Current issue: 55(3)
Under compilation: 55(4)
In Thailand and various other countries tree seedlings are generally planted using simple manual tools, often a ‘planting stick’, but the method requires time-consuming, labour-intensive teamwork. However, use of a ‘planting tube’ allows a single person to perform both the preparation and planting work. Thus, in a classical time study and ergonomic survey we compared the productivity, cost-effectiveness, and ergonomic impact of planting Eucalyptus spp. seedlings using the two tools at the same planting site in Western Thailand. The planting tube method proved to be more productive, more cost-efficient, and less burdensome than the planting stick method (with time and cost requirements of 21 s and €0.0061 per seedling, versus 16.6 s and €0.0463 per seedling, respectively). In conclusion, the planting tube method may be a viable alternative to reduce costs and increase productivity, while maintaining reasonable workloads for the workers, despite the higher purchase price of planting tubes.
Pulpwood arriving at the mills is mainly measured by weighing. In the loading phase of forwarding and trucking, timber is weighed using scales mounted in the grapple loader. The measured weight of timber is converted into volume using a conversion factor defined as green density (kg m–3). At the mill, the green density factor is determined by sampling measurements, while in connection with weighing with grapple-mounted scales during transportation, fixed green density factors are used. In this study, we developed predictive regression models for the green density of pulpwood. The models were constructed separately by pulpwood assortments: pine (contains mainly Pinus sylvestris L); spruce (mainly Picea abies (L.) Karst.); decayed spruce; birch (mainly Betula pubescens Ehrh. and Betula pendula Roth); and aspen (mainly Populus tremula L.). Study material was composed of the sampling-based measurements at the mills between 2013–2019. The models were specified as linear mixed models with both fixed and random parameters. The fixed effect produced the expected value of green density as a function of delivery week, storage time, and meteorological conditions during storage. The random effects allowed the model calibration by utilizing the previous sampling weight measurements. The model validation showed that the model predictions faithfully reproduced the observed seasonal variation in green density. They were more reliable than those obtained with the current practices. Even the uncalibrated (fixed) predictions had lower relative root mean squared prediction errors than those obtained with the current practices.
Over 20% of regeneration operations will be on drained peatland in the next decade in Finland. There are only a few studies comparing the planting success and the risk of pine weevil (Hylobius abetis (L.) feeding damage on mineral soil and drained peatland. Thirty sites planted with Norway spruce (Picea abies (L.) H. Karst.) container seedlings in 2009 in Southern and Central Finland were inventoried three growing seasons after planting. Prediction models for the probability of survival, pine weevil damage and the presence of ground vegetation cover were done separately for peatland and mineral soil sites. The planting success was 17% lower on peatland sites (1379 surviving seedlings ha–1) than on mineral soil (1654 seedlings ha–1). The factors explaining the survival were the ground vegetation cover and type of the planting spot on the peatland, and the ground vegetation cover on mineral soil. On mineral soil, 76% of the planting spots were on cultivated mineral soil while on peatland only 28% of the seedlings were planted on similar spots. There were also fewer seedlings that were surrounded by dense ground vegetation on mineral soil (4%) than on peatland (14%). Pine weevil feeding damage did not differ significantly on peatland (23%) or mineral soil (18%). The more time there was from clear-cutting, the more the probability of pine weevil feeding damage was reduced on both soil classes. Additionally, cover vegetation in the vicinity of the seedlings increased on mineral soil. Cultivated planting spots, especially those covered by mineral soil, prevented pine weevil feeding and reduced the harmful effects of vegetation on the seedlings both on mineral soil and peatland.
Current remote sensing methods can provide detailed tree species classification in boreal forests. However, classification studies have so far focused on the dominant tree species, with few studies on less frequent but ecologically important species. We aimed to separate European aspen (Populus tremula L.), a biodiversity-supporting tree species, from the more common species in European boreal forests (Pinus sylvestris L., Picea abies [L.] Karst., Betula spp.). Using multispectral drone images collected on five dates throughout one thermal growing season (May–September), we tested the optimal season for the acquisition of mono-temporal data. These images were collected from a mature, unmanaged forest. After conversion into photogrammetric point clouds, we segmented crowns manually and automatically and classified the species by linear discriminant analysis. The highest overall classification accuracy (95%) for the four species as well as the highest classification accuracy for aspen specifically (user’s accuracy of 97% and a producer’s accuracy of 96%) were obtained at the beginning of the thermal growing season (13 May) by manual segmentation. On 13 May, aspen had no leaves yet, unlike birches. In contrast, the lowest classification accuracy was achieved on 27 September during the autumn senescence period. This is potentially caused by high intraspecific variation in aspen autumn coloration but may also be related to our date of acquisition. Our findings indicate that multispectral drone images collected in spring can be used to locate and classify less frequent tree species highly accurately. The temporal variation in leaf and canopy appearance can alter the detection accuracy considerably.
This study’s aim was to identify how the application season and the method of early cleaning (EC), the first stage of multistage pre-commercial thinning (PCT), affected the time consumption in EC and in the subsequent second PCT operation. The worktime in EC was recorded in the spring, summer, and autumn in 22 sites, which were either totally cleaned or point cleaned. Later, these sites were measured at the time of the second PCT. Time consumption was estimated in PCT, based on the removal of the sites. The time consumption in EC was 5.3 productive work hours (pwh) ha–1, 7.3 pwh ha–1, and 6.2 pwh ha–1 respectively in the spring, summer, and autumn. EC in the spring instead of the summer saved 27–30% of working time, depending on the cleaning method. Point cleaning was 0.8 pwh ha–1 quicker than total cleaning, but the difference was statistically insignificant. The second stage, PCT, was 1 pwh ha–1 slower to conduct in sites which had been early cleaned in the spring instead of the summer. However, at the entire management program level, EC applied in the spring or autumn instead of the summer saved 11% or 5% respectively of the total discounted costs (3% interest rate) of multistage pre-commercial thinning. Today, the commonest time to conduct EC is in the summer, which was the most expensive of the analyzed management alternatives here. We can expect savings in juvenile stand management in forestry throughout boreal conifer forests by rethinking the seasonal workforce allocation.
Timber production and profitability were evaluated for spontaneously-regenerated mixtures on two formerly clearcut areas. The abandoned areas developed into birch-dominated (Betula pendula Roth and Betula pubescens Ehrh.) stands with successional ingrowth of Norway spruce (Picea abies (L.) H. Karst.). An experiment with randomized treatments within blocks was established, using three management strategies and one unthinned control, resulting in variation in optimal rotation age, merchantable volume and species composition. The management strategies were evaluated based on total production (volume) by using measured growth data 42 years after clearcutting and the modelled future stand development. The long-term effects of spontaneous regeneration and management strategies were evaluated based on land expectation value (LEV) and compared with a fifth management strategy using artificial regeneration and intense thinnings. 12 years after treatment, at a stand age of 42 years, the unthinned control had produced the highest total stem volume. At interest rates of 2% or higher, the unmanaged forest was an economically viable strategy, even compared to an intensive management strategy with a preferred merchantable timber species. Interest rates clearly impacted the profitability of the different management strategies. This study shows that when spontaneous regeneration is successful and dense, the first competition release can have a high impact on the development of future crop trees and on the species mixture.
Dalbergia latifolia Roxb., commonly known as rosewood, is one of the highly valuable tropical timber species of Nepal. The tree species was widely distributed in the past, however, over-exploitation of natural habitat, deforestation, forest conversion for agriculture, illegal logging and the invasion of alien species resulted in the classification of this species as vulnerable by the IUCN (International Union for Conservation of Nature) category. So, the prediction of habitat suitability and potential distribution of the species is required to develop restoration mechanisms and conservation interventions. In this study, we modelled the suitable habitat of D. latifolia over the entire possible range of Nepal using a Maxent model. We compiled 23 environmental variables (19 bioclimatic, 3 topographic and a vegetative layer), however, only 12 least correlated variables along with 43 spatially representative presence locations were retained for model prediction. We used a receiver operating characteristic (ROC) curve to assess the model’s performance and a Jackknife procedure to evaluate the relative importance of predictor variables. The model was statistically significant with an area under the curve (AUC) value of 0.969. The internal Jackknife test indicated that elevation was the most important variable for the model prediction with 71.3% contribution followed by mean temperature of driest quarter (9.8%). The most (>0.6) suitable habitat for the D. latifolia was 235 484 hectares with large sections of area in two provinces whereas, the western most provinces were not suitable for D. latifolia as per Maxent model. The information presented here can provide a framework for nature conservation planning, monitoring and habitat management of this rare and endangered species.
Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m2 collected over 350 km2 of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.
Terrestrial laser scanning (TLS) provides a unique opportunity to study forest canopy structure and its spatial patterns such as foliage quantity and dispersal. Using TLS point clouds for estimating leaf area density with voxel-based methods is biased by the physical dimensions of laser beams, which violates the common assumption of beams being infinitely thin. Real laser beams have a footprint size larger than several millimeters. This leads to difficulties in estimating leaf area density from light detection and ranging (LiDAR) in vegetation, where the target objects can be of similar or even smaller size than the beam footprint. To compensate for this bias, we propose a method to estimate the per-pulse cover fraction, defined as the fraction of laser beams’ footprint area that is covered by vegetation targets, using the LiDAR return intensity and an experimental calibration measurement. We applied this method to a Leica P40 single-return instrument, and report our experimental results. We found that conifer foliage had a lower average per-pulse cover fraction than broadleaved foliage, indicating an increased number of partial hits in conifer foliage. We further discuss limitations of our method that stem from unknown target properties that influence the LiDAR return intensity and highlight potential ways to overcome the limitations and manage the remaining uncertainty. Our method’s output, the per-beam cover fraction, may be useful in a weight function for methods that estimate leaf area density from LiDAR point clouds.