Metsän erirakenteisuutta edistävien hakkuiden kannattavuustarkastelu metsäaluetasolla

Tutkimuksessa vertailtiin metsän erirakenteisuutta edistävien poimintahakkuiden ja pienaukkohakkuiden kannattavuutta metsänhoitosuositusten mukaiseen metsänkasvatukseen Keski-Suomessa. Poimintahakkuut ja pienaukkohakkuut ovat menetelmiä, joilla voidaan lisätä luonnonmetsän häiriödynamiikan mukaista pienipiirteistä elinympäristöjen vaihtelua ja siksi ne sopivat etenkin erityiskohteisiin monimuotoisuuden, maiseman tai metsien monikäytön vuoksi. Ne johtavat yleensä vähitellen eri-ikäisrakenteiseen metsään, jossa puuston läpimittaluokkajakauma muistuttaa käänteistä J-kirjainta. Eri-ikäisrakenteisen metsänkäsittelyn taloudellista kannattavuutta puoltavat uudistumiskustannusten poisjäänti ja tukkipuihin painottuvat säännöllisin väliajoin toteutuvat hakkuut. Menetelmän soveltumista Suomen olosuhteisiin pidetään kuitenkin epävarmana. Tässä tutkimuksessa tarkasteltiin tasaikäisrakenteisen metsän muuttamista eri-ikäisrakenteiseksi 40 vuoden siirtymäaikana Metsähallituksen hallinnoimassa Isojäven ympäristöarvometsässä Kuhmoisissa. Tutkimusaineisto koostui 405 kuusivaltaisesta tasaikäisestä kuviosta, joiden pinta-alasta metsämaata on 636 hehtaaria. Metsän kehitystä simuloitiin puutason kasvumalleja käyttäen ja käsittelytoimenpiteet simuloitiin viisivuotiskausittain SIMO-metsäsuunnitteluohjelmistolla. Simulointien avulla selvitettiin jokaisen käsittelyskenaarion hakkuumäärät puutavaralajeittain, diskontatut kassavirrat ja puustopääoman muutos tarkasteluajanjakson aikana. Puunkorjuun yksikkökustannusten laskennan apuna käytettiin automatisoitua seurantajärjestelmää, jossa metsäkoneisiin asennettuilla matkapuhelimilla kerättiin MobiDoc2-sovelluksella metsäkoneiden käytöstä kiihtyvyystiedot, GPS-paikkatiedot ja syötetiedot. Lopulta jokaiselle käsittelyskenaariolle laskettiin metsän puuntuotannollista arvoa kuvaavalla tuottoarvon yhtälöllä nettonykyarvot, josta vähennettiin diskontatut puunkorjuun kustannukset. Tutkimuksen tulosten mukaan poimintahakkuun NPV oli 3 prosentin korkokannalla noin 91 % (7420 €/ha) ja pienaukkohakkuiden noin 99 % (8076 €/ha) metsänhoitosuositusten mukaisesta käsittelystä (8176 €/ha). Komparatiivinen statiikka osoitti, että korkokannan kasvattaminen 5 prosenttiin ei olennaisesti lisännyt nettonykyarvojen eroja. Poimintahakkuiden puunkorjuun yksikkökustannukset olivat 0,8 €/m3 harvennushakkuita pienemmät ja 7,2 €/m3 uudistushakkuita suuremmat. Pienaukkohakkuiden yksikkökustannukset olivat 0,7 €/m3 uudistushakkuita suuremmat.Tulosten perusteella on väistämätöntä että siirtymävaihe tasaikäisrakenteisesta eri-ikäisrakenteiseksi metsäksi aiheuttaa taloudellisia tappioita siitäkin huolimatta, että hakkuut ovat voimakkaita ja tehdään varttuneeseen kasvatusmetsään. Tappion määrä on metsän peitteisyyden ylläpidosta aiheutuva vaihtoehtoiskustannus.

Influência do relevo na produtividade e custos do harvester

Among the several variables that influence timber harvesting is the slope, which influences the productivity of forest machines. In this experiment the harvester was evaluated technically and economically while cutting and processing eucalyptus activity on different slope classes. The technical analysis included a study of time and movements by the method of continuous time; productivity was determined by the volume in cubic meters of wood processing. The economic analysis included the parameters of operational cost, production cost and energy consumption. The analysis of the data showed that productivity decreased according to the increase of the percent slope inclination, resulting in an effective work hour productivity increase from 18.72 to 39.71 m(3)sc, with a mean of operating cost of US$ 78.78 per work hour.

Infuěncia do relevo na produtividade e custos do harvester

Among the several variables that influence timber harvesting is the slope, which influences the productivity of forest machines. In this experiment the harvester was evaluated technically and economically while cutting and processing eucalyptus activity on different slope classes. The technical analysis included a study of time and movements by the method of continuous time; productivity was determined by the volume in cubic meters of wood processing. The economic analysis included the parameters of operational cost, production cost and energy consumption. The analysis of the data showed that productivity decreased according to the increase of the percent slope inclination, resulting in an effective work hour productivity increase from 18.72 to 39.71 m 3sc, with a mean of operating cost of US$ 78.78 per work hour.

Atributos físicos do solo em áreas de plantio direto com e sem escarificação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliação econômica de dois sistemas de colheita florestal mecanizada de Eucalipto

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Compactação do solo causada pelo tráfego de máquinas na colheita de madeira de Eucalyptus grandis Hill ex Maiden

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Reducing the babel in plant volatile communication: using the forest to see the trees

While plants of a single species emit a diversity of volatile organic compounds (VOCs) to attract or repel interacting organisms, these specific messages may be lost in the midst of the hundreds of VOCs produced by sympatric plants of different species, many of which may have no signal content. Receivers must be able to reduce the babel or noise in these VOCs in order to correctly identify the message. For chemical ecologists faced with vast amounts of data on volatile signatures of plants in different ecological contexts, it is imperative to employ accurate methods of classifying messages, so that suitable bioassays may then be designed to understand message content. We demonstrate the utility of `Random Forests' (RF), a machine-learning algorithm, for the task of classifying volatile signatures and choosing the minimum set of volatiles for accurate discrimination, using datam from sympatric Ficus species as a case study. We demonstrate the advantages of RF over conventional classification methods such as principal component analysis (PCA), as well as data-mining algorithms such as support vector machines (SVM), diagonal linear discriminant analysis (DLDA) and k-nearest neighbour (KNN) analysis. We show why a tree-building method such as RF, which is increasingly being used by the bioinformatics, food technology and medical community, is particularly advantageous for the study of plant communication using volatiles, dealing, as it must, with abundant noise.

The Random Forest Kernel and other kernels for big data from random partitions

We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random Forest. To demonstrate the potential of this method, we propose two new kernels, the Random Forest Kernel and the Fast Cluster Kernel, and show that these kernels consistently outperform standard kernels on problems involving real-world datasets. Finally, we show how the form of these kernels lend themselves to a natural approximation that is appropriate for certain big data problems, allowing $O(N)$ inference in methods such as Gaussian Processes, Support Vector Machines and Kernel PCA.

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

An Optimum-Path Forest framework for intrusion detection in computer networks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Efficient supervised optimum-path forest classification for large datasets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Diagnosis of a compacted area for mining through the mechanical resistance to the penetration using geostatistical methods in the Amazon Forest

The mining process promotes land modification and complete landscape alteration. Those alterations in the surface are shown more obviously in the aesthetical aspect as the visual elements of form, texture, climbs, complexity and color which composes the landscape. As a consequence, mining has impacts on the topography, in the soil, in the vegetation and in the area's drainage, with a direct influence on the enterprise. A quite common problem in the recovery of degraded areas in mineral exploration is the compaction of the soil due to the intense traffic of machines and earth movement. The most common problem of the compaction of a degraded surface is an increase of the mechanical resistance to the penetration of plant roots, a reduction of the aeration, an alteration of the flow of water and heat, also in the availability of water and nutrients. Thus, the present work had the basic objective of diagnosing the compaction of an area degraded by mining in a spacial way, through the mechanical resistance and the penetration, to guide the future subsoiling in the area requiring recovery. Through the studies, it was concluded that the krigagem method in agreement with the space variation allows the division of the area under study into sub areas facilitating a future work to reduce costs and unnecessary interference to the atmosphere. The method was shown to be quite appropriate and it can be used in the diagnosis of compaction in a degraded area by mining, foreseeing the subsoiling requirement.

Oropharyngeal dysphagia identification using wavelets and optimum path forest

The swallowing disturbers are defined as oropharyngeal dysphagia when present specifies signals and symptoms that are characterized for alterations in any phases of swallowing. Early diagnosis is crucial for the prognosis of patients with dysphagia and the potential to diagnose dysphagia in a noninvasive manner by assessing the sounds of swallowing is a highly attractive option for the dysphagia clinician. This study proposes a new framework for oropharyngeal dysphagia identification, having two main contributions: a new set of features extract from swallowing signal by discrete wavelet transform and the dysphagia classification by a novel pattern classifier called OPF. We also employed the well known SVM algorithm in the dysphagia identification task, for comparison purposes. We performed the experiments in two sub-signals: the first was the moment of the maximal peak (MP) of the signal and the second is the swallowing apnea period (SAP). The OPF final accuracy obtained were 85.2% and 80.2% for the analyzed signals MP and SAP, respectively, outperforming the SVM results. ©2008 IEEE.

Infrared face recogniton by optimum-path forest

This paper presents a novel, fast and accurate appearance-based method for infrared face recognition. By introducing the Optimum-Path Forest classifier, our objective is to get good recognition rates and effectively reduce the computational effort. The feature extraction procedure is carried out by PCA, and the results are compared to two other well known supervised learning classifiers; Artificial Neural Networks and Support Vector Machines. The achieved performance asserts the promise of the proposed framework. ©2009 IEEE.

Fast non-technical losses identification through Optimum-Path Forest

Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as Artificial Neural Networks and Support Vector Machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the Optimum-Path Forest classifier for a fast non-technical losses recognition, which has been demonstrated to be superior than neural networks and similar to Support Vector Machines, but much faster. Comparisons among these classifiers are also presented. © 2009 IEEE.