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Classifying natural forests using LiDAR data
Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Klassificering av nyckelbiotoper med hjälp av LiDAR-data (Swedish)
Abstract [en]

In forestry, natural forests are forest areas with high biodiversity, in need of preservation. The current mapping of natural forests is a tedious task that requires manual labor that could possibly be automated.

In this paper we explore the main features used by a random forest algorithm to classify natural forest and managed forest in northern Sweden. The goal was to create a model with a substantial strength of agreement, meaning a Kappa value of 0.61 or higher, placing the model in the same range as models produced in previous research.

We used raster data gathered from airborne LiDAR, combined with labeled sample areas, both supplied by the Swedish Forest Agency. Two experiments were performed with different features. Experiment 1 used features extracted using methods inspired from previous research while Experiment 2 further added upon those features. From the total number of used sample areas (n=2882), 70% was used to train the models and 30% was used for evaluation.

The result was a Kappa value of 0.26 for Experiment 1 and 0.32 for Experiment 2. Features shown to be prominent are features derived from canopy height, where the supplied data also had the highest resolution. Percentiles, kurtosis and canopy crown areas derived from the canopy height were shown to be the most important for classification. The results fell short of our goal, possibly indicating a range of flaws in the data used. The size of the sample areas and resolution of raster data are likely important factors when extracting features, playing a large role in the produced model’s performance.

Place, publisher, year, edition, pages
2019. , p. 39
Keywords [en]
Geographic information systems, Classification and regression trees, Supervised learning by classification
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:hj:diva-45267ISRN: JU-JTH-DTA-1-20190076OAI: oai:DiVA.org:hj-45267DiVA, id: diva2:1334914
External cooperation
Skogsstyrelsen
Subject / course
JTH, Computer Engineering
Presentation
2019-06-13, E1022, Gjuterigatan 5, Jönköping, 10:00 (English)
Supervisors
Examiners
Available from: 2019-07-05 Created: 2019-07-03 Last updated: 2019-07-05Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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More styles
Language
  • de-DE
  • en-GB
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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