Tokola, T. & Heikkilä, J. 1997. Improving satellite image based forest inventory by using a priori site quality information. Silva Fennica 31(1): 67-78.The purpose of this study was to test the benefits of a forest site quality map, when applying satellite image based forest inventory. By combining field sample plot data from national forest inventories with satellite imagery and forest site quality data, it is possible to estimate forest stand characteristics with higher accuracy for smaller areas. The reliability of the estimates was evaluated using the data from a standwise survey for area sizes ranging from 0.06 ha to 300 ha. When the mean volume was estimated, a relative error of 14 per cent was obtained for areas of 50 ha; for areas of 30 ha the corresponding figure was below 20 per cent. The relative gain in interpretation accuracy, when including the forest site quality information, ranged between 1 and 6 per cent. The advantage increased according to the size of the target area. The forest site quality map had the effect of decreasing the relative error in Norway spruce volume estimations, but it did not contribute to Scots pine volume estimation procedure.

Detection of the need for seedling stand tending using high-resolution remote sensing data Korhonen L., Pippuri I., Packalén P., Heikkinen V., Maltamo M., Heikkilä J. (2013). Detection of the need for seedling stand tending using high-resolution remote sensing data. Silva Fennica vol. 47 no. 2 article id 952. 20 p. AbstractSeedling stands are problematic in airborne laser scanning (ALS) based stand level forest management inventories, as the stem density and species proportions are difficult to estimate accurately using only remotely sensed data. Thus the seedling stands must still be checked in the field, which results in an increase in costs. In this study we tested an approach where ALS data and aerial images are used to directly classify the seedling stands into two categories: those that involve tending within the next five years and those which involve no tending. Standard ALS-based height and density features, together with texture and spectral features calculated from aerial images, were used as inputs to two classifiers: logistic regression and the support vector machine (SVM). The classifiers were trained using 208 seedling plots whose tending need was estimated by a local forestry expert. The classification was validated on 68 separate seedling stands. In the training data, the logistic model's kappa coefficient was 0.55 and overall accuracy (OA) 77%. The SVM did slightly better with a kappa = 0.71 and an OA = 86%. In the stand level validation data, the performance decreased for both the logistic model (kappa = 0.38, OA = 71%) and the SVM (kappa = 0.37, OA = 72%). Thus our approach cannot totally replace the field checks. However, in considering the stands where the logistic model predictions had high reliability, the number of misclassifications reduced drastically. The SVM however, was not as good at recognizing reliable cases.

No abstract

As a part of a large research and development program in Finland, a system calibration for an UAV measurement method having practical experiments in a Jorvas Railway Yard design and construction project. The Railway yard construction are was measured using an UAV measurement device by Smartplanes (Smartone) and using some reference points measured by a GNSS device. The measured data was cleaned, combined and adjusted through Least Squares Optimization using the software developed by a Finnish company Pieneering Oy. As references, two different laser scans as well as total station surveys were used. For applicability evaluation, the accuracy of measurement, the reliability of measurement, and the economy of measurement were used. Based on the comparisons, the achieved precision of the UAV measurement was high, instead the accuracy of measurement was not sufficient in this case. The principle of this photogrammetric measurement includes a lot of redundant observations making it possible to achieve a high level of reliability. The economy in this Railway Yard Design project was not able to achieve. KEYWORDSUnmanned aerial surveying, digital terrain model.

TaustaTaimikoiden etäinventointimenetelmien puutteellisuus on aiheuttanut tarpeen kehittää kaukokartoitusmenetelmiä, jotka soveltuvat nykyistä paremmin nimenomaan pienpuustojen tulkintaan. Tällä hetkellä taimikoiden ongelmallisuus pelkkään laserkeilaukseen pohjautuvissa inventointimenetelmissä johtuu puuston pienestä latvuskoosta ja puuston ryhmittyneestä tilajärjestyksestä. Ongelmia tuottaa varsinkin nuorten taimikoiden puulajisuhteiden ja runkoluvun määritys. Tutkimuksissa taimikon runkoluvun keskivirhe on ollut kaksinkertainen verrattuna uudistuskypsien metsien runko luvun keskivirheeseen. Tulosten heikkous selittyy maanpinnan ja pensaskerroksen heijastuksilla, jotka tekevät laserkeilausinventoinnin tuloksista epäluotettavia. Osin tästä syystä taimikoiden kunnostustoimenpiteiden tutkiminen etäinventointimenetelmiä apuna käyttäen on keskittynyt taimikonhoitoon eikä esimerkiksi täydennysistutukseen tai varhaisperkaukseen.Maa-ja metsätalousministeriön taimikon tiedonkeruun kehittämishankkeen yleisenä tavoitteena oli korvata mahdollisimman suuri osa maastovierailuista uudella menetelmällä. Nykyisessä metsävaratiedossa taimikkoinventoinnit muodostavat merkittävän kustannuserän, joka on pinta-alayksikköä kohti moninkertainen verrattuna varttuneiden metsiköiden tiedonkeruuseen. Tavoitteena on tuottaa etähavainnoinnilla kerätyn tiedon pohjalta taimikon kehitys-ja hoitotoimenpide-ennusteet riittävän tarkasti operatiivista käyttöä varten ilman erillistä taimikkokohtaista maastokäyntiä. Päätöksentekijälle toimitetaan arvio tiedon luotettavuudesta, jotta voidaan paikantaa epävarmimmat etähavainnointitulokset. Seuraavissa kappaleissa käydään läpi MMMtaimikkohankkeen keskeisiä tuloksia.Metsätieto ja sähköiset palvelut