Elevation Error Research Articles

Quantifying the Interactions of Noah‐MP Land Surface Processes on the Simulated Runoff Over the Tibetan Plateau

AbstractThe quantification of uncertainties in runoff over the Tibetan Plateau (TP), simulated by land surface models (LSMs), is of paramount importance for effective water resources management within this region. However, the interactions of land surface processes on simulated runoff, where the effectiveness of one process depends on the chosen scheme for another, have rarely been studied. To address this gap, we conducted ensemble simulations with the Noah‐MP (Noah with multiparameterization) LSM by varying the optional parameterization schemes of six land surface processes and quantified the sensitivities of the simulated runoff to these processes. Results showed that the simulated runoff over the TP was most sensitive to the RUN (runoff‐groundwater) process. The interplay of RUN and FRO (frozen soil permeability) accounted for up to 30% of the variation in the annual mean surface runoff in the TP's permafrost regions. The interactions of RUN and VEG (dynamic vegetation) on summer and autumn subsurface runoff exceeded 10% in the southeast TP. In regions where these interactions among land surface processes significantly affected simulated runoff, we observed elevated model errors and reduced model controllability. Therefore, this study underscores the imperative need to categorize land regions based on the interactions of land surface processes as a foundational step toward enhancing the performance of LSMs. Prioritizing improvements in model physics should be particularly directed toward regions marked by high interactions.

Asymmetric Influences of ENSO Phases on the Predictability of North Pacific Sea Surface Temperature

AbstractThe North Pacific sea surface temperature (SST) has a profound climatic influence. The El Niño‐Southern Oscillation (ENSO) significantly impacts the North Pacific SST; however, the influence of the distinct phases of ENSO on SST predictability remains unclear. To overcome the model limitations, this study assessed SST predictability under diverse ENSO phases using reanalysis. The predictability limit of the North Pacific SST under La Niña (8.4 months) is longer than that under Neutral (5.9 months) and El Niño (5.5 months) conditions, which unveils asymmetry. This asymmetry mirrors contemporary multimodal prediction skills. Error growth dynamics reveal La Niña's robust signal strength with a slow error growth rate, in contrast to El Niño's weaker signal and faster error growth. There exhibits intermediate signal strength and elevated error growth in Neutral condition. Physically, predictability signal strength aligns with SST variability, whereas the error growth rate correlates with atmospheric‐ocean heating anomalies. La Niña, which induces positive heating anomalies, minimizes the impact of atmospheric noise, resulting in lower error growth. The result is beneficial for improving North Pacific SST predictions.

ZUST Campus: A Lightweight and Practical LiDAR SLAM Dataset for Autonomous Driving Scenarios

This research proposes a lightweight and applicable dataset with a precise elevation ground truth and extrinsic calibration toward the LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) task in the field of autonomous driving. Our dataset focuses on more cost-effective platforms with limited computational power and low-resolution three-dimensional LiDAR sensors (16-beam LiDAR), and fills the gaps in the existing literature. Our data include abundant scenarios that include degenerated environments, dynamic objects, and large slope terrain to facilitate the investigation of the performance of the SLAM system. We provided the ground truth pose from RTK-GPS and carefully rectified its elevation errors, and designed an extra method to evaluate the vertical drift. The module for calibrating the LiDAR and IMU was also enhanced to ensure the precision of point cloud data. The reliability and applicability of the dataset are fully tested through a series of experiments using several state-of-the-art LiDAR SLAM methods.

2018年浙江古田山24公顷亚热带常绿阔叶林动态监测样地林冠结构和地形数据集

The canopy structure and topographic variables jointly influence the spatiotemporal variation of habitats for important species. However, there remains a considerable scarcity of precise forest canopy structure and topographic data obtained from large-scale forest dynamic plots. LiDAR data, acquired via near-surface remote sensing platforms, facilitates precise computation of both canopy structure and topographic variables. This study selected a 24-hectare dynamic plot in the subtropical evergreen broadleaf forest, located in the core conservation area of Qianjiangyuan National Park, as its focal point for survey. We conducted an analysis and quality control process on the point cloud data in 2018 using a near-surface remote sensing platform for this area, aiming to acquire corresponding canopy structure and topography data. Ground data collected through Real-Time Kinematic (RTK) surveying were used to verify and validate the accuracy of the digital elevation model (DEM). The analysis revealed a mean square error of elevation of 0.07 meters, suggesting a high level of accuracy of the dataset. The 24-hectare subtropical evergreen broad-leaved forest dynamic plot represents a typical low-altitude evergreen broad-leaved forest in the central subtropical region. This dataset can provide essential data support for the monitoring and research of biodiversity in subtropical broadleaved evergreen forests.

Abstract P347: Variability in Predictive Error of Waist Circumference Models: A Stratified Analysis Based on Body Mass Index, Waist Circumference, and Their Ratio

Introduction: Waist circumference (WC) independently predicts cardiovascular risks, yet clinical measurements are sporadic. Some models have been developed for WC prediction, with body mass index (BMI) as a key predictor. While these models are generally efficient, variability in error across distinct subpopulations is possible. This study aimed to assess the prediction error (PE) variability of WC models across different strata of BMI, WC, and their ratio, and to evaluate the role of BMI for WC prediction. Hypothesis: 1) PE varies across BMI, WC, and their interrelated strata. 2) BMI is a key feature for WC prediction. Methods: Data from 30,298 individuals from the National Health and Nutrition Examination Survey (2007-2018) were analyzed. After outlier removal and kNN-imputation for missing values, machine learning predictive models for WC were developed. The base model included age, gender, ethnicity, height, weight, and BMI. A non-BMI model was also formulated, incorporating demographics and clinical variables selected via a feature-selection process. Root Mean Square Error (RMSE) across BMI, WC, and ratio of BMI-decile to WC-decile categories were assessed. Results: The base model's RMSE for the entire cohort was 5.70. Stratification by BMI revealed higher error rates for obesity classes II (5.88) and III (12.28), contrasting with lower RMSEs in participants with normal BMI (3.95), overweight (4.63), and obesity class I (5.41). Individuals with high WC had an increased RMSE (6.28) compared to those with low WC (5.04). Those with a BMI-decile/WC-decile below one had elevated error (6.98) compared to those with ratios equal to (4.60) or above one (5.52). Across all categories, the base model outperformed the non-BMI model (Figure 1). Conclusion: WC model PE demonstrated variability across BMI, WC, and their respective ratios, underscoring the predictive significance of BMI for WC. Considering population-specific variations in predictive WC modeling is important within clinical and research sets.

New approach for satellite DEM accuracy enhancement by combing machine learning, fuzzy majority voting, and weighted interpolation techniques

The digital elevation model (DEM) is crucial in many global and regional scientific studies in civilian and military applications. The aim of this research is to develop and test a new DEM approach for correcting the various errors in the Shuttle Radar Topography Mission (SRTM) digital elevation model. Firstly, the DEMs with the feature attributes from Sentinel-2 multispectral imagery are generated. Secondly, SRTM DEM with one band and attributes of a sentinel-2 image with eight bands are used as input data in supervised max-like hood, an artificial neural network (ANN), and support vector machine (SVM) classification models. Thirdly, ANN, supervised max-like hood, and SVM classification models, which have various properties, are fused by fuzzy majority voting (probability fusion). Finally, the fused probability is assigned for each pixel of the image, which has 12 fixed ground control points (GCPs), which is considered new input data for the inverse probability weighted interpolation (IPWI) approach to create the corrected SRTM elevations. The results were contrasted with a reference DEM (RD) created by image matching with Worldview-1 stereo satellite images, which had a 1-m vertical accuracy. The results of this study demonstrated that the RMSE of the original SRTM DEM was 5.95. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 1.98 compared with that of the MLR method (3.01). The study shows a series of significant improvements in the SRTM when assessed with the reference DEM, with an RMSE reduction of (66.72%) when compared to the widely utilized multiple linear regression (MLR) method. It can be concluded that the elevation error of the original SRTM DEM is clearly reduced by the suggested approach.

High-performance deep-sea long-range underwater acoustic communication: Deconvolved conventional beamforming based approach

To address the challenges posed by large propagation loss and severe multipath delay spread, while aiming to improve communication rates in long-range underwater acoustic (UWA) communication, this paper introduces a novel approach: multi-beam (MB) Orthogonal Frequency Division Multiplexing (OFDM) based on deconvolved conventional beamforming (dCv). The proposed method establishes a signal processing framework for UWA channel receivers, wherein received signals corresponding to each angle beam are isolated and concentrated using the dCv technique. Subsequently, the separated signals from each angle undergo maximal-ratio combining (MRC) channel equalization followed by demodulation. Compared to conventional single-beam (SB) processing methods, the proposed approach capitalizes on multipath diversity gain achieved by combining MB outputs originating from various arrival angles. Moreover, employing dCv processing demonstrates superior performance compared to popularly employed beamforming techniques, especially when dealing with paths arriving from closely aligned angles. Additionally, the array beamforming utilized significantly enhances the signal-to-noise ratio (SNR) of each beam output, mitigating the elevated error rates in channel estimation associated with combining low SNR signals received from individual elements. Simulation results utilizing BELLHOP and experimental data from the South China Sea showcase notably improved bit error rate (BER) performance for the proposed method compared to SB equalization, MB-MRC equalization based on conventional beamforming (CBF), minimum variance distortionless response (MVDR), and worst-case performance optimization (WCPO), as well as standard MRC equalization techniques. The proposed receiver achieves error-free decoding results at a communication distance of 80 km with a data rate of 247 bps.

DIGITAL ELEVATION MODEL CORRECTION IN URBAN AREAS USING EXTREME GRADIENT BOOSTING, LAND COVER AND TERRAIN PARAMETERS

Abstract. The accuracy of digital elevation models (DEMs) in urban areas is influenced by numerous factors including land cover and terrain irregularities. Moreover, building artefacts in global DEMs cause artificial blocking of surface flow pathways. This compromises their quality and adequacy for hydrological and environmental modelling in urban landscapes where precise and accurate terrain information is needed. In this study, the extreme gradient boosting (XGBoost) ensemble algorithm is adopted for enhancing the accuracy of two medium-resolution 30-metre DEMs over Cape Town, South Africa: Copernicus GLO-30 and ALOS World 3D (AW3D). XGBoost is a scalable, portable and versatile gradient boosting library that can solve many environmental modelling problems. The training datasets are comprised of eleven predictor variables including elevation, urban footprints, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover and bare ground cover. The target variable (elevation error) was calculated with respect to highly accurate airborne LiDAR. After training and testing, the model was applied for correcting the DEMs at two implementation sites. The corrections achieved significant accuracy gains which are competitive with other proposed methods. There was a 46–53% reduction in the root mean square error (RMSE) of Copernicus DEM, and a 72–73% reduction in the RMSE of AW3D DEM. These results showcase the potential of gradient-boosted decision trees for enhancing the quality of global DEMs, especially in urban areas.

Harnessing the joint effect of approach bridges in arch bridge construction: an analytical study on thrust stiffness and elevation error mitigation

Achieving full equilibrium for the horizontal component force of the backstay in cable-stayed arch bridges is challenging, and the stiffness of the buckle tower has a notable influence on the overall shape of the main arch structure. Increased stiffness in the buckle tower leads to reduced construction complexity. Therefore, this study proposed a method of enhancing the longitudinal thrust stiffness of the buckle tower using the joint effect of approach bridges. A sensitivity analysis was conducted on the approach bridge–composite buckle tower structure to determine the optimal combination method, resulting in the formulation of an analytical expression for the thrust stiffness of this structure. In this study, numerical analysis was performed to explore the composition mechanism of the thrust stiffness influenced by the pier–girder connection, and we discussed the applicability of the joint effect of approach bridges during the cantilever assembly process of arch ribs. The following conclusions were obtained: 1) prior to installing the main girder of the approach bridge, when the steel buckle tower and the junction pier have already been secured, the most effective approach is to form a “T” rigid structure by firmly connecting the main girder of the approach bridge with the composite buckle tower. This configuration provides self-weight deflection and pier–girder rotation restriction effects. 2) The study presents analytical formulas for the completely rigid pier–girder connection of the approach bridge–composite buckle tower structure, partially rigid pier–girder connection, and pre-deviation. Combined with the calculation program, this can guide structural design. 3) When a large downward elevation error of the arch ribs occurs in the middle and later stages, the cable force needed to install new arch segments becomes overly large. Therefore, the joint effect of approach bridges can be utilized to substitute for a portion of the cable force, effectively reducing potential elevation errors that might arise in subsequent arch ribs in the absence of this joint effect.

Assessment of the global Copernicus, NASADEM, ASTER and AW3D digital elevation models in Central and Southern Africa

ABSTRACT Validation studies of global Digital Elevation Models (DEMs) in the existing literature are limited by the diversity and spread of landscapes, terrain types considered and sparseness of groundtruth. Moreover, there are knowledge gaps on the accuracy variations in rugged and complex landscapes, and previous studies have often not relied on robust internal and external validation measures. Thus, there is still only partial understanding and limited perspective of the reliability and adequacy of global DEMs for several applications. In this study, we utilize a dense spread of LiDAR groundtruth to assess the vertical accuracies of four medium-resolution, readily available, free-access and global coverage 1 arc-second (30 m) DEMs: NASADEM, ASTER GDEM, Copernicus GLO-30, and ALOS World 3D (AW3D). The assessment is carried out at landscapes spread across Cape Town, Southern Africa (urban/industrial, agricultural, mountain, peninsula and grassland/shrubland) and forested national parks in Gabon, Central Africa (low-relief tropical rainforest and high-relief tropical rainforest). The statistical analysis is based on robust accuracy metrics that cater for normal and non-normal elevation error distribution, and error ranking. In Cape Town, Copernicus DEM generally had the least vertical error with an overall Mean Error (ME) of 0.82 m and Root Mean Square Error (RMSE) of 2.34 m while ASTER DEM had the poorest performance. However, ASTER GDEM and NASADEM performed better in the low-relief and high-relief tropical forests of Gabon. Generally, the DEM errors have a moderate to high positive correlation in forests, and a low to moderate positive correlation in mountains and urban areas. Copernicus DEM showed superior vertical accuracy in forests with less than 40% tree cover, while ASTER and NASADEM performed better in denser forests with tree cover greater than 70%. This study is a robust regional assessment of these global DEMs.

A robust and machine learning-driven identification scheme for malicious nodes in UASNs

Underwater Acoustic Sensor Networks (UASNs) have garnered widespread adoption across maritime domains and national security sectors. Their deployment, often in remote and challenging conditions, necessitates robust trust models to assess node integrity and to pinpoint compromised units, particularly under adversarial assault. Prevailing trust models, however, predominantly focus on nodes’ communicative actions and energy consumption, neglecting the influence of ambient aquatic noise on trust assessments. Moreover, these models typically falter amidst the UASN-specific impediments of high packet collision rates and elevated error probabilities inherent to underwater signal transmission, leading to consequential inaccuracies in node evaluation. To address these limitations, this paper proposes a robust and machine learning-driven malicious node identification scheme (RMIS) for UASNs. The proposed scheme first models and quantifies the impact of packet collisions and the underwater environment on node communication, utilizing existing environment models and MAC protocols. Second, communication traffic is exploited as effective and reliable evidence of trust. The traffic evidence can reflect multiple types of attacks by analyzing changes in traffic in a UASN. Third, the evaluation models are trained using support vector machine and K-means++ algorithms. Finally, two types of trust update mechanisms are proposed to handle the dynamic underwater environment and on–off attack. The simulation results indicate that compared to the other three identification schemes, RMIS is more effective in identifying malicious nodes. The effectiveness of the RMIS becomes particularly noticeable as the ratio of malicious nodes increases. Furthermore, the trust update mechanisms effectively limit the impact of the on–off attack and improve the robustness of the RMIS.

Reconstruction of synthetic aperture radar data using hybrid compressive sensing and deep neural network algorithm

SummaryThe reconstruction of reflectivity profile of the synthetic aperture radar (SAR) is an important field of research. SAR tomography is an advanced 3D imaging technique for the spectrum estimation in the elevation direction for each azimuth resolution cell. This work presents the processing chain for the tomographic reconstruction from ALOS PALSAR data for an urban region. First, the data are preprocessed by removing the speckle noise followed by atmospheric phase screen and topographic correction. Then the SAR images are stacked together with one master image and the remaining slave images on the baseline value. After the images are coregistered, the interferogram is generated from the image to obtain the difference of the phase value. Then the proposed super resolution SAR (SRS) algorithm is attempted for TomoSAR processing, which combines the functionality of modern machine learning method like deep learning with parametric block‐based compressive sensing approach. Finally, a 3D image is reconstructed from the input data. Evaluation is carried out by comparing the results of the proposed method with other spectrum estimation methods such as nonlinear least square, Capon, and multisignal classification. The normal baseline of the interferometric fringes is about 368.54 m. The proposed SRS algorithm gives improved results with less mean elevation error of 1.8 m and the less standard deviation error of 4.85 m. Finally, the result reveals that the SRS algorithm performed better than other TomoSAR algorithms with the less relative error 0.003.

Utilization of a Low-Cost Sensor Array for Mobile Methane Monitoring.

The use of low-cost sensors (LCSs) for the mobile monitoring of oil and gas emissions is an understudied application of low-cost air quality monitoring devices. To assess the efficacy of low-cost sensors as a screening tool for the mobile monitoring of fugitive methane emissions stemming from well sites in eastern Colorado, we colocated an array of low-cost sensors (XPOD) with a reference grade methane monitor (Aeris Ultra) on a mobile monitoring vehicle from 15 August through 27 September 2023. Fitting our low-cost sensor data with a bootstrap and aggregated random forest model, we found a high correlation between the reference and XPOD CH4 concentrations (r = 0.719) and a low experimental error (RMSD = 0.3673 ppm). Other calibration models, including multilinear regression and artificial neural networks (ANN), were either unable to distinguish individual methane spikes above baseline or had a significantly elevated error (RMSDANN = 0.4669 ppm) when compared to the random forest model. Using out-of-bag predictor permutations, we found that sensors that showed the highest correlation with methane displayed the greatest significance in our random forest model. As we reduced the percentage of colocation data employed in the random forest model, errors did not significantly increase until a specific threshold (50 percent of total calibration data). Using a peakfinding algorithm, we found that our model was able to predict 80 percent of methane spikes above 2.5 ppm throughout the duration of our field campaign, with a false response rate of 35 percent.

Calibration and validation of the rule-based human driver model for car-following behaviors at roundabout using naturalistic driving data

Understanding driver behavior is crucial for introducing roundabouts. This study focuses on calibrating the parameters of the car-following model using naturalistic data and analyzing the appropriateness of different car-following models on the roundabout. We utilize rounD trajectory dataset. This dataset allows for the precise definition of lead and follow vehicles, enabling the calibration of model parameters accordingly. We compared the calibration results for roundabouts with those obtained for signalized intersections from CitySim. Our results show that the Krauss and intelligent driver models (IDM) achieve mean absolute percentage errors of 10.09% and 23.21%, respectively. Furthermore, IDM exhibited higher errors in the circulation segment of the roundabout, while in the exit segment, the Krauss model showed elevated errors. It contrasted with the homogenous results obtained in the signalized intersection. These findings provide valuable insights into driver's behavior on roundabouts.

Surveying and mapping of large-scale 3D digital topographic map based on oblique photography technology

In the fields of geographic information systems and cartography, accurate topographic maps are crucial. Although traditional topographic mapping methods have certain accuracy, they have limitations in capturing features and building details. Therefore, this article proposes a large-scale 3D digital terrain mapping method based on oblique photography technology. Firstly, the UAV route is planned and image control points are arranged, to collect large-scale geographical object data by using UAV oblique photography technology. Then Smart 3D software is used to complete the 3D modeling of real large-scale topographic map through data preprocessing, air-to-air survey, control point import, 3D modeling, texture mapping and other steps based on the obtained geographical object data. The 2D and 3D linkage integrated mapping mode is adopted to obtain geomorphic information and elevation information, complete the data collection of indoor features, confirm the features that cannot be read on both sides of the indoor industry, complete the field supplementary mapping and adjustment, and realize the mapping of large-scale 3D digital topographic map. The experimental results show that this method can use oblique photography technology to collect data and establish a three-dimensional model of large-scale 3D digital terrain maps. The mean square error of accuracy detection is 0.202m, the mean square error of elevation is 0.257m, the mean square error of plane is 0.201m, and the mean square error of elevation is 0.197m. It has high drawing accuracy and excellent aerial triangulation performance.

Evaluation of multiple digital elevation models for hypsometric analysis in the watersheds affected by the opening of the Red Sea

Climate change is increasingly affecting the Red Sea-related terrains in Egypt and Saudi Arabia with a notable increase in heavy precipitation events. This highly vulnerable region to flashfloods and other climate change-driven hazards encompasses rough terrains with more than 11,000 basins/subbasins, which necessitates the accurate estimation of their hydrological and geomorphological parameters among which the hypsometric analysis. In this regard, The study examines the accuracy of the hypsometric analysis extracted using open source SRTM-1, ASTER-GDEM, Copernicus-GLO30, ALOS-DEM against high-resolution Topo-1 m and Topo-2.8 m DEMs for Talat Hamdh basin in Egypt and Wadi El-Salwely basin in Saudi Arabia, respectively. Copernicus-GLO30 shows the highest accuracy among all DEMs with the root-mean-squared–error (RMSE), mean elevation error, standard deviation, maximum and minimum absolute errors of 3.03, 2.0, 2.3, 11.7 and 0.1 m, respectively for Talat Hamdh basin. The findings also show that, regardless of the geology and geomorphic evolution of the basin, the hypsometric analysis is sensitive to the DEM type rather than the spatial resolution as Copernicus DEM yields similar basin numbers (a single basin) and area (1.366 and 141.9 km2) compared to the reference DEMs (1.408 and 154.4 km2) for Talat Hamdh and Wadi El-Salwely basins, respectively. Contrariwise, other open source DEMs yield multiple basins and thus significantly smaller basin area. Given the DEM-type dependence of the hypsometric analysis, the study recommends that large-scale hydrological and geomorphological analyses should consider using a high-resolution reference DEM on a local-scale basin to examine the accuracy of open source DEMs prior to conducting the analysis.

The effect of data sources on calculating mean temperature and integrated water vapor in Iran

AbstractThe weighted mean temperature () plays a crucial role in calculating Precipitable Water Vapor (PWV) and integrated water vapor (IWV) using Global Navigation Satellite Systems (GNSS) techniques. Currently, the primary sources for meteorological parameters are radiosonde measurements and Numerical Weather Models (NWMs). This study focuses on assessing the influence of different data sources on the computation of and IWV in Iran. The investigation involved comparing several datasets: ERA5 numerical data with spatial resolutions of 0.125° and 2.5° (ERA5 0.125, ERA5 2.5), ERA‐Interim, NCEP numerical data and results derived from the GPT3 model. Validation of the results utilized data from 12 radiosonde stations situated across Iran. In addition, the precision of the IWV parameter was evaluated by utilizing measurements from the only available IGS station in the region, situated in Tehran. The results revealed that ERA5 0.125 exhibited superior accuracy in estimation compared with the other datasets, showing a discrepancy of approximately 1–2 K. In contrast, the GPT3 model displayed an accuracy of about 3 K. Analysing the results across different months of the year revealed elevated root mean square error (RMSE) values during warmer months, with little variability based on station height in the region for the four datasets. Regarding IWV, the ERA5 0.125 dataset outperformed the other three datasets, demonstrating an accuracy of about 0.07 kg m−2. Notably, RMSE values during summer were approximately 50% higher compared with the annual RMSE.

Inaccurate fossil placement does not compromise tip‐dated divergence times

AbstractTime‐scaled phylogenies underpin the interrogation of evolutionary processes across deep timescales, as well as attempts to link these to Earth's history. By inferring the placement of fossils and using their ages as temporal constraints, tip dating under the fossilized birth–death (FBD) process provides a coherent prior on divergence times. At the same time, it also links topological and temporal accuracy, as incorrectly placed fossil terminals should misinform divergence times. This could pose serious issues for obtaining accurate node ages, yet the interaction between topological and temporal error has not been thoroughly explored. We simulate phylogenies and associated morphological datasets using methodologies that incorporate evolution under selection, and are benchmarked against empirical datasets. We find that datasets of 300 characters and realistic levels of missing data generally succeed in inferring the correct placement of fossils on a constrained extant backbone topology, and that true node ages are usually contained within Bayesian posterior distributions. While increased fossil sampling improves the accuracy of inferred ages, topological and temporal errors do not seem to be linked: analyses in which fossils resolve less accurately do not exhibit elevated errors in node age estimates. At the same time, inferred divergence times are biased, probably due to a mismatch between the FBD prior and the shape of our simulated trees. While these results are encouraging, suggesting that even fossils with uncertain affinities can provide useful temporal information, they also emphasize that palaeontological information cannot overturn discrepancies between model priors and the true diversification history.

Assessing Tidal Hydrodynamics in a Tropical Seascape Using Structure‐from‐Motion Photogrammetry and 2D Flow Modelling

Tidal wetlands continue to be threatened by changes in seascape hydrological regime and connectivity resulting from human activities (e.g. urbanisation, engineered barriers) and climate change. Reliable and parsimonious models that can be used by managers and practitioners to simulation tidal wetland hydroperiod dynamics (duration, depth, and frequency of tidal inundation) at high-resolution are limited presumably because these ecosystems have very low elevation across their flooding plain. Here, we developed a two-dimensional hydrodynamic model parameterised using a high-resolution (3 cm) and accurate (8-cm RMSE elevation error) digital elevation model (DEM) and land cover map (2-cm resolution) derived from unoccupied aerial vehicles (UAVs) structure from motion photogrammetry (SfM) to assist in the understanding of tidal wetland hydroperiod and hydrological connectivity of an upper tidal Australian tropical seascape. Ground-based water level datasets were used to calibrate and validate the model with higher accuracy (RMSE = 7 cm between maximum observed and simulated depth). The high-resolution approach demonstrates how small changes in topography such as vehicle tracks can interfere with hydrological connectivity. Centimetre-changes in tidal height resulted in important variations (10 ha) in the total area of the wetland being inundated, suggesting that small anthropogenic modifications of tidal inputs (e.g. culverts and sea-level rise) might have important implications on tidal wetland inundation patterns. Despite challenges related to reconstructing topography in densely vegetated areas and obtaining bathymetric data, the method developed here represents an accurate and cost-effective approach to quantify tidal wetland hydroperiod. This approach assists in planning, defining, and implementing effective and measurable restoration and protection projects of tidal wetland ecosystems. Graphical

The FABDEM Outperforms the Global DEMs in Representing Bare Terrain Heights

Many remote sensing and geoscience applications require a high-precision terrain model. In 2022, the Forest And Buildings removed Copernicus digital elevation model (FABDEM) was released, in which trees and buildings were removed at a 30 m resolution. Therefore, it was necessary to make a comprehensive evaluation of this model. This research aims to perform a qualitative and quantitative analysis of fabdem in comparison with the commonly used global dems. We investigated the effect of the terrain slope, aspect, roughness, and land cover types in causing errors in the topographic representation of all dems. The fabdem had the highest overall vertical accuracy of 5.56 m. It was the best dem in representing the terrain roughness. The fabdem and Copernicus dem were equally influenced by the slopes more than the other models and had the worst accuracy of slope representation. In the tree, built, and flooded vegetation areas of the fabdem, the mean errors in elevation have been reduced by approximately 3.34 m, 1.26 m and 1.55 m, respectively. Based on Welch's t-test, there was no significant difference between fabdem and Copernicus dem elevations. However, the slight improvements in the fabdem make it the best filtered dem to represent the terrain heights over different land cover types.