Browsing by department "Department of Forest Resource Management"

The use of remote sensing imagery as auxiliary data in forest inventory is based on the correlation between features extracted from the images and the ground truth. The bidirectional reflectance and radial displacement cause variation in image features located in different segments of the image but forest characteristics remaining the same. The variation has so far been diminished by different radiometric corrections. In this study the use of sun azimuth based converted image co-ordinates was examined to supplement auxiliary data extracted from digitised aerial photographs. The method was considered as an alternative for radiometric corrections. Additionally, the usefulness of multi-image interpretation of digitised aerial photographs in regression estimation of forest characteristics was studied. The state owned study area located in Leivonmäki, Central Finland and the study material consisted of five digitised and ortho-rectified colour-infrared (CIR) aerial photographs and field measurements of 388 plots, out of which 194 were relascope (Bitterlich) plots and 194 were concentric circular plots. Both the image data and the field measurements were from the year 1999. When examining the effect of the location of the image point on pixel values and texture features of Finnish forest plots in digitised CIR photographs the clearest differences were found between front-and back-lighted image halves. Inside the image half the differences between different blocks were clearly bigger on the front-lighted half than on the back-lighted half. The strength of the phenomenon varied by forest category. The differences between pixel values extracted from different image blocks were greatest in developed and mature stands and smallest in young stands. The differences between texture features were greatest in developing stands and smallest in young and mature stands. The logarithm of timber volume per hectare and the angular transformation of the proportion of broadleaved trees of the total volume were used as dependent variables in regression models. Five different converted image co-ordinates based trend surfaces were used in models in order to diminish the effect of the bidirectional reflectance. The reference model of total volume, in which the location of the image point had been ignored, resulted in RMSE of 1,268 calculated from test material. The best of the trend surfaces was the complete third order surface, which resulted in RMSE of 1,107. The reference model of the proportion of broadleaved trees resulted in RMSE of 0,4292 and the second order trend surface was the best, resulting in RMSE of 0,4270. The trend surface method is applicable, but it has to be applied by forest category and by variable. The usefulness of multi-image interpretation of digitised aerial photographs was studied by building comparable regression models using either the front-lighted image features, back-lighted image features or both. The two-image model turned out to be slightly better than the one-image models in total volume estimation. The best one-image model resulted in RMSE of 1,098 and the two-image model resulted in RMSE of 1,090. The homologous features did not improve the models of the proportion of broadleaved trees. The overall result gives motivation for further research of multi-image interpretation. The focus may be improving regression estimation and feature selection or examination of stratification used in two-phase sampling inventory techniques.

Vapo Oy:n kehittämät maataloustraktorivetoiset HavuHukka -hakkuutähdeperävaunut ovat olleet liikenteessä jo vuosikymmenen ajan. Vaunun erikoisuus, kuormaa hydraulisesti tiivistävät laidat, mahdollistavat hakkuutähteen jatketun lähikuljetuksen suoraan palstalta terminaalivarastoon. Tällöin erillistä välivarastointivaihetta tienvarteen ei tarvita. Vapon alkuperäinen tarkoitus oli, että kesäisin turvetuotannossa olevaa traktorikalustoa voitaisiin talviaikaan käyttää HavuHukkien vetotraktoreina, jolloin urakoitsijat saisivat lisätyötä ja investoinnit konekalustoon pysyisivät kohtuullisina. Tässä tutkimuksessa selvitettiin miten HavuHukkien toimintaympäristö on vuosien aikana muuttunut ja miten toimintaympäristön muuttuminen on vaikuttanut HavuHukka -urakoinnin kustannusrakenteeseen. Tutkimus toteutettiin laadullisena, ohjaavana tapaustutkimuksena, sillä tutkimuksen tuloksia hyödynnetään päätöksenteossa HavuHukkien tulevaisuudesta. Tutkimus jakautui kolmeen osaan: haastattelututkimukseen, aikatutkimukseen ja kustannustarkasteluun. Haastattelututkimukseen osallistui yhteensä seitsemän henkilöä. HavuHukka -urakoitsijoiden lisäksi haastateltiin Vapon toimihenkilöitä seuraavilta aloilta: suunnittelu ja konekehitys, huolto ja varaosat sekä energiapuunhankinta. Aikatutkimuksessa seurattiin kahden kuljettajan työskentelyä maastossa. Kolmelta palstalta ajettiin kantoja ja yhdeltä hakkuutähdettä. Aikatutkimuksen lisäksi tehtiin kantojen koemurskaus, jolla selvitettiin kuinka monta irtokuutiota kantomursketta yhdestä HavuHukan kuormallisesta saadaan. Kustannustarkastelussa käytettiin konekustannuslaskennan periaatteita ja hyödynnettiin aiempien vuosien tutkimustuloksia sekä kevään 2008 haastattelu- ja aikatutkimuksen tuloksia. Nykyään yksi HavuHukka toimii alkuperäisellä idealla, eli se on pois käytöstä turvetuotantokauden ajan. Bioenergian käytön lisäämistavoitteen kasvaessa markkinoille on myös tullut uusia hakkuutähdettä tiivistäviä kuormatilaratkaisuja. Tutkimuksen tulokset ovat samansuuntaisia aiempien vuosien tutkimusten tulosten kanssa. Kustannustarkastelun todellisia tuloksia ei ole raportissa esitetty, sillä kustannuksista tehtiin tarkempi selvitys työn tilaajan käyttöön. Raportissa esimerkkilaskelmat on toteutettu kuvitteellisilla luvuilla.

The purpose of this study was to investigate the usability of aerial images and Landsat TM in estimating Scots pine defoliation. Estimation methods tested were unsupervised classification, maximum likelihood method, mixed model and linear regression model. Image features for needle loss detection were selected with stepwise linear regression and mixed model technique. As a part of the study the relationship between needle loss and leaf area index (LAI) was examined. The relationship between image features, needle loss and leaf area index was also examined. Numerical aerial images and Landsat TM satellite images were used. Textural features were calculated from aerial images and spectral vegetation indices from the satellite image. The study site was located in Ilomantsi, Finland. 71 field sample plots were measured and located with GPS. Field plots were circular plots. Trees with diameter brest height (dbh) over 13,9 cm were measured from 13 meter radius and trees with dbh 5,0 - 13,8 cm were measured from 7 meter radius. Needle loss of all pines was estimated. Needle loss for the plot was calculated as an average weighted by tree height. Four different class combinations were tested in classification. Plots were divided in 2, 3, 4 and 9 classes depending on their needle loss. Different image feature combinations and classification methods were tested. Classification was done by cross validation. Classification results were compared with original classes. The reliability of the classification was tested using accuracy matrix and kappa value. A mixed model was also used for aerial image features. The best image feature combination with all classification methods was the aerial image feature combination selected with stepwise selection method. Both spectral and textural features were included in the stepwise selection result. Classification accuracy varied between 38,0 % (9 classes) and 88,7 % (2 classes). The best explanatory variable for needle loss was the aerial image NIR channel maximum radiation (r2=0,69). However, unsupervised and supervised classification might have produced too positive results because of correlation in the data. Mixed model technique was used to select the variables for the linear model. Mixed model was used to reduce the effects of the correlation. The model classification accuracy varied between 35,2 % (9classes) and 87,3 % (2classes). According to mixed model selection result no textural features were significant predictors for needle loss. Classification results with Landsat image features were slightly poorer than with the best aerial image feature set (accuracy between 25,4 % and 88,7 %). The relationship between needle loss and LAI was poor (r2=0,27). Needle loss and LAI also correlated with different image features. LAI correlated slightly better with textural features than needle loss. Spectral vegetation indices calculated from Landsat TM correlated moderately with both needle loss and LAI. Indices VI3 (r2=0,56), MIR/NIR (r2=0,51) and RSR (r2=0,44) had the strongest connection to needle loss. Spectral vegetation indices could be a potential way for large area needle loss detection.

There is need for information about stands and their future development in forest planning decision making. This information is collected by inventories. In general inventory is repeated with some before-hand set intervals, irrespective of the method. Between inventories information is updated with growth models. Both inventory and using of growth models causes errors in forest planning results, for example in management options. Erroneous predictions can lead to wrong conclusions and inoptimal decisions. If the optimal result is known, economical losses caused by wrong conclusions can be described with so called inoptimality losses. The aim of this study was to answer the question how long forest inventory information, updated with growth models, can be used in forest planning purposes. Study approach was economical, so evaluation of information`s usefulness was based on inoptimality losses which arise when development of the stand is predicted incorrectly with growth models. The study material included 99 stands. Their development was simulated with the SIMO software for 60 years from present. In the 60 years period influencies of growth prediction errors were studied with inventory periods which lengths were 5, 10, 15, 20, 30 and 60 years. It was assumed that new error-free forest inventory information was received in the beginning of each of the inventory periods. In order to study effects of different inventory periods, it was assumed that the growth models were able to predict the true development of stands. Erroneous developments were yielded with error model which was developed for this study and added to the growth models. Inoptimality losses were calculated with the information derived from the optimization of stands` true and erroneous developments. Inoptimality losses increased when the inventory period became longer. Absolute inoptimality loss was approximately 230 eur/ha when the inventory period was 5 years and approximately 860 eur/ha when the inventory period was 60 years. Relative inoptimality loss was 3,3 % when the inventory period was 5 years and 11,6 % when the inventory period was 60 years. The average inoptimality losses were different between different development classes, site classes and main tree species. Study results show that the length of the updating period has an effect on the developing economical losses. It seems also that the inventory period should be different for example in different development classes. However, it is difficult to specify the optimal updating period because total losses are a sum of losses of inventory errors, losses of growth prediction errors and losses caused by other uncertainty sources. The effects of both inventory errors and growth prediction errors are different in different kinds of stands. So estimation of total losses and estimation of inoptimality losses caused by different error sources requires more research.

Industrial plantations of eucalyptus are sharply increasing in Asia. Although supplying raw material for the pulp and paper industry, easing deforestation on native forests and increasing carbon sequestration to help counter global warming, there are several concerns about the environmental effects of industrial eucalyptus plantations. These concerns include invasiveness of eucalyptus and loss of biodiversity, loss of land for food production, loss of soil fertility due to short rotation times and biomass removal, and excessive water-use and reduced catchment water yields. With protagonists on both sides, there is a need to research and examine the environmental effects of industrial eucalyptus plantations in southern China. We modelled and mapped the spatial distribution of water balance components across a small (752 ha) catchment in Guangxi province in relation to land-use, including industrial and local community plantations of eucalyptus and agriculture. Studies about the spatial distribution of water-use by eucalyptus across the landscape are few. WATBAL, a water balance model with a monthly time step, was parameterized and used to derive water balance components for 180 selected locations in the catchment. From the model output, continuous (predictive) surfaces for monthly (long-term average) potential (PET) and actual evapotranspiration (AET), evapotranspiration deficit (PET- AET), surface runoff and drainage below rooting zone were generated using GIS (ArcGIS 9.2). Averaged across the catchment, annual (October- September) actual evapotranspiration accounted for 77 %, surface runoff for 15 % and drainage below rooting zone for 8 % of rainfall. Differences between land-use types were relatively small, but areas of highest actual evapotranspiration and lowest surface runoff were associated with the oldest (6-7 years old) forested areas, including pure and mixed eucalyptus industrial plantations and local community, coppiced plantations on the slopes. The areas with the lowest actual evapotranspiration were associated with agricultural crops in the bottom of the catchment. The clear dominance of actual evapotranspiration in the water balance of all land-use types reflects the dominating role of the evaporative potential of the climate, with land-use cover, soil and topography factors playing secondary roles. While water-use was the highest for the forested areas, eucalyptus per se did not use more water than mixed plantations or those of the local community. Tree cover in general reduced surface runoff and therefore would reduce the risk of erosion. Using our modelling and mapping approach, we were able to assess the water-use and other components of the water balance of eucalyptus plantations and other land use types for this catchment. The study showed the importance of having suitable and adequate ground truth data in order to derive reliable and useful interpolation surfaces using ArcGIS.

This study investigates the effect of the data input on the forest management plan. The objective was to determine the differences between a forest plan where simulation units were either traditional stand compartments, or alternatively subcompartments delineated around measured sample plots. The simulations were compared with respect to the growth of the compartments as well as timing, income and yield from the first harvet operation suggested. The data was collected from a forest area of 72 hectares in Juuka, Eastern Finland. It consisted of 682 sample plots placed in a 30 m x 30 m grid. Independently of the sample plots, the area was divided into compartments as in normal compartmentwise planning, with the exception that no stand data was collected. Instead, the compartmentwise data was calculated from the systematic sample plot inventory. Three simulations were carried out with a planning package SIMO for a period of ten years and using one-year time step. Sim(I) presented the traditional compartmentwise planning, where the variables on compartment level were aggregated from sample plot data in the beginning of the simulation, and then used as simulation units. The other two simulations used a mosaic of sample-plot-based subcompartments as the simulation unit, and aggregate compartmentwise values were only used to determine the harvest decisions and for the comparison of the simulations. Of these mosaic setups, sim(II) was used to evaluate differences in growth rate and harvest yield to sim(I). In this simulation, the operations were adopted from sim(I) and applied simultaneously for each subcompartment of the respective compartment. The third simulation, mosaic setup sim(III) used the same simulation data as sim(II), but harvested the compartments according to the subcompartmentwise values, although using the compartments as harvest units to enable direct comparison in operation timing. Only compartments where harvests were expected during the simulation period were studied further, resulting in 14 compartments in the study. The simulations resulted in a greater growth rate estimate for sim(I). The difference between sim(I) and sim(II) varied among the compartments from 0.1 m3ha-1a-1 up to 2.0 m3ha-1a-1. The timing differences of harvest operations were 0-3 years. Income estimates were 5-10 % greater in the mosaic simulations, as well as especially the yield estimate of logwood. The differences in pulpwood estimations were more moderate, except on final cuts where mosaic simulations expected a better yield in minor species pulpwood than sim(I) which neglected these almost totally. The most effective single factor behind the differences in the simulations seemed to be the variation of site class within the compartment. The amount of compartments in the study was, however, too small and the variation between the compartment results too large to allow the application of the results elsewhere. Based on this study, the choice of data unit has an effect on the forest plan. Especially the effects of variation in site class are likely to be taken better into account if the spatial information of stand characteristics is maintained in the planning calculations. Still, small units are not necessarily better in describing the forest development, as they may result in biased estimations in the growth models.