Topographic Information Research Articles

Evaluation of Machine Learning Models for Predicting Soil Texture Using Sentinel-1A SAR and Topographic Information

Evaluation of Machine Learning Models for Predicting Soil Texture Using Sentinel-1A SAR and Topographic Information

Assessment of the Impact of LID Techniques on Water Cycle Recovery in Industrial Complexes

Urbanization and industrialization have significantly disrupted the natural water cycle, leading to increased runoff, flooding, and groundwater depletion. Research on LID (Low Impact Development) techniques is ongoing in order to address these challenges. This study has analyzed the improvement in the water cycle system by applying LID techniques to the Daejeongcheon watershed in Ulsan. Using SWMM, this study has assessed the long-term effects of LID on runoff reduction and water cycle recovery rate improvement. Based on local climate data, topographical information, and regional characteristics, LID elements such as permeable pavements, green roofs, bioretention, and vegetative swales were applied. The results showed that the LID techniques reduced runoff by 8.44% and also improved the water cycle recovery rate by 13.52%. Additionally, the study confirmed that LID techniques effectively contributed to managing runoff from the 25.5 mm rainfall management target. LID techniques are expected to serve as sustainable water management strategies in urbanized and industrialized areas, offering effective restoration and management solutions.

WRF-HEATS coupling: Incorporating human behaviors and city topography into urban heat stress evaluation

Urban human thermal stress can be inaccurately estimated along with less-understood heat heterogeneity due to the absence of high-resolution meteorological information and realistic human behavior representation. To this end, we coupled a regional climate model (weather research and forecasting model, WRF) and a human energy balance model (human-environment adaptive thermal stress model, HEATS) to predict pedestrian's dynamic thermal stress at a neighborhood scale. The WRF-human coupling system resolves human-environment heat exchanges based on meteorological and topographical information with the consideration of dynamic human activities. The coupling system has been tested and utilized to study dynamic heat stress in a typical hot, humid, and mountainous city, Hong Kong. Our results revealed widespread heat heterogeneity with up to 7 °C difference in Physiological Subjective Temperature (PST) in the core urban area, and extreme heat exposure (up to 45 °C PST) in calm-wind zones at noon. Heat stress can be further aggregated considering realistic human behaviors such as extra clothing (e.g., protective facemask during pandemics) and physical exercise (e.g., walking along inclined terrain). Optimal-thermal-comfort routes have been designed and suggested based on the simulated neighborhood-scale heat stress map.

Estimating Grassland Carrying Capacity in the Source Area of Nujiang River and Selinco Lake, Tibetan Plateau (2001–2020) Based on Multisource Remote Sensing

Estimating the spatiotemporal variations in natural grassland carrying capacity is crucial for maintaining the balance between grasslands and livestock. However, accurately assessing this capacity presents significant challenges due to the high costs of biomass measurement and the impact of human activities. In this study, we propose a novel method to estimate grassland carrying capacity based on potential net primary productivity (NPP), applied to the source area of the Nujiang River and Selinco Lake on the Tibetan Plateau. Initially, we utilize multisource remote sensing data—including soil, topography, and climate information—and employ the random forest regression algorithm to model potential NPP in areas where grazing is banned. The construction of the random forest model involves rigorous feature selection and hyperparameter optimization, enhancing the model’s accuracy. Next, we apply this trained model to areas with grazing, ensuring a more accurate estimation of grassland carrying capacity. Finally, we analyze the spatiotemporal variations in grassland carrying capacity. The main results showed that the model achieved a high level of precision, with a root mean square error (RMSE) of 4.89, indicating reliable predictions of grassland carrying capacity. From 2001 to 2020, the average carrying capacity was estimated at 9.44 SU/km2, demonstrating a spatial distribution that decreases from southeast to northwest. A slight overall increase in carrying capacity was observed, with 65.7% of the area exhibiting an increasing trend, suggesting that climate change has a modest positive effect on the recovery of grassland carrying capacity. Most of the grassland carrying capacity is found in areas below 5000 m in altitude, with alpine meadows and alpine meadow steppes below 4750 m being particularly suitable for grazing. Given that the overall grassland carrying capacity remains low, it is crucial to strictly control local grazing intensity to mitigate the adverse impacts of human activities. This study provides a solid scientific foundation for developing targeted grassland management and protection policies.

Physics‐Informed Neural Networks for the Augmented System of Shallow Water Equations With Topography

AbstractPhysics‐informed neural networks (PINNs) are gaining attention as an alternative approach to solve scientific problems governed by differential equations. This work aims at assessing the effectiveness of PINNs to solve a set of partial differential equations for which this method has never been considered, namely the augmented shallow water equations (SWEs) with topography. Differently from traditional SWEs, the bed elevation is considered as an additional conserved variable, and therefore one more equation expressing the fixed‐bed condition is included in the system. This approach allows the PINN model to leverage automatic differentiation to compute the bed slopes by learning the topographical information during training. PINNs are here tested for different one‐dimensional cases with non‐flat topography, and results are compared with analytical solutions. Though some limitations can be highlighted, PINNs show a good accuracy for the depth and velocity predictions even in the presence of non‐horizontal bottom. The solution of the augmented system of SWEs can therefore be regarded as a suitable alternative strategy to deal with flows over complex topography using PINNs, also in view of future extensions to realistic problems.

Digital mapping of dates of transplanting and accumulated thermal requirement of rice (Oryza sativa L.) in the subtropics of North Eastern Hill Region, India

ABSTRACT Accurate and timely mapping of rice areas is crucial for effective agricultural planning and food security management in the North Eastern Hill Region of India, especially amid changing climate conditions. This study developed a method to estimate rice transplanting dates by utilizing VH polarized backscattering-coefficients rom Sentinel-1A SAR data and also assessed thermal requirements for rice productivity in Ri-Bhoi district, Meghalaya during kharif 2021. The methodology involved 3 steps- (i) preprocessing data to construct time-series SAR images (ii) rule-based classification of SAR data integrating phenological and topographical information to identify transplanting windows (iii) assessing mapping accuracy. The results when compared with field data, revealed five distinct transplanting windows from early June to early August across 9031 ha of rice area. Overall accuracy was 85%, with a kappa coefficient of 0.74. User and producer accuracies ranged from 0.74 to 0.93 and 0.75 to 0.92, respectively for various transplanting windows. GDD varied between 1432.8°C day and 3290.4°C day, with higher values corresponding to earlier planting dates. This study formulates a robust methodology for rice mapping in hilly, small-field landscapes with multiple transplanting dates by integrating Sentinel-1A SAR data with ancillary and crop phenology information, contributing to improved crop management and climate change adaptations.

Строение центральной части палеопротерозойского Лосевского террейна по данным плотностного моделирования

The results of detailed three-dimensional density modeling of the upper crust of the central part of the Losevsky terrane located in the northern part of the Voronezh crystal massif are considered. The distribution of absolute crustal density values of to a depth of 16 km was obtained as a result of the inversion of local anomalies of the gravitational field in the Bouguer reduction. The solution of the inverse problem of gravimetry was implemented within the framework of the initial model, which was formed using digital geological and geophysical data from the study area: the regional density model of the East European Platform lithosphere and its corresponding regional gravity field; the field of local gravity anomalies, representing the difference between the observed and regional fields of the lithosphere model; generalized information on the density of sedimentary cover rocks and the crystal basement; the thickness scheme of the «gravity-active» layer, obtained on the basis of statistical analysis of the anomalous field; geological and topographic information. Quantitative interpretation of the gravimetry data allowed us to obtain fundamentally new information about the geological structure of the upper crust of the study area. The reliability of the obtained results is confirmed by the consistency of a priori data and the correspondence of the total model (local plus regional) and observed gravitational fields.

Probabilistic crop type mapping for ex-ante modelling and spatial disaggregation

Agricultural land use and management fundamentally impacts the condition of natural resources like waterbodies, soils, and biodiversity. Modelling the anthropogenic effects on those resources over time requires detailed knowledge of the temporal and spatial distribution of crops. However, currently available crop type maps for Europe either lack the required spatial resolution or the temporal and spatial coverage. We develop and apply a probabilistic, spatially explicit crop type mapping approach that is suitable for ex-post and ex-ante modelling. The approach allows to quantify epistemic and aleatoric uncertainty related to estimated crop shares by providing an ensemble of maps. We implement the method for the EU-28 for the years 2010 – 2020, distinguishing between 28 different crop types at 1 km resolution. Based on a model of the data generating process that conceptually links field-, grid cell- and region-level crop acreages, our approach considers soil, climate, and topography information, as well as administrative data. The validation with ground-truthing data for France indicates that the generated crop type maps are plausible. The provided uncertainty intervals capture differences in uncertainty across space and time and correctly identify grid cells and crops where estimations are less precise. The generated maps constitute a unique data product of high practical value, e.g., for agri-environmental modelling applications. We see additional potential in using the approach to disaggregate the regional or national predictions of socio-economic ex-ante prediction models.

UAV, GNSS, Total Station, and Data Management Applied to an Ancient Clay Structure as a Historic Building Information Modeling Proposal: A Case Study of Huaca Arco Iris (Trujillo, Peru)

In light of current risks and environmental impacts, HBIM (historic building information modeling) offers a highly efficient and interactive method for managing historical data and representing the current states of ancient clay structures. In this study, traditional geodetic techniques were employed to digitally locate a structure without compromising its topographic information to create an accurate model. Tools such as total stations, GNSS receivers, and UAVs were utilized to generate detailed topography of the study site and its surroundings. An ontology-based data management structure was also developed to store historical data and site intervention projects, adhering to the ISO 12006-2 standard. This was achieved through automated scripts in Dynamo softwarev.2.18.1. A comparison between the point cloud (279 images) and total station data (600 points) revealed a georeferencing accuracy difference of +/−0.003 m. Consequently, the developed methods can effectively represent similar structures digitally. The proposed ontological structure facilitates automated storage of internal and external information.

An adaptive low‐rank group sparse model based on edge‐preserving for eliminating mixed noise in SRTM

AbstractThe Shuttle Radar Topography Mission (SRTM) is a digital representation of the terrain surface morphology that contains rich terrain information and is widely used in environmental analyses. However, SRTM is adversely affected by mixed noise, which typically include random and stripe noise. Mixed noise results in the significant loss of topographic information, which reduce the validity of related research. To eliminate mixed noise in SRTM data, we propose an adaptive low‐rank group sparse model based on edge preservation (ALGS_EP) to remove mixed noise from datasets. The method relies on a low‐rank group sparse model that considers the gradient features of the terrain. It calculates a terrain factor to adapt the noise elimination model to terrain changes. Additionally, it integrates with the edge structure of elevation data and applies a double‐gradient constraint to preserve the structural details of the elevation data. The proposed model, built upon the alternating direction multiplier method framework, enhances the traditional weighted kernel paradigm minimization algorithm by introducing variable weights that adjust according to the gradient of elevation data during iterations. Additionally, it incorporates the correlation between strip noise and residual data blocks when computing the iteration count, ensuring an iterative solution approach that converges to the optimal solution. We used ALGS_EP to process global SRTM 1 data and published a higher‐quality and higher‐precision elevation dataset. The elevation data noise before and after noise elimination were statistically analyzed. Simulated and empirical results show that the model is highly robust and more effective than existing methods in both visual and quantitative evaluations. The noise elimination rate was 97.6%, compared to the original data. Therefore, this research was valuable for applications that use digital elevation model as an important data layer.

Prediction of quantitative in-situ local corrosion via deep learning

A deep understanding of corrosion behavior is critical for improving steel durability and reliability. Typically, it requires significant experimental effort and extensive measurements over a long period of time. Therefore, exploring the time-dependent corrosion at an early stage to predict its progress at a later stage can effectively understand it. In this study, we quantitatively predicted later stages of local corrosion behavior using deep learning methods based on the early stage topographical information. Furthermore, we predicted and visualized the formation, growth, and accumulation of the particle-like oxides. Our proposed method can be extended to other types of corrosion-resistant electrochemical materials.

A review of ion scattering spectroscopy studies at liquid interfaces with noble gas ion projectiles

Ion scattering spectroscopy (ISS) is an analytical tool that provides direct structural, topographical, and atomic compositional information at interfaces when ions are used as projectiles. Since its development in 1967, ISS is commonly used to obtain quantitative information about solid interfaces. Over the last couple of decades, ISS has emerged as an important technique to probe liquid interfaces and their studies employing ISS has become not uncommon, more so with Neutral impact collision ion scattering spectroscopy (NICISS). Therefore, here the principle of ISS with a particular focus on NICISS and its data evaluation are summarised while reviewing some important studies at vapor-liquid interfaces that provide direct information for molecular orientation of liquids (including ionic liquids), composition and distribution of atoms (or solutes) and charges as a function of depth to gain vast variety of thermodynamical information. Employing ISS such information can be achieved with high depth resolution of ∼1–2 Å (depending on the nature of the experiment). These examples highlight the significance of ISS and show potential for its application for studies related to specific ion effects, atmospheric reaction in aerosol and sea water droplets, and even determining the fate of environmental pollutants like heavy metal ions and per-fluoroalkyl substances (PFAS). Furthermore, some limitations of ISS are also discussed relating to investigation of high-vapor pressure liquids and probing buried interfaces like liquid-liquid interfaces while presenting progresses made in probing solid-liquid interfaces.

메이지시기에 발행된 조선지도와 국경

This study categorizes and analyzes the characteristics of maps depicting Korea published during the Meiji era, focusing on the representation of borders. Approximately 100 such maps were published during this period, with publication peaks coinciding with significant events in Japan-Korea relations, such as the Unyō Incident, the Imo Mutiny, the Sino-Japanese War, the Russo-Japanese War, and the Japan-Korea Annexation Treaty. The maps of Korea published in Japan during this era can be classified into five types based on their border representations. Type A maps, which include the Manchurian region south of the Heilong (Amur) River, were only published before the Unyō Incident. Type B maps, incorporating neutral zones on the opposite shores of the Yalu and Tumen Rivers, were published only until the Sino-Japanese War. Both A and B type maps reflect an extended territorial perception of Korea north of the Yalu and Tumen Rivers. Type C maps, depicting the Yalu River, Mount Baekdu, and the Tumen River as borders, became the predominant representation of Korea during the Meiji era. These maps reflected more accurate topographical information from late Joseon period maps adopted by Japan. Type D maps, which exclude Mount Baekdu, were published from immediately after the Imo Mutiny until shortly after the outbreak of the Russo-Japanese War. They were frequently used in Northeast Asian maps to represent Korea. Type E maps, including Gando 間島 as Korean territory, began to appear in 1894 and were published intensively during the Russo-Japanese War period, continuing until 1910. The concentrated publication of these maps around the Russo-Japanese War reflects Japan’s growing interest in Gando as it aimed to advance into Manchuria. The surge in demand for maps of Korea followed the rise of Seikanron (the “Conquest of Korea” debates) after the Meiji Restoration. Initially, due to insufficient geographical data from direct surveys, Japan had to rely on re-edited versions of previously collected historical Korean maps. As Japan conducted more direct surveys of Korea, their understanding of Korean borders became clearer, particularly around the time of the Sino-Japanese War.

One-pot radiolytically synthesized reduced graphene oxide-gold nanoparticles composites and exploration in energy storage

Graphene and its composites are of primary importance, particularly in the field of energy storage. Numerous bottom-up and top-down methods have been proposed in the literature to produce these materials with enhanced physicochemical properties. Recently, an innovative and green methodology was developed based on ionizing radiation, allowing the quantitative synthesis of graphene oxide-based materials. With the aim to extend this strategy for the preparation of hybrid nanomaterials, the objective of this work was the one-pot synthesis of nanocomposites composed of reduced graphene oxide – gold nanoparticles (rGO-AuNPs) via gamma ray-induced radiolytic reduction. UV–Vis Absorption Spectroscopy, Fourier Transform Infrared Spectroscopy, Raman Spectroscopy and X-ray Photoemission Spectroscopy were utilized to comprehensively evaluate the reduction degree of graphene oxide and the formation of gold nanoparticles. Atomic Force Microscopy and Scanning Electron Microscopy were employed to obtain the morphological and topographical information on synthesized nanocomposites. Thermal properties were investigated by Thermogravimetric Analysis and electrical properties were investigated using Cyclic Voltammetry and Potentiostatic Charge and Discharge method. The results demonstrated the successful reduction of graphene oxide and gold ions through the drastic transformation of oxygen-containing functional groups and the formation of gold nanoparticles. Electrical characterization results highlighted the excellent electrical properties of radiosynthesized rGO-AuNPs composites and their great potential for supercapacitance application.

A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery

Seasonal snow plays a crucial role in society and understanding trends in snow depth and mass is essential for making informed decisions about water resources and adaptation to climate change. However, quantifying snow depth in remote, mountainous areas with complex topography remains a significant challenge. The increasing availability of high-resolution synthetic aperture radar (SAR) observations from active microwave satellites has prompted opportunistic use of the data to retrieve snow depth remotely across large spatial and frequent temporal scales and at a high spatial resolution. Nevertheless, these novel SAR-based snow depth retrieval methods face their own set of limitations, including challenges for shallow snowpacks, high vegetation cover, and wet snow conditions. In response, here we introduce a machine learning approach to enhance SAR-based snow depth estimation over the European Alps. By integrating Sentinel-1 SAR imagery, optical snow cover observations, and topographic, forest cover and snow class information, our machine learning retrieval method more accurately estimates snow depth at independent in-situ measurement sites than current methods. Further, our method provides estimates at 100 m horizontal resolution and is capable of better capturing local-scale topography-driven snow depth variability. Through detailed feature importance analysis, we identify optimal conditions for SAR data utilization, thereby providing insight into future use of C-band SAR for snow depth retrieval.

Revisiting the tension water storage capacity distribution in conceptual rainfall-runoff modeling: A large-sample approach

Accurately characterizing the spatial variability of tension water storage capacity (TWC) within a catchment is challenging due to limited in-situ hydrologic data availability. Conventional conceptual rainfall-runoff models typically rely on an empirically specified TWC distribution. However, this empirical distribution lacks a physical foundation and fails to effectively redistribute critical hydrologic components, such as local capacity and contributing area, to real-world contexts. To overcome this limitation, the topographic wetness index (TWI) and its generalized form (GTWI) are introduced to bridge local topographic information with hydrologic components. Four TWC distribution curves are contrived based on the empirical parabolic distribution, empirical linear distribution, TWI, and GTWI, respectively. The effects of these alternate TWC distributions on streamflow are investigated within the framework of the HYdrologic MODel (HYMOD) across 460 Australian catchments. The results illustrate that the GTWI-based HYMOD (GTHYMOD) outperforms other models in terms of daily streamflow, with high Kling-Gupta Efficiency (KGE) attained in 74.8% of the study catchments during the validation period. The eastern coast of Australian catchments presents a superior streamflow performance compared to that in the western coast. GTHYMOD demonstrates its superiority in characterizing spatial variability, an aspect HYMOD lacked. This study has the potential to refine the empirical TWC distribution from a physical perspective and advance our comprehension of underlying hydrologic behaviors.

Assessing GEDI data fusions to map woodpecker distributions and biodiversity hotspots

Abstract In forested systems, woodpecker species richness has been linked with songbird diversity, and identifying woodpecker biodiversity hotspots may contribute important information for conservation planning. The availability of global forest structure data via the Global Ecosystem Dynamics Investigation (GEDI) instrument provides a new tool for examining broad extent relationships amongst environmental variables, forest structure, and woodpecker diversity hotspots. Within the Marine West Coast Forest ecoregion, USA, we used eBird data for 7 woodpecker species to model encounter rates based on bioclimatic variables, process data (e.g. duration and timing of survey), MODIS forest land cover data, and GEDI-fusion metrics. The GEDI-fusion metrics included foliage height diversity (fhd), rh98 (a representation of canopy height), and canopy cover, which were created by combining GEDI data with Landsat, Sentinel-1, topographic, and climatic information within a random forest modeling framework. AUCs for the species-specific models ranged from 0.77–0.98, where bioclimatic and process predictors were amongst the most important variables for all species. GEDI-fusion forest structure metrics were highly ranked for all species, with fhd included as a highly ranked predictor for all species. The structural metrics included as top predictors for each species were reflective of known species-specific habitat associations. Hotspots in this ecoregion tended to be inland and occurred most often on privately-owned lands. Identification of hotspots is the first step towards management plans focused on biodiversity, and understanding ownership patterns is important for future conservation efforts. The near-global extent of GEDI data, along with recent studies that recommend woodpeckers as indicators of biodiversity across multiple forest types at local and global scales, suggest that synthesis of GEDI-derived data applied to woodpecker detection information might be a powerful approach to identifying biodiversity hotspots.

Numerical simulation for large baseline interferometric imaging altimeter

Sea topography information holds significant importance in oceanic research and the climate change detection. Radar imaging altimetry has emerged as the leading approach for global ocean observation, employing synthetic aperture radar (SAR) interferometry to enhance the spatial resolution of Sea topography. Nevertheless, current payload capacity and satellite hardware limitations prevent the extension of the interferometric baseline by enlarging the physical antenna size. This constraint hinders achieving centimeter-level accuracy in interferometric altimetry. To address this challenge, we conducted a numerical simulation to assess the viability of a large baseline interferometric imaging altimeter (LB-IIA). By controlling the baseline within the range of 600–1000 m through spiral orbit design in two satellites and mitigating baseline de-correlation with the carrier frequency shift (CFS) technique, we aimed to overcome the above limitations. Our findings demonstrate the efficacy of the CFS technique in compensating for baseline decoherence, elevating coherence from less than 0.1 to over 0.85. Concurrently. The height difference accuracy between neighboring sea surfaces reaches 1 cm within a 1 km resolution. This study is anticipated to serve as a foundational reference for future interferometric imaging altimeter development, catering to the demand for high-precision sea topography data in accurate global bathymetry inversion.

Three-Dimensional Computed Tomography Scanning Study of the Superior Labial Artery in Chinese Individuals for Assessing Filler Injection Safety.

Lip filler injection is one of the most common minimally invasive cosmetic procedures involving the face; however, vascular complications are not uncommon. The aim of this study was to investigate the anatomy of the superior labial artery (SLA) and provide precise topographic information for dermal filler injection into the lips. Computed tomography (CT) scans of 52 cadaveric heads injected with lead oxide were obtained. We then used Mimics software to construct 3D images of the SLA described by a coordinate system based on the bilateral external auditory canal and the left orbit. This study aimed to classify the SLA in the Han Chinese population, measure its diameter at specific points, and determine the thickness of the lip at those points. Ultimately, we utilized a thermal imaging technique to illustrate the course and depth of the SLA within the lip. The objective of this study was to provide safe guidance for clinical injections. In this study, the SLA was successfully identified in all cadavers. The mean overall diameter of the superior labial arteries was 1.36±0.28 mm. The superior labial artery showed a general course from deep to shallow with an average depth of 5.68±1.68 mm from the oral commissure to the midline. There are anatomical differences in the superior labial arteries among Chinese people. Furthermore, 3D CT images can digitally elucidate the exact positions of the superior labial artery via a coordinate system, improving the safety of upper lip filler injections in clinical settings. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Integrating Topographical Map Information in SUMO to Simulate Realistic Micromobility Trips in Hilly and Steep Terrains

Nowadays, shared micromobility has become a trend in cities as an alternative to conventional automotive vehicles, especially for short-distance travel. It also plays an important role in the reduction of the number of automotive vehicles which results in a decrease of air pollution and traffic congestion. Shared micromobility is, however, influenced by the terrain characteristics. Varying elevation within a fleet operational area can cause imbalances in the use of micromobility stations if a steep terrain lies between stations. It also impacts the energy consumption of electric micromobility vehicles such as e-bicycles and e-scooters. Therefore, to simulate the state of charge (SOC) of traction batteries for micromobility close to reality, it is essential to include elevation data into the simulation model. This paper proposes a workflow for Simulation of Urban MObility (SUMO) comprising several steps with concrete implementation and validation in order to prepare and define the simulation model with micromobility stations and the integration of elevation data using a REST API. The integration of elevation and bike station data is validated with a defined vehicle type following a route in the hilly part of Stuttgart, Germany. A comparison of micromobility trips, with and without elevation data, was performed through a simulation by recording changes in energy consumption and driven altitude differences. The proposed workflow provides a basis for more complex use cases such as analysing micromobility business areas, improving vehicle.