/10.1080/15481603.2023.2203303, 2023.
Yang, Z., Su, Y., Li, W., Cheng, K., Guan, H., Ren, Y., Hu, T., Xu, G., and Guo, Q.: Segmenting Individual Trees From Terrestrial LiDAR Data Using Tree Branch Directivity, IEEE J. Sel. Top. Appl., 17, 956–969, https://doi.org/10.1109/JSTARS.2023.3334014, 2024.
Yao, Y., Piao, S., and Wang, T.: Future biomass carbon sequestration capacity of Chinese forests, Sci. Bull., 63, 1108–1117, https://doi.org/10.1016/j.scib.2018.07.015, 2018.
Yin, D., Wang, L., Lu, Y., and Shi, C.: Mangrove tree height growth monitoring from multi-temporal UAV-LiDAR, Remote Sens. Environ., 303, 114002, https://doi.org/10.1016/j.rse.2024.114002, 2024.
Yun, T., Jiang, K., Li, G., Eichhorn, M. P., Fan, J., Liu, F., Chen, B., An, F., and Cao, L.: Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach, Remote Sens. Environ., 256, 112307, https://doi.org/10.1016/j.rse.2021.112307, 2021.
Zemp, D. C., Guerrero-Ramirez, N., Brambach, F., Darras, K., Grass, I., Potapov, A., Röll, A., Arimond, I., Ballauff, J., Behling, H., Berkelmann, D., Biagioni, S., Buchori, D., Craven, D., Daniel, R., Gailing, O., Ellsäßer, F., Fardiansah, R., Hennings, N., Irawan, B., Khokthong, W., Krashevska, V., Krause, A., Kückes, J., Li, K., Lorenz, H., Maraun, M., Merk, M. S., Moura, C. C. M., Mulyani, Y. A., Paterno, G. B., Pebrianti, H. D., Polle, A., Prameswari, D. A., Sachsenmaier, L., Scheu, S., Schneider, D., Setiajiati, F., Setyaningsih, C. A., Sundawati, L., Tscharntke, T., Wollni, M., Hölscher, D., and Kreft, H.: Tree islands enhance biodiversity and functioning in oil palm landscapes, Nature, 618, 316–321, https://doi.org/10.1038/s41586-023-06086-5, 2023.
Zhang, G., Ganguly, S., Nemani, R. R., White, M. A., Milesi, C., Hashimoto, H., Wang, W., Saatchi, S., Yu, Y., and Myneni, R. B.: Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data, Remote Sens. Environ., 151, 44–56, https://doi.org/10.1016/j.rse.2014.01.025, 2014.
Zhao, X., Su, Y., Hu, T., Cao, M., Liu, X., Yang, Q., Guan, H., Liu, L., and Guo, Q.: Analysis of UAV lidar information loss and its influence on the estimation accuracy of structural and functional traits in a meadow steppe, Ecol. Indic., 135, 108515, https://doi.org/10.1016/j.ecolind.2021.108515, 2022.
Short summary
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.
The national-scale continuous maps of arithmetic mean height and weighted mean height across...
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