{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T21:30:30Z","timestamp":1777498230029,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2015,7,9]],"date-time":"2015-07-09T00:00:00Z","timestamp":1436400000000},"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>Multi-temporal satellite images are available at very high revisit frequency, allowing the characterization of land-cover transitions and trajectories in greater detail. However, most change detection methods aim to capture a snapshot of land cover, for instance on an annual scale, which do not describe changes that occurred between the annual time points. In this study, we present a sub-annual change detection (SCD) approach to detect change dates from dense satellite time series. SCD estimates change dates by analyzing differences between two consecutive annual segments in two steps. To validate the proposed method, SCD was applied to real and simulated time series of MODIS 16-day NDVI from 2000 to 2012 for MODIS tile h11v05 and a sub-area in southeast Ohio, USA. The results show that SCD can successfully detect from dense time series the dates of changes representing  land-cover transitions among various vegetated land covers. In addition, SCD can achieve comparable accuracy on the date of abrupt land-cover changes for the real time series tested in the study area when compared with the trend and seasonal changes identified by a Breaks for Additive Seasonal and Trend (BFAST) method. Future effort will be given to apply the proposed approach to various remotely sensed time series for other areas with extreme climates.<\/jats:p>","DOI":"10.3390\/rs70708705","type":"journal-article","created":{"date-parts":[[2015,7,10]],"date-time":"2015-07-10T02:02:23Z","timestamp":1436493743000},"page":"8705-8727","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Detecting Change Dates from Dense Satellite Time Series Using a Sub-Annual Change Detection Algorithm"],"prefix":"10.3390","volume":"7","author":[{"given":"Shanshan","family":"Cai","sequence":"first","affiliation":[{"name":"Department of Geography, The Ohio State University, 1036 Derby Hall, 154 North Oval Mall, Columbus, OH 43210, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6088-5985","authenticated-orcid":false,"given":"Desheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Geography, The Ohio State University, 1036 Derby Hall, 154 North Oval Mall, Columbus, OH 43210, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,7,9]]},"reference":[{"key":"ref_1","unstructured":"Meyer, W.B., and Turner, I.B.L. (1994). Changes in Land Use and Land Cover: A Global Perspective, Cambridge University Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1126\/science.277.5325.494","article-title":"Human domination of earth\u2019s ecosystems","volume":"277","author":"Vitousek","year":"1997","journal-title":"Science"},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change detection techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.rse.2007.03.010","article-title":"Trajectory-based change detection for automated characterization of forest disturbance dynamics","volume":"110","author":"Kennedy","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2009.08.017","article-title":"An automated approach for reconstructing recent forest disturbance history using dense landsat time series stacks","volume":"114","author":"Huang","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1080\/00045608.2011.596357","article-title":"A spatial-temporal modeling approach to reconstructing land-cover change trajectories from multi-temporal satellite imagery","volume":"102","author":"Liu","year":"2012","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/S0167-8809(01)00190-6","article-title":"Change detection, accuracy, and bias in a sequential analysis of landsat imagery in the pearl river delta, china: Econometric techniques","volume":"85","author":"Kaufmann","year":"2001","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_9","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."},{"key":"ref_10","unstructured":"Basseville, M., and Nikiforov, V. (1993). Detection of Abrupt Changes: Theory and Application, Prentice-Hall."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/9789812565402_0001","article-title":"Segmenting time series: A survey and novel approach","volume":"Volume 57","author":"Last","year":"2004","journal-title":"Data Mining in Time Series Databases"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3310","DOI":"10.1016\/j.csda.2007.10.027","article-title":"Multiscale spectral analysis for detecting short and long range change points in time series","volume":"52","author":"Olsen","year":"2008","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/11535331_8","article-title":"Change detection in time series data using wavelet footprints","volume":"3633","author":"Sharifzadeh","year":"2005","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.rse.2014.03.021","article-title":"Development of an automated method for mapping fire history captured in landsat tm and etm plus time series across queensland, australia","volume":"148","author":"Goodwin","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2009.08.014","article-title":"Detecting trend and seasonal changes in satellite image time series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.rse.2014.09.010","article-title":"Detecting changes in vegetation trends using time series segmentation","volume":"156","author":"Jamali","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_17","unstructured":"MODIS Modis Vegetation Index (mod 13): Algorithm Theoretical Basis Document (Version 3), Available online:http:\/\/modis.Gsfc.Nasa.Gov\/data\/atbd\/atbd_mod13.Pdf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2006.06.018","article-title":"Land-cover change detection using multi-temporal modis ndvi data","volume":"105","author":"Lunetta","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_19","unstructured":"Short, N. (1992). The Landsat Tutorial Workbook: Basics of Satellite Remote Sensing, NASA. publication 1078."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/0034-4257(94)90144-9","article-title":"Change-vector analysis in multitemporal space\u2014A tool to detect and categorize land-cover change processes using high temporal-resolution satellite data","volume":"48","author":"Lambin","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1080\/01431169208904212","article-title":"The best index slope extraction (bise): A method for reducing noise in ndvi time-series","volume":"13","author":"Viovy","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"67","DOI":"10.2747\/1548-1603.43.1.67","article-title":"Denoising and wavelet-based feature extraction of MODIS multi-temporal vegetation signatures","volume":"43","author":"Bruce","year":"2006","journal-title":"GISci. Remote Sens."},{"key":"ref_23","unstructured":"Burrus, C.S., and Guo, R.A.G.H. (1998). Introduction to Wavelets and Wavelet Transforms: A Primer, Prentice Hall."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1080\/1350485042000200213","article-title":"Bai and perron\u2019s and spectral density methods for structural change detection in the us inflation process","volume":"11","author":"Boutahar","year":"2004","journal-title":"Appl. Econ. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"207","DOI":"10.5194\/hess-12-207-2008","article-title":"Detecting changes in extreme precipitation and extreme streamflow in the dongjiang river basin in southern china","volume":"12","author":"Wang","year":"2008","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_26","first-page":"103","article-title":"Off-line statistical-analysis of change-point models using non parametric and likelihood methods","volume":"77","author":"Deshayes","year":"1986","journal-title":"Lect. Notes Control Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1080\/01621459.1951.10500769","article-title":"The kolmogorov-smirnov test for goodness of fit","volume":"46","author":"Massey","year":"1951","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3291","DOI":"10.1080\/014311697217099","article-title":"The igbp-dis global 1 km land cover data set, discover: First results","volume":"18","author":"Loveland","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"Modis collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/S0034-4257(02)00078-0","article-title":"Global land cover mapping from modis: Algorithms and early results","volume":"83","author":"Friedl","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rse.2014.03.012","article-title":"Enhancing modis land cover product with a spatial-temporal modeling algorithm","volume":"147","author":"Cai","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1080\/0142639032000172433","article-title":"Outbacks: The popular construction of an emergent landscape","volume":"29","author":"McSweeney","year":"2004","journal-title":"Landsc. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2970","DOI":"10.1016\/j.rse.2010.08.003","article-title":"Phenological change detection while accounting for abrupt and gradual trends in satellite image time series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/TGRS.2002.802519","article-title":"Seasonality extraction by function fitting to time-series of satellite sensor data","volume":"40","author":"Jonsson","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.cageo.2004.05.006","article-title":"Timesat\u2014A program for analyzing time-series of satellite sensor data","volume":"30","author":"Jonsson","year":"2004","journal-title":"Comput. Geosci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/7\/8705\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:48:57Z","timestamp":1760215737000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/7\/8705"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,7,9]]},"references-count":35,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2015,7]]}},"alternative-id":["rs70708705"],"URL":"https:\/\/doi.org\/10.3390\/rs70708705","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,7,9]]}}}