{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T10:34:43Z","timestamp":1783766083645,"version":"3.55.0"},"reference-count":48,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T00:00:00Z","timestamp":1716854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB3902100"],"award-info":[{"award-number":["2022YFB3902100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Yellow River Delta (YRD), known for its vast and diverse wetland ecosystem, is the largest estuarine delta in China. However, human activities and climate change have significantly degraded the wetland ecosystem in recent decades in the YRD. Therefore, an understanding of the land use modifications is essential for the efficient management and preservation of ecosystems in this region. This study utilized time series of remote sensing data and the extreme gradient boosting method to generate land use maps of the YRD from 2000 to 2020. Several methods, including transition matrix, land use dynamic degree, and standard deviation ellipse, were employed to explore the characteristics of land use transitions. The results underscore significant spatial variations in land use over the past two decades. The most rapid increase was observed in built-up area, followed by terrestrial water and tidal flats, while unutilized land experienced the fastest decrease, followed by forest\u2013grassland. The spatial distribution patterns of agricultural land, built-up area, terrestrial water, and forest\u2013grassland demonstrated stronger directionality compared to other land use types. The wetlands have expanded in size and improved in structure. Unutilized land has been converted into artificial wetlands comprising ponds, reservoirs, salt ponds, shrimp and crab ponds, and natural wetlands featuring mudflats and forest\u2013grassland. The wetland conservation efforts after 2008 have proven very effective, playing a positive role in ecological and environmental preservation, as well as in regional sustainable development.<\/jats:p>","DOI":"10.3390\/rs16111946","type":"journal-article","created":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T13:32:55Z","timestamp":1716903175000},"page":"1946","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Monitoring Land Use Changes in the Yellow River Delta Using Multi-Temporal Remote Sensing Data and Machine Learning from 2000 to 2020"],"prefix":"10.3390","volume":"16","author":[{"given":"Yunyang","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Geography, Nanjing Normal University, Nanjing 210046, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1647-1950","authenticated-orcid":false,"given":"Linlin","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5027-8121","authenticated-orcid":false,"given":"Zilu","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiqing","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Yao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjin","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4849-775X","authenticated-orcid":false,"given":"Rajiv","family":"Pandey","sequence":"additional","affiliation":[{"name":"Indian Council of Forestry Research and Education (ICFRE), Dehradun 248006, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1196-1248","authenticated-orcid":false,"given":"Aqil","family":"Tariq","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Department of Wildlife, Fisheries and Aquaculture, College of the Forest Resources, Mississippi State University, Starkville, MS 39762-9690, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Luo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6322-8307","authenticated-orcid":false,"given":"Qingting","family":"Li","sequence":"additional","affiliation":[{"name":"Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,28]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1126\/science.1150195","article-title":"Global Change and the Ecology of Cities","volume":"319","author":"Grimm","year":"2008","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Huang, C., Zhang, C., He, Y., Liu, Q., Li, H., Su, F., Liu, G., and Bridhikitti, A. (2020). Land Cover Mapping in Cloud-Prone Tropical Areas Using Sentinel-2 Data: Integrating Spectral Features with Ndvi Temporal Dynamics. Remote Sens., 12.","DOI":"10.3390\/rs12071163"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"381","DOI":"10.2307\/2339461","article-title":"On the Probable Errors of Frequency-Constants","volume":"71","author":"Edgeworth","year":"1908","journal-title":"J. R. Stat. Soc."},{"key":"ref_5","first-page":"S1","article-title":"On the Generalized Distance in Statistics","volume":"80","author":"Mahalanobis","year":"2018","journal-title":"Sankhy\u0101 Indian J. Stat. Ser. A (2008-)"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support Vector Machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/S0020-7373(87)80053-6","article-title":"Simplifying Decision Trees","volume":"27","author":"Quinlan","year":"1987","journal-title":"Int. J. Man-Mach. Stud."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","article-title":"Learning Deep Architectures for AI","volume":"2","author":"Bengio","year":"2009","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/TGRS.2004.837325","article-title":"Results and Implications of a Study of Fifteen Years of Satellite Image Classification Experiments","volume":"43","author":"Wilkinson","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.rse.2018.11.032","article-title":"Deep Learning Based Multi-Temporal Crop Classification","volume":"221","author":"Zhong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"042609","DOI":"10.1117\/1.JRS.11.042609","article-title":"Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools, and Challenges for the Community","volume":"11","author":"Ball","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1016\/j.csr.2007.01.031","article-title":"An Overview of Physical and Ecological Processes in the Rio de La Plata Estuary","volume":"28","author":"Mianzan","year":"2008","journal-title":"Cont. Shelf Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.jhazmat.2009.03.125","article-title":"Characterization, Ecological Risk Assessment and Source Diagnostics of Polycyclic Aromatic Hydrocarbons in Water Column of the Yellow River Delta, One of the Most Plenty Biodiversity Zones in the World","volume":"169","author":"Wang","year":"2009","journal-title":"J. Hazard. Mater."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1007\/s12665-016-5864-2","article-title":"Social\u2013Ecological Challenges in the Yellow River Basin (China): A Review","volume":"75","author":"Wohlfart","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1016\/j.ecoleng.2009.03.022","article-title":"Evaluating the Ecological Performance of Wetland Restoration in the Yellow River Delta, China","volume":"35","author":"Cui","year":"2009","journal-title":"Ecol. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107301","DOI":"10.1016\/j.catena.2023.107301","article-title":"The Salinization Process and Its Response to the Combined Processes of Climate Change\u2013Human Activity in the Yellow River Delta between 1984 and 2022","volume":"231","author":"Guo","year":"2023","journal-title":"Catena"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"140585","DOI":"10.1016\/j.scitotenv.2020.140585","article-title":"Reclamation Shifts the Evolutionary Paradigms of Tidal Channel Networks in the Yellow River Delta, China","volume":"742","author":"Xie","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1080\/13504509.2011.556814","article-title":"Water Shortages and Countermeasures for Sustainable Utilisation in the Context of Climate Change in the Yellow River Delta Region, China","volume":"18","author":"Zhang","year":"2011","journal-title":"Int. J. Sustain. Dev. World Ecol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106201","DOI":"10.1016\/j.ocecoaman.2022.106201","article-title":"Monitoring and Projecting Sustainable Transitions in Urban Land Use Using Remote Sensing and Scenario-Based Modelling in a Coastal Megacity","volume":"224","author":"Lu","year":"2022","journal-title":"Ocean Coast. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105892","DOI":"10.1016\/j.ecolind.2019.105892","article-title":"Vegetation Dynamics and the Relations with Climate Change at Multiple Time Scales in the Yangtze River and Yellow River Basin, China","volume":"110","author":"Zhang","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/0025-3227(93)90025-Q","article-title":"Historical Changes in the Yellow River Delta, China","volume":"113","author":"Xue","year":"1993","journal-title":"Mar. Geol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3898","DOI":"10.1002\/grl.50758","article-title":"Land Subsidence at Aquaculture Facilities in the Yellow River Delta, China","volume":"40","author":"Higgins","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s12665-011-1491-0","article-title":"Wetland Loss and Degradation in the Yellow River Delta, Shandong Province of China","volume":"67","author":"Wang","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(97)00104-1","article-title":"On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index","volume":"62","author":"Carlson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.rse.2012.01.003","article-title":"Image Texture as a Remotely Sensed Measure of Vegetation Structure","volume":"121","author":"Wood","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kupidura, P. (2019). The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11101233"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/36.752194","article-title":"Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co-Occurrence Matrices","volume":"37","author":"Soh","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liping, C., Yujun, S., and Saeed, S. (2018). Monitoring and Predicting Land Use and Land Cover Changes Using Remote Sensing and GIS Techniques\u2014A Case Study of a Hilly Area, Jiangle, China. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0200493"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4327","DOI":"10.1002\/ldr.4039","article-title":"Dynamic Changes of Land Use\/Cover and Landscape Pattern in a Typical Alpine River Basin of the Qinghai-Tibet Plateau, China","volume":"32","author":"Li","year":"2021","journal-title":"Land Degrad. Dev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1086\/214027","article-title":"Measuring Geographic Concentration by Means of the Standard Deviational Ellipse","volume":"32","author":"Lefever","year":"1926","journal-title":"Am. J. Sociol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s41748-018-0083-5","article-title":"Assessing Land Use\/Land Cover Dynamic and Its Impact in Benin Republic Using Land Change Model and CCI-LC Products","volume":"3","author":"Guidigan","year":"2019","journal-title":"Earth Syst. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"166239","DOI":"10.1016\/j.scitotenv.2023.166239","article-title":"Dynamic Landscapes and the Influence of Human Activities in the Yellow River Delta Wetland Region","volume":"899","author":"Dou","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Murshed, M.G.S., Murphy, C., Hou, D., Khan, N., Ananthanarayanan, G., and Hussain, F. (2021). Machine Learning at the Network Edge: A Survey. ACM Comput. Surv., 54.","DOI":"10.1145\/3469029"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1111\/tgis.12174","article-title":"Modeling Urban Land Use Changes Using Support Vector Machines","volume":"20","author":"Bajat","year":"2016","journal-title":"Trans. GIS"},{"key":"ref_38","first-page":"100351","article-title":"Performance Evaluation of MLE, RF and SVM Classification Algorithms for Watershed Scale Land Use\/Land Cover Mapping Using Sentinel 2 Bands","volume":"19","author":"Rana","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15481603.2019.1650447","article-title":"Land Cover and Land Use Classification Performance of Machine Learning Algorithms in a Boreal Landscape Using Sentinel-2 Data","volume":"57","author":"Abdi","year":"2020","journal-title":"GISci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/LGRS.2018.2803259","article-title":"Very High Resolution Object-Based Land Use\u2013Land Cover Urban Classification Using Extreme Gradient Boosting","volume":"15","author":"Georganos","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Shao, Z., Ahmad, M.N., and Javed, A. (2024). Comparison of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Features for Mapping Urban Impervious Surface. Remote Sens., 16.","DOI":"10.3390\/rs16040665"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.ocecoaman.2017.06.004","article-title":"Need to Link River Management with Estuarine Wetland Conservation: A Case Study in the Yellow River Delta, China","volume":"146","author":"Zhou","year":"2017","journal-title":"Ocean Coast. Manag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.4028\/www.scientific.net\/AMR.864-867.2403","article-title":"Effect of Ecological Water Supplement on Land Use and Land Cover Changes in Diaokou River","volume":"864\u2013867","author":"Dong","year":"2013","journal-title":"Adv. Mater. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1075914","DOI":"10.3389\/fevo.2022.1075914","article-title":"Evolution of Habitat Quality and Analysis of Influencing Factors in the Yellow River Delta Wetland from 1986 to 2020","volume":"10","author":"Zhang","year":"2022","journal-title":"Front. Ecol. Evol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.ecoleng.2015.11.017","article-title":"Landscape and Avifauna Changes as an Indicator of Yellow River Delta Wetland Restoration","volume":"86","author":"Chen","year":"2016","journal-title":"Ecol. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"147644","DOI":"10.1016\/j.scitotenv.2021.147644","article-title":"Dynamic Landscapes and the Driving Forces in the Yellow River Delta Wetland Region in the Past Four Decades","volume":"787","author":"Zhang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yin, L., Zheng, W., Shi, H., Wang, Y., and Ding, D. (2023). Spatiotemporal Heterogeneity of Coastal Wetland Ecosystem Services in the Yellow River Delta and Their Response to Multiple Drivers. Remote Sens., 15.","DOI":"10.3390\/rs15071866"},{"key":"ref_48","first-page":"213","article-title":"Land Use Change and Its Correlation with Habitat Quality in High Efficiency Eco-Economic Zone of Yellow River Delta","volume":"40","author":"Jia","year":"2020","journal-title":"Bull. Soil Water Conserv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1946\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:49:45Z","timestamp":1760107785000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1946"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,28]]},"references-count":48,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16111946"],"URL":"https:\/\/doi.org\/10.3390\/rs16111946","relation":{"is-referenced-by":[{"id-type":"doi","id":"10.1007\/s00704-025-05590-0","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,28]]}}}