Small-scale non-industrial private forest ownership in the United States : rationale and implications for forest management

Abstract

Applying geostatistics for investigations of forest ecosystems using remote sensing imagery

Abstract

Structure of old Pinus sylvestris dominated forest stands along a geographic and human impact gradient in mid-boreal Fennoscandia

Abstract

Temporal trends in chemical parameters of upland forest soils in southern Finland

Abstract

Stratified estimation of forest inventory variables using spatially summarized stratifications

Abstract

Inventory of sparse forest populations using adaptive cluster sampling

Abstract

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

Abstract

Generating a raster map presentation of a forest resource by solving a transportation problem

Abstract

Scheduling forest road maintenance using the analytic hierarchy process and heuristics

Abstract

Landsat TM imagery and high altitude aerial photographs in estimation of forest characteristics

Abstract

Educating Pulp and Paper Engineers for the Global Forest Industry

Vol.23, No. 5, pp. 1024-1037, 2007.

Mobile ERP in the use of maintenance operations of forest industry

Toiminnanohjausjärjestelmien käyttö on muuttanut metsäteollisuuden kunnossapidon työntekijän toimenkuvaa kunnossapidon perustoiminnoista itseohjautuvampaan tietojen käsittelyyn ja jalostamiseen. Työn tavoitteena oliselvittää mobiilin työtilausjärjestelmän vaikutuksia metsäteollisuuden kunnossapidon kenttätyössä. Mobiilin työtilausjärjestelmän käyttö metsäteollisuuden kunnossapidossa mahdollistaa tärkeän kunnossapitotiedon keräämisen ja tarkentamisen kentällä, jossa se usein on ajanmukaisinta ja tarkinta. Haasteellisinta on oikeanlaisen teknologian löytäminen ja standardoiminen kunnossapidon toimintaympäristöihin. Myös organisaation toimintoprosessien on oltava selkeät ennen kuin prosesseja voidaan tehostaa teknologisin keinoin.

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.