ArcGIS ModelBuilder Research Articles

Watershed Delineation from DEM by Model Builder in ArcGIS

The water level fluctuates continuously at local, continental, and global levels. Consequently, remote sensing offers extensive data for assessing the location, extent, and variability of change, including the causes and changes that occur and the reactions and repercussions of change. Considering the significance of the water level, this research focuses on describing the methodology and process to determine the water level in the Al-Zab River. This study offers a straightforward and cost-effective approach and procedure for precisely delineating flood danger regions in the Al-Zab River basin utilizing a geographic information system and the existing database. The proposed strategy for regionally dispersing flood risks combines the size of prior flood events and the geographical distribution of their causes. This approach allows for the calculation of a weighted score for each participant's causative component. The regional distribution of the flood hazard intensity level is obtained by categorizing the spatial patterns of the flood hazard index. The 30-m digital elevation model with a high resolution was utilized to evaluate the water level in this river. Furthermore, this work is considered the first step towards enhancing information management in the studied area. This study mainly aims to explain how to outline and define watersheds using ArcGIS for flood mapping. Doi: 10.28991/CEJ-2023-09-11-011 Full Text: PDF

Combining Fuzzy, Multicriteria and Mapping Techniques to Assess Soil Fertility for Agricultural Development: A Case Study of Firozabad District, Uttar Pradesh, India

Soil fertility (SF) assessment is an important strategy for identifying agriculturally productive lands, particularly in areas that are vulnerable to climate change. This research focuses on detecting SF zones in Firozabad district, Uttar Pradesh, India, for agricultural purposes, so that they can be prioritized for future management using the fuzzy technique in the Arc GIS model-builder. The model computing technique was also deployed to determine the different fertility zones, considering 17 soil parameters. The derived fuzzy technique outperformed the traditional method of dividing the sampling sites into clusters to correlate soil fertility classes with the studied soil samples. The prioritization of the soil factors and a spatial analysis of the fertility areas were carried out using the Analytic Hierarchy Process (AHP) and GIS tools, respectively. The AHP analysis outcome indicated that hydraulic properties had the highest weighted value, followed by physical and chemical properties, regarding their influence on SF. The spatial distribution map of physico-chemical properties also clearly depicts the standard classification. A fuzzy priority map was implemented based on all the classes parameters to identify the five fertility classes of the soil, namely very high (0.05%); high (16.59%); medium (60.94%); low (22.34%); and very low (0.07% of total area). This study will be of significant value to planners and policymakers in the future planning and development of activities and schemes that aim to solve similar problems across the country.

WATER QUALITY ASSESSMENT USING WATER QUALITY INDEX: A CASE STUDY OF HIGH ALTITUDE AREA, GILGIT CITY, GILGIT BALTISTAN, PAKISTAN

The research aimed to evaluate the surface water quality of Gilgit city by comparingthe actual values of physio-chemical and microbiological parameters with that of the national standardsfor drinking water quality of Pakistan. A total of Sixteen surface water samples were gathered from thestudy area to analyze their quality. Multiple parameters including fluoride, hardness, pH, nitrite,nitrate, chloride, and TDS were mapped spatially using inverse distance weighting. To compute waterquality index, ArcGIS Model Builder and ArcPy were employed. The key informants interviews andfocus group discussions were also conducted to list the water-related problems. The findings of thewater quality index ensured the good quality of collected surface water samples using physicochemicalparameters but the occurrence of microbiological contamination in both rivers declared water unfit fordrinking. It showed that the water of the city is being contaminated due to anthropogenic activities.The mixing of wastewater into drinking water sources was also reported by the respective authoritiesand community. The residents also suggested to repair filtration plants as well as cover the waterstorage structures on an urgent basis. The study will be helpful to concerned departments of the studyarea to plan their resources and provide their services to the residents efficiently.

DEVELOPING WATERSHED ANALYSIS TOOL IN GIS ENVIRNOMENT

The process of deriving the hydrological parameters in the GIS environment requires semi-automated procedures that depend on the user’s experience with the tools designed within the system, and this in turn may cause technical errors as a result of implementing the sequential steps that address an issue, so there was a need to develop tools that depend on the total automated derivation reduce the number of steps while reducing the time required for implementation. As a result, in the presented work, a hydrological toll was created as an automated workflow using Python and ArcGIS Model Builder, which will be explained. The workflow for this tool was developed in Python and applied to Iraq's northeast region. Through the statistical comparison of all derived stream configuration results using DEM, an ideal threshold value was found to maximize drainage configuration. The code was successful in automatically estimating the right ranking of specified stream orders without any further manual processes. Therefore, the developed tool saves effort and time, making it an extremely valuable function for large catchment basins.

Integration of Geostatistical and Sentinal-2AMultispectral Satellite Image Analysis for Predicting Soil Fertility Condition in Drylands

For modelling and predicting soil indicators to be fully operational and facilitate decision-making at any spatial level, there is a requirement for precise spatially referenced soil information to be available as input data. This paper focuses on showing the capacity of Sentinal-2A(S2A) multispectral imaging to predict soil properties and provide geostatistical analysis (ordinary kriging) for mapping dry land soil fertility conditions (SOCs). Conditioned Latin hypercube sampling was used to select the representative sampling sites within the study area. To achieve the objectives of this work, 48 surface soil samples were collected from the western part of Matrouh Governorate, Egypt, and pH, soil organic matter (SOM), available nitrogen (N), phosphorus (P), and potassium (K) levels were analyzed. Multilinear regression (MLR) was used to model the relationship between image reflectance and laboratory analysis (of pH, SOM, N, P, and K in the soil), followed by mapping the predicted outputs using ordinary kriging. Model fitting was achieved by removing variables according to the confidence level (95%).Around 30% of the samples were randomly selected to verify the validity of the results. The randomly selected samples helped express the variety of the soil characteristics from the investigated area. The predicted values of pH, SOM, N, P, and K performed well, with R2 values of 0.6, 0.7, 0.55, 0.6, and 0.92 achieved for pH, SOM, N, P, and K, respectively. The results from the ArcGIS model builder indicated a descending fertility order within the study area of: 70% low fertility, 22% moderate fertility, 3% very low fertility, and 5% reference terms. This work evidence that which can be predicted from S2A images and provides a reference for soil fertility monitoring in drylands. Additionally, this model can be easily applied to environmental conditions similar to those of the studied area.

Automated Generalization of Road Networks for Topographic Base Map

Automated generalization is a very challenging problem for map producers and plays an important role in map creation. Road networks are important features on topographic base maps. They are sensitive to scale change, therefore multiple representations are required to maintain visual and geographic logic at smaller scales. This paper focuses on the characteristics, hierachies, constraint parameters and automated generalization of the road features on the topographic base map at different scales. The generalization steps were implemented in ArcGIS Model Builder using mentioned out-of-the-box functionality, which removes features by feature hierarchy and network connectivity, yet preserves characteristic urban local density patterns that can be lost through simple category removals. The tool and constraint parameters are used to automatically generalize road networks from maps scale of 1: 2,000 to scales of 1:10,000 area of Ho Chi Minh City, Viet Nam. Research result shows that using reasonable parameters and tools could provide a good way to generalize and create base map layers meeting different demands and building multi-purpose map database in the future.

A Method for Object Classification in Aerial/Satellite Images with Incorporating Geospatial Information

Aerial and satellite multispectral images are important source of intelligence information. However, the object classification accuracy in those images for reasons such as camouflage, use of decoys, and others often turns out to be insufficient. The objective of the study is to develop a method for computer-aided analysis of aerial and satellite multispectral images, which allows improving classification accuracy. This objective is achieved by incorporating geospatial information (topographic, geodetic, about land cover types) into the classification process. As a mathematical basis of the method is used subjective logic of A. Jøsang. The effectiveness of the proposed method has been demonstrated by computer modeling using ArcGIS ModelBuilder tools.

Utilization of Satellite Images-Based Indices for Assessment of Al-Hammar Marsh Restoration plan

Wetland landscape characterization is an important component of determining the degree to which wetlands improve environmental conditions. The present study aims to create a model used to automated extraction of the land cover of the western part of the Al-Hammar Marsh south of Iraq, and then monitor the change in land cover overtime. A model builder in ArcGIS created based on a series of spectral-based indices included the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and the Normalized Difference Water Index (NDWI), the OLI satellite images from 2013 to 2020, ENVI 5.3 and ArcGIS 10.4 were used to achieve this goal. The results were six land cover classes: water, density vegetation, medium dense vegetation, low dense vegetation, wet barren land and, dry barren land. From the monitoring of the changing trend, it is clear that there is no improvement in the vegetation area, only a slight temporal improvement to 48% in 2017, an increase in water area for the years 2019 and 2020 to 47.33%, and 42.85% from the total area of the marsh respectively. The highest percentage was in 2019 while decreasing to the lowest rate of 14.05% for the year 2018. The developed model was accepted and can be applied for reflectance Landsat 8 data in the study area and can be applied in the southern Iraqi marshes. It also can be applied to other types of sensors, but according to determinants.

Training students in Python programming skills and WPS wrapping for geoprocessing tasks by using examples of less commonly applied thematic mapping methods

Abstract. Python programming and the application of web services technology can be considered important skills for geomatics students when looking for employment later. A course on Web Processing Services (WPS) as part of the International Geomatics master programme at Karlsruhe University of Applied Sciences takes this into consideration while using the opportunity of making students aware of less-known thematic mapping methods. Students are introduced to automating a complex geoprocessing task and finally wrapping it into a WPS. It is achieved by following the phases of first conceptualizing the single steps before then using ArcGIS ModelBuilder and Python scripts in combination to build the processing chain. The alternate band map method serves as example in the hands-on exercise. Later students are asked to automate the generation of the geometries for the map face and/or map legend supporting other thematic mapping methods like the square density map or the absolute value raster map. The paper discusses the teaching approach and experiences made so far. Conceptualizing the automatization of a given complex geoprocessing problem proved to be the most challenging part for the students.

Semi-automating the calculation of catchment scale geomorphic controls on river diversity using publically available datasets

The global availability of Digital Elevation Models (DEMs) has enabled rapid, catchment scale geomorphic analysis of river systems. In this paper, we present a semi-automated GIS approach to quickly and accurately extract catchment scale geomorphic controls: slope, gross stream power and valley bottom width, and import bed material texture data along a drainage network. To enable rapid applicability of this approach, we provide the workflow for calculating elevation, slope, contributing catchment area and gross stream power embedded within ArcGIS toolkit, ArcGIS ModelBuilder and Python script as supplementary data. We also present an approach for disaggregating the drainage network and deciding the most appropriate reach length over which to calculate slope based on catchment topography. We demonstrate the importance of investigating DEM quality prior to use, strategic selection of the flow accumulation algorithm, and validation of the drainage network output using aerial imagery.

Integrated Multi-criteria Land Suitability Evaluation and Mapping for Scaling Malt Barley Varieties in Rain-Fed Production Areas of Ethiopia

Information on variety specific land suitability analysis was not available in Ethiopia. Therefore, integrated multi-criteria land suitability analysis and mapping for contrasting malt barley varieties was carried out to identify where and how much potentially suitable land exists in the country. The main factors considered for analysis include rainfall and temperature during the growing period, length of growing period, digital elevation models, (altitude and slope data) and soil characteristics (types, pH, depth, texture and drainage). The malt barley varieties included are late maturing Bekoji-1, EH1847 and Holker; and early maturing Grace, IBON 174/03 and Sabini. For classification of the data layers according to the degree of suitability for each variety, various reports and other relevant information were reviewed and used in defining the limits of the suitability ranges of malt barley varieties. The overall suitability was computed by multiplying the selected criteria weight by the assigned sub-criteria score and summing these values in the ArcGIS Model Builder. The analysis showing the extent and patterns of suitable land area available for the selected malt barley varieties are presented in the form of tabular data and maps. Highly suitable areas for these varieties include: 125,332 ha for Bekoji-1; 124,004 ha for EH1847; 775,312 ha for Grace; 125,356 ha for Holker; 1,677,388 ha for IBON 174/03; and 307,952 ha for Sabini. The results suggest that current improved malt barley varieties can be targeted for scaling out in the identified land suitability classes in the highlands of Ethiopia. Results also suggest that future research and development works should give priority for developing early maturing, acidic and waterlogging soil tolerant malt barley varieties. The results can be useful for policy and decision making to ensure land resources are used in the most productive and sustainable ways and solve the mismatches between current land use and land suitability for malt barley varieties in the country.

Assessing trends and predicting the cover management factor in a tropical island state using Enhanced Vegetation Index

Soil erosion is a complex process involving multiple factors that contribute to the amount of soil loss. The amount of vegetation cover is one of the main factors used to estimate soil loss and is an important risk factor in informing land use management and soil conservation policies. Cover/crop management (C-factor) is a dynamic soil loss factor and analysing C-factor trends in the context of both space and time, to build a multi-dimensional data structure, increased the value of the trend analysis. The objectives of the study were to (1) utilise EVI dependent static and dynamic predictive equations to compute the C-factor, and (2) to investigate the spatio-temporal changes of the C-factor and hotspots in a tropical small island developing state (SIDS) from 2010 to 2019. ArcGIS Model Builder was utilised to automate the computation of the C-factor using Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data and compute the ordinary least square regression. Spatio-temporal analysis was performed using the novel emerging hot spot analysis in ArcGIS to identify statistically significant hot and cold spot trends of cover management to locate new, intensifying, persistent, or sporadic hot spot patterns at different time-step intervals. The regression output for the C-factor and EVI values indicated a strong r2, explaining on average 90% of the models. There was no statistically significant (P value > 0.05) increase in C-factor trend over a 10 year period (2010–2019, P-value = 0.92), a 5 year period (2015–2019; P-value = 0.59), or a 3 year period (2017–2019; P-Value = 0.31). Our results showed that intensifying hot spots (27%) were concentrated along the north–south corridor of the study area, highlighting areas with a statistically significant increase of the C-factor, thus a reduction in vegetation cover over the study period. Integrating spatio-temporal data and spatial technology provided vital cover management estimates for soil practitioners and farmers to guide conservation strategies.

Locating Site Selection for Rainwater Harvesting Structure using Remote Sensing and GIS

Rainfall is a key source to diminish the problem of water scarcity in the arid and semi-arid regions. Rainwater harvesting is considered an imperious tool for rainwater conservation. Locating the appropriate location for rainwater harvesting structure plays an important role to increase water availability and improve water resources planning. The main goal of this paper is to recognize the proper location for a rainwater harvesting structure using a suitability model generated with ModelBuilder in ArcGIS. Six thematic layers i.e. soil structure, slope, drainage density, vegetation cover, distance to the roads, and runoff depth, are considered to find the proper site for rainwater harvesting structure. The result shows that 12% represents the suitable zone of the total study area, 42% represents the medium suitable area, and 46% represents not suitable areas to implement rainwater harvesting structure. The application of this scheme should maintain any policy adoption for site selection for rainwater harvesting.

Evaluating the generalization ability of convolutional neural networks for built-up area extraction in different cities of China

The difficulty of build-up area extraction is due to complexity of remote sensing data in terms of heterogeneous appearance with large intra-class variations and lower inter-class variations. In order to extract the built-up area from Landsat 8-OLI images provided by Google earth engine (GEE), we propose a convolutional neural networks (CNN) utilizing spatial and spectral information synchronously, which is built in Google drive using Colaboratory-Keras. To train a CNN model with good generalization ability, we choose Beijing, Lanzhou, Chongqing, Suzhou and Guangzhou of China as the training sites, which are very different in term of natural environments. The ArcGIS-Model Builder is employed to automatically select 99 332 samples from the 38-m global built-up production of the European Space Agency (ESA) in 2014. The validate accuracy of the five experimental sites is higher than 90%. We compare the results with other existing building data products. The classification results of CNN can be very good for the details of the built-up areas, and greatly reduce the classification error and leakage error. We applied the well-trained CNN model to extract built-up areas of Chengdu, Xi’an, Zhengzhou, Harbin, Hefei, Wuhan, Kunming and Fuzhou, for the sake of evaluating the generalization ability of the CNN. The fine classification results of the eight sites indicate that the generalization ability of the well-trained CNN is pretty good. However, the extraction results of Xi’an, Zhengzhou and Hefei are poor. As for the training data, only Lanzhou is located in the northwest region, so the trained CNN has poor image classification ability in the northwest region of China.

A Study on Storm and Flood Insurance Management Mapping: Case Study of Incheon Metropolitan City

In this research, we have used spatial information analysis techniques and procedures to process storm, flood, and snow damage risks, and apply premium rates to produce a Storm and Flood Insurance Management Map. To calculate risk, we used ArcGIS’s main features to overlay, integrate, and classify data. Moreover, we designed an ArcGIS Model Builder program to process very large amounts of risk data quickly and accurately. Excel’s pivot feature was used to calculate areas and premium rates according to flood depth. In the case of Incheon metropolitan city, the average risk was 2.85 on the 4-level scale, which lies between "alert" and "danger" and corresponds to the 1st of the 4 premium rate grades. In particular, there were high risks and high premium rates in areas around ports, ocean beaches, and beaches connected to rivers. We expect that this insurance management map created using spatial information analysis techniques will provide useful data for scientific natural disaster response and prevention planning, rational insurance rate calculation and application, and promotion of policies, which identify and prevent areas at risk for frequent storm and flood damage.

Optimization of waste management regions using recursive Thiessen polygons

Geographic Information Systems (GIS) are commonly employed to solve problems related to landfill siting and optimization of waste collection. This research aims to develop an easily implementable tool to optimize the topology of waste management regions in various Canadian jurisdictions using ArcGIS ModelBuilder. Landfill count, populated places, and road length are minimized using standard deviation to determine optimized tessellations. In Nova Scotia, reductions in standard deviation of 9.6–30.4% are observed between original and optimized tessellations. The results suggest that an optimized tessellation of Nova Scotia's Federal subdivisions may perform better than that of their waste management regions. In Saskatchewan, reductions in standard deviation of 4.9–46.1% were observed between original and optimized tessellations. Considering all Saskatchewan Federal Subdivisions, no optimization occurred. However, partitions of Saskatchewan Federal Subdivisions yielded better results, with vertical partitions yielding a 30% decrease in standard deviation of roads, while landfills and population were reduced in the horizontal subdivision by 20.0% and 38.0%, respectively. This suggests that a different approach may be required for waste management regions in Northern Saskatchewan. Saskatchewan transportation planning committees regions had the highest standard deviation across all parameters, and optimized at the fourth iteration (landfills and populated places), and first iterations (roads), despite the fact that this tessellation was developed in direct relation to roads in the province. The proposed tool, however, showed a limited application in the City of Regina given that land use planning within City limits. This work will improve the data driven aspect of regional waste management system design.

Evaluación de la vulnerabilidad sísmica de las edificaciones de la zona urbana del distrito de Chiquián, utilizando el model builder del ArcGIS

<p>El presente trabajo de investigación descriptivo, no experimental y transversal, tiene como objetivo evaluar la vulnerabilidad sísmica de las edificaciones de la zona urbana del distrito de Chiquián, aplicando el model builder del ArcGIS.<br />La metodología propuesta permite la automatización y la creación de un modelo de vulnerabilidad sísmica utilizando la herramienta model builder del ArcGIS, donde se evaluó once parámetros para la determinación del índice de vulnerabilidad y el índice de daños en las edificaciones de la zona urbana del distrito de Chiquián.<br />De un total de 1417 viviendas se encontraron que el 14,7% de las viviendas están en vulnerabilidad baja, el 21,2% en vulnerabilidad media, el 48,8% en vulnerabilidad alta y el 15,2% en vulnerabilidad muy alta. Además, teniendo en cuenta el índice de vulnerabilidad y una aceleración horizontal del terreno de 0,32g, se calcularon que 1209 viviendas (85,32%) sufrían el colapso total y 208 viviendas (14,68%) seguirían en pie con algún daño estructural. Según el índice de daño calculado y teniendo en cuenta el precio por metro cuadrado de construcción, si ocurriera un sismo con las características descritas anteriormente los daños económicos se cuantificarían a la suma de S/.70,043,266.14</p>

A Semi-Automatic Approach in GIS for 3D Modeling and Visualization of Utility Networks: Application for Sewer & Stormwater networks

This paper presents a semi-automatic methodology proposed for 3D modeling of utility networks in GIS environment. The ModelBuilder in ArcGIS (ESRI) software is used for implementing this methodology, by developing two tools to automate the construction processes of 3D networks. The first presents a tool to create a 3D Manhole layer from points layer, and the second is a tool to create a 3D pipe layer. For both tools, a work algorithm has been built, in addition to designing user interfaces elements. These tools are stored in a Toolbox called “3D Manhole & Pipe.tbx”. The two previous tools were tested and applied to spatial data for a proposed residential area. The final 3D model of the residential area includes the sewage and stormwater networks, as well as other spatial data such as buildings, parks, roads, etc. This model is able to spot the intersection points in the network, visually or using the 3D analysis available in the software, allowing us to identify problems to be processed and resolved before starting a project, leading consequently to time and cost savings, effort and money. The proposed methodology is an easy and an effective way to build 3D network models (sewer, water..etc), and the developed tools allow the implementation of a set of necessary processes needed to build 3D networks.

Automated Geospatial Models of Varying Complexities for Pine Forest Evapotranspiration Estimation with Advanced Data Mining

The study goal was to develop automated user-friendly remote-sensing based evapotranspiration (ET) estimation tools: (i) artificial neural network (ANN) based models, (ii) ArcGIS-based automated geospatial model, and (iii) executable software to predict pine forest daily ET flux on a pixel- or plot average-scale. Study site has had long-term eddy-flux towers for ET measurements since 2006. Cloud-free Landsat images of 2006−2014 were processed using advanced data mining to obtain Principal Component bands to correlate with ET data. The regression model’s r2 was 0.58. The backpropagation neural network (BPNN) and radial basis function network (RBFN) models provided a testing/validation average absolute error of 0.18 and 0.15 Wm−2 and average accuracy of 81% and 85%, respectively. ANN models though robust, require special ANN software and skill to operate; therefore, automated geospatial model (toolbox) was developed on ArcGIS ModelBuilder as user-friendly alternative. ET flux map developed with model tool provided consistent ET patterns for landuses. The software was developed for lay-users for ET estimation.

Regolith thickness modeling using a GIS approach for landslide distribution analysis, NW Himalayas

Regolith thickness is considered as a contributing factor for the occurrence of landslides. Although, mostly it is ignored because of complex nature and as it requires more time and resources for investigation. This study aimed to appraise the role of regolith thickness on landslide distribution in the Muzaffarabad and surrounding areas, NW Himalayas. For this purpose regolith thickness samples were evenly collected from all the lithological units at representative sites within different slope and elevation classes in the field. Topographic attributes (slope, aspect, drainage, Topographic Wetness Index, elevation and curvature) were derived from the Digital Elevation Model (DEM) (12.5 m resolution). ArcGIS Model Builder was used to develop the regolith thickness model. Stepwise regression technique was used to explore the spatial variation of regolith thickness using topographic attributes and lithological units. The derived model explains about 88% regolith thickness variation. The model was validated and shows good agreement (70%) between observed and predicted values. Subsequently, the derived regolith model was used to understand the relationship between regolith thickness and landslide distribution. The analysis shows that most of the landslides were located within 1–5 m regolith thickness. However, landslide concentration is highest within 5–10 m regolith thickness, which shows that regolith thickness played a significant role for the occurrence of landslide in the studied area.