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

Abstract

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.

The geography and progression of blowouts in the coastal dunes along the eastern shore of Lake Michigan since 1938

“…After resampling, we selected an unsupervised ML algorithm known as the Iterative Self-Organizing Data Analysis Technique (ISODATA) method. The ISODATA classification method is often used in landscape analyses (e.g., Lemenkova, 2021), including in analyses of coastal environments (Tojo and Udo, 2018), due to its efficiency in processing large remote sensing datasets without the need for supervised training samples (Ma et al, 2020). Moreover, unsupervised ML methods, including the ISODATA algorithm, “offer the promise of objective anomaly assignment” (Kvamme et al, 2019, p. 313), thus potentially reducing the bias of the authors to guide these procedures (Campbell, 1996, p. 318).…”

Identifying Shoreline Changes in Tanjung Burung Forest Reserve and its Impact on Mangrove Stands Using Geospatial Technique

“…This tool contains a combination of Iso Cluster and Maximum Likelihood Classification tools. It was chosen for this study because it is simple and machine-based, involving minimum human intervention [29]. The output raster image will be reclassified into 0 for land, 1 for water and no-data.…”

Heath forest identification using remote sensing in the surrounding area of the new capital city of Indonesia

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