Digital Map Research Articles

Spatializing soil elemental concentration as measured by X-ray fluorescence analysis using remote sensing data

Spatial soil information has significantly contributed to public policy and planning. The use of X-ray fluorescence spectrometry (XRF) in soil science, primarily in laboratory settings or for isolated point analyses, has been well documented. This study broadens the application of XRF data by integrating it with remote sensing data for a comprehensive understanding of soil patterns and processes. Focusing on a 2574 km2 area in Brazil, we utilized a lab-based XRF instrument to measure the total concentrations of key chemical elements in the soil, such as Al, Si, Ti, and Fe. These geochemical data were then spatialized using the digital soil mapping (DSM) framework. We employed a synthetic bare soil image and elevation data as covariates in the analysis. The spatial accuracy assessment yielded R2 values ranging from 0.65 to 0.81 for Fe, 0.62 to 0.78 for Ti, 0.52 to 0.63 for Si, and 0.48 to 0.63 for Al. The digital soil maps of geochemical elements aligned well with existing pedological, and geological maps as evaluated using multiple correspondence analysis. The integration of spatialized XRF data with DSM and remote sensing techniques shows significant promise in assessing soil variations across landscapes based on their chemical composition.

Eco-Driving on Hilly Roads in a Mixed Traffic Environment: A Model Predictive Control Approach

Human driving behavior significantly affects vehicle fuel economy and emissions on hilly roads. This paper presents an ecological (eco) driving scheme (EDS) on hilly roads using nonlinear model predictive control (NMPC) in a mixed traffic environment. A nonlinear optimization problem with a relevant prediction horizon and a cost function is formulated using variables impacting the fuel economy of vehicles. The EDS minimizes vehicle fuel usage and emissions by generating the optimum velocity trajectory considering the longitudinal motion dynamics, the preceding vehicle’s state, and slope information from the digital road map. Furthermore, the immediate vehicle velocity and angle of the road slope are used to tune the cost function’s weight utilizing fuzzy inference methods for smooth maneuvering on slopes. Microscopic traffic simulations are used to show the effectiveness of the proposed EDS for different penetration rates on a real hilly road in Fukuoka City, Japan, in a mixed traffic environment with the conventional (human-based) driving scheme (CDS). The results show that the fuel consumption and emissions of vehicles are significantly reduced by the proposed NMPC-based EDS compared to the CDS for varying penetration rates. Additionally, the proposed EDS significantly increases the average speed of vehicles on the hilly road. The proposed scheme can be deployed as an advanced driver assistance system (ADAS).

An internal perturbation method to counteract the dynamical degradation of digital chaotic maps and its application

An internal perturbation method to counteract the dynamical degradation of digital chaotic maps and its application

Development of Activity Data for Greenhouse Gas Inventory in Settlements in South Korea

In South Korea, Agriculture, Forestry, and Other Land Use (AFOLU) collates greenhouse gas (GHG) inventories. However, the settlement category lacks a clear definition of land use and activity data. This study proposed a method for examining the settlement spatial extent and constructing activity data to estimate GHG emissions and absorption as a pilot calculation, as well as to provide data for land use classification. Utilizing cadastral maps (CDMs), settlement spatial extents were determined, with settlements occupying approximately 11% of the total land area in 2019, or 9% excluding overlaps. Activity data for settlements were established through a sampling method and analysis of aerial orthoimages from 2000 and 2019. After removing overlaps with digital forest type maps and smart farm maps, settlement activity data covered approximately 18.47% based on CDMs, or 12.66% excluding overlaps. In 2019, CO2 emissions and absorptions were estimated at 622.16 ktCO2yr−1 based on CDMs and 242.16 ktCO2yr−1, excluding overlaps. To enhance GHG inventory calculation consistency and compliance with TACCC principles, clear spatial extents for settlements must be established. This entails constructing activity data and assessing GHG inventories accordingly. GHG inventory statistics should also inform future nationally determined contributions.

Geomorphological Response of Alluvial Streams to Flood Events during Base-Level Lowering: Insights from Drone-Based Photogrammetric Surveys in Dead Sea Tributaries

The geomorphological impact of base-level lowering on ephemeral alluvial streams has been extensively investigated through fieldwork, experimentation, and modeling. Yet, the understanding of hydrological parameters governing the dynamics of the stream’s geometry during discrete flood events is lacking due to limited direct measurements of flood-scale erosion/deposition. The emergence of novel remote sensing methods allows for quantifying morphological modifications caused by floods in alluvial streams. This study utilizes drone surveys and hydrological data to quantitatively investigate the relation between channel evolution in alluvial tributaries draining to the receding Dead Sea and the hydrological characteristics of flash floods. Drone-based photogrammetric surveys were conducted before and after 25 floods, over a period of four years, to generate centimeter-scale Digital Elevation Models (DEM) and orthophoto maps of two major streams. The outcomes of these DEMs are maps of ground elevation changes (erosion/deposition), thalweg longitudinal profiles, and channel cross sections, revealing the incision/aggradation along and across the streams. Statistical comparison of results with flow hydrographs identified potential relations linking the hydrological characteristics of each flood and the corresponding geomorphological modifications. Peak discharge emerged as the primary factor influencing sediment removal, leading to more efficient sediment evacuation and a negative sediment budget with increased discharge. Water volumes of floods also exhibited a secondary effect on the sediment budget. The chronological order of floods, whether first or later in the season, was identified as the primary factor determining incision magnitude. Knickpoints formed at the streams’ outlets during the dry period, when lake-level drops, amplifying the impact of the first flood. These findings have potential implications for infrastructure planning and environmental management in the context of climate change and altered water runoff. The research highlights the efficiency of drone-based photogrammetry for cost-effective and timely data collection, providing invaluable flexibility for field research.

Metric localization for lunar rovers via cross-view image matching

Accurate localization is critical for lunar rovers exploring lunar terrain features. Traditionally, lunar rover localization relies on sensor data from odometers, inertial measurement units and stereo cameras. However, localization errors accumulate over long traverses, limiting the rover’s localization accuracy. This paper presents a metric localization framework based on cross-view images (ground view from a rover and air view from an orbiter) to eliminate accumulated localization errors. First, we employ perspective projection to reduce the geometric differences in cross-view images. Then, we propose an image-based metric localization network to extract image features and generate a location heatmap. This heatmap serves as the basis for accurate estimation of query locations. We also create the first large-area lunar cross-view image (Lunar-CV) dataset to evaluate the localization performance. This dataset consists of 30 digital orthophoto maps (DOMs) with a resolution of 7 m/pix, collected by the Chang’e-2 lunar orbiter, along with 8100 simulated rover panoramas. Experimental results on the Lunar-CV dataset demonstrate the superior performance of our proposed framework. Compared to the second best method, our method significantly reduces the average localization error by 26% and the median localization error by 22%.

Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics

Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment’s findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4–5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis.

CROSSOVER AND EXTENSION OF HAND-DRAWN MAP IN HERITAGE TOURISM: A CASE STUDY

In the realm of thematic map development, hand-drawn maps tailored for tourism hold considerable significance. Nonetheless, their progress is impeded by inherent limitations and challenges, thereby constraining their potential advancement. Concurrently, the emergence and progression of computer and network technology have furnished a proficient platform for fostering interdisciplinary growth in the realm of hand-drawn cartography. The aim of this study is establishing a customized, culturally relevant, metaphorical and de-identified design framework for enhancing visualization in digital interactions, drawing upon the universal design principles inherent in traditional animation. A comprehensive depiction of the design workflow, along with intricate design particulars pertaining to each stage, was demonstrated through a case study centered on the hand-drawn style drawing and map design for Dahuaishu Ancestor Memorial Garden. By adopting such a design framework, the potential outcome entails a more user-friendly and captivating means of disseminating information within digital tourist maps of the same genre. This approach preserves the inherent merits of hand-drawn maps while simultaneously broadening their scope and capabilities.

A framework for optimizing environmental covariates to support model interpretability in digital soil mapping

A common practice in digital soil mapping (DSM) is to incorporate many environmental covariates into a machine-learning algorithm to predict the spatial patterns of soil attributes. Variance inflation factor (VIF), principal component analysis (PCA), and recursive feature elimination (RFE) are three statistical methods that can be used to reduce the number of covariates. This study aims 1) to compare VIF and PCA approaches; 2) to identify an approach to determine the minimum number of covariates in DSM to ensure model parsimony using RFE after using VIF; and 3) to examine methods to interpret the impact of covariates on the variability of the predicted soil properties. The study area was the province of British Columbia (BC), Canada. This study used legacy data for four soil properties to make digital soil maps: soil organic carbon (SOC%), pH, clay%, and coarse fragment (CF%). Seven models were made for each soil property to determine the influence on validation results by using a different number of covariates produced by various methods on validation results. The results showed that the number of covariates could be reduced from 70 to 4 to 12 with only a little or no difference in concordance correlation coefficient (CCC) validation results. The CCC results of pH models using 70 and 7 covariates were both 0.74, and for other soil properties, this difference was negligible. The validation results obtained from PCA models showed that the performance of PCA in reducing the number of covariates was not as effective as when using VIF. Moreover, this study showed that covariates related to precipitation were the most important for modeling SOC%, soil pH, and clay%. Topographic covariates were the most influential covariates for modeling soil CF%. This study emphasizes the potential benefits of combining various data reduction methods to achieve optimal outcomes and generate the most parsimonious and interpretable models.

Analysis of faults and pit chains in Noctis Labyrinthus: Implications for early extension and possible magmatic plumbing

Noctis Labyrinthus has been a region of disputed origin due to its complexity and poor understanding of how various processes and mechanisms may have combined to form it. The surface is an integrated record of intensive tectonic activity expressed by a multiple extended sets of dip-slip faults oriented in different directions, and thought to have acted on this region over its history. These faults are always coalescent to pits and pit chains displaying a complicated geological history in the region. To understand this geological history, we mapped the surface features in Noctis Labyrinthus using the High-Resolution Stereo Camera (HRSC) onboard Mars Express ND2 nadir channel basemaps, and we adapted the Digital Terrain Map (DTM) from the Mission Experiment Gridded Data Record (MEGDR) of Mars Orbiter Laser Altimeter (MOLA) onboard Mars Global Surveyor (MGS) for the topography. We have investigated the spatial distribution and trend of fault systems, the pit chains' morphology, and the correlation between these two types of features. Our results show three fault systems: i) NS and NNE-SSW, ii) EW and ENE-WSW, and iii) NNW-SSE and NW. The analysis of the faults trending, cross-cutting correlation and the superimposition led to identify multiple intersections between these faults that have been alongside with the reactivations of some inherited faults. We interpreted the first system of fault to be related to coeval lateral extension, generated by regional stress tensor, which is probably related to the slight bending of Valles Marineris within two phases of bidirectional extension. The second system of faults has been generated by the radial oblate stress tensor related to the formation of the small shield volcanoes in Syria Planum. However, the third system is likely related to the external driving process, probably in the Tharsis province. We classified pits in four evolutionary stages based on their morphometric attributes. We believe that the formation of the pit chains in Noctis Labyrinthus is related to a surface collapse after a pressure drop related to the magma chamber deflation associated with Syria Planum volcanic province. We propose a deformational model based on early extension and magmatic plumbing as driving processes for the formation of Noctis Labyrinthus.

Rheological‐based digital approach for gel curve analysis of alcohol ethoxylates

AbstractMany surfactants, such as higher mole alcohol ethoxylates like C12‐15–7EO and C9‐11–8EO, when diluted in water, will form a gel at specific concentrations and temperatures. Gels can be highly viscous and semi‐solid and should be avoided since they take time and energy to disperse once formed. Historically, the creation of gel diagrams or maps for our technical product brochures primarily has depended on visual observation, leading to variable interpretations and inconsistent results over time. Also, completing a gel map for one surfactant grade requires a minimum of one day, due to testing many samples across various concentrations and temperatures. To improve objectivity, consistency, and speed in gel mapping, oscillatory rheology was utilized to identify gels using viscoelastic properties by testing samples prepared at various concentrations. The digitization of the gel mapping technique provides two significant benefits. It offers a rheological‐based approach giving a non‐subjective, digital gel map and it is faster than our visual‐based method. Furthermore, this digital method is consistent with our visual‐based method giving good discrimination between surfactant grades and reproducibility within batches of the same grade. This work also demonstrates the promising potential of utilizing machine learning algorithms to model the rheological behavior of gel maps effectively. R and Python, programming languages widely used for data analysis, graphing, and machine learning, were employed. Overall, the new digital approach presented yields several benefits for surfactant gel behavior study, including a reduction in subjectivity, faster data generation, and increased efficiency in the gel map analysis process.

Evaluation of Robot Motion Trajectory Based on Selected Mapping Algorithms

This paper presents the concept of a remotely controlled mobile robot that generates a two-dimensional map of its surroundings. The hardware platform developed relies on the LINUX operating system with the Robot Operating System (ROS) to function properly. The authors focused on discussing the robot's hardware and presenting the software used. In line with the assumptions made, the robot is capable of generating a two-dimensional digital map of its surroundings, as well as of recording images of those surroundings. The robot relies on lidar odometry for identifying its position, meaning that the developed algorithm calculates the vehicle's location based on data from a laser scanner. The main sources of environment-related information acquired by the robot include the following: RPLidar A3M1 laser scanner by Slamtec (generating a digital map) and a 5mpx HD OV5647 camera (capturing images of the surroundings). These devices are coupled with the Raspberry Pi 3B on-board computer via a CSI interface.

Comparison of SWAT and WEPP for Modeling Annual Runoff and Sediment Yield in Agewu - Mariyam Watershed, Northern Ethiopia

The Soil and Water Assessment Tool (SWAT) and the Geographic Water Erosion Prediction Project (Geo-WEPP) were applied to compare modeling of annual runoff and sediment yield in the Agewmariam watershed, eastern Amhara Region, Ethiopia. Spatial and temporal data distributions were required as inputs to run both models. Soil texture and other soil properties were measured in the field and in the laboratory, and soil maps were generated from global digital soil maps. Land use maps were created by manually digitizing Google Earth images. Watersheds were defined using watershed DEMs and gradient maps were created for each runoff event. Runoff samples were collected and analyzed for sediment concentrations in the laboratory; average annual runoff and sediment volumes were estimated using the WEPP and SWAT models. The results were satisfactory compared to the observed values, with R2 values of 0.86 and 0.91 for the SWAT and WEPP models, respectively, and NSE values of 0.54 and 0.71 for the monthly runoff. The estimated annual mean runoff and sediment yield at the watershed outlets were 65.54 mm, 146.14 mm, 43t/ha/yr and 41.7t/ha/yr for the WEPP and SWAT models, respectively. Several sub watersheds were determined to be susceptible to soil erosion and were prioritized, so more attention was given to this area to reduce runoff and soil erosion. Therefore, the SWAT and WEPP models were suitable for estimating annual runoff and sediment volumes. Sediment yields simulated from both models were high and alarming and far exceeded the allowable rate of soil loss.

Szanse reintegracji a remediacja cyfrowa geografii

Nowadays, geographers and specialists in other disciplines perceive information and communication technologies, including GIS software, as part of the inevitable changes in practice and science (including geography). Recent ways of generating, processing, storing, analyzing and sharing data, creating and sharing texts, visualizing, mapping, analyzing, communicating ideas, video, podcasts, and presentations, sharing information, social networks and mainstream media are entirely dependent on computing technologies. Modern science in the digital age uses big data, artificial intelligence – machine learning methods (data mining, pattern recognition, geovisualization, spatial statistics, optimization and spatial simulation), which in turn have renewed interest in and ties with the fields of computational social sciences and data-driven geography, to gain deeper insights into quantitative, time-scale, multi-resolution and multi-scale research. The tool and product of a geographer’s research is, among other things, the map, a medium, a vehicle for geographic theories and ideas and a tool for conveying geo-information. However, the remediation of a traditional map into a digital map involves the transformation, addition, restriction or change of the meaning system of the original. The new digital medium arouses different attitudes, different emotions and triggers new practices of receiving geo-information among professionals and the public. Undoubtedly, this also applies to researchers–geographers who apply in their geospatial research multi-, inter- or transdisciplinary approaches based on different research paradigms. Integrated and problem-focused scientific discourse in geographic research and digital remediation of maps allow us to look at the future of (currently diversified) geographic research with some hope for its reintegration. The aim of the paper is to identify the possibilities, current and prospective paths and research topics of geography in Poland, which are interdisciplinary, combining the field of interest of physical, socio-economic geographers and specialists of space economy, using the modern instrumentarium of information and communication technologies in geography, i.e. geographic information systems.

Field-scale digital mapping of top- and subsoil Chernozem properties

Field-scale digital mapping of top- and subsoil Chernozem properties

Corrigendum to: On-Site Applicability of Terrain Interpolation Based on Semantic Segmentation in 3D Digital Maps of Earthwork Sites

Corrigendum to: On-Site Applicability of Terrain Interpolation Based on Semantic Segmentation in 3D Digital Maps of Earthwork Sites

Introducing the Concept of Sanctuary Sites in Ischemic Stroke.

The topography of arterial territories has been defined using digital maps of supratentorial infarcts. Regions with a high probability of infarction (Pi) exist in the deep compartment due to a paucity of collaterals. However, less attention has been given to regions with a low Pi. Using published digital maps, patients with cortical stroke and documented vessel occlusion were included. Infarcts from T2-weighted magnetic resonance images were segmented and registered onto a standard brain template (Montreal Neurological Institute 152). Segmented magnetic resonance images were averaged to yield the Pi at a voxel level. The overall Pi for the combined arterial territories was calculated to ensure that Pi was in the range of 0 to 1. Sanctuary sites were identified as regions with Pi <0.1. There were 154 patients (63% men; median age, 69 years; and interquartile range, 57-78 years). The magnetic resonance imaging scan used for segmentation was performed at a median interval of 35 (interquartile range, 3-66) days after stroke onset. Sanctuary sites were present in the frontal (gyrus rectus, the paracentral lobule, and orbitofrontal and precentral gyrus), parietal (postcentral, supramarginal, and angular gyrus, superior and inferior parietal lobule, and precuneus and posterior cingulate), and occipital cortex (cuneus, middle, and superior occipital gyrus). We propose that following vessel occlusion, there are cortical regions with a low Pi, which we termed sanctuary sites. The anatomic basis for this observation is the compensatory capacity of leptomeningeal collaterals.

Exploring the driving forces and digital mapping of soil biological properties in semi-arid regions

Soil biological properties (SBPSs) are crucial for soil health and ecosystem functioning. Mapping these properties can provide insights into soil ecosystems and help develop management and conservation strategies. The objective of this study was to map five important indicators of SBPSs, namely urease (Ur), acid phosphatase (ACP), alkaline phosphatase enzyme (ALP) activity, metabolic quotient (qCO2), and soil microbial biomass to soil organic carbon ratio (MBC: SOC) using machine learning algorithms (MLA) and to identify the most important driving factors of these properties. The study was conducted in Honam sub-basin, Lorestan, Iran, on an area of 14,000 ha. 90 surface soil samples (0–30 cm) were collected and analyzed in the laboratory. 60 soil and environmental covariates, including soil, topography, and remote sensing data (RS), were used to represent influencing factors. The most relevant covariates were selected based on correlation and recursive feature elimination (RFE) analysis using two state-of-the-art MLAs, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) trees, were used to predict SBPSs based on the selected covariates. Relative importance analysis and partial dependence plots were used to evaluate the effects of different factors on the prediction of SBPSs. The results showed that 28 soil and environmental covariates were selected as relevant factors. Both RF and XGBoost models performed well and achieved acceptable accuracy (as measured by R2 value). XGBoost outperformed RF on qCO2 (R2 = 0.61), ALP (R2 = 0.73) and Ur (R2 = 0.75), while RF performed better on ACP (R2 = 0.65) and the MBC: SOC (R2 = 0.81). Relative importance analysis showed that ACP and Ur were most strongly associated with the soil variables at 79 % and 42 %, respectively. ALP and qCO2 were strongly correlated with topographic attributes at 56 % and 52 %, respectively. MBS: SOC was about 40 % associated with the RS indices. The influence of each SBPS varied depending on the specific types of covariates. These results highlight the potential of targeted strategies for effective management and protection of soil ecosystem leading to improved soil health and ecosystem function. Further research should focus on developing methods that encompass a broader range of indices that influence soil microbial activity in drylands.

Automatic Extraction Method of Landslide Based on Digital Elevation Model and Digital Orthophoto Map Data Combined in Complex Terrain

This study aims to accurately determine the distribution of landslides in the complex terrain of Jiangdingya, Nanyu Township, Zhouqu County, Gansu Province. The digital orthophoto map (DOM) and digital elevation model (DEM) are used to accurately identify landslide areas and analyze associated data. Based on image-based supervised classification, the influence factor constraint analysis is used to further identify and delineate the landslide area. Three mathematical morphology operations—erosion, dilation, and opening—are then applied to automatically identify and extract landslides. Experimental results demonstrate that achieving an accuracy, precision, and recall of 98.02%, 85.24%, and 84.78% shows that it is possible to better avoid interference caused by complex terrain with rich features. High-resolution DEM and DOM data contain rich spectral and texture information. These data can accurately depict geomorphic features of complex terrain and aid in identifying landslide-prone areas when combined with mathematical morphology processing. This contribution is important for identifying landslides in complex terrain and emergency disaster management.

LOVE Is in the R—Two R Tools for Local Vegetation Reconstruction

Pollen deposition in small lakes and peatlands is composed of local pollen deposition arriving from the nearby vegetation and of regional pollen deposition arriving from farther away. The LOVE model aims to reconstruct past vegetation on a small local scale by extracting only the local pollen signal. To this end, pollen deposition from small sites is related to pollen deposition from large lakes with predominantly regional pollen deposition. We here present a new implementation of the LOVE model in the R environment for statistical computing that is more user friendly than existing implementations. It allows more readily application with fossil pollen data and facilitates extensive testing of the approach in simulated landscapes. The LOVE model derives from critical mathematical assumptions that strongly limit its application. We additionally present LOVEoptim as an adjusted approach to local-scale reconstructions. Other than in LOVE, past local plant abundances are approximated using numerical optimization. Tests in a simulated landscape, which is based on true landscape patterns from digital maps, show that both approaches are valid. Due to fewer mathematical assumptions, LOVEoptim is more widely applicable. The modeling results help to better interpret the spatial scale of vegetation reflected in LOVE/LOVEoptim reconstructions.