Evaluating a new dual-polarimetric decomposition for forest mapping in Australian tropical savannas

Le résumé fourni par la source

Savanna ecosystems cover approximately 20% of the global land surface and support substantial woody biomass, yet their forest extent remains poorly quantified. Accurate forest extent maps are essential for ecosystem monitoring, carbon accounting, and biodiversity conservation. However, current methods are optimised for tall-tree forests and may be inaccurate in other biomes. This work evaluated a recently published dual-polarimetric decomposition method, applicable to SAR data with and without phase, for mapping forests in tropical savannas. The decomposition was applied to Sentinel-1, NovaSAR-1, and PALSAR-2 data, each stacked with Sentinel-2 multispectral imagery. Random forest and extreme gradient boosting classification algorithms were trained on canopy height models produced by drone-mounted lidar. The new decomposition significantly outperformed the dual-polarisation entropy-alpha method, and decomposition features ranked among the most important predictors. PALSAR-2 produced the highest single-sensor accuracies, while PALSAR-2 and Sentinel-1 stack combined with Sentinel-2 yielded the best overall results. The XGB classifier achieved a k -fold cross-validated weighted average F1 score of 77.7% and an F1 score of 86.6% for forest prediction. The total detected forest area of 1033 km 2 vastly exceeds the estimate from Australia's National Forest Inventory for the same region, indicating a substantial underestimation of forest extent in existing national datasets. These methods represent a traceable path for forest extent estimates, with important implications for regulated biodiversity and carbon market policy.

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