{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T01:59:20Z","timestamp":1784167160380,"version":"3.55.0"},"reference-count":63,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,15]],"date-time":"2019-07-15T00:00:00Z","timestamp":1563148800000},"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":["2016YFB0501404"],"award-info":[{"award-number":["2016YFB0501404"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Finer Resolution Imagery Based Urban Refine Management and Application Demonstration System","award":["06-Y30B04-9002-13\/15"],"award-info":[{"award-number":["06-Y30B04-9002-13\/15"]}]},{"name":"Science and Technology Plans of Ministry of Housing and Urban-Rural Development of the Peoples Republic of China, , and Opening projects of Beijing Advanced Innovation Centre for Future Urban Design, Beijing University of Civil Engineering and Architectur","award":["VDC2017021422"],"award-info":[{"award-number":["VDC2017021422"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Urban Land Use\/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.<\/jats:p>","DOI":"10.3390\/s19143120","type":"journal-article","created":{"date-parts":[[2019,7,16]],"date-time":"2019-07-16T02:23:16Z","timestamp":1563243796000},"page":"3120","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme"],"prefix":"10.3390","volume":"19","author":[{"given":"Guoyin","family":"Cai","sequence":"first","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"},{"name":"Beijing advanced innovation center for future urban design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiqun","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liuzhong","family":"Yang","sequence":"additional","affiliation":[{"name":"Remote Sensing Application Center, Ministry of Housing and Urban-Rural Development of the People\u2019s Republic of China, Beijing 100835, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[{"name":"Remote Sensing Application Center, Ministry of Housing and Urban-Rural Development of the People\u2019s Republic of China, Beijing 100835, China"},{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyi","family":"Du","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"},{"name":"Beijing advanced innovation center for future urban design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0207-9299","authenticated-orcid":false,"given":"Changshan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"},{"name":"Beijing advanced innovation center for future urban design, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"},{"name":"Department of Geography, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1080\/014311600210092","article-title":"Land cover mapping of large areas from satellites: Status and research priorities","volume":"21","author":"Cihlar","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","unstructured":"National Research Council (2005). Radiative Forcing of Climate Change: Expanding the Concept and Addressing Uncertainties, The National Academies Press."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1111\/j.1475-4959.2007.232_3.x","article-title":"Urbanization and global environmental change: Local effects of urban warming","volume":"173","author":"Grimmond","year":"2007","journal-title":"Geogr. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2793","DOI":"10.1007\/s11434-012-5268-y","article-title":"Remote sensing of environmental change over China: A review","volume":"57","author":"Gong","year":"2012","journal-title":"Chin. Sci. Bull."},{"key":"ref_5","unstructured":"(2018, August 05). Global Urban Footprint. Available online: http:\/\/www.dlr.de\/eoc\/en\/desktopdefault.aspx\/tabid-9628\/16557_read-40454\/."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.isprsjprs.2017.10.012","article-title":"Breaking new ground in mapping human settlements from space\u2014The Global Urban Footprint","volume":"134","author":"Esch","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.rse.2016.12.026","article-title":"Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD)","volume":"191","author":"Wickham","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.isprsjprs.2014.09.002","article-title":"Global land cover mapping at 30m resolution: A POK-based operational approach","volume":"103","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2004.09.005","article-title":"A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets","volume":"94","author":"Giri","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.rse.2017.05.001","article-title":"Multi-level monitoring of subtle urban changes for the megacities of China using high-resolution multi-view satellite imagery","volume":"196","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2018.05.019","article-title":"Mining regularity of landscape-structure heterogeneity to improve urban land-cover mapping","volume":"214","author":"Zheng","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1080\/01431160500297956","article-title":"Urban land cover multi-level region-based classification of VHR data by selecting relevant features","volume":"27","author":"Carleer","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2011.06.024","article-title":"Impervious surface quantification using a synthesis of artificial immune networks and decision\/regression trees from multi-sensor data","volume":"117","author":"Im","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5973","DOI":"10.1080\/01431161.2012.675451","article-title":"Land-cover classification of an intra-urban environment using high-resolution images and object-based image analysis","volume":"33","author":"Fonseca","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1016\/j.rse.2009.02.014","article-title":"A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification","volume":"113","author":"Pacifici","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2014.2380779","article-title":"Spatiotemporal detection and analysis of urban villages in mega city regions of china using high-resolution remotely sensed imagery","volume":"53","author":"Huang","year":"2015","journal-title":"IEEE Tran. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.isprsjprs.2016.02.005","article-title":"Identifying tree crown delineation shapes and need for remediation on high resolution imagery using an evidence based approach","volume":"114","author":"Leckie","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.isprsjprs.2013.11.007","article-title":"Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery","volume":"87","author":"Belgiu","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.rse.2018.05.006","article-title":"Integrating bottom-up classification and top-down feedback for improving urban land-cover and functional-zone mapping","volume":"212","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.rse.2016.03.015","article-title":"Learning selfhood scales for urban land cover mapping with very-high-resolution satellite images","volume":"178","author":"Zhang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1080\/15481603.2018.1502399","article-title":"Modelling relational contexts in GEOBIA framework for improving urban land-cover mapping","volume":"56","author":"Du","year":"2019","journal-title":"GIsci Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, J., Du, P.J., WU, C.S., Xia, J.S., and Chanussot, J. (2018). Mapping urban land cover of a large area using multiple sensors multiple features. Remote Sens., 10.","DOI":"10.3390\/rs10060872"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7232","DOI":"10.3390\/s8117323","article-title":"A comprehensive automated 3D approach for building extraction, reconstruction, and regularization from airborne laser scanning point clouds","volume":"8","author":"Dorninger","year":"2008","journal-title":"Sensors"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.rse.2016.02.019","article-title":"Fractional vegetation cover estimation algorithm for chinese gf-1 wide field view data","volume":"177","author":"Jia","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.isprsjprs.2015.05.009","article-title":"Improved capabilities of the Chinese high-resolution remote sensing satellite GF-1 for monitoring suspended particulate matter (SPM) in inland waters: Radiometric and spatial considerations","volume":"106","author":"Li","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Lin, C.X., Wang, S.X., Liu, W.L., and Tian, Y. (2016). Estimation of Building Density with the integrated use of GF-1 PMS and Radarsat-2 data. Remote Sens., 8.","DOI":"10.3390\/rs8110969"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.resconrec.2016.06.026","article-title":"Monitoring wind farms occupying grasslands based on remote-sensing data from China\u2019s GF-2 HD satellite\u2014A case study of Jiuquan city, Gansu province, China","volume":"121","author":"Shen","year":"2017","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.isprsjprs.2014.02.009","article-title":"Automated bias-compensation of rational polynomial coefficients of high resolution satellite imagery based on topographic maps","volume":"100","author":"Oh","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"691","DOI":"10.14358\/PERS.76.6.691","article-title":"Mapping Lantana camara: Accuracy Comparison of Various Fusion Techniques","volume":"76","author":"Taylor","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3230","DOI":"10.1109\/TGRS.2007.901007","article-title":"Improving Component Substitution PansharpeningThrough Multivariate Regression of MS +Pan Data","volume":"45","author":"Aiazzi","year":"2007","journal-title":"IEEE Tran. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.culher.2011.12.003","article-title":"Surveying the roofs of Rome","volume":"13","author":"Fiumi","year":"2012","journal-title":"J. Cult. Herit."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Momeni, R., Aplin, P., and Boyd, D.S. (2016). Mapping complex urban land cover from spaceborne imagery: The influence of spatial resolution, spectral band set and classification approach. Remote Sens., 8.","DOI":"10.3390\/rs8020088"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1080\/01431160902894475","article-title":"Segmentation performance evaluation for object-based remotely sensed image analysis","volume":"31","author":"Corcoran","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","unstructured":"Wagner, W., and Sz\u00e9kely, B. (2010). A review on image segmentation techniques with remote sensing perspective. ISPRS TC VII Symposium\u2014100 Years ISPRS, IAPRS."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1016\/j.rse.2011.05.013","article-title":"Object-oriented mapping of landslides using Random Forests","volume":"115","author":"Norman","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","article-title":"Change detection from remotely sensed images: From pixel-based to object-based approaches","volume":"80","author":"Hussain","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7941","DOI":"10.1080\/01431161.2014.978042","article-title":"Well site extraction from Landsat-5 TM imagery using an object-and pixel-based image analysis method","volume":"35","author":"Salehi","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2017.05.010","article-title":"Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery","volume":"310","author":"Mahdianpari","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/13658810903174803","article-title":"ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data","volume":"24","author":"Tiede","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2011.11.020","article-title":"A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery","volume":"118","author":"Duro","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_44","first-page":"259","article-title":"Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping","volume":"35","author":"Poursanidis","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4502","DOI":"10.1080\/01431161.2011.649864","article-title":"Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests","volume":"33","author":"Duro","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1080\/01431161.2014.1001086","article-title":"Assessing machine-learning algorithms and image-and lidar-derived variables for GEOBIA classification of mining and mine reclamation","volume":"36","author":"Maxwell","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"153","DOI":"10.3390\/rs70100153","article-title":"Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery","volume":"7","author":"Qian","year":"2015","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kaszta, Z., Kerchove, R.V.D., Ramoelo, A., Cho, M.A., Madonsela, S., Mathieu, R., and Wolff, E. (2016). Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel- and Object-Based Approaches and Selected Classification Algorithms. Remote Sens., 8.","DOI":"10.3390\/rs8090763"},{"key":"ref_49","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_50","unstructured":"(2018, August 05). Random Forests. Available online: http:\/\/www.stat.berkeley.edu\/~breiman\/RandomForests\/cc_home.htm."},{"key":"ref_51","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_52","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_53","doi-asserted-by":"crossref","first-page":"3105","DOI":"10.1080\/01431160701469016","article-title":"Textural and local spatial statistics for the object-oriented classification of urban areas using high resolution imagery","volume":"29","author":"Su","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Tang, H. (2019). A comprehensive evaluation of approaches for build-up area extraction from Landsat OLI images using massive samples. Remote Sens., 11.","DOI":"10.20944\/preprints201812.0067.v1"},{"key":"ref_55","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_56","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_57","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compenvurbsys.2012.06.003","article-title":"A review of regional science applications of satellite remote sensing in urban settings","volume":"37","author":"Patino","year":"2013","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.landurbplan.2009.08.006","article-title":"Monitoring loss of biodiversity in cultural landscapes. New methodology based on satellite data","volume":"94","author":"Ramil","year":"2010","journal-title":"Landsc. Urban Plan."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.compenvurbsys.2017.02.002","article-title":"Employing crowdsourced geographic data and multi-temporal\/multi-sensor satellite imagery to monitor land cover change: A case study in an urbanizing region of the Philippines","volume":"64","author":"Johnson","year":"2017","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_61","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_62","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1559\/152304010792194949","article-title":"Spatial autoregressive model for population estimation at the census block level using LIDAR-derived building volume information","volume":"37","author":"Qiu","year":"2010","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.ufug.2014.11.006","article-title":"Understanding the dynamic of greenspace in the urbanized area of Beijing based on high resolution satellite images","volume":"14","author":"Qian","year":"2015","journal-title":"Urban For. Urban Green."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/14\/3120\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:05:48Z","timestamp":1760187948000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/14\/3120"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,15]]},"references-count":63,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["s19143120"],"URL":"https:\/\/doi.org\/10.3390\/s19143120","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,15]]}}}