A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

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 proba...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2015, Vol.7 (5), p.4973-4996
Main Authors: Reiche, Johannes, de Bruin, Sytze, Hoekman, Dirk, Verbesselt, Jan, Herold, Martin
Format: Article
Language:English
Subjects:
Accuracy
accuracy assessment
ALOS (satellite)
ALOS PALSAR
Bayesian analysis
Bayesian approach
brazilian amazon
classification
Conditional probability
conditional-probability networks
Deforestation
disturbance
forest cover loss
fusion
Landsat
near real-time
Real time
Remote sensing
remotely-sensed images
sar
Temporal logic
Time series
tropical deforestation
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs70504973