Application of Most Similar Neighbor Inference for Estimating Marked Stand Characteristics Using Harvester and Inventory Generated Stem Databases

The purpose of this study was to develop and test the application of non-parametric Most Similar Neighbor Inference (MSN) for wood procurement planning. An application developed using this method would be a part of a stem database in Finnish forest enterprises and could predict characteristics of a marked stand with accuracy demanded by bucking simulation. A stem database includes representative samples of stands and stems, applications to control and update data and applications to utilize the database. The study materials used consist of two different kinds of data: data collected by harvesters and historical forest inventory data. The harvester collected stem data came from stands in central Finland, whereas forest inventory data was obtained from all over Finland. The accuracy of the MSN method was analyzed by estimating characteristics of tree stocks and by comparing simulated spruce, pine and birch log length-diameter distributions with the information from actual stands. The application presented was found to be a useful and flexible tool for predicting characteristics of marked stands based on the stem data collected by a harvester. The forest inventory data was found less suitable for reference data. The most efficient way to create a length-diameter distribution was to calculate length-diameter class estimates from reference stands as weighted averages of the corresponding length-diameter class. The proposed method appears robust against measurement errors of search variables

Männikön laatujakauman kuvaaminen oksarajatiedon avulla kuvioittaisessa arvioinnissa

TutkimusartikkeliTutkimuksessa selvitettiin erilaisten kuvioittaisessa metsänarvioinnissa käyttökelpoisten menetelmien hyvyyttä männyn ulkoisen oksikkuuden huomioon ottamisessa. Oksikkuustunnuksista käsiteltiin rungon laatuun vaikuttavista tekijöistä alimman kuolleen oksan (kuivaoksaraja) ja elävän latvusrajan (latvusraja) korkeutta. Ko. oksarajatunnusten sekä puun rinnankorkeusläpimitan ja pituuden avulla rungot arvoapteerattiin dynaamisella optimoinnilla käyttäen laatuluokkina ulkoisesti oksatonta (I-laatu), terveoksaista (II-laatu) ja kuivaoksaista (III-laatu) laatua. §§ Tutkimus jakautui kahteen osaan. Ensimmäisessä osassa koealojen läpimittajakauma muodostettiin Weibull-jakauman avulla, estimoitiin jakauman puille pituudet, kuivaoksa- ja latvusrajat ja optimoitiin rungot. Lopuksi laskettin hehtaarikohtaiset tunnukset. Aineistona oli 65 VAPU (Valtakunnallinen puututkimus) -koealaa. Toisessa osassa simuloitiin kahteen metsikköön (Jaamankangas, Kontiolahti ja Mekrijärvi, Ilomantsi) relaskooppikoealoja, joissa kokeiltiin erilaisten mittausyhdistelmien ja koealojen lukumäärän vaikutusta eri laatuluokkien hehtaarikohtaisiin tuloksiin. §§ Rungon tukkiosan jakaminen eri laatuluokkiin onnistui tarkastellussa tilanteessa harhattomimmin menetelmällä, jossa koealan keskipuun oksarajatunnukset yleistetään suoraan kaikille koealan puille. Kokeillut mallit sekä kuivaoksa- että latvusrajalle osoittautuivat epätarkoiksi kalibrointienkin jälkeen. Koealojen lukumäärän vaikutus tulosten tarkkuuteen riippui käytetystä menetelmästä

Key structural features of Boreal forests may be detected directly using L-moments from airborne lidar data

This article introduces a novel methodology for automated classification of forest areas from airborne laser scanning (ALS) datasets based on two direct and simple rules: L-coefficient of variation Lcv=0.5 and L-skewness Lskew=0, thresholds based on descriptors of the mathematical properties of ALS height distributions. We observed that, while Lcv>0.5 may represent forests with large tree size inequality, Lskew>0 can be an indicator for areas lacking a closed dominant canopy. Lcv=0.5 discriminated forests with trees of approximately equal sizes (even tree size classes) from those with large tree size inequality (uneven tree size classes) with kappa κ = 0.48 and overall accuracy OA = 92.4%, while Lskew=0 segregated oligophotic and euphotic zones with κ = 0.56 and OA = 84.6%. We showed that a supervised classification could only marginally improve some of these accuracy results. The rule-based approach presents a simple method for detecting structural properties key to tree competition and potential for natural regeneration. The study was carried out with low-density datasets from the national program on ALS surveying of Finland, which shows potential for replication with the ALS datasets typically acquired at nation-wide scales. Since the presented method was based on deductive mathematical rules for describing distributions, it stands out from inductive supervised and unsupervised classification methods which are more commonly used in remote sensing. Therefore, it presents an opportunity for deducing physical relations which could partly eliminate the need for supporting ALS applications with field plot data for training and modelling, at least in Boreal forest ecosystems

Comparing basal area diameter distributions estimated by tree species and for the entire growing stock in a mixed stand.

The purpose of this study was to compare the Weibull distributions estimated for the entire growing stock of a stand and separately for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies Karst.) in describing the basal area diameter distributions in mixed stands. The material for this study was obtained by measuring 553 stands located in eastern Finland. The parameters of the Weibull distribution were estimated using the method of maximum likelihood. The models for these parameters were derived using regression analysis. Also, some parameter models from previous studies were compared with the measured distribution. The obtained distributions were compared using the diameter sums of the entire growing stock, diameter sums by tree species and of the sawtimber part of the growing stock. The results showed that far more accurate results were obtained when the distributions were formed using parameter models separately for the different tree species than when using parameter models for the entire growing stock. This was already true when considering the entire growing stock of the stand and especially when the results were examined by tree species. When the models for the entire growing stock were applied by tree species in relation to basal areas, the results obtained were overestimates for Norway spruce and underestimates for Scots pine. The models from earlier studies, where parameter models were estimated separately for tree species from the National Forest Inventory data, showed good fits also in regard to the data of this study

Canonical Correlation Analysis for Interpreting Airborne Laser Scanning Metrics along the Lorenz Curve of Tree Size Inequality

Canonical Correlation Analysis for Interpreting Airborne Laser Scanning Metrics along the Lorenz Curve of Tree Size Inequalit