{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T17:32:18Z","timestamp":1770485538450,"version":"3.49.0"},"reference-count":52,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,10,4]],"date-time":"2017-10-04T00:00:00Z","timestamp":1507075200000},"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>In recent years, sequential tests for detecting structural changes in time series have been adapted for deforestation monitoring using satellite data. The input time series of such sequential tests is typically a vegetation index (e.g., NDVI), which uses two or three bands and ignores all other bands. Being limited to a vegetation index will not benefit from the richer spectral information provided by newly launched satellites and will bring two bottle-necks for deforestation monitoring. Firstly, it is hard to select a suitable vegetation index a priori. Secondly, a single vegetation index is typically affected by seasonal signals, noise and other natural dynamics, which decrease its power for deforestation detection. A novel multispectral time series change monitoring method that combines dimension reduction methods with a sequential hypothesis test is proposed to address these limitations. For each location, the proposed method automatically chooses a \u201csuitable\u201d index for deforestation monitoring. To demonstrate our approach, we implemented it in two study areas: a dry tropical forest in Bolivia (time series length: 444) with strong seasonality and a moist tropical forest in Brazil (time series length: 225) with almost no seasonality. Our method significantly improves accuracy in the presence of strong seasonality, in particular the temporal lag between disturbance and its detection.<\/jats:p>","DOI":"10.3390\/rs9101025","type":"journal-article","created":{"date-parts":[[2017,10,4]],"date-time":"2017-10-04T13:53:55Z","timestamp":1507125235000},"page":"1025","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Dimension Reduction of Multi-Spectral Satellite Image Time Series to Improve Deforestation Monitoring"],"prefix":"10.3390","volume":"9","author":[{"given":"Meng","family":"Lu","sequence":"first","affiliation":[{"name":"Institute for Geoinformatics, Westf\u00e4lische Wilhelms-Universit\u00e4t M\u00fcnster (WWU), Heisenbergstra\u00dfe 2, 48149 M\u00fcnster, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eliakim","family":"Hamunyela","sequence":"additional","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7923-4309","authenticated-orcid":false,"given":"Jan","family":"Verbesselt","sequence":"additional","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8049-7069","authenticated-orcid":false,"given":"Edzer","family":"Pebesma","sequence":"additional","affiliation":[{"name":"Institute for Geoinformatics, Westf\u00e4lische Wilhelms-Universit\u00e4t M\u00fcnster (WWU), Heisenbergstra\u00dfe 2, 48149 M\u00fcnster, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.rse.2015.11.006","article-title":"Using spatial context to improve early detection of deforestation from Landsat time series","volume":"172","author":"Hamunyela","year":"2016","journal-title":"Remote Sens. 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