A Note on Statistical Techniques and Biological Background in Analysis of Remote Sensed Data in Forest Inventory

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F23%3A97939" target="_blank" >RIV/60460709:41320/23:97939 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.jstage.jst.go.jp/browse/formath/list/-char/en" target="_blank" >https://www.jstage.jst.go.jp/browse/formath/list/-char/en</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15684/formath.22.003" target="_blank" >10.15684/formath.22.003</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Note on Statistical Techniques and Biological Background in Analysis of Remote Sensed Data in Forest Inventory

Popis výsledku v původním jazyce

In this work we discuss possibilities and challenges in utilization of several statistical methods for assessment of forest resources related to forest inventories, especially question of dataset size where the time and resources required for data collection are often in contrast to sample size and analysis of all potential parameters of potential models. The combination of a priori knowledge of the phenomena being studied (tree number, wood volume, etc.) and understanding of behavior of individual variables provided by remote sensing instruments (different predictor variables) is crucial for production of reliable models for forest resource assessment. Using our dataset, we compared two regression techniques and one machine learning for predictor analysis for wood volume estimation. All techniques in general provided similar results in terms of variable importance and accuracy, but in more detailed analysis differences appeared, indicating that if possible biological knowledge and understanding of variables should not be neglected.

Název v anglickém jazyce

A Note on Statistical Techniques and Biological Background in Analysis of Remote Sensed Data in Forest Inventory

Popis výsledku anglicky

In this work we discuss possibilities and challenges in utilization of several statistical methods for assessment of forest resources related to forest inventories, especially question of dataset size where the time and resources required for data collection are often in contrast to sample size and analysis of all potential parameters of potential models. The combination of a priori knowledge of the phenomena being studied (tree number, wood volume, etc.) and understanding of behavior of individual variables provided by remote sensing instruments (different predictor variables) is crucial for production of reliable models for forest resource assessment. Using our dataset, we compared two regression techniques and one machine learning for predictor analysis for wood volume estimation. All techniques in general provided similar results in terms of variable importance and accuracy, but in more detailed analysis differences appeared, indicating that if possible biological knowledge and understanding of variables should not be neglected.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20705 - Remote sensing

Projekt

<a href="/cs/project/QK21010435" target="_blank" >QK21010435: Monitoring stavu a vývoje souší po kůrovcové kalamitě</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

FORMATH

ISSN

2188-5729

e-ISSN

2188-5729

Svazek periodika

22

Číslo periodika v rámci svazku

2023

Stát vydavatele periodika

JP - Japonsko

Počet stran výsledku

8

Strana od-do

1-8

Kód UT WoS článku

001126804800001

EID výsledku v databázi Scopus

—