Naslov
Comparing Different Machine Learning Options to Map Bark Beetle Infestations in Croatia

Autori
Kranjčić, Nikola ; Cetl, Vlado ; Matijević, Hrvoje ; Markovinović, Danko

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences / Minghini, M ; Ciolli, M ; Neziri, G. - Prizren : Copernicus Publications, 2023, 83-88

Skup
FOSS4G Conference 2023

Mjesto i datum
Prizren, Kosovo, 28.06.2023. - 30.06.2023

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
supervised classification, machine learning options, QGIS, SAGA GIS, Copernicus data

Sažetak
This paper presents different approaches to map bark beetle infested forests in Croatia. Bark beetle infestation presents threat to forest ecosystems. Due to large unapproachable area, it also presents difficulties in mapping infested areas. This paper analyses available machine learning options in open-source software QGIS and SAGA GIS. All options are performed on Copernicus data, Sentinel 2 satellite imagery. Machine learning and classification options are maximum likelihood classifier, minimum distance, artificial neural network, decision tree, K Nearest Neighbor, random forest, support vector machine, spectral angle mapper and Normal Bayes. Kappa values respectively are: 0.71 ; 0.72 ; 0.81 ; 0.68 ; 0.69 ; 0.75 ; 0.26 ; 0.60 ; 0.41 which shows highest classification accuracy for artificial neural networks method and lowest for support vector machine accuracy.

Izvorni jezik
Engleski

Znanstvena područja
Geodezija, Interdisciplinarne tehničke znanosti, Informacijske i komunikacijske znanosti

Napomena
Indeksirano u: DOAJ, E/I Compendex

POVEZANOST RADA