Jari Varjo. (1997). Change detection and controlling forest information using multi-temporal Landsat TM imagery. Acta Forestalia Fennica no. 258 article id 7524. https://doi.org/10.14214/aff.7524

Keywords: forest inventory; change detection; continuous updating; satellite image; radiometric calibration; stand information; nonparametric discrimination

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A method was developed for relative radiometric calibration of single multitemporal Landsat TM image, several multitemporal images covering each other, and several multitemporal images covering different geographical locations. The radiometrically calibrated different images were used for detecting rapid changes on forest stands. The nonparametric Kernel method was applied for change detection. The accuracy of the change detection was estimated by inspecting the image analysis results in field.

The change classification was applied for controlling the quality of the continuously updated forest stand information. The aim was to ensure that all the manmade changes and any forest damages were correctly updated including the attribute and stand delineation information. The image analysis results were compared with the registered treatments and the stand information base. The stands with discrepancies between these two information sources were recommended to be field inspected.

Varjo, E-mail: jv@mm.unknown

Category : Research article

article id 111, category Research article

Ilona Pietilä, Annika Kangas, Antti Mäkinen, Lauri Mehtätalo. (2010). Influence of growth prediction errors on the expected losses from forest decisions. Silva Fennica vol. 44 no. 5 article id 111. https://doi.org/10.14214/sf.111

Keywords: growth prediction; uncertainty; forest information; updating; inoptimality loss

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In forest planning, forest inventory information is used for predicting future development of forests under different treatments. Model predictions always include some errors, which can lead to sub-optimal decisions and economic loss. The influence of growth prediction errors on the reliability of projected forest variables and on the treatment propositions have previously been examined in a few studies, but economic losses due to growth prediction errors is an almost unexplored subject. The aim of this study was to examine how the growth prediction errors affected the expected losses caused by incorrect harvest decisions, when the inventory interval increased. The growth models applied in the analysis were stand-level growth models for basal area and dominant height. The focus was entirely on the effects of growth prediction errors, other sources of uncertainty being ignored. The results show that inoptimality losses increased with the inventory interval. Average relative inoptimality loss was 3.3% when the inventory interval was 5 years and 11.6% when it was 60 years. Average absolute inoptimality loss was 230 euro ha^–1 when the inventory interval was 5 years and 860 euro ha^–1 when it was 60 years. The average inoptimality losses varied between development classes, site classes and main tree species.

Pietilä, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ip@nn.fi
Kangas, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: annika.kangas@helsinki.fi
Mäkinen, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: am@nn.fi
Mehtätalo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lm@nn.fi

article id 345, category Research article

Pekka Hyvönen, Perttu Anttila. (2006). Change detection in boreal forests using bi-temporal aerial photographs. Silva Fennica vol. 40 no. 2 article id 345. https://doi.org/10.14214/sf.345

Keywords: forest inventory; discriminant analysis; change detection; aerial photography; continuous updating

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Increased need for timely forest information is leading to continuous updating of stand databases. In continuous updating, stand attributes are estimated in the field after an operation and stored in databases. To find the changes caused by operations and forest damage, a semi-automatic method based on bi-temporal aerial photographs was developed. The test data were classified into three classes: No-change (952 stands), Moderate-change (163 stands) and Considerable-change (44 stands). The aerial photographs were acquired in years 2001 and 2004 with almost the same image specifications. Altogether 110 features at stand level were extracted and used in change detection analysis. The test data were classified with stepwise discriminant analysis. The overall accuracy of classification varied between 75.3 and 84.7%. The considerable changes were found without error, whereas the Moderate-change and No-change classes were often confused. However, 84.2% of thinned stands were classified correctly. The best accuracy in classification was obtained by using the histogram and textural features extracted from the original, uncorrected images. Radiometric correction did not improve the accuracy of classification. Soil type, characteristics of the growing stock and the location of a stand in an image were found to affect the change detection. Before the method can be applied operationally, issues related to, e.g., confusion between No-change and Moderate-change must be solved.

Hyvönen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: pekka.hyvonen@metla.fi
Anttila, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: pa@nn.fi

article id 544, category Research article

Perttu Anttila. (2002). Updating stand level inventory data applying growth models and visual interpretation of aerial photographs. Silva Fennica vol. 36 no. 2 article id 544. https://doi.org/10.14214/sf.544

Keywords: aerial photographs; stand level inventory; MELA; updating of inventory data; visual interpretation

Abstract | View details | Full text in PDF | Author Info

In this study two procedures for updating stand level inventory data were developed and tested. The development of the growing stock of 62 stands over 12 years was simulated in the MELA stand simulator with no prior information of rapid changes, such as clear-cuttings. The acceptability of the simulation was decided standwise with visual interpretation of aerial false-colour photographs. If the simulated data were not accepted, new stand attributes were assessed with photo interpretation in procedure 1. In procedure 2, on the other hand, it was possible to utilise old management proposals. In case a cutting or other operation had been proposed and it looked like the operation had been realised, the interpreters accepted the proposal. Otherwise the last implemented operation and implementation year were interpreted. In case no operation had been carried out during the updating period but the growth model updated data were not acceptable, the same stand characteristics were estimated as in procedure 1. Stands where a proposal had been accepted or an operation interpreted were later updated again in MELA so that the program simulated the operations. The Root Mean Squared Errors of stem volume were 62 and 57 m3 per ha (34 and 30%) with procedures 1 and 2. With procedure 2 the accuracy of updating was comparable with a stand level field inventory carried out in the study area. The productivity of the photo interpretation procedures was 57 and 84 ha per h, respectively, whereas the productivity of a field inventory has been 3.3–5 ha per h.

Anttila, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: perttu.anttila@joensuu.fi

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