Abstract
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.
Highlights
The goal of this paper is to present the processing of the Landsat TM image covering the study area i.e.Vegetation mapping is one of the most important tools for environmental monitoring
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 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
Summary
The goal of this paper is to present the processing of the Landsat TM image covering the study area i.e. Vegetation mapping is one of the most important tools for environmental monitoring. Using remote sensing data processed by GIS is the fastest way that helps land cover types to be visualized and assessed. The specific geologic setting including volcanism in the southwestern part of Iceland (Figure 1) resulted in the development of erosion prone soils and fragile vegetation (Eckert and Engesser, 2013; Eddudottir et al, 2020). Technical approaches include ISODATA i.e. Iterative SelfOrganizing Data Analysis (Memarsadeghi et al, 2007) and K-means image classification (Fard et al, 2020; Peña et al, 1999; Zhao et al, 2020; Bottou and Bengio, 1995), which aim at grouping image pixels into classes of similar properties representing land cover types
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
