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

Johannes Reiche,Dirk Hoekman,Martin Herold,Jan Verbesselt,Sytze De Bruin

doi:10.3390/rs70504973

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.

Highlights

Information on deforestation is crucial to effectively manage and protect forest resources in the tropics [1,2]
We demonstrate a proof of concept using Landsat NDVI and ALOS PALSAR L-band backscatter time series of an evergreen forest plantation (Pinus caribaea) located in Fiji
Providing deforestation information in near real-time (NRT) and at medium-resolution scale can provide a powerful tool for governments and forestry stakeholders to improve the management and conservation of forest resources

Summary

Introduction

Information on deforestation is crucial to effectively manage and protect forest resources in the tropics [1,2]. Managed and illegal activities in natural and plantation forest cause a wide range of negative environmental effects, significant financial losses and depression of the world timber price [3]. For example, represents 5%–10% of the commercial value of all wood products traded on the global market [4]. To fully understand illegal forest activities, such as laundering, for example, deforestation information from both natural forest and plantation forest are essential [3]. In this context, the importance of forest plantations is often undervalued [5]. Communities and forestry stakeholders in improving forest management and enacting timely actions against illegal deforestation, near real-time (NRT) deforestation monitoring can provide a powerful tool [1,2,7,8]

Methods

Results

Conclusion