Search Results (97)

Foraminifera are widely used in paleoclimatic and paleoceanographic studies, providing information about past ocean conditions. However, in order to use these tracers, it is essential to obtain an accurate chronology. Radiocarbon has proven to be a powerful tool in developing robust chronologies. Sample sizes of a few milligrams of carbonate material are needed for precise radiocarbon determination using accelerator mass spectrometry (AMS). In the specific case of paleoceanographic and paleoenvironmental studies, Foraminifera microfossils are the most important indicator of oceanic conditions. However, for establishing the chronology of deposition, sample availability is often limited. In AMS facilities using solid ion sources, such as the Radiocarbon Laboratory of the Universidade Federal Fluminense (LAC-UFF), in Brazil, CO₂ samples need to be converted to graphite after physical and chemical pre-treatment to remove contamination. Reducing the sample sizes increases the relative contribution of contamination and can favor increased background levels. In this work, we tested different amounts of ¹⁴C-free carbonate samples as a means to evaluate the pattern of contamination. For the sealed tube Zn/TiH₂ graphitization method, we tested prebaking the graphitization tubes and compared storage procedures. As a result, the background for regular-sized samples was decreased, and accurate measurement of carbonate samples containing ca. 0.5 mg C could be performed. Prebaked graphitization tubes can safely be stored in desiccator cabinets for up to 4 weeks. Foraminifera samples with mass as low as 1 mg (ca. 0.1 mg C) can now be measured at the LAC-UFF AMS facility, provided that C contamination can be estimated and corrected. The developments presented in this work allowed for the study of species-specific Foraminifera and other small-sized carbonate samples. Full article

The creation of crop type maps from satellite data has proven challenging and is often impeded by a lack of accurate in situ data. Street-level imagery represents a new potential source of in situ data that may aid crop type mapping, but it requires automated algorithms to recognize the features of interest. This paper aims to demonstrate a method for crop type (i.e., maize, wheat and others) recognition from street-level imagery based on a convolutional neural network using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery using the Picture Pile application. The classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for others. Given that wheat and maize are two of the most common food crops grown globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved global crop type monitoring. Challenges remain in addressing the noise aspect of street-level imagery (i.e., buildings, hedgerows, automobiles, etc.) and uncertainties due to differences in the time of day and location. Such an approach could also be applied to developing other in situ data sets from street-level imagery, e.g., for land use mapping or socioeconomic indicators. Full article

An enumeration of spatial autocorrelation’s (SA’s) polyvalent forms occurred nearly three decades ago. Attempts to conceive and disseminate a clearer explanation of it employ metaphors seeking to better relate SA to a student’s or spatial scientist’s personal knowledge databank. However, not one of these uses the jigsaw puzzle metaphor appearing in this paper, which exploits an analogy between concrete visual content organization and abstract map patterns of attributes. It not only makes SA easier to understand, which furnishes a useful pedagogic tool for teaching novices and others about it, but also discloses that many georeferenced data should contain a positive–negative SA mixture. Empirical examples corroborate this mixture’s existence, as well as the tendency for marked positive SA to characterize remotely sensed and moderate (net) positive SA to characterize socio-economic/demographic, georeferenced data. Full article

There has been a growth in the number of composite indicator tools used to assess community risk, vulnerability, and resilience, to assist study and policy planning. However, existing research shows that these composite indicators vary extensively in method, selected variables, aggregation methods, and sample size. The result is a plethora of qualitative and quantitative composite indices to choose from. Despite each providing valuable location-based information about specific communities and their qualities, the results of studies, each using disparate methods, cannot easily be integrated for use in decision making, given the different index attributes and study locations. Like many regions in the world, the Arctic is experiencing increased variability in temperatures as a direct consequence of a changing planetary climate. Cascading effects of changes in permafrost are poorly characterized, thus limiting response at multiple scales. We offer that by considering the spatial interaction between the effects of permafrost, infrastructure, and diverse patterns of community characteristics, existing research using different composite indices and frameworks can be augmented. We used a system-science and place-based knowledge approach that accounts for sub-system and cascade impacts through a proximity model of spatial interaction. An estimated ‘permafrost vulnerability surface’ was calculated across Alaska using two existing indices: relevant infrastructure and permafrost extent. The value of this surface in 186 communities and 30 military facilities was extracted and ordered to match the numerical rankings of the Denali Commission in their assessment of permafrost threat, allowing accurate comparison between the permafrost threat ranks and the PVI rankings. The methods behind the PVI provide a tool that can incorporate multiple risk, resilience, and vulnerability indices to aid adaptation planning, especially where large-scale studies with good geographic sample distribution using the same criteria and methods do not exist. Full article

Reference evapotranspiration (ETo) estimation is essential for water resources management. The present research compares four different ETo estimators based on reanalysis data (ERA5-Land) and in situ observations from three different cultivation sites in Greece. ETo based on FAO56-Penman–Monteith (FAO-PM) is compared to ETo calculated from the empirical methods of Copais, Valiantzas and Hargreaves-Samani using both reanalysis and in situ data. The daily and monthly biases of each method are calculated against the FAO56-PM method. ERA5-Land data are also compared to ground-truth observations. Additionally, a sensitivity analysis is conducted on each site for different cultivation periods. The present research finds that the use of ERA5-Land data underestimates ground-truth-based ETo by 35%, approximately, when using the FAO56-PM method. Additionally, the use of other methodologies also shows underestimation of ETo when calculated with ERA5-Land data. On the contrary, the use of the Valiantzas and Copais methodologies with in situ observations shows overestimation of ETo when compared to FAO56-PM, in the ranges of 32–62% and 24–56%, respectively. The sensitivity analysis concludes that solar radiation and relative humidity are the most sensitive variables of the Copais and Valiantzas methodologies. Overall, the Hargreaves-Samani methodology was found to be the most efficient tool for ETo estimation. Finally, the evaluation of the ERA5-Land data showed that only air temperature inputs can be utilized with high levels of confidence. Full article

Climate change is altering the conditions to which communities have adapted. The Köppen–Geiger classification system can provide a compact metric to identify regions with notable changes in climatic conditions. Shifting Köppen–Geiger climate zones will be especially impactful in regions with large populations. This study uses high-resolution datasets on Köppen–Geiger climate zones and populations to quantify the number of people affected by shifting climate zones (i.e., population exposure to shifting climate zones). By the end of this century, 9–15% of the Earth’s land surface is projected to shift its climate zone. These shifts could affect 1.3–1.6 billion people (14–21% of the global population). Many of the affected people live in areas that were classified as temperate in the historical period. These areas are projected to be classified as tropical or arid in the future. This study presents a new metric for exposure to climate change: the number of people living in areas whose climate zone classification is projected to shift. It also identifies populations that may face climatic conditions in the future that deviate from those to which they have adapted. Full article

Colonia communities, which host forgotten Americans, lack essential services such as portable water, adequate wastewater and solid waste disposal, adequate drainage, and adequate paved roads. The aim of this study is to investigate five key aspects of the colonias in the Rio Grande Valley (RGV), which include the total count of colonias in the valley, their susceptibility to public health hazards, flooding occurrences, the transformations that have occurred over the past two decades, and community resilience. This research utilizes two datasets, namely the Colonia Database from the Texas Secretary of State and the community resiliency estimates from the Census Bureau. Geographical information systems (GIS) methods are employed to analyze the spatial and temporal distribution of colonia communities. The principal results reveal that colonia communities host 14% of the RGV’s total 1.37 million population. About half of the total colonia population resides in Hidalgo County, followed by Starr, Cameron, and Willacy counties. About 87% of the total colonia communities exist in census tracts characterized by low or very low community resiliency. Furthermore, 26% of the total colonia communities experiencing flooding after rainfall are in tracts with low or very low community resiliency. This study provides the major conclusion that while there have been slight improvements in the colonias’ susceptibility to public health risks within the past two decades, there still remains significant developmental work. Without tackling these challenges, achieving meaningful progress in community resilience becomes a daunting task. Applying an environmental justice lens to the issues faced by colonia communities helps shed light on the systemic inequalities and injustices they experience. Full article

This paper analyzes the performance of the open-source OpenDroneMap image processing software (ODM) across multiple platforms. We tested desktop and laptop computers as well as high-performance cloud computing and supercomputers. Multiple machine configurations (CPU cores and memory) were used. We used eBee S.O.D.A. drone image datasets from Namibia and northern Finland. For testing, we used the OpenDroneMap command line tool with default settings and the fast orthophoto option, which produced a good quality orthomosaic. We also used the “rerun-all option” to ensure that all jobs started from the same point. Our results show that ODM processing time is dependent upon the number of images, a high number of which can lead to high memory demands, with low memory leading to an excessively long processing time. Adding additional CPU cores is beneficial to ODM up to a certain limit. A 20-core machine seems optimal for a dataset of about 1000 images, although 10 cores will result only in slightly longer processing times. We did not find any indication of improvement when processing larger datasets using 40-core machines. For 1000 images, 64 GB memory seems to be sufficient, but for larger datasets of about 8000 images, higher memory of up to 256 GB is required for efficient processing. ODM can use GPU acceleration, at least in some processing stages, reducing processing time. In comparison to commercial software, ODM seems to be slower, but the created orthomosaics are of equal quality. Full article

The most extensive coverage of surficial sediment samples collected to date on Egypt’s Nile Delta coast and shelf is needed to better define sediment dispersal patterns across this setting’s rapidly eroding margin. Changes in time are now induced by River Nile sediment cutoff by dams, sea level rise, marked shelf subsidence, and regional climate changes, which have altered the amounts and components of sediments; these require replacement, along with the implementation of more effective coastal protection measures. Multiple computer-generated offshore maps depict the distributions and proportions of sand, silt, and mud; the mean grain size and standard deviation (sorting); heavy mineral concentrations; and carbonate content. Heavy mineral lobes at the coast and offshore identify former Nile branch sites. Channel lobes extending seaward resulted from their progradational phase and from the delta’s altered sedimentation from the early to late Holocene. The progressive deposition and erosion of these fossil fluvial lobes, and of two active Nile channels, selectively removed their quartz and less dense minerals, thus concentrating heavy minerals on the coast and inner shelf. The prolonged dispersal of original sediment effluence from relict and recent Nile tributaries induced variable depositional patterns on the present shelf. These coastal depocenters, along with extensive sand, silt, and mud from shelf sediments, were reworked further seaward and dispersed by bottom currents, thus masking most previous onshore-to-offshore transport patterns. The major surficial features document long-term responses to the diverse dispersal that influenced the shoreline to the outer shelf deposits from the Pleistocene to the present. Full article

This paper presents the development procedure and significance of a web mapping application designed for disseminating, exploring, and analyzing Kaupert’s 19th-century Maps of Attica, Greece. The application facilitates historical and geographical study by providing access to high-resolution map images and overlaying multiple vector layers of geospatial data. The paper outlines the methods used to create the application, which includes the process of interpreting, digitizing, and organizing the original mapped data, georeferencing the historical cartographic sheets, and developing the web-based mapping application. The results of this work include a comprehensive and interactive digital reference tool for studying the ancient topography of Attica, as well as a framework for future research. Overall, this work highlights the potential of digital technologies to transform the way we approach and study historical maps and other cultural artifacts. Full article

Jamaica, as a Small Island Developing State (SIDS), is highly vulnerable to weather extremes. As precipitation persistence is a critical factor in determining the susceptibility of an area to risks, this work assesses the spatial and temporal variations of rainfall persistence in Jamaica from 1981 to 2020, using satellite-based information. The Hurst exponent (H) and the serial correlation coefficient (SCC) are used to evaluate the long-term persistence of precipitation and the Persistence Threshold (PT) concept is introduced to provide a description of rainfall characteristics over short periods, specifically, the number of consecutive days with precipitation above or below a set threshold value. The PT method is a novel concept that expands upon the Consecutive Dry Days (CDD) and Consecutive Wet Days (CWD) methods that only consider a threshold of 1 mm. Results show notable temporal and spatial variations in persistence over the decades, with an overall increasing trend in high precipitation persistence and a decreasing trend in low precipitation persistence. Geographically, the northern mountainous area of Jamaica received the most persistent rainfall over the study period with an observed increase in extreme rainfall events. The excess rainfall of the 2001–2010 decade is remarkable in this study, coinciding with the global unprecedented climate extremes during this time. We conclude that the data used in this study is viable for understanding and modeling rainfall trends in SIDS like Jamaica, and the derived PT method is a useful tool for short-term rainfall trends, but it is just one step toward determining flood or drought risk. Further research will focus on developing drought and flood indices. Full article

As the world’s urban population increases to the predicted 70% of the total population, urban infrastructure and built-up land will continue to grow as well. This growth will continue to have an impact on the urban heat island effect in all of the world’s cities. The urban tree canopy has been found to be one of the few factors that can lessen the effects of the urban heat island effect. This study seeks to accomplish two objectives: first, we examine the use of a commonly used machine learning classifier (e.g., Support Vector Machine) for identifying the urban tree canopy using no-cost high resolution NAIP imagery. Second, we seek to use Land Surface Temperature (LST) maps derived from no-cost Landsat thermal imagery to identify correlations between canopy loss and temperature hot spot increases over a 14-year period in Columbia, SC, USA. We found the SVM imagery classifier was highly accurate in classifying both the 2005 imagery (94.3% OA) and the 2019 imagery (94.25% OA) into canopy and other classes. We found the color infrared image available in the 2019 NAIP imagery better for identifying canopy than the true color images available in 2005 (97.8% vs. 90.2%). Visual analysis based on the canopy maps and LST maps showed temperatures rose near areas where tree canopy was lost, and urban development continued. Future studies will seek to improve classification methods by including other classes, other ancillary data sets (e.g., LiDAR), new classification methods (e.g., deep learning), and analytical methods for change detection analysis. Full article

Microhabitats can provide thermal niches that affect geographic range shifts of species as the climate changes and provide refuges for pest and beneficial insect populations in agricultural regions. The spatial distribution of microhabitats is influenced by topography that can influence local extinction and recolonization by animal populations. Scaling local temperature-dependent processes to a regional scale of population expansion, and contraction requires the validation of biophysical models of near surface temperatures. We measured temperature at 2.5 cm above and below ground at 25 sites in each of the two regions: southern and northern Utah, USA. Using NichMapR version 3.2.0, we modeled the temperature at these same sites with local slopes and aspects for four years for the former and eight years for the latter region. Empirical and modeled air temperatures differed by 7.4 °C, on average, and soil temperatures differed less (4.4 °C, on average). Site-specific additions of hill shading at 25 m distance or soil parameters did not improve the agreement of the empirical and modeled temperatures. A hybrid model for air temperature that incorporated soil temperature at 0 cm depth when snow depth exceeded 3 cm resulted in an average improvement of 8% that was as great as 31%. Understanding biological processes at the regional scale and in projected future climates will continue to require biophysical modeling. To achieve the widest applications possible, biophysical models such as NichMapR need to be validated with empirical data from as wide a variety of altitudes, latitudes, soil types, and topographies wherein organisms currently inhabit and where their ranges might expand to in the future. Full article

Automation in map production has created the need for modeling the map composition process. Generalization is the most critical process in map composition, with considerable impact on the quality of features portrayed on the maps. Modeling of the generalization process has been an area of research for several years in the international cartographic community. Constraint-based generalization modeling prevailed, and it is evolving to an agent model or to other optimization models. The generalization model presented in this paper is based on constraint-based modeling. It introduces the standardization of the semantic and cartographic generalization process together with an evaluation mechanism for the assessment of the quality of the resulting cartographic data considering simultaneously the preservation of the shape of the portrayed linear and area features. For cartographers, quality management is a key factor in creating an evidence-based, reliable product. To achieve this objective, cartographers, drawing on international experience, should implement a quality policy and adopt a quality management system (QMS) as an integral part of the map production process, starting with the quality assessment of the input data and finishing with the evaluation of the final product. Full article

Examining the similarity of event environments or surroundings—more precisely, settings—provides additional insight in analyzing event sequences, as it provides information about the context and potential common factors that may have influenced them. This article proposes a new similarity measure for event setting sequences, which involve the space and time in which events occur. While similarity measures for spatiotemporal event sequences have been studied, the settings and setting sequences have not yet been studied. While modeling event setting sequences, we consider spatial and temporal scales to define the bounds of the setting and incorporate dynamic variables alongside static variables. Using a matrix-based representation and an extended Jaccard index, we developed new similarity measures that allow for the use of all variable data types. We successfully used these similarity measures coupled with other multivariate statistical analysis approaches in a case study involving setting sequences and pollution event sequences associated with the same monitoring stations, which validate the hypothesis that more similar spatial-temporal settings or setting sequences may generate more similar events or event sequences. In conclusion, the developed similarity measures have wide application beyond the case study to other disciplinary contexts and geographical settings. They offer researchers a powerful tool for understanding different factors and their dynamics corresponding to occurrences of spatiotemporal event sequences. Full article