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Implementation of policies to reduce forest loss challenges the Earth observation community to improve forest monitoring. An important avenue for progress is the use of new satellite missions and the combining of optical and synthetic aperture radar sensor data. Monitoring of changes in tropical forest cover has relied predominantly on optical satellite sensors because of their relative ease of processing and interpretation and the continuity of medium-resolution (10–30 m) observations since the 1970s1,2. Spaceborne synthetic aperture radar (SAR) data have the advantage of providing cloud-free observations, but these data have been comparatively underutilized in operational programmes1,2. It is rarer still for optical and SAR data to be used in combination, despite increasing evidence of the benefits of this approach

The potential role of a spaceborne SAR component within a dedicated global monitoring system for tropical rain forest areas is investigated. Use is made of NASA's airborne radar system AirSAR, which acquired C-band, L-band, and P-band polarimetric data of a colonization area located at the edge of the Colombian Amazon. Classification accuracy for primary forest, secondary forest, recently deforested areas, and pastures are studied to determine optimal wave parameter combinations, using an extensive database of 778 plots. Kappa statistics are used to compare results for different combinations. The relevance of polarimetry and the effect of speckle level are studied by incorporating the multilook pdf's of polarimetric phase differences and the polarimetric correlations. Kolmogorov-Smirnov tests of fit well confirm the agreement of theoretical pdf's used and experimental observations. In addition, possibilities for biomass estimation are studied using detailed vegetation structure measurements of bush-invaded grasslands (5 plots), secondary forest (10 plots), and primary forest (13 plots). Accuracy for land cover-type classification over 90% can only be obtained when two frequency bands are combined. L-band with HV polarization and P-band show the best possibilities for biomass estimation. After land cover-type classification, eight biomass classes can be differentiated at a high level of confidence. The results clearly indicate how SAR systems may be designed to accurately monitor processes of deforestation, land and forest degradation, and secondary forest regrowth. The effect of Faraday rotation on P-band data collected from spaceborne SAR is also taken into consideration.Index Terms-Land cover change, radar polarimetry, remote sensing by radar, tropical forests.

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.