{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T05:55:44Z","timestamp":1770962144721,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2014,9,23]],"date-time":"2014-09-23T00:00:00Z","timestamp":1411430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spurious change is a common problem in urban vegetation change detection by using multi-temporal remote sensing images of high resolution. This usually results from the false-absent and false-present vegetation patches in an obscured and\/or shaded scene. The presented approach focuses on object-based change detection with joint use of spatial and spectral information, referring to it as multi-level spatial analyses. The analyses are conducted in three phases: (1) The pixel-level spatial analysis is performed by adding the density dimension into a multi-feature space for classification to indicate the spatial dependency between pixels; (2) The member-level spatial analysis is conducted by the  self-adaptive morphology to readjust the incorrectly classified members according to the spatial dependency between members; (3) The object-level spatial analysis is reached by the self-adaptive morphology involved with the additional rule of sharing boundaries. Spatial analysis at this level will help detect spurious change objects according to the spatial dependency between objects. It is revealed that the error from the automatically extracted vegetation objects with the pixel- and member-level spatial analyses is no more than 2.56%, compared with 12.15% without spatial analysis. Moreover, the error from the automatically detected spurious changes with the object-level spatial analysis is no higher than 3.26% out of all the dynamic vegetation objects, meaning that the fully automatic detection of vegetation change at a joint maximum error of 5.82% can be guaranteed.<\/jats:p>","DOI":"10.3390\/rs6099086","type":"journal-article","created":{"date-parts":[[2014,9,23]],"date-time":"2014-09-23T10:20:41Z","timestamp":1411467641000},"page":"9086-9103","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Multi-Level Spatial Analysis for Change Detection of Urban Vegetation at Individual Tree Scale"],"prefix":"10.3390","volume":"6","author":[{"given":"Jianhua","family":"Zhou","sequence":"first","affiliation":[{"name":"Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5628-0003","authenticated-orcid":false,"given":"Bailang","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China"}]},{"given":"Jun","family":"Qin","sequence":"additional","affiliation":[{"name":"Shanghai Botanical Garden, Shanghai 200231, China"}]}],"member":"1968","published-online":{"date-parts":[[2014,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1080\/0143116031000101675","article-title":"Digital change detection methods in ecosystem monitoring: A review","volume":"25","author":"Coppin","year":"2004","journal-title":"Int. J. Remote Sens"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.rse.2005.10.023","article-title":"Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis","volume":"100","author":"Small","year":"2006","journal-title":"Remote Sens. Environ"},{"key":"ref_3","first-page":"46","article-title":"Assessing different remote sensing techniques to detect land use\/cover changes in the eastern Mediterranean","volume":"11","author":"Berberoglu","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wulder, M.A., and Franklin, S.E. (2006). Understanding Forest Disturbance and Spatial pattern: Remote Sensing and GIS Approaches, CRC Press.","DOI":"10.1201\/9781420005189"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6426","DOI":"10.1080\/01431161.2012.688148","article-title":"Potential effects in multi-resolution post-classification change detection","volume":"33","author":"Colditz","year":"2012","journal-title":"Int. J. Remote Sens"},{"key":"ref_6","first-page":"213","article-title":"Detecting the nature of change in an urban environment: A comparison of machine learning algorithms","volume":"67","author":"Chan","year":"2001","journal-title":"Photogram. Eng. Remote Sens"},{"key":"ref_7","first-page":"431","article-title":"Assessment of changes in urban green spaces of Mashad city using satellite data","volume":"11","author":"Rafiee","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1080\/014311698216053","article-title":"Land-use mapping and change detection in a coal mining area\u2014A case study in the Jharia Coalfield, India","volume":"19","author":"Prakash","year":"1998","journal-title":"Int. J. Remote Sens"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1016\/j.rse.2010.01.014","article-title":"Ice sheet change detection by satellite image defferencing","volume":"114","author":"Bindschadler","year":"2010","journal-title":"Remote Sens. Environ"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1080\/02757259609532305","article-title":"Digital change detection in forest ecosystems with remote sensing imagery","volume":"13","author":"Coppin","year":"1996","journal-title":"Remote Sens. Rev"},{"key":"ref_11","unstructured":"Malila, W.A. (1980, January 3\u20136). Change vector analysis: An approach for detecting forest changes with Landsat. West Lafayette, IN, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"455","DOI":"10.5589\/m08-062","article-title":"Impact of sun-surface-sensor geometry upon multitemporal high spatial resolution satellite imagery","volume":"34","author":"Wulder","year":"2008","journal-title":"Can. J. Remote Sens"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4434","DOI":"10.1080\/01431161.2011.648285","article-title":"Object-based change detection","volume":"33","author":"Chen","year":"2012","journal-title":"Int. J. Remote Sens"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1080\/01431160151144369","article-title":"Estimation of urban vegetation abundance by spectral mixture analysis","volume":"22","author":"Small","year":"2001","journal-title":"Int. J. Remote Sens"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1016\/j.rse.2007.07.005","article-title":"Spectral mixture analysis for mapping abundance of urban surface components from the Terra\/ASTER data","volume":"112","author":"Pu","year":"2007","journal-title":"Remote Sens. Environ"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.1016\/j.rse.2009.06.004","article-title":"The influence of urban structures on impervious surface maps from airborne hyperspectral data","volume":"113","author":"Hostert","year":"2009","journal-title":"Remote Sens.Environ"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1109\/TPAMI.2003.1206520","article-title":"Detecting moving shadows: Algorithms and evaluations","volume":"25","author":"Prati","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1661","DOI":"10.1109\/TGRS.2006.869980","article-title":"A comparative study on shadow compensation of color aerial images in invariant color models","volume":"44","author":"Tsai","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_19","first-page":"1","article-title":"Methods of extracting distribution information of plants at urban darken areas and repairing their brightness","volume":"6","author":"Zhou","year":"2011","journal-title":"J. E. China Norm. Univ. (Natl. Sci. Ed.)"},{"key":"ref_20","first-page":"1299","article-title":"Automatic shadow detection and compensation of aerial remote sensing images","volume":"37","author":"Gao","year":"2012","journal-title":"Geomat. Inf. Sci. Wuhan Univ"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7359","DOI":"10.1080\/01431161.2010.523727","article-title":"Quantifying the robustness of fuzzy rule sets in object based image analysis","volume":"32","author":"Hofmann","year":"2011","journal-title":"Int. J. Remote Sens"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic object-based image analysis: A new paradigm in remote sensing and geographic information science","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS Int. J. Photogram. Remote Sens"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS Int. J. Photogram. Remote Sens"},{"key":"ref_24","unstructured":"Strobl, J., Blaschke, T., and Griesebner, G. (2000). Angewandte Geographische Informationsverarbeitung XII, Wichmann-Verlag."},{"key":"ref_25","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 Int. J. Photogram. Remote Sens"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/380995.380999","article-title":"Support vector machines: Hype or Hallelujah?","volume":"2","author":"Bennett","year":"2000","journal-title":"SIGKDD Explor"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/0034-4257(92)90058-R","article-title":"Estimating vegetation amount from visible and near infrared reflectances","volume":"41","author":"Price","year":"1992","journal-title":"Remote Sens. Environ"},{"key":"ref_28","first-page":"524","article-title":"Mathematic descriptors for identifying plant species: A case study on urban landscape vegetation","volume":"15","author":"Zhou","year":"2011","journal-title":"J. Remote Sens"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/9\/9086\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:16:15Z","timestamp":1760217375000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/9\/9086"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,9,23]]},"references-count":29,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2014,9]]}},"alternative-id":["rs6099086"],"URL":"https:\/\/doi.org\/10.3390\/rs6099086","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,9,23]]}}}