Aboveground Deadwood Biomass and Composition Along Elevation and Land-Use Gradients at Mount Kilimanjaro

Deadwood is an important structural and functional component of forest ecosystems and biodiversity. As deadwood can make up large portions of the total aboveground biomass, it plays an important role in the terrestrial carbon (C) cycle. Nevertheless, in tropical ecosystems and especially in Africa, quantitative studies on this topic remain scarce. We conducted an aboveground deadwood inventory along two environmental gradients—elevation and land use— at Mt. Kilimanjaro, Tanzania. We used a huge elevation gradient (3690 m) along the southern slope of the mountain to investigate how deadwood is accumulated across different climate and vegetation zones. We also compared habitats that differed from natural forsts in land-use intensity and disturbance history to assess anthropogenic influence on deadwood accumulation. In our inventory we distinguished coarse woody debris (CWD) from fine woody debris (FWD). Furthermore, we calculated the C and nitrogen (N) content of deadwood and how the C/N ratio varied with decomposition stages and elevation. Total amounts of aboveground deadwood ranged from 0.07 ± 0.04 to 73.78 ± 36.26 Mg ha–1 (Mean ± 1 SE). Across the elevation gradient, total deadwood accumulation was highest at mid-elevations and reached a near-zero minimum at very low and very high altitudes. This unimodal pattern was mainly driven by the corresponding amount of live aboveground biomass and the combined effects of decomposer communities and climate. Land-use conversion from natural forests into traditional homegardens and commercial plantations, in addition to frequent burning, significantly reduced deadwood biomass, but not past selective logging after 30 years of recovery time. Furthermore, we found that deadwood C content increased with altitude. Our study shows that environmental gradients, especially temperature and precipitation, as well as different anthropogenic disturbances can have considerable effects on both the quantity and composition of deadwood in tropical forests.

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.

Natural regeneration in logging gaps of different sizes in Subri River Forest Reserve (Ghana)

Subri River Forest Reserve (SR) is the most extensive forest area in Ghana with an accompanying rich floral species. Over the years, logging from both legally prescribed and illegal operations remain the predominant forest disturbance in SR. Gap creation following logging is crucial in determining tree species composition and diversity. Hence, the study evaluated the composition and diversity of naturally regenerated tree species in logging gaps of different sizes and, again examined the roles of these tree species in fulfilling the economic and ecological agenda of sustainable forest management after logging in SR. Twelve gaps were randomly selected: 4 each were grouped into small size (≤ 200 m2), medium size (201–300 m2), and large size (≥ 300 m2). Data were gathered from 1 m2 circular area at gap centres and repeatedly inside 1 m width strip along 20 m individual N-S-E-W transects. Species diversity differed significantly between gap sizes. Higher diversity indices were measured in large size gaps. Gap sizes shared similar species. There were significant differences among various height groupings of tree species across all three gap sizes. Pioneers preferred medium to large size gaps, while shade-tolerant tree species preferred small size gaps for their abundance. Vulnerable and Lower Risk Near Threatened tree species under Conservation Status and, Premium and Commercial tree species under Utilisation Status preferred small size gaps for their proliferation and conservation. Therefore, we recommend the single tree-based selective logging for ensuring creations of small to medium size (200–300 m2) gaps through adjustments to the logging permit process, revision of Allocation Quota Permit, strict adherence to the 40-year polycyclic selection system, along with more dedicated enforcement and monitoring. Changes along these protocols would tremendously facilitate natural regeneration of different suites of timber species resulting in the improvement of the overall biodiversity conservation associated with the forest, more sustainable forest harvests and more income to those who receive permits.

Combining Sentinel-1 and Landsat 8 Does Not Improve Classification Accuracy of Tropical Selective Logging

Tropical forests play a key role in the global carbon and hydrological cycles, maintaining biological diversity, slowing climate change, and supporting the global economy and local livelihoods. Yet, rapidly growing populations are driving continued degradation of tropical forests to supply wood products. The United Nations (UN) has developed the Reducing Emissions from Deforestation and Forest Degradation (REDD+) programme to mitigate climate impacts and biodiversity losses through improved forest management. Consistent and reliable systems are still needed to monitor tropical forests at large scales, however, degradation has largely been left out of most REDD+ reporting given the lack of effective monitoring and countries mainly focus on deforestation. Recent advances in combining optical data and Synthetic Aperture Radar (SAR) data have shown promise for improved ability to monitor forest losses, but it remains unclear if similar improvements could be made in detecting and mapping forest degradation. We used detailed selective logging records from three lowland tropical forest regions in the Brazilian Amazon to test the effectiveness of combining Landsat 8 and Sentinel-1 for selective logging detection. We built Random Forest models to classify pixel-based differences in logged and unlogged regions to understand if combining optical and SAR improved the detection capabilities over optical data alone. We found that the classification accuracy of models utilizing optical data from Landsat 8 alone were slightly higher than models that combined Sentinel-1 and Landsat 8. In general, detection of selective logging was high with both optical only and optical-SAR combined models, but our results show that the optical data was dominating the predictive performance and adding SAR data introduced noise, lowering the detection of selective logging. While we have shown limited capabilities with C-band SAR, the anticipated opening of the ALOS-PALSAR archives and the anticipated launch of NISAR and BIOMASS in 2023 should stimulate research investigating similar methods to understand if longer wavelength SAR might improve classification of areas affected by selective logging when combined with optical data.

GERMINAÇÃO E PROPAGAÇÃO IN VITRO DE MOGNO BRASILEIRO (Swietenia macrophylla King)

A intensificação da exploração seletiva de madeiras tem ocasionado grandes perdas na biodiversidade de espécies nativas de alto valor econômico, comprometendo, a sua sobrevivência. O potencial madeireiro do mogno brasileiro é mundialmente reconhecido e, por isso, é também motivo de grande preocupação da comunidade científico. Esta pesquisa objetiva avaliar o efeito de concentrações de reguladores de crescimento na germinação e multiplicação in vitro de mogno brasileiro e analisar aspectos físicos para determinar a eficiência na produção de mudas. Para isso, as sementes foram incubadas em meio de cultura MS no delineamento inteiramente casualisado em esquema fatorial 2 x 4 (duas intensidades de luz e quatro tempos de hipoclorito de sódio), com cinco repetições e quatro sementes por repetição. Aos trinta dias, os explantes isentos de contaminação foram transferidos para tubos de ensaio contendo meio MS e suplementados com diferentes concentrações de BAP e mantidos em sala de crescimento. Para multiplicação os brotos foram transferidos para meio MS e suplementados com diferentes concentrações de BAP e ANA. Obtiveram-se a maior porcentagem de brotações (83%) de explantes da porção intermediária de mogno e a utilização de concentrações superiores de ANA e BAP para formação de calos permitindo êxito na produção clonal. Palavras-chave: espécie nativa; plantas lenhosas; micropropagação. Germination and propagation in vitro of brazilian mahogany (Swietenia macrophylla King) ABSTRACT: The intensification of selective logging causes great losses biodiversity of native species of high economic value, compromising their survival. The wood industry potential of brazilian mahogany is recognized worldwide and, therefore, it is also a cause of great preoccupations of the scientific community. This research aims to evaluate the effect of concentrations of growth regulators on germination and in vitro multiplication of brazilian mahogany and analyze physical aspects to determining the efficiency in the production of seedlings. For this, the seeds were incubated in MS culture medium in a completely randomized design in a factorial scheme 2 x 4 (two light intensities and four sodium hypochlorite times) with five repetitions and four seeds per supplemented with different concentrations of BAP and kept in the repetition. At thirty days, explants free from contamination were transferred to test tubes containing MS medium and supplemented with different BAP concentrations and kept in the growth room. For multiplication the shoots were transferred to MS medium and supplemented with different concentrations of BAP and ANA. The highest number of percentage of shoots (83%) in the use of explants of the intermediate mahogany and the higher concentrations of ANA and BAP for callus formation enabling success in clonal production. Keywords: native species; woody plants; micropropagation.

Occupancy of wild southern pig-tailed macaques in intact and degraded forests in Peninsular Malaysia

Deforestation is a major threat to terrestrial tropical ecosystems, particularly in Southeast Asia where human activities have dramatic consequences for the survival of many species. However, responses of species to anthropogenic impact are highly variable. In order to establish effective conservation strategies, it is critical to determine a species’ ability to persist in degraded habitats. Here, we used camera trapping data to provide the first insights into the temporal and spatial distribution of southern pig-tailed macaques (Macaca nemestrina, listed as ‘Vulnerable’ by the IUCN) across intact and degraded forest habitats in Peninsular Malaysia, with a particular focus on the effects of clear-cutting and selective logging on macaque occupancy. Specifically, we found a 10% decline in macaque site occupancy in the highly degraded Pasoh Forest Reserve from 2013 to 2017. This may be strongly linked to the macaques’ sensitivity to intensive disturbance through clear-cutting, which significantly increased the probability that M. nemestrina became locally extinct at a previously occupied site. However, we found no clear relationship between moderate disturbance, i.e., selective logging, and the macaques’ local extinction probability or site occupancy in the Pasoh Forest Reserve and Belum-Temengor Forest Complex. Further, an identical age and sex structure of macaques in selectively logged and completely undisturbed habitat types within the Belum-Temengor Forest Complex indicated that the macaques did not show increased mortality or declining birth rates when exposed to selective logging. Overall, this suggests that low to moderately disturbed forests may still constitute valuable habitats that support viable populations of M. nemestrina, and thus need to be protected against further degradation. Our results emphasize the significance of population monitoring through camera trapping for understanding the ability of threatened species to cope with anthropogenic disturbance. This can inform species management plans and facilitate the development of effective conservation measures to protect biodiversity.

Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks

It is estimated that, in the Brazilian Amazon, forest degradation contributes three times more than deforestation for the loss of gross above-ground biomass. Degradation, in particular those caused by selective logging, result in features whose detection is a challenge to remote sensing, due to its size, space configuration, and geographical distribution. From the available remote sensing technologies, SAR data allow monitoring even during adverse atmospheric conditions. The aim of this study was to test different pre-trained models of Convolutional Neural Networks (CNNs) for change detection associated with forest degradation in bitemporal products obtained from a pair of SAR COSMO-SkyMed images acquired before and after logging in the Jamari National Forest. This area contains areas of legal and illegal logging, and to test the influence of the speckle effect on the result of this classification by applying the classification methodology on previously filtered and unfiltered images, comparing the results. A method of cluster detections was also presented, based on density-based spatial clustering of applications with noise (DBSCAN), which would make it possible, for example, to guide inspection actions and allow the calculation of the intensity of exploitation (IEX). Although the differences between the tested models were in the order of less than 5%, the tests on the RGB composition (where R = coefficient of variation; G = minimum values; and B = gradient) presented a slightly better performance compared to the others in terms of the number of correct classifications for selective logging, in particular using the model Painters (accuracy = 92%) even in the generalization tests, which presented an overall accuracy of 87%, and in the test on RGB from the unfiltered image pair (accuracy of 90%). These results indicate that multitemporal X-band SAR data have the potential for monitoring selective logging in tropical forests, especially in combination with CNN techniques.