SAGA GIS for Information Extraction on Presence and Conditions of Vegetation of Northern Coast of Iceland Based on the Landsat TM

Acta agriculturae Serbica

2021

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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.

Section: Discussionmentioning

confidence: 99%

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

Acta agriculturae Serbica

2021

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“…GMT is a powerful mapping instrument often used for geophysical data analysis, effective visualization and interpretation (Lemenkova, 2019d;2020a;2020b, 2020c. Other examples of geodata analysis include application of remote sensing data analysis in environmental, spatial, marine biological or oceanographocal studies (Jamieson, 2018;Stewart & Jamieson, 2019;Lemenkova, 2019g;Abdel-Mageed et al 2020;.…”

Section: Methodsmentioning

confidence: 99%

GRASS GIS for topographic and geophysical mapping of the Peru-Chile Trench

2020

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The study area is located along the western continental margins of South America, Peru-Chile Trench south-east Pacific Ocean, geographically encompasses 90° to 60°W longitude and 55°S to 0° latitude. The study aims to perform a spatial analysis using GRASS GIS approach applied for processing and visualizing topographic and geophysical data on the study area. Data include following raster grids: topographic SRTM_15PLUS raster grid with 15 arc-second resolution, geoid model (EGM96), geophysical fields and gravity maps (marine free-air gravity and vertically corrected free-air gravity). The thematic grids were mapped by GRASS GIS modules and visualized for comparative analysis. Spatial analysis included plotting slope aspect, profile curvature, terrain elevation and modeled topographic classes based on the neighborhood analysis. The results include visualized geophysical and topographic maps of the study area showing correlation between the geophysical fields and topographic elevation. The variety of forms of the submarine relief of the Pacific Ocean seafloor was formed as a result of complex factors: tectonic movements, dislocations, active volcanism, geologic variations in rock density, which is reflected in visualized gravity and geoid maps. The actuality of the study is explained by high potential commercial interests in deep-water mineral resource deposits, oil and gas, which make ocean seafloor studies one of the most promising topics in geoscience. At the same time, the need for programming applications for big data analysis in geosciences requires testing advanced scripting approaches in cartography. Therefore, the current paper presents a multi-disciplinary approach combining geological analysis with technical cartographic aspects of data analysis and visualization.

“…Due to its availability and quality, the analysis of the Landsat TM data presents a variety of existing applications (Cao et al, 2020;Lemenkova, 2011Lemenkova, , 2015Lemenkova, , 2020cLemenkova, , 2020dFoga et al, 2017;Nagol et al, 2015;Healey et al, 2018;Chowdhury et al, 2021;Homer et al, 2015). With a history of nearly 50 years of continuous global data collection, the Landsat mission has a constant development.…”

Section: Introductionmentioning

confidence: 99%

ISO Cluster classifier by ArcGIS for unsupervised classification of the Landsat TM image of Reykjavík

2021

Bull Nat Sci Research

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The paper presents the use of the Landsat TM image processed by the ArcGIS Spatial Analyst Tool for environmental mapping of southwestern Iceland, region of Reykjavik. Iceland is one of the most special Arctic regions with unique flora and landscapes. Its environment is presented by vulnerable ecosystems of highlands where vegetation is affected by climate, human or geologic factors: overgrazing, volcanism, annual temperature change. Therefore, mapping land cover types in Iceland contribute to the nature conservation, sustainable development and environmental monitoring purposes. This paper starts by review of the current trends in remote sensing, the importance of Landsat TM imagery for environmental mapping in general and Iceland in particular, and the requirements of GIS specifically for satellite image analysis. This is followed by the extended methodological workflow supported by illustrative print screens and technical description of data processing in ArcGIS. The data used in this research include Landsat TM image which was captured using GloVis and processed in ArcGIS. The methodology includes a workflow involving several technical steps of raster data processing in ArcGIS: 1) coordinate projecting, 2) panchromatic sharpening, 3) inspection of raster statistics, 4) spectral bands combination, 5) calculations, 6) unsupervised classification, 7) mapping. The classification was done by clustering technique using ISO Cluster algorithm and Maximum Likelihood Classification. This paper finally presents the results of the ISO Cluster application for Landsat TM image processing and concludes final remarks on the perspectives of environmental mapping based on Landsat TM image processing in ArcGIS.The results of the classification present landscapes divided into eight distinct land cover classes: 1) bare soils; 2) shrubs and smaller trees in the river valleys, urban areas including green spaces; 3) water areas; 4) forests including the Reykjanesfólkvangur National reserve; 5) ice-covered areas, glaciers and cloudy regions; 6) ravine valleys with a sparse type of the vegetation: rowan, alder, heathland, wetland; 7) rocks; 8) mixed areas. The final remarks include the discussion on the development of machine learning methods and opportunities of their technical applications in GIS-based analysis and Earth Observation data processing in ArcGIS, including image analysis and classification, mapping and visualization, machine learning and environmental applications for decision making in forestry and sustainable development.