Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Classifying natural forests using LiDAR data
Högskolan i Jönköping, Tekniska Högskolan, JTH, Datateknik och informatik.
Högskolan i Jönköping, Tekniska Högskolan, JTH, Datateknik och informatik.
2019 (engelsk)Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgaveAlternativ tittel
Klassificering av nyckelbiotoper med hjälp av LiDAR-data (svensk)
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.

sted, utgiver, år, opplag, sider
2019. , s. 39
Emneord [en]
Geographic information systems, Classification and regression trees, Supervised learning by classification
HSV kategori
Identifikatorer
URN: urn:nbn:se:hj:diva-45267ISRN: JU-JTH-DTA-1-20190076OAI: oai:DiVA.org:hj-45267DiVA, id: diva2:1334914
Eksternt samarbeid
Skogsstyrelsen
Fag / kurs
JTH, Computer Engineering
Presentation
2019-06-13, E1022, Gjuterigatan 5, Jönköping, 10:00 (engelsk)
Veileder
Examiner
Tilgjengelig fra: 2019-07-05 Laget: 2019-07-03 Sist oppdatert: 2019-07-05bibliografisk kontrollert

Open Access i DiVA

Classifying natural forests using LiDAR data(6740 kB)33 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 6740 kBChecksum SHA-512
68d3852b3d0d72d7a1870136fedf09e0841b81f9d89a74434d098707ebc8bfb3ac36ffc22df90809a436bdf09b93ba23c85a21f2dde2e136c03d3c65edf45ece
Type fulltextMimetype application/pdf

Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 33 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 111 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf