J. For. Sci., 2016, 62(3):137-142 | DOI: 10.17221/73/2015-JFS

Development of models for forest variable estimation from airborne laser scanning data using an area-based approach at a plot levelOriginal Paper

J. Sabol1, D. Procházka2, Z. Patočka1
1 Department of Forest Management and Applied Geoinformatics, Faculty of Forestry and Wood Technology, Mendel University in Brno, Brno, Czech Republic
2 Department of Informatics, Faculty of Business and Economics, Mendel University in Brno, Brno, Czech Republic

Airborne laser scanning (ALS) is increasingly used in the forestry over time, especially in a forest inventory process. A great potential of ALS lies in providing quick high precision data acquisition for purposes such as measurements of stand attributes over large forested areas. Models were developed using an area-based approach to predict forest variables such as wood volume and basal area. The solution was performed through developing an object-oriented script using Python programming language, Python Data Analysis Library (Pandas), which represents a very flexible and powerful data analysis tool in conjunction with interactive computational environment the IPython Notebook. Several regression models for estimation of forest inventory attributes were developed at a plot level.

Keywords: Python; Fusion; forest inventory; linear regression; Norway spruce

Published: March 31, 2016  Show citation

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Sabol J, Procházka D, Patočka Z. Development of models for forest variable estimation from airborne laser scanning data using an area-based approach at a plot level. J. For. Sci.. 2016;62(3):137-142. doi: 10.17221/73/2015-JFS.
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