{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T01:51:47Z","timestamp":1773193907127,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T00:00:00Z","timestamp":1638144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land use and land cover change (LUCC) modeling has continuously been a major research theme in the field of land system science, which interprets the causes and consequences of land use dynamics. In particular, models that can obtain long-term land use data with high precision are of great value in research on global environmental change and climate impact, as land use data are important model input parameters for evaluating the effect of human activity on nature. However, the accuracy of existing reconstruction and prediction models is inadequate. In this context, this study proposes an integrated convolutional neural network (CNN) LUCC reconstruction and prediction model (CLRPM), which meets the demand for fine-scale LUCC reconstruction and prediction. This model applies the deep learning method, which far exceeds the performance of traditional machine learning methods, and uses CNN to extract spatial features and provide greater proximity information. Taking Baicheng city in Northeast China as an example, we verify that CLRPM achieved high-precision annual LUCC reconstruction and prediction, with an overall accuracy rate 9.38% higher than that of the existing models. Additionally, the error rate was reduced by 49.5%. Moreover, this model can perform multilevel LUCC classification category reconstructions and predictions. This study casts light on LUCC models within the high-precision and fine-grained LUCC categories, which will aid LUCC analyses and help decision-makers better understand complex land-use systems and develop better land management strategies.<\/jats:p>","DOI":"10.3390\/rs13234846","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4846","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["An Integrated CNN Model for Reconstructing and Predicting Land Use\/Cover Change: A Case Study of the Baicheng Area, Northeast China"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3531-9498","authenticated-orcid":false,"given":"Yubo","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Earth Sciences, Jilin University, Changchun 130021, China"},{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiuchun","family":"Yang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongyan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Jilin University, Changchun 130021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Public Administration and Law, Northeast Agricultural University, Harbin 150036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5565-535X","authenticated-orcid":false,"given":"Lingxue","family":"Yu","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengqin","family":"Yan","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liping","family":"Chang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yubo, Z., Zhuoran, Y., Jiuchun, Y., Yuanyuan, Y., and Shuwen, Z. (2020). A Novel Model Integrating Deep Learning for Land Use\/Cover Change Reconstruction: A Case Study of Zhenlai County, Northeast China. Remote Sens., 12.","DOI":"10.3390\/rs12203314"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.rse.2015.12.040","article-title":"A time series analysis of urbanization induced land. use and land cover change and its impact on land surface temperature with Landsat imagery","volume":"175","author":"Fu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lyu, R., Clarke, K.C., Zhang, J., Jia, X., Feng, J., and Li, J. (2019). The impact of urbanization and climate change on ecosystem services: A case study of the city belt along the Yellow River in Ningxia, China. Comput. Environ. Urb. Syst., 77.","DOI":"10.1016\/j.compenvurbsys.2019.101351"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1177\/030913339702100303","article-title":"Modelling and monitoring land-cover change processes in tropical regions","volume":"21","author":"Lambin","year":"1997","journal-title":"Prog. Phys. Geogr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"20666","DOI":"10.1073\/pnas.0704119104","article-title":"The emergence of land change science for global environmental change and sustainability","volume":"104","author":"Lambin","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1641\/0006-3568(2003)053[0077:TIOLUL]2.0.CO;2","article-title":"The importance of land-use legacies to ecology and conservation","volume":"53","author":"Foster","year":"2003","journal-title":"Bioscience"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/08941920500394857","article-title":"Land use legacies and the future of southern Appalachia","volume":"19","author":"Gragson","year":"2006","journal-title":"Soc. Nat. Resour."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/S0959-3780(01)00007-3","article-title":"The causes of land-use and land-cover change: Moving beyond the myths","volume":"11","author":"Lambin","year":"2001","journal-title":"Glob. Environ. Chang.-Hum. Policy Dimens."},{"key":"ref_9","unstructured":"Mora, B., Romijn, E., and Herold, M. (2016, January 9\u201310). Monitoring progress towards Sustainable Development Goals\u2014The role of land monitoring. Proceedings of the 5thGEOSS Science and Technology Stakeholder Workshop Linking the Sustainable Development Goals to Earth Observations, Models and Capacity Building, Berkeley, CA, USA."},{"key":"ref_10","unstructured":"Porter, J.R. (2005). Science Plan and Implementation Strategy, GLP. IGPB Report No. 53\/IHDP Report No. 19."},{"key":"ref_11","unstructured":"Liverman, D., Rockstr\u00f6m, J., Visbek, M., Leemans, R., Abrahamse, T., Becker, B., D\u2019Sousa, R., Jones, K., Mooney, H., and Niang, I. (2013). Future Earth Initial Design, ICSU."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3882","DOI":"10.3390\/en8053882","article-title":"Using a Cellular Automata-Markov Model to Reconstruct Spatial Land-Use Patterns in Zhenlai County, Northeast China","volume":"8","author":"Yang","year":"2015","journal-title":"Energies"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.scitotenv.2017.06.241","article-title":"Effects of land use and climate change on ecosystem services in Central Asia\u2019s arid regions: A case study in Altay Prefecture, China","volume":"607\u2013608","author":"Fu","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100963","DOI":"10.1016\/j.ecoser.2019.100963","article-title":"Effects of land use and land cover change on ecosystem services in the Koshi River Basin, Eastern Nepal","volume":"38","author":"Rimal","year":"2019","journal-title":"Ecosyst. Serv."},{"key":"ref_15","first-page":"64","article-title":"Assessment of land cover change and desertification using remote sensing technology in a local region of Mongolia","volume":"57","author":"Munkhnasan","year":"2016","journal-title":"Adv. Space Res. Off. J. Comm. Space Res. (COSPAR)"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s002679900095","article-title":"Using Dynamic Modeling to Scope Environmental Problems and Build Consensus","volume":"22","author":"Costanza","year":"1998","journal-title":"Environ. Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1080\/10807039.2018.1468994","article-title":"A review of approaches to land use changes modeling","volume":"25","author":"Noszczyk","year":"2019","journal-title":"Hum. Ecol. Risk Assess."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.ecolind.2018.01.047","article-title":"Modeling land use change using Cellular Automata and Artificial Neural Network: The case of Chunati Wildlife Sanctuary, Bangladesh","volume":"88","author":"Islam","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nath, B., Wang, Z., Ge, Y., Islam, K., and Niu, Z. (2020). Land Use and Land Cover Change Modeling and Future Potential Landscape Risk Assessment Using Markov-CA Model and Analytical Hierarchy Process. Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9020134"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1111\/1365-2664.12453","article-title":"Evaluating the combined effects of climate and land-use change on tree species distributions","volume":"52","author":"Svenning","year":"2015","journal-title":"J. Appl. Ecol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.envsoft.2013.09.010","article-title":"Inductive pattern-based land use\/cover change models: A comparison of four software packages","volume":"51","author":"Mas","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1970","DOI":"10.1016\/j.scib.2020.07.005","article-title":"70 years of urban expansion across China: Trajectory, pattern, and national policies","volume":"65","author":"Kuang","year":"2020","journal-title":"Sci. Bull."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.scib.2020.10.022","article-title":"Global observation of urban expansion and land-cover dynamics using satellite big-data","volume":"66","author":"Kuang","year":"2020","journal-title":"Sci. Bull."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"63","DOI":"10.5194\/essd-13-63-2021","article-title":"A 30 m resolution dataset of China\u2019s urban impervious surface area and green space, 2000\u20132018","volume":"13","author":"Kuang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Koomen, E., and Stillwell, J. (2007). Modelling Land-Use Change. Modelling Land-Use Change, Springer.","DOI":"10.1007\/1-4020-5648-6"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"41275","DOI":"10.1038\/srep41275","article-title":"Analyzing historical land use changes using a Historical Land Use Reconstruction Model: A case study in Zhenlai County, northeastern China","volume":"7","author":"Yang","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Aung, S.W.Y., Khaing, S.S., and Aung, S.T. (2018). Multi-label Land Cover Indices Classification of Satellite Images Using Deep Learning. Proceedings of the International Conference on Big Data Analysis and Deep Learning Applications, Miyazaki, Japan, 14\u201315 May 2018, Springer.","DOI":"10.1007\/978-981-13-0869-7_11"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Lyu, H.B., Lu, H., and Mou, L.C. (2016). Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection. Remote Sens., 8.","DOI":"10.3390\/rs8060506"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1109\/TIFS.2020.3013204","article-title":"A Siamese CNN for Image Steganalysis","volume":"16","author":"You","year":"2021","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10189","DOI":"10.1109\/JSTARS.2021.3106481","article-title":"GAN-Based LUCC Prediction via the Combination of Prior City Planning Information and Land-Use Probability","volume":"14","author":"Sun","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Guo, L., Xi, X., Yang, W., and Liang, L. (2021). Monitoring Land Use\/Cover Change Using Remotely Sensed Data in Guangzhou of China. Sustainability, 13.","DOI":"10.3390\/su13052944"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s11442-014-1082-6","article-title":"Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s","volume":"24","author":"Liu","year":"2014","journal-title":"J. Geogr. Sci."},{"key":"ref_35","first-page":"970","article-title":"Research and compilation of the Geomorphological Atlas of the People\u2019s Republic of China","volume":"29","author":"Zhou","year":"2010","journal-title":"Geogr. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"111425","DOI":"10.1016\/j.rse.2019.111425","article-title":"Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product","volume":"235","author":"Shao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2018.11.014","article-title":"Joint Deep Learning for land cover and land use classification","volume":"221","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"111411","DOI":"10.1016\/j.rse.2019.111411","article-title":"Deep learning based winter wheat mapping using statistical data as ground references in Kansas and northern Texas, US","volume":"233","author":"Zhong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.jfo.2018.02.029","article-title":"Simulateur de chirurgie de cataracte EyeSi: Validit\u00e9 de construction des modules capsulorhexis, phaco\u00e9mulsification et aspiration des masses cristalliniennes","volume":"42","author":"Conart","year":"2019","journal-title":"J. Fran\u00e7ais D\u2019ophtalmol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, K., Feng, M., Biswas, A., Su, H., Niu, Y., and Cao, J. (2020). Driving Factors and Future Prediction of Land Use and Cover Change Based on Satellite Remote Sensing Data by the LCM Model: A Case Study from Gansu Province, China. Sensors, 20.","DOI":"10.3390\/s20102757"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5061","DOI":"10.1016\/j.eswa.2009.12.004","article-title":"A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm","volume":"37","author":"Aci","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2016.2616418","article-title":"Advanced Spectral Classifiers for Hyperspectral Images A review","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.cageo.2013.10.008","article-title":"Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information","volume":"63","author":"Cracknell","year":"2014","journal-title":"Comput. Geosci."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","first-page":"e00971","article-title":"Land use\/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms","volume":"22","author":"Ge","year":"2020","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_52","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv Preprint."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017, January 4\u201310). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Laurens, V., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1007\/s10980-010-9519-5","article-title":"Diagnostic tools to evaluate a spatial land change projection along a gradient of an explanatory variable","volume":"25","author":"Chen","year":"2010","journal-title":"Landsc. Ecol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.compenvurbsys.2012.01.001","article-title":"A comparative analysis of cellular automata models for simulation of small urban areas in Galicia, NW Spain","volume":"36","author":"Crecente","year":"2012","journal-title":"Comput. Environ. Urb. Syst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s00168-007-0138-2","article-title":"Comparing the input, output, and validation maps for several models of land change","volume":"42","author":"Pontius","year":"2008","journal-title":"Ann. Reg. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.isprsjprs.2011.03.003","article-title":"Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics","volume":"66","author":"Bodart","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_60","first-page":"203","article-title":"Evaluation of forest cover estimates for Haiti using supervised classification of Landsat data","volume":"30","author":"Churches","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.isprsjprs.2014.09.004","article-title":"A parametric model for classifying land cover and evaluating training data based on multi-temporal remote sensing data","volume":"97","author":"Glanz","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2015.08.004","article-title":"Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data","volume":"169","author":"Kontgis","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.rse.2005.08.012","article-title":"Spatial and temporal patterns of China\u2019s cropland during 1990\u20132000: An analysis based on Landsat TM data","volume":"98","author":"Liu","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Liu, J., Tian, H., Liu, M., Zhuang, D., Melillo, J.M., and Zhang, Z. (2005). China\u2019s changing landscape during the 1990s: Large-scale land transformations estimated with satellite data. Geophys. Res. Lett., 32.","DOI":"10.1029\/2004GL021649"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s11442-010-0483-4","article-title":"Spatial patterns and driving forces of land use change in China during the early 21st century","volume":"20","author":"Liu","year":"2010","journal-title":"J. Geogr. Sci."},{"key":"ref_66","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_67","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.landurbplan.2012.01.004","article-title":"Assessing spatiotemporal variations of greenness in the Baltimore\u2014Washington corridor area","volume":"105","author":"Tang","year":"2012","journal-title":"Landsc. Urb. Plan."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4846\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:37:20Z","timestamp":1760168240000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4846"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,29]]},"references-count":67,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234846"],"URL":"https:\/\/doi.org\/10.3390\/rs13234846","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,29]]}}}