Elevation Data Research Articles

Sediment dynamic on the tidal flat sheltered by artificial engineering: A case study on eddies

Tidal flats are important ecological and terrestrial resources. With the increasing development of coastal engineering projects, it is important to understand the dynamic response of tidal flats to human activities. Hence, the time series of wave, current, and suspended sediment during the spring tide from April 30, 2013 to May 1, 2013 were recorded by three tripod observation systems placed on the upper, middle, and lower parts of the tidal flats south of a constructed harbor in Wanggang, Jiangsu, China. The results showed that eddies formed south of the reclamations during flood tides. The directions of local tidal currents changed from their original southwest to the present northwest, and the ebb tides also changed but continued to flow northeast. The sediment transport process was basically consistent with the currents. The total net sediment fluxes during the tidal cycle (mean values of two tides) on the mudflat were two orders of magnitude larger than those on the salt marsh. Most of the sediments on both the mudflat and the salt marsh were transported northward alongshore, and a small portion of the sediments was transported seaward on the mudflat but shoreward on the salt marsh. The portion of the tidal flat with an elevation >1 m was wide in the north but narrow in the south, as determined by analysis of LiDAR elevation data, which coincided with the local sediment dynamics. The sediments were transported northward from southern deep waters during flood tides, which assisted in the expansion of the tidal flats in the north. This phenomenon reflected unique local sediment dynamics that were highly constrained by reclamations and Dafeng Harbor in the north as well as seawalls in the west. The tidal flat influenced by eddies can extend up to approximately 10 km south of the Dafeng Harbor. This case study has significant implications for the protection of tidal flats and the construction of coastal artificial engineering projects.

Comparative analysis of sandy beach and foredune geomorphic change measurements from Apple lidar and small-unoccupied aerial systems

Sandy beach and foredune environments are common throughout coastlines globally. Coastal landscapes are dynamic and vulnerable to water level fluctuations, storm events, and human disturbances. Standard methods for measuring geomorphic changes include small-unoccupied aircraft systems paired with structure-from-motion photogrammetry (sUAS-SfM), but this can be costly and logistically challenging. We evaluated the accuracy of Apple lidar in comparison to high precision sUAS-SfM and RTK-GPS to map sandy beach and foredune geomorphic change. Checkpoint elevations were measured via RTK-GPS and both the sUAS-SfM and Apple lidar surveyed elevations were compared against these checkpoints to evaluate the performance of both methods for measuring elevations. The sUAS-SfM elevation data were on average around 0.004 m above/below the checkpoint elevations while the Apple Lidar elevations were around 0.039 m. Apple lidar and sUAS-SfM-derived volumetric measurements and spatial patterns of erosion and accretion were compared to evaluate the Apple lidar’s ability to detect geomorphic change over time. The geomorphic changes documented from these two methods were similar though the Apple lidar appeared to capture finer-scale erosion and accretion patterns. Our findings indicate that the Apple lidar can capture sandy beach and foredune geomorphic changes rapidly and accurately, which can promote proactive and resilient coastal management.

Enhancing Assessments of Coastal Wetland Migration Potential with Sea-level Rise: Accounting for Uncertainty in Elevation Data, Tidal Data, and Future Water Levels

Enhancing Assessments of Coastal Wetland Migration Potential with Sea-level Rise: Accounting for Uncertainty in Elevation Data, Tidal Data, and Future Water Levels

Vertical Accuracy of Google Earth Data

Digital Elevation Models (DEMs) are an important data source used in many engineering and Geographic Information System (GIS) applications. This paper illustrates a strategy for creating a DEM by utilizing elevation data from Google Earth and evaluating the vertical positional accuracy of the generated DEM adopting a well-defined methodology. To ensure the accuracy of the elevation data obtained from Google Earth, a thorough evaluation was done in three diverse small districts of the northern shoreline in Egypt. The evaluation process involved determining the ground coordinates of reference points utilizing two surveying techniques: total station and Real-Time Kinematic (RTK) Global Positioning System (GPS) surveys. These coordinates were compared with the ones predicated by the DEM generated by putting into service Google Earth's elevation data. Furthermore, the vertical accuracy was assessed using Shuttle Radar Topographic Mission (SRTM) data of Google Earth collected at two different periods in 2015 and 2023. The vertical accuracy of the Google Earth data is detailed utilizing Mean Error (ME), Maximum Absolute Error (MAE), and Root Mean Square Error (RMSE). According to the results, Google Earth's elevation data accuracy remains consistent from 2015 to 2023, and refining SRTM data does not improve the vertical accuracy. The vertical accuracy of the total station survey surpasses the one of the RTK GPS survey, and the elevation accuracy of the RTK GPS survey decreases with increasing height difference. In addition, the vertical accuracy of DEMs was found to be sufficient for some engineering applications but not accurate enough for precise engineering studies. The accuracy achieved in small height difference terrain can be utilized to produce large-scale cadastral maps, city plans, or land use maps. Finally, the elevation data offered by Google Earth can be utilized for preliminary studies at a low cost. However, to ensure the accuracy of these data, it is recommended that users compare them with reference data before implementation.

Tsunami model of the 2021 Flores earthquake and its impact on Labuan Bajo, East Nusa Tenggara

Abstract The Flores earthquake on December 14, 2021, was recorded to cause a weak tsunami in East Nusa Tenggara. The epicenter was in the Flores Sea within 10 km of the hypocenter that triggered M 7.3 and impacted some coastline regions of East Nusa Tenggara, including Labuan Bajo. This study aimed to hindcast this phenomenon and mitigate the coastline region by applying the forecasting scenarios related to the amplification of the earthquake magnitudes and type of the sub-bottom displacements. The TUNAMI Software has been selected to illustrate tsunami wave propagation and inundation. The elevation data for the inputting model used BATNAS-DEMNAS National, while the rest of the parameters were applied using the two scenarios and the best guess. Based on the model results, it can be concluded that the source characteristics and the magnitudes played an essential role in inundating the coastal region in Labuan Bajo, West Manggarai Regency, East Nusa Tenggara.

Spatial interpolation techniques comparison and evaluation: The case of ground-based gravity and elevation datasets of the central Main Ethiopian rift

The ground-based gravity data reveals diverse anomaly signatures in areas of the Main Ethiopian rift where active volcanic and tectonic activities are dominant. In such a region ground-based data collection is restricted to existing roads and relies on accessible stations. These resulted in gaps in data, either missing, uneven, or insufficient spatial coverage that must be estimated with proper interpolation techniques. Comparison and evaluations of the spatial interpolation methods that are commonly used in potential field geophysical data analysis were made for the terrestrial gravity and elevation data of the central Main Ethiopian rift. In this research, two widely used interpolation techniques, minimum curvature interpolation, and Ordinary Kriging were compared and assessed. A 10 % hold-out validation was employed, where 90 % of the data points were used to generate interpolated surfaces, which were then evaluated against the remaining 10 %. Following interpolation with each technique, the generated grid was converted into discrete data points (estimated values). These are then compared with the available gravity data, which were deliberately excluded from the gridding process (10 % remaining dataset). The accuracy of each method was assessed by evaluation metrics such as mean value, variance, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correlation coefficient (r), and R-squared. The results showed that the ordinary Kriging interpolation method outperformed the minimum curvature interpolants for gravity data with all performance metrics, while both interpolants seem to perform equally well for the elevation dataset. Therefore, it is proposed to use the Kriging interpolation method for potential field gravity studies conducted in the central Main Ethiopia rift.

Study on Optimization of Placement Method for Transmission Line Monitoring Devices Based on Probability of Meteorological Disasters

Abstract To ensure the reliability and safety of the power grid system, it is of great significance to carry out electric meteorological monitoring for transmission channels. In this paper, a method for optimizing the placement of transmission line monitoring devices based on the probability of meteorological disasters is proposed. By considering the climatic characteristics near the transmission line, the probability of occurrence of adverse meteorological faults for each transmission tower in the transmission line is analyzed and calculated by using existing meteorological and GIS elevation data. Meanwhile, the historical failure data and voltage levels of transmission lines are combined to achieve the optimization of the installation position of monitoring devices in transmission channels. This method was applied to a 220 kV transmission line for practical analysis, achieving dense monitoring in areas with high failure rates and sparse monitoring in areas with low failure rates.

A Comprehensive Approach to Quantitative Risk Assessment of Rockfalls on Buildings Using 3D Model of Rockfall Runout

Rockfalls are incidents of nature that take place when rocks or boulders break from a steep slope and fall to the ground. They can pose considerable threats to buildings placed in high-risk zones. Despite the fact that the impact of a rockfall on a building can cause structural and non-structural damage, few studies have been undertaken to investigate the danger associated with this event. Most of these studies indicated that the risk resulting from rockfall hazards is hard to forecast and assess. A comprehensive quantitative risk assessment approach for rockfalls on buildings is developed and described in this paper and applied for the Mtein village in Mount Lebanon. This method employs a 3D model to simulate the rockfall trajectories using a combination of digital elevation data, field surveys, and orthorectified aerial photographs. The spatial and temporal probability of rockfalls were evaluated using the analysis of historical data in two triggering-factor scenarios: earthquake and precipitation. The findings show that, during the period of 1472 years between the years 551 (the first observed large earthquake in Lebanon) and the current year of the study (2023), the temporal probability will potentially be equal to 0.002 and 0.105 in the cases of earthquake- and rainfall-triggered rockfalls, respectively, while the maximal damage values are expected to be 232 USD and 10,511 USD per year, respectively. The end result is a final map presenting the risk values assigned to each building that could be damaged by rockfalls.

Overview and evolutionary path of Estonian coastal lagoons

Coastal lagoons are highly productive and biologically diverse landscape features that sustain a range of valuable natural services to societies. Acting as an interface between land and sea, they evolve naturally, but are also vulnerable to various forms of human activity. Based on old cartographic material, LiDAR elevation data, Holocene sea level histories, and shoreline modelling, we study the development of coastal lagoons on the postglacially uplifting, tideless coast of the brackish water Baltic Sea. Ecological succession of habitats along the path from bays to semi-enclosed lagoons, coastal lakes or bogs is discussed utilizing community structure analysis of habitat building bottom vegetation. There are about 600 lagoons in Estonia, but they are typically small (bigger ones up to 6 km2) and mostly <1 m deep. Due to ongoing postglacial uplift (1.5−3.4 mm/a), lagoons are just a relatively short (50−500-years long) transitional phase in a long (up to ∼10 000-years) succession. Locating at an altitude of up to 20−30 m, some older, more resilient palaeolagoons can be nowadays distinguished as lakes or bogs, while smaller ones have blended into the surrounding landscape. The contemporary lagoons are ephemeral too, because the emerging terrain has already been flattened by erosion and sedimentation. After separation from the sea, the brackish water habitat is gradually replaced with freshwater habitat and both biodiversity and plants coverage usually increases. As organic deposits build up, low-growth shores are replaced with high-growth vegetation; reedbeds expand. Detached from their marine past, communities typical for lakes, fens, bogs or forests are finally formed. As a result of eutrophication, ongoing climate change and sea level rise, the balance between emergence of new lagoons and their disappearance due to distancing and swamping has shifted over the past 100 years.

Pattern Analysis of Periodic Sediment Elevation Data: A Decision Support Framework for Deposited Sediment Management in Coastal Environment

This research explores sediment deposition patterns in coastal areas by employing synthetic data representing wandering set and recurrent patterns, utilizing Fourier transformation and power-law analysis to characterize frequency content and scaling behavior. The study employs fundamental models to interpret observed patterns and investigates the relevance of Poincaré's theorem in understanding recurrent patterns. Leveraging Python-based computation systems and open-source satellite data, the findings reveal contrasting behaviors in power-law exponents between scenarios, with wandering set patterns exhibiting scale-free characteristics and recurrent patterns displaying structured frequency distributions. This holistic approach advances scientific knowledge on sediment dynamics, informing evidence-based decision-making for effective sediment management and coastal resilience planning. Through its methodology and insights, the research contributes to understanding sediment deposition processes, elucidating underlying mechanisms driving coastal sedimentation, and providing practical approaches for monitoring and studying sediment dynamics in coastal environments, ultimately enhancing strategies for coastal management and conservation.

Machine learning feature importance selection for predicting aboveground biomass in African savannah with landsat 8 and ALOS PALSAR data

In remote sensing, multiple input bands are derived from various sensors covering different regions of the electromagnetic spectrum. Each spectral band plays a unique role in land use/land cover characterization. For example, while integrating multiple sensors for predicting aboveground biomass (AGB) is important for achieving high accuracy, reducing the dataset size by eliminating redundant and irrelevant spectral features is essential for enhancing the performance of machine learning algorithms. This accelerates the learning process, thereby developing simpler and more efficient models. Our results indicate that compared individual sensor datasets, the random forest (RF) classification approach using recursive feature elimination (RFE) increased the accuracy based on F score by 82.86 % and 26.19 respectively. The mutual information regression (MIR) method shows a slight increase in accuracy when considering individual sensor datasets, but its accuracy decreases when all features are taken into account for all models. Overall, the combination of features from the Landsat 8, ALOS PALSAR backscatter, and elevation data selected based on RFE provided the best AGB estimation for the RF and XGBoost models. In contrast to the k-nearest neighbors (KNN) and support vector machines (SVM), no significant improvement in AGB estimation was detected even when RFE and MIR were used. The effect of parameter optimization was found to be more significant for RF than for all the other methods. The AGB maps show patterns of AGB estimates consistent with those of the reference dataset. This study shows how prediction errors can be minimized based on feature selection using different ML classifiers.

Modeling the impact of sea level rise on endangered deer habitat

Numerous unique flora and fauna inhabit the Lower Florida Keys, including the endangered Florida Key deer, found nowhere else. In this vulnerable habitat of flat islands with low elevation, accelerated sea level rise poses a threat. Predicting the impact of sea level rise on vegetation and wildlife is crucial. This study used 5 Intergovernmental Panel on Climate Change (IPCC) sea level rise scenarios to assess their effects on No Name Key, Florida. The goal was to estimate changes in the Florida Key deer population relative to sea level rise using a lidar-derived elevation data and a vegetation map. The method used 2 cases to model the sea level rise impact. In Case 1, total non-submerged area at current sea level was determined. Using 5 IPCC scenarios, a new total non-submerged land area was estimated, and deer numbers were predicted for each scenario. In Case 2, upward migration of coastal vegetation combined with the coastal squeeze process was modeled. A distinct elevation range for each vegetation type at the current sea level was determined. Vegetation ranges were redistributed based on respective elevation ranges in the sea level rise scenarios. Areas for each vegetation type were recalculated, and Key deer numbers were estimated for each sea level rise scenario. Results under the worst emission scenario showed the following: (1) for case 1, the land area was reduced to 30 % of the current land area, corresponding to having about 27 deer, and (2) for case 2, the land area was reduced to 70 % of the current land area, having about 54 deer on No Name Key. The results indicated reduced non-submerged land area and less upland vegetation, particularly hardwoods/hammocks, by the year 2100. As less land area is available, a decline in Key deer population is expected as sea levels rise. Since Key deer favor upland vegetation, habitat affected by sea level rise will likely support a smaller deer population. The findings emphasize the need for precise, timely predictions of sea level rise impacts and long-term conservation strategies. Specifically designed measures are required to protect and maintain endangered wildlife, such as the Florida Key deer, residing on these vulnerable islands.

GIS-based spatial approaches to refining urban catchment delineation that integrate stormwater network infrastructure

Rapid urbanization and escalating climate change impacts have heightened stormwater-related concerns (e.g., pluvial flooding) in cities. Understanding catchment dynamics and characteristics, including precise catchment mapping, is essential to accurate surface water monitoring and management. Traditionally, topography is the primary data set used to model surface water flow dynamics in undisturbed natural landscapes. However, urban systems also contain stormwater drainage infrastructure, which can alter catchment boundaries and runoff behavior. Acknowledging both natural and built environmental influences, this study introduces three GIS-based approaches to enhance urban catchment mapping: (1) Modifying DEM elevations at inlet locations; (2) Adjusting DEM elevations along pipeline paths; (3) Applying the QGRASS plug-in to systematically incorporate infrastructure data. Our evaluation using the geographical Friedman test (p > 0.05) and Dice Similarity Coefficient (DSC = 0.80) confirms the statistical and spatial consistency among the studying methods. Coupled with onsite flow direction validation, these results support the feasibility and reliability of integrating elements of nature and built infrastructure in urban catchment mapping. The refined mapping approaches explored in this study offer improved and more accurate and efficient urban drainage catchment zoning, beyond using elevation and topographic data alone. Likewise, these methods bolster predictive stormwater management at catchment scales, ultimately strengthening urban stormwater and flooding resilience.

Observed and future shifts in climate zone of Borneo based on CMIP6 models

Climate change has significantly altered the characteristics of climate zones, posing considerable challenges to ecosystems and biodiversity, particularly in Borneo, known for its high species density per unit area. This study aimed to classify the region into homogeneous climate groups based on long-term average behavior. The most effective parameters from the high-resolution daily gridded Princeton climate datasets spanning 65 years (1950–2014) were utilized, including rainfall, relative humidity (RH), temperatures (Tavg, Tmin, Tmax, and diurnal temperature range (DTR)), along with elevation data at 0.25° resolution. The FCM clustering method outperformed K-Mean and two Ward's hierarchical methods (WardD and WardD2) in classifying Borneo's climate zones based on multi-criteria assessment, exhibiting the lowest average distance (2.172–2.180) and the highest compromise programming index (CPI)-based correlation ranking among cluster averages across all climate parameters. Borneo's climate zones were categorized into four: ‘Wet and cold’ (WC) and ‘Wet’ (W) representing wetter zones, and ‘Wet and hot’ (WH) and ‘Dry and hot’ (DH) representing hotter zones, each with clearly defined boundaries. For future projection, EC-Earth3-Veg ranked first for all climate parameters across 961 grid points, emerging as the top-performing model. The linear scaling (LS) bias-corrected EC-Earth3-Veg model, as shown in the Taylor diagram, closely replicated the observed datasets, facilitating future climate zone reclassification. Improved performance across parameters was evident based on MAE (35.8–94.6%), MSE (57.0–99.5%), NRMSE (42.7–92.1%), PBIAS (100–108%), MD (23.0–85.3%), KGE (21.1–78.1%), and VE (5.1–9.1%), with closer replication of empirical probability distribution function (PDF) curves during the validation period. In the future, Borneo's climate zones will shift notably, with WC elongating southward along the mountainous spine, W forming an enclave over the north-central mountains, WH shifting northward and shrinking inland, and DH expanding northward along the western coast. Under SSP5-8.5, WC is expected to expand by 39% and 11% for the mid- and far-future periods, respectively, while W is set to shrink by 46%. WH is projected to expand by 2% and 8% for the mid- and far-future periods, respectively. Conversely, DH is expected to expand by 43% for the far-future period but shrink by 42% for the mid-future period. This study fills a gap by redefining Borneo's climate zones based on an increased number of effective parameters and projecting future shifts, utilizing advanced clustering methods (FCM) under CMIP6 scenarios. Importantly, it contributes by ranking GCMs using RIMs and CPI across multiple climate parameters, addressing a previous gap in GCM assessment. The study's findings can facilitate cross-border collaboration by providing a shared understanding of climate dynamics and informing joint environmental management and disaster response efforts.

Positioning Improvement for Spaceborne Laser Footprint Based on Precisely Terrain Data

Abstract. Spaceborne laser altimetry represents a novel active remote sensing technology applicable to earth observation, which together with imaging spectroscopy and synthetic aperture radar as a core technology for data acquisition in the earth observation systems. However, the accuracy of horizontal positioning for laser footprints from spaceborne laser altimeters declines due to various factors such as the changes in the orbital environment and the deterioration of performance. Moreover, the limited frequency of in-orbit calibration of the spaceborne laser altimeters and the non-disclosure of calibration parameters mean that users are heavily reliant on positioning accuracy of the altimetry data provided. To address this issue, a new algorithm is proposed in this study for enhancing the accuracy of horizontal positioning for laser footprints in the absence of satellite altimeter pointing and ranging parameters. In this algorithm, high-resolution DSM is taken as the reference terrain data to take advantage of the higher precision in elevation over horizontal positioning of the laser footprints. By adjusting the horizontal position of the laser footprint within a small area, the algorithm achieves the optimal alignment of laser elevation data with the reference terrain. Then, the resulting shift in the horizontal position of the laser footprints is referenced to correct their horizontal positioning during that period. Based on the high-accuracy DSM data collected from the Xinjiang autonomous region in China and the data collected by the GF-7 satellite, simulation experiments are performed in this study to analyze and validate the proposed algorithm. According to the experimental results, the horizontal accuracy of the laser footprints improves significantly from 12.56 m to 3.11 m after optimization by the proposed method. With the elimination of 9.45 m horizontal error, accuracy is improved by 75.23%. This method is demonstrated as effective in further optimizing the horizontal position of laser altimetry data products in the absence of altimeter parameters and original data, which promotes the application of spaceborne laser data.

Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds

Satellite remote sensing plays a significant role in the detection of smoke from forest fires. However, existing methods for detecting smoke from forest fires based on remote sensing images rely solely on the information provided by the images, overlooking the positional information and brightness temperature of the fire spots in forest fires. This oversight significantly increases the probability of misjudging smoke plumes. This paper proposes a smoke detection model, Forest Smoke-Fire Net (FSF Net), which integrates wildfire smoke images with the dynamic brightness temperature information of the region. The MODIS_Smoke_FPT dataset was constructed using a Moderate Resolution Imaging Spectroradiometer (MODIS), the meteorological information at the site of the fire, and elevation data to determine the location of smoke and the brightness temperature threshold for wildfires. Deep learning and machine learning models were trained separately using the image data and fire spot area data provided by the dataset. The performance of the deep learning model was evaluated using metric MAP, while the regression performance of machine learning was assessed with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The selected machine learning and deep learning models were organically integrated. The results show that the Mask_RCNN_ResNet50_FPN and XGR models performed best among the deep learning and machine learning models, respectively. Combining the two models achieved good smoke detection results (Precisionsmoke=89.12%). Compared with wildfire smoke detection models that solely use image recognition, the model proposed in this paper demonstrates stronger applicability in improving the precision of smoke detection, thereby providing beneficial support for the timely detection of forest fires and applications of remote sensing.

Influence of the Road Model on the Optimal Maneuver of a Racing Motorcycle

Motorcycle motion is largely influenced by the road geometry, which alters the allowable accelerations in longitudinal and lateral directions and influences the vertical wheel loads. Recently, a method for three-dimensional road reconstruction and its incorporation into transient and quasi-steady-state (QSS) minimum lap time simulations (MLTSs) has been proposed. The main purpose of this work is to demonstrate how significantly different results from a minimum lap time optimal control problem can be obtained when using inappropriate elevation data sources in the track reconstruction problem, and how the road model reconstructed using poor input data can lead to misleading conclusions when analyzing real vehicle and driver performances. Two road models derived from high- and low-resolution digital elevation models (DEMs) are compared and their impact on the optimal maneuver of a racing motorcycle is examined. The essentials of track identification are presented, as well as vehicle positioning on the 3D road and the generalized QSS motorcycle model. Obtained 3D and 2D road models are analyzed in detail, on a case example of the Road Atlanta racetrack, and used in minimum lap time simulations, which are validated by the experimental data recorded on the Supersport motorcycle. The comparative analysis showed that great care should be taken when selecting the elevation dataset in the track reconstruction process, and that the 1 m resolution local DEMs seem to be sufficient to obtain MLTS results close to the measured ones. The example of using the 3D free-trajectory QSS minimum lap time problem to localize the track segments where real driver actions can be improved is also presented. The differences between simulation results on different road models of the same racetrack can be large and influence the interpretation of optimal maneuver.

Gravity Interpretation of Mud Volcano based on Satellite Data (Study Case Kuwu and Cangkring Mud Volcano)

&lt;p class="Abstract"&gt;A mud volcano is one type of mountain in the world. Mud volcano has specific characteristics. In Java, several mud volcanoes spread from west to east of Java. Bledug Kuwu and Bledug Cangkring are mud volcanoes in Central Java. Research on the Bledug Kuwu and Cangkring mud volcanoes systems was not done. In this preliminary study, the gravitational field analysis of the Kuwu-Cangkring mud volcano system was done by using GGmPlus satellite data with a 220 m grid and elevation data using ERTM. Free air anomaly data processing obtained a complete Bouguer anomaly value of 23 to 34 mGal. The separation process of anomalies using the upward continuation method produces a local of -0.5 to 0.5 mGal and a regional of 23 mGal to 34 mGal. The local anomaly value of Bledug Kuwu was -0.275 to - 0.05 mGal and Bledug Cangkring-0.125 to 0.1 mGal. The local anomaly around Bledug Cangkring is higher than Bledug Kuwu, indicating a lower density beneath Bledug Kuwu than in Cangkring.&lt;/p&gt;

Spatial targeted delivery of riboflavin with a controlled corneal iontophoresis delivery system in theranostic-guided UV-A light photo-therapy.

Seven human donor eye globes underwent corneal cross-linking using theranostic UV-A device with accessory corneal iontophoresis system for patterned delivery of a 0.22% riboflavin solution. Theranostic-guided UV-A light illumination assessed riboflavin distribution and treated corneas at 10 mW/cm2 for 9 min with a 5.0-mm beam size. Corneal topography maps were taken at baseline and 2-h post-treatment. Analysis utilized corneal topography elevation data, with results showing controlled riboflavin delivery led to a consistent gradient, with 40% higher levels centrally (248 ± 79 μg/cm3) than peripherally (180 ± 72 μg/cm3 at ±2.5 mm from the center). Theranostic-guided UV-A light irradiation resulted in significant changes in corneal topography, with a decrease in best-fit sphere value (-0.7 ± 0.2 D; p < 0.001) and consistent downward shift in corneal elevation map (-11.7 ± 3.7 μm). The coefficient of variation was 2.5%, indicating high procedure performance in achieving significant and reliable corneal flattening.

Enhancing surface drainage mapping in eastern Canada with deep learning applied to LiDAR-derived elevation data.

Agricultural dykelands in Nova Scotia rely heavily on a surface drainage technique called land forming, which is used to alter the topography of fields to improve drainage. The presence of land-formed fields provides useful information to better understand land utilization on these lands vulnerable to rising sea levels. Current field boundaries delineation and classification methods, such as manual digitalization and traditional segmentation techniques, are labour-intensive and often require manual and time-consuming parameter selection. In recent years, deep learning (DL) techniques, including convolutional neural networks and Mask R-CNN, have shown promising results in object recognition, image classification, and segmentation tasks. However, there is a gap in applying these techniques to detecting surface drainage patterns on agricultural fields. This paper develops and tests a Mask R-CNN model for detecting land-formed fields on agricultural dykelands using LiDAR-derived elevation data. Specifically, our approach focuses on identifying groups of pixels as cohesive objects within the imagery, a method that represents a significant advancement over pixel-by-pixel classification techniques. The DL model developed in this study demonstrated a strong overall performance, with a mean Average Precision (mAP) of 0.89 across Intersection over Union (IoU) thresholds from 0.5 to 0.95, indicating its effectiveness in detecting land-formed fields. Results also revealed that 53% of Nova Scotia's dykelands are being used for agricultural purposes and approximately 75% (6924 hectares) of these fields were land-formed. By applying deep learning techniques to LiDAR-derived elevation data, this study offers novel insights into surface drainage mapping, enhancing the capability for precise and efficient agricultural land management in regions vulnerable to environmental changes.