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Investigating the potential of object-based image analysis to identify tree avenues in high resolution aerial imagery and lidar data : a literature review

Moreen Wistrand, Greta LU (2016) In Student thesis series INES NGEK01 20161
Dept of Physical Geography and Ecosystem Science
Abstract
The aim of this study was to investigate the potential of geographic object-based image analysis (GEOBIA) for the identification and monitoring of tree avenues with the help of aerial imagery and lidar data. For this, a literature review into three topics was conducted: First, scientific literature in form of journal articles, text books, and reports were used to highlight key features of GEOBIA. Second, eCognition, a widely used GEOBIA software, was introduced by referring to user manuals, and web-tutorials. Third, a detailed overview of tree avenues and their characteristics was derived from reports and text books. The main finding of the study is that, although there have been no earlier attempts to identify tree avenues using GEOBIA,... (More)
The aim of this study was to investigate the potential of geographic object-based image analysis (GEOBIA) for the identification and monitoring of tree avenues with the help of aerial imagery and lidar data. For this, a literature review into three topics was conducted: First, scientific literature in form of journal articles, text books, and reports were used to highlight key features of GEOBIA. Second, eCognition, a widely used GEOBIA software, was introduced by referring to user manuals, and web-tutorials. Third, a detailed overview of tree avenues and their characteristics was derived from reports and text books. The main finding of the study is that, although there have been no earlier attempts to identify tree avenues using GEOBIA, there is considerable potential for doing so. This is motivated mainly due to the characteristics of tree avenues and their similarity with landscape objects that have been successfully identified with eCognition. As a progressive result of this literature review, an action plan for practitioners interested in mapping tree avenues is designed and discussed. (Less)
Abstract (Swedish)
Syftet med den här studien var att undersöka potentialen hos geografisk objektbaserad bildanalys (GEOBIA) för att identifiera och övervaka alléer med hjälp av fjärranalysbilder och lidardata. För detta genomfördes en trefaldig litteraturstudie: först användes artiklar, böcker och rapporter för att skapa en bild av de viktigaste funktionerna inom GEOBIA. Sedan introducerades eCognition, ett välkänt GEOBIA-program genom att referera till användarmanualer och webbaserade tutorials. Och slutligen togs en detaljerad beskrivning av alléer fram med hjälp av rapporter och böcker. Den huvudsakliga upptäckten med studien var att, trots att inga tidigare studier gjorts med syftet att identifiera alléer med hjälp av GEOBIA så finns det stora... (More)
Syftet med den här studien var att undersöka potentialen hos geografisk objektbaserad bildanalys (GEOBIA) för att identifiera och övervaka alléer med hjälp av fjärranalysbilder och lidardata. För detta genomfördes en trefaldig litteraturstudie: först användes artiklar, böcker och rapporter för att skapa en bild av de viktigaste funktionerna inom GEOBIA. Sedan introducerades eCognition, ett välkänt GEOBIA-program genom att referera till användarmanualer och webbaserade tutorials. Och slutligen togs en detaljerad beskrivning av alléer fram med hjälp av rapporter och böcker. Den huvudsakliga upptäckten med studien var att, trots att inga tidigare studier gjorts med syftet att identifiera alléer med hjälp av GEOBIA så finns det stora möjligheter med den här metoden. Detta motiveras framförallt genom alléers attribut och likheter med andra landskapsobjekt som framgångsrikt kunnat identifieras i eCognition. Som ett framåtsträvande resultat av litteraturstudien föreslås och diskuteras ett tillvägagångssätt riktat till användare intresserade av att kartlägga alléer. (Less)
Please use this url to cite or link to this publication:
author
Moreen Wistrand, Greta LU
supervisor
organization
course
NGEK01 20161
year
type
M2 - Bachelor Degree
subject
keywords
eCognition, remote sensing, (geographic) object-based image analysis, (GE)OBIA, lidar, tree avenues, landscape elements, monitoring, physical geography and ecosystem science
publication/series
Student thesis series INES
report number
388
language
English
id
8886544
date added to LUP
2016-07-06 19:26:11
date last changed
2016-07-06 19:26:11
@misc{8886544,
  abstract     = {The aim of this study was to investigate the potential of geographic object-based image analysis (GEOBIA) for the identification and monitoring of tree avenues with the help of aerial imagery and lidar data. For this, a literature review into three topics was conducted: First, scientific literature in form of journal articles, text books, and reports were used to highlight key features of GEOBIA. Second, eCognition, a widely used GEOBIA software, was introduced by referring to user manuals, and web-tutorials. Third, a detailed overview of tree avenues and their characteristics was derived from reports and text books. The main finding of the study is that, although there have been no earlier attempts to identify tree avenues using GEOBIA, there is considerable potential for doing so. This is motivated mainly due to the characteristics of tree avenues and their similarity with landscape objects that have been successfully identified with eCognition. As a progressive result of this literature review, an action plan for practitioners interested in mapping tree avenues is designed and discussed.},
  author       = {Moreen Wistrand, Greta},
  keyword      = {eCognition,remote sensing,(geographic) object-based image analysis,(GE)OBIA,lidar,tree avenues,landscape elements,monitoring,physical geography and ecosystem science},
  language     = {eng},
  note         = {Student Paper},
  series       = {Student thesis series INES},
  title        = {Investigating the potential of object-based image analysis to identify tree avenues in high resolution aerial imagery and lidar data : a literature review},
  year         = {2016},
}