{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T01:43:56Z","timestamp":1772588636736,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T00:00:00Z","timestamp":1625616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Heilongjiang Bureau of Surveying, Mapping Geographic Information","award":["202004"],"award-info":[{"award-number":["202004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Land use and land cover (LULC) are fundamental units of human activities. Therefore, it is of significance to accurately and in a timely manner obtain the LULC maps where dramatic LULC changes are undergoing. Since 2017 April, a new state-level area, Xiong\u2019an New Area, was established in China. In order to better characterize the LULC changes in Xiong\u2019an New Area, this study makes full use of the multi-temporal 10-m Sentinel-2 images, the cloud-computing Google Earth Engine (GEE) platform, and the powerful classification capability of random forest (RF) models to generate the continuous LULC maps from 2017 to 2020. To do so, a novel multiple RF-based classification framework is adopted by outputting the classification probability based on each monthly composite and aggregating the multiple probability maps to generate the final classification map. Based on the obtained LULC maps, this study analyzes the spatio-temporal changes of LULC types in the last four years and the different change patterns in three counties. Experimental results indicate that the derived LULC maps achieve high accuracy for each year, with the overall accuracy and Kappa values no less than 0.95. It is also found that the changed areas account for nearly 36%, and the dry farmland, impervious surface, and other land-cover types have changed dramatically and present varying change patterns in three counties, which might be caused by the latest planning of Xiong\u2019an New Area. The obtained 10-m four-year LULC maps in this study are supposed to provide some valuable information on the monitoring and understanding of what kinds of LULC changes have taken place in Xiong\u2019an New Area.<\/jats:p>","DOI":"10.3390\/ijgi10070464","type":"journal-article","created":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T12:31:25Z","timestamp":1625661085000},"page":"464","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Characterizing the Up-To-Date Land-Use and Land-Cover Change in Xiong\u2019an New Area from 2017 to 2020 Using the Multi-Temporal Sentinel-2 Images on Google Earth Engine"],"prefix":"10.3390","volume":"10","author":[{"given":"Jiansong","family":"Luo","sequence":"first","affiliation":[{"name":"Heilongjiang Institute of Geomatics Engineering, Harbin 150081, China"}]},{"given":"Xinwen","family":"Ma","sequence":"additional","affiliation":[{"name":"Heilongjiang Institute of Geomatics Engineering, Harbin 150081, China"}]},{"given":"Qifeng","family":"Chu","sequence":"additional","affiliation":[{"name":"Heilongjiang Institute of Geomatics Engineering, Harbin 150081, China"}]},{"given":"Min","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Agronomy, Inner Mongolia Agricultural University, Hohhot 010019, China"}]},{"given":"Yujia","family":"Cao","sequence":"additional","affiliation":[{"name":"Heilongjiang Institute of Geomatics Engineering, Harbin 150081, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1016\/j.isprsjprs.2011.04.001","article-title":"Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery","volume":"66","author":"Saadat","year":"2011","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2011.07.020","article-title":"Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data","volume":"117","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2015.01.001","article-title":"Global land cover mapping using Earth observation satellite data: Recent progresses and challenges","volume":"103","author":"Ban","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hao, B., Ma, M., Li, S., Li, Q., Hao, D., Huang, J., Ge, Z., Yang, H., and Han, X. (2019). Land Use Change and Climate Variation in the Three Gorges Reservoir Catchment from 2000 to 2015 Based on the Google Earth Engine. Sensors, 19.","DOI":"10.3390\/s19092118"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s11442-018-1462-4","article-title":"Examining urban land-cover characteristics and ecological regulation during the construction of Xiong\u2019an New District, Hebei Province, China","volume":"28","author":"Kuang","year":"2018","journal-title":"J. Geog. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yu, M., Guo, S., Guan, Y., Cai, D., Zhang, C., Fraedrich, K., Liao, Z., Zhang, X., and Tian, Z. (2021). Spatiotemporal Heterogeneity Analysis of Yangtze River Delta Urban Agglomeration: Evidence from Nighttime Light Data (2001\u20132019). Remote Sens., 13.","DOI":"10.3390\/rs13071235"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1016\/j.gloenvcha.2013.03.006","article-title":"Land-cover change in the conterminous United States from 1973 to 2000","volume":"23","author":"Sleeter","year":"2013","journal-title":"Glob. Environ. Chang."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, J., Zheng, X., Zhang, C., and Chen, Y. (2018). Impact of Land-Use and Land-Cover Change on Meteorology in the Beijing\u2013Tianjin\u2013Hebei Region from 1990 to 2010. Sustainability, 10.","DOI":"10.3390\/su10010176"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.buildenv.2018.03.035","article-title":"Predicting effect of forthcoming population growth\u2013induced impervious surface increase on regional thermal environment: Xiong\u2019an New Area, North China","volume":"136","author":"Xu","year":"2018","journal-title":"Build. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, Z., de Jong, M., Li, F., Brand, N., Hertogh, M., and Dong, L. (2020). Towards Developing a New Model for Inclusive Cities in China\u2014The Case of Xiong\u2019an New Area. Sustainability, 12.","DOI":"10.3390\/su12156195"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.scib.2018.05.002","article-title":"Long-term surface water changes and driving cause in Xiong\u2019an, China: From dense Landsat time series images and synthetic analysis","volume":"63","author":"Song","year":"2018","journal-title":"Sci. Bull."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Cao, J. (2021). Assessing and Predicting the Impact of Multi-Scenario Land Use Changes on the Ecosystem Service Value: A Case Study in the Upstream of Xiong\u2019an New Area, China. Sustainability, 13.","DOI":"10.3390\/su13020704"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106503","DOI":"10.1016\/j.agwat.2020.106503","article-title":"Impact of urbanization on precipitation and temperature over a lake-marsh wetland: A case study in Xiong\u2019an New Area, China","volume":"243","author":"Su","year":"2021","journal-title":"Agric. Water Manage."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Z., Cao, J., Zhu, C., and Yang, H. (2020). The Impact of Land Use Change on Ecosystem Service Value in the Upstream of Xiong\u2019an New Area. Sustainability, 12.","DOI":"10.3390\/su12145707"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, S., Cui, Y., Li, N., Deng, X., Shi, X., Liu, X., and Zhao, F. (2018, January 28\u201330). In the study of interannual change of urban expansion, precipitation and water area of baiyang lake in Xiong\u2019an new area. Proceedings of the 2018 26th International Conference on Geoinformatics, Kunming, China.","DOI":"10.1109\/GEOINFORMATICS.2018.8557084"},{"key":"ref_16","unstructured":"Gao, P., Wang, S., Li, W., and Gao, X. (2021, July 03). Analysis of Spatial and Temporal Variation of Land Use in Xiong\u2019an New Area Based on Re-mote Sensing Data. Available online: https:\/\/www.x-mol.com\/paper\/1376443291721510912?adv."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Kumar, L., and Mutanga, O. (2018). Google Earth Engine Applications since Inception: Usage, Trends, and Potential. Remote Sens., 10.","DOI":"10.3390\/rs10101509"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","article-title":"Google Earth Engine for geo-big data applications: A meta-analysis and systematic review","volume":"164","author":"Tamiminia","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2011.12.003","article-title":"Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture","volume":"121","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dabija, A., Kluczek, M., Zagajewski, B., Raczko, E., Kycko, M., Al-Sulttani, A.H., Tard\u00e0, A., Pineda, L., and Corbera, J. (2021). Comparison of Support Vector Machines and Random Forests for Corine Land Cover Mapping. Remote Sens., 13.","DOI":"10.3390\/rs13040777"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Naboureh, A., Ebrahimy, H., Azadbakht, M., Bian, J., and Amani, M. (2020). RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12213484"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.rse.2018.02.055","article-title":"High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform","volume":"209","author":"Liu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Mondal, P., Liu, X., Fatoyinbo, T.E., and Lagomasino, D. (2019). Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa. Remote Sens., 11.","DOI":"10.3390\/rs11242928"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, M., Huang, H., Li, Z., Hackman, K.O., Liu, C., Andriamiarisoa, R.L., Ny Aina Nomenjanahary Raherivelo, T., Li, Y., and Gong, P. (2020). Automatic High-Resolution Land Cover Production in Madagascar Using Sentinel-2 Time Series, Tile-Based Image Classification and Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12213663"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pratic\u00f2, S., Solano, F., Di Fazio, S., and Modica, G. (2021). Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation. Remote Sens., 13.","DOI":"10.3390\/rs13040586"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Forstmaier, A., Shekhar, A., and Chen, J. (2020). Mapping of Eucalyptus in Natura 2000 Areas Using Sentinel 2 Imagery and Artificial Neural Networks. Remote Sens., 12.","DOI":"10.3390\/rs12142176"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.isprsjprs.2020.01.001","article-title":"Examining earliest identifiable timing of crops using all available Sentinel 1\/2 imagery and Google Earth Engine","volume":"161","author":"You","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"111951","DOI":"10.1016\/j.rse.2020.111951","article-title":"Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images","volume":"247","author":"Wang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from TM imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","first-page":"87","article-title":"Using thematic mapper data to identify contrasting soil plains and tillage practices","volume":"63","author":"Ward","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.12.009","article-title":"Mapping paddy rice agriculture in southern China using multi-temporal MODIS images","volume":"95","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"111205","DOI":"10.1016\/j.rse.2019.05.024","article-title":"Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4\u20138 and Sentinel-2 imagery","volume":"231","author":"Qiu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"112163","DOI":"10.1016\/j.rse.2020.112163","article-title":"Investigating ESA Sentinel-2 products\u2019 systematic cloud cover overestimation in very high altitude areas","volume":"252","author":"Tiede","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.isprsjprs.2020.11.024","article-title":"Per-pixel land cover accuracy prediction: A random forest-based method with limited reference sample data","volume":"172","author":"Ebrahimy","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2016.10.010","article-title":"Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas","volume":"187","author":"Pelletier","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wessels, J.K., Van den Bergh, F., Roy, P.D., Salmon, P.B., Steenkamp, C.K., MacAlister, B., Swanepoel, D., and Jewitt, D. (2016). Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers. Remote Sens., 8.","DOI":"10.3390\/rs8110888"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2012.10.031","article-title":"Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation","volume":"129","author":"Olofsson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"111510","DOI":"10.1016\/j.rse.2019.111510","article-title":"Annual maps of global artificial impervious area (GAIA) between 1985 and 2018","volume":"236","author":"Gong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"110987","DOI":"10.1016\/j.rse.2018.11.030","article-title":"Tracking annual changes of coastal tidal flats in China during 1986\u20132016 through analyses of Landsat images with Google Earth Engine","volume":"238","author":"Wang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Xiong, J., Thenkabail, S.P., Tilton, C.J., Gumma, K.M., Teluguntla, P., Oliphant, A., Congalton, G.R., Yadav, K., and Gorelick, N. (2017). Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine. Remote Sens., 9.","DOI":"10.3390\/rs9101065"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, Q., Qiu, C., Ma, L., Schmitt, M., and Zhu, X.X. (2020). Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12040602"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/7\/464\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:27:00Z","timestamp":1760164020000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/7\/464"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,7]]},"references-count":54,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["ijgi10070464"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10070464","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,7]]}}}