{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T16:39:57Z","timestamp":1784047197106,"version":"3.55.0"},"reference-count":47,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,24]],"date-time":"2019-05-24T00:00:00Z","timestamp":1558656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871223"],"award-info":[{"award-number":["41871223"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2652017116"],"award-info":[{"award-number":["2652017116"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Tracking cropland change and its spatiotemporal characteristics can provide a scientific basis for assessments of ecological restoration in reclamation areas. In 1998, an ecological restoration project (Converting Farmland to Lake) was launched in Dongting Lake, China, in which original lake areas reclaimed for cropland were converted back to lake or to poplar cultivation areas. This study characterized the resulting long-term (1998\u20132018) change patterns using the LandTrendr algorithm with Landsat time-series data derived from the Google Earth Engine (GEE). Of the total cropland affected, ~447.48 km2 was converted to lake and 499.9 km2 was converted to poplar cultivation, with overall accuracies of 87.0% and 83.8%, respectively. The former covered a wider range, mainly distributed in the area surrounding Datong Lake, while the latter was more clustered in North and West Dongting Lake. Our methods based on GEE captured cropland change information efficiently, providing data (raster maps, yearly data, and change attributes) that can assist researchers and managers in gaining a better understanding of environmental influences related to the ongoing conversion efforts in this region.<\/jats:p>","DOI":"10.3390\/rs11101234","type":"journal-article","created":{"date-parts":[[2019,5,24]],"date-time":"2019-05-24T11:20:46Z","timestamp":1558696846000},"page":"1234","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery"],"prefix":"10.3390","volume":"11","author":[{"given":"Lihong","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangnan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yibo","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1002\/eco.1637","article-title":"Effects of hydrological regulation and anthropogenic pollutants on Dongting Lake in the Yangtze floodplain","volume":"9","author":"Xu","year":"2016","journal-title":"Ecohydrology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3623","DOI":"10.1007\/s12665-014-3651-5","article-title":"Environmental monitoring and change assessment of Toshka lakes in southern Egypt using remote sensing","volume":"73","author":"Hereher","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_3","first-page":"101","article-title":"Inundation extent and flood frequency mapping using LANDSAT imagery and digital elevation models","volume":"46","author":"Qi","year":"2009","journal-title":"Mapp. Sci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"054012","DOI":"10.1088\/1748-9326\/10\/5\/054012","article-title":"Long-term agricultural land-cover change and potential for cropland expansion in the former Virgin Lands area of Kazakhstan","volume":"10","author":"Kraemer","year":"2015","journal-title":"Environ. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1126\/science.320.5879.1011a","article-title":"Free access to Landsat imagery","volume":"320","author":"Woodcock","year":"2008","journal-title":"Science"},{"key":"ref_6","first-page":"43","article-title":"Analysing land cover change using time series analysis of Landsat data and geoinformation processing. A natural experiment in Northern Greece","volume":"7104","author":"Stellmes","year":"2008","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/S0034-4257(01)00296-6","article-title":"A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery","volume":"80","author":"Rogan","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.apgeog.2017.06.004","article-title":"Multi-faceted land cover and land use change analyses in the Yellow River Basin based on dense Landsat time series: Exemplary analysis in mining, agriculture, forest, and urban areas","volume":"85","author":"Wohlfart","year":"2017","journal-title":"Appl. Geogr."},{"key":"ref_9","first-page":"1218","article-title":"Monitoring the spatio-temporal dynamics of swidden agriculture and fallow vegetation recovery using Landsat imagery in northern Laos","volume":"25","author":"Liao","year":"2015","journal-title":"Acta Geogr. Sin."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Forkuor, G., Conrad, C., Thiel, M., Zoungrana, B., and Tondoh, J. (2017). Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa. Remote Sens., 9.","DOI":"10.3390\/rs9080839"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"750","DOI":"10.5589\/m11-011","article-title":"Parcel-based classification of agricultural crops via multitemporal Landsat imagery for monitoring habitat availability of western burrowing owls in the Imperial Valley agro-ecosystem","volume":"36","author":"Falkowski","year":"2010","journal-title":"Can. J. Remote Sens."},{"key":"ref_12","unstructured":"Justice, C.J. (2015, January 17). Landsat-derived cropland mask for Tanzania using 2010\u20132013 time series and decision tree classifier methods. Proceedings of the Agu Fall Meeting, College Park, MD, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2018.09.008","article-title":"Tracking annual cropland changes from 1984 to 2016 using time-series Landsat images with a change-detection and post-classification approach: Experiments from three sites in Africa","volume":"218","author":"Xu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.isprsjprs.2017.06.013","article-title":"Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications","volume":"130","author":"Zhe","year":"2017","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1080\/19479832.2013.868372","article-title":"Current situation and needs of change detection techniques","volume":"5","author":"Lu","year":"2014","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.rse.2011.11.006","article-title":"Using annual time-series of Landsat images to assess the effects of forest restitution in post-socialist Romania","volume":"118","author":"Griffiths","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_17","first-page":"230","article-title":"Landsat time series analysis for temperate forest cover change detection in the Sierra Madre Occidental, Durango, Mexico","volume":"73","author":"Franks","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Verbesselt, J., Herold, M., Hyndman, R., Zeileis, A., and Culvenor, D. (2011, January 12\u201314). A robust approach for phenological change detection within satellite image time series. Proceedings of the Analysis of Multi-Temporal Remote Sensing Images, Trento, Italy.","DOI":"10.1109\/Multi-Temp.2011.6005042"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.12928\/telkomnika.v16i3.6876","article-title":"Optimization of Parallel K-means for Java Paddy Mapping Using Time-series Satellite Imagery","volume":"16","author":"Fatikhunnada","year":"2018","journal-title":"Telkomnika"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"012070","DOI":"10.1088\/1755-1315\/17\/1\/012070","article-title":"Extreme Drought-induced Trend Changes in MODIS EVI Time Series in Yunnan, China","volume":"17","author":"Huang","year":"2014","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2017.04.012","article-title":"Assessment of spatio-temporal changes of smallholder cultivation patterns in the Angolan Miombo belt using segmentation of Landsat time series","volume":"195","author":"Schneibel","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.rse.2018.05.005","article-title":"Mapping the timing of cropland abandonment and recultivation in northern Kazakhstan using annual Landsat time series","volume":"213","author":"Dara","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous change detection and classification of land cover using all available Landsat data","volume":"144","author":"Zhe","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.rse.2014.08.023","article-title":"Effectiveness of the BFAST algorithm for detecting vegetation response patterns in a semi-arid region","volume":"154","author":"Watts","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.ecolmodel.2016.07.019","article-title":"Land use\/cover change and regional climate change in an arid grassland ecosystem of Inner Mongolia, China","volume":"353","author":"Li","year":"2017","journal-title":"Ecol. Model."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3389\/feart.2017.00017","article-title":"Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping","volume":"5","author":"Shelestov","year":"2017","journal-title":"Front. Earth Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7494","DOI":"10.3390\/rs70607494","article-title":"Monitoring Spatial and Temporal Dynamics of Flood Regimes and Their Relation to Wetland Landscape Patterns in Dongting Lake from MODIS Time-Series Imagery","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6339","DOI":"10.1038\/srep06339","article-title":"Effects of young poplar plantations on understory plant diversity in the Dongting Lake wetlands, China","volume":"4","author":"Li","year":"2014","journal-title":"Sci. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.12.024","article-title":"Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity","volume":"185","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1109\/LGRS.2015.2409982","article-title":"First Results From the Phenology-Based Synthesis Classifier Using Landsat 8 Imagery","volume":"12","author":"Simonetti","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.gloplacha.2013.06.012","article-title":"NDVI-based vegetation changes and their responses to climate change from 1982 to 2011: A case study in the Koshi River Basin in the middle Himalayas","volume":"108","author":"Zhang","year":"2013","journal-title":"Glob. Planet. Chang."},{"key":"ref_33","first-page":"882","article-title":"Spatio-temporal Change of Vegetation NDVI and Its Relations with Regional Climate in Northern Shaanxi Province in 2000\u20132010","volume":"34","author":"Bai","year":"2014","journal-title":"Sci. Geogr. Sin."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2013.08.010","article-title":"Monitoring coniferous forest biomass change using a Landsat trajectory-based approach","volume":"139","author":"Mainknorn","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1016\/j.rse.2010.07.008","article-title":"Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr\u2014Temporal segmentation algorithms","volume":"114","author":"Kennedy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of land cover classification accuracy assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_37","first-page":"641","article-title":"Practical look at the sources of confusion in error matrix generation","volume":"59","author":"Congalton","year":"1993","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2684","DOI":"10.1016\/j.proenv.2011.09.417","article-title":"Measurement of Dongting Lake Area Based on Visual Interpretation of Polders","volume":"10","author":"Bo","year":"2011","journal-title":"Proced. Environ. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"492","DOI":"10.4028\/www.scientific.net\/KEM.500.492","article-title":"An Object-based Basic Farmland Change Detection Using High Spatial Resolution Image and GIS Data of Land Use Planning","volume":"500","author":"Zhang","year":"2012","journal-title":"Key Eng. Mater."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2018.02.050","article-title":"Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series","volume":"210","author":"Yin","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Schmidt, M., Pringle, M., Devadas, R., Denham, R., and Dan, T. (2016). A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics. Remote Sens., 8.","DOI":"10.3390\/rs8040312"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.rse.2016.03.027","article-title":"Quantifying drought-induced tree mortality in the open canopy woodlands of central Texas","volume":"181","author":"Schwantes","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Long, T., Zhang, Z., He, G., Jiao, W., Tang, C., Wu, B., Zhang, X., Wang, G., and Yin, R. (2019). 30 m resolution Global Annual Burned Area Mapping based on Landsat images and Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11050489"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.016","article-title":"Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine","volume":"185","author":"Dong","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/S2095-3119(16)61502-2","article-title":"Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer","volume":"16","author":"Shrestha","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tian, H., Wu, M., Wang, L., and Niu, Z. (2018). Mapping Early, Middle and Late Rice Extent Using Sentinel-1A and Landsat-8 Data in the Poyang Lake Plain, China. Sensors, 18.","DOI":"10.3390\/s18010185"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.rse.2017.01.030","article-title":"Revisiting the coupling between NDVI trends and cropland changes in the Sahel drylands: A case study in western Niger","volume":"191","author":"Tong","year":"2017","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/10\/1234\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:54:43Z","timestamp":1760187283000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/10\/1234"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,24]]},"references-count":47,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["rs11101234"],"URL":"https:\/\/doi.org\/10.3390\/rs11101234","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,24]]}}}