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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F16%3A43909008" target="_blank" >RIV/62156489:43110/16:43909008 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62156489:43410/16:43909008

Výsledek na webu

<a href="http://dx.doi.org/10.17221/73/2015-JFS" target="_blank" >http://dx.doi.org/10.17221/73/2015-JFS</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.17221/73/2015-JFS" target="_blank" >10.17221/73/2015-JFS</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

GK - Lesnictví

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Forest Science

ISSN

1212-4834

e-ISSN

—

Svazek periodika

62

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

6

Strana od-do

137-142

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-84963620274