{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T11:11:20Z","timestamp":1771845080953,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T00:00:00Z","timestamp":1580688000000},"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":["No. 2017YFB0504205"],"award-info":[{"award-number":["No. 2017YFB0504205"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 41571378)"],"award-info":[{"award-number":["No. 41571378)"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-precision information regarding the location, time, and type of land use change is integral to understanding global changes. Time series (TS) analysis of remote sensing images is a powerful method for land use change detection. To address the complexity of sample selection and the salt-and-pepper noise of pixels, we propose a bidirectional segmented detection (BSD) method based on object-level, multivariate TS, that detects the type and time of land use change from Landsat images. In the proposed method, based on the multiresolution segmentation of objects, three dimensions of object-level TS are constructed using the median of the following indices: the normalized difference vegetation index (NDVI), the normalized difference built index (NDBI), and the modified normalized difference water index (MNDWI). Then, BSD with forward and backward detection is performed on the segmented objects to identify the types and times of land use change. Experimental results indicate that the proposed BSD method effectively detects the type and time of land use change with an overall accuracy of 90.49% and a Kappa coefficient of 0.86. It was also observed that the median value of a segmented object is more representative than the commonly used mean value. In addition, compared with traditional methods such as LandTrendr, the proposed method is competitive in terms of time efficiency and accuracy. Thus, the BSD method can promote efficient and accurate land use change detection.<\/jats:p>","DOI":"10.3390\/rs12030478","type":"journal-article","created":{"date-parts":[[2020,2,5]],"date-time":"2020-02-05T03:18:48Z","timestamp":1580872728000},"page":"478","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Bidirectional Segmented Detection of Land Use Change Based on Object-Level Multivariate Time Series"],"prefix":"10.3390","volume":"12","author":[{"given":"Yuzhu","family":"Hao","sequence":"first","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"},{"name":"Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation, Nanjing University, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3033-8470","authenticated-orcid":false,"given":"Zhenjie","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"},{"name":"Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation, Nanjing University, Nanjing 210023, China"},{"name":"Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China"}]},{"given":"Qiuhao","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"},{"name":"Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation, Nanjing University, Nanjing 210023, China"}]},{"given":"Feixue","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"},{"name":"Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation, Nanjing University, Nanjing 210023, China"}]},{"given":"Beibei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"},{"name":"Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation, Nanjing University, Nanjing 210023, China"}]},{"given":"Lei","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"},{"name":"Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation, Nanjing University, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,3]]},"reference":[{"key":"ref_1","unstructured":"Turner, B., Moss, R.H., and Skole, D.L. (2020, January 31). Relating Land Use and Global Land-Cover Change: A Proposal for an IGBP-HDP Core Project. Global Change Report 1993. Available online: https:\/\/bit.ly\/2ScdeL6."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.2307\/1941591","article-title":"Beyond Global Warming: Ecology and Global Change","volume":"75","author":"Vitousek","year":"1994","journal-title":"Ecology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.isprsjprs.2016.10.007","article-title":"Urban land use extraction from Very High Resolution remote sensing imagery using a Bayesian network","volume":"122","author":"Li","year":"2016","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.rse.2017.10.030","article-title":"Detecting irrigation extent, frequency, and timing in a heterogeneous arid agricultural region using MODIS time series, Landsat imagery, and ancillary data","volume":"204","author":"Chen","year":"2018","journal-title":"Remote. Sens. Environ."},{"key":"ref_5","first-page":"119","article-title":"Surface area change detection of the Burullus Lagoon, North of the Nile Delta, Egypt, using water indices: A remote sensing approach","volume":"16","author":"Hereher","year":"2013","journal-title":"Egypt. J. Remote. Sens. Space Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.rse.2014.11.005","article-title":"An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites","volume":"158","author":"Hermosilla","year":"2015","journal-title":"Remote. Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1080\/15481603.2013.817150","article-title":"Object-oriented crop classification using multitemporal ETM+ SLC-off imagery and random forest","volume":"50","author":"Long","year":"2013","journal-title":"GISci. Remote Sens."},{"key":"ref_9","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":"Zhu","year":"2014","journal-title":"Remote. Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Zhang, J., Yang, Z., Aljaddani, A.H., Cohen, W.B., Qiu, S., and Zhou, C. (2019). Continuous monitoring of land disturbance based on Landsat time series. Remote. Sens. Environ., 111116.","DOI":"10.1016\/j.rse.2019.03.009"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"839","DOI":"10.14358\/PERS.80.9.839","article-title":"Generation of Pixel-Level SAR Image Time Series Using a Locally Adaptive Matching Technique","volume":"80","author":"Cheng","year":"2014","journal-title":"Photogram. Eng. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"035006","DOI":"10.1117\/1.JRS.11.035006","article-title":"Detecting spatio-temporal and typological changes in land use from Landsat image time series","volume":"11","author":"Wang","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.rse.2017.07.020","article-title":"The relationship between threshold-based and inflexion-based approaches for extraction of land surface phenology","volume":"199","author":"Shang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/79.790984","article-title":"Dynamic programming search for continuous speech recognition","volume":"16","author":"Ney","year":"1999","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1109\/TGRS.2011.2179050","article-title":"Satellite Image Time Series Analysis Under Time Warping","volume":"50","author":"Petitjean","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1007\/s10115-004-0154-9","article-title":"Exact indexing of dynamic time warping","volume":"7","author":"Keogh","year":"2005","journal-title":"Knowl. Inf. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.ins.2010.09.024","article-title":"Discovering multi-label temporal patterns in sequence databases","volume":"181","author":"Chen","year":"2011","journal-title":"Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2218","DOI":"10.1016\/j.ins.2009.02.016","article-title":"Mining frequent trajectory patterns in spatial\u2013temporal databases","volume":"179","author":"Lee","year":"2009","journal-title":"Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guan, X., Huang, C., Liu, G., Meng, X., and Liu, Q. (2016). Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance. Remote Sens., 8.","DOI":"10.3390\/rs8010019"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1109\/LGRS.2013.2288358","article-title":"Efficient Satellite Image Time Series Analysis Under Time Warping","volume":"11","author":"Petitjean","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.isprsjprs.2019.10.003","article-title":"A time-series classification approach based on change detection for rapid land cover mapping","volume":"158","author":"Yan","year":"2019","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1016\/j.patcog.2010.09.022","article-title":"Weighted dynamic time warping for time series classification","volume":"44","author":"Jeong","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Nasrallah, A., Baghdadi, N., Mhawej, M., Faour, G., Darwish, T., Belhouchette, H., and Darwich, S. (2018). A Novel Approach for Mapping Wheat Areas Using High Resolution Sentinel-2 Images. Sensors, 18.","DOI":"10.3390\/s18072089"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3729","DOI":"10.1109\/JSTARS.2016.2517118","article-title":"A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping","volume":"9","author":"Maus","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.apgeog.2018.05.020","article-title":"Global trends analysis of the main vegetation types throughout the past four decades","volume":"97","author":"Faour","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1109\/TGRS.2006.885408","article-title":"A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in the Polar Domain","volume":"45","author":"Bovolo","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.isprsjprs.2006.09.004","article-title":"Multiple support vector machines for land cover change detection: An application for mapping urban extensions","volume":"61","author":"Nemmour","year":"2006","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.compag.2009.06.004","article-title":"Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery","volume":"68","year":"2009","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_30","unstructured":"Cecchi, G., Engman, E.T., and Zilioli, E. (1997). Object-Based Approach to Integrate Remotely Sensed Data within a GIS Context for Land Use Changes Detection at Urban-Rural Fringe Areas, SPIE."},{"key":"ref_31","unstructured":"Blaschke, T., Burnett, C., and Pekkarinen, A. New contextual approaches using image segmentation for object-based classification. Proceedings of the Remote Sensing Image Analysis: Including the Spatial Domain."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s40808-016-0088-8","article-title":"Quantitative assessment of 2014\u20132015 land-cover changes in Azerbaijan using object-based classification of LANDSAT-8 timeseries","volume":"2","author":"Bayramov","year":"2016","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"13208","DOI":"10.3390\/rs71013208","article-title":"An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series","volume":"7","author":"Matton","year":"2015","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.rse.2011.01.009","article-title":"Object-based crop identification using multiple vegetation indices, textural features and crop phenology","volume":"115","author":"Ngugi","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_35","first-page":"119","article-title":"Object-oriented and multi-feature hierarchical change detection based on CVA for high-resolution remote sensing imagery","volume":"22","author":"Zhao","year":"2018","journal-title":"J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2006.01.013","article-title":"Forest change detection by statistical object-based method","volume":"102","author":"Bogaert","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.rse.2017.03.014","article-title":"Use of time-series L-band UAVSAR data for the classification of agricultural fields in the San Joaquin Valley","volume":"193","author":"Whelen","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3181","DOI":"10.1016\/j.rse.2008.03.013","article-title":"An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution","volume":"112","author":"Bontemps","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1016\/j.patrec.2012.06.009","article-title":"Spatio-temporal reasoning for the classification of satellite image time series","volume":"33","author":"Petitjean","year":"2012","journal-title":"Pattern Recognit. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat Surface Reflectance Data Set for North America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","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_42","unstructured":"(2012, June 20). Pat Scaramuzza; Esad Micijevic; Gyanesh Chander SLC Gap-Filled Products Phase One Methodology, Available online: http:\/\/landsat.usgs.gov\/documents\/SLC_Gap_Fill_Methodology.pdf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s12524-012-0235-2","article-title":"Air Pollution Modeling from Remotely Sensed Data Using Regression Techniques","volume":"41","author":"Mozumder","year":"2013","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_44","first-page":"171","article-title":"Effects of post-fire wood management strategies on vegetation recovery and land surface temperature (LST) estimated from Landsat images","volume":"44","author":"Vlassova","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2786","DOI":"10.1080\/01431161.2015.1047991","article-title":"Comparison of data gap-filling methods for Landsat ETM+ SLC-off imagery for monitoring forest degradation in a semi-deciduous tropical forest in Mexico","volume":"36","author":"Franklin","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","first-page":"12","article-title":"Multiresolution Segmentation: An optimization approach for high quality multi-scale image segmentation","volume":"12","author":"Baatz","year":"2000","journal-title":"Beitr\u00e4ge zum AGIT-Symposium"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2013.11.018","article-title":"Automated parameterisation for multi-scale image segmentation on multiple layers","volume":"88","author":"Csillik","year":"2014","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","article-title":"Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery","volume":"115","author":"Myint","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Shen, L., Wu, L., Dai, Y., Qiao, W., and Wang, Y. (2017). Topic Modelling for Object-Based Unsupervised Classification of VHR Panchromatic Satellite Images Based on Multiscale Image Segmentation. Remote Sens., 9.","DOI":"10.3390\/rs9080840"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1109\/TGRS.2008.2009355","article-title":"Texture and Scale in Object-Based Analysis of Subdecimeter Resolution Unmanned Aerial Vehicle (UAV) Imagery","volume":"47","author":"Laliberte","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.isprsjprs.2011.02.006","article-title":"Unsupervised image segmentation evaluation and refinement using a multi-scale approach","volume":"66","author":"Johnson","year":"2011","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2825","DOI":"10.1080\/01431161003745608","article-title":"Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: Scale, texture and image objects","volume":"32","author":"Kim","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","first-page":"21","article-title":"Automated object-based classification of topography from SRTM data","volume":"141\u2013142","author":"Eisank","year":"2012","journal-title":"Geomorphology"},{"key":"ref_54","first-page":"628","article-title":"Parameters of Multi-Segmentation based on Segmentation Evaluation Function","volume":"33","author":"Shi","year":"2018","journal-title":"Remote Sens. Tec. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Cui, W., Gao, L., Le, W., and Li, D. (2008, January 29). Study on geographic ontology based on object-oriented remote sensing analysis. Proceedings of the International Conference on Earth Observation Data Processing and Analysis (ICEODPA), Wuhan, China.","DOI":"10.1117\/12.815691"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s11355-011-0158-z","article-title":"Object-based classification of land cover and tree species by integrating airborne LiDAR and high spatial resolution imagery data","volume":"8","author":"Sasaki","year":"2012","journal-title":"Landscape Ecol. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.isprsjprs.2014.12.026","article-title":"Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery","volume":"102","author":"Ma","year":"2015","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pce.2009.11.014","article-title":"Landsat TM image segmentation for delineating geological zone correlated vegetation stratification in the Kruger National Park, South Africa","volume":"55\u201357","author":"Munyati","year":"2013","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Wang, B., Chen, Z., Zhu, A.-X., Hao, Y., and Xu, C. (2019). Multi-Level Classification Based on Trajectory Features of Time Series for Monitoring Impervious Surface Expansions. Remote Sens., 11.","DOI":"10.3390\/rs11060640"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1016\/S1671-2927(11)60136-3","article-title":"NDVI-Based Lacunarity Texture for Improving Identification of Torreya Using Object-Oriented Method","volume":"10","author":"Han","year":"2011","journal-title":"Agric. Sci. China"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/j.eswa.2007.09.067","article-title":"Object-oriented change detection for the city of Harare, Zimbabwe","volume":"36","author":"Gamanya","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1007\/s10980-005-5238-8","article-title":"Quantifying Spatiotemporal Patterns of Urban Land-use Change in Four Cities of China with Time Series Landscape Metrics","volume":"20","author":"Seto","year":"2005","journal-title":"Landsc. Ecol."},{"key":"ref_64","first-page":"931","article-title":"Radarsat Time Series Analysis and Short-time Change Detection of Regional Land-use\/Land-cover","volume":"11","author":"Qian","year":"2007","journal-title":"J. Remote Sens."},{"key":"ref_65","unstructured":"Tan, Y., Bai, B., and Mohammad, M.S. (2016, January 4\u20136). Time series remote sensing based dynamic monitoring of land use and land cover change. Proceedings of the IEEE 2016 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA), Guangzhou, China."},{"key":"ref_66","unstructured":"Congalton, R.G. (1989, January 10\u201314). Considerations And Techniques For Assessing The Accuracy of Remotely Sensed Data. Proceedings of the IEEE 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1016\/j.rse.2010.05.003","article-title":"Assessing the accuracy of land cover change with imperfect ground reference data","volume":"114","author":"Foody","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_68","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/478\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:54:09Z","timestamp":1760172849000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/478"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,3]]},"references-count":68,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12030478"],"URL":"https:\/\/doi.org\/10.3390\/rs12030478","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,3]]}}}