Forest Mapping Through Object-based Image Analysis of Multispectral and LiDAR Aerial Data
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43410%2F14%3A00217314" target="_blank" >RIV/62156489:43410/14:00217314 - isvavai.cz</a>
Výsledek na webu
<a href="http://www.aitjournal.com/articleView.aspx?ID=863" target="_blank" >http://www.aitjournal.com/articleView.aspx?ID=863</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5721/EuJRS20144708" target="_blank" >10.5721/EuJRS20144708</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Forest Mapping Through Object-based Image Analysis of Multispectral and LiDAR Aerial Data
Popis výsledku v původním jazyce
The objective of this research was to establish a set of rules for using Object-based Image Analysis (OBIA) to semi-automatically map a given densely forested area. Aerial images (RGB and NIR bands) and LiDAR elevation data provided the raw data and wereanalysed using eCognition Developer 8 software. All the non-forested areas, including built-up areas, water surfaces or agricultural land, were identified first. The forested areas were then classified as stands composed principally of broadleaf trees,coniferous trees, mixed forest or clear-cuts, which was achieved with an accuracy of almost 90%. Subsequently the stands were classified on the basis of height, using 5 metres intervals, and this was achieved with an accuracy of just over 70%.
Název v anglickém jazyce
Forest Mapping Through Object-based Image Analysis of Multispectral and LiDAR Aerial Data
Popis výsledku anglicky
The objective of this research was to establish a set of rules for using Object-based Image Analysis (OBIA) to semi-automatically map a given densely forested area. Aerial images (RGB and NIR bands) and LiDAR elevation data provided the raw data and wereanalysed using eCognition Developer 8 software. All the non-forested areas, including built-up areas, water surfaces or agricultural land, were identified first. The forested areas were then classified as stands composed principally of broadleaf trees,coniferous trees, mixed forest or clear-cuts, which was achieved with an accuracy of almost 90%. Subsequently the stands were classified on the basis of height, using 5 metres intervals, and this was achieved with an accuracy of just over 70%.
Klasifikace
Druh
J<sub>x</sub> - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)
CEP obor
GK - Lesnictví
OECD FORD obor
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Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2014
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
European Journal of Remote Sensing
ISSN
2279-7254
e-ISSN
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Svazek periodika
47
Číslo periodika v rámci svazku
2014
Stát vydavatele periodika
IT - Italská republika
Počet stran výsledku
15
Strana od-do
117-131
Kód UT WoS článku
335955600002
EID výsledku v databázi Scopus
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