Geographic Information System Layers Research Articles

ANALYSIS OF SUITABLE DUMP SITES IN KAFANCHAN, JEMA’A LOCAL GOVERNMENT AREA KADUNA STATE, NIGERIA

The research focuses on assessing the suitability of dump sites for solid waste disposal in Kafanchan town, Kaduna state, utilizing Surveying and Geo informatics techniques. With the escalating environmental impact of waste due to population growth, urbanization, and industrialization, the study aims to identify appropriate locations for waste disposal. The methodology employs Geographic Information System (GIS) tools, involving the overlaying of datasets and criteria such as drainage, land use, and road networks. The process includes investigating solid waste disposal in Kafanchan, acquiring data through a Global Positioning System (GPS) receiver, converting criteria into GIS layers, and processing the collected information on a computer system using ArcGIS software. The spatial analysis reveals potential sites suitable for solid waste disposal based on connectivity, contiguity, and overlay methods. The results advocate for the effectiveness of GIS in promptly selecting suitable waste disposal sites. The conclusion emphasizes the recommendation to adopt the identified sites for solid waste disposal in the area. Overall, the study underscores the significance of GIS in addressing contemporary environmental challenges related to waste management and highlights the practical application of spatial tools for sustainable solutions.

Hybridizing genetic random forest and self-attention based CNN-LSTM algorithms for landslide susceptibility mapping in Darjiling and Kurseong, India

Landslides are a prevalent natural hazard in West Bengal, India, particularly in Darjeeling and Kurseong, resulting in substantial socio-economic and physical consequences. This study aims to develop a hybrid model, integrating a Genetic-based Random Forest (GA-RF) and a novel Self-Attention based Convolutional Neural Network and Long Short-term Memory (SA-CNN-LSTM), for accurate landslide susceptibility mapping (LSM) and generate landslide vulnerability-building map in these regions. To achieve this, we compiled a database with 1830 historical data points, incorporating a landslide inventory as the dependent variable and 32 geo-environmental parameters from Remote Sensing (RS) and Geographic Information Systems (GIS) layers as independent variables. These parameters include features like topography, climate, hydrology, soil properties, terrain distribution, radar features, and anthropogenic influences. Our hybrid model exhibited superior performance with an AUC of 0.92 and RMSE of 0.28, outperforming standalone SA-CNN-LSTM, GA-RF, RF, MLP, and TreeBagger models. Notably, slope, Global Human Modification (gHM), Combined Polarization Index (CPI), distances to streams and roads, and soil erosion emerged as key layers for LSM in the region. Our findings identified around 30% of the study area as having high to very high landslide susceptibility, 20% as moderate, and 50% as low to very low. The vulnerability-building map for 244,552 building footprints indicated varying landslide risk levels, with a significant proportion (27.74%) at high to very high risk. Our model highlighted high-risk zones along roads in the northeastern and southern areas. These insights can enhance landslide risk management in Darjeeling and Kurseong, guiding sustainable strategies for future damage qualification.

Suitability location for landfill using a fuzzy-GIS model: A case study in Hillah, Iraq

Abstract One of the most significant issues facing local governments and municipalities worldwide is choosing an appropriate location for landfills. In order to suggest appropriate places for waste sites, this study proposes a strategy using a fuzzy logic model and spatial analytic tools. The proposed approach utilized five parameters extracted from Geographic Information System (GIS) layers in Hillah city in Iraq, which include distances from hydrology features, residential or urban areas, transportation, and historical locations. These parameters were used as standards to construct a geodatabase for generating suitability maps. These indicators are relevant factors in determining the suitability of a site for an urban landfill. It is important to consider the distance from residential or urban areas when selecting a landfill site. A significant distance helps reduce the potential negative impacts on nearby communities, such as odors, noise, traffic, and health concerns associated with waste disposal, and for the other indicators. The fuzzy logic model is more accurate than conventional methods such as the weighted overlay approach. Based on a fuzzy logic model, the total surface area of acceptable locations is 1.1 km². According to the fuzzy logic model, some frequent adequate places were discovered to be beyond the city borders, to the west and south of the research region. To minimize the negative impacts on the environment and its residents, this study demonstrated how the fuzzy logic model and GIS might be combined for waste management decision concerns connected to landfill site selection.

Modeling Flood Susceptible Areas Using Deep Learning Techniques with Random Subspace: A Case Study of the Mae Chan Basin in Thailand

Flooding is a recurring global issue that leads to substantial loss of life and property damage. A crucial tool in managing and mitigating the impact of flooding is using flood hazard maps, which help identify high-risk areas and enable effective planning and management. This study presents a study on developing a predictive model to identify flood-prone areas in the Mae Chan Basin of Thailand using machine learning techniques, precisely the random sub-space ensemble method combined with a deep neural network (RS-DNN) and Nadam optimizer. The model was trained using 11 geographic information system (GIS) layers, including rainfall, elevation, slope, distance from the river, soil group, NDVI, road density, curvature, land use, flow accumulation, geology, and flood inventory data. Feature selection was carried out using the Gain Ratio method. The model was validated using accuracy, precision, ROC, and AUC metrics. Using the Wilcoxon signed-rank test, the effectiveness was compared to other machine learning algorithms, including random tree and support vector machines. The results showed that the RS-DNN model achieved a higher classification accuracy of 97% in both the training and testing datasets, compared to random tree (93%) and SVM (82%). The model's performance was also validated by its high AUC value of (0.99), compared to a random tree (0.93) and SVM (0.82) at a significance level of 0.05. In conclusion, the RS-DNN model is a highly accurate tool for identifying flood-prone areas, aiding in effective flood management and planning.

Reducing non-revenue water in Luxor-Egypt using GIS

Unaccounted for Water, referring to all losses from the water system, illegal or otherwise, frequently exceeds 40% of all water produced. This represents a major strain on utility operations and budgets. A large amount of Unaccounted for Water indicates large losses in the revenue of the utilities. Deficiency in financial resources normally leads to a deficiency in the performance of the utility. This deficiency in performance may affect the operation and maintenance of treatment plants and network components. The main objective is to reduce Unaccounted for Water rates. The multi-stage systematic process involves measuring water flow, leak detection, installing and fixing meters, reducing illegal connections, and increasing billing and collections, and developing Geographic Information System layers and databases for all of them. Another objective is to connect Geographic Information System maps with Billing, hydraulic systems, hotline systems, etc. After choosing a pilot area, we review similar activities, conduct field visits, and hold meetings, prepare the timeline schedule, prepare, and implement base maps and the water network, analyze, and compare end-line results, and report the results. A high reduction in the Unaccounted for Water percentage is expected due to fixing the discovered leaks. The issue of Unaccounted for Water, encompassing losses from the system, whether legal or illegal. This places a significant burden on both utility operations and budgets. A substantial volume of Unaccounted for Water signifies considerable revenue losses for the utilities, and the financial constraints often translate into performance deficiencies within the utility. We formulated a methodology aimed at assisting utilities in lowering Unaccounted for Water rates. This structured, multi-stage process includes activities such as measuring water flow, implementing leak detection measures, installing, and repairing meters, minimizing illegal connections, and enhancing billing and collection.

A Data Processing Methodology to Analyze Construction and Demolition Dynamics in the European Metropolis of Lille, France

In the absence of industry data, organisms, and researchers leverage free and available data, specifically building and demolition permits. Geospatial processing is essential to integrate information from various files into a single GIS layer containing all relevant attributes for analysis. This article proposes a Geographic Information System (GIS) processing model aimed at monitoring construction and demolition dynamics in the European metropolis of Lille to quantify the urban production of mineral waste from buildings. Author methodology is based on that that the deposit potential can be analyzed using the observation of the spatiotemporal dynamics of building and demolition permits. The results demonstrate that combining construction and demolition (C&D) permits with other GIS layers allows us to produce data to quantify demolition surfaces per year in a given French area. The applicability of this methodology extends to all French regions, providing insights into the impact of crises on deconstruction activities and C&D waste generation. The study focuses on C&D French public data bases (French government and European Metropolis of Lille) attributed to the region (area) of the European Metropolis of Lille (MEL) between 2013 and 2022. Some data for 2022 were incomplete due to ongoing treatment, emphasizing the importance of understanding the dynamics of demolition rates or surfaces to identify data gaps or errors. Historical trajectories of C&D permits were quantified and analyzed, revealing over 21,000 permits granted from 2013 to 2022, categorized by site type (new construction, rehabilitations, prior declarations, and demolitions). Construction sites during this period covered approximately 3,345,948 m2, constituting 20% of the MEL’s building stock, while demolition sites amounted to 1,977,911 m2, equivalent to 5% of the total area of buildings in the metropolis. Employing GIS allowed for a spatial analysis, visualizing data by municipality, urban fabric, and year. The analysis highlighted territories with high and low potential for demolition and construction, as well as the most impacted urban fabrics and dynamic periods. The article discusses potential crisis impacts (e.g., COVID-19 or economic downturns) and the implications of incomplete data. Finally, the study demonstrates how these findings can be utilized to quantify C&D waste, leveraging GIS and the production rate calculation method (GRC).

Hydric erosion rates in Raouz watershed, Morocco: RUSLE, GIS, and remote sensing

Water erosion significantly contributes to global soil degradation, inducing detrimental consequences. One of the most adverse impacts is the sedimentation buildup in reservoirs, which results in reduced water storage capacity and damage to the agro-pedological environment. In Morocco's western Rif region, the Raouz watershed faces soil losses annually that disrupt the basin's ecosystems and impede socio-economic progress. These erosional losses manifest in the Moulay El Hassan Ben Mehdi (MHBM) Dam's sedimentation, causing substantial financial repercussions of several million dirhams/year. To comprehensively assess and quantify this soil degradation phenomenon, this study evaluates soil losses employing the revised universal soil loss equations (RUSLE), strategically integrating geographic information system (GIS) layers that encapsulate pertinent environmental variables such as climate, topography, soil composition, and land use. By utilizing these empirical equations, the identification of predominant factors responsible for soil water erosion within the Raouz watershed is achieved. The outcomes of this study methodology demonstrate an average soil loss estimate of 16.53 t/ha/yr through RUSLE application. Furthermore, comparing soil loss values from this study using RUSLE to those derived from prior research using the Caesium-137 method reveals relatively minor disparities.

Remote sensing and geographic information system (GIS)-based high-resolution mapping of potential groundwater recharge zones on the hard rock terrains of the Cameroon volcanic line (CVL)

Groundwater is the major water reserve in the present context of global warming-related droughts that appear to be more intense in hard rock terrains. The use of Geographical Information Systems (GIS) and Remote Sensing (RS) technologies are increasingly beneficial to groundwater research, by allowing for low cost and larger-scale high-resolution mapping compared to conventional hydrogeological exploration methods. This study aimed at developing a high-resolution map of potential groundwater recharge (GWRpot) zones for the drought-stricken Banka hard rock terrain, straddling the Cameroon Volcanic Line (CVL). Shuttle Radar Tomography Mission (SRTM)-30m and Landsat 8 satellite images constituted the main data source that was ground-truthed through field mapping and used to produce various thematic GIS layers: geology slope, aspect, land use & land cover, drainage density and lineament density of spatial resolution 16m x 16m. The layers were each attributed a fixed score and weight to groundwater recharge, computed using Multi-Influencing Factor (MIF) and Analytical Hierarchy Process of Multi-Criteria Decision Analysis (AHP-MCDA) techniques. Lastly, a Weighted Overlay Analysis was done using the layers to produce the GWRpot zones for the study area. The resulting map shows that 60% of the study area, covering the south, west-northwest and the north-northeast portions of the map have moderate to very high recharge potentials. This result is particularly useful for groundwater targeting in the area and demonstrates the effectiveness of the method in hard rock terrains where traditional methods have been less efficient in properly delineating groundwater recharge zones.Article highlightsGroundwater is generally the safest and most reliable source of water in water scarce environments, and forms when surface water goes into the ground.More areas where water can enter the ground means more chances of getting enough groundwater. Conventional ways of knowing these areas are time consuming and costly, whereas RS and GIS-based methods are less costly and use lesser time.We used the RS, GIS, high-resolution field mapping and statistical methods of blending several factors to produce the potential groundwater recharge zones on a part of the hardrocks of the CVL, that shows 60% of the study area, covering the south, west-northwest and the north-northeast portions of the area have moderate to very high recharge potentials.

Evaluating the Eligibility of Abandoned Agricultural Land for the Development of Wind Energy in Lithuania

Land requirements of wind power (WP) are often seen as a constraint to future broad-scale deployment. The aim of the study is to evaluate the eligibility of abandoned agricultural land (AAL) areas, covered with woody plants, for the development of wind energy (WE) in Lithuania. Agricultural land abandonment (ALA) has numerous negative aspects and its use for WE must be a profitable choice for the landowner, as for the energy developers also. A newly developed methodological approach, a multi-criteria decision-making (MCDM) method known as TOPSIS (the Technique for Order Preference by Similarity to Ideal Solution) was applied to select suitable areas for wind power plants (WPP). The authors have used various data sets, as follows: protected areas (reserves, parks) combined into one common geographic information system (GIS) layer, forest cadaster data, water (lakes, rivers) area layer; abandoned land area layer; buildings layer, taken from the Lithuania Georeferenced Data Base. The results were generated for the entire territory of Lithuania and separately for AAL using the algorithms of the open source QGIS program. The results showed that the central part of Lithuania is most suitable for the development of WE. However, ALA in this part is low, because of the high soil yield potential and suitable conditions for farming. According to the selection criteria, about 7% of AAL are suitable for WE, and more than 18% of AAL have an average suitability.

Identification of Suitable Sites Using GIS for Rainwater Harvesting Structures to Meet Irrigation Demand

This study uses a multi-criteria decision analysis approach based on geographic information system (GIS) to identify suitable sites for rainwater harvesting (RWH) structures (such as farm dam, check dam and contour bund) to meet irrigation demand in Greater Western Sydney region, New South Wales, Australia. Data on satellite image, soil, climate, and digital elevation model (DEM) were stored in GIS layers and merged to create a ranking system, which were then used to identify suitable RWH (rainwater harvesting) areas. The resulting thematic layers (such as rainfall, land use/land cover, soil type, slope, runoff depth, drainage density, stream order and distance from road) were combined into one overlay to produce map of RWH suitability. The results showed that 9% of the study region is ‘very highly suitable’ and 25% is ‘highly suitable’. On the other hand, 36% of the area, distributed in the north-west, west and south-west of the study region, is ‘moderately suitable’. While 21% of the region, distributed in east and south-east part of the region, has ‘low suitability’ and 9% is found as ‘unsuitable area’. The findings of this research will contribute towards wider adoption of RWH in Greater Western Sydney region to meet irrigation demand. The developed methodology can be adapted to any other region/country.

China's provincial wind power potential assessment and its potential contributions to the "dual carbon" targets.

Wind power development is one of the important measures to achieve China's committed dual carbon targets (carbon peak before 2030 and carbon neutrality before 2060). This study assessed the technical and economic potential of China's onshore and offshore wind power potential through Geographic Information System (GIS) layer overlay and raster calculations. Based on the assessment, provincial contributions of wind power under the 'dual carbon' targets are also estimated. The results show that (a) the technical potential of China's wind power is 25.57PWh/year, and the economic potential is 11.69PWh/year. Fifty-six percent of the potential is located in Inner Mongolia, Xinjiang, and Gansu provinces, while 75% of the offshore potential is distributed over the coastal provinces (Liaoning, Zhejiang, Fujian, Guangdong, and Shandong). (b) Ten provinces, including Anhui, Jiangxi, and Henan, have insufficient remaining economic potential. In comparison, five provinces, including Jiangxi, Henan, Hubei, Yunnan, and Ningxia, have advanced wind power development, with 2020 as the base year. (c) For carbon neutrality, China's potential contributions of wind power may need 1088-2620GW by 2060. Eight provinces can constitute 72% of the new potential contributions. China may need approximately 9.23 trillion CNY (2020 constant price) investment in wind power for the contributions.

Resource Selection of Domestic Sheep on Mountainous Summer Habitat in Utah, United States

Past research has documented livestock grazing patterns and dynamics across pastoral landscapes. We used spatial locations of domestic range sheep (Ovis aries) obtained from Global Positioning System collars to study habitat selection on summer mountain habitat North of Scofield Reservoir Utah, United States. Data were collected between the months of July to September 2020. We developed a resource selection function to determine the influence of slope, distance to water, aspect, ruggedness, elevation, and vegetation types on habitat selection by sheep while grazing on summer mountain habitat. We found sheep selected for sites closer to water, with more gentle terrain, higher in elevation, and north-facing slopes. Vegetation types were less predictive, ostensibly due to the lack of species composition information in available geographic information system layers and the possibility of sheep being herded to avoid areas of overuse. Although it is often assumed that sheep use slopes more than their heavier and larger cattle counterparts, overall they tended to avoid steep slopes compared with all other predictor variables. This information on selection for higher elevations and avoidance of steep slopes can be used to improve livestock management practices including flock management that increases sheep foraging patterns and energy efficiency.

Allocation of Regional Logistics Hubs and Assessing Their Contribution to Saudi Arabia’s Logistics Performance Index Ranking

In 2016, Saudi Arabia published its vision for the year 2030, which is based on the Kingdom’s geographical, financial, social and religious potentials. Developing the logistics sector and improving the Kingdom’s ranking in the World Bank’s Logistics Performance Index (LPI) to the twenty-fifth rank was one of the most targeted success factors. Unfortunately, Saudi Arabia’s rank in this index has declined over the past years, until it reached the fifty-fifth rank as per the last published LPI report. This research proposes a set of logistics hubs (LHs) which are located at key multi-logistics areas within the regional trading zones of the country. A spatial model was implemented on a macro level to integrate multi-logistical, infrastructural and natural geographic information system (GIS) layers, and highlight their intersections as initial feasible areas. Subsequently, an optimization model based on integer linear programming (ILP) was used to maximize the number of allocated LHs and minimize the overall distances between allocated LHs and international trading nodes considering multiple factors. More than 80 selected subject matter experts (SMEs) from 9 different countries have participated in World Bank’s driven surveys that assess the contribution of the LHs’ allocation proposal on Saudi Arabia’s LPI ranking. An improvement of about 10% in the LPI overall score with a 20 rank promotion has been estimated as per the SMEs responses. These results demonstrate that investing in logistics infrastructure and ranking in LPI are perfectly, positively and highly related.

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.

A multi-criteria CCUS screening evaluation of the Gulf of Mexico, USA

Continued research into reservoir characterization along with offshore carbon dioxide (CO2) transportation and infrastructure assets is needed to facilitate development of safe and successful carbon capture, utilization, and storage (CCUS) projects. This paper outlines a multi-criteria evaluation methodology that incorporates disparate sets of quantitative, spatially variable data into a decision-making framework for screening the Gulf of Mexico (GOM) outer continental shelf (OCS) for potentially viable CO2 storage and enhanced oil recovery (EOR) sites. Criteria categories include favorable geologic characteristics, logistics, and potential risks. Data compiled for 14 criteria from several publicly available geographic information system (GIS) layers was aggregated over 2559 spatially balanced points across the study area using the National Energy Technology Laboratory (NETL)-developed Cumulative Spatial Impact Layers™ (CSIL) GIS tool. Criteria are weighted by qualitative expert opinion relative to their perceived importance to given scenarios— the output of combined criteria values and weights enables regional CO2 storage suitability differentiation. The methodology considers both technical and non-technical factors impacting CCUS decision-making. The flexible methodology enables a systematic approach to regional ranking at high spatial resolution over a large study domain. Additionally, the framework enables high-grading of priority sites that warrant further characterization and follow-on analysis. Areas along the Louisiana coast and Mississippi River Delta consistently rank high for all scenarios largely a result of the favorable geology with the potential for stacked storage, as well as the density of existing pipelines and platforms, and proximity to several onshore CO2 sources. High-graded regions for the CO2 EOR-related scenarios are typically located further offshore towards the middle and edge of the OCS compared to higher priority regions for the geologic storage scenarios which fall closer to the Louisiana coastline.

Satellite-based approach for forest fire prediction: A case study from Lebanon

Forest fires are one of the severe natural hazards that frequently occur in the Mediterranean Region, as a result of the increased temperature from global warming. The damage in the forest area has been exacerbated lately, making it necessary to adopt systematic tools based on scientific techniques. This can be performed by producing vulnerability maps for forest fires. The production of vulnerability maps can be attained by integrating several indices to determine the likelihood of fires. The most applicable indices are the Structural Fire Index (SFI), Fire Risk Index (FRI), and Hybrid Fire Index (HFI). However, the geographical location of the study area (exposed to hot and dry east winds during spring and autumn) requires adopting a new indicator for forest fire prediction, with enhanced methodology according to the temporal and spatial dimensions. In this study, satellite datasets were used to identify inducing factors and then mapping these factors as GIS layers with all required calculations. For this purpose, Sentinel-2 satellite images were processed with the Google Earth Engine (GEE) to compare the Normalized Difference Vegetation Index (NDVI) change, related to the Normalized Burn Ratio (NBR) changes at the same time. A comparison was also made between the years 2015 and 2020 for (NDVI) and (NBR) (Pre, Post). The considered dataset parameters are: elevations, slopes, aspect, NDVI, temperature, humidity, wind direction and distance from residential areas and roads. The extracted geospatial data from satellite images for building GIS layers were manipulated using a Geographic Information System (GIS) which enabled the integration of different indices to end up with the vulnerability map for the forest fire. It represents a supportive tool for decision-makers to adopt the best forest fire prediction and intervention plans.

A Multi-Stage Approach Combining Very High-Resolution Satellite Image, GIS Database and Post-Classification Modification Rules for Habitat Mapping in Hong Kong

Identification and mapping of various habitats with sufficient spatial details are essential to support environmental planning and management. Considering the complexity of diverse habitat types in a heterogeneous landscape, a context-dependent mapping framework is expected to be superior to traditional classification techniques. With the aim to produce a territory-wide habitat map in Hong Kong, a three-stage mapping procedure was developed to identify 21 habitats by combining very-high-resolution satellite images, geographic information system (GIS) layers and knowledge-based modification rules. In stage 1, several classification methods were tested to produce initial results with 11 classes from a WorldView-2/3 image mosaic using a combination of spectral, textural, topographic and geometric variables. In stage 2, modification rules were applied to refine the classification results based on contextual properties and ancillary data layers. Evaluation of the classified maps showed that the highest overall accuracy was obtained from pixel-based random forest classification (84.0%) and the implementation of modification rules led to an average 8.8% increase in the accuracy. In stage 3, the classification scheme was expanded to all 21 habitats through the adoption of additional rules. The resulting habitat map achieved &gt;80% accuracy for most of the evaluated classes and &gt;70% accuracy for the mixed habitats when validated using field-collected points. The proposed mapping framework was able to utilize different information sources in a systematic and controllable workflow. While transitional mixed habitats were mapped using class membership probabilities and a soft classification method, the identification of other habitats benefited from the hybrid use of remote-sensing classification and ancillary data. Adaptive implementation of classification procedures, development of appropriate rules and combination with spatial data are recommended when producing an integrated and accurate map.

A comparative study of the proximity to nomadic travel routes and environmental factors on the occurrence of cutaneous leishmaniasis in Kohgiluyeh and Boyer-Ahmad province, southwestern Iran.

Cutaneous leishmaniasis (CL) is one of the most important health challenges in hyperendemic countries like Iran. Geospatial information systems-based studies have shown that factors, including land cover, altitude, slope temperature, rainfall and animal livestock, affect CL distribution in Kohgyloyeh and Boyerahmad province, southwestern Iran. However, the question of the influence of nomadic tribes, who travel with their goats and sheep, on CL is unanswered. We, therefore, investigated their role in CL epidemiology from 2008 to 2017 and compare them with geoclimatic factors. CL patient demographic data and their village/city addresses were retrieved from Provincial Health Center and mapped on the geographic information system (GIS) layer of the province's political divisions. Nomadic travel routes (NTRs) with a 2km buffer were generated and their effect on CL was investigated together with the interpolated layers of rainfall, temperatures, humidity, slope, elevation, land covers, by binary regression. CL was significantly more common in villages/cities in the 2km NTR zone (pvalue<.001; OR=1.96; 95% CI=1.4-2.745). Geoclimatic factors, including slope, elevation, rainfall, temperatures, humidity and most of the landcovers, were not significantly different inside and outside the NTR. Areas of irrigated farm were the only effective landcover on CL (p value=.049; OR=2.717; 95% CI=1.003-7.361) within the NTR versus non-NTR. Living within NTRs almost doubled the risk of acquiring CL. Several factors for this include passage through areas of high sand fly activity, increased contact between sandflies and humans, sheep and goats, and feeding on their blood and faeces, and low availability of health facilities that should be more investigated and considered in the future control programs.

UAV technique to localize landslide susceptibility and mitigation proposal: A case of Rinchending Goenpa landslide in Bhutan

Natural hazards such as landslides impose very high risks to the community and infrastructures. To mitigate such risk, proper long-term engineering solutions are must. Several studies conducted in the past proposed countermeasures on many occasions. Despite such interventions, the Rinchending Goenpa area still suffers high vulnerability to landslide hazard which continues every monsoon. To assess landslide characteristics in detail, we performed landslide susceptibility mapping using the unmanned aerial vehicle (UAV) technique and conducted a site investigation. High-resolution digital elevation model (DEM) was generated with the site-specific aerial photographs and processed in Agisoft PhotoScan to developed thematic layers in geographical information system (GIS) software. We implemented a multi-influencing factor (MIF) for deriving the influence of landslide conditioning factors and developed a landslide susceptibility map (LSM) using the weighted overlay method (WOM). The landslide conditioning factors include slope, elevation, aspect, topographical wetness index (TWI), and normalized difference vegetation index (NDVI). According to LSM, the areal coverage of landslide susceptibility of study area reveals 2.41% in very low susceptibility, 37.14% in low susceptibility, with highest in moderate susceptibility zone of 49.55% followed by 10.60% in high susceptibility zone, and 0.30% in the very high susceptible zone. Based on the zonal distribution of LSM, and considering findings from several other site investigation such as geophysical survey, the performance of existing countermeasures, soil characteristics, and footprint of last landslide occurrences, we proposed mitigation measures under each zone to provide long-term solutions.

High Voltage Overhead Powerlines Routing in Central Nigeria using GIS-MCDA Approach and Environmental Impact Factors

High voltage overhead transmission line route selection has become a vital component of the power transmission line project life cycle as demand for electricity has increased and global awareness of the environmental implications of large-scale power projects has grown. The project necessitates meticulous planning, engineering designs, facility installations, commissioning, and maintenance, as any errors made at this stage may have a flowing detrimental impact on the following phase, which may be too costly to fix. Using geographic information system (GIS), multi-criteria decision analysis approach, and environmental impact factors strategies, this study determines the best route from existing geographical space between Lafia 330/132/33kV Substation in Nasarawa State and New Apo (Pigba) 330/132/33/kV Substation in the Federal Capital Territory, Central Nigeria, Abuja. Land use, land cover, and other environmental data were obtained by performing image classification on Landsat 8 satellite image using ArcGIS 10.1 to achieve this goal. Three key terrain elements were identified to define the land cover types: built-up areas, vegetation, and water bodies. These layers were created to reveal the locations of significant settlements, geological formations, and the HV-OHTL route that avoided crossing protected areas. Using pair-wise comparison of analytic hierarchy process (AHP) approach, each criterion evaluated was assigned relative weight that shows their level of influence on the proposed power transmission line, and these were provided as GIS layers for visualizations. To build the cost surfaces assessed during route selection for HV-OHTL, weights derived from AHP method for each decision makers were applied to all layers to create the cost surfaces analyzed during route selection for HV-OHTL.