Abstract
Hundreds of millions of hectares of tropical forest have been selectively logged, either legally or illegally. Methods for detecting and monitoring tropical selective logging using satellite data are at an early stage, with current methods only able to detect more intensive timber harvest (>20 m3 ha−1). The spatial resolution of widely available datasets, like Landsat, have previously been considered too coarse to measure the subtle changes in forests associated with less intensive selective logging, yet most present-day logging is at low intensity. We utilized a detailed selective logging dataset from over 11,000 ha of forest in Rondônia, southern Brazilian Amazon, to develop a Random Forest machine-learning algorithm for detecting low-intensity selective logging (<15 m3 ha−1). We show that Landsat imagery acquired before the cessation of logging activities (i.e. the final cloud-free image of the dry season during logging) was better at detecting selective logging than imagery acquired at the start of the following dry season (i.e. the first cloud-free image of the next dry season). Within our study area the detection rate of logged pixels was approximately 90% (with roughly 20% commission and 8% omission error rates) and approximately 40% of the area inside low-intensity selective logging tracts were labelled as logged. Application of the algorithm to 6152 ha of selectively logged forest at a second site in Pará, northeast Brazilian Amazon, resulted in the detection of 2316 ha (38%) of selective logging (with 20% commission and 7% omission error rates). This suggests that our method can detect low-intensity selective logging across large areas of the Amazon. It is thus an important step forward in developing systems for detecting selective logging pan-tropically with freely available data sets, and has key implications for monitoring logging and implementing carbon-based payments for ecosystem service schemes.
Highlights
Earth’s tropical forests are being rapidly lost and degraded by agricultural expansion and commercial logging operations, with population growth projected to further increase pressures on forests globally (Asner et al, 2005; DeFries et al, 2010)
The ability to monitor forest disturbances is an important component in sustainable forest management, understanding the global carbon budget, and implementing climate policy initiatives, such as the United Nation’s (UN) Reducing Emissions from Deforestation and Forest Degradation (REDD+) programme, which seeks to mitigate climate change and biodiversity losses through improved forest management practices (GOFC-GOLD, 2016)
3.1 Data inputs for detecting selective logging For the Landsat scenes given in Table 1, the surface reflectance values for the Blue, Green, Red, Near Infrared, Shortwave Infrared 1 and Shortwave Infrared 2 bands were measured at each pixel where logging occurred (n = 13699) and 2000 randomly selected pixels in an adjacent forest management units (FMUs) that remained unlogged
Summary
Earth’s tropical forests are being rapidly lost and degraded by agricultural expansion and commercial logging operations, with population growth projected to further increase pressures on forests globally (Asner et al, 2005; DeFries et al, 2010). Forest degradation is an ambiguous term, with over 50 different definitions and no internationally established description (Ghazoul et al, 2015; Simula, 2009). This makes generalizing its impacts difficult, in part because degradation can include forests subject to varying intensities of selective logging, fire, artisanal gold mining, fuelwood extraction, etc., which has hampered the development of coordinated international forest policies to track and monitor forest degradation (Ghazoul et al, 2015; Sasaki and Putz, 2009)
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