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

被引：2
作者：
Sabol J. [1 ]
Procházka D. [2 ]
Patočka Z. [1 ]
机构：
[1] Department of Forest Management and Applied Geoinformatics, Faculty of Forestry and Wood Technology, Mendel University in Brno, Zemědělská 1/1665, Brno
[2] Department of Informatics, Faculty of Business and Economics, Mendel University in Brno, Brno
来源：
Journal of Forest Science | 2016年 / 62卷 / 03期
关键词：
Forest inventory; Fusion; Linear regression; Norway spruce; !text type='Python']Python[!/text;
D O I：
10.17221/73/2015-JFS
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：137 / 142
页数：5
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