A Low-Cost and Robust Landsat-Based Approach to Study Forest Degradation and Carbon Emissions from Selective Logging in the Venezuelan Amazon

Abstract

Selective logging in the tropics is a major driver of forest degradation by altering forest structure and function, including significant losses of aboveground carbon. In this study, we used a 30-year Landsat time series (1985–2015) to analyze forest degradation and carbon emissions due to selective logging in a Forest Reserve of the Venezuelan Amazon. Our work was conducted in two phases: the first, by means of a direct method we detected the infrastructure related to logging at the sub-pixel level, and for the second, we used an indirect approach using buffer areas applied to the results of the selective logging mapping. Pre- and post-logging forest inventory data, combined with the mapping analysis were used to quantify the effects of logging on aboveground carbon emissions for three different sources: hauling, skidding and tree felling. With an overall precision of 0.943, we demonstrate the potential of this method to efficiently map selective logging and forest degradation with commission and omission errors of +7.6 ± 4.5 (Mean ± SD %) and −7.5% ± 9.1 respectively. Forest degradation due to logging directly affected close to 24,480 ha, or about ~1% of the total area of the Imataca Forest Reserve. On average, with a relatively low harvest intensity of 2.8 ± 1.2 trees ha−1 or 10.5 ± 4.6 m3 ha−1, selective logging was responsible for the emission of 61 ± 21.9 Mg C ha−1. Lack of reduced impact logging guidelines contributed to pervasive effects reflected in a mean reduction of ~35% of the aboveground carbon compared to unlogged stands. This research contributes to further improve our understanding of the relationships between selective logging and forest degradation in tropical managed forests and serves as input for the potential implementation of projects for reducing emissions from deforestation and forest degradation (REDD+).

Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon

“…UAV LiDAR highlighted the importance of detecting such events, as we found that, in an area of selectively logged forest, more than half of canopy gaps were smaller than 0.05 ha and 62% of disturbed area was caused by gaps below 0.1 ha: a degradation mapping tool that excluded these disturbances could severely underestimate degradation, and miss whole regions of degradation typified by multiple small clearances. For comparison, many previous attempts to detect selective logging from satellite data have worked at the 0.09 ha scale of Landsat pixels [64][65][66][67]. Although there is evidence that disturbances as small as 25% of a Landsat pixel can be detected [68], this relies on a cloud free image being available from the short period during which the disturbed area shows bare ground and therefore a strong optical difference to the canopy, which is likely to lead to high missed detection rates in cloudy tropical regions.…”

Strategies to achieve a carbon neutral society: a review

“…The field data were obtained from the Industria Técnica de Maderas C.A (INTECMACA) and Empresa Nacional Forestal (ENAFOR) inventories, and reports from logging companies were used to obtain trees properties. Then, the analytical approach was done by mapping selective logging using the TerraAmazon system and validating them, then construction and validation of degradation maps, then the estimation of Aboveground Biomass and Carbon, and estimation of Committed Carbon Emissions (Pacheco-Angulo et al 2021 ) Buildings, transportation Sumida, Tokyo, Japan The authors used spatial micro–Big Data, 3D carbon mapping, and a bottom-up approach model. Total emissions were estimated from Japan’s greenhouse gas Inventory Office, and unit emissions were estimated from the Japan Institute of Energy report.…”

“…Additionally, the forest had 27,800 m 3 of green biomass and 13,066 t of carbon (Mihut et al 2019 ). Another study on forest degradation as a result of logging was conducted in Venezuela’s Amazon (Pacheco-Angulo et al 2021 ). The findings indicated that forest degradation directly impacted 24,480 ha of the Imataca forest reserve.…”

Changes in tropical terrestrial vertebrate communities along two anthropogenic gradients: Forest degradation and accessibility

“…As NDMI may not be very sensitive in high moisture environments, we also considered ACD. While factors other than forest degradation affect ACD, forest carbon storage is a good measure of forest complexity and degradation from logging activities (Pacheco et al, 2021;Wekesa et al, 2016;Yohannes & Soromessa, 2015), and a strong relationship between the two has been demonstrated in our study region (Asner et al, 2018). To quantify forest accessibility, we calculated remoteness as hours walking time to each point in the landscape from a set of start points using a hiking function/least-cost paths analysis (Rees, 2004).…”