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Browsing by discipline "Skogsresursvetenskap och -teknologi (skogsinventering)"

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  • Järnstedt, Janne (2010)
    The objective of this study was to develop a method for estimation of forest stand variables and updating the forest resource data, based on a well known and widely used method among forest sector, aerial photography. The second objective was to produce information of cost-effectiveness and accuracy of digital surface model (DSM) generated from very high resolution aerial images in comparison of methods based on aerial laser scanning (ALS). The study area covering circa 2000 hectares is located in state owned forest in Hämeenlinna, Southern Finland. The study material consisted of 85 digitised and orthorectified colour-infrared (CIR) aerial photographs, LiDAR measurements of the corresponding area and field measurements of 402 concentric circular plots. Both the remote sensing data and the field measurements were acquired in 2009. In this study, the accuracy of DSM generated from very high resolution CIR - aerial images was examined in the estimation of forest stand variables. Estimation of forest stand variables was made using non-parametric k-nearest neighbour method. Sequential forward selection was used for selecting features from remote sensing data and the examination of accuracy was done with cross validation. The variables examined were mean diameter, basal area, mean height, dominant height and mean volume. Relative RMSE -values of DMS estimation were at the best with mean diameter, basal area, mean height, dominant height and mean volume 33,67 %, 36,23 %, 25,33 %, 23,53 % and 40,39 %. For the reference ALS-data, relative RMSE-values were 25,26 %, 27,89 %, 19,94 %, 16,76 % ja 31,26 %. Photogrammetric DSM was best suited for estimating dominant and mean height and produced estimates slightly more inaccurate than those of reference ALS-data. When estimating mean diameter, photogrammetric DSM was slightly better, but at mean volume estimation, ALS-data proved again to be a little more a accurate than photogrammetric DSM. At basal area estimation, ALS-data gave considerably better results than photogrammetric DSM. This research showed that the photogrammetric DSM suits well for updating the forest resource data, and also satisfies the requirements in a more economic way.
  • Vanhatalo, Kalle M. (2012)
    As an alternative to complex 3D-modelling of structure, the canopy spectral invariants are a novel concept to describe the average behavior of photons in a vegetated canopy. The probabilities of canopy absorption and scattering can be summarized with only three parameters (?L, p and R): The green leaf single scattering albedo (?L) describes the wavelength-dependent probabilities of absorption for each time a photon interacts with a leaf. In the event of scattering, a photon’s probabilities of reinteraction (photon recollision probability p) and exiting the canopy in a given direction (directional escape factor R) can be described as independent of wavelength; as the size of the scattering elements is considerably larger than wavelengths in the shortwave radiation budget, p and R depend only upon the structural arrangement of the scattering elements. In this work, a recently published (2011) approach to infer remotely sensed (spaceborne) hyperspectral imagery (also referred to as imaging spectroscopy data) based on the canopy spectral invariants was tested in a case study on southern boreal forests at full leaf development. An atmospherically corrected image taken with the Hyperion imaging spectrometer aboard the National Aeronautics and Space Administration’s (NASA) Earth Observing-1 (EO-1) spacecraft was interpreted with a single reference transformed green leaf scattering albedo. Transforming of a traditionally defined leaf albedo means correcting the measurements for the effect of surface reflectance, resulting in probabilities of leaf scattering and absorption given a photon interacts with the leaf internal constituents. Utilizing such transformed albedo as reference results in reference (canopy) spectral invariants describing the relative difference between the reference and the scattering properties of (theoretical) mean leaves at the scale of inference (pixel). The results of the study are parallel to those of previously published and ongoing research: In essence, even while the individual parameters p and R depend on the reference, the ratio R/(1–p) (directional escape factor to total escape probability) was found practically independent of the selection of the reference, thus implicating a possibility to develop a physically-based algorithm to infer hyperspectral imagery in vegetated areas. Moreover, the reference (canopy) spectral invariants were found as highly applicable in retrieval of forest structural properties such as dominant forest type (broadleaved, coniferous, mixed) and a quantitative estimate of the broadleaf fraction of a forest area.
  • Kantola, Tuula (2010)
    Coarse woody debris (CWD) is an important indicator of biodiversity in forests, the source of organic material and carbon dioxide in the atmosphere and the habitat for a wide variety of organisms. In southern Finland, the amount of CWD per hectare in fresh mineral soils of old spruce-dominant forests can be as much as 90–120 m3 ha-1. In managed forests, however, it is only about 2–10 m3 ha-1, due to the management methods used in forests. The spatial pattern of CWD in managed forests is an essential research area, although it has rarely been studied. With knowledge of the spatial pattern of CWD in managed forests, it is possible to investigate inventory methods of rare phenomena, such as adaptive cluster sampling or line intersect sampling. The field measurements were performed in eastern Finland as part of one of the most extensive projects in Finland to inventory rare phenomena. Altogether 340 hectares of managed forest were inventoried by strip survey and over 11 600 dead trees were measured. The spatial pattern of CWD was examined with Ripley’s K –method. The method allows spatial assessment at different scales among and between species and enables one to determine how CWD is located in the study area used. The results of this study indicate that the CWD is located clustered in the area level in every spatial scale below 25 m. The spatial pattern of the CWD was complete random in approximately 63% of the forest management compartments in every studied spatial scale. The spatial pattern was clustered in 12% of the compartments. The spatial pattern was a mixture of random and clustered pattern in the rest (25%) of the compartments. In the future, the results of the study will be used as background information for examining inventory methods of rare phenomena and damages in managed forests.
  • Puolakka, Paula (2010)
    Leaf and needle biomasses are key factors in forest health. Insects that feed on needles cause growth losses and tree mortality. Insect outbreaks in Finnish forests have increased rapidly during the last decade and due to climate change the damages are expected to become more serious. There is a need for cost-efficient methods for inventorying these outbreaks. Remote sensing is a promising means for estimating forests and damages. The purpose of this study is to investigate the usability of airborne laser scanning in estimating Scots pine defoliation caused by the common pine sawfly (Diprion pini L.). The study area is situated in Ilomantsi district, eastern Finland. Study materials included high-pulse airborne laser scannings from July and October 2008. Reference data consisted of 90 circular field plots measured in May-June 2009. Defoliation percentage on these field plots was estimated visually. The study was made on plot-level and methods used were linear regression, unsupervised classification, Maximum likelihood method, and stepwise linear regression. Field plots were divided in defoliation classes in two different ways: When divided in two classes the defoliation percentages used were 0–20 % and 20–100 % and when divided in four classes 0–10 %, 10–20 %, 20–30 % and 30–100 %. The results varied depending on method and laser scanning. In the first laser scanning the best results were obtained with stepwise linear regression. The kappa value was 0,47 when using two classes and 0,37 when divided in four classes. In the second laser scanning the best results were obtained with Maximum likelihood. The kappa values were 0,42 and 0,37, correspondingly. The feature that explained defoliation best was vegetation index (pulses reflected from height > 2m / all pulses). There was no significant difference in the results between the two laser scannings so the seasonal change in defoliation could not be detected in this study.
  • Majasalmi, Titta (2011)
    Kasvuston G-funktio kuvaa säteilyn vähenemistä tai ”sammumista” auringon eri korkeuskulmissa varjostavan lehtialan suhteen. Sen vuoksi sitä kutsutaan sammumiskertoimeksi. Tutkimuksen tavoitteena oli selvittää vaihteleeko G-funktion muoto eri puulajien metsiköissä, ja voidaanko puulajit erottaa toisistaan G-funktion muodon avulla. Arvioin lisäksi G-funktioiden muodossa kasvukaudenaikana tapahtuvia muutoksia. Muita tutkimuksen kannalta mielenkiintoisia tutkimuskysymyksiä olivat voidaanko ryhmittymistä tai latvusmuotoa arvioida G-funktion muodon avulla. Maastomittaukset suoritettiin 3.5.2010 -30.9.2010 välisenä aikana. Tutkimusalueena oli Hyytiälän metsäaseman (68?59`N, 35?72`E) ympäristö, jonka metsät edustavat tyypillistä boreaalista havu- ja lehtimetsää. Tutkimusta varten perustettiin kuusi yhden puulajin koealaa, kaksi kutakin puulajia kohden. Tutkittavat puulajit olivat mänty (Pinus sylvestris), kuusi (Picea abies) ja rauduskoivu (Betula pendula). Koealaparit valittiin niin, että kullekin puulajille muodostui varttuneesta metsästä ja taimikosta muodostuva pari tai vaihtoehtoisesti tiheämpi ja harvempi koeala. Koealoille perustettiin 81 mittauspistettä sisältävä mittaushila, jossa kunkin pisteen aukkoisuustiedot mitattiin kahden viikon välein. Mittaukset suoritettiin kahdella LAI-2000 Plant Canopy Analyzer -laitteella. Laitteiden tulosteista koealoille saatiin aukkoisuustiedot T(?) ja LAI, joiden avulla saatiin laskettua tarkasteltavat G-funktiot. Saman puulajin G-funktiota vertailtiin toisiinsa puulajityypillisten trendien havaitsemiseksi. Keskikesällä eri puulajien G-funktioita verrattiin toisiinsa. Teoreettisten simulointien avulla tutkittiin latvuksen dimensioiden (latvuksen pituus ja säde) ja sisäisen ryhmittäisyyden vaikutusta puulajikohtaiseen G-funktioon. Simuloinneissa käytettiin hyväksi tietoa koealojen puustotunnuksista, lehtialasta sekä runkoluvusta. Puulajikohtaiset G-funktiot ovat erotettavissa toisistaan funktion minimi- ja maksimiarvojen sijoittumisen sekä suhteellisen vaihteluvälin perusteella. Havupuualojen G-funktiot eivät juuri muuttuneet kasvukauden aikana. Koivualoilla G-funktion kasvukaudenaikaiset muutokset (pelkät oksat, hiirenkorvat ja täysikasvuiset lehdet) olivat sitä vastoin helposti havaittavissa. G-funktion muodon avulla voidaan myös arvioida latvusmuotoa ja ryhmittymistä. Mäntyjen latvusmuoto on approksimoitavissa parhaiten ympyräkartion avulla. Kuusien ja koivujen latvusmuodon approksimointiin parhaana vaihtoehtona voidaan pitää ellipsoidia. Teoreettisten simulointien perusteella nuori kuusikko on muita havupuualoja ryhmittyneempi. Tutkimuksen mukaan säteily sammuu satelliittien yleisimmässä kuvaussuunnassa tehokkaammin kuin kaikkien suuntien yli laskettu keskiarvo (0,5) antaa olettaa. Puulajikohtaisten G-funktioiden avulla voidaan epäsuorasti arvioida sekä metsästä tapahtuvaa heijastusta että metsikön sisäisiä säteilyolosuhteita, sillä puulaji yhdessä metsikön rakenteen kanssa vaikuttaa metsästä heijastuvaan säteilyyn. Puulajikohtainen G-funktio on parametri, jonka avulla voidaan kalibroida malleja, joissa tarvitaan tietoa säteilyn kulusta erilaisissa kasvustoissa.
  • Ilvesniemi, Saara (2009)
    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.