Dimension Reduction of Multi-Spectral Satellite Image Time Series to Improve Deforestation Monitoring

Lu Meng, Hamunyela Eliakim, Verbesselt Jan, Pebesma Edzer

Forschungsartikel (Zeitschrift) | Peer reviewed

Zusammenfassung

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 “suitable” 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.

Details zur Publikation

FachzeitschriftRemote Sensing (Remote Sens.)
Jahrgang / Bandnr. / Volume9
Ausgabe / Heftnr. / Issue10
StatusVeröffentlicht
Veröffentlichungsjahr2017 (04.10.2017)
Sprache, in der die Publikation verfasst istEnglisch
DOI10.3390/rs9101025
Link zum Volltexthttp://www.mdpi.com/2072-4292/9/10/1025
Stichwörtermulti-spectral; dimension reduction; deforestation monitor; Landsat time series

Autor*innen der Universität Münster

Lu, Meng
Institut für Geoinformatik (ifgi)
Pebesma, Edzer
Professur für Geoinformatik (Prof. Pebesma)