Geospatial Tools Research Articles

An Update on the Habitat Suitability Model of <i>Dacrycarpus imbricatus</i> (Blume) de Laub. and Its Conservation Status in Bali, Indonesia

Dacrycarpus imbricatus provides essential ecosystem functions and various potential uses. Therefore, studying this distribution and conservation status in Bali Islands is crucial. The Habitat Suitability Model (HSM) and Geospatial Conservation Assessment Tool (GeoCAT) were used to predict this distribution and conservation status. The results showed changes in the predicted habitat suitability in 2050. Climate change conditions will impact the preferential habitat of the current location. The analysis classifies D. imbricatus as an endangered (EN) species in Bali. The model does not consider anthropogenic factors which change the land use/land cover. Therefore, more severe conservation efforts in Bali are needed for this species.

Eugenia (Myrtaceae) from Mexico: checklist, distribution, and conservation assessments

Myrtaceae is represented in Mexico by 10 genera and ca. 109 species mainly occurring in warmer climatic zones. Eugenia is one of the most diverse genera in Myrtaceae, with about 1,150 species, distributed from Mexico, Cuba and the Antilles to Uruguay and Argentina, with fewer representatives in Africa and Southeast Asia. This study presents a checklist of the 87 species of Eugenia that occur in Mexico as well as provides assessments of environmental threat for each species. The checklist was prepared in stages, gathering data from international databases, consulting relevant bibliography (mainly Mexican Floras), personal visits to key herbarium collections (MEXU, CICY and K), and analysis of specimen images available online. Extinction risk assessments follow the categories and criteria of the IUCN Red List of Threatened species version 3.1. calculating the extent of occurrence (EOO) and area of occupancy (AOO) parameters using the GeoCat software (Geospatial Conservation Assessment Tool). Extinction risks are provided for eighty-seven species of Eugenia, including notes on species distributions. Mexican states with the highest number of species of the genus are Chiapas and Veracruz, with 48 and 31 species, respectively. Forty-six species are endemic to Mexico, which represents 53% of the total of species occurring in the country. Regarding extinction risk, from a total of 87 species, 33 species (38%) are assessed with a threatened category, with 7 Critically Endangered (CR), 14 Endangered (EN), and 12 Vulnerable (VU); 54 species are not threatened, 42 of these rated as Least Concern (LC) and 12 as Near Threatened (NT). This study reveals the degree of extinction risk that exists to native or endemic Mexican Eugenia species, and highlights the urgent need to take action to protect, conserve and monitor their populations in the wild.

Utility of Environmental Complexity as a Predictor of Alzheimer’s Disease Diagnosis: A Big-Data Machine Learning Approach

BackgroundRural-urban differences and spatial navigation deficits have received much attention in Alzheimer’s Disease research. While individual environmental and neighborhood factors have been independently investigated, their integrative, multifactorial effects on Alzheimer’s diagnosis have not. Here we explore this “environmental complexity” for predictive power in classifying Alzheimer’s from cognitively-normal status.MethodsWe utilized data from the National Alzheimer’s Coordinating Center (NACC) uniform data set containing annual visits since 2005 and selected individuals with multiple visits and who remained in their zipcode (N = 22,553). We georeferenced each subject with 3-digit zipcodes of their residences since entering the program. We calculated environmental complexity measures using geospatial tools from street networks and landmarks for spatial navigation in subjects’ zipcode zones. Zipcode zones were grouped into two cognitive classes (Cognitively-Normal and Alzheimer’s-inclined) based on the ratios of AD and dementia subjects to all subjects in an individual zipcode zone. We randomly selected 80% of the data to train a neural network classifier model on environmental complexity measures to predict the cognitive class for each zone, controlling for salient demographic variables. The remaining 20% served as the test set for performance evaluation.ResultsOur proposed model reached excellent classification ability on the testing data: 83.87% accuracy, 95.23% precision, 83.33% recall, and 0.8889 F1-score (F1-score=1 for perfect prediction). The most salient features of “Alzheimer’s-inclined” zipcode zones included longer street-length average, higher circuity, and slightly fewer points of interest. Most “cognitively-normal” zipcode zones appeared in or near urban areas with high environmental complexity measures.ConclusionEnvironmental complexity, reflected in frequency and density of street networks and landmarks features, predicted with high precision the cognitive status of 3-digit zipcode zones based on the etiologic diagnoses and observed cognitive impairment of NACC subjects residing in these zones. The zipcode zones vary widely in size (1.6 km2 to 35,241 km2), and large zipcode zones suffer high spatial heterogeneity. Other proven AD risk factors, such as PM2.5, disperse across zones, and so do individual’s activities, leading to spatial uncertainty. Nevertheless, the model classifies diagnosis well, establishing the need for prospective experiments to quantify effects of environmental complexity on Alzheimer’s development.

Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models

Wildfires are one of the gravest and most momentous hazards affecting rich forest biomes worldwide; India is one of the hotspots due to its diverse forest types and human-induced reasons. This research aims to identify wildfire risk zones in two contrasting climate zones, the Wayanad Wildlife Sanctuary in the Western Ghats and the Kedarnath Wildlife Sanctuary in the Himalayas, using geospatial tools, analytical hierarchy process (AHP), and fuzzy-AHP models to assess the impacts of various conditioning factors and compare the efficacy of the two models. Both of the wildlife sanctuaries were severely battered by fires in the past, with more than 100 fire incidences considered for this modeling. This analysis found that both natural and anthropogenic factors are responsible for the fire occurrences in both of the two sanctuaries. The validation of the risk maps, utilizing the receiver operating characteristic (ROC) method, proved that both models have outstanding prediction accuracy for the training and validation datasets, with the F-AHP model having a slight edge over the other model. The results of other statistical validation matrices such as sensitivity, accuracy, and Kappa index also confirmed that F-AHP is better than the AHP model. According to the F-AHP model, about 22.49% of Kedarnath and 17.12% of Wayanad fall within the very-high risk zones. The created models will serve as a tool for implementing effective policies intended to reduce the impact of fires, even in other protected areas with similar forest types, terrain, and climatic conditions.

RePlant Alfa: Integrating Google Earth Engine and R Coding to Support the Identification of Priority Areas for Ecological Restoration

Land degradation and climate change are among the main threats to the sustainability of ecosystems worldwide. As a result, the restoration of degraded landscapes is essential to maintaining the functionality of ecosystems, especially those with greater social, economic, and environmental vulnerability. Nevertheless, policymakers are frequently challenged by deciding where to prioritize restoration actions, which usually includes dealing with multiple and complex needs under an always limited budget. If these decisions are not taken based on proper data and processes, restoration implementation can easily fail. In order to help decision-makers take informed decisions on where to implement restoration activities, we have developed a semiautomatic geospatial platform to prioritize areas for restoration activities based on ecological, social, and economic variables. This platform takes advantage of the potential to integrate R coding, Google Earth Engine cloud computing, and GIS visualization services to generate an interactive geospatial decision-maker tool for restoration. Here, we present a prototype version called “RePlant alpha”, which was tested with data from the Central Zone of Chile. This exercise proved that integrating R and GEE was feasible, and that the analysis with at least six indicators for a specific region was also feasible to implement even from a personal computer. Therefore, the use of a virtual machine in the cloud with a large number of indicators over large areas is both possible and practical.

Morphometric Analysis of River Basin: A Case Study of River Indrayani Using Geospatial Tools

Morphometric Analysis of River Basin: A Case Study of River Indrayani Using Geospatial Tools

A geospatial risk analysis graphical user interface for identifying hazardous chemical emission sources.

Performing back trajectory and forward trajectory using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a reliable approach for assessing particle transport after release among mid-field atmospheric models. HYSPLIT has an externally facing online interface that allows non-expert users to run the model trajectories without requiring extensive training or programming. However, the existing HYSPLIT interface is limited if simulations have a large amount of meteorological data and timesteps that are not coincident. The objective of this study is to design and develop a more robust tool to rapidly evaluate hazard transport conditions and to perform risk analysis, while still maintaining an intuitive and user-friendly interface. HYSPLIT calculates forward and backward trajectories of particles based on wind speed, wind direction, and the corresponding location, timestamp, and Pasquill stability classes of the regions of the atmosphere in terms of the wind speed, the amount of solar radiation, and the fractional cloud cover. The computed particle transport trajectories, combined with the online Proton Transfer Reaction-Mass Spectrometry (PTR-MS) data (https://figshare.com/articles/dataset/ARL_Data_from_PROS_station_at_Hanford_site/19993964), can be used to identify and quantify the sources and affected area of the hazardous chemicals' emission using the potential source distribution function (PSDF). PSDF is an improved statistical function based on the well-known potential source contribution function (PSCF) in establishing the air pollutant source and receptor relationship. Performing this analysis requires a range of meteorological and pollutant concentration measurements to be statistically meaningful. The existing HYSPLIT graphical user interface (GUI) does not easily permit computations of trajectories of a dataset of meteorological data in high temporal frequency. To improve the performance of HYSPLIT computations from a large dataset and enhance risk analysis of the accidental release of material at risk, a geospatial risk analysis tool (GRAT-GUI) is created to allow large data sets to be processed instantaneously and to provide ease of visualization. The GRAT-GUI is a native desktop-based application and can be run in any Windows 10 system without any internet access requirements, thus providing a secure way to process large meteorological datasets even on a standalone computer. GRAT-GUI has features to import, integrate, and convert meteorological data with various formats for hazardous chemical emission source identification and risk analysis as a self-explanatory user interface. The tool is available at https://figshare.com/articles/software/GRAT/19426742.

Identification of suitable zones and sites for rainwater harvesting using GIS and multicriteria decision analysis

Identifying suitable zones for surface water storage and groundwater recharge is needed to enhance irrigation water availability. This study was undertaken to map rainwater harvesting (RWH) potential zones using geospatial tools and analytic hierarchy process (AHP). The suitable locations for RWH were also mapped using the Boolean logic concept. The surface runoff is a vital factor to demarcate the appropriate zones for RWH. The curve number approach was used to estimate the surface runoff potential. The runoff coefficient (RC) map was generated based on rainfall and surface runoff depth. Weights have been allocated to selected themes of RC, drainage density, and slope. The themes were integrated using geographic information system (GIS) and AHP to demarcate the suitable zones for RWH. The derived RWH potential map was categorized into zones like "very good," "good," "moderate," "poor," and "very poor" with an aerial extent of 14.3%, 24.7%, 28.3%, 20.2%, and 12.6%, respectively. The area suitable for farm ponds was found to be about 9% (408 km2), 13% (329 km2), and 4% (244 km2) in Mirzapur, Chandauli, and Sonbhadra districts, respectively. Furthermore, 22, 15, and 21 locations were found suitable for check dams in Mirzapur, Chandauli, and Sonbhadra districts, respectively. At a large scale, effective planning of water management strategies based on multicriteria decision analysis and GIS offers increased availability of surface and groundwater resources and may help for enhancing the agricultural land use options. The higher resolution maps may be further utilized to plan RWH strategies at village level.

The usage of geospatial tools in traffic sign detection

The topic of this scientific paper is the detection of traffic signs based on the data obtained using modern geodetic methods (LiDAR, mobile laser scanning), by using geospatial tools. In a total of 100 photos taken in an urban area in Germany, in a length of 500 m, 75 traffic signs were detected. Each sign is assigned a corresponding code from the catalog of German traffic signs, after what the photos are connected to the point cloud. Then the extraction of bounding boxes and central points of traffic signs was done. The visualization was done in two softwares -Bentley MicroStation and QGIS, which completed the detection process. The accuracy of traffic sign detection in photos is 83% (62/75). The extraction of bounding boxes and central points on the point cloud was performed with a high degree of accuracy (85%).

The State of Trajectory Visualization in Notebook Environments

Gaining insights from trajectory datasets is a challenging task that requires suitable visual data representations. There is a considerable gap between the state-of-the-art cartographic techniques presented in the literature and currently available spatial data science toolboxes. This review paper presents the current state of geospatial visualization tools for trajectory data, focusing on the Python and Jupyter notebooks ecosystem. The shortcomings identified provide pointers for further scientific software development, as well as a reference for data scientists in choosing the best-fitting tool for a specific job.

Deep Learning-based Sensing and Extended Reality Technologies, Visual Recognition and Geospatial Mapping Tools, and Virtual Simulation and Spatial Cognition Algorithms in Digital Twin Cities and Immersive 3D Environments

Deep Learning-based Sensing and Extended Reality Technologies, Visual Recognition and Geospatial Mapping Tools, and Virtual Simulation and Spatial Cognition Algorithms in Digital Twin Cities and Immersive 3D Environments

Virtual Data Modeling and Geospatial Mapping Tools, Data-driven Internet of Things and Sustainable Urban Computing Systems, and Digital Twin Simulation and Extended Reality Technologies in Augmented Reality-powered Immersive Spaces and 3D Digital Environments

Virtual Data Modeling and Geospatial Mapping Tools, Data-driven Internet of Things and Sustainable Urban Computing Systems, and Digital Twin Simulation and Extended Reality Technologies in Augmented Reality-powered Immersive Spaces and 3D Digital Environments

Potential application of GIS and remote sensing to evaluate suitable site for livestock production in Northwestern part of Bangladesh

The livestock resources of Bangladesh are under tremendous strain due to several natural and anthropogenic causes. Especially in the Northwestern region of Bangladesh, these resources are more vulnerable to deterioration resulting from human actions, a lack of environmental and rangeland legislation, climate change, drought, poor management, and inadequate disaster mitigation plans. GIS based multicriteria decision analysis (MCDA) and remote sensing techniques have been used in this research to locate ideal land for sheep, goats, buffalo and cow production. In this study, the suitability analysis for livestock production has been considered in eight thematic layers: slope, land use & land cover (LULC), soil types, rainfall, water accessibility, road distance, relative humidity, and average temperature. Besides, this research had been used geospatial tools for combining these eight geographical layers, whenanalytical hierarchy process, aMCDA approach helped tomeasure the weight of each criterion. The final map that is perfect for raising cattle has been divided into four categories, such as low, medium, high and excellent. Each of these rangeland suitability groups has portions in the Northwestern region of Bangladesh that fall into the following percentages: 11.14%, 26.07%, 35.27%, and 27.53%. This research also depicts that the western part of the study region, which includes Thakurgaon, Panchagar, Dinajpur, Naogaon, Joypurhat and Bogra has a low suitability index for livestock production while the eastern region, which includes Kurigram, Nilphamari, Pabna, Lalmonirhat, Gaibandha, Rangpur and Sirajganj contributes to an excellent zone. The outcome of this study will be useful to identify the best places for livestock production in the Northwestern part of Bangladesh. Finally, this research may additionally assist government officials in creating environmental and rangeland strategies for the livestock population in the study area.

A remote sensing approach to explore the available water spread for fisheries development in the reservoirs of Godavari River Basin, Telangana, India

Reservoirs have tremendous fisheries potential amongst the inland water resources and aptly referred as ‘sleeping giants’, as the fish yield potential of these resources are yet to be tapped. Existing gaps in the availability of absolute information and data on the inland water resources hinder the applicability and implementation of various fisheries development programmes, which in turn impact inland fisheries production. The present study was carried out in the reservoirs viz., Sri Ram Sagar, Kaddam and Swarna from the Godavari Basin, covering the period 2016-2021, as a case study to demonstrate the use of remote sensed data in fisheries stock enhancement planning. The perennial and seasonal water spread area of the reservoirs under study, estimated through composite water maps prepared using Sentinel 2A data ranged between 8 to 19 and 4 to 29%, respectively. Further, the potential area for enclosure fish culture (both cage and pen culture) in these reservoirs were found to range between 14.89% (Sir Ram Sagar) to 48.54% (Kaddam). The field validated results revealed less than 0.1% of the perennial water spread area mapped in these reservoirs is under use for cage culture. This highlights under-utilisation of these reservoirs and demonstrates scope for developing enclosure fish culture or other culture-based fisheries for enhancing the fish production. The study also demonstrates the use of geospatial tools in development planning for expanding enclosure fish culture in the reservoirs and in turn enhancing fisheries production from the reservoirs in India. Keywords: Composite water maps, Enclosure fish culture, Geospatial tools, Godavari River, NDWI, Reservoir, Sentinel 2A data, Water spread area

Analyzing Water Poverty Components Using Geospatial Tools: Resource and Environmental Constraint in Kathmandu District

This current paper examines water poverty situation in Kathmandu district in terms of water resource and environment constraints. Integrated methodology was adopted for the study. Water source sample survey was carried out using GPS tool. Study area comprised total of 13 water sample sites. Household questionnaire survey was carried out for water poverty mapping and analysis. Resource component of water poverty comprised indicators, namely seasonal variation in water availability, water supply frequency and groundwater recharge potential. The environment constraint component comprised water quality rate of soil erosion and topographic wetness index. The result show that the average calculated value of water resource limitation component is 6.31 and out of 13 studied communities, 7 communities fall below average. It is found that environmental constraint is less associated with urban housing density. It is found that environmental constraint is less associated with urban housing density. The average calculated value of environmental component is 3.43 and out of 13 studied communities, 8 communities fall below average. Spatial variability in water poverty is prominent and highest water poverty is found in urban cores. Communities with lower water poverty are found in peri-urban location near the foothills. The average calculated value of water poverty is 9.74 and out of 13 studied communities, 9 communities fall below the average. The study concluded that water poverty index and resultant map is a very effective tool to visualize the distribution of water poverty at local spatial scale and to present the complex nature of water poverty.

Anthropogenic risk assessment of riverine habitat using geospatial modelling tools for conservation and restoration planning: a case study from a tropical river Pranhita, India.

The riverine ecosystem provides multiple benefits to human community and contributes to the sustainable development of the ecoregion. The growing dependency on these ecosystems has largely contributed to aggravating the ecological risks, habitat degradation, and loss of ecosystem services. The present study evaluates the ecological risk emanating from nine anthropogenic stressors including river use, hydro-morphology, catchment pollution, and biological stressor on river Pranhita in Godavari Basin of Peninsular India using InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) Habitat Risk Assessment model. The primary field survey, remote sensing, and secondary data-assisted spatial modelling results revealed low ecological risk (R = 0.65 of 3) in river Pranhita due to anthropogenic activities. Sediment loading, the inflow of nitrogen, and habitat fragmentation were the major stressors with relatively higher risk score (> 1); influence on a sizeable portion of riverine habitat (29-75% of the total area under high-risk zone) indicates the mounting threat from catchment activities. The low-risk value observed in protected river reaches as compared to unprotected areas is likely to be influenced by the abundant presence of intact riparian vegetation which mitigate the catchment stressors and minimal anthropogenic activity within protected areas. This study demonstrates the application of InVEST HRA model for ecological risk assessment of riverine ecosystems and fish assemblages along with their input data generation framework. This has the potential for prioritization of sensitive habitats based on computed ecological risk and stressor identification based on their exposure and consequences for developing appropriate mitigation measures. This model is spatially explicit and accommodates user-defined criteria for ecosystem-level assessment at a regional and national scale to facilitate the resource managers and policymakers for conservation and restoration planning and implementation of targeted management measures for sustainable development.

Urban Heat Island vulnerability mapping using advanced GIS data and tools

Urban Heat Island (UHI) is a phenomenon that can cause hotspots in city areas due to dense, impervious infrastructure and minimal vegetation cover. UHI hotspots may become worse in extreme heat events that are already affecting many regions across the globe due to increased frequent hot extremes, human-induced warming in cities, and rapidly growing urbanization, as documented by the latest IPCC report 2021. In seeking to support designers, planners, and decision-makers in developing and implementing adaptation strategies and measures to make our cities sustainable and resilient, reliable projections and modelling are required. In this study, we modelled UHI vulnerability using high-resolution spatial data, advanced geospatial tools, and socio-demographic data. This modified vulnerability approach drew upon UHI index maps and 20 select customized indicators of heat exposure, population sensitivity, and mobility/adaptive capacity. The indicators were Delphi evaluated and weighted, and the methodology was applied against the City of Greater Geelong municipality in Australia. The resulting UHI index maps indicated significant hotspots in areas of high building density, commercial/industrial zones, newly constructed sites, and zones with low urban green infrastructure. These UHI maps, in combination with selected indicators, highlighted the areal concentration of heat risk areas and vulnerable locations for the sensitive human population. The highlighted areas were primarily concentrated in high building density and high population density areas, which was seen through correlation curves. However, the building density showed a weak correlation, and population per meshblock indicated a strong correlation with UHI measurements. This study provides a comprehensive analysis of risk mapping and vulnerability assessment using GIS geospatial data for the advancement of a major local government area and concludes that this methodology has replicability incomparable geographical regions.

Used of Geospatial tools to detect the Flood Hazards in Dibru River Basin, Assam (India)

Mapping of Flood hazards is an essential component of land use planning. It creates awareness among the mass people about the impact of floods within the region. This study mainly focuses on the flood hazards within the Dibru River Basin. The region is the plain region near the bank of the river, or in the low land area where flood inundation is being observed. The primary aim of this study is to categorize the region into a very high flood zone, moderate flood zone and low flood zone. Different parametric are used in order to prepare the flood hazards mapping. GIS plays a vital role in processing all these parameters at one platform. GIS tool is frequently used to prepare flood hazard maps. It provides an effective way of assembling information from different maps. It can support planning and development by identifying high-risk locations and steering development away from them.

Leveraging data driven approaches for enhanced tsunami damage modelling: Insights from the 2011 Great East Japan event

This study aims at developing an empirical, multi-variable tsunami damage model for buildings, based on machine-learning algorithms which leverage about 250.000 ex-post data surveyed by the Japanese Ministry of Land, Infrastructure and Transportation after the 2011 Great East Japan event in the Tōhoku region. By implementing simple geospatial tools, the dataset is integrated with additional explanatory variables, including, among others, factors accounting for the mutual interaction between the inundated structures. Tests on models’ sensitivity to the number and type of input features used for model development reveal the importance, on the predictive performance, of considering usually neglected mechanisms like the shielding effect and the debris impact generation. The analysis for the potential spatial transferability indicates a reduction in the accuracy, thus suggesting a better suitability of empirical models for descriptive purposes, limiting their predictive ability only to region-specific cases.

Application of a sustainable location index approach to landfill site selection in Monterrey, Mexico.

Landfilling is the main method to manage municipal solid waste (MSW) in Latin America due to the economic, technological and political characteristics of the region. The disposal of MSW in landfill sites may affect the quality of the environment and compromise a considerable share of the municipal budgets. The selection of suitable sites reduces the environmental and economic impact of landfills. In the present study the sustainable location index (SLI) is proposed as a methodology to assess environmentally, and economically, sanitary landfill site selection in the Metropolitan Area of Monterrey, a representative large-size city of Latin America. EVIAVE methodology was modified to include administrative and economic dimensions, which are assessed as a sustainable approach using together the SLI integrated with geospatial and multicriteria analysis tools. The assessment showed that the zones with the lowest SLI changes drastically when the importance of the economic or environmental factor varies. This result suggest that the inclusion of sustainability in landfill site selection decision-making is complex and it may requires the inclusion of local particularities such as municipal budgets, policies of MSW management and public perception about environmental deterioration.