Home About Browse Search
Svenska


Udali, Alberto, 2019. Assessing the accuracy for area-based tree species classification using Sentinel-1 C-band SAR data. Second cycle, A2E. Umeå: SLU, Dept. of Forest Resource Management

[img] PDF
870kB

Abstract

Forest type (FTY) and tree species classification (SPP) over the Remn-ingstorp test site were performed using ground-based field observations and remote sensing data sources. The field inventory for the forest estate and for the surrounding natural reserve of Eahagen was carried out in 2016. The re-mote sensing data used were C-band Synthetic Aperture Radar (SAR) data from Sentinel-1. Dual polarization backscatter values were extracted for the period October 2017 - February 2019 and the area-based method was applied. The metrics obtained, i.e. monthly mean backscatter, were used to perform classification by machine learning models’ random forest (RF) and linear dis-criminant analysis (LDA). The models were evaluated with the leave-one-out cross-validation method and the classification outcomes were compared with reference values in terms of confusion matrixes. The best performing model was LDA with an overall accuracy of 88% for FTY and 61% for SPP, whereas RF achieved values of 84% for FTY and 56% for SPP. It was concluded that C-band SAR data can be used for FTY and SPP classification, but further investigation is needed to determine which factors affect the backscatter in order to obtain more accurate classifications.

Main title:Assessing the accuracy for area-based tree species classification using Sentinel-1 C-band SAR data
Authors:Udali, Alberto
Supervisor:Persson, Henrik
Examiner:Fransson, Johan
Series:Arbetsrapport / Sveriges lantbruksuniversitet, Institut-ionen för skoglig resurshushållning
Volume/Sequential designation:504
Year of Publication:2019
Level and depth descriptor:Second cycle, A2E
Student's programme affiliation:Other
Supervising department:(S) > Dept. of Forest Resource Management
Keywords:Sentinel-1, tree species, random forest, linear discrimi-nant analysis, classification
URN:NBN:urn:nbn:se:slu:epsilon-s-15246
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-s-15246
Subjects:Forestry - General aspects
Forestry production
Language:English
Deposited On:17 Dec 2019 06:50
Metadata Last Modified:04 Jun 2020 12:30

Repository Staff Only: item control page

Downloads

Downloads per year (since September 2012)

View more statistics

Downloads
Hits