An object based image analysis applied for volcanic and glacial landforms mapping in Sahand Mountain, Iran

• Published in: Catena (Giessen) 2021-03, Vol.198, p.105073, Article 105073
• Main Authors: Feizizadeh, Bakhtiar, Kazemi Garajeh, Mohammad, Blaschke, Thomas, Lakes, Tobia
• Format: Article
• Language: English
• Subjects: Iran; Object-based image analysis; Sahand Mountain; Semi-automated approach; Volcanic and glacier landform
• ISSN: 0341-8162; 1872-6887
• DOI: 10.1016/j.catena.2020.105073

•We developed a semi-automated object-based rule set for landform detecting and mapping.•A novel object based algorithms has developed and examined for landform mapping.•The Fuzzy Synthetic Evaluation technique was applied for accuracy assessment.•Results presents new and efficient approach for Geo/GISciences development. Landform mapping has increasingly become part of the digital domain. While the majority of approaches evaluates Digital Elevation Models (DEM) on a per-pixel basis, some examples exist were object-based image analysis (OBIA) has been applied to terrain data to identify a variety of landforms, including glacial landforms. The main objective of this study is to develop a semi-automated object-based rule set for detecting and delineating volcanic and glacier landforms in the area of the Sahand Mountain, Northern Iran. First, we applied a multi-resolution segmentation algorithm on a freely available Sentinel-2 optical satellite image and then selected the relevant features to define appropriate segmentation scales for each landform category. Object-based rule sets were then developed using spatial (DEM and its derivatives, e.g. slope, aspect, curvature and flow accumulation) and spectral information. Volcanic and glacial landforms were detected and classified into eight classes: caldera, volcanic cone, tuff formation, andesite lava, dacite lava, glacier valley, suspension valley, glacier cirque. An accuracy assessment was applied based on the fuzzy synthetic evaluation technique, together with the error matrix and kappa coefficient, using field data and geomorphological units derived from geological maps and very high resolution aerial photographs. The resulting overall accuracies for each class were 96.2%, 93.3%, 92.4%, 94.2%, 93.01, 95.1, 90.1 and 90.5, respectively. Our research demonstrated that spatial (e.g. density, shape index, length/width) and spectral (e.g. mean, brightness and standard deviation) algorithms together with a grey-level co-occurrence matrix (GLCMs) were efficient features for detecting and mapping volcanic and glacial landforms. We conclude that the OBIA-based algorithms and features provide a high potential for detecting and classifying landforms. Results of this study are of great importance for geomorphology and geology as well as geo-tourism societies and the semi-automated landform mapping contributes to the framework of GIScience.