Abstract
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.
Highlights
The rainforests of Malaysia are one of the most species-rich and biologically diverse tropical ecosystems with a considerable number of endemic species
These studies discovered that: (i) Integration of template matching (TM) and object-based image analysis (OBIA) has the higher accuracy compared to OBIA only; (ii) support vector machine (SVM) classification is a suitable classification for identifying the selective logging impacts; and (iii) the forest canopy gaps area impacted by the logging activities was 18.6% of the total study area
Previous studies showed that tree species maps obtained from commonly used medium-spatial-resolution [19], but for this study indicates that the imagery below 0.10 m from the unmanned aerial vehicle (UAV) is very suitable for selective logging impacts identification and mapping
Summary
The rainforests of Malaysia are one of the most species-rich and biologically diverse tropical ecosystems with a considerable number of endemic species. 10,000 flowering plant species, of which 2830 are tree species from Peninsular Malaysia. Malaysia’s cumulative natural forest area was about 18.27 million ha or 55.60% of the country’s total land area of 32.86 million ha [1]. There are concerns about the degradation and depletion of forest resources and many species are known to have a risk of extinction [2]. An extensive area of tropical forests has been logged, either legal or illegally [3]. The degradation produced by selective logging will have an impact on the region and the long-term viability of forest management [4]. Time limitations and labour costs are major challenges [5]
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