Abstract
Imagery from medium resolution satellites, such as Landsat, have long been used to map forest disturbances in the tropics. However, the Landsat spatial resolution (30 m) has often been considered too coarse for reliably mapping small-scale selective logging. Imagery from the recently launched Sentinel-2 sensor, with a resampled 10 m spatial resolution, may improve the detection of forest disturbances. This study compared the performance of Landsat 8 and Sentinel-2 data for the detection of selective logging in an area located in the Brazilian Amazon. Logging impacts in seven areas, which had governmental authorization for harvesting timber, were mapped by calculating the difference of a self-referenced normalized burn ratio (ΔrNBR) index over corresponding time periods (2016–2017) for imagery of both satellite sensors. A robust reference dataset was built using both high- and very-high-resolution imagery. It was used to define optimum ΔrNBR thresholds for forest disturbance maps, via a bootstrapping procedure, and for estimating accuracies and areas. A further assessment of our approach was also performed in three unlogged areas. Additionally, field data regarding logging infrastructure were collected in the seven study sites where logging occurred. Both satellites showed the same performance in terms of accuracy, with area-adjusted overall accuracies of 96.7% and 95.7% for Sentinel-2 and Landsat 8, respectively. However, Landsat 8 mapped 36.9% more area of selective logging compared to Sentinel-2 data. Logging infrastructure was better detected from Sentinel-2 (43.2%) than Landsat 8 (35.5%) data, confirming its potential for mapping small-scale logging. We assessed the impacted area by selective logging with a regular 300 m × 300 m grid over the pixel-based results, leading to 1143 ha and 1197 ha of disturbed forest on Sentinel-2 and Landsat 8 data, respectively. No substantial differences in terms of accuracy were found by adding three unlogged areas to the original seven study sites.
Highlights
Selective logging is a pervasive activity in tropical forests
Based on the ∆rNBR approach proposed by Reference [50], selective logging activities were mapped in the seven sustainable forest management (SFM) areas near the town of Santo Antônio do Matupi, State of Amazonas, Brazil
We compared the performance of the Sentinel-2 MSI and Landsat 8 OLI sensors for detecting forest canopy disturbances caused by selective logging in a focus area in the State of Amazonas, Brazil
Summary
In the Brazilian Amazon, it is a major source of forest degradation, possibly encompassing an area larger than that reported as deforested [1]. Despite the fact that carbon emissions caused by forest degradation in the tropics are to some extent compensated by forest regrowth [2], forests that have been selectively logged or burned store up to 40%. REDD+ policies require the use of reliable methods to measure carbon emissions from deforestation and forest degradation [4]. While Brazil has a consistent methodology for measuring deforestation [5], the approach used to estimate greenhouse gas emissions from forest degradation still needs improvement [6]. Research on automatic methods for mapping selective logging, a source of forest degradation, is imperative. The term “forest disturbance” is preferred, given that not all forest disturbance events necessarily result in forest degradation [9]
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