{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T09:48:02Z","timestamp":1772963282969,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T00:00:00Z","timestamp":1725667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u2018EXCELSIOR\u2019 project (European Union\u2019s Horizon 2020 research and innovation programme)","award":["857510"],"award-info":[{"award-number":["857510"]}]},{"name":"Remote Sensing MDPI","award":["857510"],"award-info":[{"award-number":["857510"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Long-term monitoring studies on the transition of different land cover units to barren areas are crucial to gain a better understanding of the potential challenges and threats that land surface ecosystems face. This study utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products (MCD12C1) to conduct geospatial analysis based on the maximum extent (MaxE) concept, to assess the spatiotemporal changes in barren areas from 2001 to 2022, at global and continental scales. The MaxE area includes all the pixels across the entire period of observations where the barren land cover class was at least once present. The relative expansion or reduction of the barren areas can be directly assessed with MaxE, as any annual change observed in the barren distribution is comparable over the entire dataset. The global barren areas without any land change (UA) during this period were equivalent to 12.8% (18,875,284 km2) of the global land surface area. Interannual land cover changes to barren areas occurred in an additional area of 3,438,959 km2 (2.3% of the global area). Globally, barren areas show a gradual reduction from 2001 (91.1% of MaxE) to 2012 (86.8%), followed by annual fluctuations until 2022 (88.1%). These areas were mainly interchanging between open shrublands and grasslands. A relatively high transition between barren areas and permanent snow and ice is found in Europe and North America. The results show a 3.7% decrease in global barren areas from 2001 to 2022. Areas that are predominantly not barren account for 30.6% of the transitional areas (TAs), meaning that these areas experienced short-term or very recent transitions from other land cover classes to barren. Emerging barren areas hotspots were mainly found in the Mangystau region (Kazakhstan), Tibetan plateau, northern Greenland, and the Atlas Mountains (Morocco, Tunisia).<\/jats:p>","DOI":"10.3390\/rs16173317","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T04:15:01Z","timestamp":1725855301000},"page":"3317","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Temporal Dynamics of Global Barren Areas between 2001 and 2022 Derived from MODIS Land Cover Products"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0715-9511","authenticated-orcid":false,"given":"Marinos","family":"Eliades","sequence":"first","affiliation":[{"name":"ERATOSTHENES Centre of Excellence, Limassol 3012, Cyprus"},{"name":"Remote Sensing and GeoEnvironment Lab, Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol 3036, Cyprus"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1678-1556","authenticated-orcid":false,"given":"Stelios","family":"Neophytides","sequence":"additional","affiliation":[{"name":"ERATOSTHENES Centre of Excellence, Limassol 3012, Cyprus"},{"name":"Remote Sensing and GeoEnvironment Lab, Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol 3036, Cyprus"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5281-4175","authenticated-orcid":false,"given":"Michalis","family":"Mavrovouniotis","sequence":"additional","affiliation":[{"name":"ERATOSTHENES Centre of Excellence, Limassol 3012, Cyprus"},{"name":"Remote Sensing and GeoEnvironment Lab, Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol 3036, Cyprus"}]},{"given":"Constantinos F.","family":"Panagiotou","sequence":"additional","affiliation":[{"name":"ERATOSTHENES Centre of Excellence, Limassol 3012, Cyprus"}]},{"given":"Maria N.","family":"Anastasiadou","sequence":"additional","affiliation":[{"name":"ERATOSTHENES Centre of Excellence, Limassol 3012, Cyprus"},{"name":"Remote Sensing and GeoEnvironment Lab, Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol 3036, Cyprus"}]},{"given":"Ioannis","family":"Varvaris","sequence":"additional","affiliation":[{"name":"ERATOSTHENES Centre of Excellence, Limassol 3012, Cyprus"},{"name":"Remote Sensing and GeoEnvironment Lab, Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol 3036, Cyprus"}]},{"given":"Christiana","family":"Papoutsa","sequence":"additional","affiliation":[{"name":"ERATOSTHENES Centre of Excellence, Limassol 3012, Cyprus"},{"name":"Remote Sensing and GeoEnvironment Lab, Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol 3036, Cyprus"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6181-0187","authenticated-orcid":false,"given":"Felix","family":"Bachofer","sequence":"additional","affiliation":[{"name":"Earth Observation Center (EOC), German Aerospace Center (DLR), 82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3853-5065","authenticated-orcid":false,"given":"Silas","family":"Michaelides","sequence":"additional","affiliation":[{"name":"ERATOSTHENES Centre of Excellence, Limassol 3012, Cyprus"},{"name":"Remote Sensing and GeoEnvironment Lab, Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol 3036, Cyprus"}]},{"given":"Diofantos","family":"Hadjimitsis","sequence":"additional","affiliation":[{"name":"ERATOSTHENES Centre of Excellence, Limassol 3012, Cyprus"},{"name":"Remote Sensing and GeoEnvironment Lab, Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol 3036, Cyprus"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wu, J., Niu, B., He, Y., Zu, J., Li, M., and Zhang, X. (2020). Vegetation Expansion on the Tibetan Plateau and Its Relationship with Climate Change. Remote Sens., 12.","DOI":"10.3390\/rs12244150"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ding, Y., Feng, Y., Chen, K., and Zhang, X. (2024). Analysis of Spatial and Temporal Changes in Vegetation Cover and Its Drivers in the Aksu River Basin, China. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-60575-9"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"158618","DOI":"10.1016\/j.scitotenv.2022.158618","article-title":"Linking Land Use Land Cover Change to Global Groundwater Storage","volume":"853","author":"Dasgupta","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Eliades, M., Michaelides, S., Evagorou, E., Fotiou, K., Fragkos, K., Leventis, G., Theocharidis, C., Panagiotou, C.F., Mavrovouniotis, M., and Neophytides, S. (2023). Earth Observation in the EMMENA Region: Scoping Review of Current Applications and Knowledge Gaps. Remote Sens., 15.","DOI":"10.20944\/preprints202307.0683.v1"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1080\/10106049.2019.1629647","article-title":"Land Use\/Land Cover in View of Earth Observation: Data Sources, Input Dimensions, and Classifiers\u2014A Review of the State of the Art","volume":"36","author":"Pandey","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Buchhorn, M., Lesiv, M., Tsendbazar, N.-E., Herold, M., Bertels, L., and Smets, B. (2020). Copernicus Global Land Cover Layers\u2014Collection 2. Remote Sens., 12.","DOI":"10.3390\/rs12061044"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 Global Land Cover: Algorithm Refinements and Characterization of New Datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V.R., Murayama, Y., and Ranagalage, M. (2020). Sentinel-2 Data for Land Cover\/Use Mapping: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12142291"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, C., and Li, X. (2022). Land Use and Land Cover Mapping in the Era of Big Data. Land, 11.","DOI":"10.3390\/land11101692"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"168354","DOI":"10.1016\/j.scitotenv.2023.168354","article-title":"Analysis of the Spatiotemporal Changes in Global Land Cover from 2001 to 2020","volume":"908","author":"Jing","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.5194\/essd-16-1353-2024","article-title":"GLC_FCS30D: The First Global 30 m Land-Cover Dynamics Monitoring Product with a Fine Classification System for the Period from 1985 to 2022 Generated Using Dense-Time-Series Landsat Imagery and the Continuous Change-Detection Method","volume":"16","author":"Zhang","year":"2024","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nie, T., Dong, G., Jiang, X., and Lei, Y. (2021). Spatio-Temporal Changes and Driving Forces of Vegetation Coverage on the Loess Plateau of Northern Shaanxi. Remote Sens., 13.","DOI":"10.3390\/rs13040613"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhai, H., Lv, C., Liu, W., Yang, C., Fan, D., Wang, Z., and Guan, Q. (2021). Understanding Spatio-Temporal Patterns of Land Use\/Land Cover Change under Urbanization in Wuhan, China, 2000\u20132019. Remote Sens., 13.","DOI":"10.3390\/rs13163331"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"169690","DOI":"10.1016\/j.scitotenv.2023.169690","article-title":"Insight into Land Cover Dynamics and Water Challenges under Anthropogenic and Climatic Changes in the Eastern Nile Delta: Inference from Remote Sensing and GIS Data","volume":"913","author":"Youssef","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_15","first-page":"63","article-title":"Land Use\/Land Cover Change Assessment of Halda Watershed Using Remote Sensing and GIS","volume":"23","author":"Chowdhury","year":"2020","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_16","first-page":"100699","article-title":"Monitoring of Land Use and Land Cover Changes by Using Remote Sensing and GIS Techniques at Human-Induced Mangrove Forests Areas in Bangladesh","volume":"25","author":"Faruque","year":"2022","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Quamar, M.M., Al-Ramadan, B., Khan, K., Shafiullah, M., and El Ferik, S. (2023). Advancements and Applications of Drone-Integrated Geographic Information System Technology\u2014A Review. Remote Sens., 15.","DOI":"10.3390\/rs15205039"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kumar, M., Singh, R.B., Singh, A., Pravesh, R., Majid, S.I., and Tiwari, A. (2023). Introduction of Geographic Information System. Geographic Information Systems in Urban Planning and Management, Springer Nature.","DOI":"10.1007\/978-981-19-7855-5"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1002\/esp.3943","article-title":"The Geomorphology of the Anthropocene: Emergence, Status and Implications","volume":"42","author":"Brown","year":"2017","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"160943","DOI":"10.1016\/j.scitotenv.2022.160943","article-title":"Land Cover Change in Global Drylands: A Review","volume":"863","author":"Wang","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2501","DOI":"10.1038\/s41467-021-22702-2","article-title":"Global Land Use Changes Are Four Times Greater than Previously Estimated","volume":"12","author":"Winkler","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.5194\/essd-12-1217-2020","article-title":"Annual Dynamics of Global Land Cover and Its Long-Term Changes from 1982 to 2015","volume":"12","author":"Liu","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1002\/ldr.4466","article-title":"Global Desert Expansion during the 21st Century: Patterns, Predictors and Signals","volume":"34","author":"Wu","year":"2023","journal-title":"Land Degrad. Dev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1038\/s41586-018-0411-9","article-title":"Global Land Change from 1982 to 2016","volume":"560","author":"Song","year":"2018","journal-title":"Nature"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lamchin, M., Bilintoh, T.M., Lee, W.-K., Ochir, A., and Lim, C.-H. (2022). Exploring Spatio-Temporal Change in Global Land Cover Using Categorical Intensity Analysis. Front. For. Glob. Chang., 5.","DOI":"10.3389\/ffgc.2022.994713"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104203","DOI":"10.1016\/j.jaridenv.2020.104203","article-title":"Land Desertification and Its Influencing Factors in Kazakhstan","volume":"180","author":"Hu","year":"2020","journal-title":"J. Arid Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, Y., and Xu, E. (2023). The Spatiotemporal Change in Land Cover and Discrepancies within Different Countries on the Qinghai\u2013Tibet Plateau over a Recent 30-Year Period. Land, 12.","DOI":"10.3390\/land12091797"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4279","DOI":"10.1007\/s40808-023-01752-z","article-title":"Desertification Modeling in the Moroccan Middle Atlas Using Sentinel-2A Images and TCT Indexes (Case of the Ain Nokra Forest)","volume":"9","author":"Ouiaboub","year":"2023","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"159942","DOI":"10.1016\/j.scitotenv.2022.159942","article-title":"Multifaceted Responses of Vegetation to Average and Extreme Climate Change Over Global Drylands","volume":"858","author":"He","year":"2023","journal-title":"Sci. Total Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3317\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:50:07Z","timestamp":1760111407000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3317"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,7]]},"references-count":29,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173317"],"URL":"https:\/\/doi.org\/10.3390\/rs16173317","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,7]]}}}