Geographic Information System Analysis Research Articles

Integration of LCSA and GIS-based MCDM for sustainable landfill site selection: a case study.

The paper aims at developing a framework for decision-support to select a sustainable landfill site in Bardaskan City (Iran) by combining life cycle sustainability assessment (LCSA) concepts and geographic information system (GIS)-based multi-criteria decision-making (MCDM). Overall, 13 criteria were chosen (three constraints and 10 factors) and classified into three main aspects of sustainability (i.e., environmental, social, and economic) to achieve the research goals. Boolean and fuzzy logic were employed to standardize the classified constraints and factors, respectively. Analytic hierarchy process (AHP) was used to calculate the factors' weights and then suitability maps were produced using the GIS analysis. The layers were combined using simple additive weighting (SAW). Next, the most sustainable sites were obtained. The results indicated that distance from city backline, groundwater depth, and distance from rural areas were the most significant factors with the weight of 0.338, 0.141, and 0.129, respectively. The final map of suitable sites was created by classifying the SAW layer according to 75, 80, and 85% of suitability to show the high, medium, and low priority areas for landfill site selection, respectively. Therefore, integration of LCSA and GIS-based MCDM to select the sustainable landfill site for municipal solid waste (MSW) is highly important, which can be effectively employed in regional and urban planning to select the location of appropriate and sustainable landfills.

Exploring the Use of Geographic Information Systems to Identify Spatial Patterns of Remote UAS Pilots and Possible National Airspace Risk

The proliferation of Unmanned Aircraft Systems (UAS) in the United States National Airspace System (NAS) has resulted in an increasing number of close encounters between manned aircraft and UAS, which correlates with the increasing number of remote pilots in the Federal Aviation Administration (FAA) airmen database. This research explores spatial patterns of registered airmen using Geographic Information Systems (GIS) analyses that provide notable spatial distribution patterns of pilots and how they relate to UAS sightings and airspace categories. The application of GIS to these aviation data may assist safety practitioners with identifying geographic patterns, areas of higher risk, and ultimately improve safety management. The authors analyzed publicly available airmen data to examine spatial distribution patterns, data correlations, and inferences. Airmen addresses were first geocoded into ArcPro 10.4 GIS software as a vector data layer containing attribute values of the database. The spatial analysis tool set was then utilized to establish clustering, density patterns, and spatial relationships between various categories of registered airmen. These density analyses revealed implicitly that commercial registered pilots tend to have the highest clustering near major commercial use controlled airspace, yet registered remote (UAS) pilots are also clustered in these and other densely populated areas. UAS sighting data were also geocoded using zip code values of the reported city to potentially correlate UAS sighting with registered remote pilots, yet the lack of spatial precision in the database made establishing any type of spatial relationship ineffective. The implicit spatial relationships between commercial and remote registered pilots revealed further research is needed to integrate UAS safely and effectively into the national airspace. The poor quality of UAS sighting data also demonstrates the need to better utilize GIS to monitor and track UAS flights within the context of an Unmanned Traffic Management System.

PENGARUH KOSENTRASI INDUSTRI TERHADAP PERTUMBUHAN INDUSTRI KECIL DI ACEH, INDONESIA

This study aims to analyze the effect of industrial concentration on the growth of small industries in Aceh. Spatial industrial growth is often associated with industrial agglomeration, where industrial growth is centralized due to efficiency, agglomeration is an activity that can increase the efficiency of the industry within it. This study uses secondary data sources from the Aceh Industry, Trade and Cooperative Office. By using the Geographic Information System (GIS) and correlation analysis, it appears that the growth of small industries in Aceh is concentrated and the pattern of concentration influences the growth of small industries in Aceh. The pattern of concentration causes each district to have a certain type of industry. Based on panel data analysis using random effect, it is found that economic growth is influenced by the concentration index and the agglomeration index of SMEs in Aceh. The higher the concentration index and agglomeration index, the higher the economic growth. This is because concentration and agglomeration together increase the growth in SME employment absorption which has an impact on economic growth. Keywords : Index of concentration, agglomeration, economic growth

Leveraging Geographic Information in Organization Studies: Beginning the Conversation

We propose geographic information systems (GIS) as a framework in organization studies, particularly for scholars who consider the nuances of space and geography in various organizational contexts. GIS are computer-based systems that manage, store, analyze, and distribute spatial data. While more and more scholars recognize the theoretical significance of organizational space, suggestions for conducting empirical research around organizational space using alternative frameworks – such as GIS – are seldom made. We present an introduction to GIS and various spatial analyses through a case study of organizations in the reproductive healthcare field and offer future directions related to the geographic implications of understanding organizations and organizing through GIS.

Sustainability of Urban Regions and Migration in Pakistan: A GIS Analysis

The concept of sustainable migration has emerged recently after realizing the potential of migration in framing and altering the social, economic and environmental structures at destination, especially in the context of sustainable development goals (SDGs). An empirical investigation on the link between regional sustainability and sustainable migration is rare in literature, especially in the context of Pakistan’s urban areas. Present study aims at analysing the relationship between the two by geographic information system (GIS) spatially. This study shows that out of thirteen urban regions, hosting above-average migration along with positive in-migration growth, the migration towards nine regions is unsustainable. Two mega cities namely, Karachi and Lahore are included. This highlights the sustainable growth of regions, specifically and the nation generally. Therefore, in the national policy framework, migration policies should appear as an integral part.

Designing a GIS‐based supply chain for producing carinata‐based sustainable aviation fuel in Georgia, USA

AbstractCarinata is a potential crop for sustainable aviation fuel (SAF) production in the southern USA. However, as a novel crop, the cost‐effectiveness and environmental feasibility of carinata feedstock are unknown, and there are questions about the optimal supply chain configuration for carinata‐based SAF production. This study aims to design a supply chain model for carinata‐based SAF production by optimizing the location of farms and facilities (e.g. storage units, crushing mills, biorefineries) for a minimum transportation cost under a set of supply and demand conditions. An integrated mixed‐integer linear programming (MILP) model was combined with geographical information system (GIS) analysis to design a spatially explicit supply chain configuration. The GIS‐based network analysis considered all of the counties in Georgia to set the candidate locations of carinata farms and facilities, and determined minimum cost and emission routes between those counties and the airport using existing transportation networks and modes (e.g. road, rail and pipeline). The MILP model determined the final selection of the farms and the number of facilities and their locations over those minimum‐cost routes. With this supply chain configuration, the minimum price of SAF was $0.92 L−1, which is $0.44 higher than conventional aviation fuel (CAF). The associated carbon intensity of SAF was estimated at 940.7 g CO2e L−1, a reduction of 66% relative to the carbon intensity of equivalent CAF. The study found that a carbon tax (or subsidy) of $230.48 t CO2e−1 would be needed to overcome the cost differential with CAF and promote carinata‐based SAF in Georgia.

Spatial evaluation of healthcare accessibility across archipelagic communities of Maluku Province, Indonesia.

The Maluku Province is an underdeveloped region in Indonesia with over 1,340 scattered islands. Due to the limited health facilities and transportation infrastructure, access to healthcare is very challenging. Here, we combined data from various sources to locate the population clusters, health facilities, roads, and ports/docks, and then utilize geographic information systems (GIS) to estimate distances from residents to health facilities. Health workforce distribution data was then integrated to elucidate overall healthcare equity among districts in the province. The average distances to puskesmas (primary health clinics) were 8.89 km (by land) and 18.43 km (by land and water) respectively, and the average distances to hospitals were 56.19 km (by land) and 73.09 km (by land and water), with large disparities within and among districts. Analysis of health workforce data shows that 65% of 207 puskesmas lack physicians, while 49% lack midwives. Ambon, Tual, and Southeast Maluku have the highest health equity, while East Ceram, Buru, and South Buru have the lowest. In general, this study demonstrates the utility of GIS and spatial analyses, which can help identify problem areas in healthcare accessibility and equity in archipelago settings, and provide recommendations to stakeholders such as public health officials and district administrators.

Analysis of the Effect of Soil Erosion in Abandoned Agricultural Areas: The Case of NE Area of Basilicata Region (Southern Italy)

Land abandonment is among the most complex la nd use change processes driven by a multiplicity of anthropogenic and natural factors, such as agricultural over-exploitation, implementation of agricultural policies, socio-economic and climatic aspects. Therefore, it is necessary to deepen the effects of land abandonment based on methodologies that are as multidisciplinary as possible. Environmental and social problems related to abandonment include soil erosion and environmental degradation. Approaches combining GIS (Geographic Information System), remote sensing, and image analysis techniques allow for assessments and predictions based on integrating theoretical models with advanced geospatial and geostatistical models. One of the most widely used models for soil erosion estimation is the Revised Universal Soil Loss Equation (RUSLE). The present work developed a model using remote sensing and GIS tools to investigate some factors of the RUSLE equation to evaluate the adverse effects of soil erosion in areas covered by arable crops and subsequently abandoned. To identify potentially degraded areas, two factors of the RUSLE were related: the C Factor describing the vegetation cover of the soil and the A Factor representing the amount of potential soil erosion. Through statistical correlation analysis with the RUSLE factors, based on the deviations from the average erosion values and mapping of the areas of vegetation degradation relating to arable land, the areas identified and mapped are susceptible to soil degradation.

Enrichment Characteristics, Source Apportionment, and Risk Assessment of Heavy Metals in the Industrial and Mining Area of Northern Guangdong Province

In order to understand the heavy metal pollution of the industrial and mining area in northern Guangdong Province, topsoil samples (0-20 cm) from 209 sites in study area were collected, and the concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were analyzed. The source and distribution characteristics of Cd, Cr, Hg, Ni, Pb, As, Cu, and Zn in soil samples were analyzed using the geographic information system (GIS) and principal component analysis (PCA), and the geo-accumulation index and potential ecological hazard index were used to evaluate their pollution status and ecological risk. The results showed that:① Except Ni, all seven heavy metal elements exceeded the national soil background value but were below the pollution risk screening value. The soil environment as a whole was relatively clean. ② The spatial distribution of heavy metals in soil differed. The contents of As and Pb showed northwest-southeast zonal distribution. The contents of Cd, Cu, Hg, and Zn generally decreased from the middle to the surrounding, which was consistent with the industrial and mining enterprise locations. However, the spatial distribution of Cr and Ni had no direct relationship with the location of pollution sources such as industrial enterprises. ③ The eight heavy metals could be identified as three principal components (PCs). PC1 (Cd, Cu, Pb, and Zn) was mainly affected by human activities such as lead-zinc deposit dressing, traffic emissions, and agricultural production. However, PC2 (Cr and Ni) was a natural source, mainly affected by soil parent material. In addition, PC3 (As and Hg) was mainly affected by industrial activities such as non-ferrous metal smelting and thermal power generation. ④ According to the geo-accumulation index method, the risk degree of the eight heavy metals was:Cd>As>Zn>Hg>Pb>Cu>Cr=Ni. The contents of Cr and Ni in soil were at a no-risk level; the contents of As, Cu, Hg, Pb, and Zn were at a low-risk level; and the content of Cd was at a high-risk level. Most potential ecological hazards for single elements in the study area were at a mild risk level. In contrast, a small proportion of the surface soils in areas of intense industrial activity were subject to substantial levels of heavy metal stress and require further attention.

Analysis of the Levelized Cost of Renewable Hydrogen in Austria

Austria is committed to the net-zero climate goal along with the European Union. This requires all sectors to be decarbonized. Hereby, hydrogen plays a vital role as stated in the national hydrogen strategy. A report commissioned by the Austrian government predicts a minimum hydrogen demand of 16 TWh per year in Austria in 2040. Besides hydrogen imports, domestic production can ensure supply. Hence, this study analyses the levelized cost of hydrogen for an off-grid production plant including a proton exchange membrane electrolyzer, wind power and solar photovoltaics in Austria. In the first step, the capacity factors of the renewable electricity sources are determined by conducting a geographic information system analysis. Secondly, the levelized cost of electricity for wind power and solarphotovoltaics plants in Austria is calculated. Thirdly, the most cost-efficient portfolio of wind power and solar photovoltaics plants is determined using electricity generation profiles with a 10-min granularity. The modelled system variants differ among location, capacity factors of the renewable electricity sources and the full load hours of the electrolyzer. Finally, selected variables are tested for their sensitivities. With the applied model, the hydrogen production cost for decentralized production plants can be calculated for any specific location. The levelized cost of hydrogen estimates range from 3.08 EUR/kg to 13.12 EUR/kg of hydrogen, whereas it was found that the costs are most sensitive to the capacity factors of the renewable electricity sources and the full load hours of the electrolyzer. The novelty of the paper stems from the model applied that calculates the levelized cost of renewable hydrogen in an off-grid hydrogen production system. The model finds a cost-efficient portfolio of directly coupled wind power and solar photovoltaics systems for 80 different variants in an Austria-specific context.

Racial and Ethnic Disparities in Access to Pediatric Trauma Centers in the United States: A Geographic Information Systems Analysis.

Injury is the leading cause of death and disability for children, making access to pediatric trauma centers crucial to pediatric trauma care. Our objective was to describe the pediatric population with timely access to a pediatric trauma center by demographics and geography in the United States. Level 1, 2, and 3 pediatric trauma center locations were provided by the American Trauma Society. Geographic information systems road network and rotor wing analysis determined US Census Block Groups with the ground and/or air access to a pediatric trauma center within a 60-minute transport time. We then described, at the national and state levels, the 2020 pediatric population (< 15 years old) with and without pediatric trauma center access by ground and air, stratified by race, ethnicity, and urbanicity. There were 157 pediatric trauma centers (82 Level 1, 64 Level 2, 11 Level 3). Of the 2020 US pediatric population, 33,352,872 (54.5%) had timely access to Level 1-3 pediatric trauma centers by ground and 45,431,026 (74.1%) by air. The percentage of children with access by race and ethnicity were (by ground, by air): American Indian/Alaskan Native (31.0%, 43.5%), White (48.7%, 71.3%), Native Hawaiian/Pacific Islander (59.3%, 61.0%), Hispanic (60.2%, 76.9%), Black (64.2%, 78.0%), and Asian (76.5%, 89.5%). Only 48.2% of children living in rural block groups had access, compared with 83.6% in urban block groups. Significant disparities in current access to pediatric trauma centers exist by race and ethnicity, and geography, leaving some children at risk for poor trauma outcomes.

MAPPING AND ASSESSMENT OF FLOOD EVACUATION SITES IN A GEOSPATIAL ENVIRONMENT: A CASE IN LAS NIEVES, AGUSAN DEL NORTE, PHILIPPINES

Abstract. The municipality of Las Nieves is one of the most vulnerable areas prone to flooding, usually caused by tropical cyclones. With these, preparing for an emergency response is relatively significant, and one strategic response is planning for systematic management of evacuation shelters. Hence, this study assessed the spatial exposure to flooding, the spatial capacity and mapped the suitability of evacuation shelters using Geographic Information System and site suitability analysis. This study also developed a web-based application that will address the needs of the Las Nieves LGU, specifically in providing the locality with spatial information and other flood-related data. Weighted Overlay Analysis was performed based on Multi-Criteria Decision Making and weights from related literature to generate a suitability map for flood evacuation shelters. Factors included slope, elevation, land use/land cover, road networks, distance to water sources, distance to electrical supplies, and proximity to fuel stations. The results indicated that 85.1% of the areas were moderately suitable, 0.3% were highly suitable, and 14.6% were low suitable for flood evacuation sites in Las Nieves. From the analysis of 11 existing evacuation centers, there are ten evacuation centers moderately suitable for use during an emergency, while only the Maningalao Evacuation Center was not suitable because the location of the center was affected by the 100-year flood event. Moreover, the web mapping application developed in this study will help the stakeholders and community of Las Nieves to effectively manage evacuation centers and access real-time spatial data regarding evacuation centers' status and spatial capacity.

Remote sensing-based deformation monitoring and geological characterisation of an active deep-seated rock slide (Tellakopf/Cima di Tella, South Tyrol, Italy)

This study presents a 5-year long field campaign combined with 75 years of remote sensing data on the active, deep-seated Tellakopf rock slide in South Tyrol, Italy. Detailed field data and remote sensing techniques from airborne and terrestrial laser scanning (ALS, TLS) and orthoimages from federal flight campaigns and a helicopter-based monitoring campaign, allowed the development of a geometrical-kinematical rock slide model to investigate the initial failure and temporal-variable deformation mechanisms. The rock compound slide formed in foliated, highly fractured metamorphic rock masses at the SE-facing Tellakopf slope, directly above the commuter-rich national road SS 41. A total volume of approximately 3–4 Mm3 was estimated based on geographic information system (GIS) analysis by comparing the pre-failure surface topography and the reconstructed basal shear zone. Multi-temporal deformation analyses based on orthoimages, ALS and TLS data show alarmingly high velocities of at least 9 m/month during the initial formation phase in 2014, followed by a continuous velocity reduction to the current mean annual values of 1–2.5 m/year. Results indicate internal slab formation along discrete shear zones displayed on surface as main and minor scarps. These slabs show a translational movement behaviour along a fully persistent, slightly curvilinear basal shear zone. Rock mass yielding and fracturing conditions extensive mass loss manifested by high rock fall activity and large rock scree deposits at the rock slide toe. The cause for rock slide formation is attributed to retrogressive processes caused by long-term stress release due to topographical and hydrogeological changes by adjacent rock slides.

Mapping photovoltaic power stations and assessing their environmental impacts from multi-sensor datasets in Massachusetts, United States

Solar energy is often viewed as a sustainable alternative to non-renewable energy, yet the debate between solar energy promotion and environmental cost has received growing attention. Accurate geographic information of photovoltaic power stations is a prerequisite for quantifying cost and benefit of clean energy promotion. Therefore, this study aims to estimate the environmental impacts of photovoltaic power stations by geo-mapping solar panels over space and time. Based on the case of Massachusetts, United States, we classified the solar panel arrays using object-based image analysis on Sentinel-2 monthly composites, identified the per-array construction year based upon 20-year all available Landsat time-series dataset, and assessed the solar-induced environmental impacts with various environmental datasets. The accuracy assessment suggests that our classification performs well for detecting solar arrays (overall accuracy: 96%), depicting photovoltaic power stations geometry (average Jaccard Index value: 0.70), and capturing the construction years (percentage of temporal bias less than one year: 73%). Solar-induced land use and cover changes have largely occurred in forest and cropland, where 49% and 23% of the solar arrays have been installed, respectively. Geographic Information System (GIS) analysis uncovers that more than half of the mapped solar arrays were built in proximity (within 500 m) to rare wildlife habitats or adjacent to wetlands. This work exemplifies a new framework for identifying multifaceted land change information through a combination of finer-resolution Sentinel-2 images and long Landsat-based data archive. The findings can be useful for informing spatial planning and contributing to the growth, expansion, advancement, and location selection of solar installation arrays. The study also provides a new perspective for monitoring forest loss due to clean energy promotion and addressing critical issues of local conflicts between solar energy and environmental conservation.

Spatial Distribution, Contamination Assessment and Origin of Soil Heavy Metals in the Danjiangkou Reservoir, China.

Soil heavy metal contamination is crucial due to menacing food safety and mortal health. At present, with the fast advancement of urbanization and industrialization, heavy metals are increasingly released into the soil by anthropogenic activities, and the soil ecosystem contamination around the Danjiangkou Reservoir is directly associated with water quality security of the reservoir. In this paper, using 639 soil samples from the Danjiangkou Reservoir, Henan Province, China, we studied a variety of space distribution characteristics of heavy metals in soil. Geographic information system analysis (GIS), geo-accumulation index (Igeo), contamination factor (CF), principal component analysis (PCA) model, and positive matrix factorization (PMF) model were used together to recognize and quantify the distribution, contamination, and origin of heavy metals. We uncovered an exceptional variety of heavy metal concentrations among the tested soils: the mean arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), manganese (Mn), nickel (Ni), zinc (Zn), lead (Pb) and mercury (Hg) concentrations (14.54, 0.21, 18.69, 81.69, 898.42, 39.37, 79.50, 28.11, 0.04 mg/kg, respectively, in the topsoil (0-20 cm depth)), all exceed their background values. The mean Igeo value and CF values of these trace elements are both in descending order: Cd > Co > Mn > Ni > Pb > Zn > Cr > As > Hg. Cd was the highest contributor to the assessment of heavy metal pollution, with an average Igeo value over three, indicating that the study area is modestly contaminated by Cd. The PCA analysis and PMF model revealed three potential sources, including natural sources (PC1) for Cr, Co, Mn and Ni; agricultural sources (PC2) for Cd, Zn and Hg; and industrial emissions and transportation sources (PC3) for Pb. This study displays a map of heavy metal contamination in the eastern area topsoil of the Danjiangkou Reservoir, showing the most severe pollutant is Cd, which poses a threat to the water quality security of Danjiangkou Reservoir and provides a significant source identification for future contamination control.

Assessment of groundwater potential zones for urban development site suitability analysis in Srivaikundam region, Thoothukudi district, South India

To demarcate groundwater potential zones (GWPZ) for the Srivaikundam region in Thoothukudi district, Tamil Nadu, India, an effort was made in this study to locate the subsurface basin for groundwater storage and to identify the suitable destination for the urban development, agricultural, and drinking needs. This study using remote sensing and geographical information system (GIS) with analytical hierarchy process (AHP). According to their respective importance for groundwater occurrence, Saaty's scale was appropriately weighted to give the thematic layers and their attributes. The selected thematic maps were then merged using the weighted linear combination method to produce the final groundwater potential zone map after the layers' assigned weights and their features had been standardized using the Analytic Hierarchy Process (AHP) and eigenvector approach. Based on Saaty's 9-point scale, each criterion or factor was given the proper weight, and the weights were then normalized using the analytic hierarchy approach (AHP). To create the groundwater potential prediction map for the research area, the method was incorporated into the GIS environment. For the purpose of identifying potential urban residential development sites, the generated GWPZ is combined with eight different thematic layers. Groundwater conditions have been used to define GIS analysis criteria, and each information layer has been assigned an appropriate rank and weightage. Finally, groundwater recharge zones have been selected and classified into low, medium, and high based on cumulative weighted value. The moderate groundwater potential zone covers the majority of the land, with areas of very low 0.5 km2 (0.1%), low 112.3 km2 (17.7%), medium 428.7 km2 (67.6%), high 87.8 km2 (13.8%), and very high 5.1 km2 (0.8%) accordingly. Furthermore, higher groundwater yields are estimated in medium to good zones, indicating greater potentiality of groundwater sources.

GIS and SDM-Based Methodology for Resource Optimisation: Feasibility Study for Citrus in Mediterranean Area

South Italy is characterised by a semi-arid climate with scarce rain and high evaporative demand. Since climate change could worsen this condition, the need to optimise water resources in this area is crucial. In citrus cultivation, which involves one of the most important crops bred in Southern Italy, and more generally in Mediterranean regions, deficit irrigation strategies are implemented in order to cope with limited resource availability. On this basis, knowledge on how the territorial distribution of citrus would change in relation to these strategies represents valuable information for stakeholders. Therefore, the objective of this study was to determine the probability of the presence of citrus in Sicily based on changes in the percentage of water deficit in order to identify and analyse change in the surface area as well as the location of the crop. The methodology was based on the application of species distribution models (SDM) and Geographic Information Systems (GIS) to the case study of the province of Syracuse in Sicily. Different geostatistical and machine learning models were applied based on bioclimatic variables measured over three decades, a Digital Terrain Model and irrigation. Assessment of the outcomes was carried out using classification evaluation metrics. The analysis of the outcomes showed that uncorrelated predictor layers mainly included water input that most affected the probability of the presence of citrus fruits. Moreover, GIS analyses showed that deficit irrigation strategies would generate an overall reduction of cultivation surfaces in the territory (e.g., for the Random Forest model the surface reduction was equal to 41.15%) and a decrease of citrus presence in southern areas of the considered territory. In this area, climate conditions are less favourable in terms of temperature and precipitation; thus, these analyses provide useful information for decision support tools in agriculture and land use policy.

The Agrivoltaic Potential of Canada

Canada has committed to reducing greenhouse gas (GHG) emissions by increasing the non-emitting share of electricity generation to 90% by 2030. As solar energy costs have plummeted, agrivoltaics (the co-development of solar photovoltaic (PV) systems and agriculture) provide an economic path to these goals. This study quantifies agrivoltaic potential in Canada by province using geographical information system analysis of agricultural areas and numerical simulations. The systems modeled would enable the conventional farming of field crops to continue (and potentially increase yield) by using bifacial PV for single-axis tracking and vertical system configurations. Between a quarter (vertical) and more than one third (single-axis tracking) of Canada’s electrical energy needs can be provided solely by agrivoltaics using only 1% of current agricultural lands. These results show that agrivoltaics could be a major contributor to sustainable electricity generation and provide Canada with the ability to render the power generation sector net zero/GHG emission free. It is clear that the potential of agrivoltaic-based solar energy production in Canada far outstrips current electric demand and can, thus, be used to electrify and decarbonize transportation and heating, expand economic opportunities by powering the burgeoning computing sector, and export green electricity to the U.S. to help eliminate their dependence on fossil fuels.

Assessment and Mapping of Salt- Affected Soils Using Biophysical Soil Indicators, Rremote Sensing and GIS Analysis in Wadi Elgaab Area Northern State, Sudan

Salt-affected soils is a prime indicator of land degradation (LD). Thus, this study was conducted to assess, and mapping of salt-affected soils, evaluating the land suitability for irrigated agriculture and updating information through soil, remote sensing and geographical information system analysis techniques in the study area. By estimating and monitoring some soil properties namely, soil salinity, sodicity and alkalinity, due to drought and LD process. The soil survey was carried for the project area (31105 feddans). This study depends on extracted data and information from soil survey, soil analysis (SA), and remote sensed (RS), in addition to fieldwork (observation points) in the study area.&nbsp; SA and RS data indicated that LD was taken place in the study area particularly in the western, south western and north western parts of the area which were affected by salinity, sodicity and alkalinity. The study showed the study area was highly eroded by wind. According to the framework of the land suitability (FAO, 1990 and 2006), soil properties and degradation hazard, were estimated. The results showed that the soil of the study area is moderately suitable (S2), marginally suitable lands (S3), currently unsuitable land (N1) and unsuitable land for irrigated agriculture (N2), for 15200 feddans, 2580 feddans, 11035 feddans and 2436 feddans respectively.

Society for Education and the Advancement of Research in Connected Health SEARCH 2022 – The National Telehealth Research Symposium Abstracts Virtual Meeting November 16–18, 2022

Society for Education and the Advancement of Research in Connected Health SEARCH 2022 – The National Telehealth Research Symposium Abstracts Virtual Meeting November 16–18, 2022