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

J. Sabol, D. Procházka and Z. Patočka
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J. Sabol: Department of Forest Management and Applied Geoinformatics, Faculty of Forestry and Wood Technology, Mendel University in Brno, Brno, Czech Republic
D. Procházka: Department of Informatics, Faculty of Business and Economics, Mendel University in Brno, Brno, Czech Republic
Z. Patočka: Department of Forest Management and Applied Geoinformatics, Faculty of Forestry and Wood Technology, Mendel University in Brno, Brno, Czech Republic

Journal of Forest Science, 2016, vol. 62, issue 3, 137-142

Abstract: 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 (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnljfs:v:62:y:2016:i:3:id:73-2015-jfs

DOI: 10.17221/73/2015-JFS

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