A Machine Learning approach for automatic land cover mapping from DSLR images over the Maltese Islands. (January 2018)

• Record Type: Journal Article
• Authors: Gauci, A.; Abela, J.; Austad, M.; Cassar, L.F.; Zarb Adami, K.
• Is Part Of: Environmental modelling & software. Volume 99(2018)
• Page Start: 1
• Page End: 10
• Publication Date: 2018-01
• Subjects: Machine learning -- Pattern recognition -- Land cover mapping -- Decision trees; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.09.014
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: High resolution raster data for land cover mapping or change analysis are normally acquired through satellite or aerial imagery. Apart from the incurred costs, the available files might not have the required temporal resolution. Moreover, cloud cover and atmospheric absorptions may limit the applicability of existing algorithms or reduce their accuracy. This paper presents a novel technique that is capable of mapping garrigue and/or phrygana vegetation as well as karst or ground-armour terrain in photos captured by a digital camera. By including a reference pattern in every frame, the automated method estimates the total area covered by each land type. Pixel based classification is performed by supervised decision tree methods. Although the intention is not to replace traditional surface cover analysis, the proposed technique allows accurate land cover mapping with quantitative estimates to be obtained. Since no expensive hardware or specialised personnel are required, vegetation monitoring of local sites can be carried out cheaply and frequently. The developed proof of concept is tested on photos taken in thirteen different sites across the Maltese Islands. Highlights: Mapping of garrigue and/or phrygana vegetation as well as karst or ground-armour terrain in photos captured by a DSLR. By including a reference pattern in every frame, the automated method estimates the total area covered by each land type. Pixel based classification is performed by supervised decision tree methods. Accurate land cover mapping with quantitative estimates. Vegetation monitoring of local sites can be carried out cheaply and frequently.