Paper Info
Title
A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection
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.
Year
DOI
Venue
2015
10.3390/rs70504973
REMOTE SENSING
Keywords
Field
DocType
deforestation,bayesian approach,near real time
Reference data (financial markets),Bayesian inference,Conditional probability,Remote sensing,Normalized Difference Vegetation Index,Evergreen forest,Deforestation,Missing data,Geology,Bayesian probability
Journal
Volume
Issue
ISSN
7
5
2072-4292
Citations 
PageRank 
References 
8
0.62
21
Authors
5
Name
Order
Citations
PageRank
johannes reiche1553.75
Sytze de Bruin28112.35
Dirk H. Hoekman320827.80
Jan Verbesselt49516.93
herold martin510126.05