Awk and Gnu Octave Programming Languagesintegrated With Generic Mapping Tools for Geomorphological Analysis

Acta Biologica Marisiensis

2020

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The paper aims to evaluate the presence and condition of vegetation by SAGA GIS. The study area covers northern coasts of Iceland including two fjords, the Eyjafjörður and the Skagafjörður, prosperous agricultural regions. The vegetation coverage in Iceland experience the impact of harsh climate, land use, livestock grazing, glacial ablation and volcanism. The data include the Landsat TM image. The methodology is based on computing raster bands for simulating Tassel Cap Transformation (wetness, greenness and brightness) and Enhanced Vegetation Index (EVI) sensitive to high biomass. The results include modelled three bands of brightness, greenness and wetness. Greenness variation shows the least values in ice-covered areas (-56.98 to -18.69). High values (-23.48 to 9.12) are in the valleys with dense vegetation, correlating with the geomorphology of the river network, the vegetation-free areas and ocean which corresponds to the peak of 30.87 to 41.19. The bell-shaped data distribution shows frequency 43.19–141.74 for vegetation indicating healthy state and canopy density. Maximal values are in ice-covered regions and glaciers (64°N-65°N). Very low values (0 to -20) show desertification and mountainous rocks. Moderate values (20-40) indicate healthy vegetation. The most frequent data: -28,17 to 11,8. The EVI shows data variations (-0.14 to 0.04). The study contributes both to the regional studies of Arctic Iceland and methodological approach of remote sensing data processing by SAGA GIS.

Section: Discussionmentioning

confidence: 99%

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

Acta Biologica Marisiensis

2020

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“…Other examples of the geospatial mapping for analysis of the datasets is illustrated by automated vectorization of the isolines using AutoTrace [56], geostatistical approaches by ArcGIS [57][58][59][60][61][62][63]. Effective way of the data analysis is a combination of various methods, for instance, C++, Matlab, cartographic mapping by ArcGIS [64][65], CARIS HIPS and GMT [66], or AWK and Octave with GMT [67]. However, few works use programming full with codes available for geological modelling.…”

Section: Discussionmentioning

confidence: 99%

R Libraries {dendextend} and {magrittr} and Clustering Package scipy.cluster of Python For Modelling Diagrams of Dendrogram Trees

Carpathian Journal of Electronic and Computer Engineering

2020

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The paper presents a comparison of the two languages Python and R related to the classification tools and demonstrates the differences in their syntax and graphical output. It indicates the functionality of R and Python packages {dendextend} and scipy.cluster as effective tools for the dendrogram modelling by the algorithms of sorting and ranking datasets. R and Python programming languages have been tested on a sample dataset including marine geological measurements. The work aims to detect how bathymetric data change along the 25 bathymetric profiles digitized across the Mariana Trench. The methodology includes performed hierarchical cluster analysis with dendrograms and plotted clustermap with marginal dendrograms. The statistical libraries include Matplotlib, SciPy, NumPy, Pandas by Python and {dendextend}, {pvclust}, {magrittr} by R. The dendrograms were compared by the model-simulated clusters of the bathymetric ranges. The results show three distinct groups of the profiles sorted by the elevation ranges with maximal depths detected in a group of profiles 19-21. The dendrogram visualization in a cluster analysis demonstrates the effective representation of the data sorting, grouping and classifying by the machine learning algorithms. The programming codes presented in this study enable to sort a dataset in a similar research aimed to group data based on the similarity of attributes. Effective visualization by dendrograms is a useful modelling tool for the geospatial management where data ranking is required. Plotting dendrograms by R, comparing to Python, presented functional and sophisticated algorithms, refined design control and fine graphical data output. The interdisciplinary nature of this work consists in application of the coding algorithms for spatial data analysis.

“…Other approaches include, for example, reconstructed wave heights and ComMIT tsunami modelling (Meservy et al, 2020), method of the radiometric age determination of granitic rocks from the islands for analysis of the geological evolution and geochronology of west Indonesia based on the plate-tectonics concept (Katili, 1973). Development of cartographic methods is presented in works on marine mapping and visualization (Mammerickx et al, 1976), as well as automatization approaches in the GIS techniques (Schenke and Lemenkova, 2008;Klinger et al, 2012;Lemenkova, 2019e).…”

Section: Methodsmentioning

confidence: 99%

Analysis of the difference in depths and variation in slope steepness of the Sunda Trench, Indonesia, east Indian Ocean

2020

Rev. Geomorf.

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The paper discusses geomorphology of the Sunda Trench, an oceanic trench located in eastern Indian Ocean along the Sumatra and Java Islands of the Indonesian archipelago. In particular, it analysis the difference in depths and variation in slope steepness between the two segments of the trench: the southern Java transect (coordinates 108.8°E 10.10°S to 113.0°E 10.75°S) and the northern Sumatra transect (97.5°E 1.1°S to 101.0°E 5.5°S). The thematic maps and geomorphological modelling were plotted using Generic Mapping Tools (GMT). The materials include high-resolution data on topography, geology and geophysics: GEBCO 15 arc-minute resolution grid, EGM2008 2.5 minute Earth Gravitation Model of 2008, GlobSed global 5‐arc‐minute total sediment thickness and vector geological datasets. In addition to the GEBCO-based bathymetric data, geological, topographic and geophysical maps, the results include enlarged transects for the Java and Sumatra segments, their slope gradients and cross-section profiles, derived from the bathymetric GEBCO dataset. The geomorphology framework of the Sunda Trench is largely controlled by the subduction of the Australian plate underneath the Sunda microplate. The geological processes take place in basin of the Indian Ocean at different stages of its evolution and influence the nature of the submarine geomorphology and geometric shape of the trench. Sunda Trench is seismically active part of the Pacific Ring of Fire. A large number of the catastrophic earthquakes are recorded around the trench. The histograms shows variation in depths along the segments of the Sumatra and Java. The Java segment has a bell-shaped data distribution in contrast to the Sumatra with bimodal pattern. The Java segment has the most repetitive depths at -2,500 to -5,200 m. The Sumatra transect has two peaks: 1) a classic bell-shaped peak at depths -4,500 m to -5,500 m; 2) shelf area with a peak from 0 to -1,750 m. The data at middle depths (-1,750 to -4,500 m) have a frequency <300 samples. The most frequent bathymetry for the Sumatra segment corresponds to the -4,750 m to -5,000 m (2,151 samples). Comparing to the Sumatra segment, the Java segment is deeper. For the depths >-6,000 m, there are only 138 samples for the Sumatra while 547 samples for Java. Furthermore, Java segment has more symmetrical geometric shape while Sumatra segment is asymmetric, one-sided. The Sumatra segment has a steepness of 57.86° on its eastern side (facing Sumatra Island) and a contrasting 14.58° on the western part. The Java segment has a steepness of 64.34° on its northern side (facing Java Island) and 24.95° on the southern part (facing Indian Ocean). The paper contributes to the studies of the submarine geomorphology in Indonesia.