New, low estimate for carbon stock in global forest vegetation based on inventory data

Abstract

Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data

Abstract

Age and size structure of gap-dynamic, old-growth boreal forest stands in Newfoundland

[Abstract]

Geometrically accurate time series of archived aerial images and airborne lidar data in a forest environment

Abstract

Bayesian classification of forest ecological type from satellite data

The main objective of this study was todo a statistical analysis of ecological type from optical satellite data, using Tipping's sparse Bayesian algorithm. This thesis uses "the Relevence Vector Machine" algorithm in ecological classification betweenforestland and wetland. Further this bi-classification technique was used to do classification of many other different species of trees and produces hierarchical classification of entire subclasses given as a target class. Also, we carried out an attempt to use airborne image of same forest area. Combining it with image analysis, using different image processing operation, we tried to extract good features and later used them to perform classification of forestland and wetland.

Value added logistics in supply and demand chain. Smile. Part 2. eBusiness in a service business. Case: A maintenance and operations network in forest industry

VVALOSADE is a research project of professor Anita Lukka's VALORE research team in the Lappeenranta University of Technology. The VALOSADE includes the ELO technology program of Tekes. SMILE is one of four subprojects of the VALOSADE. The SMILE study focuses on the case of the company network that is composed of small and micro-sized mechanical maintenance service providers and forest industry as large-scale customers. The basic principle of the SMILE study is the communication and ebusiness in supply and demand networks. The aim of the study is to develop ebusiness strategy, ebusiness model and e-processes among the SME local service providers, and onthe other hand, between the local service provider network and the forest industry customers in a maintenance and operations service business. A literature review, interviews and benchmarking are used as research methods in this qualitative case study. The first SMILE report, 'Ebusiness between Global Company and Its Local SME Supplier Network', concentrated on creating background for the SMILE study by studying general trends of ebusiness in supply chains and networks of different industries. This second phase of the study concentrates on case network background, such as business relationships, information systems and business objectives; core processes in maintenance and operations service network; development needs in communication among the network participants; and ICT solutions to respond needs in changing environment. In the theory part of the report, different ebusiness models and frameworks are introduced. Those models and frameworks are compared to empirical case data. From that analysis of the empirical data, therecommendations for the development of the network information system are derived. In process industry such as the forest industry, it is crucial to achieve a high level of operational efficiency and reliability, which sets up great requirements for maintenance and operations. Therefore, partnerships or strategic alliances are needed between the network participants. In partnerships and alliances, deep communication is important, and therefore the information systems in the network also are critical. Communication, coordination and collaboration will increase in the case network in the future, because network resources must be optimised to improve competitive capability of the forest industry customers and theefficiency of their service providers. At present, ebusiness systems are not usual in this maintenance network. A network information system among the forest industry customers and their local service providers actually is the only genuinenetwork information system in this total network. However, the utilisation of that system has been quite insignificant. The current system does not add value enough either to the customers or to the local service providers. At present, thenetwork information system is the infomediary that share static information forthe network partners. The network information system should be the transaction intermediary, which integrates internal processes of the network companies; the network information system, which provides common standardised processes for thelocal service providers; and the infomediary, which share static and dynamic information on right time, on right partner, on right costs, on right format and on right quality. This study provides recommendations how to develop this system in the future to add value to the network companies. Ebusiness scenarios, vision, objectives, strategies, application architecture, ebusiness model, core processes and development strategy must be considered when the network information system will be developed in the next development step. The core processes in the case network are demand/capacity management, customer/supplier relationship management, service delivery management, knowledge management and cash flow management. Most benefits from ebusiness solutions come from the electrifying of operational level processes, such as service delivery management and cash flow management.

Analysis of Satellite Data for Forest Segmentation

In this work we study the classification of forest types using mathematics based image analysis on satellite data. We are interested in improving classification of forest segments when a combination of information from two or more different satellites is used. The experimental part is based on real satellite data originating from Canada. This thesis gives summary of the mathematics basics of the image analysis and supervised learning , methods that are used in the classification algorithm. Three data sets and four feature sets were investigated in this thesis. The considered feature sets were 1) histograms (quantiles) 2) variance 3) skewness and 4) kurtosis. Good overall performances were achieved when a combination of ASTERBAND and RADARSAT2 data sets was used.

Building Dynamic Data Centers for Fast Delivery of New Data and Data Updates

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

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.

Modelling ecological values in heterogeneous and dynamic landscapes with geospatial data

Our surrounding landscape is in a constantly dynamic state, but recently the rate of changes and their effects on the environment have considerably increased. In terms of the impact on nature, this development has not been entirely positive, but has rather caused a decline in valuable species, habitats, and general biodiversity. Regardless of recognizing the problem and its high importance, plans and actions of how to stop the detrimental development are largely lacking. This partly originates from a lack of genuine will, but is also due to difficulties in detecting many valuable landscape components and their consequent neglect. To support knowledge extraction, various digital environmental data sources may be of substantial help, but only if all the relevant background factors are known and the data is processed in a suitable way. This dissertation concentrates on detecting ecologically valuable landscape components by using geospatial data sources, and applies this knowledge to support spatial planning and management activities. In other words, the focus is on observing regionally valuable species, habitats, and biotopes with GIS and remote sensing data, using suitable methods for their analysis. Primary emphasis is given to the hemiboreal vegetation zone and the drastic decline in its semi-natural grasslands, which were created by a long trajectory of traditional grazing and management activities. However, the applied perspective is largely methodological, and allows for the application of the obtained results in various contexts. Models based on statistical dependencies and correlations of multiple variables, which are able to extract desired properties from a large mass of initial data, are emphasized in the dissertation. In addition, the papers included combine several data sets from different sources and dates together, with the aim of detecting a wider range of environmental characteristics, as well as pointing out their temporal dynamics. The results of the dissertation emphasise the multidimensionality and dynamics of landscapes, which need to be understood in order to be able to recognise their ecologically valuable components. This not only requires knowledge about the emergence of these components and an understanding of the used data, but also the need to focus the observations on minute details that are able to indicate the existence of fragmented and partly overlapping landscape targets. In addition, this pinpoints the fact that most of the existing classifications are too generalised as such to provide all the required details, but they can be utilized at various steps along a longer processing chain. The dissertation also emphases the importance of landscape history as an important factor, which both creates and preserves ecological values, and which sets an essential standpoint for understanding the present landscape characteristics. The obtained results are significant both in terms of preserving semi-natural grasslands, as well as general methodological development, giving support to science-based framework in order to evaluate ecological values and guide spatial planning.

Effect of data acquisition accuracy on timing of stand harvests and expected net present value

Abstract