Tool In ArcMap Research Articles

A MACHINE LEARNING APPROACH FOR SPATIAL SUITABILITY MODELLING FOR COTTON CULTIVATION IN NORTH-EAST BALUCHISTAN-PAKISTAN

Over the past few years, cotton cultivation in Pakistan has witnessed a decline, with farmers increasingly turning to sugarcane and maize. This shift has had detrimental effects on the local textile industry. To revive cotton farming in the region, this study sought to identify vacant, naturally available land suitable for cotton cultivation. The research focused on the suitability of cotton cultivation in Northeast Baluchistan, a challenging terrain characterized by extreme slopes and mountainous areas. The data were processed in Google Earth Engine (GEE) and for different parameters i.e. Land use and Landcover (LULC), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Moisture Index (NDMI), Rainfall, Temperature, Geology, Slope, DEM, Soil pH and Soil texture. Different satellite data were used to measure the parameters in the study area. Then all the parameters were assigned different weights and the Weighted Overlay Method was applied from the Spatial Analyst tool in Arc Map and suitability modelling was done. The study revealed that Barkhan in Northeast Baluchistan exhibited the highest suitability for cotton cultivation, covering 0.71% of the area. On the other hand, approximately 47.86% of the region displayed suitability despite restrictions on cultivation, including well-furnished drainages and irrigation and important infrastructures. The remaining land was not suitable for cotton cultivation in the northeast of Baluchistan. Overall, this publication offers information on land suitability for cotton farming in Northeast Baluchistan and creates a path for the discovery of feasible cotton farming.

CN-N: A Python-based ArcGIS Tool for Generating SCS Curve Number and Manning’s Roughness

Water resources engineers and geospatial analysts often face the challenge of spatially estimating parameters such as the Soil Conservation Service (SCS) Curve Number (CN) and Manning’s roughness number (n), which are critical for predicting runoff and streamflow in hydrologic studies. Addressing the above challenge, this paper presents an innovative ArcMap tool developed using Python. This tool streamlines the SCS-CN and Manning’s n spatial calculations and is designed to handle large datasets, even at the scale of the entire US. Additionally, it offers the unique capability of geoprocessing mixed soil types and seamlessly integrating data if the watershed spans over different states. Our tool automates the integration of land cover data, hydrologic soil group data, and hydrologic boundaries. The tool reads watershed boundaries and uses the National Land Cover Database (NLCD) and the Gridded Soil Survey Geographic Database (gSSURGO) to develop SCS-CN and Manning’s n spatial layers. The tool also offers users the unique flexibility to add any desired values for CN or Manning’s n in the form of a so-called lookup table, which is a great help with the iterative process of calibrating hydrologic or hydraulic models. Our tool addressed one of the major limitations of its predecessors, acknowledging the existence of mixed hydrologic soil groups, e.g., B/C or C/D, and allowing for user adjustments to address hydrologic or hydraulic models’ calibration needs. The tool was developed with a flexible framework to incorporate additional spatial parameters soon, such as the spatial green-ampt parameters. With a user-friendly interface and integration capabilities, the tool is invaluable for hydrologic and hydraulic studies at local, regional, and global scales.

Neighborhood Walkability, Historical Redlining, and Childhood Obesity in Denver, Colorado.

Childhood obesity is a precursor to future health complications. In adults, neighborhood walkability is inversely associated with obesity prevalence. Recently, it has been shown that current urban walkability has been influenced by historical discriminatory neighborhood disinvestment. However, the relationship between this systemic racism and obesity has not been extensively studied. The objective of this study was to evaluate the association of neighborhood walkability and redlining, a historical practice of denying home loans to communities of color, with childhood obesity. We evaluated neighborhood walkability and walkable destinations for 250 participants of the Healthy Start cohort, based in the Denver metropolitan region. Eligible participants attended an examination between ages 4 and 8. Walkable destinations and redlining geolocations were determined based on residential addresses, and a weighting system for destination types was developed. Sidewalks and trails in Denver were included in the network analyst tool in ArcMap to calculate the precise walkable environment for each child. We implemented linear regression models to estimate associations between neighborhood characteristics and child body mass index (BMI) z-scores and fat mass percent. There was a significant association between child BMI and redlining (β: 1.36, 95% CI: 0.106, 2.620). We did not find an association between walkability measures and childhood obesity outcomes. We propose that cities such as Denver pursue built environment policies, such as inclusionary zoning and direct investments in neighborhoods that have been historically neglected, to reduce the childhood health impacts of segregated poverty, and suggest further studies on the influences that redlining and urban built environment factors have on childhood obesity.

Geospatial assessment of land suitability for oil palm (Elaeis guineensis Jacq.) growing in Northern Uganda.

Under the second phase of the National Oil Palm Project, the Government of Uganda plans to extend the oil palm project to Northern Uganda. According to the Final Project Design Report (2017) of the National Oil Palm Project, and based on the rainfall, soil and temperature of the region, areas in Northern Uganda have already been mapped for the project. However, no detailed information on the degree of suitability of the areas has been provided. In this research, other parameters such as land cover, elevation and slope were identified through the literature review. Furthermore, on the basis of the reclassify tool in ArcMap 10.8, the data were then reclassified into four classes, namely, highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and unsuitable (N.) With the aid of the Analytical Hierarchical Process (AHP), pairwise comparison matrices were constructed and the weight of each parameter was computed. The suitability map obtained from a weighted linear combination identified 38.18%, 35.54%, 21.41% and 4.87% of the land area as highly suitable, moderately suitable, marginally suitable and unsuitable, respectively. A geospatial assessment of the suitability of the land for oil palm growing was carried out. It was based on only the soil types, but excluded the chemical properties of the soil. Therefore, further research on the chemical properties of the soils at suitable sites should be carried out. In-depth research should be carried out While considering social and economic factors among the criteria to determine the willingness and financial capability of the people to venture into oil palm growing as a source of income, Oil Palm Uganda Limited should conduct in-depth research into this issue.

UTILISING VIIRS DNB NIGHTTIME LIGHT DATA TOGETHER WITH LANDSAT DAYTIME DATA TO ASSESS CHANGES IN LIGHT POLLUTION IN ETHEKWINI METROPOLITAN MUNICIPALITY (EMM)

Abstract. Night-time light (NTL) data has become widely used in different studies, including urbanization and light pollution subjects. Some sources of NTL data have made the data freely available on online platforms, making it easy to study subjects that relate to the use of artificial light at night. African countries have not published much on the use of NTL data. Still, there has been a development in different countries in the continent, such as economic growth and the provision of new and improved infrastructure. These developments can be considered primary contributors to the increase in light pollution worldwide. This study aimed at using NTL data together with familiar daytime data to assess how light pollution has been changing in eThekwini Metropolitan Municipality and again assess the changes in the sources of light pollution through the assessment of Land-use Land-cover (LULC) change of the study area. NTL images were reclassified into five different classes of light pollution using the reclassification tool in ArcMap 10.7. Supervised classification was performed to assess the changes in LULC. The confusion/error matrix was done to verify the accuracy of these classifications. These showed that light pollution is increasing in eThekwini Metropolitan Municipality, with the increased built-up area also being witnessed. NTL data is becoming useful worldwide, and it is highly recommended for African countries also to do research using this data in their studies.

Mangrove Biodiversity, GIS Weighted Overlay Analysis, and Mapping of Suitable Areas in Alabel, Sarangani Province, Philippines

The utility of Geographic Information System (GIS) has been adopted in mapping and predicting sites for mangrove suitability in Alabel, Sarangani Province, Province. Biodiversity assessment of coastal mangroves in Alabel, Sarangani Province, Philippines was performed in purposive manner using sampling quadrats. There were 13 observed mangrove species in sand-dominated (51.48%) soil. The sampled field data points are scattered irregularly, showing slight curvature on the east-west trendline but a flatter surface on the north-south trendline. The abundance and soil data were converted into shapefiles and subjected to inverse distance weighted (IDW) interpolation. The rasterized and reclassified thematic map layers were assigned with factor influence consisting of mangrove abundance (40%), land cover (30%), sand composition (20%), and slope (10%) for suitability analysis. The suitable mangrove areas were computed using weighted overlay suitability analysis, and the final score for the suitability index was converted into suitability classes. Prediction of mangrove suitable areas was performed under the ModelBuilder workflow tool in ArcMap. The mangrove suitability classes were extracted using a zonal geometry tool depicting three suitability classes. The predicted mangrove high suitability areas covered 463.37 km2, the low suitability areas covered 573.02 km2, and the unsuitable areas covered 9,403.77 km2. The study utilized a combination of field gathering and available online data for GIS analysis which local government units could perform. The results could be used to plan and manage mangrove resources in the area effectively.

Spatio-temporal pattern of urban vegetation in the central business district of the Wa municipality of Ghana

Increasing physical infrastructure development influences the spatial distribution of vegetation and temperature in many cities around the globe. However, in emerging cities such as Wa Municipality in Ghana and elsewhere, research attention has focused on urban expansion or urban sprawl and the impacts thereof on the peri‑urban vegetation. Considering the importance of vegetation in regulating temperature, reducing windstorm, carbon sequestration and creating beauty, this paper concentrates on the threat posed by urban retrofitting activities on vegetation in open spaces in the Wa Central Business District (CBD). The study area was gridded into 1 ha plots using the Fishnet tool in ArcMap. One hundred and fifty-seven full plots and 75 plots of various sizes less than 1 ha were obtained from the fishnet gridding. One hundred and thirteen full plots were sampled for the counting of the trees. Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) maps were prepared from 2014, 2017 and 2020 Landsat 8 satellite images. Point NDVI and LST values were extracted for 600 random points and used to analyze the spatial distribution using Global and Local Moran I tests. Two-sample t-test and Pearson correlation statistical tests were performed to assess the significance of any differences in the NDVI, LST and the tree density for 2014, 2017 and 2020. The results revealed that the average tree densities for 2017 and 2020 in the Wa CBD were 4 and 3 trees ha−1, respectively which were classified as very low. The spatial distribution of the NDVI values showed low, high and insignificant clusters. The NDVI values were within the range of 0.019–0.219. The clusters of high values were found around state institutional structures, where large open spaces existed while clusters of low values were found around the market, transport stations and densely populated areas where open spaces were very few. The land surface temperature trend revealed increasing minimum and maximum values. The minimum LST values were 32 °C, 33 °C and 36 °C in 2014, 2017 and 2020, respectively and the maximum LST values were 36 °C, 37 °C and 40 °C for 2014, 2017 and 2020, respectively. It is concluded that tree density and NDVI values in the CBD are low, signifying isolated trees and patches of vegetation in the area. Also, it was revealed that as greenness reduces LST increases. The Wa Municipal Assembly and the Regional Forestry Commission should intensify public education on tree planting and maintenance in the city.

Shoreline change rate dynamics analysis and prediction of future positions using satellite imagery for the southern coast of Kuwait: A case study

The studied shoreline extends 73 kilometers, starting from the Kuwait-Saudi Arabia borders northward. This work represents a limited work that analyses the historical shoreline positions. Five-dates satellite images along a period of 35 years between 1986 and 2021 were used to calculate the historical shoreline change rates and predict future shoreline positions in 2030 and 2050. First, the historical shoreline was extracted using the Normalized Difference Wader index (WI 2015). Then, the shoreline change rates were quantified statistically using the End Point Rate (EPR) and the Linear Regression Rate (LRR) models of the Digital Shoreline Analysis System (DSAS) tool in ArcMap. It has been found that the maximum erosion rate is –9.73 m/year, and the highest accretion is 10.88 m/year. Also, the positions of shorelines in the year 2030 and 2050 were predicted and defined on the map, with mapping of gain and loss surfaces. The results defined the most stable areas for future development and the areas needing urgent protection. It has been found that the resulting model can be affected by the topographical changes of the beaches due to human activities, where the coast alongside the Al-Khiran project will be eroded and accreted less than predicted because of the presence of protection facilities.

Fusion of Landsat 8 OLI and PlanetScope Images for Urban Forest Management in Baton Rouge, Louisiana

In recent years image fusion method has been used widely in different studies to improve spatial resolution of multispectral images. This study aims to fuse high resolution satellite imagery with low multispectral imagery in order to assist policymakers in the effective planning and management of urban forest ecosystem in Baton Rouge. To accomplish these objectives, Landsat 8 and PlanetScope satellite images were acquired from United States Geological Survey (USGS) Earth Explorer and Planet websites with pixel resolution of 30m and 3m respectively. The reference images (observed Landsat 8 and PlanetScope imagery) were acquired on 06/08/2020 and 11/19/2020. The image processing was performed in ArcMap and used 6-5-4 band combination for Landsat 8 to visually inspect healthy vegetation and the green spaces. The near-infrared (NIR) panchromatic band for PlanetScope was merged with Landsat 8 image using the Create Pan-Sharpened raster tool in ArcMap and applied the Intensity-Hue-Saturation (IHS) method. In addition, location of urban forestry parks in the study area was picked using the handheld GPS and recorded in an excel sheet. This sheet was converted into Excel (.csv) file and imported into ESRI ArcMap to identify the spatial distribution of the green spaces in East Baton Rouge parish. Results show fused images have better contrast and improve visualization of spatial features than non-fused images. For example, roads, trees, buildings appear sharper, easily discernible, and less pixelated compared to the Landsat 8 image in the fused image. The paper concludes by outlining policy recommendations in the form of sequential measurement of urban forest over time to help track changes and allows for better informed policy and decision making with respect to urban forest management.

Determination of Soil Erosion and Sediment Yield in the Bonsa River Basin Using GIS and Revised Universal Soil Loss Equation (RUSLE)

The Bonsa river is an important tributary of the Ankobra river in the Western Region of Ghana. The catchment of the Bonsa river has been undergoing rapid land cover changes due to human activities such as farming, illegal mining, population growth, among others which are likely to promote soil erosion and sediment yield in the river basin. To estimate the amount of soil eroded over a period and subsequent sediment yielded along the Bonsa river basin, the Revised Universal Soil Loss Equation (RUSLE) was integrated with Geographic Information System (GIS) to model the spatial distribution patterns in soil erosion and sediment yield within the catchment. Data used included annual rainfall records, soil map, Digital Elevation Model (DEM) and land-use map of the study area. Parameters of the model were determined and converted into raster layers using the raster calculator tool in ArcMap to produce a soil erosion map. The concept of Sediment Delivery Ratio (SDR) was applied to determine the annual sediment yield by combining a raster SDR layer with soil erosion map. The predicted soil loss and sediment yield values were found to be low. This may be due to high soil protective cover provided by vegetation as well as low topographic relief in the river basin. Though, the elements and processes responsible for soil erosion and sediment yield prevailing in the basin was found to be low, adverse situations could be developed with time if the prevailing conditions are not checked, as soil erosion is a natural gradual slow process. The gains made could be sustained by putting measures in place to control human activities, particularly, illegal mining (galamsey) in the basin, indiscriminate cutting down of trees and farmining activities along the Bansa river basin. This study will support monitoring, planning of water resources and help to improve sustainable water quality.

Geo-spatial distribution of hydrological nephrotoxic characteristics in Kalawewa and Tissawewa reservoirs in Anuradhapura, Sri Lanka

Reservoirs are one of the major water resources subjected to different forms of pollution due to anthropogenic activities. Heavy metals and water hardness are a particular concern due to their potential toxic effect and bio-accumulate ability. Therefore, the objective of this investigation was to analyze the spatial distribution of selected heavy metals Cadmium (Cd), Lead (Pb), and Arsenic (As)) and total water hardness in Kalawewa and Tissawewa reservoirs, Anuradhapura District in Sri Lanka. Cd, Pb, and As contents of water samples were analyzed using Inductive Coupled Plasma Mass Spectrometry (ICP-MS-Agilent-7800). Total water hardness values were analyzed based on Ca and Mg concentrations of water samples. Spatial distribution patterns of analyzed heavy metals and total water hardness values were interpolated using Inverse Distance Weighted (IDW) and Spatial autocorrelation tool in ArcMap 10.2.2 software. Overall, the mean heavy metal concentrations in two reservoirs complied with World Health Organization agricultural water standards. The mean total water hardness values of the two reservoirs reflect “very hard water”, and the distributions of analyzed heavy metals and water hardness were higher in the center. As long term exposure to nephrotoxic heavy metals adversely affects human health, taking all necessary changes before consumption can be recommended.

Identifying Suitable Areas for Plantation of Organic Products Using GIS and AHP

Organic farming is a technique, which involves cultivation of plants in natural ways. In Malaysia, because the organic food industry is still small-scale, over 60% of organic foods are still imported and it is not possible for the availability and output of organic goods to meet the local demand and need. This study was intended to utilize the GIS and AHP technique to identify suitable areas for organic farming in Sabak Bernam, Malaysia. The study was conducted based on the objectives where criteria for organic farming were identified through background research. Pair-wise comparison (PWCM) method using questionnaires answered by experts were used to determine the weights for each of the parameters used. For this study, seven (7) criteria were considered. The criteria were then weighted according to importance and those weighted criteria were combined to produce a suitability map. A site suitability model was built using the Modelbuilder tool in Arcmap. The model used the AHP and Weighted Overlay basis which provides promising result for the analysis of finding suitable sites for organic farming. Results obtained showed that the majority of land within the Sabak Bernam district is suitable to carry out organic cultivation where the land is far from road networks, contains high organic matter content, gentle slopes with flat aspects low elevation and less than 10 meters from drainages. While land deemed not suitable involves land in dense urban area. This simultaneously means that the land is too close to road networks where underground contamination is a major possibility.

Geographical access to point-of-care testing for hypertensive disorders of pregnancy as an integral part of maternal healthcare in Ghana

BackgroundHypertensive disorders of pregnancy (HDP) are associated with high maternal mortality in Ghana and globally. Evidence shows that there is poor availability of pregnancy-related point-of-care (POC) tests in Ghana’s primary healthcare (PHC) clinics (health centre or community-based health planning services facilities). Therefore, we employed geographic information systems to estimate the geographical distribution of and physical accessibility to HDP POC testing services in the Upper East Region (UER), Ghana.MethodsWe collected data on 100 out of 365 PHC clinics, public hospitals providing HDP testing, PHC clinic type, ownership, and availability of urine dipsticks and blood pressure (BP) devices. We also obtained the geo-located data of the PHC clinics and hospitals using the global positioning system. We employed ArcGIS 10.4 to measure the distance and travel time from the location of each PHC clinic without HDP POC testing services as well as from all locations of each district to the nearest hospital/clinic where the service is available. The travel time was estimated using an assumed motorised tricycle speed of 20 km/hour. We further calculated the spatial distribution of the hospitals/clinics providing HDP POC testing services using the spatial autocorrelation tool in ArcMap, and Stata version 14 for descriptive statistical analysis.ResultsOf the 100 participating PHC clinics, POC testing for HDP was available in 19% (14% health centres and 5% community-based health planning services compounds) in addition to the 10 hospitals use as referral points for the service. The findings indicated that the spatial pattern of the distribution of the health facilities providing HDP POC testing was random (z-score = -0.61; p = 0.54). About 17% of the PHC clinics without HDP POC testing service were located > 10 km to the nearest facility offering the service. The mean distance and travel time from PHC clinics without HDP POC testing to a health facility providing the service were 11.4 ± 9.9 km and 31.1 ± 29.2 min respectively. The results suggest that if every 19% of the 365 PHC clinics are offering HDP POC testing in addition to these 10 hospitals identified, then the estimated coverage (health facility-to-women in fertility age ratio) in the UER is 1: 3,869.ConclusionsThere is poor physical accessibility to HDP POC testing services from PHC clinics without HDP POC testing in the UER. Mothers who obtain maternal healthcare in about 17% of the PHC clinics travel long distances (> 10 km) to access the service when needed. Hence, there is a need to improve the availability of HDP POC diagnostic tests in Ghana’s rural clinics.

Geochemical Mapping of Sulfur Within Olkaria Geothermal Field, Kenya

This research paper focuses on the geochemical mapping of Sulfur to characterize the Olkaria geothermal field, a field associated with geothermal power generation in the northeast of Nairobi. The current geochemical methods used are CO<sub>2</sub> and N<sub>2</sub> however, a discrepancy associated with the biogenic sources of CO<sub>2</sub> renders it less reliable. Sulfur in geothermal systems is magmatic, hence its utilization can help solve these discrepancies. The study utilized sulfur present in the geothermal well waters to identify the main features of a magmatic hydrothermal system, their distribution within the Olkaria volcanic complex and how they relate with the Olkaria structures. Secondary data of the concentrations of H<sub>2</sub>S and SO<sub>4 </sub><sup>2-</sup> was used for mapping using ArcMap tool within ArcGIS. The distribution of the magmatic H<sub>2</sub>S facilitated the mapping of the possible up-flow zones and the heat sources within the study area. SO<sub>4 </sub><sup>2-</sup> was used to map possible recharge zones within the study area. The mappings, that is, the up-flow zones, the heat source and the recharge zones are important, because they increase knowledge on where exactly to drill production wells, make up wells and the reinjection wells. Geochemical mapping performed on other fluid chemical species such as CO<sub>2</sub>, Cl<sup>-</sup>, N<sub>2</sub>, as well as the temperature, facilitated the correlation with the sulfur concentration variations. The distribution of various concentrations of sulfur as well as the correlation parameters were shown by different color scales on the geochemical maps. From the maps, the field-scale distributions, enabled the visualization of which faults establish the fluid ascension areas and which are more closely related to recharge zones. The findings indicate that up-flow zones were affiliated to the NW-SE trending faults as well as the Olkaria Fault while the recharge zones were associated with the Gorge farm fault, the ring structure and the Ololbutot fault. Geochemical mapping of Sulfur proved to be an effective method in the characterization of a geothermal field. Its utilization to complement conventional methods, improves precision for well siting. It should therefore, not be ignored during exploration campaign.<

Classificação do relevo com base nos elementos de Geomorphons e sua relação com a vegetação florestal nativa na Serra do Caverá - Sudoeste do RS, Brasil

As formas e a composição do relevo são fatores que contribuem na determinação da ocorrência e dos tipos de vegetação de determinado local. A associação da vegetação com determinados ambientes, em função da insolação recebida, da disponibilidade hídrica, entre outros fatores, está diretamente relacionada à configuração do relevo de uma área. Este trabalho busca demonstrar a relação existente entre a configuração do relevo e a distribuição da vegetação florestal nativa na Serra do Caverá, na região sudoeste do Rio Grande do Sul. A classificação do relevo da área de estudo foi realizada com base nos Geomorphons, a partir do MDE oriundo dos dados SRTM, obtendo-se dez elementos de relevo. Após o mapeamento da vegetação, realizado com base na interpretação de imagens do satélite LANDSAT 8 OLI, foi realizado o cruzamento das informações em ambiente SIG, com o software ArcGis 10.3, ferramenta ArcMap, e também através do cálculo da razão de frequência (Fr). A maior correlação observada foi com os elementos vales e áreas planas (42,52%), seguida da vegetação ocorrente em encostas, bases de encostas e relevos escavados, que juntos somaram 40,85% do total da vegetação florestal da área de estudo.
 
 Abstract
 
 RELIEF CLASSIFICATION BASED ON THE ELEMENTS OF GEOMORPHONS AND THEIR RELATIONS WITH NATIVE FOREST VEGETATION IN THE SERRA DO CAVERÁ - SOUTHWEST OF RS, BRAZIL
 
 The composition and the forms of the relief are factors that contribute in the determination of the occurrence and the types of vegetation of a certain place. The association of vegetation with certain environments due to the sunshine received, water availability, among others, are directly related to the configuration of the relief of an area. The objective of this work is to demonstrate the relations between the configuration of the relief and the distribution of the native forest vegetation in the Serra do Caverá, in the southwest region of Rio Grande do Sul. The relief classification of the study area was based on the Geomorphons, from the DEM generated through the SRTM data, obtaining ten elements. After the vegetation mapping was carried out based on the interpretation of images of the satellite LANDSAT 8 OLI, the information was cross-referenced in a GIS environment, with ArcGis 10.3 software, ArcMap tool, also by calculating the frequency ratio (Fr). The highest correlation observed was with the valleys and flat areas (42.52%), followed by vegetation on hillsides, slope bases and excavated reliefs, which together added up to 40.85% of the total forest vegetation in the study area.

Long-and short-term variations in shore morphology of Van Island in gulf of Mannar using remote sensing images and DSAS analysis

Trends of shoreline changes need a scientific study as erosion affects the coastal ecosystem and environment. This study focuses on the trends of shoreline changes along the Van Island, Gulf of Mannar, during the period from 2000 to 2016 using the Digital Shoreline Analysis System model of a tool in Arc Map. Shorelines were extracted from the Landsat OLI and ETM+ satellite data from the years 2000, 2005, 2010 and 2015 and short-term changes obtained by TSS with the help of GPS. The rates of changes were calculated by the standard method of end point rate based on 157 transects lines and baseline. The average value of EPR observed was −5.03 m/year during the study period. Statistics of EPR further calculated the average long-time intervals during the period of 15 years and also short-term changes. It is noted that the study area of Van Island’s degradation is very rapid. The analysis shows that the surveyed years from 2015 to 2016 record the highest erosion and retreat of shoreline changes in December 2014 to May2015 than in May2015 to December 2015. Based on the predicted EPR value of −125 m/year of erosion in the next 25 years, it is concluded that the Van Island will vanish.

Geospatial approach towards enumerative analysis of suspended sediment concentration for Ganges–Brahmaputra Bay

This study presents an easy, modular, user-friendly, and flexible software package for processing of Landsat 7 ETM and Landsat 8 OLI-TIRS data for estimating suspended particulate matter concentrations in the coastal waters. This package includes 1) algorithm developed using freely downloadable SCILAB package, 2) ERDAS Models for iterative processing of Landsat images and 3) ArcMAP tool for plotting and map making. Utilizing SCILAB package, a module is written for geometric corrections, radiometric corrections and obtaining normalized water-leaving reflectance by incorporating Landsat 8 OLI-TIRS and Landsat 7 ETM+ data. Using ERDAS models, a sequence of modules are developed for iterative processing of Landsat images and estimating suspended particulate matter concentrations. Processed images are used for preparing suspended sediment concentration maps. The applicability of this software package is demonstrated by estimating and plotting seasonal suspended sediment concentration maps off the Bengal delta. The software is flexible enough to accommodate other remotely sensed data like Ocean Color monitor (OCM) data, Indian Remote Sensing data (IRS), MODIS data etc. by replacing a few parameters in the algorithm, for estimating suspended sediment concentration in coastal waters.

An integrated user-friendly ArcMAP tool for bivariate statistical modelling in geoscience applications

Abstract. Modelling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modelling. Bivariate statistical analysis (BSA) assists in hazard modelling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time-consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, bivariate statistical modeler (BSM), for BSA technique is proposed. Three popular BSA techniques, such as frequency ratio, weight-of-evidence (WoE), and evidential belief function (EBF) models, are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and created by a simple graphical user interface (GUI), which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve (AUC) is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications.

DECISION SUPPORT SYSTEM FOR PRECISION FARMING

A decision support system for precision farming is designed to assist farmers, agricultural experts, research workers or any intellectuals with guidance in making various farming related decisions and help them to access, display and analyze data that have geographic content and meaning. The concept of precision farming is not only related with the use of technologies but it is also about the five R’s that is use of right input (nutrients, water, fertilizer, money, machinery etc.), at the right time, at the right place, in the right amount and in the right manner. There is need to have accurate information and suitable decisions regarding the right inputs required for the farming practices and to initiate the step towards the precision farming. DSS calculates irrigation requirement of crops. In this paper, Maps that are shown generated with the help of ArcGIS software (ArcMap tool). The system has been developed using Hypertext Pre Processor (PHP) at front end and MySQL at back end.

Automated Identification and Extraction of Horizontal Curve Information from Geographic Information System Roadway Maps

Roadway horizontal alignment has long been recognized as one of the most significant contributing factors to lane departure crashes. Knowledge of the location and geometric information of horizontal curves can greatly facilitate the development of appropriate countermeasures. When curve information is unavailable, obtaining curve data in a cost-effective way is of great interest to practitioners and researchers. To date, many approaches have been developed to extract curve information from commercial satellite imagery, Global Positioning System survey data, laser-scanning data, and AutoCAD digital maps. As geographic information system (GIS) roadway maps become more accessible and more widely used, they become another cost-effective source for extraction of curve data. This paper presents a fully automated method for the extraction of horizontal curve data from GIS roadway maps. A specific curve data–extraction algorithm was developed and implemented as a customized add-in tool in ArcMap. With this tool, horizontal curves could be automatically identified from GIS roadway maps. The length, radius, and central angle of the curves were also computed automatically. The only input parameter of the proposed algorithm was calibrated to have the least curve identification errors. Finally, algorithm validation was conducted through a comparison of the algorithm-extracted curve data with the ground truth curve data for 76 curves that were obtained from Bing aerial maps. The validation results indicated that the proposed algorithm was very effective and that it identified completely 96.7% of curves and computed accurately their geometric information.