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SFMToolbox: an ArcGIS Python Toolbox for Automatic Production of Maps of Soil Fertility

Velamala Ranga Rao, Pant Pawan Kumar

Geomatics and Environmental Engineering

2023

Vol. 17, no. 2

105--145

SFMToolbox is an ArcGIS Python toolbox developed in ArcGIS Desktop (ArcMap) to perform preprocessing tasks for the automatic creation of maps of soil fertility parameters. Through SFMToolbox, users can automatically produce 12 soil fertility parameter maps as a batch at one time. It is easy to use, where users can only provide input; the output files are automatically created from the name of the sample point and saved in the defined workspace. During the execution of the tools, various processes, such as Inverse Distance Weighted (IDW) – a technique of interpolation, reclassification, adding color, merging, projection, area calculation, and legend are done automatically for all 12 parameters at the same time. The SFMToolbox was validated as part of the following case study: village – Kashipur, tehsil – Balrampur, district – Balrampur, state – Uttar Pradesh, Country – India. The results show that the user can quickly generate maps and save time, improve accuracy, and reduce human intervention and ensure uniformity among maps. This toolbox also applied to Cycle II data from the Government of India’s Soil Health Card (SHC) scheme and timely produced 12-parameters soil nutrient maps for 630 districts in a uniform format. The toolbox may be used by public and private organizations to make timely decisions on agricultural and environmental issues.

Estimating the validity of the recognition results of earthquake-prone areas using the ArcMap

Gorshkov A., Novikova O.

Acta Geophysica

2018

Vol. 66, no. 5

843--853

In 1972, V. Keilis-Borok and I. Gelfand introduced the phenomenological approach based on the morphostructural zoning and pattern recognition for identification of earthquake-prone areas. This methodology identifies seismogenic nodes capable of generating strong earthquakes on the basis of geological, morphological, and geophysical data, which do not contain information on past seismicity. In the period 1972–2018, totally, 26 worldwide seismic regions have been studied and maps showing the recognized earthquake-prone areas in each region have been published. After that, 11 of these regions were hit by earthquakes of the relevant sizes. The goal of this work is to analyze the correlation of the post-publication events with seismogenic nodes defined in these 11 regions. The test was performed using the NEIC earthquake catalog because it uniformly defines the location and magnitudes of earthquakes over the globe. The ArcMap facilities were exploited to plot the post-publication events on the maps showing the recognized seismogenic nodes. We found that about 86% of such events fall in the recognized seismogenic nodes. The performed test proved the sufficient validity of the methodology for identifying areas capable of strong earthquakes and confirms the idea on nucleating strong earthquakes at the nodes.