<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Two change detection techniques (NDVI differencing and post-classification analysis) were compared, in order to detect canopy cover changes in forests on the area of twelve forest districts in the Sudety and West Beskidy Mountains in Poland, using 2012 and 2013 Black-Bridge satellite images. Although the classification accuracy of the respective images was high (about 95%), the accuracy of the difference in bi-temporal images was much worse because of the short time between the dates of images and the imperfection of the algorithm calculating the unclear boundary between the forest and no-forest areas. NDVI differencing method and thresholding brought much better overall results, although roads, clouds and fogs caused much problem performing pseudo-changes.</p></abstract>

Two change detection techniques (NDVI differencing and post-classification analysis) were compared, in order to detect canopy cover changes in forests on the area of twelve forest districts in the Sudety and West Beskidy Mountains in Poland, using 2012 and 2013 Black-Bridge satellite images. Although the classification accuracy of the respective images was high (about 95%), the accuracy of the difference in bi-temporal images was much worse because of the short time between the dates of images and the imperfection of the algorithm calculating the unclear boundary between the forest and no-forest areas. NDVI differencing method and thresholding brought much better overall results, although roads, clouds and fogs caused much problem performing pseudo-changes.

Black-Bridge data in the detection of forest area changes in the example of Sudety and Beskidy

Forest Research Institute, Department of Forest Resources Management

Folia Forestalia Polonica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Two change detection techniques (NDVI differencing and post-classification analysis) were compared, in order to detect canopy cover changes in forests on the...

Black-Bridge data in the detection of forest area changes in the example of Sudety and Beskidy

Data publikacji: 10 lut 2018

Zakres stron: 272 - 280

Otrzymano: 01 mar 2017

Przyjęty: 29 wrz 2017

DOI: https://doi.org/10.1515/ffp-2017-0029

Słowa kluczowe
remote sensing, Black-Bridge, change detection, NDVI, post-classification analysis

© 2017 Tomasz Hycza et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Black-Bridge data in the detection of forest area changes in the example of Sudety and Beskidy

Data publikacji: 10 lut 2018

Zakres stron: 272 - 280

Otrzymano: 01 mar 2017

Przyjęty: 29 wrz 2017

DOI: https://doi.org/10.1515/ffp-2017-0029

Słowa kluczoweremote sensing, Black-Bridge, change detection, NDVI, post-classification analysis

© 2017 Tomasz Hycza et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
remote sensing, Black-Bridge, change detection, NDVI, post-classification analysis