2.3 Classification with random forest classifier
Image classification is a method for extracting environmental parameters, such as land cover and land use (LCLU) (Jensen, 2004). An expert who conducts a supervised classification should have prior knowledge about study area conditions and land uses. For this study, training samples were determined by using available maps, aerial images, field data and ancillary spatial information, such as soil types. We used a random forest classifier implemented in the package “random Forest” in statistical R software. Based on the literature and preliminary tests, we used 1,000 trees to parameterize the random forest classifier. We used the Blue, Green, Red, NIR, SWIR-1, and SWIR-2 bands for Landsat 4, 5 TM, Landsat 7 ETM+ and Landsat 8 OLI to produce LCLU change maps with a random forest classifier. The iterative classification approach was used with an increase of training samples selected for each thematic class until the classification accuracy for individual thematic classes did not increase anymore.
We developed a classification catalog, which presented a mix of LCLU classes (Table 1). Our classification catalog consisted of eight thematic classes: ‘forest’, ‘agriculture’, ‘rangeland’, ‘water’, ‘rocks’, ‘industry’, ‘residences’, and ‘other’. The ‘forest’ class consisted of urban and industrial green space with trees and tree plantations, ‘agriculture’ consisted of tilled agricultural land used for crops and clean or green fallow land as a part of crop rotation. The ‘rangeland’ class consisted of native natural plants with low (type I) to medium (type II) density. ‘Water’ represented water objects, such as rivers, natural wetlands and man-made water ponds. The ‘rock ‘class consisted of rocky outcrops and the mountains. ‘Industry’ consisted of industrial areas and mines. ‘residences’ comprised urban and rural settlements and main paved roads and the ‘other’ class was bare soil, industrial and mineral wastelands, and dirt roads.
<Table 1>