Evaluating land cover types from Landsat TM using SAGA GIS for vegetation mapping based on ISODATA and K-means clustering

• Published in: Acta agriculturae Serbica
• Main Author: Lemenkova Polina
• Format: Article in Journal/Newspaper
• Language: English; srp
• Published: University of Kragujevac, Faculty of Agronomy, Cacak 2021
• Subjects: saga gis; mapping; vegetation; k-means; isodata; clustering; cartography; machine learning; Agriculture; S; Iceland
• Online Access: https://doi.org/10.5937/AASer2152159L; https://doaj.org/article/05274d76c1504b94b16e3bd6154b6624

The paper presents the cartographic processing of the Landsat TM image by the two unsupervised classification methods of SAGA GIS: ISODATA and K-means clustering. The approaches were tested and compared for land cover type mapping. Vegetation areas were detected and separated from other land cover types in the study area of southwestern Iceland. The number of clusters was set to ten classes. The processing of the satellite image by SAGA GIS was achieved using Imagery Classification tools in the Geoprocessing menu of SAGA GIS. Unsupervised classification performed effectively in the unlabeled pixels for the land cover types using machine learning in GIS. Following an iterative approach of clustering, the pixels were grouped in each step of the algorithm and the clusters were reassigned as centroids. The paper contributes to the technical development of the application of machine learning in cartography by demonstrating the effectiveness of SAGA GIS in remote sensing data processing applied for vegetation and environmental mapping.