Abstract
Selective logging is the primary driver of forest degradation in the tropics and reduces the capacity of forests to harbour biodiversity, maintain key ecosystem processes, sequester carbon, and support human livelihoods. While the preceding decade has seen a tremendous improvement in the ability to monitor forest disturbances from space, large-scale (spatial and temporal) forest monitoring systems have almost universally relied on optical satellite data from the Landsat program, whose effectiveness is limited in tropical regions with frequent cloud cover. Synthetic aperture radar (SAR) data can penetrate clouds and have been utilized in forest mapping applications since the early 1990s, but only recently has SAR data been widely available on a scale sufficient to facilitate pan-tropical selective logging detection systems. Here, a detailed selective logging dataset from three lowland tropical forest regions in the Brazilian Amazon was used to assess the effectiveness of SAR data from Sentinel-1, RADARSAT-2, and Advanced Land Observing Satellite-2 Phased Arrayed L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) for monitoring tropical selective logging. We built Random Forests models aimed at classifying pixel-based differences between logged and unlogged areas. In addition, we used the Breaks For Additive Season and Trend (BFAST) algorithm to assess if a dense time series of Sentinel-1 imagery displayed recognizable shifts in pixel values after selective logging. In general, Random Forests classification with SAR data (Sentinel-1, RADARSAT-2, and ALOS-2 PALSAR-2) performed poorly, having high commission and omission errors for logged observations. This suggests little to no difference in pixel-based metrics between logged and unlogged areas for these sensors, particularly at lower logging intensities. In contrast, the Sentinel-1 time series analyses indicated that areas under higher intensity selective logging (> 20 m3 ha−1) show a distinct spike in the number of pixels that included a breakpoint during the logging season. BFAST detected breakpoints in 50% of logged pixels and exhibited a false alarm rate of approximately <5% in unlogged forest. Overall our results suggest that SAR data can be used in time series analyses to detect tropical selective logging at high intensity logging locations (> 20 m3 ha−1) within the Amazon.
Highlights
Selective logging is the primary driver of forest degradation in the tropics (Curtis et al, 2018; Hosonuma et al, 2012)
The single image detection results for all sensors revealed that in order to obtain a sufficiently low false discovery rate (e.g. < 10%), the corresponding detection rates (DR) of selective logging were of almost no value (< 5–10%) for reliable forest monitoring
We demonstrated that L-band PALSAR-2, C-band RADARSAT-2, and C-band Sentinel-1 data performed inadequately at detecting tropical selective logging when using single image pixel-based attributes for classification
Summary
Selective logging is the primary driver of forest degradation in the tropics (Curtis et al, 2018; Hosonuma et al, 2012). The ability to reliably map forest degradation from selective logging is a key element in understanding the terrestrial portion of the carbon budget and the role of land-use in turning tropical forests into net carbon emitters (Baccini et al, 2017). While the preceding decade has seen a tremendous improvement in the ability to detect forest disturbances from space (Hansen et al, 2013; Hethcoat et al, 2019; Tyukavina et al, 2017), forest monitoring at large spatial and temporal scales has largely relied on optical satellite data from the Landsat program. Uptake by users has been more limited than optical data and the full potential of SAR has likely been under-utilized (Reiche et al, 2016)
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