Empirical errors of small area estimates from the multisource National Forest Inventory in Eastern Finland

Abstract

Bayesian methods for estimation, optimization and experimental design

Mathematical models often contain parameters that need to be calibrated from measured data. The emergence of efficient Markov Chain Monte Carlo (MCMC) methods has made the Bayesian approach a standard tool in quantifying the uncertainty in the parameters. With MCMC, the parameter estimation problem can be solved in a fully statistical manner, and the whole distribution of the parameters can be explored, instead of obtaining point estimates and using, e.g., Gaussian approximations. In this thesis, MCMC methods are applied to parameter estimation problems in chemical reaction engineering, population ecology, and climate modeling. Motivated by the climate model experiments, the methods are developed further to make them more suitable for problems where the model is computationally intensive. After the parameters are estimated, one can start to use the model for various tasks. Two such tasks are studied in this thesis: optimal design of experiments, where the task is to design the next measurements so that the parameter uncertainty is minimized, and model-based optimization, where a model-based quantity, such as the product yield in a chemical reaction model, is optimized. In this thesis, novel ways to perform these tasks are developed, based on the output of MCMC parameter estimation. A separate topic is dynamical state estimation, where the task is to estimate the dynamically changing model state, instead of static parameters. For example, in numerical weather prediction, an estimate of the state of the atmosphere must constantly be updated based on the recently obtained measurements. In this thesis, a novel hybrid state estimation method is developed, which combines elements from deterministic and random sampling methods.

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.

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

Näytteenotto teurasruhojen pintakontaminaation tutkimiseen

Summary: Sampling methods to monitor the microbiological contamination of carcasses

Ympäristövaikutusten velvoitetarkkailun tulosten epävarmuuden arviointi

Tämä diplomityö tehtiin Neste Oil Oyj:n Kehittäminen ja Laboratoriot yksikön HSE-palveluille. Työn tavoitteena oli arvioida Neste Oilin ympäristövaikutusten velvoitetarkkailujen mittaustulosten epävarmuutta. Tarkastelu koski ilmanlaadun SO2-, NO2-, TRS- sekä O3-mittauksia, ympäristömelumittauksia sekä pohjavesinäytteenottoa. Ympäristönsuojelulaki (86/2000) velvoittaa tuotantolaitoksia selvittämään toimintansa ympäristövaikutukset. Myös esimerkiksi akkreditoitaessa menetelmiä mittausepävarmuus on tunnettava. On arvioitu, että tulevaisuudessa direktiivit tulevat tiukentamaan päästöraja-arvoja ja mittausepävarmuuden käsite tulee käyttöön kaikilla ympäristösektoreilla.Tässä työssä ilmanlaadun mittausepävarmuus arvioitiin vertaamalla Neste Oilin mittaustuloksia Ilmatieteenlaitoksen vertailumittausten ja kalibrointien tuloksiin. Ympäristömelun mittausepävarmuus arvioitiin Ympäristöministeriön ympäristömelunmittausohjeen (1/1995)mukai-sesti. Pohjavesinäytteenoton mittausepävarmuus arvioitiin laskemalla haitta-aineiden ajallisen vaihtelun, näytteenottomenetelmien, näytteiden kuljetuksenja säilytyksen aiheuttaman kontaminaation sekä analyysivaiheen epävarmuustekijöiden yhdistetty mittausepävarmuus. Tarkastelussa todettiin, että ilmanlaadunmittaustulokset eivät poikenneet merkittävästi vertai-lumittausten ja kalibrointien tuloksista. Menetelmien laajennetuksi mittausepävarmuudeksi saatiin 6-8 %. Ympäristömelun mittausepävarmuus vastasi ympäristömelunmittausohjeessa esitettyjä arvoja ja vaihtelivat 2-10 dB:n välillä, riippuen mittausetäisyydestä ja mittauskertojen lukumäärästä. Pohjavesinäytteenoton mittausepävarmuudelle ei ole asetettu laatutavoitteita. Tässä tarkastelussa pohjavesinäytteenoton mittausepävarmuudeksi saatiin 33 %.

Hakevirrasta otettavan näytteen luotettavuus ja näytteenottimen kehittäminen

Työn tavoitteena oli saada tietoa yleisesti partikkelivirtauksessa tapahtuvasta näytteenotosta ja näytteenottomenetelmistä. Työssä tutkittiin UPM-Kymmenen Kaukaan tehtaan nykyisen ostohakkeen automaattisen näytteenottimen toimintaa sekä sen ottamien näytteiden luotettavuutta kokeellisesti ja tilastollisesti. Lopuksi tutkimuksessa ideoitiin uusi näytteenottimen konstruktio. Uuden konstruktion suunnittelun lähtökohtana oli, että näytteenottimen konstruktiolla tulisi voida ottaa toistuvasti ja turvallisesti luotettavia hakenäytteitä aiheuttamatta muulle vastaanottoaseman toiminnalle häiriöitä Tutkimusta varten tehtiin eri haketoimittajien kanssa koenäytteenottoja. Hakenäytteet otettiin kolmessa eri haketoimituksen vaiheessa niiden keskinäistä vertailua varten. Hakenäytteistä määritettiin kuiva-ainepitoisuus ja laatuanalyysin tekoa varten palakokojakauma koeseulonnoilla. Palakokojakauma-analyysistä saatuja jakaumapainoprosenttiosuuksien ja niistä laskettujen laatuarvojen pohjalta tutkittiin automaattisen näytteenottimen ottaman näytteen luotettavuutta ja näytteenottimen toimintaa. Konstruointityössä noudatettiin pääpiirteittäin ohjeistoa VDI 2221. Uuden näytteenottimen suunnittelutyön tavoitteet määriteltiin tutkimuksessa tehtyjen päätelmien ja kokeiden pohjalta. Uudelle näytteenottimen konstruktiolle etsittiin toteutuskelpoisia ratkaisuja jo olemassa olevista laitteistoista ja uusista ideoista. Uudet konstruktiovaihtoehdot suunniteltiin noudattamaan näytteenottoteoriaa ja sijoituspaikan asettamia vaatimuksia.

Metsähakkeen jalostusarvon nostaminen eri kuivausmenetelmillä

The thesis was made for Hyötypaperi Oy. To the business activity of the company belongs the recycling of materials and carry out for re-use and the manufacture of solid biofuels and solid recovered fuels. Hyötypaperi Oy delivers forest chips to its partner incineration plants by day and night though the year. The value of the forest chips is based on its percentage of dry material. It is important to dry forest chips well before its storage in piles and delivering to incineration plants. In the thesis was examed the increasing of the degree of refinement of forest chips by different drying methods. In the thesis was examined four different drying methods of the forest chips. The methods were field-, plate-, platform- and channel drying. In the channel drying was used a mechanical blower and the other drying methods were based on the weather conditions. By all drying methods were made test dryings during the summer 2007. In the thesis was examined also the economical profitableness of the field- and channel drying. The last examination in the thesis was measuring of the forest chips’s humidity by humidity measuring equipment of sawn timber during November 2007. The field drying on the surface of asphalt is the only method of drying, which is used by Hyötypaperi Oy in its own production. There do not exist earlier properly examined facts of any drying methods of forest chips, because the drying of forest chips is a new branch. By field- and platform drying achieved lower humidity of forest chips than by plate drying. The object by using the humidity measuring equipment was to be informed of the humidity of forest chips. At present the humidity will find out after 24 hours when the sample of humidity quantity has been dried in the oven. The Lappeenranta University of Technology had the humidity measuring equipment of sawn timber. The values of humidity measured by the equipment from the sample of forest chips were 2 – 9 percent lower than the real values of humidity specified by drying in oven.

Models in country scale carbon accounting of forest soils

Selective papers of the workshop on "Development of models and forest soil surveys for monitoring of soil carbon", Koli, Finland, April 5-9 2006.

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

Abstract

Functions for estimating stem diameter and tree age using tree height, crown width and existing stand database information

Abstract

Metsäbiomassan kartoittaminen monilähdeinventoinnilla

Seloste artikkelista: Tuominen, S., Eerikäinen, K., Schibalski, A., Haakana, M. & Lehtonen, A. / Mapping biomass variables with a multi-source forest inventory technique. Silva Fennica 44 (2010) : 1, 109-119.

Comparison of model-based above ground biomass predictions from LiDAR

In order to reduce greenhouse emissions from forest degradation and deforestation the international programme REDD (Reducing Emissions from Deforestation and forest Degradation) was established in 2005 by the United Nations Framework Convention on Climate Change (UNFCCC). This programme is aimed to financially reward to developing countries for any emissions reductions. Under this programm the project of setting up the payment system in Nepal was established. This project is aimed to engage local communities in forest monitoring. The major objective of this thesis is to compare and verify data obtained from di erect sources - remotely sensed data, namely LiDAR and field sample measurements made by two groups of researchers using two regression models - Sparse Bayesian Regression and Bayesian Regression with Orthogonal Variables.