Integrated Segmentation Approach with Machine Learning Classifier in Detecting and Mapping Post Selective Logging Impacts Using UAV Imagery

Selective logging can cause significant impacts on the residual stands, affecting biodiversity and leading to environmental changes. Proper monitoring and mapping of the impacts from logging activities, such as the stumps, felled logs, roads, skid trails, and forest canopy gaps, are crucial for sustainable forest management operations. The purpose of this study is to assess the indicators of selective logging impacts by detecting the individual stumps as the main indicators, evaluating the performance of classification methods to assess the impacts and identifying forest gaps from selective logging activities. The combination of forest inventory field plots and unmanned aerial vehicle (UAV) RGB and overlapped imaged were used in this study to assess these impacts. The study area is located in Ulu Jelai Forest Reserve in the central part of Peninsular Malaysia, covering an experimental study area of 48 ha. The study involved the integration of template matching (TM), object-based image analysis (OBIA), and machine learning classification—support vector machine (SVM) and artificial neural network (ANN). Forest features and tree stumps were classified, and the canopy height model was used for detecting forest canopy gaps in the post selective logging region. Stump detection using the integration of TM and OBIA produced an accuracy of 75.8% when compared with the ground data. Forest classification using SVM and ANN methods were adopted to extract other impacts from logging activities such as skid trails, felled logs, roads and forest canopy gaps. These methods provided an overall accuracy of 85% and kappa coefficient value of 0.74 when compared with conventional classifier. The logging operation also caused an 18.6% loss of canopy cover. The result derived from this study highlights the potential use of UAVs for efficient post logging impact analysis and can be used to complement conventional forest inventory practices.

Assessment of the CLASlite forest monitoring system in detecting disturbance from selective logging in the Selva Maya, Mexico

Detecting and monitoring forest disturbance from selective logging is necessary to develop effective strategies and polices that conserve tropical forests and mitigate climate change. We assessed the potential of using the remote sensing tool, CLASlite forest monitoring system, to detect disturbance from timber harvesting in four community forests () of the Selva Maya on the Yucatan Peninsula, Mexico. Selective logging impacts (e.g. felling gaps, skid trails, logging roads and log landings) were mapped using GPS in the 2014 annual cutting areas (ACAs) of each ejido. We processed and analyzed two pre-harvest Landsat images (2001 and 2013) and one post-harvest image (November 2014) with the CLASlite system, producing maps of degraded, deforested and unlogged areas in each ACA. Based on reference points of disturbed (felling and skidding), deforested (log landings and roads) and unlogged areas in each ACA, we applied accuracy assessments which showed very low overall accuracies (<19.1%). Selective logging impacts, mainly from log landings and new logging road construction, were detected in only one ejido which had the highest logging intensity (7 m ha).ejidos3–1

Can partial‐cut harvesting be used to manage terrestrial lichen habitat? A review of recent evidence

Recent research suggests that partial-cut harvesting techniques can be used to alter successional trajectories in pine- and spruce-lichen woodlands, allowing forest managers to extend the period of reindeer lichen growth in mid- to late seral boreal forest stands. In Quebec, a fully replicated partial-cutting trial found that terrestrial lichen abundance remained at least as high in the partial cut as in the clearcuts or unlogged stands, and that the partial cut appeared to be on a trajectory to have even more terrestrial lichen due to sustained higher growth rates. In Alberta, a retrospective study found higher terrestrial lichen abundance in an early horse-logged partial cut than in undisturbed adjacent old forests or in clearcuts. Follow-up studies of partial-cut harvesting trials in British Columbia found that group selection plots 10 years after harvesting had lichen cover equivalent to that of undisturbed forest. In contrast, studies on lichen woodlands that have been defoliated by mountain pine beetle showed a major decline in reindeer lichen cover and a corresponding increase in vascular plant cover, similar to the results of previous studies on clear-cut logging impacts. Taken together these studies provide qualified support for the hypothesis that partial-cut harvesting can be used to enhance, or at least maintain, terrestrial lichen mats used as forage by caribou.