Forest inventory information needs in Northwest Russia

This research report applies the customer value hierarchy model to forestry in order to determine strategic options to enhance the value of LiDAR technology in Russian forestry. The study is conducted as a qualitative case study with semi-structured interviews as a main source of the primary data. The customer value hierarchy model constitutes a theoretical base for the research. Secondary data incorporates information on forest resource management, LiDAR technology and Russian forestry. The model is operationalised using forestry literature and forms a basis for analyses of primary data. Analyses of primary data coupled with comprehension of Russian forest inventory system and knowledge on global forest inventory have led to conclusions on the forest inventory methods selection criteria and the organizations that would benefit the most from LiDAR technology use. The report recommends strategic options for LiDAR technology’s value enhancement in Russian forestry. This work has been conducted as a part of the project ‘Finnish-Russian Forest Academy 2 - Exploiting and Piloting’, which has been supported financially by the South-East Finland- Russia ENPI CBC 2007-2014 Programme.

Suitability of LiDAR technology for forest inventory in Russia

LiDAR is an advanced remote sensing technology with many applications, including forest inventory. The most common type is ALS (airborne laser scanning). The method is successfully utilized in many developed markets, where it is replacing traditional forest inventory methods. However, it is innovative for Russian market, where traditional field inventory dominates. ArboLiDAR is a forest inventory solution that engages LiDAR, color infrared imagery, GPS ground control plots and field sample plots, developed by Arbonaut Ltd. This study is an industrial market research for LiDAR technology in Russia focused on customer needs. Russian forestry market is very attractive, because of large growing stock volumes. It underwent drastic changes in 2006, but it is still in transitional stage. There are several types of forest inventory, both with public and private funding. Private forestry enterprises basically need forest inventory in two cases – while making coupe demarcation before timber harvesting and as a part of forest management planning, that is supposed to be done every ten years on the whole leased territory. The study covered 14 companies in total that include private forestry companies with timber harvesting activities, private forest inventory providers, state subordinate companies and forestry software developer. The research strategy is multiple case studies with semi-structured interviews as the main data collection technique. The study focuses on North-West Russia, as it is the most developed Russian region in forestry. The research applies the Voice of the Customer (VOC) concept to elicit customer needs of Russian forestry actors and discovers how these needs are met. It studies forest inventory methods currently applied in Russia and proposes the model of method comparison, based on Multi-criteria decision making (MCDM) approach, mainly on Analytical Hierarchy Process (AHP). Required product attributes are classified in accordance with Kano model. The answer about suitability of LiDAR technology is ambiguous, since many details should be taken into account.

Study of the physical and physicochemical characteristics of fruits of the licuri palm (Syagrus coronata (Mart.) Becc.) found in the Atlantic Forest of Minas Gerais, Brazil

AbstractThe Atlantic Forest has species of native fruits, consumed fresh and processed, which have an important contribution to food sovereignty of families that consume it. This study examined the physical and physicochemical characteristics, proximate composition, concentration of carotenoids, vitamin C, vitamin E and minerals in the pulp and kernels of fruits of licuri (Syagrus coronata (Mart.) Becc.). Titratable acidity was analyzed by volumetric neutralization, soluble solids by refractometry, proteins by the micro-Kjeldahl method, lipids by gravimetry using soxhlet, dietary fiber by non-enzymatic gravimetry, carotenoids and vitamin C by HPLC-DAD, vitamin E by HPLC-fluorescence, and minerals by ICP-AES. Pulp were a source of Zn (0.95 mg 100–1), a good source of fiber (6.15 g 100–1), excellent source of provitamin A (758.75 RAE 100–1), Cu (0.69 mg 100–1), Fe (3.81 mg 100–1), Mn (3.40 mg 100–1) and Mo (0.06 mg 100–1). The kernel were a source of Fe (3.36 mg 100–1) and excellent source of Mn (6.14 mg 100–1), Cu (0.97 mg 100–1) and Mo (0.07 mg 100–1). The nutritional value and wide availability of licuri fruit make it an important resource for reducing food insecurity and improving nutrition of the rural population and other individuals who have access to it.

Use of airborne laser scanner data in demanding forest conditions

Most of the applications of airborne laser scanner data to forestry require that the point cloud be normalized, i.e., each point represents height from the ground instead of elevation. To normalize the point cloud, a digital terrain model (DTM), which is derived from the ground returns in the point cloud, is employed. Unfortunately, extracting accurate DTMs from airborne laser scanner data is a challenging task, especially in tropical forests where the canopy is normally very thick (partially closed), leading to a situation in which only a limited number of laser pulses reach the ground. Therefore, robust algorithms for extracting accurate DTMs in low-ground-point-densitysituations are needed in order to realize the full potential of airborne laser scanner data to forestry. The objective of this thesis is to develop algorithms for processing airborne laser scanner data in order to: (1) extract DTMs in demanding forest conditions (complex terrain and low number of ground points) for applications in forestry; (2) estimate canopy base height (CBH) for forest fire behavior modeling; and (3) assess the robustness of LiDAR-based high-resolution biomass estimation models against different field plot designs. Here, the aim is to find out if field plot data gathered by professional foresters can be combined with field plot data gathered by professionally trained community foresters and used in LiDAR-based high-resolution biomass estimation modeling without affecting prediction performance. The question of interest in this case is whether or not the local forest communities can achieve the level technical proficiency required for accurate forest monitoring. The algorithms for extracting DTMs from LiDAR point clouds presented in this thesis address the challenges of extracting DTMs in low-ground-point situations and in complex terrain while the algorithm for CBH estimation addresses the challenge of variations in the distribution of points in the LiDAR point cloud caused by things like variations in tree species and season of data acquisition. These algorithms are adaptive (with respect to point cloud characteristics) and exhibit a high degree of tolerance to variations in the density and distribution of points in the LiDAR point cloud. Results of comparison with existing DTM extraction algorithms showed that DTM extraction algorithms proposed in this thesis performed better with respect to accuracy of estimating tree heights from airborne laser scanner data. On the other hand, the proposed DTM extraction algorithms, being mostly based on trend surface interpolation, can not retain small artifacts in the terrain (e.g., bumps, small hills and depressions). Therefore, the DTMs generated by these algorithms are only suitable for forestry applications where the primary objective is to estimate tree heights from normalized airborne laser scanner data. On the other hand, the algorithm for estimating CBH proposed in this thesis is based on the idea of moving voxel in which gaps (openings in the canopy) which act as fuel breaks are located and their height is estimated. Test results showed a slight improvement in CBH estimation accuracy over existing CBH estimation methods which are based on height percentiles in the airborne laser scanner data. However, being based on the idea of moving voxel, this algorithm has one main advantage over existing CBH estimation methods in the context of forest fire modeling: it has great potential in providing information about vertical fuel continuity. This information can be used to create vertical fuel continuity maps which can provide more realistic information on the risk of crown fires compared to CBH.

Trophic cascades in boreal landscapes: top predator protection on tree seedlings and forest grouse

Changes in the abundance of top predators have brought about notable, cascading effects in ecosystems around the world. In this thesis, I examined several potential trophic cascades in boreal ecosystems, and their separate interspecific interactions. The main aim of the thesis was to investigate whether predators in the boreal forests have direct or indirect cascading effects on the lower trophic levels. First, I compared the browsing effects of different mammalian herbivores by excluding varying combinations of voles, hares and cervids from accessing the seedlings of silver birch (Betula pendula), Scots pine (Pinus sylvestris) and Norway spruce (Picea abies). Additionally, I studied the effect of simulated predation risk on vole browsing by using auditory cues of owls. Moving upwards on the trophic levels, I examined the intraguild interactions between the golden eagle (Aquila chrysaetos), and its mesopredator prey, the red fox (Vulpes vulpes) and the pine marten (Martes martes). To look at an entire potential trophic cascade, I further studied the combined impacts of eagles and mesopredators on the black grouse (Tetrao tetrix) and the hazel grouse (Tetrastes bonasia), predicting that the shared forest grouse prey would benefit from eagle presence. From the tree species studied, birch appears to be the most palatable one for the mammalian herbivores. I observed growth reductions in the presences of cervids and low survival associated with hares and voles, which suggests that they all weaken regeneration in birch stands. Furthermore, the simulated owl predation risk appeared to reduce vole browsing on birches in late summer, although the preferred grass forage is then old and less palatable. Browsing by voles and hares had a negative effect on the condition and survival of Scots pine, but in contrast, the impact of mammalian herbivores on spruce was found to be small, at least when more preferred food is available. I observed that the presence of golden eagles had a negative effect on the abundance of adult black grouse but a positive, protective effect on the proportion of juveniles in both black grouse and hazel grouse. Yet, this positive effect was not dependent on the abundance foxes or martens, nor did eagles seem to effectively decrease the abundance of these mesopredators. Conversely, the protection effect on grouse could arise from fear effects and also be mediated by other mesopredators. The results of this thesis provide important new information about trophic interactions in the boreal food webs. They highlight how different groups of mammalian herbivores vary in their effects on the growth and condition of different tree seedlings. Lowered cervid abundances could improve birch regeneration, which indirectly supports the idea that the key predators of cervids could cause cascading effects also in Fennoscandian forests. Owls seem to reduce vole browsing through an intimidation effect, which is a novel result of the cascading effects of owl vocalisation and could even have applications for protecting birch seedlings. In the third cascade examined in this thesis, I found the golden eagle to have a protective effect on the reproducing forest grouse, but it remains unclear through which smaller predators this effect is mediated. Overall, the results of this thesis further support the idea that there are cascading effects in the forests of Northern Europe, and that they are triggered by both direct and non‐lethal effects of predation.

Utilization of Statistical Methods for Management in the Forest Industry

The strongest wish of the customer concerning chemical pulp features is consistent, uniform quality. Variation may be controlled and reduced by using statistical methods. However, studies addressing the application and benefits of statistical methods in forest product sector are scarce. Thus, the customer wish is the root cause of the motivation behind this dissertation. The research problem addressed by this dissertation is that companies in the chemical forest product sector require new knowledge for improving their utilization of statistical methods. To gain this new knowledge, the research problem is studied from five complementary viewpoints – challenges and success factors, organizational learning, problem solving, economic benefit, and statistical methods as management tools. The five research questions generated on the basis of these viewpoints are answered in four research papers, which are case studies based on empirical data collection. This research as a whole complements the literature dealing with the use of statistical methods in the forest products industry. Practical examples of the application of statistical process control, case-based reasoning, the cross-industry standard process for data mining, and performance measurement methods in the context of chemical forest products manufacturing are brought to the public knowledge of the scientific community. The benefit of the application of these methods is estimated or demonstrated. The purpose of this dissertation is to find pragmatic ideas for companies in the chemical forest product sector in order for them to improve their utilization of statistical methods. The main practical implications of this doctoral dissertation can be summarized in four points: 1. It is beneficial to reduce variation in chemical forest product manufacturing processes 2. Statistical tools can be used to reduce this variation 3. Problem-solving in chemical forest product manufacturing processes can be intensified through the use of statistical methods 4. There are certain success factors and challenges that need to be addressed when implementing statistical methods

Finding Methods for Recognizing and Classifying Spare Part Criticality – Case Full Deposit Stripping Machines

The aim of this Master’s thesis is to find a method for classifying spare part criticality in the case company. Several approaches exist for criticality classification of spare parts. The practical problem in this thesis is the lack of a generic analysis method for classifying spare parts of proprietary equipment of the case company. In order to find a classification method, a literature review of various analysis methods is required. The requirements of the case company also have to be recognized. This is achieved by consulting professionals in the company. The literature review states that the analytic hierarchy process (AHP) combined with decision tree models is a common method for classifying spare parts in academic literature. Most of the literature discusses spare part criticality in stock holding perspective. This is relevant perspective also for a customer orientated original equipment manufacturer (OEM), as the case company. A decision tree model is developed for classifying spare parts. The decision tree classifies spare parts into five criticality classes according to five criteria. The criteria are: safety risk, availability risk, functional criticality, predictability of failure and probability of failure. The criticality classes describe the level of criticality from non-critical to highly critical. The method is verified for classifying spare parts of a full deposit stripping machine. The classification can be utilized as a generic model for recognizing critical spare parts of other similar equipment, according to which spare part recommendations can be created. Purchase price of an item and equipment criticality were found to have no effect on spare part criticality in this context. Decision tree is recognized as the most suitable method for classifying spare part criticality in the company.

Evaluation of the Efficiency of Line-Start Permanent-Magnet Machines as a Function of the Operating Temperature

The standard squirrel-cage induction machine has nearly reached its maximum efficiency. In order to further increase the energy efficiency of electrical machines, the use of permanent magnets in combination with the robust design and the line start capability of the induction machine is extensively investigated. Many experimental designs have been suggested in literature, but recently, these line-start permanent-magnet machines (LSPMMs) have become off-the-shelf products available in a power range up to 7.5 kW. The permanent magnet flux density is a function of the operating temperature. Consequently, the temperature will affect almost every electrical quantity of the machine, including current, torque, and efficiency. In this paper, the efficiency of an off-the-shelf 4-kW three-phase LSPMM is evaluated as a function of the temperature by both finite-element modeling and by practical measurements. In order to obtain stator, rotor, and permanent magnet temperatures, lumped thermal modeling is used.

Design and Material Selection of High-Speed Rotating Electrical Machines

The increasing emphasis on energy efficiency is starting to yield results in the reduction in greenhouse gas emissions; however, the effort is still far from sufficient. Therefore, new technical solutions that will enhance the efficiency of power generation systems are required to maintain the sustainable growth rate, without spoiling the environment. A reduction in greenhouse gas emissions is only possible with new low-carbon technologies, which enable high efficiencies. The role of the rotating electrical machine development is significant in the reduction of global emissions. A high proportion of the produced and consumed electrical energy is related to electrical machines. One of the technical solutions that enables high system efficiency on both the energy production and consumption sides is high-speed electrical machines. This type of electrical machines has a high system overall efficiency, a small footprint, and a high power density compared with conventional machines. Therefore, high-speed electrical machines are favoured by the manufacturers producing, for example, microturbines, compressors, gas compression applications, and air blowers. High-speed machine technology is challenging from the design point of view, and a lot of research is in progress both in academia and industry regarding the solution development. The solid technical basis is of importance in order to make an impact in the industry considering the climate change. This work describes the multidisciplinary design principles and material development in high-speed electrical machines. First, high-speed permanent magnet synchronous machines with six slots, two poles, and tooth-coil windings are discussed in this doctoral dissertation. These machines have unique features, which help in solving rotordynamic problems and reducing the manufacturing costs. Second, the materials for the high-speed machines are discussed in this work. The materials are among the key limiting factors in electrical machines, and to overcome this limit, an in-depth analysis of the material properties and behavior is required. Moreover, high-speed machines are sometimes operating in a harsh environment because they need to be as close as possible to the rotating tool and fully exploit their advantages. This sets extra requirements for the materials applied.