Conference Agenda

Synthetic Aperture Radar (SAR) has emerged as a critical data source for continuous and reliable forest monitoring, particularly in tropical regions where persistent cloud cover limits the applicability of optical remote sensing. Owing to its sensitivity to structural and dielectric properties of vegetation, SAR enables the study of forest dynamics through a variety of polarimetric and interferometric derivatives. This study investigates the potential of polarimetric and interferometric SAR variables derived from C-band Sentinel-1 data for deforestation mapping across tropical and sub-tropical forest regions. Specifically, the research focuses on the Haldwani Forest Range in India, which is characterized by complex forest management activities and seasonal soil moisture variations that pose challenges to accurate change detection.

A statistical change detection algorithm, Cumulative Sums of Change (CuSUM) was employed to analyze backscatter time-series data for identifying abrupt and persistent changes indicative of deforestation events. The backscatter-driven change detection algorithm was applied using VH-polarized Sentinel-1 data. However, while this approach effectively captured major deforestation events, it also resulted in a considerable number of false positives, primarily arising from grazing activity and short-term soil moisture variations that caused fluctuations in backscatter intensity unrelated to forest loss. To address this limitation, a coherence-based compensation mechanism was developed to refine the backscatter-derived change maps.

In this proposed framework, temporal coherence values were used as a weighting factor to assess the reliability of detected changes. Coherence served as an indicator to distinguish between genuine deforestation and temporary disturbances. A rule-based decision-making approach was then implemented to retain or discard changes flagged by the backscatter-based method based on the corresponding coherence values. Specifically, changes associated with low coherence were examined to confirm whether they represented consistent structural alteration (e.g., tree removal) or temporary effects (e.g., grazing or moisture fluctuations). This coherence-based decision mechanism significantly reduced the number of false positives, thereby improving the accuracy and reliability of deforestation detection.

The results demonstrated a marked improvement in classification accuracy through the integration of coherence information. Using a backscatter-only approach, the overall accuracy and kappa coefficient achieved were 0.58 and 0.23, respectively, for VH polarization. After applying the coherence-based compensation approach, these values improved by approximately 39.6% (overall accuracy = 0.81) and 120.8% (κ = 0.53), indicating a substantial reduction in commission errors. The study highlights the importance of incorporating temporal coherence as an auxiliary variable in SAR-based change detection to mitigate false alarms caused by dynamic environmental factors such as soil moisture variations, isolated rainfall events, and inconsistencies in land cover classification.