Spatial Analysis Tools Research Articles

Open Source Software for Spatial Data Science

Much progress has been made in the development of software tools for spatial analysis since the special issue of Geographical Analysis appeared in 2006, devoted to “Recent advances in software for spatial analysis in the social sciences” (Rey and Anselin 2006). The 15 some years since the publication of the issue have been marked by major changes in the spatial analytical software landscape. Arguably, three important and somewhat related phenomena can be distinguished that drove these changes: the embedding of spatial analysis into spatial data science; the growing recognition of open science/open source principles in empirical work; and the increasing adoption of a literate programming perspective.

Post-Graduate Geographical Education in China: Can Talents Meet the Need of Sustainable Development?

As widely acknowledged and targeted in Transforming Our World: The 2030 Agenda for Sustainable Development, talents education and training is an important measure to systematically solve the problem of economically, societally, resource, and environmentally sustainable development, and so as the post-graduate geographical talents education and training. Whether post-graduate geographical talents education and training can meet the need of sustainable development is an increasingly significant issue in geography science. Therefore, from the perspective of population scale, education quality, and education input, taking Chinese post-graduate geographical education as an example, the paper empirically investigated the spatial differentiation and talents production mechanism. With the support of spatial analysis tools by ArcGIS and GeoDa software, the strong inter-regional differentiation and imbalance characteristics of post-graduate geographical talents education were detected, outlining a general east-west geographical pattern in China. Moreover, the spatial production mechanism of post-graduate geographical talents has its own global and national scale, regional comprehensive and province-related characteristics, and production of the talents education and training in human geography, physical geography, and cartography. GIS also has its own focuses and demands.

Groundwater Potential Assessment Using GIS and Remote Sensing Techniques: Case Study of West Arsi Zone, Ethiopia

Groundwater is a crucial source of water supply due to its continuous availability, reasonable natural quality, and being easily diverted directly to the poor community more cheaply and quickly. The West Arsi Zone residents remain surface water dependent due to traditional exploration of groundwater, which is a tedious approach in terms of resources and time. This study uses remote sensing data and geographic information system techniques to evaluate the groundwater potential of the study area. This technique is a fast, accurate, and feasible technique. Groundwater potential and recharge zone influencing parameters were derived from Operational Land Imager 8, digital elevation models, soil data, lithological data, and rainfall data. Borehole data were used for results validation. With spatial analysis tools, the parameters affecting groundwater potential (LULC, soil, lithology, rainfall, drainage density, lineament density, slope, and elevation) were mapped and organized. The weight of the parameters according to percent of influence on groundwater potential and recharge was determined by Analytical Hierarchy Process according to their relative influence. For weights allocated to each parameter, the consistency ratio obtained was 0.033, which is less than 0.1, showing the weight allocated to each parameter is acceptable. In the weighted overlay analysis, from a percent influence point of view, slope, land use/cover, and lithology are equally important and account for 24% each, while the soil group has the lowest percent of influence, which accounts only 2% according to this study. The generated groundwater potential map has four ranks, 2, 3, 4, and 5, in which its classes are Low, Moderate, High, and Very High, respectively, based on its groundwater potential availability rank and class. The area coverage is 9825.84 ha (0.79%), 440,726.49 ha (35.46%), 761,438.61 ha (61.27%), and 30,748.68 ha (2.47%) of the study area, respectively. Accordingly, the western part of district is expected to have very high groundwater potential. High groundwater potential is concentrated in the central and western parts whereas moderate groundwater potential distribution is dominant in the eastern part of the area. The validation result of 87.61% confirms the very good agreement among the groundwater record data and groundwater potential classes delineated.

Spatial performance analysis in basketball with CART, random forest and extremely randomized trees

This paper proposes tools for spatial performance analysis in basketball. In detail, we aim at representing maps of the court visualizing areas with different levels of scoring probability of the analysed player or team. To do that, we propose the adoption of algorithmic modeling techniques. Firstly, following previous studies, we examine CART, highlighting strengths and weaknesses. With respect to what done in the past, here we propose the use of polar coordinates, which are more consistent with the basketball court geometry. In order to overcome CART’s drawbacks while maintaining its points of force, we propose to resort to CART-based ensemble learning algorithms, namely to Random Forest and Extremely Randomized Trees, which are shown to be able to give excellent results in terms of interpretation and robustness. Finally, an index is defined in order to measure the map’s graphical goodness, which can be used—jointly with measures of the out-of-sample error—to tune the algorithm’s parameters. The functioning of the proposed approaches is shown by the analysis of real data of the NBA regular season 2020/2021.

Risk-prone territories for spreading tuberculosis, temporal trends and their determinants in a high burden city from São Paulo State, Brazil

ObjectivesTo identify risk-prone areas for the spread of tuberculosis, analyze spatial variation and temporal trends of the disease in these areas and identify their determinants in a high burden city.MethodsAn ecological study was carried out in Ribeirão Preto, São Paulo, Brazil. The population was composed of pulmonary tuberculosis cases reported in the Tuberculosis Patient Control System between 2006 and 2017. Seasonal Trend Decomposition using the Loess decomposition method was used. Spatial and spatiotemporal scanning statistics were applied to identify risk areas. Spatial Variation in Temporal Trends (SVTT) was used to detect risk-prone territories with changes in the temporal trend. Finally, Pearson's Chi-square test was performed to identify factors associated with the epidemiological situation in the municipality.ResultsBetween 2006 and 2017, 1760 cases of pulmonary tuberculosis were reported in the municipality. With spatial scanning, four groups of clusters were identified with relative risks (RR) from 0.19 to 0.52, 1.73, 2.07, and 2.68 to 2.72. With the space–time scan, four clusters were also identified with RR of 0.13 (2008–2013), 1.94 (2010–2015), 2.34 (2006 to 2011), and 2.84 (2014–2017). With the SVTT, a cluster was identified with RR 0.11, an internal time trend of growth (+ 0.09%/year), and an external time trend of decrease (− 0.06%/year). Finally, three risk factors and three protective factors that are associated with the epidemiological situation in the municipality were identified, being: race/brown color (OR: 1.26), without education (OR: 1.71), retired (OR: 1.35), 15 years or more of study (OR: 0.73), not having HIV (OR: 0.55) and not having diabetes (OR: 0.35).ConclusionThe importance of using spatial analysis tools in identifying areas that should be prioritized for TB control is highlighted, and greater attention is necessary for individuals who fit the profile indicated as “at risk” for the disease.

Mapping the impact of a large municipal waste disposal area on surface water: 1993–2017, case of Laogang, Shanghai

In China, the impact of waste disposal facilities is always a cause of concern for the government and the public. Laogang Municipal Waste Disposal Area (LMDA), Shanghai, one of the largest municipal waste disposal areas in the world was selected as case in this study, and it was attempted to analyze the changes in the surface water quality, and map the impacted area by LMDA on surrounding streams from its operation period of 1993–2017. The results showed that, during the whole period, only biochemical oxygen demand (BOD5) showed a continuous improvement with a percentage of 85.92%, however, chemical oxygen demand (CODcr), ammonia (NH4+-N) and total phosphorus (TP) significantly improved but BOD5 slightly deteriorated began from 2013. Using spatial analysis tools and Kendall’s concordance test, CODcr and phenol at LMDA showed a significant impact on surrounding surface water; especially, the impacted area for CODcr decreased from 106.30 km2 to 22.86 km2 from 1993 to 2017, which dropped from 4.3 to 0.9 times the area of LMDA. Surprisingly, NH4+-N and TP at LMDA were affected by the surrounding streams, instead of having an impact on them. Interestingly, heavy metals and non-metals such as Hg, As, Zn, and Se in the surrounding streams were unlikely affected by LMDA. The driving forces for surface water quality improvement included the eco-remediation of closed unsanitary landfills, upgrade in waste shipping and terminals, operation of sanitary landfills and incineration plants for landfill diversion.Capsule: Impacted area of municipal waste disposal area is not so large.

التحلیل المکاني للصناعات الغذائیة في قضاء سوران

Abstract: The industry is a branch of the economic sector and is an essential fundamental in developing economic and social lifestyle. This sector has a role in solving unemployment and in increasing an individual income. The ailment industry is considered to be a branch of the manufacturing industry and is continually growing and expanding due to higher consumer demands and it’s helpful to the population’s needs. The research issue is an unequal geographical distribution of ailment industry in Soran district it is due to not having an industrial plan. This research aims to analyze the structure of the ailment industry in the researched area and conducting this research about this topic helps us to understand the directions of the food industry nowadays and the effects of the spreading of the Covid-19. To write this paper we have relied on several geographical industrial methods to reach the purpose of this paper “the inductive approach” the qualitative method, and SPSS program have been used, and this can be done by depending on geographic information systems and through what the technique includes of spatial analysis tools. An Empirical search was conducted for the location of manufacturers by providing questionnaires to gain data and information from the governmental institutions but nothing was attained. This research has been divided into five major parts despite the introduction, conclusions, recommendations.

Application of Spatial Risk Assessment Integrated With a Mobile App in Fighting Against the Introduction of African Swine Fever in Pig Farms in Thailand: Development Study.

BackgroundAfrican swine fever (ASF), a highly contagious disease affecting both domestic and wild pigs, has been having a serious impact on the swine industry worldwide. This important transboundary animal disease can be spread by animals and ticks via direct transmission and by contaminated feed and fomites via indirect transmission because of the high environmental resistance of the ASF virus. Thus, the prevention of the introduction of ASF to areas free of ASF is essential. After an outbreak was reported in China, intensive import policies and biosecurity measures were implemented to prevent the introduction of ASF to pig farms in Thailand.ObjectiveEnhancing prevention and control, this study aims to identify the potential areas for ASF introduction and transmission in Thailand, develop a tool for farm assessment of ASF risk introduction focusing on smallholders, and develop a spatial analysis tool that is easily used by local officers for disease prevention and control planning.MethodsWe applied a multi-criteria decision analysis approach with spatial and farm assessment and integrated the outputs with the necessary spatial layers to develop a spatial analysis on a web-based platform.ResultsThe map that referred to potential areas for ASF introduction and transmission was derived from 6 spatial risk factors; namely, the distance to the port, which had the highest relative importance, followed by the distance to the border, the number of pig farms using swill feeding, the density of small pig farms (<50 heads), the number of pigs moving in the area, and the distance to the slaughterhouse. The possible transmission areas were divided into 5 levels (very low, low, medium, high, and very high) at the subdistrict level, with 27 subdistricts in 10 provinces having very high suitability and 560 subdistricts in 34 provinces having high suitability. At the farm level, 17 biosecurity practices considered as useful and practical for smallholders were selected and developed on a mobile app platform. The outputs from the previous steps integrated with necessary geographic information system layers were added to a spatial analysis web-based platform.ConclusionsThe tools developed in this study have been complemented with other strategies to fight against the introduction of ASF to pig farms in the country. The areas showing high and very high risk for disease introduction and transmission were applied for spatial information planning, for example, intensive surveillance, strict animal movement, and public awareness. In addition, farms with low biosecurity were improved in these areas, and the risk assessment developed on a mobile app in this study helped enhance this matter. The spatial analysis on a web-based platform helped facilitate disease prevention planning for the authorities.

High-performance solutions of geographically weighted regression in R

ABSTRACT As an established spatial analytical tool, Geographically Weighted Regression (GWR) has been applied across a variety of disciplines. However, its usage can be challenging for large datasets, which are increasingly prevalent in today’s digital world. In this study, we propose two high-performance R solutions for GWR via Multi-core Parallel (MP) and Compute Unified Device Architecture (CUDA) techniques, respectively GWR-MP and GWR-CUDA. We compared GWR-MP and GWR-CUDA with three existing solutions available in Geographically Weighted Models (GWmodel), Multi-scale GWR (MGWR) and Fast GWR (FastGWR). Results showed that all five solutions perform differently across varying sample sizes, with no single solution a clear winner in terms of computational efficiency. Specifically, solutions given in GWmodel and MGWR provided acceptable computational costs for GWR studies with a relatively small sample size. For a large sample size, GWR-MP and FastGWR provided coherent solutions on a Personal Computer (PC) with a common multi-core configuration, GWR-MP provided more efficient computing capacity for each core or thread than FastGWR. For cases when the sample size was very large, and for these cases only, GWR-CUDA provided the most efficient solution, but should note its I/O cost with small samples. In summary, GWR-MP and GWR-CUDA provided complementary high-performance R solutions to existing ones, where for certain data-rich GWR studies, they should be preferred.

Spatial Matching Analysis and Development Strategies of County Night-Time Economy: A Case of Anning County, Yunnan Province

The Chinese government regards the night-time economy as one of the essential means to expand domestic demand and enhance sustainable economic development. Scientifically choosing the night-time economic development path of the suburban counties of the Chinese metropolis (SCCM) and proposing a reasonable spatial matching planning strategy is an urgent problem for Chinese local governments. This study takes Anning county, a suburban Kunming metropolitan area, as the research area. Using Python to capture multi-spatial data, such as POI and Baidu heatmap, we use ArcGIS spatial analysis and statistical tools to show the spatial distribution characteristics of the night-time economic formats in Anning County. At the same time, the spatial coupling coordination model is used to calculate the coupling coordination degree of the night-time economic formats distribution and comprehensive traffic distribution (D1), night-time economic formats distribution and night-crowd vitality (D2), and the spatial coupling coordination of the three (D3). It is divided into five spatial matching levels and analyzes the shortage of night-time economic development in each subdistrict. The research results show that the spatial development of the night-time economy in Anning county is unbalanced at the current stage. The northern part of the county has a good development trend, and the Lianran subdistrict has the highest coupling coordination degree (0.995). In contrast, the southern part of the county has the lowest coupling coordination degree due to a lack of economic formats and traffic restrictions (0.115). According to the subdistricts’ differences, the sustainable development strategy of the county’s night-time economy should be formulated from the perspective of the long-term development of metropolitan areas. We hope that this research can provide valuable inspiration and a development reference for relevant countries and regions to stimulate the sustainable power of the night-time economy.

Photocontrollable Probes for Mitochondrial Protein Profiling.

The mitochondrion is the core site of cell signaling, energy metabolism and biosynthesis. Here, taking advantage of activity-based probes, we synthesized two photocontrollable probes (YGH-1 and YGH-2), composed of a mitochondrial localization moiety "triphenylphosphonium", a photo-triggered group to achieve spatially and temporally controlled protein capture, and an alkyne group to enrich the labeled protein. Proteomic validation was further carried out to facilitate identification of the mitochondrial proteome in HeLa cells. The results show that half of the identified protein hits (∼300) labeled by YGH-1 and YGH-2 belong to mitochondria, and are mostly localized in the mitochondrial matrix and inner mitochondrial membrane. Our results provide a new tool for spatial and temporal analysis of subcellular proteomes.

Mapping the landscapes of the Stalinist mass repressions.

In this article, we focus on the ways in which a variety of different carceral techniques used to punish and exploit people's labour during the Stalin period (1927-1953) in the Union of Soviet Socialist Republics (USSR) created a distinctive landscape of repression. Using the tools of historical geographic information science (GIS) to map the material landscape, we foreground space in the discussion of the USSR's exceptional history of repression. The 'carceral conditions' frame allows us to deconstruct boundaries erected over more than half a century of writing the history of the USSR that have maintained artificial distinctions between the victims and impacts of different punishment modalities. In the article, we follow the example of the Stanford Holocaust Geographies Project in combining quantitative and textual data with the spatial analytical tools of geovisualisation to reveal the patterns of events as the Stalinist repressive apparatus extended its reach across Soviet space. In fixing the geolocation of carceral institutions and layering the resultant pattern with different types of qualitative and quantitative information in the same visual space, we hope to counter some of the myths and generalizations that exist in the literature about the geography of Soviet gulag. We use the case study of Perm' region in the Urals to highlight the spatiality of the production of the material landscape of repression in one region. Our aim is to position the USSR in the now substantial geographical literature discussing the twentieth century history of crimes against humanity and genocide and to suggest to historians that the geovisualisation of data can add a new dimension their studies of the Stalin period.

Investigating Health Context Using a Spatial Data Analytical Tool: Development of a Geospatial Big Data Ecosystem

BackgroundEnabling the use of spatial context is vital to understanding today’s digital health problems. Any given location is associated with many different contexts. The strategic transformation of population health, epidemiology, and eHealth studies requires vast amounts of integrated digital data. Needed is a novel analytical framework designed to leverage location to create new contextual knowledge. The Geospatial Analytical Research Knowledgebase (GeoARK), a web-based research resource has robust, locationally integrated, social, environmental, and infrastructural information to address today’s complex questions, investigate context, and spatially enable health investigations. GeoARK is different from other Geographic Information System (GIS) resources in that it has taken the layered world of the GIS and flattened it into a big data table that ties all the data and information together using location and developing its context.ObjectiveIt is paramount to build a robust spatial data analytics framework that integrates social, environmental, and infrastructural knowledge to empower health researchers’ use of geospatial context to timely answer population health issues. The goal is twofold in that it embodies an innovative technological approach and serves to ease the educational burden for health researchers to think spatially about their problems.MethodsA unique analytical tool using location as the key was developed. It allows integration across source, geography, and time to create a geospatial big table with over 162 million individual locations (X-Y points that serve as rows) and 5549 attributes (represented as columns). The concept of context (adjacency, proximity, distance, etc) is quantified through geoanalytics and captured as new distance, density, or neighbor attributes within the system. Development of geospatial analytics permits contextual extraction and investigator-initiated eHealth and mobile health (mHealth) analysis across multiple attributes.ResultsWe built a unique geospatial big data ecosystem called GeoARK. Analytics on this big table occur across resolution groups, sources, and geographies for extraction and analysis of information to gain new insights. Case studies, including telehealth assessment in North Carolina, national income inequality and health outcome disparity, and a Missouri COVID-19 risk assessment, demonstrate the capability to support robust and efficient geospatial understanding of a wide spectrum of population health questions.ConclusionsThis research identified, compiled, transformed, standardized, and integrated multifaceted data required to better understand the context of health events within a large location-enabled database. The GeoARK system empowers health professionals to engage more complex research where the synergisms of health and geospatial information will be robustly studied beyond what could be accomplished today. No longer is the need to know how to perform geospatial processing an impediment to the health researcher, but rather the development of how to think spatially becomes the greater challenge.

Issues in Replication and Stability of Least-cost Path Calculations

An important and frequently used tool in archaeological spatial analysis is least-cost path (LCP) analysis to compute routes connecting a set of targets. The outcome depends on the cost model chosen and the topographic data used. A slope-dependent cost model requires a digital elevation model (DEM) that should reflect the landscape in the past. It is often impossible to reconstruct the past terrain, and modern high-resolution elevation data results in problematic storage requirements and computation times. This article presents a case study that explores issues in replication and stability of LCP calculations for pairs of targets that are close to known old trade routes. A large number of cost models is tested based on two topographic data sets, including DEMs of two different resolutions (25 m and 50 m). The cost models use six different slope-dependent cost functions suggested by various authors for pedestrian movement. Moreover, a slope-dependent cost function is applied that results in LCPs including hairpin curves if the slope exceeds a predefined critical value. It is shown that the best-performing LCP sets for the two topographic data sets are closely related but not identical. Moreover, reasons for the failure of LCP reconstructions for some old route sections are discussed.

Analyzing Spatial Transcriptomics Data Using Giotto.

Spatial transcriptomic technologies have been developed rapidly in recent years. The addition of spatial context to expression data holds the potential to revolutionize many fields in biology. However, the lack of computational tools remains a bottleneck that is preventing the broader utilization of these technologies. Recently, we have developed Giotto as a comprehensive, generally applicable, and user-friendly toolbox for spatial transcriptomic data analysis and visualization. Giotto implements a rich set of algorithms to enable robust spatial data analysis. To help users get familiar with the Giotto environment and apply it effectively in analyzing new datasets, we will describe the detailed protocols for applying Giotto without any advanced programming skills. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Getting Giotto set up for use Basic Protocol 2: Pre-processing Basic Protocol 3: Clustering and cell-type identification Basic Protocol 4: Cell-type enrichment and deconvolution analyses Basic Protocol 5: Spatial structure analysis tools Basic Protocol 6: Spatial domain detection by using a hidden Markov random field model Support Protocol 1: Spatial proximity-associated cell-cell interactions Support Protocol 2: Assembly of a registered 3D Giotto object from 2D slices.

An Analysis of the Optimal Facility Location of Tourism Industry in Plain Region by Utilizing GIS

In this study, we utilize regional public facilities as the factors of evaluation criteria, apply buffering and weighted overlay analysis in GIS as the multi-criteria spatial information analysis tools, and take the Lanyang Plain, Yilan, Taiwan as the test area for the optimal facility location analysis of tourism industry. We take the “optimal service/influence distance” of each regional public facility from literature review as the radius of neighborhood for buffering and build the buffer objects in GIS. The process of weighted overlay analysis is conducted by using grid method. After a series of grid size testing, we find that while the grid size decreases to 320 m × 320 m, the distribution of grids with weighted overlay values is close to the distribution of all the buffer objects mentioned above. The distribution of grids with higher weighted overlap values obviously presents the optimal facility location of tourism industry that is apparently consistent with the distribution of the famous tour sites or facilities of tourism industry and the four to five star hotels. Therefore, we conclude that this optimal facility location model of tourism industry should be quite suitable for land use or site selection planning of tourism and relative industries.

Computerized Road network modeling using open street map sources in GIS: Basrah province as a case study

Information sources have developed considerably in recent years; many electronic platforms are able to provide valuable information regards engineering topics. One of the most important data sources is the open street map (OSM) platform, providing editable geographic information for most of the world, with different levels of accuracy and at different points in time. Road network mapping requires a high level of effort and accuracy, due to the complexity of the modelling and the amount of information that needs to be included in the feature class. OSM can support road network modelling by providing a different kind of data. In this paper, a systematic procedure was investigated for the production of an automated road network for Basrah city, as a case study for the use of OSM in Geographic Information System (GIS) 10.8 software. Specific spatial analysis tools such as road density and network analysis were also implemented. This study validated a computerised procedure to extract OSM data via two methods of validation and demonstrated the immediate applicability of this data for density and network analysis. The research results show a significant reduction in time and effort required to produce an accurate Basrah city road network using OSM data sources. Density analysis and network analysis show the importance and validity of the produced road network.

Does High-Speed Rail Operation Reduce Ecological Environment Pressure?—Empirical Evidence from China

With improved productivity, the impact of social and economic development on the ecological environment is becoming more and more significant, and the transformation of transportation modes often accompanies the transformation of the economic development mode. It is essential to study the impact of high-speed rail (HSR) on the environment. This article constructs a compiled index Ecological Environment Pressure (EEP) evaluation system. The spatial analysis tool is then used to explore the temporal and spatial evolution characteristics of EEP in China. The Difference-in-Difference (DID) method and the Propensity Score Matching (PSM) method are used to quantitatively calculate the impact of HSR operations on EEP based on Panel Data. The results show that the EEP in China decreased significantly from 2003 to 2018, and therefore the quality of China’s ecological environment is improving. The regression coefficient of HSR on EEP is significantly negative, indicating that HSR operations will reduce EEP. Additionally, the HSR operation in the eastern and central regions negatively impacts the EEP. At the same time, the HSR operations will also reduce the EEP of resource-based cities, especially for resource-based cities in the central region. The degree of industrial transformation (DIT) and degree of employment transformation (DET), combined with the implementation of HSR operations, can significantly reduce the EEP. It is suggested to formulate more focused actions and policies to reduce EEP and effectively promote sustainable social development.

An Interpretation Approach of Ascending–Descending SAR Data for Landslide Identification

The technique of interferometric synthetic aperture radar (InSAR) is increasingly employed for landslide detection over large areas, even though the limitations of initial InSAR analysis results have been well acknowledged. Steep terrain in mountainous areas may cause geometric distortions of SAR images, which could affect the accuracy of InSAR analysis results. In addition, due to the existence of massive ground deformation points in the initial InSAR analysis results, accurate landslide recognition from the initial results is challenging. To efficiently identify potential landslide areas from the ascending–descending SAR datasets, this paper presents a novel interpretation approach to analyze the initial time-series InSAR analysis results. Within the context of the proposed approach, SAR visibility analysis, conversion analysis of deformation rates obtained from the time-series InSAR analysis, and spatial analysis and statistics tools for cluster extraction are incorporated. The effectiveness of the proposed approach is illustrated through a case study of landslide identification in Danba, a county in Sichuan, China. The potential landslide regions in the study area are identified based on the interpretation of small baseline subset InSAR (SBAS-InSAR) results, obtained with ascending–descending Sentinel-1A datasets. Finally, on the basis of the field survey results, a total of 21 landslides are detected in the potential landslide regions identified, through which the results obtained from the proposed interpretation approach are tested.

Can statistical remote sensing aid in predicting the potential productivity of inland lakes? Case study: Lake Qaroun, Egypt

This study aimed at creating a sustainable and inexpensive Landsat-based electrical conductivity model that can easily notify fisheries managers of changes in electrical conductivity and hence the potential fish yield of Lake Qaroun in Egypt. The study integrated geospatial technology, field measurements, mathematical computations, and fish yield empirical model into the adopted methodology. Seventeen sampling sites covering the entire study area were selected to measure the electrical conductivity (EC; mS/cm) and water depths (D; m) of Lake Qaroun, Egypt, during November 2018. Spatial analysis tools within ArcGIS were used to extract EC data from non-surveyed sites. A high-resolution Sentinel-2B MSI and a cloud-free medium-resolution Landsat-8 OLI scenes for Lake Qaroun were used for morphometric and regression analyses, respectively. For regression, 75% of the dataset was used to build up the regression model, while the remaining 25% was used for validation. The study selected Landsat band ratios that correlated with the highest certainty (R > 0.80) with the examined EC. Stepwise regression model was then developed to predict EC from Landsat-8 data. In choosing the best regression model, the study selected the significant model (P < 0.05) with the highest coefficient of determination (R2) and the least error metrics. Finally, the developed model was applied in calculating the potential yield of Lake Qaroun. The innovative EC model derived in the current study using Landsat-8 OLI for Lake Qaroun showed a very good performance in estimating 95% of EC values significantly with high acceptable accuracy. In closure, the model can be used very efficiently as a decision support tool in assisting managers not only in monitoring the lake’s electrical conductivity regularly, during the month of November, but also in making preliminary estimates of the lake’s potential yield.