ANALYSIS OF LAVA FLOW PATHS USING REMOTE SENSING AND GEOMORPHOLOGICAL TECHNIQUES

<p>Stromboli Volcano was very active in the summer of 2019:  Two paroxysms dramatically changed the summit craters of the volcano on July 3 and August 28. The first intense paroxysmal eruptive sequence involved both the North and the Central-South (C-S) crater areas and has generated an eruptive plume rising 4 km above the summit (924 m a.s.l.) while the incandescent material set fire to vegetation on the flanks of the volcano. Volcanic products from the laterally directed explosions and from the collapse of the external crater terrace generated two pyroclastic flows that travelled down the Sciara del Fuoco (SdF) and for several hundred of meters out to sea. Between July 3 and August 28, the activity was characterised by lava flows in the Southern sector of the SdF and by very intense Strombolian activity at a set of small scoria cones that grew around the vents, particularly in the N crater area. The second paroxysmal eruption occurred on August 28 again involving the two crater areas and producing an eruptive column that rose 4 km above the summit. Material from the eruption and from the collapse of the rim of the C-S area contributed to the generation of a pyroclastic flow that travelled down the SdF and out to sea. Important morphological variations to the crater terrace were evident after the two paroxysms.</p><p>We used UAVs to monitor morpho-structural changes of the Stromboli volcano following the paroxysmal eruptions; in particular, five high-resolution UAV survey campaigns have been performed since May 2019. The aerial images were acquired using two different UAVs, a DJI Mavic 2 Pro and a Wingcopter. Using Structure-from-Motion (SfM) techniques we generated DEMs (Digital Elevation Model) and orthoimages with a resolution ranging between 0.2 and 0.5 m. An additional 1 m-resolution DEM was extracted from available tri-stereo Pleiades satellite imagery and chosen as pre-paroxysm surface. Using the orthoimages it was possible to map the distribution of eruption products and determine the morpho-structural changes. Furthermore, the topographic approach (subtraction between two different surfaces DEMs) with a cut-and-fill procedure was chosen to calculate the volume gain (in the southern sector of the SdF) and loss (in the crater areas).</p><p>This work demonstrates the usefulness of the combined use of UAVs and SfM techniques to map volcanic products, to highlight morphological changes and perform volume estimations. The data collected during these field efforts and the temporal comparisons of the DEMs represent a fundamental contribution to both volcanic hazard assessment and risk mitigation, and can be used to support civil protection operations.</p>

Digital Elevation Models (DEMs) contribute to geomorphological and hydrological applications. DEMs can be derived using different remote sensing-based datasets, such as Interferometric Synthetic Aperture Radar (InSAR) (e.g., Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR) and Shuttle Radar Topography Mission (SRTM) DEMs). In addition, there is also the Digital Surface Model (DSM) derived from optical tri-stereo ALOS Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) imagery. In this study, we evaluated satellite-based DEMs, SRTM (Global) GL1 DEM V003 28.5 m, ALOS DSM 28.5 m, and PALSAR DEMs 12.5 m and 28.5 m, and their derived channel networks/orders. We carried out these assessments using Light Detection and Ranging (LiDAR) Digital Surface Models (DSMs) and Digital Terrain Models (DTMs) and their derived channel networks and Strahler orders as reference datasets at comparable spatial resolutions. We introduced a pixel-based method for the quantitative horizontal evaluation of the channel networks and Strahler orders derived from global DEMs utilizing confusion matrices at different flow accumulation area thresholds (ATs) and pixel buffer tolerance values (PBTVs) in both ±X and ±Y directions. A new Python toolbox for ArcGIS was developed to automate the introduced method. A set of evaluation metrics—(i) producer accuracy (PA), (ii) user accuracy (UA), (iii) F-score (F), and (iv) Cohen’s kappa index (KI)—were computed to evaluate the accuracy of the horizontal matching between channel networks/orders extracted from global DEMs and those derived from LiDAR DTMs and DSMs. PALSAR DEM 12.5 m ranked first among the other global DEMs with the lowest root mean square error (RMSE) and mean difference (MD) values of 4.57 m and 0.78 m, respectively, when compared to the LiDAR DTM 12.5 m. The ALOS DSM 28.5 m had the highest vertical accuracy with the lowest recorded RMSE and MD values of 4.01 m and −0.29 m, respectively, when compared to the LiDAR DSM 28.5 m. PALSAR DEM 12.5 m and ALOS DSM 28.5 m-derived channel networks/orders yielded the highest horizontal accuracy when compared to those delineated from LiDAR DTM 12.5 m and LiDAR DSM 28.5 m, respectively. The number of unmatched channels decreased when the PBTV increased from 0 to 3 pixels using different ATs.

In orbit since the April 2018, the Colour and Stereo Surface Imaging System (CaSSIS) on board of the ExoMars Trace Gas Orbiter (TGO) has recently entered its 6th year of scientific phase. Since then, CaSSIS has provided us with a rich catalog of up to four bands stereo images of the Martian surface. The stereo capability is achieved through an innovative telescope rotation approach composing a convergence angle of almost 22 degrees between the stereo couple. In this way CaSSIS can produce detailed color 3D maps, thus providing crucial data for the analysis of the surfaces and their composition.The pipeline for three-dimensional modelling is developed and maintained by the INAF team located at the Astronomical Observatory of Padova, making available the Digital Terrain Models (DTMs) and orthorectified images to the entire team and scientific community since the start of the mission.The 3DPD software (Simioni et al., 2021; Re et al., 2022), at the core of the pipeline, allows the exploitation of data according to the principles of stereogrammetry. The DTMs are produced at 13.5 m ground sample distance from 4.5 m/px images, with an estimated vertical accuracy below 3 pixels size (15 m)  (Fig.1).To date, more than 2100 stereo couples are available on a total of 40.000 images acquired. Of these about 400 stereo pairs have already been processed and available for download at  the OAPD-hosted repository (https://cassis.oapd.inaf.it/archive/).Since the framework was first founded by Simioni et al., 2021, the pipeline has been continuously developing to improve the performance of the data product generation.The goal of this work is to present the actual state of the framework and all the improvements made. Recent changes are here described and supported by an assessment of the quality and precision of the generated products and their derivatives.Recent developments include the integration of the Bundle Block Adjustment, employing the jigsaw routines made available with the USGS ISIS platform (Laura et al., 2023). Thanks to the jigsaw output, we are able to refine the projection matrices affected otherwise by distortions that introduce geometric effects of misalignments between the acquisitions. The misalignments not adequately modelled and resolved by the Bundle Adjustment could otherwise result in steps artefacts that can reach even hundreds of m in the worst cases.Further important update is given by an innovative approach of aligning DTMs to MOLA-HRSC (Fergason et al., 2018), further improving the surface projection and the absolute elevation, reaching values generally below 50 m/px of standard deviation in comparison with it. This process was also extended to the entire database of DTMs already produced as one major update, bringing similar results (Fig.2).Fig.1 Comparison between a CaSSIS DTM (MY34_003673_018) and a HiRISE DTM (DTEEC_005533_1975_005388_1975) at 1 m/px, demonstrating a vertical accuracy of about 8m. Fig.2 The standard deviation on the vertical accuracy, achieved as a result of the recent alignment with the MOLA-HRSC and applied to the entire CaSSIS DTMs database.

One of the most important parameters when formalising the specifications for a photogrammetric project is the ground sample distance (GSD). While there are a variety of methods for the computation of the mean GSD for an aerial image, the aim of this research paper is to propose a method for the computation of the GSD that balances both precision and speed of computation. The rigorous computation of the mean GSD on an image requires the evaluation of the ground projection for each pixel over the digital terrain model. Three large photogrammetric projects within the Spanish National Plan for Aerial Orthophotography (PNOA) are evaluated to determine the degree of fulfilment of the required GSDs. The statistical distribution of the GSD is analysed to confirm its (lack of) normality. The rigorous approach to compute the mean GSD was presented, tested and compared with ten approximate solutions based on regular patterns of GSDs. The results achieved with the mean GSD value based on two object points with maximum and minimum ground elevations per image are also presented as well as the base/height ratios achieved on the three large PNOA projects. It is empirically demonstrated that the computation of the mean GSD based on a pattern of 3 × 3 GSDs is recommended. An error better than 1 cm is achieved for 99.8 % of the images, independent of the topography of the mission.

Abstract. Assessment and management of volcanic risk are important scientific, economic, and political issues, especially in densely populated areas threatened by volcanoes. The Virunga volcanic province in the Democratic Republic of the Congo, with over 1 million inhabitants, has to cope permanently with the threat posed by the active Nyamulagira and Nyiragongo volcanoes. During the past century, Nyamulagira erupted at intervals of 1–4 years – mostly in the form of lava flows – at least 30 times. Its summit and flank eruptions lasted for periods of a few days up to more than 2 years, and produced lava flows sometimes reaching distances of over 20 km from the volcano. Though most of the lava flows did not reach urban areas, only impacting the forests of the endangered Virunga National Park, some of them related to distal flank eruptions affected villages and roads. In order to identify a useful tool for lava flow hazard assessment at Goma Volcano Observatory (GVO), we tested VORIS 2.0.1 (Felpeto et al., 2007), a freely available software (http://www.gvb-csic.es) based on a probabilistic model that considers topography as the main parameter controlling the lava flow propagation. We tested different parameters and digital elevation models (DEM) – SRTM1, SRTM3, and ASTER GDEM – to evaluate the sensitivity of the models to changes in input parameters of VORIS 2.0.1. Simulations were tested against the known lava flows and topography from the 2010 Nyamulagira eruption. The results obtained show that VORIS 2.0.1 is a quick, easy-to-use tool for simulating lava-flow eruptions and replicates to a high degree of accuracy the eruptions tested when input parameters are appropriately chosen. In practice, these results will be used by GVO to calibrate VORIS for lava flow path forecasting during new eruptions, hence contributing to a better volcanic crisis management.

In recent decades, an important number of regional and global digital elevation models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs need to be expressed in the same coordinate reference system (CRS) and sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention) with heights relative to the same vertical reference system (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements or disparities may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect, or curvature. Therefore, before any comparison, the control of DEM planimetric accuracy is certainly a very important task to perform. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have already been computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been introduced through bicubic interpolation in an 11 × 11 grid with a 0.1-pixel step in both directionsThis validation method has been applied in four approximately 10 km × 10 km DEMIX tiles showing different roughness (height distribution). Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with typical errors (eb) ranging from 12 to 20% of the pixel size (with the worst case in Croatia). These errors in displacement retrievals are not equally distributed in the 11 × 11 grid, and the overall error Eb depends on the roughness encountered in the different tiles. The second aim of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d = 1 of the interpolated point) on the accuracy of the displacement retrieval. By considering Eb as a quality indicator, tests have been performed in the four DEMIX tiles, making the bicubic parameter vary between −1.5 and 0.0 by a step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four Eb minimal values. This BBC parameter b* is low for flat areas (around −0.95) and higher in mountainous areas (around −0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r = 0.717). The logarithmic regression formula b~σslope estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC b~.

The Surface Extraction by TIN-based Search-space Minimization (SETSM) algorithm provides automatic generation of stereo-photogrammetric Digital Surface Models (DSMs) from single stereopairs of stereoscopic images (i.e., stereopairs), eliminating the need for terrain-dependent parameters. SETSM has been extensively validated through the ArcticDEM and Reference Elevation Models for Antarctica (REMA) DSM mapping projects. To enhance DSM coverage, quality, and accuracy by addressing stereopair occlusions, we expand the capabilities of the SETSM algorithm from single stereopair to multiple-pair matching. Building on SETSM’s essential components, we present a SETSM multiple-pair matching procedure (SETSM MMP) that modifies 3D voxel construction, similarity measurement, and blunder detection, among other components. A novel Three-Dimensional Kernel-based Weighted Height Estimation (3D KWHE) algorithm specialized for SETSM accurately determines optimal heights and reduces surface noise. Additionally, an adaptive pixel-to-pixel matching strategy mitigates the effect of differences in ground sample distance (GSD) between images. Validation using space-borne Worldview-2 and air-borne DMC multiple images over urban landscapes, compared to USGS lidar DSM, confirms improved height accuracy and matching success rates. The results from the DMC air-borne images demonstrate efficient elimination of occlusions. SETSM MMP enables high-quality DSM generation in urban environments while retaining the original, single-stereopair SETSM’s high performance.

Modeling vegetation heights from high resolution stereo aerial photography: An application for broad-scale rangeland monitoring

Abstract. Generating precise and up-to-date landslide susceptibility maps (LSMs) in landslide-prone areas is important to identify hazard potential in the future. The data quality and the method selection affect the accuracy of the LSMs. In this context, the accuracy and precision of the digital elevation models (DEMs) used as input are among the most important performance elements. Therefore, the influence of DEM accuracy and spatial resolution in producing LSMs was investigated here. A high accuracy DEM with 5 m grid spacing produced from aerial photographs and the EU-DEM v1.1 freely accessible from Copernicus Land Monitoring Service with 25 m spatial resolution were used for producing two different LSMs using the Random Forest (RF) method in this study. The RF method has proven success for this purpose. A total of eight conditioning factors, which include topographical and geological features, was used as model input. The landslide inventory was derived with the help of aerial stereo images with 20 cm and 30 cm ground sampling distances. The performances of the LSMs were assessed with receiver operating characteristics (ROC) area under curve (AUC) values. In addition, the results were compared with visual inspection. The results show that although the AUC values obtained from the aerial DEM (0.95) and EU-DEM v1.1 (0.93) were comparable; based on the visual assessments, the LSM obtained from the higher resolution DEM was found more successful in detecting the landslides and thus exhibited better prediction performance.

An automated, open-source pipeline for mass production of digital elevation models (DEMs) from very-high-resolution commercial stereo satellite imagery

The integration of satellite data and modeling represents a step toward the next generation of quantitative hazard assessment in response to effusive volcano eruption onset. Satellite-based thermal remote sensing of hotspots related to effusive activity can effectively provide a variety of products suited to timing, locating, and tracking the radiant character of lava flows. Hotspots show the location and occurrence of eruptive events (vents). Discharge rate estimates may indicate the current intensity (effusion rate) and potential magnitude (volume). High-spatial-resolution multispectral satellite data can complement field observations for monitoring the front position (length) and extension of flows (area). Physics-based models driven, or validated, by satellite-derived parameters are now capable of fast and accurate forecast of lava flow inundation scenarios (hazard). Here, we demonstrate the potential of the integrated application of satellite remote-sensing techniques and lava flow models by using a retrospective analysis of the 2004–2005 effusive eruption at Mount Etna in Italy. The lava flow hazard was assessed by using the HOTSAT volcano hotspot detection system, which works with satellite thermal infrared data, and the MAGFLOW lava flow emplacement model, which is able to relate the flow evolution to eruption conditions at the vent. We used HOTSAT to analyze Moderate Resolution Imaging Spectroradiometer (MODIS) and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data to output hotspot location, lava thermal flux, and effusion rate estimation. This output was used to drive the MAGFLOW simulations of lava flow paths and to continuously update flow simulations. We also show how Landsat-7 Enhanced Thematic Mapper+ (ETM+) and Earth Observing 1 ( EO-1 ) Advanced Land Imager (ALI) images complement the field observations to track the flow front position in time and add valuable data on lava flow advancement with which to validate the numerical simulations. Such integration at last makes timely forecasts of lava flow hazards during effusive crises possible at the great majority of volcanoes for which no monitoring exists.

Digital Elevation Model (DEM) is main input for watershed modelling. Recently, (DEM) is available online for free in different accuracies, and spatial resolutions as a product of several remote sensing satellites. Hence, it is necessary to find out which one is the best for watershed modeling in the study area. In this study, the different accuracies 30m spatial resolution DEMs of (Copernicus, SRTM, and ASTER) can be examined by using Remote sensing (RS) and Geographic Information systems (GIS) techniques to delineate and calculate the topographic characteristics for five different size and topography watersheds (Swaidy, Garlond, Khuwayr Hirah, Naqab, and Kalak) located on both sides of the Mosul reservoir in the northeastern part of Iraq. The analysis results can be led to find that the Copernicus (GLO-30) 30 m resolution DEM is the optimum and most accurate DEM in the selected study area at the vertical accuracy (1.3521 m) and with 95% confidence level is (2.6502 m) represented by the minimum Root Mean Square Error (RMSE) of the elevations differences between check points (MOWR elevations and LIDAR DEM 1 m resolution) and the used DEMs. The watershed delineation and calculated topographic characteristics (watersheds boundary, elevations, area, perimeter and slope areas) are affected by DEM accuracy. Where the considerable accuracy of the differences is with the (Copernicus-SRTM) DEMS at the minimum RMSE of watersheds characteristics.

Forecasting lava flow hazards during the 2006 Etna eruption: Using the MAGFLOW cellular automata model

Comparison of different DEMs for hydrological studies in the mountainous areas

The main objective of the study is Digital Terrain Model (DTM) generation from aerial photogrammetric data and identify and map the potential avalanche prone zones in Manali region. Avalanche is a dynamic hazardous phenomenon in the snow-bound mountainous terrain. Mapping of avalanche prone terrain is crucial to minimize the avalanche hazard. Nowadays, airborne data capturing technology, such as large-format Photogrammetry, has opened new vistas for the mapping of complex and inaccessible mountainous areas. In the present study, large format digital Photogrammetry data of 20 cm ground sample distance (GSD) have been used to generate high-resolution and accurate Digital Elevation Model and ortho-images. Digital terrain model along with its derivative terrain products and land cover map generated from land cover classification of derived ortho-photo is analyzed to locate the probable avalanche zone. The terrain characteristics, snow-pack condition and prevailing meteorological conditions are the groups of variables that influence the occurrence of avalanche. Amongst these, the terrain characteristics is the most influencing factor, and easier to map due to its stable nature along the time. Therefore advanced geo-informatics techniques have been used by mixing terrain property, Digital Elevation Model (DEM) and satellite imagery to determine the different geographical factors that affect the avalanche triggering. Also the derived information was combined in Analytic Hierarchy Process to extract a map of the avalanche prone zones in the study area standard mapping techniques as coarse-resolution data are not very appropriate for such studies.