<p>Forest quantification methods have evolved from a simple graphical approach to complex regression models with stochastic structural components. Currently, mixed effects models methodology is receiving attention in the forestry literature. However, the review work (Paper I) indicates a tendency to overlook appropriate covariance structures in the NLME modeling process.</p><p>A nonlinear mixed effects modeling process is demonstrated in Paper II using Cupressus lustanica tree merchantable volume data and compared several models with and without covariance structures. For simplicity and clarity of the nonlinear mixed effects modeling, four phases of modeling were introduced. The nonlinear mixed effects model for C. lustanica tree merchantable volume with the covariance structures for both the random effects and within group errors has shown a significant improvement over the model with simplified covariance matrix. However, this statistical significance has little to explain in the prediction performance of the model.</p><p>In Paper III, using several performance indicator statistics, tree taper models were compared in an effort to propose the best model for the forest management and planning purpose of the C. lustanica plantations. Kozak's (1988) tree taper model was found to be the best for estimating C. lustanica taper profile.</p><p>Based on the Kozak (1988) tree taper model, a Ds optimal experimental design study is carried out in Paper IV. In this study, a Ds-optimal (sub) replication free design is suggested for the Kozak (1988) tree taper model.</p>

Historically the U.S. Forest Service has used uncorrected aerial photographs to delineate proposed and past management activities on the land base it manages. Transferring a boundary from an image not planimetrically correct to a planimetrically corrected image introduces errors. Positional accuracy of boundaries affects the number of acres the Forest is accountable for managing, and the annual sale quantity (ASQ) or annual board feet targets. The purpose of this study was to develop a methodology that eliminated the need to transfer the boundary from an uncorrected to a corrected image. Raster and vector warping methods were evaluated with reference to positional accuracy and efficiency. Due to the rugged topography of the Siuslaw National Forest, selection of ground control points (GCPs) was an important function in the accurate transformation of images. A Vector warping method, Rubber Sheeting the ARC/INFO projective transformation for all digital GCPs, to all of the Global Position System (GPS) ground control points, provided the most accurate rectification of vector boundaries that had been digitized or scanned from an uncontrolled low elevation photograph. / Graduation date: 1992

Forest quantification methods have evolved from a simple graphical approach to complex regression models with stochastic structural components. Currently, mixed effects models methodology is receiving attention in the forestry literature. However, the review work (Paper I) indicates a tendency to overlook appropriate covariance structures in the NLME modeling process. A nonlinear mixed effects modeling process is demonstrated in Paper II using Cupressus lustanica tree merchantable volume data and compared several models with and without covariance structures. For simplicity and clarity of the nonlinear mixed effects modeling, four phases of modeling were introduced. The nonlinear mixed effects model for C. lustanica tree merchantable volume with the covariance structures for both the random effects and within group errors has shown a significant improvement over the model with simplified covariance matrix. However, this statistical significance has little to explain in the prediction performance of the model. In Paper III, using several performance indicator statistics, tree taper models were compared in an effort to propose the best model for the forest management and planning purpose of the C. lustanica plantations. Kozak's (1988) tree taper model was found to be the best for estimating C. lustanica taper profile. Based on the Kozak (1988) tree taper model, a Ds optimal experimental design study is carried out in Paper IV. In this study, a Ds-optimal (sub) replication free design is suggested for the Kozak (1988) tree taper model.

yes / Drug vehicles are chemical carriers that provide beneficial aid to the drugs they bear. Taking advantage of their favourable properties can potentially allow the safer use of drugs that are considered highly toxic. A means for vehicle selection without experimental trial would therefore be of benefit in saving time and money for the industry. Although machine learning is increasingly used in predictive toxicology, to our knowledge there is no reported work in using machine learning techniques to model drug-vehicle relationships for vehicle selection to minimise toxicity. In this paper we demonstrate the use of data mining and machine learning techniques to process, extract and build models based on classifiers (decision trees and random forests) that allow us to predict which vehicle would be most suited to reduce a drug’s toxicity. Using data acquired from the National Institute of Health’s (NIH) Developmental Therapeutics Program (DTP) we propose a methodology using an area under a curve (AUC) approach that allows us to distinguish which vehicle provides the best toxicity profile for a drug and build classification models based on this knowledge. Our results show that we can achieve prediction accuracies of 80 % using random forest models whilst the decision tree models produce accuracies in the 70 % region. We consider our methodology widely applicable within the scientific domain and beyond for comprehensively building classification models for the comparison of functional relationships between two variables.

Abstract Cross-country route optimization, particularly from a pedestrian’s perspective, is a relatively uncommon research topic that has many application possibilities. In this research, route optimization is examined in the context of forest inventories. The background for the research is the change in the forest data collection practices carried out by the Finnish Forest Centre. In this new inventory procedure, targets to be inventoried in the field are often located far apart from each other. The research indicated that the collection of data from sparsely distributed targets is comparatively inefficient. In order to address this issue, a method allowing the traversability of cross-country terrain to be expressed in a numeric form was developed such that it makes cross-country route optimization possible. In addition, a method for the determination of a route inside an areal object, allowing the collection of reliable inventory data from the target, was constructed in the study. Finally, the route optimization method developed in the study was tested in actual field work. The main hypothesis for the research was that it is possible to apply the route optimization method to forest inventory, and that it would be considered useful. The result of the study was that the method is, indeed, suitable for forest data collection. However, the results also suggested that route optimization does not necessarily make the work more efficient, but its utility depends on the qualities of the field workers and the area where the targets are located. The results have both theoretical and practical significance. The route optimization system constructed in the study is the most accurate national system realized thus far which has also been tested and evaluated in actual field work. A number of ancillary GIS-based analyses for route optimization were also developed in the study, and they turned out to be suitable for the calculation of inventory routes for field workers. A new route optimization problem, coined as the Areal Inventory Problem (AIP), was defined during the research. While the route optimization procedure developed in the study can be put into operation in the forest data collection practices of the Finnish Forest Centre, many of its principles are also applicable to purposes outside the domain of forestry. Data needed in other applications can be tailored using the methods presented in this research. Several prospects and needs for further research and development were recognized. By addressing these questions, the route optimization procedure can be further improved, while also strengthening the theoretical knowledge concerning cross-country route optimization. / Tiivistelmä Reitinoptimointi maastossa, etenkin jalankulkijan näkökulmasta, on melko vähän tutkittu aihe, jolla on erilaisia sovellusmahdollisuuksia. Tässä tutkimuksessa reitinoptimointia on tarkasteltu metsäninventoinnin näkökulmasta. Tutkimuksen taustana on Suomen metsäkeskuksen metsävaratiedon keruun menetelmien uudistuminen, jonka myötä maastossa inventoidaan hajallaan sijaitsevia kohteita. Tutkimuksessa havaittiin, että hajallaan sijaitsevien kohteiden inventointi on melko tehotonta. Tämän vuoksi kehitettiin menetelmä, jossa maaston kulkukelpoisuutta voidaan kuvata numeerisessa muodossa niin, että se mahdollistaa reitinoptimoinnin. Lisäksi luotiin menetelmä, jolla voidaan tuottaa inventointireitti aluemaisen kohteen sisälle niin, että kohteelta voidaan kerätä riittävän luotettavat tiedot. Lopuksi menetelmää testattiin metsävaratiedon keruun tuotantotyössä. Hypoteesina oli, että reitinoptimointia on mahdollista soveltaa metsäninventoinnissa ja että menetelmä koettaisiin hyödylliseksi. Tutkimuksessa vahvistettiin menetelmän soveltuvuus metsäninventointiin. Samalla havaittiin, ettei reitinoptimointia voida aukottomasti todistaa työtä tehostavaksi, vaan sen hyödyllisyys riippuu maastotyöntekijän ja maastotyöalueen ominaisuuksista. Tutkimustuloksilla on sekä teoreettista että käytännöllistä merkitystä. Tutkimuksessa luotiin tähän mennessä tarkin kotimainen reitinoptimointimenetelmä, jota on myös testattu maastossa. Samalla kehitettiin reitinoptimointiin liittyviä paikkatietomenetelmiä ja havaittiin, että paikkatietojärjestelmällä on mahdollista tuottaa maastotyöntekijän apuvälineeksi sopivia inventointireittejä. Tutkimuksen aikana määriteltiin uusi reitinlaskentaongelma, aluemaisen kohteen inventoinnin ongelma (AIP). Reitinoptimointimenetelmä on otettavissa käyttöön metsävaratiedon keruussa Suomen metsäkeskuksessa, ja sitä voidaan soveltaa myös metsäalan ulkopuolella. Tutkimuksessa esitellyillä menetelmillä voidaan tuottaa sovellustarvetta vastaavat aineistot reitinlaskennan lähtötiedoiksi. Tutkimuksessa tunnistettiin monia jatkotutkimus- ja kehittämistarpeita. Näihin kysymyksiin vastaamalla voidaan luoda yhä paremmin toimiva työkalu metsävaratiedon keruun apuvälineeksi ja syventää edelleen teoreettista tietämystä reitinoptimoinnista maastossa.