High-resolution Geospatial Data Research Articles

Detection of Urban Growth in Taiz City, Yemen, Between 1981 and 2022 by Using Google Earth™ and Geographical Information System Data

Rapid urbanization brought serious socio-economic and environmental problems in many cities around the world, particularly in developing countries like Yemen. Urban "sprawl", a synonym of rapid and unplanned urban growth, is considered an obstacle for sustainable development, which causes serious long- and short-term problems, such as extreme infrastructure expenses, economic inefficiency, environmental destruction, and social instability. This is the case of Taiz City, the third largest city in Yemen, which is facing a huge challenge to manage the spatial extension of its built-up area at the expense of the reduction of natural resources and environment. This study aims to investigate urban growth process in Taiz City, focusing on urban sprawl and agricultural land-use change during 1981–2022. The data used are high resolution geospatial data, which are the topographic map for 1981 and Google Earth (GE) images for 2003, 2012, and 2022. The methodology adopted was manual digitizing using Geographic Information System (GIS) software to extract urban land use features from the images of the different dates. Four land-use maps were produced and used for calculation the urban sprawl and land-use changes. The urban expansion rate and patterns were also identified in three phases: 1981–2003, 2003–2012, and 2012–2022. The results show that the built-up area increased six times from 6.2 km2 in 1981 to 34 km2 in 2022, while the agriculture area decreased by 77% from 18 km2 in 1981 to 4.2 km2 in 2022. The urban sprawl in Taiz covered about 75% of its municipal area and extended to the agricultural and rural areas. The sprawl index (USI) is measured to be 5.4%, indicating a high degree of sprawl in Taiz. There is no open space left for future development, and the water resources are at risk of population and depletion. The study demonstrated that informal and haphazard expansion must be controlled, a development strategy should be prepared, so that sustainable urban growth can be achieved. The results of this study could be used as a decision support tool for urban management activities. The vector layers produced by this study could provide the required input data for the future urban modeling of the city.

Unveiling and modelling: Land degradation, poverty nexus in Nigeria's semi-arid (jigawa state) using cloud-based geospatial data

Land degradation and multidimensional poverty represent global challenges whose interconnected dynamics are poorly understood and hinder effective interventions. The aim of this study was to address these issues by integrating the RULSE model, the Vegetation Condition Index (VCI), and the Multidimensional Poverty Index (MPI). Using high-resolution geospatial data from Sentinel-2 via the Google Earth Engine, land-use changes were identified as indicators of degradation, with VCI critical for assessing vegetation health. A holistic poverty assessment using MPI data from the Nigerian National Bureau found strong evidence of land degradation in these regions, with approximately 60–75% experiencing multidimensional poverty. Spatial overlap highlights the close connection between severe land degradation and high multidimensional poverty rates. The correlation analysis provided insights into the relationships between land degradation variables (vegetation status, soil loss, and digital elevation) and poverty patterns (population dynamics). This study highlights how soil erosion negatively impacts agriculture and perpetuates the vicious cycle of poverty. This study illuminates the complex relationship between land degradation and poverty, and argues for further exploration, technological integration, and a nuanced understanding of the components of poverty. Advanced assessments can guide targeted policies and interventions to break the cycle of land degradation and poverty.

Automated geovisualization of flood disaster impacts in the global South cities with open geospatial data sets and ICEYE SAR flood data

Flooding is a highly destructive natural disaster affecting millions of people annually, causing substantial global economic losses. Especially in the Global South, vulnerable populations with limited resources for flood protection and recovery suffer the most. However, effective flood impact analysis and standardized geospatial data processing and visualization is lacking, hindering effective disaster risk reduction. This study employs Python to develop a standardized, automated analysis and visualization model to address flood impacts after a high magnitude event anywhere in the world by using global geospatial data sets.In order to test its applicability two relevant cases, with different locations and scales, were selected, (Bangkok, Thailand, and Tula de Allende, Mexico). In both cases the automated flood model efficiently processed flood extent and depth data from ICEYE SAR images, added the population estimates from the German Aerospace Center, and key infrastructure elements from OpenStreetMap. The output of the automated process is presented as web-based interactive maps, offering insights into flooding severity and impacts with minimal user input. This quick approach (processing time between 41 s and 10,5 min) facilitates timely responses by first responders, with great potential to aid and improve the efficiency of international humanitarian efforts.By leveraging global high-resolution geospatial data for local-level analysis, this study demonstrates the versatility and time-saving benefits of this automated analysis and visualization. Automated models standardize results, minimizing human errors, and enabling consistent historical flood data comparison. Even though the databases are gathered from trusted sources, additional evaluation of uncertainties of the results with field data should be considered in further development of this tool. Nevertheless, this research highlights the worldwide potential, and especially the Global South, of automated global geospatial analysis to improve disaster impact reduction by enhancing efficient response efforts.

Design of Mixed Reality Visualization System for Operational Situation Using Cloud-based Geospatial Information

The importance of geospatial information is increasingly highlighted in the defense domain. Accurate and up-to-date geospatial data is essential for situational awareness, target analysis, and mission planning in millitary operations. The use of high-resolution geospatial data in military operations requires large storage and fast image processing capabilities. Efficient image processing is required for tasks such as extracting useful information from satellite images and creating 3D terrain for mission planning, In this paper, we designed a cloud-based operational situation mixed reality visualization system that utilizes large-scale geospatial information distributed processed on a cloud server based on the container orchestration platform Kubernetes. We implemented a prototype and confirmed the suitability of the design.

Methods and insights on enabling geovisualisation for coastal communities of the North Shore of Vancouver

Contemporary sea level rise (SLR) research seldom considers enabling effective geovisualisation for the communities. This lack of knowledge transfer impedes raising awareness on climate change and its impacts. The goal of this study is to produce an online SLR map accessible to the public that allows them to interact with evolving high-resolution geospatial data and techniques. The study area was the North Shore of Vancouver, British Columbia, Canada. While typically coarser resolution (10 m+/pixel) Digital Elevation Models have been used by previous studies, we explored an open access airborne 1 m LiDAR which has a higher resolution and vertical accuracy and can penetrate tree cover at a higher degree than most satellite imagery. A bathtub method model with hydrologic connectivity was used to delineate the inundation zones for various SLR scenarios which allows for a not overly complex model and process using standard tools such as ArcGIS and QGIS with similar levels of accuracy as more complex models, especially with the high-resolution data. Deep learning and 3D visualizations were used to create past, present, and modelled future Land Use/Land Cover and 3D flyovers. Analysis of the possible impacts of 1, 2, 3, and 4 m SLR over the unique coastline, terrain, and land use was detailed. The generated interactive online map helps local communities visualize and understand the future of their coastlines. We have provided a detailed methodology and the methods and results are easily reproducible for other regions. Such initiatives can help popularize community-focused geovisualisation to raise awareness about SLR.

Assessment of green space benefits and burdens for urban health with spatial modeling

We present a new spatial model to quantify and map health-related impacts of urban green space (UGS), with the aim to address major shortcomings of existing models and to meet the needs of urban spatial planners for quantitative, spatially-explicit assessment of both the health benefits and burdens of UGS. This GIS-based model uses publicly available high-resolution geospatial data to estimate street-level values of five major determinants of urban health that are significantly influenced by urban spatial design (including UGS): unattractive views, heat stress, air pollution, perceived unsafety and tick-bite risk. We also describe a potential major application of the model for urban spatial planners, a so-called hotspot analysis for a set of five urban design-related health problems, which can assist in setting spatial priorities in urban greening strategies as well as in designing effective greenspace interventions. Hotspots are areas within a city where urban design-related health problems are the largest. An analysis for Maastricht (The Netherlands) showed that in hotspots UGS was mostly a net burden, but also that redesign of UGS could make its role more beneficial for human health.

Quantifying agricultural productive use of energy load in Sub-Saharan Africa and its impact on microgrid configurations and costs

The use of advanced energy technologies for agricultural purposes—such as irrigation, refrigeration, crop processing, and egg incubation—has the potential to increase crop yield, reduce vulnerability to changing precipitation patterns, increase shelf life, strengthen income and employment opportunities in rural areas, and reduce emissions by displacing fossil fuel-based technologies. These productive uses of energy (PUE) in remote areas could potentially be powered by microgrids that additionally serve otherwise unelectrified communities, most of which are located in rural Sub-Saharan Africa. In this paper, we use high-resolution geospatial data to estimate the end-use electricity demand for a range of agricultural PUE across Sub-Saharan Africa, and we share these data in an open-access mapping tool. Next, we use REopt®, a techno-economic optimization model of energy systems, to determine the cost and system sizing implications of incorporating agricultural PUE into microgrid designs in Kenya and Zambia. We estimate the upper bound of agricultural PUE demand for irrigation, milling, shelling, refrigeration, and egg incubation across Sub-Saharan to be 16.8 TWh/yr. We find that incorporating local agricultural PUE into microgrid system designs increases the required system sizing while having minimal impact on the levelized cost of energy of these systems. Our analysis is the first to demonstrate the PUE potential in the agricultural sector at a 10x10-kilometer resolution across Sub-Saharan Africa and to show, at scale, how site-specific PUE can impact the cost and sizing of microgrids that are otherwise deployed to serve local household and community load.

Development of a high-resolution top-down model to estimate actual household-level heat pump electricity consumption

Heat pumps can play an important part in decarbonizing the residential sector due to their use of electricity instead of fossil fuels, and their high efficiency, which often exceeds 100%. However, heat pump performance and energy savings vary with climate and individual household energy usage. Recent studies have used geospatial models to estimate potential heat pump energy consumption across the United States. Yet most studies use generic and oversimplified heat pump models. We contribute to this field with a geospatial model based on manufacturer data and measured test data for 16 different R410A, high efficiency, variable speed compressor heat pumps. Using linear regression, we estimate a market average of COP with respect to ambient temperature. From this, we can identify the variation in efficiency with temperature across this technology class. We also use linear regression to estimate demand for heating and cooling as a function of ambient temperature and household characteristics. We compare the performance of both the predicted energy demand and heat pump efficiency against measured data from a heat pump-equipped house in West Lafayette, Indiana, and find that the model predicts daily heat pump electricity consumption with 27.8% relative error, comparable to other building simulation models. By incorporating high-resolution geospatial data inputs, such top-down models can still maintain a large scope across technologies and diverse climates while increasing spatial and temporal resolution.

Use of High-Resolution Geospatial and Genomic Data to Characterize Recent Tuberculosis Transmission, Botswana.

Combining genomic and geospatial data can be useful for understanding Mycobacterium tuberculosis transmission in high-burden tuberculosis (TB) settings. We performed whole-genome sequencing on M. tuberculosis DNA extracted from sputum cultures from a population-based TB study conducted in Gaborone, Botswana, during 2012-2016. We determined spatial distribution of cases on the basis of shared genotypes among isolates. We considered clusters of isolates with ≤5 single-nucleotide polymorphisms identified by whole-genome sequencing to indicate recent transmission and clusters of ≥10 persons to be outbreaks. We obtained both molecular and geospatial data for 946/1,449 (65%) participants with culture-confirmed TB; 62 persons belonged to 5 outbreaks of 10-19 persons each. We detected geospatial clustering in just 2 of those 5 outbreaks, suggesting heterogeneous spatial patterns. Our findings indicate that targeted interventions applied in smaller geographic areas of high-burden TB identified using integrated genomic and geospatial data might help interrupt TB transmission during outbreaks.

UAV-Based Remote Sensing for Detection and Visualization of Partially-Exposed Underground Structures in Complex Archaeological Sites

The utilization of remote sensing technologies for archaeology was motivated by their ability to map large areas within a short time at a reasonable cost. With recent advances in platform and sensing technologies, uncrewed aerial vehicles (UAV) equipped with imaging and Light Detection and Ranging (LiDAR) systems have emerged as a promising tool due to their low cost, ease of deployment/operation, and ability to provide high-resolution geospatial data. In some cases, archaeological sites might be covered with vegetation, which makes the identification of below-canopy structures quite challenging. The ability of LiDAR energy to travel through gaps within vegetation allows for the derivation of returns from hidden structures below the canopy. This study deals with the development and deployment of a UAV system equipped with imaging and LiDAR sensing technologies assisted by an integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) for the archaeological mapping of Dana Island, Turkey. Data processing strategies are also introduced for the detection and visualization of underground structures. More specifically, a strategy has been developed for the robust identification of ground/terrain surface in a site characterized by steep slopes and dense vegetation, as well as the presence of numerous underground structures. The derived terrain surface is then used for the automated detection/localization of underground structures, which are then visualized through a web portal. The proposed strategy has shown a promising detection ability with an F1-score of approximately 92%.

Four-century history of land transformation by humans in the United States (1630–2020): annual and 1 km grid data for the HIStory of LAND changes (HISLAND-US)

Abstract. The land of the conterminous United States (CONUS) has been transformed dramatically by humans over the last four centuries through land clearing, agricultural expansion and intensification, and urban sprawl. High-resolution geospatial data on long-term historical changes in land use and land cover (LULC) across the CONUS are essential for predictive understanding of natural–human interactions and land-based climate solutions for the United States. A few efforts have reconstructed historical changes in cropland and urban extent in the United States since the mid-19th century. However, the long-term trajectories of multiple LULC types with high spatial and temporal resolutions since the colonial era (early 17th century) in the United States are not available yet. By integrating multi-source data, such as high-resolution remote sensing image-based LULC data, model-based LULC products, and historical census data, we reconstructed the history of land use and land cover for the conterminous United States (HISLAND-US) at an annual timescale and 1 km × 1 km spatial resolution in the past 390 years (1630–2020). The results show widespread expansion of cropland and urban land associated with rapid loss of natural vegetation. Croplands are mainly converted from forest, shrub, and grassland, especially in the Great Plains and North Central regions. Forest planting and regeneration accelerated the forest recovery in the Northeast and Southeast since the 1920s. The geospatial and long-term historical LULC data from this study provide critical information for assessing the LULC impacts on regional climate, hydrology, and biogeochemical cycles as well as achieving sustainable use of land in the nation. The datasets are available at https://doi.org/10.5281/zenodo.7055086 (Li et al., 2022).

An open access geospatial database for the sub-Antarctic Prince Edward Islands

Researchers of projects at the sub-Antarctic Prince Edward Islands are increasingly considering geospatial data as an essential component in answering scientific questions. A need exists for high-resolution geospatial data in both multi- and transdisciplinary research to better analyse fine-scale biotic–abiotic interactions of the Islands’ landscape and ecosystems within the context of climate change and the impacts of invasive species. However, much of the geospatial data that currently exist have limitations in spatial coverage and/or resolution, are outdated, or are not readily available. To address these issues, we present an online geospatial database for the Prince Edward Islands (both islands) produced from a high-resolution digital surface model and satellite imagery. This database contains vector files, raster data sets, and maps of topographical and hydrological parameters. It is freely available to download from Figshare – an open access data repository. We encourage the South African polar science community to make use of similar platforms for improved data sharing practices. Significance: A topographical and hydrological geospatial database – produced from a 1 m x 1 m digital surface model of the Prince Edward Islands – is provided. These fine-scale geospatial data allow for a more comprehensive assessment of biotic–abiotic interactions at an island scale. Also included are locality maps specifying place names and established long-term marine mammal monitoring beaches and coastal zones for improved cross-referencing. The dataset is downloadable from an open access data repository and intended to promote open science and data sharing practices. Open data set: https://doi.org/10.6084/m9.figshare.19248626

Developing High-Resolution Population and Settlement Data for Impactful Malaria Interventions in Zambia.

Foundational high-resolution geospatial data products for population, settlements, infrastructure, and boundaries may greatly enhance the efficient planning of resource allocation during health sector interventions. To ensure the relevance and sustainability of such products, government partners must be involved from the beginning in their creation, improvement, and/or management, so they can be successfully applied to public health campaigns, such as malaria control and prevention. As an example, Zambia had an ambitious strategy of reaching the entire population with malaria vector control campaigns by late 2020 or early 2021, but they lacked the requisite accurate and up-to-date data on infrastructure and population distribution. To address this gap, the Geo-Referenced Infrastructure and Demographic Data for Development (GRID3) program, Akros, and other partners developed maps and planning templates to aid Zambia's National Malaria Elimination Program (NMEP) in operationalizing its strategy.

Estimates of species-level tolerance of urban habitat in North American birds.

Species vary in their responses to urban habitat; most species avoid these environments, whereas others tolerate or even thrive in them. To better characterize the extent to which species vary in their responses to urban habitat (from this point forwards "urban tolerance"), we used several methods to quantify these responses at a continental scale across all birds. Using open access community science-derived data from the eBird Status and Trends Products and two different types of high-resolution geospatial data that quantify urbanization of landscapes, we calculated urban tolerance for 432 species with breeding ranges that overlap large cities in Canada or the USA. We developed six different calculations to characterize species-level urban tolerance, allowing us to assess how each species' relative abundance across their breeding range varied with estimates of urban habitat use and intensity. We assessed correlations among these six indices, then compressed the two best-performing indices into a single principal component (multivariate urban tolerance index) that captured variation in urban tolerance among species. We assessed the accuracy of our single and multivariate urban tolerance indices using 24 test species that have been well characterized for their tolerance or avoidance of the urban habitat, as well as with previously published, independent urban tolerance estimates. Here, we provide this new dataset of species-level urban tolerance estimates that improves upon previous metrics by incorporating continental-scale, continuous estimates that better differentiate species' tolerance of urban habitat compared with existing, categorical methods. These refined metrics can be used to test hypotheses that link ecological, life history, and behavioral traits to avian urban tolerance. The dataset is licensed as CC-By Attribution 4.0 International. Users must appropriately cite the data paper and dataset if used in publications and scientific presentations.

Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties

Abstract. Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here, we present a data-driven ozone-mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone from sparse, irregularly placed measurement stations to a high-resolution regular grid using machine learning methods. The produced map contains the average tropospheric ozone concentration of the years 2010–2014 with a resolution of 0.1∘ × 0.1∘. The machine learning model is trained on AQ-Bench (“air quality benchmark dataset”), a pre-compiled benchmark dataset consisting of multi-year ground-based ozone measurements combined with an abundance of high-resolution geospatial data. Going beyond standard mapping methods, this work focuses on two key aspects to increase the integrity of the produced map. Using explainable machine learning methods, we ensure that the trained machine learning model is consistent with commonly accepted knowledge about tropospheric ozone. To assess the impact of data and model uncertainties on our ozone map, we show that the machine learning model is robust against typical fluctuations in ozone values and geospatial data. By inspecting the input features, we ensure that the model is only applied in regions where it is reliable. We provide a rationale for the tools we use to conduct a thorough global analysis. The methods presented here can thus be easily transferred to other mapping applications to ensure the transparency and reliability of the maps produced.

SNOW AVALANCHE SUSCEPTIBILITY MAPPING FOR DAVOS, SWITZERLAND

Abstract. Snow avalanches are among destructive hazards occurring in mountainous regions and spatial distribution (susceptibility) of their occurrences needs to be considered for spatial planning and disaster risk mitigation efforts. The susceptibility assessment is the first step in avalanche disaster management and can be carried out using high resolution geospatial data and machine learning (ML) algorithms. In this study, we have assessed the snow avalanche susceptibility in Davos, Switzerland using an inventory delineated on satellite imagery in a previous study. The conditioning factors used for the avalanche susceptibility assessment include elevation, slope, plan curvature, profile curvature, aspect, topographic position index, topographic ruggedness index, topographic wetness index, land use and land cover, lithology, distance to road, and distance to the river. Two ML algorithms, the logistic regression (LR) and the random forest (RF), were comparatively assessed using validation data split from the training data (30/70). The prediction performances of both models were assessed based on the area under the receiver operating characteristic curve (ROC-AUC) value. Although the AUC value obtained from the LR method was relatively low (0.74), the value obtained from the RF (0.96) demonstrated high performance and usability of this approach. The results indicate that the RF method can successfully produce an avalanche susceptibility map for the region, although potential improvements may be possible by investigating various input features and ML algorithms as well as by classifying the starting and runout zones of the avalanche data separately. Furthermore, the accuracy is expected to increase by using a larger training dataset.

Climate Change, Drought, and Potential Environmental Migration Flows Under Different Policy Scenarios

Unmitigated climate change will likely produce major problems for human populations worldwide. Although many researchers and policy-makers believe that drought may be an important “push” factor underlying migration in the future, the precise relationship between drought and migration remains unclear. This article models the potential scope of such movements for the emissions policy choices facing all nation-states today. Applying insights from climate science and computational modeling to migration research, we examine the likely surge of drought-induced migration and assess the prospects of different policy scenarios to mitigate involuntary displacement. Using an ensemble of 16 climate models in conjunction with high-resolution geospatial population data and different policy scenarios, we generate drought projections worldwide and estimate the potential for internal and international population movement due to extreme droughts through the remainder of the 21stcentury. Our simulations suggest that a potential for drought-induced migration increases by approximately 200 percent under the current international policy scenario (corresponding to the current Paris Agreement targets). In contrast, total migration increases by almost 500 percent, should current international cooperation fail and should unrestricted policies toward greenhouse gas emissions prevail. We argue that despite the continued growth projections of drought-induced migration in all cases, international cooperation on climate change can substantially reduce the global potential for such migration, in contrast to unilateral policy approaches to energy demands. This article highlights the importance of modeling future environmental migrations, in order to manage the pressures and unprecedented policy challenges which are expected to dramatically increase under conditions of unmitigated climate change.

UAV Survey for Landslide Hazard Assessment in the Former Min-Kush Uranium Processing Site

In March 2013 a large landslide of almost one million cubic meters happened in the Min-Kush uranium processing site in the Kyrgyz Republic. This hazardous landslide area is connected to radioactive and toxic tailings of mining industry located in the center of Tien-Shan mountain system. The present research represents the results of work done by using Unmanned Aerial Vehicle (UAV) for photographing the landslide area, which was made for monitoring of this potentially hazardous phenomenon. Based on these images and geodetic measurements of the landslide, orthophotos as well as generated Digital Surface Model (DSM) with an accuracy of less than ±5 cm resulted. The application of the UAV in the man-made landslide study has been established as a cost and time effective monitoring tool that provides researchers and engineers with high-resolution geospatial data.

Artificial intelligence for air quality

Artificial intelligence for air quality IntelliAQ is an ERC Advanced Grant project to explore the application of cutting-edge machine learning techniques to global air quality data in combination with high resolution geospatial and weather data. It combines novel data management and data science approaches to build the foundation for innovative air quality information services.

High-resolution analysis of observed thermal growing season variability over northern Europe

Strong historical and predicted future warming over high-latitudes prompt significant effects on agricultural and forest ecosystems. Thus, there is an urgent need for spatially-detailed information of current thermal growing season (GS) conditions and their past changes. Here, we deployed a large network of weather stations, high-resolution geospatial environmental data and semi-parametric regression to model the spatial variation in multiple GS variables (i.e. beginning, end, length, degree day sum [GDDS, base temperature + 5 °C]) and their intra-annual variability and temporal trends in respect to geographical location, topography, water and forest cover, and urban land use variables over northern Europe. Our analyses revealed substantial spatial variability in average GS conditions (1990–2019) and consistent temporal trends (1950–2019). We showed that there have been significant changes in thermal GS towards earlier beginnings (on average 15 days over the study period), increased length (23 days) and GDDS (287 °C days). By using a spatial interpolation of weather station data to a regular grid we predicted current GS conditions at high resolution (100 m × 100 m) and with high accuracy (correlation ≥ 0.92 between observed and predicted mean GS values), whereas spatial variation in temporal trends and interannual variability were more demanding to predict. The spatial variation in GS variables was mostly driven by latitudinal and elevational gradients, albeit they were constrained by local scale variables. The proximity of sea and lakes, and high forest cover suppressed temporal trends and inter-annual variability potentially indicating local climate buffering. The produced high-resolution datasets showcased the diversity in thermal GS conditions and impacts of climate change over northern Europe. They are valuable in various forest management and ecosystem applications, and in adaptation to climate change.