Browsing by Subject "laserkeilaus"

The effect of forest health and structure to the relative surface temperature captured by airborne thermal imagery was investigated in Norway Spruce-dominated stands in Southern Finland. Canopy surface temperature has long been recognized useful in monitoring vegetation water status. Recent studies have shown also its potential in monitoring vegetation health. Airborne thermal imagery, Airborne Light Detection and Ranging (LiDAR) and field measurements were acquired from the area of interest (AOI). The relative surface temperature correlated most negatively with the logarithm of stem volume, Lorey’s height and logarithm of basal area at resolution of 254m2 (9-m radius). In other words, taller and older stands had colder surface temperatures. In addition, LiDAR metrics, such as height percentiles and canopy cover percentage, were compared with surface temperature. Standard deviation of canopy height model, height features (H90, CHM_max) and canopy cover percentage were most strongly negatively correlated with the surface temperature. On average, higher surface temperatures were detected in defoliated canopies indicating that thermal images may provide some additional information for classifying forests health status. However, the surface temperature of defoliated plots varied considerably. It was also found that surface temperature differences between canopy and ground responses were higher in defoliated plots. Based on the results, forest health and structure affect to the surface temperature captured by airborne thermal imagery and these effects should be taken into account when developing forest health mapping applications using thermal imagery.

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.

The purpose of this Master’s thesis is to find out what kind of views Finnish forest professionals have concerning forest data that’s based on laser scanning. Lately there have been a lot of changes in forest organizations and in the forest professional’s work environment. One of these big changes is that forest data based on laser scanning is becoming a part of operative forest planning. Extensive mapping of forest areas is more often done with airplane or satellite remote sensing. This way it is possible to cut down costs and make mapping more efficient by reducing sample plots and using more remote sensing data. This study focuses on this change from the perspective of an employee of a forest planning or advisory organization. The theoretical frame is action theory. The aim is to find out how this new forest data suits forest planning and is the quality of the data good enough to make forest plans. This research is a very topical subject because forest data that is based on laser scanning is going to bring significant changes to the forest planning field practices. The strengths of laser scanning are that it is objective and it gives at least as accurate tree data measures as other methods. Forest data based on laser scanning is also quite inexpensive, it can be acquired very quickly and it is easy to update. There is very little previous research about this subject. It is likely that new researched information is going to improve the usability of forest data that is based on laser scanning at forest planners’ everyday work. This research was implemented with web-based questionnaire and both quantitative and qualitative methods where used. The sample was taken from three forest organizations (The Finnish Forest Centre, OTSO Forest Services and The Forest Management Associations). The aim was to select forest professionals that use laser scanning based forest information at their work. The whole sample size was 929 respondents and the response rate was 37 percent. The research methods used were statistical description, cross tables and qualitative thematic analysis. When we look at the results it is clear that there are differences on how regularly forest data based on laser scanning is used in these three organizations. The Finnish Forest Centre and OTSO Forest services are using forest data based on laser scanning more often than The Forest Management Associations, but in all of these organizations there was a desire to use it more. The results of this study clearly show that a main part of forest professionals think that this new method is suitable for forest planning at least as an aid to making forest plans. The forest professionals also thought that there is still room for improvement in this new forest data collection method. There are too many errors at reliability especially in young and untreated forest. There is also a difficulty to determine the ratio of tree species. Generally it can be said that forest data based on laser scanning is liked among the forest professionals and they would like to use it more in the future.

There is an ever growing interest in coarse woody debris (CWD). This is because of its role in maintaining biodiversity and storing atmospheric carbon. The aim of this study was to create an ALS-data utilizing model for mapping CWD and estimating its volume. The effect of grid cell size change to the model's performance was also considered. The study area is located in Sonkajärvi in eastern Finland and it consisted mostly of young commercially managed forests. The study utilized low-frequency ALS-data and precise strip-wise field inventory of CWD. The data was divided into two parts: one fourth of the data was used for modeling and the remaining three fourths for validating the models that were constructed. Both parametric and non-parametric modelling practices were used for modelling the area's CWD. Logistic regression was used to predict the probability of encountering CWD in grid cells of different sizes (0.04, 0.20, 0.32, 0.52 and 1.00 ha). The explanatory variables were chosen among 80 ALS-based variables and their conversions in three stages. Firstly, the variables were plotted against CWD volumes. Secondly, the best variables plotted in the first stage were examined in single-variable variable models. Thirdly, variables to the final multivariable model were chosen using 95 % level of significance. The 0.20 ha model was parametrized to other grid cell sizes. In addition to parametric model constructed with logistic regression, 0.04 ha and 1.0 ha grid cells were also classified with CART-modelling (Classification and Regression Trees). With CARTmodelling, non-linear dependecies were sought between ALS-variables and CWD. CART-models were constructed for both CWD existence and volume. When the existence of CWD in the study grid cells was considered, CART-modelling resulted in better classification than logistic regression. With logistic model the goodness of classification was improved as the grid cell size grew from 0.04 ha (kappa 0.19) to 0.32 ha (kappa 0.38). On 0.52 ha cell size, kappa value of the classification started to diminish (kappa 0.32) and was futhermore diminished to 1.0 ha cell size (kappa 0.26). The CART classification improved as the cell size grew larger. The results of CART-modelling were better than those of the logistic model in both 0.04 ha (kappa 0.24) and 1.0 ha (kappa 0.52) cell sizes. The relative RMSE of the cellwise CWD volume predicted with CART-models diminished as the cell size was enlarged. On 0.04 ha grid cell size the RMSE of the total CWD volume of the study area was 197.1 % and it diminished to 120.3 % as the grid cell size was enlarged to 1.0 ha. On the grounds of the results of this study it can be stated that the link between CWD and ALS-variables is weak but becomes slightly stronger when cell size increases. However, when cell size increases, small-scale variation of CWD becomes more difficult to spot. In this study, the existence of CWD could be estimated somewhat accurately, but the mapping of small-scale patterns was not successful with the methods that were used. Accurate locating of small-scale CWD variation requires further research, particularly on the use of high density ALS-data in CWD inventories.

Site index and site type are methods for describing a habitat’s highest annual production of tree (m3/ha/a). Site index is based on the growth of trees’ dominant height, and site type on the existence of plant communities in a habitat. This research examined whether site type can be estimated with site index derived by airborne laser scanning (ALS). ALS is a remote sensing method by which trees’ characteristics are predicted based on the height distribution and density values of laser pulses. Data were gathered from Syöte, Northern Ostrobothnia. Arbonaut Oy provided the ALS-data, and Metsähallitus provided age and habitat data of microstands. Site type, trees’ ages and dominant heights were also measured by field surveying. Experimental data from plots were generalized to microstand data. There were 208 experimental plots that were generalized to 39 microstands. Site indexes for experimental plots and microstands were calculated to index age 100 years with Vuokila and Väliaho’s (1980) growth models for dominant height and with Schumacher’s (1939) guide curve. Site indexes were converted to site types with Vuokila and Väliaho’s (1980) conversion diagram and then compared to site types of the field surveys. The accuracy of the site indexes was evaluated with an error matrix. Accuracy of age and dominant height was evaluated with a paired t-test. Variation of index aged dominant height inside a site type was examined with root mean square error (RMSE). The effect of the proportions of tree species on the site index was analyzed with a correlation coefficient. The percentage of properly classified site types was 57 % at best when using Schumacher’s (1939) guide curve and two site types (rich and poor). With Vuokila and Väliaho’s (1980) growth models for dominant height the best percentage of correctly classified site types was 46 %. The variation (RMSE) of index aged dominant height as an average in all site types was 3,2 m. The maximum variation of index aged dominant height in one site type was 6 m. This indicated that the dependency between site index and site type was poor with this data. The proportions of tree species did not have a significant effect on site index. Based on this study, predicting site types with ALS does not work. One problem is the absence of very rich site types. There are also many sources of error: SutiGIS’ (position knowledge system) age data, prescribed burning, possible fertilization of stands, and the problems of fitting site index and site type. Vuokila and Väliaho’s (1980) growth models for dominant height were made in the 1980s, so they do not acknowledge the increased growth of present-day trees. Site index cannot reliably predict the site type, but site index gives valuable extra information about the trees for example when evaluating the value of forest estates.

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.

Metsäkanalintujen elinympäristövaatimukset tunnetaan Suomessa hyvin ja elinympäristöihin kiinnitetään yhä enemmän huomiota osana metsän- ja luonnonhoidon toimia. Metso (Tetrao urogallus) ja pyy (Tetrastes bonasia) ovat elinympäris-töjen valinnassaan melko tarkkoja ja lajien välillä on selkeitä eroja. Elinympäristöjen ennakkotarkasteluun ei tällä hetkellä ole olemassa monipuolisia metsäsuunnittelun työkaluja ja lentolaserkeilauksella kerättyä aineistoa ei ole käytetty metsä-kanalintujen elinympäristötutkimuksiin Suomessa. Tässä kvalitatiivisessa tutkimuksessa selvitettiin Maanmittauslaitoksen vuonna 2012 keräämän lentolaserkeilausai-neiston soveltuvuutta potentiaalisten metso- ja pyyelinympäristöjen ohjaamattomaan luokitukseen. Aineiston pulssitiheys oli 0,74–0,8 / m2. Tutkimusalueena oli noin 1 200 hehtaaria Metsähallituksen omistamaa metsätalousmetsää Hämeenlinnan Evolla. Tutkimusalue jaettiin 16 x 16 metrin soluihin. Laserkeilausaineistosta laskettiin viisi elinympäristöä kuvaavaa lisäpiirrettä: latvuspeitto CC, alikasvostunnus US, puuston korkeusosuuden tiheystunnus k60_den, korkeusosuuden keski-hajonta H60_stdv sekä pituustunnus H_max. Menetelmänä käytettiin solukohtaista ohjaamatonta luokitusta (K-means), joka jakoi solut 43 luokkaan. Edelleen kohdennettuun maasto-otantaan valittiin 16 luokkaa. Säteeltään kiinteitä 9 metrin ympyräkoealoja mitattiin alueelta kaikkiaan 168 kpl, joista selkeitä pyykohteita ja vähintään arvosanalla 8 arvioituja metsokohteita oli molempia 12 kpl. Ohjaamaton luokitus ei nostanut esiin yhtä tai useampaa potentiaalisia kohteita kuvaavaa luokkaa, vaan potentiaalisten kohteiden lisäksi luokissa oli paljon ei-potentiaalisia soluja. Vähintään arvosanalla 8 arvioitujen metsokohteiden lisäpiirteet täyttäviä kohteita oli suhteellisesti eniten luokissa 3, 7 ja 8 osuuksilla 40,4 %, 35,6 % ja 31,8 %. Vastaavasti pyylle osuudet luokissa 4, 18 ja 20 olivat 38,4 %, 28,5 % ja 23,8 % minimiarvosanalla 6. Tutkimusalue sisältää arvioitujen kohteiden lisäpiirteiden perusteella hyviä elinympäristöjä metsolle (Arvosana ≥ 8) 15,0 % ja pyylle (Arvosana ≥ 6) 9,2 % sen pinta-alasta. Korkeimman arvosanan kohteiden lisäpiirteiden arvojen herkkyysanalyysissä alueet olivat vastaavasti 8,5 % ja 14,4 % kunkin lisäpiirteen ± 30 % marginaaleilla. Arvosanarajoite toimi loogisesti eli lisäpiirteiden vaihteluväli kapeni arvosanan kasvaessa. Tuloksena saadut poten-tiaaliset solut olivat toisiinsa erittäin kytkeytyneitä molemmissa analyyseissä vaikka käytetty menetelmä oli täysin riip-pumaton solujen sijainnista toistensa suhteen. Pyyhavainnot olivat potentiaalisuutta kuvaavalla solulla neljässä ja hyvin lähellä potentiaalisuutta kuvaavaa solua kahdessa havainnossa kuudesta, kun lisäpiirteiltään potentiaaliset solut valittiin arvosanoihin perustuen. Käytetyn aineiston kyky kuvata tässä tutkimuksessa määritettyjä potentiaalisia kohteita on siis lupaava. Aineiston kykyä tulee kuitenkin edelleen testata ennen kuin luotettavia metsäsuunnittelun työkaluja voidaan soveltaa operatiiviseen käyttöön.

In recent years objectives to increase fuelwood usage have created a need to develop new re-mote sensing based methods to map fuelwood reserves. Laser scanning (LiDAR) is a remote sensing method which has been used in traditional forest inventories on large forest areas. These inventories have mainly concentrated on the stem volume instead of the total tree bio-mass. Former studies concerning fuelwood inventory accuracy have been made on a sample plot level. The aim of this study was to determine the precision of LiDAR based fuelwood inventory on a forest stand level. Wood stem volume and biomass estimates were produced by using two dif-ferent methods: point cloud and digital terrain model. The estimates were compared with field inventory results and results from the multi-source National Forest Inventory. Of the two compared methods the point cloud method was found to be more accurate than the method based on digital terrain model. When the point cloud was used, the accuracy (RMSE %) of the most important fuelwood estimates were following: waste wood vol-ume 37.4 %, branch biomass 21.8 % and stump biomass 18.6 %. The study indicates that re-sults got by laser scanning are on similar level as results got with traditional standwise inventory. This study concentrated only to predict the amount of fuelwood on the forest stand level. The suitability of the stand for fuelwood harvesting was not estimated. In order to utilize LiDAR-based fuelwood inventory for wood acquisition in forest industry, methods to estimate the suita-bility of the stand for harvesting need to be developed.

Huoltovarmuuden merkitys kasvaa maailmalla ja Suomessa. Yksityisteiden kunto ja ylläpitäminen tukevat Suomen huoltovarmuutta ja kestävää metsätaloutta. Maastoinventoinneilla yksityisteiden kunnon määrittäminen vie paljon resursseja ja on hidasta, joten kunnon määrittämiselle tarvitaan kaukokartoituspohjaisia ratkaisuja. Laserkeilaus loisi suuren edun yksityisteiden kunnon kartoittamisessa, koska näin saadaan kerättyä laajalta alueelta nopeasti tietoa. Lisäksi samaa metsävarojeninventointiin kerättävää laserkeilausaineistoa voidaan hyödyntää myös yksityisteiden tarkastelussa. Tässä maisterintutkielmassa tutkittiin TIESIT-hankkeelle tuotetun tiheäpulssisen laserkeilaustulkinnan tarkkuutta ja epäkohtia yksityisteiden kuntotiedon määrityksessä. Maisterintutkielma on tehty Suomen metsäkeskuksen toimeksiantona ja on osana TIESIT-hanketta. Tuotettua tulkintaa vertailtiin todellisiin yksityisteiden kuntoa määrittäviin arvoihin, jotka kerättiin maastomittauksilla. Tavoitteena tutkimuksessa oli selvittää lasertulkinnan yhteneväisyys todelliseen tien kuntoon ja löytää mahdolliset ongelmakohdat. Tutkimuskysymyksissä pohdittiin tiheäpulssisen laserkeilausaineiston tulkinnan vastaavuutta yksityisteiden kunnolle verrattaessa todelliseen kuntoon, ja minkä kuntomuuttujan määritys oli lasertulkinnalle haastavinta. Lisäksi pohdittiin mahdollisia syitä virheiden ja epätarkkuuksien tapahtumiselle. Tutkimuksella oli tavoitteena tuoda esille TIESIT-hankkeelle tuotetun lasertulkinnan täsmällisyyttä sekä mahdollisia parannuskohtia. Kuntomuuttujina tutkimuksessa käytettiin ajoradan leveyttä ja urasyvyyttä. Lasertulkinnan mittausvirhettä tarkasteltiin harhan, RMSE: n ja RMSE-% avulla. Lisäksi aineistolle selvitettiin kuntoluokkien oikeinluokitukset koealoittain, tarkimmat ja epätarkimmat tulkinnat sekä paritettu t-testi ja Spearmanin korrelaatioita. Kuntoluokat jaettiin TIESIT-hankkeessa kolmeen luokkaan, joita noudatettiin myös tässä tutkielmassa: hyvä, kohtalainen ja huono. Aineistona käytettiin TIESIT-hankkeelle tuotettua lasertulkintapilottia tien eri kuntomuuttujille ja maastoaineistoa. Lisäksi käytin erilaisia lisätietolähteitä kohteiden ominaisuuksien ja mahdollisten virheidenaiheuttajien tulkinnassa. Koealoja oli tutkimuksessa 104 ja ne sijoittuivat eteläiseen Kouvolaan. Koealat satunnaistettiin ennen maastomittauksia ja otokseen varmistettiin yksityisteiden ominaisuuksien sekä ympäröivän puuston erilaisuus. Lasertulkinnan onnistuminen kuntoluokkien määritykselle oli 44 %. Eri kuntomuuttujien sekä luokkien onnistuminen asettui 6—65 % välille. Heikoiten lasertulkinta luokitteli leveydeltään huonossa kunnossa olevat tiet. Kuntomuuttujista urasyvyyden määrittäminen oli lasertulkinnalle haastavinta. Lasertulkinta tuotti tilastollisesti merkitsevästi yliarviota urasyvyyksien määrittämisessä. Lasertulkinta epäonnistui eniten niillä koealoilla, joilla esiintyi matalaa kasvillisuutta, varttunutta puustoa ja heikkoja tieprofiileja, joten nämä ominaisuudet voisivat olla syitä tulkinnassa tapahtuneisiin virheisiin. Lisäksi tuotetun tulkinnan malleissa ja algoritmeissa, jotka olivat tässä tutkimuksessa tuntemattomat, voi esiintyä myös virheidenaiheuttajia. TIESIT-hankkeelle tuotettu lasertulkinta ei ollut tarpeeksi tarkka yksityisteiden kuntomuuttujien määrityksessä ja kuntoluokkien luokittelu epäonnistui yli puolella tapauksista. Tutkimuksessa havaittiin kuitenkin myös onnistuneita kohteita ja löydettiin lasertulkinnalle mahdollisia virheidenaiheuttajia. Tiheäpulssisen laserkeilausaineiston käyttö luo silti tehokkaan apuvälineen yksityisteiden kuntotiedon arviointiin. Lasertulkinnan avulla maastokäyntejä pystytään vähentämään ja suurempia alueita saadaan inventoitua lyhyessä ajassa. Tulevaisuudessa on tärkeää ottaa huomioon erilaiset virhelähteet ja tuottaa referenssimittauksia lasertulkinnan tuloksille maastossa, jotta voidaan parantaa yksityisteiden kuntokartoituksen tarkkuutta ja luotettavuutta.