Geospatial Technology Integration in Smart City Frameworks for Achieving Climate Neutrality by 2050: A Case Study of Limassol Municipality, Cyprus

This study investigated the integration of geospatial technologies within smart city frameworks to achieve the European Union’s climate neutrality goals by 2050. Focusing on rapid urbanization and escalating climate challenges, the research analyzed how smart city frameworks, aligned with climate neutrality objectives, leverage geospatial technologies for urban planning and climate action. The study included case studies from three leading European cities, extracting lessons and best practices in implementing Climate City Contracts across sectors like energy, transport, and waste management. These insights highlighted the essential role of EU and national authorities in providing technical, regulatory, and financial support. Additionally, the paper presented the application of a WEBGIS platform in Limassol Municipality, Cyprus, demonstrating citizen engagement and acceptance of the proposed geospatial framework. Concluding with recommendations for future research, the study contributed significant insights into the advancement of urban sustainability and the effectiveness of geospatial technologies in smart city initiatives for combating climate change.

Leveraging Geospatial Technology for Smallholder Farmer Credit Scoring

According to the Food and Agriculture Organization of the United Nations (FAO), there are about 500 million smallholder farmers in the world, and in developing countries, such farmers produce about 80% of the food consumed there;their farming activities are therefore critical to the economies of their countries and to the global food security. However, these farmers face the challenges of limited access to credit, often due to the fact that many of them farm on unregistered land that cannot be offered as collateral to lending institutions;but even when they are on registered land, the fear of losing such land that they should default on loan payments often prevents them from applying for farm credit;and even if they apply, they still get disadvantaged by low credit scores (a measure of creditworthiness). The result is that they are often unable to use optimal farm inputs such as fertilizer and good seeds among others. This depresses their yields, and in turn, has negative implications for the food security in their communities, and in the world, hence making it difficult for the UN to achieve its sustainable goal no.2 (no hunger). This study aimed to demonstrate how geospatial technology can be used to leverage farm credit scoring for the benefit of smallholder farmers. A survey was conducted within the study area to identify the smallholder farms and farmers. A sample of surveyed farmers was then subjected to credit scoring by machine learning. In the first instance, the traditional financial data approach was used and the results showed that over 40% of the farmers could not qualify for credit. When non-financial geospatial data, i.e. Normalized Difference Vegetation Index (NDVI) was introduced into the scoring model, the number of farmers not qualifying for credit reduced significantly to 24%. It is concluded that the introduction of the NDVI variable into the traditional scoring model could improve significantly the smallholder farmers’ chances of accessing credit, thus enabling such a farmer to be better evaluated for credit on the basis of the health of their crop, rather than on a traditional form of collateral.

Transformation and risk assessment of the East Kolkata Wetlands (India)using fuzzy MCDM method and geospatial technology

The East Kolkata Wetlands (EKW) is located on the eastern periphery of the city of Kolkata and extends up to theBidyadhari-Matla River confluence. It is a Ramsar Site and acts as an absorber basin for a large number of con-taminants drained from Kolkata. Agricultural lands, sewage-fed fisheries, garbage dumping fields, horticulture,and built-up areas are included in this protected area, that covers approximately 125 km2. It reveals that climatechange reduces the variety of wetland ecosystem services and increases socio-economic vulnerability and eco-nomic stress. The human encroachment, reclamation of land for agriculture, aquaculture, and urban expansion inand around Kolkata has recently adversely threatened the EKW. The remotely sensed data, socio-economic data,and responses of inhabitants have been used to analyse the EKW’s risk and vulnerability. We employed geospatialanalysis by using the Multi-Criteria Decision Making (MCDM) method using nine risk factors. An in-depth analysisof the EKW using geospatial techniques and the Fuzzy Analytic Hierarchy Process (FAHP) helped to understandthe EKW transformations through vulnerability and risk analysis. The results show that the transformation of thewetland to aquaculture, eutrophication and pollution, road proximity, waste dumping, population density, andgrowth are the main factors for the deteriorating health, quality, and environment of the EKW. It also reveals thatquantitative and qualitative evaluations of ecosystem services, wetland degradation, transformation, and cause-effect rapport should be periodically assessed using scientific methods like FAHP, RS, GIS to formulate resilient,integrated plans and strategy for the sustainable management of the EKW.

Leveraging Geospatial Technologies for Resource Optimization in Livestock Management

Geospatial technologies can be leveraged to optimize the available resources for better productivity and sustainability. The resources can be human, software and hardware equipment and their effective management can enhance operational efficiency through better and informed decision making. This review article examines the application of geospatial technologies, including GPS, GIS, and remote sensing, for optimizing resource utilization in livestock management. It compares these technologies to traditional livestock management practices and highlights their potential to improve animal tracking, feed intake monitoring, disease monitoring, pasture selection, and rangeland management. Previously, animal management practices were labor-intensive, time-consuming, and required more precision for optimal animal health and productivity. Digital technologies, including Artificial Intelligence (AI) and Machine Learning (ML) have transformed the livestock sector through precision livestock management. However, major challenges such as high cost, availability and accessibility to these technologies have deterred their implementation. To fully realize the benefits and tremendous contribution of these digital technologies and to address the challenges associated with their widespread adoption, the review proposes a collaborative approach between different stakeholders in the livestock sector including livestock farmers, researchers, veterinarians, industry professionals, technology developers, the private sector, financial institutions and government to share knowledge and expertise. The collaboration would facilitate the integration of various strategies to ensure the effective and wide adoption of digital technologies in livestock management by supporting the development of user-friendly and accessible tools tailored to specific livestock management and production systems.

Rapid Evaluation and Response to Impacts on Critical End-Use Loads Following Natural Hazard-Driven Power Outages:A Modular and Responsive Geospatial Technology

The disparate nature of data for electric power utilities complicates the emergency recovery and response process.The reduced efficiency of response to natural hazards and disasters can extend the time that electrical service is not available for critical end-use loads,and in extreme events,leave the public without power for extended periods.This article presents a methodology for the development of a semantic data model for power systems and the integration of electrical grid topology,population,and electric distribution line reliability indices into a unified,cloud-based,serverless framework that supports power system operations in response to extreme events.An iterative and pragmatic approach to working with large and disparate datasets of different formats and types resulted in improved application runtime and efficiency,which is important to consider in real time decision-making processes during hurricanes and similar catastrophic events.This technology was developed initially for Puerto Rico,following extreme hurricane and earthquake events in 2017 and 2020,but is applicable to utilities around the world.Given the highly abstract and modular design approach,this technology is equally applicable to any geographic region and similar natural hazard events.In addition to a review of the requirements,development,and deployment of this framework,technical aspects related to application performance and response time are highlighted.

Soil erosion susceptibility mapping of Hangu Region,Kohat Plateau of Pakistan using GIS and RS-based models

Soil erosion is a crucial geo-environmental hazard worldwide that affects water quality and agriculture,decreases reservoir storage capacity due to sedimentation,and increases the danger of flooding and landslides.Thus,this study uses geospatial modeling to produce soil erosion susceptibility maps(SESM)for the Hangu region,Khyber Pakhtunkhwa(KPK),Pakistan.The Hangu region,located in the Kohat Plateau of KPK,Pakistan,is particularly susceptible to soil erosion due to its unique geomorphological and climatic characteristics.Moreover,the Hangu region is characterized by a combination of steep slopes,variable rainfall patterns,diverse land use,and distinct soil types,all of which contribute to the complexity and severity of soil erosion processes.These factors necessitate a detailed and region-specific study to develop effective soil conservation strategies.In this research,we detected and mapped 1013 soil erosion points and prepared 12 predisposing factors(elevation,aspect,slope,Normalized Differentiate Vegetation Index(NDVI),drainage network,curvature,Land Use Land Cover(LULC),rainfall,lithology,contour,soil texture,and road network)of soil erosion using GIS platform.Additionally,GIS-based statistical models like the weight of evidence(WOE)and frequency ratio(FR)were applied to produce the SESM for the study area.The SESM was reclassified into four classes,i.e.,low,medium,high,and very high zone.The results of WOE for SESM show that 16.39%,33.02%,29.27%,and 21.30%of areas are covered by low,medium,high,and very high zones,respectively.In contrast,the FR results revealed that 16.50%,24.33%,35.55%,and 23.59%of the areas are occupied by low,medium,high,and very high classes.Furthermore,the reliability of applied models was evaluated using the Area Under Curve(AUC)technique.The validation results utilizing the area under curve showed that the success rate curve(SRC)and predicted rate curve(PRC)for WOE are 82%and 86%,respectively,while SRC and PRC for FR are 85%and 96%,respectively.The validation results revealed that the FR model performance is better and more reliable than the WOE.

A Geospatial Analysis of Wetland Cultivated Areas in Ile-lfe, Osun State, Nigeria

Wetlands are among the world＇s most productive environment. They are cradles of bio-diversity, providing the water and primary productivity upon which large numbers of plant and animal species depend for survival. Unfortunately, they are also among the world＇s most threatened ecosystems, owing mainly to continued drainage, urbanization, pollution, over-exploitation or other unsustainable uses of their resources. The lack of baseline wetland inventory and limited accessibility to the available ones have been identified as major limitations for sustainable use and management of wetland resources. This study therefore utilized multispectral remote sensing data and global positioning system （GPS） for mapping and assessing the spatial pattern of wetlands, particularly the cultivated ones. The images were processed using ILWIS 3.2 Academic. The combined digital image processing and visual image interpretation were used to identify and segment wetlands in the image data. The coordinates of all identified wetlands and various anthropogenic activities on them were taken using GPS. Questionnaire forms were designed and randomly administered on the households residing around the wetland areas to enable the study to assess the anthropogenic factors, which are capable of destroying the ecosystem. The result indicated that the original size of wetlands in the study area has been modified as there has been reduction in their area extent. Various anthropogenic factors such as, the conversion of wetlands to agricultural, residential and commercial land uses were majorly responsible for the modification. In conclusion, remote sensing data and GIS technology were found useful in mapping and assessing wetlands for sustainable use.

Detection and Mapping of Violent Crime Hotspots in Southwestern Nigeria

The increasing rate of insecurity in Nigeria, especially the southwest requires a paradigm shift from popular approach to crime hotspots detection. This study employed geospatial technologies to integrate spatio-temporal crime, social media and field observation data from the communities in all the six states in the southwest to develop crime hotspots that can serve as preliminary information to assist in allocating resources for crime control and prevention. Historical crime data from January 1972 to April, 2021 were compiled and updated with rigorous field survey in September, 2021. The field data were encoded, input to the SPSS 17 and analyzed using descriptive statistics and multivariate analysis. A total 936 crime locations data were geolocated and exported to ArcGIS 10.5 for spatial mapping using point map operation and further imported to e-Spatial web-based and QGIS for the generation of hotspot map using heatmap tool. The results revealed that armed robbery, assassination and cultism were more pronounced in Lagos and Ogun States. Similarly, high incidences of farmers/herdsmen conflicts are observed in Oyo and Osun States. Increasing incidences of kidnapping are common in all the south-western states but very prominent in Ondo, Lagos and Oyo States. Most of the violent crime incidents took place along the highways, with forests being their hideouts. Violent crimes are dominantly caused by high rate of unemployment while farmer/herdsmen conflicts were majorly triggered by the scarcity of grazing fields and destruction of arable crops. The conflicts have resulted in the increasing cases of rape and disruption of social group, intake of hard drugs, cult-related activities, low income and revenue generation, and displacement of farmers and infrastructural damages. The study advocates regular retraining and equipping of security agents, establishment of cattle ranch, and installation of sophisticated IP Camera at the crime hotspots to assist in real-time crime monitoring and management.

Geospatial technologies for Chang’e-3 and Chang’e-4 lunar rover missions

This paper presents a brief overview of the geospatial technologies developed and applied in Chang’e-3 and Chang’e-4 lunar rover missions.Photogrammetric mapping techniques were used to produce topographic products of the landing site with meter level resolution using orbital images before landing,and to produce centimeter-resolution topographic products in near real-time after landing.Visual positioning techniques were used to determine the locations of the two landers using descent images and orbital basemaps immediately after landing.During surface operations,visual-positioning-based rover localization was performed routinely at each waypoint using Navcam images.The topographic analysis and rover localization results directly supported waypoint-to-waypoint path planning,science target selection and scientific investigations.A GIS-based digital cartography system was also developed to support rover teleoperation.

Relationships between land use types and urban heat island intensity in Hulu Langat district,Selangor,Malaysia

Background Urban heat island(UHI)is an urban climate phenomenon that primarily responds to urban conditions and land use change.The extent of hard surfaces significantly influences the thermal properties of the land.To address this issue,a novel approach quantifying the association between land use and UHI is developed.This study offers a new technique for effectively estimating the effect of land use on the UHI intensity using the combination of urban heat intensity index(UHII)and land contribution index(LCI)derived from Landsat 8 OLI images.The time-series thermal effect of land use on the UHI intensity can be determined according to the ratio in mean temperature between specific land use and the whole study site.The study was conducted in the Hulu Langat district,Malaysia during 2014-2021.Results The UHI intensity rose from 0.19 in 2014 to 0.70 in 2021.The negative value of LCI for vegetation areas and water bodies obtained its negative contribution to the urban heat island,while the positive value of LCI for bare areas and built-up areas showed its positive effect on the urban heat island.The LCI value for urban areas showed a significant increase in the 7 years such as 0.51,0.66,0.69,and 0.75 for periods 2014,2016,2018,and 2021,respectively.The change in LCI from 2014 to 2021 for the transformation of bare area and forest was recorded to be 0.23 and−0.02,respectively.Thus,the conversion of forests into urban areas had a negative effect on the increment of UHI intensity.Conclusions Overall,these findings are useful for policy-making agency in developing an effective policy for reducing high UHI intensity and planning long-term land use management.

Soil quality estimation using environmental covariates and predictive models:an example from tropical soils of Nigeria

Background:Information addressing soil quality in developing countries often depends on results from small experimental plots,which are later extrapolated to vast areas of agricultural land.This approach often results in misin-formation to end-users of land for sustainable soil nutrient management.The objective of this study was to estimate the spatial variability of soil quality index(SQI)at regional scale with predictive models using soil–environmental covariates.Methods:A total of 110 composite soil samples(0–30 cm depth)were collected by stratified random sampling schemes at 2–5 km intervals across the Cross River State,Nigeria,and selected soil physical and chemical properties were determined.We employed environmental covariates derived from a digital elevation model(DEM)and Senti-nel-2 imageries for our modelling regime.We measured soil quality using two approaches[total data set(TDS)and minimum data set(MDS)].Two scoring functions were also applied,linear(L)and non-linear(NL),yielding four indices(MDS_L,MDS_NL,TDS_L,and TDS_NL).Eleven soil quality indicators were used as TDS and were further screened for MDS using principal component analysis(PCA).Random forest(RF),support vector regression(SVR),regression kriging(RK),Cubist regression,and geographically weighted regression(GWR)were applied to predict SQI in unsampled locations.Results:The computed SQI via MDS_L was classified into five classes:≤0.38,0.38–0.48,0.48–0.58,0.58–0.68,and≥0.68,representing very low(classⅤ),low(classⅣ),moderate(classⅢ),high(classⅡ)and very high(classⅠ)soil quality,respectively.GWR model was robust in predicting soil quality(R^(2)=0.21,CCC=0.39,RMSE=0.15),while RF was a model with inferior performance(R^(2)=0.02,CCC=0.32,RMSE=0.15).Soil quality was high in the southern region and low in the northern region.High soil quality class(>49%)and moderate soil quality class(>14%)dominate the study area in all predicted models used.Conclusions:Structural stability index,sand content,soil oganic carbon content,and mean weight diameter of aggregates were the parameters used in establishing regional soil quality indices,while land surface water index,Sentinel-2 near-infrared band,plane curvature,and clay index were the most important variables affecting soil quality variability.The MDS_L and GWR are effective and useful models to identify the key soil properties for assessing soil quality,which can provide guidance for site-specific management of soils developed on diverse parent materials.

Opening the black box of donor influence on Digital Earth in Africa

Africa’s participation in Digital Earth is uneven.There is a tendency to ascribe this state to prevailing governance and cultural challenges in Africa.However,foreign actors such as donors have an apparent role in shaping geospatial policies and outcomes.Thus far,the complex linkages between external aid and improved social and environmental monitoring and decision-making have been handled as a kind of‘black box’.To better understand the situation,we open the box and focus on the interaction between donors and policy-makers.We use a heuristic from political science,as well as empirical evidence,to describe the policyinfluencing tools that donors employ based on four basic resources donors possess:organization,authority,treasure,and nodality.We show an evolution of tool usage as donors shift from‘old aid’to‘new aid’modalities.The new tools include:technical assistance for geospatial curriculum development,inscription of standards and data access requirements in contracts and grants,cross-agency project design,best-practice analysis,portfolio management,and the use of language to promote participation and accountability.Though these tools reflect donor intent to partner in the realization of Digital Earth,the tools stem from a persisting asymmetric power dynamic between donors and policy-makers.