{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T23:10:47Z","timestamp":1772752247721,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2016,11,23]],"date-time":"2016-11-23T00:00:00Z","timestamp":1479859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Project of High Resolution Earth Observation System","award":["00-Y30B14-9001-14\/16-1"],"award-info":[{"award-number":["00-Y30B14-9001-14\/16-1"]}]},{"name":"Project of High Resolution Earth Observation System","award":["00-Y30B14-9001-14\/16-2"],"award-info":[{"award-number":["00-Y30B14-9001-14\/16-2"]}]},{"name":"Project of High Resolution Earth Observation System","award":["00-Y30B15-9001-14\/16-1"],"award-info":[{"award-number":["00-Y30B15-9001-14\/16-1"]}]},{"name":"the National Natural Science Foundation","award":["41201441"],"award-info":[{"award-number":["41201441"]}]},{"name":"the National Key Research Program","award":["2016YFC0803004"],"award-info":[{"award-number":["2016YFC0803004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Building density, as a component of impervious surface fraction, is a significant indicator of population distribution as essentially all humans live and conduct activities in buildings. Because population spatialization usually occurs over large areas, large-scale building density estimation through a proper, time-efficient, and relatively precise way is urgently required. Therefore, this study constructed a decision tree by the Classification and Regression Tree (CART) algorithm combining synthetic aperture radar (SAR) with optical images. The input features included four spectral bands (B1\u20134) of GF-1 PMS imagery; Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Ratio Built-up Index (RBI) derived from them; and backscatter intensity (BI) of Radarsat-2 SAR data. In addition, a new index called amended backscatter intensity (ABI), which takes the influence created by different spatial patterns into account, was introduced and calculated through fractal dimension and lacunarity. Result showed that before the integration use of multisource data, a model using B1\u20134, NDVI, NDWI, and RBI had the highest accuracy, with RMSE of 10.28 and R2 of 0.63 for Jizhou and RMSE of 20.34 and R2 of 0.36 for Beijing. In Comparison, the best model after combining two data sources (i.e., the model employing B1\u20134, NDVI, NDWI, RBI and ABI) reduced the RMSE to 8.93 and 16.21 raised the R2 to 0.80 and 0.64, respectively. The result indicated that the synergistic use of optical and SAR data has the potential to improve the building density estimation performance and the addition of ABI has a better capacity for improving the model than other input features.<\/jats:p>","DOI":"10.3390\/rs8110969","type":"journal-article","created":{"date-parts":[[2016,11,23]],"date-time":"2016-11-23T11:12:14Z","timestamp":1479899534000},"page":"969","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data"],"prefix":"10.3390","volume":"8","author":[{"given":"Yi","family":"Zhou","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenxi","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shixin","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenliang","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Tian","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1111\/j.1538-4632.2006.00694.x","article-title":"Population estimation using Landsat enhanced thematic mapper imagery","volume":"39","author":"Wu","year":"2007","journal-title":"Geogr. Anal."},{"key":"ref_2","unstructured":"Zeng, C. (2010). Spatial and Temporal Analysis of Population Distribution in China Based on Remote Sensing Data. [Master\u2019s Thesis, Institute of Remote Sensing Applications, Chinese Acamedy of Sciences]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5635","DOI":"10.1080\/01431161.2010.496799","article-title":"Spatial refinement of census population distribution using remotely sensed estimates of impervious surfaces in haiti","volume":"31","author":"Azar","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.rse.2003.04.006","article-title":"Extending satellite remote sensing to local scales: Land and water resource monitoring using high-resolution imagery","volume":"1","author":"Sawaya","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Weng, Q. (2008). Remote Sensing of Impervious Surfaces, CRC Press.","DOI":"10.1201\/9781420043754.fmatt"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"230","DOI":"10.5589\/m02-098","article-title":"An approach for mapping large-area impervious surfaces: Synergistic use of Landsat-7 ETM+ and high spatial resolution imagery","volume":"29","author":"Yang","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.rse.2012.11.022","article-title":"Generation of fine-scale population layers using multi-resolution satellite imagery and geospatial data","volume":"130","author":"Azar","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_8","first-page":"914","article-title":"Impervious surface distribution estimation by spectral mixture analysis","volume":"11","author":"Yue","year":"2007","journal-title":"J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fan, F., Fan, W., and Weng, Q. (2015). Improving urban impervious surface mapping by linear spectral mixture analysis and using spectral indices. Can. J. Remote Sens., 41.","DOI":"10.1080\/07038992.2015.1112730"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.rse.2013.10.028","article-title":"Improving the impervious surface estimation with combined use of optical and SAR remote sensing images","volume":"141","author":"Zhang","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"161","DOI":"10.2747\/1548-1603.46.2.161","article-title":"Quantifying sub-pixel urban impervious surface through fusion of optical and insar imagery","volume":"46","author":"Yang","year":"2009","journal-title":"GISci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5447","DOI":"10.1080\/01431160701227596","article-title":"Land cover classification in the brazilian amazon with the integration of Landsat ETM+ and radarsat data","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3007","DOI":"10.1016\/j.rse.2011.06.004","article-title":"Settlement detection and impervious surface estimation in the mekong delta using optical and SAR remote sensing data","volume":"115","author":"Leinenkugel","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_14","unstructured":"Leinenkugel, P. (2010). The Combined Use of Optical and SAR Data for Large Area Impervious Surface Mapping. [Master\u2019s Thesis, University Salzburg]."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2325","DOI":"10.1080\/01431160902980324","article-title":"Block-regression based fusion of optical and SAR imagery for feature enhancement","volume":"31","author":"Zhang","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","first-page":"148","article-title":"A comparison study of impervious surfaces estimation using optical and SAR remote sensing images","volume":"18","author":"Zhang","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1080\/02757250109532436","article-title":"Remote sensing of impervious surfaces: A review","volume":"20","author":"Slonecker","year":"2001","journal-title":"Remote Sens. Rev."},{"key":"ref_18","first-page":"100","article-title":"The importance of imperviousness","volume":"1","author":"Schueler","year":"1994","journal-title":"Watershed Prot. Tech."},{"key":"ref_19","first-page":"309","article-title":"Study on extracting building density and floor area ratio based on high resolution image","volume":"3","author":"Li","year":"2007","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.landurbplan.2010.08.004","article-title":"Automated derivation of urban building density information using airborne LiDAR data and object-based method","volume":"98","author":"Yu","year":"2010","journal-title":"Landsc. Urban Plan."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wu, Q., Chen, R., Sun, H., and Cao, Y. (2011, January 11\u201313). Urban building density detection using high resolution SAR imagery. Proceedings of the 2011 Urban Remote Sensing Event (JURSE), Munich, Germany.","DOI":"10.1109\/JURSE.2011.5764715"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cao, Y., Su, C., and Liang, J. (2012, January 22\u201327). Building unit density detection from high resolution TerraSAR-X image based on mathematical morphological operators. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352246"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kajimoto, M., and Susaki, J. (2013, January 21\u201326). Urban-area extraction from polarimetric sar images using polarization orientation angle. Proceedings of the 2013 Geoscience and Remote Sensing Symposium, Melbourne, Australia.","DOI":"10.1109\/IGARSS.2012.6352274"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.rse.2014.09.006","article-title":"Urban density mapping of global megacities from polarimetric SAR images","volume":"155","author":"Susaki","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/JSTARS.2008.921099","article-title":"Spatial indexes for the extraction of formal and informal human settlements from high-resolution sar images","volume":"1","author":"Stasolla","year":"2008","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","first-page":"68","article-title":"Urban building density estimation based on VIIRS night-time satellite data-a case of Nanjing","volume":"18","author":"Zheng","year":"2014","journal-title":"Sci. Technol. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.landurbplan.2014.10.010","article-title":"A comparative study of urban expansion in Beijing, Tianjin and Shijiazhuang over the past three decades","volume":"134","author":"Wu","year":"2015","journal-title":"Landsc. Urban Plan."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1109\/TGRS.2002.802459","article-title":"A canonical problem in electromagnetic backscattering from buildings","volume":"40","author":"Franceschetti","year":"2002","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_29","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, Wadsworth International Group."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2011.02.030","article-title":"Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends","volume":"117","author":"Weng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1080\/01431160305001","article-title":"A stepwise regression tree for nonlinear approximation: Applications to estimating subpixel land cover","volume":"24","author":"Huang","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","first-page":"35","article-title":"Construction of ratio built-up index for GF-1 image","volume":"28","author":"Yang","year":"2016","journal-title":"Remote Sens. Land. Resour."},{"key":"ref_33","unstructured":"Horne, J.H., and Horne, J.H. (2003, January 4\u20135). A tasseled cap transformation for IKONOS images. Proceedings of the ASPRS 2003 Annual Conference, Anchorage, AK, USA."},{"key":"ref_34","unstructured":"Zhao, L. (2009). Building Extraction from High-Resolution SAR Imagery. [Ph.D. Thesis, National University of Defense Technology]."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Schmidt, M., Esch, T., Klein, D., Thiel, M., and Dech, S. (2010, January 25\u201330). Estimation of building density using TerraSAR-X-data. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5649543"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4963","DOI":"10.1080\/01431160600676695","article-title":"Fractal analysis of remotely sensed images: A review of methods and applications","volume":"27","author":"Sun","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"927","DOI":"10.14358\/PERS.71.8.927","article-title":"Examining lacunarity approaches in comparison with fractal and spatial autocorrelation techniques for urban mapping","volume":"71","author":"Myint","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_38","first-page":"1041","article-title":"A fractal approach to the classification of Mediterranean vegetation types in remotely sensed data","volume":"61","author":"Smde","year":"1995","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_39","first-page":"63","article-title":"Fractal characterization of hyperspectral imagery","volume":"65","author":"Qiu","year":"1999","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1559\/152304002782064600","article-title":"An evaluation of fractal methods for characterizing image complexity","volume":"29","author":"Lam","year":"2002","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_41","first-page":"725","article-title":"Fractals: Form, chance and dimension","volume":"1","author":"Mandelbrot","year":"1979","journal-title":"Phys. Today"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"373","DOI":"10.14358\/PERS.72.4.373","article-title":"Three new implementations of the triangular prism method for computing the fractal dimension of remote sensing images","volume":"72","author":"Sun","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.compenvurbsys.2005.01.007","article-title":"A study of lacunarity-based texture analysis approaches to improve urban image classification","volume":"29","author":"Myint","year":"2005","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3369","DOI":"10.1080\/014311600750019985","article-title":"Test of a new lacunarity estimation method for image texture analysis","volume":"21","author":"Dong","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","unstructured":"Voss, R., Peitgen, H.O., and Saupe, D. (1988). The Science of Fractal Images, Springer."},{"key":"ref_46","first-page":"20","article-title":"Lacunarity for spatial heterogeneity measurement in GIS","volume":"6","author":"Dong","year":"2000","journal-title":"Geogr. Inf. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2687","DOI":"10.1109\/TGRS.2002.807001","article-title":"A novel lacunarity estimation method applied to SAR image segmentation","volume":"40","author":"Gan","year":"2002","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1016\/j.patrec.2013.05.008","article-title":"A new approach to estimate lacunarity of texture images","volume":"34","author":"Backes","year":"2013","journal-title":"Pattern Recogn. Lett."},{"key":"ref_49","unstructured":"Michie, D., Spiegelhalter, D.J., Taylor, C.C., and Campbell, J. (1994). Machine Learning, Neural and Statistical Classification, Ellis Horwood Ltd."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/11\/969\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:27:16Z","timestamp":1760210836000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/11\/969"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,23]]},"references-count":49,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2016,11]]}},"alternative-id":["rs8110969"],"URL":"https:\/\/doi.org\/10.3390\/rs8110969","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,11,23]]}}}