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

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

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 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

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

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

Click this link to register to Silva Fennica.

If you are a registered user, log in to save your selected articles for later access.

Contents alert

Your selected articles

Follow @SilvaFennica