High-resolution Topographic Information Research Articles

Enhancing salt marshes monitoring: Estimating biomass with drone-derived habitat-specific models

Salt marshes play a vital role in biodiversity conservation, coastal protection, and carbon sequestration. Estimating aboveground biomass (AGB) is crucial for quantifying the carbon sequestration capacity of the system. However, traditional fieldwork methods are time-consuming, demanding, and detrimental to marsh ecosystems. In this study, we propose utilizing low-altitude remote sensing techniques to collect high-resolution LiDAR and multispectral (MS) data for biomass assessment. With these datasets, we characterized salt marsh vegetation habitats through the analysis of vegetation indices (VIs) variability. LiDAR high-resolution topographic information aids in evaluating habitat distribution. Moreover, the Anthocyanin Reflectance Index 2 (ARI2), combined with the Digital Surface Model, allows for the identification and separation of the two habitats with distinct dominant species (Sarcocornia spp. and Sporobolus maritimus). The analysis of the annual cycle of VIs reveals the presence of seasonality. However, the VIs for the two vegetation classes exhibit dissimilar seasonal changes, indicating distinct growth mechanisms for each. Biomass models are created for each season in the annual cycle. Habitat-specific models exhibit higher precision (up to 99%) than models that treat species uniformly. Depending on whether the marsh is considered as a whole or separated into dominant habitats, differences in biomass estimation trends are observed. This implies that the two dominant species exhibit varying behaviours throughout the year, contributing to diverse biomass production. The study reveals a seasonal pattern in the total AGB, with the highest biomass value in summer and the lowest peak in spring, with annual variation accounting for only 9% of total output. The reduced AGB value in spring may be due to increased soil salinity and stress. The use of LiDAR and MS data from an unmanned aerial vehicle (UAV) is crucial for accurately distinguishing primary marsh habitats and creating detailed biomass models, with unprecedented accuracy. This method is user-friendly, repeatable, and cost-effective, enabling the study of salt marshes, evolutionary trends, and climate change response requiring less fieldwork.

The influence of volcano topographic changes on infrasound amplitude: lava fountains at Mt. Etna in 2021

Infrasound signals are used to investigate and monitor active volcanoes during eruptive and degassing activity. Infrasound amplitude information has been used to estimate eruptive parameters such as plume height, magma discharge rate, and lava fountain height. Active volcanoes are characterized by pronounced topography and, during eruptive activity, the topography can change rapidly, affecting the observed infrasound amplitudes. While the interaction of infrasonic signals with topography has been widely investigated over the past decade, there has been limited work on the impact of changing topography on the infrasonic amplitudes. In this work, the infrasonic signals accompanying 57 lava fountain paroxysms at Mt. Etna (Italy) during 2021 were analyzed. In particular, the temporal and spatial variations of the infrasound amplitudes were investigated. During 2021, significant changes in the topography around the most active crater (the South East Crater) took place and were reconstructed in detail using high resolution imagery from unoccupied aerial system surveys. Through analysis of the observed infrasound signals and numerical simulations of the acoustic wavefield, we demonstrate that the observed spatial and temporal variation in the infrasound signal amplitudes can largely be explained by the combined effects of changes in the location of the acoustic source and changes in the near-vent topography, together with source acoustic amplitude variations. This work demonstrates the importance of accurate source locations and high-resolution topographic information, particularly in the near-vent region where the topography is most likely to change rapidly and illustrates that changing topography should be considered when interpreting local infrasound observations over long time scales.

Acquisition of high-resolution topographic information in forest environments using integrated UAV-LiDAR system: System development and field demonstration

High-resolution topographic information of landslide-prone areas plays an important role in accurate prediction and characterization of potential landslides and mitigation of landslides-associated hazards. This study presents an advanced geomorphological surveying system that integrates the light detection and ranging (LiDAR) with an unmanned aerial vehicle (UAV), a multi-rotor aerial vehicle in specific, for prediction, monitoring and forensic analysis of landslides, and for maintenance of debris-flow barriers. The test-flight over a vegetated area demonstrates that the integrated UAV-LiDAR system can provide high-resolution, three-dimensional (3D) LiDAR point clouds below canopy and vegetation in forest environments, overcoming the limitation of aerial photogrammetry and terrestrial LiDAR platforms. An algorithm is suggested to delineate the topographic information from the acquired 3D LiDAR point clouds, and the accuracy and performance of the developed UAV-LiDAR system are examined through field demonstration. Finally, two field demonstrations are presented: the forensic analysis of the recent Gokseong landslide event, and the sediment deposition monitoring for debris-flow barrier maintenance in South Korea. The developed surveying system is expected to contribute to geomorphological field surveys in vegetated, forest environments, particularly in a site that is not easily accessible.

Comparative Analysis of Debris Flow Numerical Simulation Based on the Difference between the Resolution of Topographic Information and Grid Size

In this study, a comparative analysis was conducted for the debris flow that occurred in Gokseong-gun, Jeollanam-do, in August 2020, using the difference between the resolution of topographic information and grid size. High-resolution topographic information (0.03 m) was established through photogrammetry. For comparison, the low-resolution topographic information was obtained from the contour lines of the 1:5000 digital map. The topographic information was depicted as a grid, with resolutions of 1, 5, and 10 m, and analyzed by setting the simulation grid size to 1, 5, and 10 m. According to the topographic resolution, no significant difference was obtained in the numerical simulation results, when high-resolution topographic information from UAV photogrammetry was used, and the analysis was similar to the actual debris flow. Additionally, when the size of the analysis grid for the simulation was 10 m, the simulation was very similar to the actual debris-flow situation. For predicting a risk area using a debris-flow model, it is very important to reflect actual information, such as topography, geology, and rainfall; technical parameters, such as the grid size, are also important. Moreover, determining the optimal grid size by considering the conditions of the study area, such as the basin area, is necessary.

Remote Sensing Perspectives on Geomorphology and Tectonic Processes

The quantity and quality of remote sensing measurements of tectonic deformation have increased dramatically over the past two decades, improving our ability to observe active geomorphological tectonic processes. High-precision and high-resolution topography is the basis for the quantitative study of active geomorphological and tectonic processes. Recently, with the rapid development of computer visual science and the growing application of light detection and ranging (LiDAR), small unmanned aerial vehicles (UAVs) and structure from motion (SfM) photogrammetry have shown great potential in providing high-resolution and high-precision topographic information. In this Special Issue, we focus on the tectonic activity of active faults and the geomorphic processes in various global tectonic regimes that are related to remote sensing measurements. This Special Issue covers major earthquake hazards and seismogenic structures, new methods in seismological studies using high-resolution data sets, and the tectonic and geomorphic application of high-resolution data sets worldwide and, in particular, in the Eastern Tibetan Plateau and Tian Shan. These contributions will provide new insights into the remote sensing perspectives of geomorphological and tectonic processes.

Determination of a new gravimetric geoid modelling for Sudan using the least-squares collocation technique

The main purpose of this study is to compute a new gravimetric geoid model for Sudan by using the least-square collocation technique (LSC method) and applying the remove-compute-restore (RCR) technique. The computation of the model contains different datasets which are the gravity contribution of the model GO_CONS_GCF_2_TIM_R6e degree/order 300, BGI free-air gravity dataset in Sudan, GPS/levelling data, and high-resolution topographic information from ASTER digital elevation model. The “residual gravity anomalies” were run through the GEOCOL program using the GRAVSOFT software package, and the effects were restored to calculate the quasi-geoid surface (height anomalies). The gravimetric geoid was computed by adding the (N − \U0001d701 h ) separation term to the quasi-geoid and was fitted to the GPS and levelling data provided by Sudan. The accuracy of our gravimetric geoid model SDN-LSC-G22 of the area of Sudan and some areas of bordering countries has been investigated by using geoid undulations computed from GPS and levelling data and by investigating the differences between the geoids of the GGM models which are EGM2008 and SGG-UGM-2. Our gravimetric geoid model (SDN-LSC-G22) has indicated an accuracy of 17.4 cm, in terms of a standard deviation compared with 66 GPS and Leveling data distributed in the area of Khartoum (most of these points are control points and benchmarks). Also, we evaluated our gravimetric geoid model by using 19 points distributed in the area of Sudan, and they indicated a standard deviation of 51.3 cm. The overall accuracy of SDN-LSC-G22 compared with the geoid undulation of all GPS and levelling has indicated an STD of 34.1 cm. The model SDN-LSC-G22 has shown better accuracy and significant differences compared with the GGM models EGM2008 and SGG-UGM-2 in terms of the differences with the available GPS and levelling data which have shown ~17 cm differences using (Abdalla, 2009) GPS and levelling data. It has demonstrated STD of ~17 cm differences by using 66 GPS and levelling data. Therefore, the model SDN-LSC-G22 provided better improvements and reliable geoid heights over Sudan compared to EGM2008 and SGG-UGM-2 gravity field models.

Emulating the Adaptation of Wind Fields to Complex Terrain with Deep Learning

Abstract Estimating the impact of wind-driven snow transport requires modeling wind fields with a lower grid spacing than the spacing on the order of 1 or a few kilometers used in the current numerical weather prediction (NWP) systems. In this context, we introduce a new strategy to downscale wind fields from NWP systems to decametric scales, using high-resolution (30 m) topographic information. Our method (named “DEVINE”) is leveraged on a convolutional neural network (CNN), trained to replicate the behavior of the complex atmospheric model ARPS, and was previously run on a large number (7279) of synthetic Gaussian topographies under controlled weather conditions. A 10-fold cross validation reveals that our CNN is able to accurately emulate the behavior of ARPS (mean absolute error for wind speed = 0.16 m s−1). We then apply DEVINE to real cases in the Alps, that is, downscaling wind fields forecast by the AROME NWP system using information from real alpine topographies. DEVINE proved able to reproduce main features of wind fields in complex terrain (acceleration on ridges, leeward deceleration, and deviations around obstacles). Furthermore, an evaluation on quality-checked observations acquired at 61 sites in the French Alps reveals improved behavior of the downscaled winds (AROME wind speed mean bias is reduced by 27% with DEVINE), especially at the most elevated and exposed stations. Wind direction is, however, only slightly modified. Hence, despite some current limitations inherited from the ARPS simulations setup, DEVINE appears to be an efficient downscaling tool whose minimalist architecture, low input data requirements (NWP wind fields and high-resolution topography), and competitive computing times may be attractive for operational applications. Significance Statement Wind largely influences the spatial distribution of snow in mountains, with direct consequences on hydrology and avalanche hazard. Most operational models predicting wind in complex terrain use a grid spacing on the order of several kilometers, too coarse to represent the real patterns of mountain winds. We introduce a novel method based on deep learning to increase this spatial resolution while maintaining acceptable computational costs. Our method mimics the behavior of a complex model that is able to represent part of the complexity of mountain winds by using topographic information only. We compared our results with observations collected in complex terrain and showed that our model improves the representation of winds, notably at the most elevated and exposed observation stations.

Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals

Milder winters and extended wetter periods in spring and autumn limit the amount of time available for carrying out ground-based forest operations on soils with satisfactory bearing capacity. Thus, damage to soil in form of compaction and displacement is reported to be becoming more widespread. The prediction of trafficability has become one of the most central issues in planning of mechanized harvesting operations.The work presented looks at methods to model field measured spatio-temporal variations of soil moisture content (SMC, [%vol]) – a crucial factor for soil strength and thus trafficability. We incorporated large-scaled maps of soil characteristics, high-resolution topographic information – depth-to-water (DTW) and topographic wetness index – and openly available temporal soil moisture retrievals provided by the NASA Soil Moisture Active Passive mission. Time-series measurements of SMC were captured at six study sites across Europe. These data were then used to develop linear models, a generalized additive model, and the machine learning algorithms Random Forest (RF) and eXtreme Gradient Boosting (XGB). The models were trained on a randomly selected 10% subset of the dataset.Predictions of SMC made with RF and XGB attained the highest R2 values of 0.49 and 0.51, respectively, calculated on the remaining 90% test set. This corresponds to a major increase in predictive performance, compared to basic DTW maps (R2 = 0.022). Accordingly, the quality for predicting wet soils was increased by 49% when XGB was applied (Matthews correlation coefficient = 0.45).We demonstrated how open access data can be used to clearly improve the prediction of SMC and enable adequate trafficability mappings with high spatial and temporal resolution. Spatio-temporal modelling could contribute to sustainable forest management.

Monitoring surface water dynamics in the Prairie Pothole Region of North Dakota using dual-polarised Sentinel-1 synthetic aperture radar (SAR) time series

Abstract. The North American Prairie Pothole Region (PPR) represents a large system of wetlands with great importance for biodiversity, water storage and flood management. Knowledge of seasonal and inter-annual surface water dynamics in the PPR is important for understanding the functionality of these wetland ecosystems and the changing degree of hydrologic connectivity between them. Optical sensors that are widely used for retrieving such information are often limited by their temporal resolution and cloud cover, especially in the case of flood events. Synthetic aperture radar (SAR) sensors can potentially overcome such limitations. However, water extent retrieval from SAR data is often impacted by environmental factors, such as wind on water surfaces. Hence, robust retrieval methods are required to reliably monitor water extent over longer time periods . The aim of this study was to develop a robust approach for classifying open water extent in the PPR and to analyse the obtained time series covering the entire available Sentinel-1 observation period from 2015 to 2020 in the hydrometeorological context. Open water in prairie potholes was classified by fusing dual-polarised Sentinel-1 data and high-resolution topographical information using a Bayesian framework. The approach was tested for a study area in North Dakota. The resulting surface water maps were validated using high-resolution airborne optical imagery. For the observation period, the total water area, the number of waterbodies and the median area per waterbody were computed. The validation of the retrieved water maps yielded producer’s accuracies between 84 % and 95 % for calm days and between 74 % and 88 % for windy days. User’s accuracies were above 98 % in all cases, indicating a very low occurrence of false positives due to the constraints introduced by topographical information. The observed dynamics of total water area displayed both intra-annual and inter-annual patterns. In addition to differences in seasonality between small (<1 ha) and large (>1 ha) waterbodies due to the effect of evaporation during summer, these size classes also responded differently to an extremely wet period from 2019 to 2020 in terms of the increase in the number of waterbodies and the total area covered. The results demonstrate the potential of Sentinel-1 data for high-resolution monitoring of prairie wetlands. Limitations of this method are related to wind inhibiting the correct water extent retrieval and to the rather long acquisition interval of 12 d over the PPR, which is a result of the observation strategy of Sentinel-1.

A Study on the Integration of Airborne LiDAR and UAV Data for High-resolution Topographic Information Construction of Tidal Flat

A Study on the Integration of Airborne LiDAR and UAV Data for High-resolution Topographic Information Construction of Tidal Flat

Assessment of applicability of mike 11-nam hydrological module for rainfall runoff modelling in a poorly studied river basin

Introduction. The need to simulate hydrological processes is caused by, among other factors, the complexity of hydrological systems and data insufficiency due to the unavailability or a small number of instrumental observations. Recently, the reanalysis of the climate data supplied by the world’s leading meteorological centres has been used quite successfully in the regions that suffer from the deficit of instrumental information. This paper assesses the applicability of climate reanalysis data to rainfall runoff (“rainfall runoff”) modelling in the poorly studied river basin in Eritrea. Materials and methods. Climate Forecast System Reanalysis (CFSR) data generated by the National Centre for Environmental Prediction (USA) were used. Besides, high-resolution topographic information, generated by the SRTM international research project, was also applied to set the drainage area boundaries and to simulate the river network using such tools as MIKE and GIS. In addition, calibration and validation (evaluation) of the hydrological model (simulation quality) were performed using the Nash-Sutcliffe efficiency criterion, the determination coefficient, and the root mean square error of volumetric and peak flow rates. Results. The results suggest that a considerable overestimation of precipitation in the reanalysis data set, which in turn has a significant effect on other variables such as potential evapotranspiration, leads to a significant discrepancy between water balance values which are simulated and registered by the hydrographs. Conclusions. The applicability of Climate Forecast System Reanalysis (CFSR) data to river flow modelling in arid and semi-arid regions such as Eritrea is questionable. The incompatibility of spatial and temporal variations of initial variables (e.g. precipitation), derived from reanalysis data sets and instrumental observations, is undoubtedly the main reason for errors. Thus, the application of reanalysis data sets and development of hydrological models for the region under study requires further intensive research aimed at identifying most effective mechanisms designated for the harmonization of differences between reanalysis data and field observations. In the course of further research, CFSR information is to be converted into more realistic data; climate reanalysis indicators, provided by other sources and designated for different time scales in the context of the “rainfall runoff” model are to be assessed, and the efficiency of other software systems is to be compared with MIKE 11-NAM.

Using machine learning to identify karst sinkholes from LiDAR-derived topographic depressions in the Bluegrass Region of Kentucky

Information about the distribution and characteristics of existing sinkholes is critical for understanding karst aquifer systems and evaluating sinkhole hazards. LiDAR provides accurate and high-resolution topographic information and has been used to improve delineation of sinkholes in many karst regions. LiDAR data also reveal many topographic depressions, however, and identifying sinkholes from these depressions through manual visual inspection can be slow and laborious. To improve the efficiency of the identification process, we applied six machine learning methods (logistic regression, naive Bayes, neural network, random forests, RUSBoost, and support vector machine) to a dataset of morphometric characteristics of LiDAR-derived topographic depressions. Sinkhole data from Bourbon, Woodford, and Jessamine Counties in the Bluegrass Region of Kentucky were used to derive the dataset for training and testing the machine learning methods. The dataset consisted of 22,884 records with 10 variables for each record. For each method, a random subset of 80% of the records was used for training and the remaining 20% was used for testing. The test receiver operating characteristic curves showed that all six methods were applicable to the dataset, as demonstrated by all area under the curves (AUCs) being greater than 0.87. Neural network emerged as the method that performed best, with an AUC of 0.95 and a testing average accuracy of 0.85. To further improve the sinkhole mapping process, we subsequently developed a two-step process that combined the trained neural network classifier and manual visual inspection and applied the process to Scott County, also in the Bluegrass region. We were able to locate 97% of the sinkholes in the county by manually inspecting only 27% of the topographic depressions the neural network classified as having relatively high probabilities of being sinkholes. This study showed that machine learning is a promising method for improving sinkhole identification efficiency in karst areas in which high-resolution topographic information is available.

High-resolution topographical information improves tree-level storm damage models

Storms cause major forest disturbances in Europe. The aim of this study was to model tree-level storm damage probability based on the properties of a tree and its environment and to examine whether fine-scale topographic information is connected to the damage probability. We used data documenting effects of two autumn storms on over 17 000 trees on permanent Finnish National Forest Inventory plots. The first storm was associated with wet snowfall that damaged trees, while exceptionally strong winds and gusts characterized the second storm. During the storms, soils were unfrozen and deciduous trees were without leaves. Generalized linear mixed models were used to study how topographical variables calculated from digital elevation models (DEM) with resolutions of 2 and 10 m (TOPO2 and TOPO10, respectively) were related to damage probability, in addition to variable groups for tree (TREE) and stand (STAND) characteristics. We compared models containing different variable groups with Akaike information criteria. The best model contained the variable groups TREE, STAND, and TOPO2. Increase in slope steepness calculated from the high-resolution DEM decreased tree-level damage probability significantly in the model. This suggests that the local topography affects the tree-level damage probability and that high-resolution topographical data improves the tree-level damage probability models.

Towards district scale flood simulations using conventional and anisotropic porosity shallow water models with high-resolution topographic information

Current topographic survey technology provides high-resolution (HR) datasets for urban environments. Incorporating this HR information in models aiming to provide flood risk assessment is desirable because the flood wave propagation is depending on the urban topographic features, i.e. buildings, bridges and street networks. Conceptual, numerical and practical challenges arise from the application of shallow water models to HR urban flood modeling. For instance, numerical challenges are occurrence of wet-dry fronts, geometric discontinuities in the urban environment and discontinuous solutions, i.e. shock waves. These challenges can be overcome by using a Godunov-type scheme. However, the computational cost of this type of schemes is high, such that HR two-dimensional shallow water simulations with practical relevance have to be run on supercomputers. The porous shallow water model is an alternative approach that aims to reduce computational cost by using a coarse resolution and accounting for unresolved processes by means of the porosity terms. Usually, a speedup between two and three orders of magnitude in comparison to HR simulations can be obtained. This study reports preliminary results of a practical test case concerning pluvial flooding in a district of the city of Nice, France, caused by the intense rainfall event on October 3rd, 2015. HR topography data set on a 1 m resolution is available for the district, whereby street features of infra-metric dimensions have been included. A reference solution is calculated by a HR shallow water model on a 1 m by 1 m structured computational grid. The porous shallow water model is run on a 10 m by 10 m grid and the influence of the drag source term is studied. The model results show a large deviation, which is caused by the poor meshing strategy of the porous shallow water (AP) model. The study also summarizes practical challenges that arise during the application of the AP and HR models to a large urban catchment. The main difficulty is to obtain a good mesh. In smaller scale investigations, the mesh is currently constructed by hand such that the cell edges align with buildings. This approach is not feasible for large scale urban catchments with a large number of buildings. Future steps that have to be taken, such as a strategy for automatic mesh generation, are reported on.

ASSESSMENT OF BOTTOM-OF-ATMOSPHERE REFLECTANCE IN LIDAR DATA AS REFERENCE FOR HYPERSPECTRAL IMAGERY

Abstract. While airborne lidar has confirmed its leading role in delivering high-resolution 3D topographic information during the last decade, its radiometric potential has not yet been fully exploited. However, with the increasing availability of commercial lidar systems which (a) make use of full-waveform information and (b) operate at several wavelengths simultaneously, this potential is increasing as well. Radiometric calibration of the full-waveform information mentioned before allows for the derivation of physical target surface parameters such as the backscatter coefficient and a diffuse reflectance value at bottom of atmosphere (BOA), i.e. the target surface. With lidar being an active remote sensing technique, these parameters can be derived from lidar data itself, accompanied by the measurement or estimation of reference data for diffuse reflectance. In contrast to this, such a radiometric calibration for passive hyperspectral imagery (HSI) requires the knowledge and/or estimation of much more unknowns. However, in case of corresponding wavelength(s) radiometrically calibrated lidar datasets can deliver an areawide reference for BOA reflectance. This paper presents criteria to check where the assumption of diffuse BOA reflectance behaviour is fulfilled and how these criteria are assessed in lidar data; the assessment is illustrated by an extended lidar dataset. Moreover, for this lidar dataset and an HSI dataset recorded over the same area, the corresponding reflectance values are compared for different surface types.

Calibration of boresight offset of LROC NAC imagery for precision lunar topographic mapping

High-resolution and high-precision lunar topographic information is essential for lunar exploration and scientific research. The lunar surface imagery acquired by the Narrow Angle Cameras (NACs) of NASA’s Lunar Reconnaissance Orbiter Camera (LROC) is a valuable dataset for lunar topographic mapping because of its very fine ground sampling distance (GSD) of 0.5–2m. The NACs have a unique configuration consisting of two separate cameras closely mounted. This configuration enables the collection of lunar surface images with both large coverage and high resolution. However, the mapping results from the NAC images depend highly on the accuracy of the relative alignment (referred to as boresight offset) between the two NACs. This paper firstly presents the details of the NAC sensor configuration and orientations. Then, an approach to calibrate the boresight offsets of the two NACs is presented. By using triple-matching tie points between the NAC images, the boresight offsets are improved based on the geometric model through a least-squares adjustment. Experiments using typical NAC stereo images show that there are inconsistencies as great as 80m in object space in the topographic models generated using the pre-flight determined SPICE kernels. The inconsistencies are well improved using the image orientation parameters derived from the temperature-dependent SPICE kernels recently released by the LROC team, however there are still small inconsistencies of about 5–10m in object space, which can be further reduced to meter level by using the method proposed in this paper. This method is of significance for generating lunar topographic models with high precision and internal consistency from NAC imagery.

Lava flow hazard at Fogo Volcano, Cabo Verde, before and after the 2014–2015 eruption

Abstract. Lava flow simulations help to better understand volcanic hazards and may assist emergency preparedness at active volcanoes. We demonstrate that at Fogo Volcano, Cabo Verde, such simulations can explain the 2014–2015 lava flow crisis and therefore provide a valuable base to better prepare for the next inevitable eruption. We conducted topographic mapping in the field and a satellite-based remote sensing analysis. We produced the first topographic model of the 2014–2015 lava flow from combined terrestrial laser scanner (TLS) and photogrammetric data. This high-resolution topographic information facilitates lava flow volume estimates of 43.7 ± 5.2 × 106 m3 from the vertical difference between pre- and posteruptive topographies. Both the pre-eruptive and updated digital elevation models (DEMs) serve as the fundamental input data for lava flow simulations using the well-established DOWNFLOW algorithm. Based on thousands of simulations, we assess the lava flow hazard before and after the 2014–2015 eruption. We find that, although the lava flow hazard has changed significantly, it remains high at the locations of two villages that were destroyed during this eruption. This result is of particular importance as villagers have already started to rebuild the settlements. We also analysed satellite radar imagery acquired by the German TerraSAR-X (TSX) satellite to map lava flow emplacement over time. We obtain the lava flow boundaries every 6 to 11 days during the eruption, which assists the interpretation and evaluation of the lava flow model performance. Our results highlight the fact that lava flow hazards change as a result of modifications of the local topography due to lava flow emplacement. This implies the need for up-to-date topographic information in order to assess lava flow hazards. We also emphasize that areas that were once overrun by lava flows are not necessarily safer, even if local lava flow thicknesses exceed the average lava flow thickness. Our observations will be important for the next eruption of Fogo Volcano and have implications for future lava flow crises and disaster response efforts at basaltic volcanoes elsewhere in the world.

High-resolution Modelling With Bi-dimensional Shallow Water Equations Based Codes – High-Resolution Topographic Data Use for Flood Hazard Assessment Over Urban and Industrial Environments

The availability of new generation of High-Resolution (HR) topographic datasets combined with high performance computing resources opens the door to HR hydraulic simulations for risk assessment. LiDAR and photo-interpreted datasets are promising for HR Digital Elevation Model (DEM) generation, allowing inclusion of fine (infra-metric) aboveground structures influencing overland flow hydrodynamic in urban environment. Nonetheless, if topographic data is one key input for free surface hydraulic modelling using standard 2D Shallow Water Equations (SWEs) based codes, several categories of technical and numerical challenges arise to use HR dataset within numerical modelling. This proceeding explores the new possibilities, advantages and limits of HR topographic data use with 2D SWEs based numerical modelling tools for flood hazard assessment and proposes an original method for uncertainty assessment. The concepts of HR topographic data and 2D SWE based numerical modelling are reviewed. Using LiDAR and photo-interpreted datasets, different 2D SWEs based codes (Mike 21, Mike 21 FM, TELEMAC-2D, FullSWOF_2D) and strategies are tested to encompass HR DEM in intense rainfall and river flood events simulations ranging from industrial site scale to a megacity district scale (Nice, France). Tools and methods for assessing uncertainties related to HR DEM use with 2D SWE based codes are developed to perform a spatial global sensitivity analysis related to HR topographic data use. Computed sensitivity indices maps quantify the importance and spatial variability of uncertainties introduced by modeller choices regarding ways HR topographic information are integrated in models, compared to measurement errors. Impact of thin aboveground features inclusion, even at a decreased resolution, appears as a crucial asset in flood risk assessment on urban area, but requires providing caution to decision makers along with models’ results.

Exploring the Nanoscale: Fifteen Years of Tip-Enhanced Raman Spectroscopy.

Spectroscopic methods with high spatial resolution are essential to understand the physical and chemical properties of nanoscale materials including biological and chemical materials. Tip-enhanced Raman spectroscopy (TERS) is a combination of surface-enhanced Raman spectroscopy (SERS) and scanning probe microscopy (SPM), which can provide high-resolution topographic and spectral information simultaneously below the diffraction limit of light. Even examples of sub-nanometer resolution have been demonstrated. This review intends to give an introduction to TERS, focusing on its basic principle and the experimental setup, the strengths followed by recent applications, developments, and perspectives in this field.

Structural Analysis of the Hero Range in the Qaidam Basin, Northwestern China, Using Integrated UAV, Terrestrial LiDAR, Landsat 8, and 3-D Seismic Data

Quantitative structural analysis is a useful approach for studying geologic structures. It is particularly important in remote and complex fold-thrust belts where outcrop data and high-quality seismic reflection images are challenging to obtain. In this study, we integrated terrestrial light detection and ranging (LiDAR), unmanned aerial vehicle (UAV), and Landsat 8 (L8) data to extract high-resolution topographic and surface geologic information and constrain interpretations of three-dimensional (3-D) seismic reflection data in the Hero Range of the Qaidam Basin (QB) in northwestern China. UAV images were used to obtain a digital elevation model (DEM) and to measure the orientation of sedimentary bedding. Terrestrial LiDAR data were used to generate high-resolution digital outcrops and to evaluate the accuracy of the UAV-based DEM. L8 images were used to distinguish different stratigraphic units. The random sample consensus (RANSAC) algorithm was adopted to ascertain the best-fit plane of bedding. The results show that UAV images can be used to construct a DEM with <;1 m resolution and orthophotos with 0.15-m resolution. Collectively, these data improve the ability to identify and measure small exposures of bedding surfaces. The RANSAC algorithm improves the accuracy of measuring bedding orientations by removing erroneous selection points and facilitating the recognition of second-order variations in bedding orientation. The integrated analysis of remotely sensed and 3-D seismic data indicates that, of the three anticlines within the Hero Range, two are fault-propagation folds (the Shizigou and Youshashan anticlines) and one is associated with a pop-up structure (Ganchaigou anticline).