Open AccessClassification and Measurement of Land Cover of Wildfires in Australia Using Remote Sensing
Author(s) -
Enas Kh. Hassan,
Harith M. Saeed,
Assad H. Thary Al-Ghrairi
Publication year - 2022
Publication title -
iraqi journal of science
Language(s) - English
Resource type - Journals
eISSN - 2312-1637
pISSN - 0067-2904
DOI - 10.24996/ijs.2022.63.1.38
Subject(s) - remote sensing , land cover , multispectral image , geography , satellite , national park , contextual image classification , vegetation (pathology) , feature (linguistics) , multispectral pattern recognition , computer science , environmental science , cartography , meteorology , land use , artificial intelligence , image (mathematics) , engineering , medicine , linguistics , philosophy , civil engineering , archaeology , pathology , aerospace engineering
Remote sensing techniques used in many studies for classfying and measuring of wildfires. Satellite Landsat8(OLI) imagery is used in the presented work. The satellite is considered as a near-polar orbit, with a high multispectral resolution for covering Wollemi National Park in Australia. The work aims to study and measure wildfire natural resources prior to and throughout fire breakout which occurred in Wollemi National Park in Australia for a year (October, 2019), as well as analyzing the harm resulting from such wildfires and their effects on earth and environment through recognizing satellite images for studied region prior to and throughout wildfires. A discussion of methods for computing the affecred area is covered regarding each one of the classes and lessening or limiting the quickly-spreading wildfires damage. This paper propose a 2-phases techniques: training and classifying. In the training phase, the number of clustering is computed by using C# Programming Language and feature extracted and clustered as a group and stored in the dataset. The classification used the moments with (K-Means) classification approach in RS (Remote Sensing) for classified image. The results of classification showed 5 distinctive classes (trees, rivers, bare earth, buildings with no trees, and buildings with trees) in which it might be indicates that the region is secured via each one of the classes prior to and throughout wildfires as well as the changed pixels with regard to all the classes. Also, the classification experimental methods results indicate an excellent performance recision with a good classifying and result analysis about the harms caused by fires in the study area.