Timo Tokola, Juho Heikkilä. (1997). Improving satellite image based forest inventory by using a priori site quality information. Silva Fennica vol. 31 no. 1 article id 5610. https://doi.org/10.14214/sf.a8511

Keywords: forest inventory; stand characteristics; remote sensing; forest surveys; site factors; ancillary data; Landsat TM

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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 stand-wise 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 (Picea abies) volume estimations, but it did not contribute to Scots pine (Pinus sylvestris) volume estimation procedure.

Tokola, E-mail: tt@mm.unknown
Heikkilä, E-mail: jh@mm.unknown

Category : Research article

article id 952, category Research article

Lauri Korhonen, Inka Pippuri, Petteri Packalén, Ville Heikkinen, Matti Maltamo, Juho Heikkilä. (2013). Detection of the need for seedling stand tending using high-resolution remote sensing data. Silva Fennica vol. 47 no. 2 article id 952. https://doi.org/10.14214/sf.952

Keywords: forest management; airborne laser scanning; logistic regression; seedling stand; tending; support vector machine

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Seedling 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.

Korhonen, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
Pippuri, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: inka.pippuri@uef.fi
Packalén, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi
Heikkinen, School of Computing, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: ville.heikkinen@uef.fi
Maltamo, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi
Heikkilä, Finnish Forest Centre, Public Services, Maistraatinportti 4 A, FI-00240 Helsinki, Finland E-mail: juho.heikkila@metsakeskus.fi

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