{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T15:38:32Z","timestamp":1760369912249,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,13]],"date-time":"2023-05-13T00:00:00Z","timestamp":1683936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19090300","2019QZKK030701","61731022"],"award-info":[{"award-number":["XDA19090300","2019QZKK030701","61731022"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program","award":["XDA19090300","2019QZKK030701","61731022"],"award-info":[{"award-number":["XDA19090300","2019QZKK030701","61731022"]}]},{"name":"National Natural Science Foundation of China","award":["XDA19090300","2019QZKK030701","61731022"],"award-info":[{"award-number":["XDA19090300","2019QZKK030701","61731022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forest cover data are fundamental to sustainable forest management and conservation. Available medium-resolution publicly shared forest-related datasets provide primary information on forest distribution. The evaluation of relevant datasets is of great importance to learn about the differences, characterize the accuracy, and provide a reference for rational use. This study presents an evaluation and analysis of the forest-related datasets in China around 2020, including TreeCover and the forest-related layer (latter referred to as the forest datasets) in WorldCover, Esri land cover, FROM-GLC10, GlobeLand30, and GLC_FCS30. These forest datasets, that are obtained by aggregating forest-related lasses based on the classification schemes, are analyzed from spatial consistency and accuracy comparison. The results illustrate that forest datasets with 10m resolution are generally more precise than those with 30m resolution in China. WorldCover shows the highest accuracy, with producer accuracy and user accuracy of 91.4% and 87.09%, respectively. These datasets exhibit high accuracy but great spatial inconsistency. The more consistent the regions are, the more accurate the accuracy is. High consistency (\u22655, i.e., classified into forests by five datasets) areas account for 56.49% of areas of forest classified (AFC), while the area of low consistency (\u22642) reach 25.51% of AFC. The analysis delves into the datasets, offering a reliable reference for the usage of these datasets.<\/jats:p>","DOI":"10.3390\/rs15102557","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T02:02:11Z","timestamp":1684116131000},"page":"2557","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["User-Aware Evaluation for Medium-Resolution Forest-Related Datasets in China: Reliability and Spatial Consistency"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0613-7230","authenticated-orcid":false,"given":"Xueli","family":"Peng","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guojin","family":"He","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Hainan Research Institute, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guizhou","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3572-4415","authenticated-orcid":false,"given":"Tengfei","family":"Long","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5594-0815","authenticated-orcid":false,"given":"Ranyu","family":"Yin","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,13]]},"reference":[{"key":"ref_1","unstructured":"Campbell, J.B., and Wynne, R.H. (2011). Introduction to Remote Sensing, Guilford Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/0031-8663(70)90007-4","article-title":"Methods of representing the results of photo interpretation","volume":"25","author":"Reinhold","year":"1970","journal-title":"Photogrammetria"},{"key":"ref_3","first-page":"717","article-title":"A hydrological comparison of Landsat TM, Landsat MSS and black & white aerial photography","volume":"Volume 7","author":"France","year":"1986","journal-title":"Proceedings of the Remote Sensing for Ressources Development and Environmental Management. International Symposium"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kangas, A., and Maltamo, M. (2006). Forest Inventory: Methodology and Applications, Springer Science & Business Media.","DOI":"10.1007\/1-4020-4381-3"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"022211","DOI":"10.1117\/1.JRS.14.022211","article-title":"Rapid generation of global forest cover map using Landsat based on the forest ecological zones","volume":"14","author":"Zhang","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gigovi\u0107, L., Pourghasemi, H.R., Drobnjak, S., and Bai, S. (2019). Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia\u2019s Tara National Park. Forests, 10.","DOI":"10.3390\/f10050408"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Peng, X., He, G., She, W., Zhang, X., Wang, G., Yin, R., and Long, T. (2022). A Comparison of Random Forest Algorithm-Based Forest Extraction with GF-1 WFV, Landsat 8 and Sentinel-2 Images. Remote Sens., 14.","DOI":"10.3390\/rs14215296"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/07038992.2016.1207484","article-title":"Remote Sensing Technologies for Enhancing Forest Inventories: A Review","volume":"42","author":"White","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_10","first-page":"46","article-title":"Statistical inferential techniques for approaching forest mapping. A review of methods","volume":"42","author":"Fattorini","year":"2018","journal-title":"Ann. Silvic. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1080\/07038992.2014.987376","article-title":"Forest Monitoring Using Landsat Time Series Data: A Review","volume":"40","author":"Banskota","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2014.04.014","article-title":"New global forest\/non-forest maps from ALOS PALSAR data (2007\u20132010)","volume":"155","author":"Shimada","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5289697","DOI":"10.34133\/2021\/5289697","article-title":"Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects","volume":"2021","author":"Liu","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Venter, Z.S., Barton, D.N., Chakraborty, T., Simensen, T., and Singh, G. (2022). Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover. Remote Sens., 14.","DOI":"10.3390\/rs14164101"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, X., Wang, Z., Cheng, H., Kang, J., Tang, H., Li, Y., Bian, Z., and Bai, Z. (2022). Consistency Analysis and Accuracy Assessment of Three Global Ten-Meter Land Cover Products in Rocky Desertification Region\u2014A Case Study of Southwest China. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11030202"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ding, Y., Yang, X., Wang, Z., Fu, D., Li, H., Meng, D., Zeng, X., and Zhang, J. (2022). A Field-Data-Aided Comparison of Three 10 m Land Cover Products in Southeast Asia. Remote Sens., 14.","DOI":"10.3390\/rs14195053"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106165","DOI":"10.1016\/j.landusepol.2022.106165","article-title":"Land use and cover changes on the Loess Plateau: A comparison of six global or national land use and cover datasets","volume":"119","author":"Sun","year":"2022","journal-title":"Land Use Policy"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, H., Yan, H., Hu, Y., Xi, Y., and Yang, Y. (2022). Consistency and Accuracy of Four High-Resolution LULC Datasets\u2014Indochina Peninsula Case Study. Land, 11.","DOI":"10.3390\/land11050758"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6185","DOI":"10.1080\/01431161.2019.1587207","article-title":"Comparisons of three recent moderate resolution African land cover datasets: CGLS-LC100, ESA-S2-LC20, and FROM-GLC-Africa30","volume":"40","author":"Xu","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"12070","DOI":"10.3390\/rs61212070","article-title":"Global Land Cover Mapping: A Review and Uncertainty Analysis","volume":"6","author":"Congalton","year":"2014","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1080\/17538947.2023.2181992","article-title":"Reliability and consistency assessment of land cover products at macro and local scales in typical cities","volume":"16","author":"Shi","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Dong, S., Guo, H., Chen, Z., Pan, Y., and Gao, B. (2022). Spatial Stratification Method for the Sampling Design of LULC Classification Accuracy Assessment: A Case Study in Beijing, China. Remote Sens., 14.","DOI":"10.3390\/rs14040865"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Galiatsatos, N., Donoghue, D.N.M., Watt, P., Bholanath, P., Pickering, J., Hansen, M.C., and Mahmood, A.R.J. (2020). An Assessment of Global Forest Change Datasets for National Forest Monitoring and Reporting. Remote Sens., 12.","DOI":"10.3390\/rs12111790"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2016.06.012","article-title":"Earth science data records of global forest cover and change: Assessment of accuracy in 1990, 2000, and 2005 epochs","volume":"184","author":"Feng","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1080\/17538947.2017.1421721","article-title":"Accuracy assessment of four cloud-free snow cover products over the Qinghai-Tibetan Plateau","volume":"12","author":"Hao","year":"2018","journal-title":"Int. J. Digit. Earth"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"106946","DOI":"10.1016\/j.compag.2022.106946","article-title":"Quantifying the accuracies of six 30-m cropland datasets over China: A comparison and evaluation analysis","volume":"197","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","unstructured":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., and Vergnaud, S. (2023, April 07). ESA WorldCover 10 m 2020 v100. Available online: https:\/\/zenodo.org\/record\/5571936#.Y0uZbnZBxaQ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J.C., Mathis, M., and Brumby, S.P. (2021, January 11\u201316). Global land use\/land cover with Sentinel 2 and deep learning. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553499"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.scib.2019.03.002","article-title":"Stable classification with limited sample: Transferring a 30-m resolution sample set collessscted in 2015 to mapping 10-m resolution global land cover in 2017","volume":"64","author":"Gong","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.isprsjprs.2014.09.002","article-title":"Global land cover mapping at 30m resolution: A POK-based operational approach","volume":"103","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1038\/514434c","article-title":"Open access to Earth land-cover map","volume":"514","author":"Jun","year":"2014","journal-title":"Nature"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.5194\/essd-13-2753-2021","article-title":"GLC_FCS30: Global land-cover product with fine classification system at 30\u2009m using time-series Landsat imagery","volume":"13","author":"Zhang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, X., Liu, L., Chen, X., Xie, S., and Gao, Y. (2019). Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach. Remote Sens., 11.","DOI":"10.3390\/rs11091056"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1831","DOI":"10.5194\/essd-14-1831-2022","article-title":"GISD30: Global 30\u2009m impervious-surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform","volume":"14","author":"Zhang","year":"2022","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data","volume":"34","author":"Gong","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","unstructured":"National Forestry and Grassland Administration (2020). China Forest Resources Report, Chinese Forestry Press."},{"key":"ref_38","first-page":"1896","article-title":"Mapping annual global land cover changes at a 30 m resolution from 2000 to 2015","volume":"25","author":"Xu","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.isprsjprs.2016.11.004","article-title":"Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative","volume":"122","author":"Zhu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ahmed, N., Saha, S., Shahzad, M., Fraz, M.M., and Zhu, X.X. (2021, January 11\u201317). Progressive Unsupervised Deep Transfer Learning for Forest Mapping in Satellite Image. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00089"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2502605","DOI":"10.1109\/LGRS.2021.3135869","article-title":"Automatic Framework of Mapping Impervious Surface Growth With Long-Term Landsat Imagery Based on Temporal Deep Learning Model","volume":"19","author":"Yin","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2022.10.005","article-title":"A full resolution deep learning network for paddy rice mapping using Landsat data","volume":"194","author":"Xia","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2557\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:34:18Z","timestamp":1760124858000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2557"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,13]]},"references-count":42,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15102557"],"URL":"https:\/\/doi.org\/10.3390\/rs15102557","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,5,13]]}}}