Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 488385
Title A Bayesian approach to combine Landsat and ALOS PALSAR time series for near real-time deforestation detection
Author(s) Reiche, J.; Bruin, S. de; Hoekman, D.H.; Verbesselt, J.; Herold, M.
Source Remote Sensing 7 (2015). - ISSN 2072-4292 - p. 4973 - 4996.
DOI https://doi.org/10.3390/rs70504973
Department(s) Laboratory of Geo-information Science and Remote Sensing
Earth System Science
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2015
Keyword(s) conditional-probability networks - remotely-sensed images - forest cover loss - tropical deforestation - brazilian amazon - accuracy assessment - classification - sar - disturbance - fusion
Abstract To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat- and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series.
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