Search Results (53)

At least 10 global digital elevation models (DEMs) at one-arc-second resolution now cover Earth. Comparing derived grids, like slope or curvature, preserves surface spatial relationships, and can be more important than just elevation values. Such comparisons provide more nuanced DEM rankings than just elevation root mean square error (RMSE) for a small number of points. We present three new comparison categories: fraction of unexplained variance (FUV) for grids with continuous floating point values; accuracy metrics for integer code raster classifications; and comparison of stream channel vector networks. We compare six global DEMs that are digital surface models (DSMs), and four edited versions that use machine learning/artificial intelligence techniques to create a bare-earth digital terrain model (DTM) for different elevation ranges: full Earth elevations, under 120 m, under 80 m, and under 10 m. We find edited DTMs improve on elevation values, but because they do not incorporate other metrics in their training they do not improve overall on the source Copernicus DSM. We also rank 17 common geomorphic-derived grids for sensitivity to DEM quality, and document how landscape characteristics, especially slope, affect the results. None of the DEMs perform well in areas with low average slope compared to reference DTMs aggregated from 1 m airborne lidar data. This indicates that accurate work in low-relief areas grappling with global climate change should use airborne lidar or very high resolution image-derived DTMs. Full article

The automated recognition of landforms holds significant importance within the framework of digital geomorphological mapping, serving as a pivotal focal point for research and practical applications alike. Over the last decade, various methods have been developed to achieve this goal, ranging from grid-based to object-based approaches, covering a range from supervised to completely unsupervised techniques. Furthermore, the vast majority of the methods mentioned depend on Digital Elevation Models (DEMs) as their primary input, highlighting the crucial significance of meticulous preparation and rigorous quality assessment of these datasets. In this study, we compare the outcomes of grid-based methods for landforms extraction and surficial process type assessment, leveraging various DEMs as input data. Initially, we employed a photogrammetric Digital Terrain Model (DTM) generated at a regional scale, along with two LiDAR datasets. The first dataset originates from an airborne survey conducted by the national government approximately a decade ago, while the second dataset was acquired by UAV as part of this study’s framework. The results highlight how the higher resolution and level of detail of the LiDAR datasets allow the recognition of a higher number of features at higher scales; but, in contrast, generally, a high level of detail corresponds with a higher risk of noise within the dataset, mostly due to unwanted natural features or anthropogenic disturbance. Utilizing these datasets for generating geomorphological maps harbors significant potential in the framework of natural hazard assessment, particularly concerning phenomena associated with geo-hydrological processes. Full article

Over the past two decades, the airborne Light Detection and Ranging (LiDAR) system has become a useful tool for acquiring high-resolution topographic data, especially in active tectonics studies. Analyzing Digital Terrain Models (DTMs) from LiDAR exposes morpho-structural elements, aiding in the understanding of fault zones, among other applications. Despite its effectiveness, challenges persist in regions with rapid deformation, dense vegetation, and human impact. We propose an adapted workflow transitioning from the conventional airborne LiDAR system to the usage of drone-based LiDAR technology for higher-resolution data acquisition. Additionally, drones offer a more cost-effective solution, both in an initial investment and ongoing operational expenses. Our goal is to demonstrate how drone-based LiDAR enhances the identification of active deformation features, particularly for earthquake-induced surface faulting. To evaluate the potential of our technique, we conducted a drone-based LiDAR survey in the Casamicciola Terme area, north of Ischia Island, Italy, known for the occurrence of destructive shallow earthquakes, including the 2017 Md = 4 event. We assessed the quality of our acquired DTM by comparing it with existing elevation datasets for the same area. We discuss the advantages and limitations of each DTM product in relation to our results, particularly when applied to fault mapping. By analyzing derivative DTM products, we identified the fault scarps within the Casamicciola Holocene Graben (CHG) and mapped its structural geometry in detail. The analysis of both linear and areal geomorphic features allowed us to identify the primary factors influencing the current morphological arrangement of the CHG area. Our detailed map depicts a nested graben formed by two main structures (the Maio and Sentinella faults) and minor internal faults (the Purgatorio and Nizzola faults). High-resolution DEMs acquired by drone-based LiDAR facilitated detailed studies of the geomorphology and fault activity. A similar approach can be applied in regions where the evidence of high slip-rate faults is difficult to identify due to vegetation cover and inaccessibility. Full article

Understory topography serves as a crucial data source, playing an instrumental role in numerous forest ecosystem applications. However, the use of synthetic aperture radar interferometry and optical stereo for the acquisition of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), SRTM (Shuttle Radar Topography Mission), and GLO-30 (Copernicus Digital Elevation Model) DEM presents unique challenges, particularly in forested environments. These challenges are primarily due to limitations in penetration capability and the effects of foreshortening. ICESat-2/ATLAS, with its higher spatial sampling rate and strong penetrability, presents a new opportunity for estimating forest height parameters and understory terrain. We assessed the vertical accuracy of ASTER, SRTM, GLO-30, and ATLAS in the forest study areas of the United States compared to the reference dataset DTM provided by G-LiHT and we will further discuss the influence of different ground altitudes, forest types, slopes, and aspects on vertical accuracy. The study reveals that in a forested environment, ICESat-2 ATL03 exhibits the highest accuracy at the footprint scale, with a correlation coefficient (R²) close to 1 and Root Mean Square Error (RMSE) = 1.96 m. SRTM exhibits the highest accuracy at the regional scale, with an R² close to 0.99, RMSE = 11.09 m. A significant decrease in accuracy was observed with increasing slope, especially for slopes above 15°. With a sudden increase in altitude, such as in mountainous situations, the accuracy of vertical estimation will significantly decrease. Aspect and forest cover indeed influence the accuracy of the four DEM products, but this influence lacks a clear pattern. Our results show that ICESat-2 and SRTM data might show sufficient and stable vertical accuracy in a forested environment. Full article

The creation of accurate elevation models (topography) from stereo images are critical for a large variety of geospatial activities, including the production of digital orthomosaics, change detection, landing site analysis, geologic mapping, rover traverse planning, and spectral analysis. The United Stated Geological Survey, Astrogeology Science Center, continues to transition the supported planetary sensor models to the Community Sensor Model (CSM) standard. This paper describes the current state of use for this photogrammetric standard, supported sensor model types, and qualitatively compares derived topography between SOCET SET and SOCET GXP (^®BAE Systems) using HiRISE stereo images of Mars. Our transition to the CSM standard will ensure an uninterrupted capability to make these valuable products for Mars and many other extraterrestrial planets and moons. Full article

Remote sensing (RS) techniques are essential for studying hazardous landslide events because they capture information and monitor sites at scale. They enable analyzing causes and impacts of ongoing events for disaster management. There has been a plethora of work in the literature mostly discussing (1) applications to detect, monitor, and predict landslides using various instruments and image analysis techniques, (2) methodological mechanics in using optical and microwave sensing, and (3) quantification of surface geological and geotechnical changes using 2D images. Recently, studies have shown that the degree of hazard is mostly influenced by speed, type, and volume of surface deformation. Despite available techniques to process lidar and image/radar-derived 3D geometry, prior works mostly focus on using 2D images, which generally lack details on the 3D aspects of assessment. Thus, assessing the 3D geometry of terrain using elevation/depth information is crucial to determine its cover, geometry, and 3D displacements. In this review, we focus on 3D landslide analysis using RS data. We include (1) a discussion on sources, types, benefits, and limitations of 3D data, (2) the recent processing methods, including conventional, fusion-based, and artificial intelligence (AI)-based methods, and (3) the latest applications. Full article

This study addresses the growing interest in utilizing remote sensing tools for locating sustainable drainage systems (SuDS) in urban environments. SuDS, recognized as Nature-based Solutions (NbS), play a crucial role in enhancing urban resilience against climate change. This study focuses on the calibration process required to establish a correlation between the Topographic Wetness Index (TWI), derived from high-precision digital elevation models (DEMs), and soil moisture (SM) data obtained from satellite imaging. This calibration serves as a method to optimize the placement of sustainable urban drainage system vegetated techniques in urban areas. This study leveraged the exceptional resolution of PAZ satellite radar data to effectively detect variations in SM, particularly in grass-type vegetated land. The sensitivity of the X-band radar signal to moisture levels and changes in ground roughness proved valuable in tracking SM dynamics. The core of the study involved deriving the TWI from a high-resolution digital terrain model (DTM). The correlation between the TWI and SM values demonstrates robustness, with an R² value of 0.77. These findings significantly advance the calibration of TWI values with SM measurements, enhancing their practicality in identifying areas prone to water accumulation. The study’s outcomes provide valuable insights for guiding the strategic placement of SuDS in urban environments, contributing to the effective management of water-related challenges in the face of urbanization and climate change. Full article

Forest canopy height estimation is essential for forest management and biomass estimation. In this study, we aimed to evaluate the capacity of TanDEM-X interferometric synthetic aperture radar (InSAR) data to estimate canopy height with the assistance of an external digital terrain model (DTM). A ground-to-volume ratio estimation model was proposed so that the canopy height could be precisely estimated from the random-volume-over-ground (RVoG) model. We also refined the RVoG inversion process with the relationship between the estimated penetration depth (PD) and the phase center height (PCH). The proposed method was tested by TanDEM-X InSAR data acquired over relatively homogenous coniferous forests (Teruel test site) and coniferous as well as broadleaved forests (La Rioja test site) in Spain. Comparing the TanDEM-X-derived height with the LiDAR-derived height at plots of size 50 m × 50 m, the root-mean-square error (RMSE) was 1.71 m (R² = 0.88) in coniferous forests of Teruel and 1.97 m (R² = 0.90) in La Rioja. To demonstrate the advantage of the proposed method, existing methods based on ignoring ground scattering contribution, fixing extinction, and assisting with simulated spaceborne LiDAR data were compared. The impacts of penetration and terrain slope on the RVoG inversion were also evaluated. The results show that when a DTM is available, the proposed method has the optimal performance on forest height estimation. Full article

Crop height is a vital indicator of growth conditions. Traditional drone image-based crop height measurement methods primarily rely on calculating the difference between the Digital Elevation Model (DEM) and the Digital Terrain Model (DTM). The calculation often needs more ground information, which remains labour-intensive and time-consuming. Moreover, the variations of terrains can further compromise the reliability of these ground models. In response to these challenges, we introduce G-DMD, a novel method based on Gated Recurrent Units (GRUs) using DEM and multispectral drone images to calculate the crop height. Our method enables the model to recognize the relation between crop height, elevation, and growth stages, eliminating reliance on DTM and thereby mitigating the effects of varied terrains. We also introduce a data preparation process to handle the unique DEM and multispectral image. Upon evaluation using a cotton dataset, our G-DMD method demonstrates a notable increase in accuracy for both maximum and average cotton height measurements, achieving a 34% and 72% reduction in Root Mean Square Error (RMSE) when compared with the traditional method. Compared to other combinations of model inputs, using DEM and multispectral drone images together as inputs results in the lowest error for estimating maximum cotton height. This approach demonstrates the potential of integrating deep learning techniques with drone-based remote sensing to achieve a more accurate, labour-efficient, and streamlined crop height assessment across varied terrains. Full article

Prescribed burning and pyric herbivory play pivotal roles in mitigating wildfire risks, underscoring the imperative of consistent biomass monitoring for assessing fuel load reductions. Drone-derived surface models promise uninterrupted biomass surveillance but require complex photogrammetric processing. In a Mediterranean mountain shrubland burning experiment, we refined a Structure from Motion (SfM) and Multi-View Stereopsis (MVS) workflow to diminish biases in 3D modeling and RGB drone imagery-based surface reconstructions. Given the multitude of SfM-MVS processing alternatives, stringent quality oversight becomes paramount. We executed the following steps: (i) calculated Root Mean Square Error (RMSE) between Global Navigation Satellite System (GNSS) checkpoints to assess SfM sparse cloud optimization during georeferencing; (ii) evaluated elevation accuracy by comparing the Mean Absolute Error (MAE) of six surface and thirty terrain clouds against GNSS readings and known box dimensions; and (iii) complemented a dense cloud quality assessment with density metrics. Balancing overall accuracy and density, we selected surface and terrain cloud versions for high-resolution (2 cm pixel size) and accurate (DSM, MAE = 57 mm; DTM, MAE = 48 mm) Digital Elevation Model (DEM) generation. These DEMs, along with exceptional height and volume models (height, MAE = 12 mm; volume, MAE = 909.20 cm³) segmented by reference box true surface area, substantially contribute to burn impact assessment and vegetation monitoring in fire management systems. Full article

The integration of multiple data sources, including satellite imagery, aerial photography, and ground-based measurements, represents an important development in the study of landslide processes. The combination of different data sources can be very important in improving our understanding of geological phenomena, especially in cases of inaccessible areas. In this context, the study of coastal areas represents a real challenge for the research community, both for the inaccessibility of coastal slopes and for the numerous drivers that can control coastal processes (subaerial, marine, or endogenic). In this work, we present a case study of the Conero Regional Park (Northern Adriatic Sea, Ancona, Italy) cliff-top retreat, characterized by Neogenic soft rocks (flysch, molasse). In particular, the study is focused in the area between the beach of Portonovo and Trave (south of Ancona), which has been studied using aerial orthophoto acquired between 1978 and 2021, Unmanned Aerial Vehicle (UAV) photographs (and extracted photogrammetric model) surveyed in September 2021 and 2012 LiDAR data. Aerial orthophotos were analyzed through the United States Geological Survey’s (USGS) tool Digital Shoreline Analysis System (DSAS) to identify and estimate the top-cliff erosion. The results were supported by the analysis of wave data and rainfall from the correspondent period. It has been found that for the northernmost sector (Trave), in the examined period of 40 years, an erosion up to 40 m occurred. Furthermore, a Digital Elevation Model (DEM) of Difference (DoD) between a 2012 Digital Terrain Model (DTM) and a UAV Digital Surface Model (DSM) was implemented to corroborate the DSAS results, revealing a good agreement between the retreat areas, identified by DSAS, and the section of coast characterized by a high value of DoD. Full article

This research is primarily focused on utilizing available airborne LiDAR data and spatial data from the OpenStreetMap (OSM) database to generate 3D models of buildings for a large-scale urban area. The city center of Ljubljana, Slovenia, was selected for the study area due to data availability and diversity of building shapes, heights, and functions, which presented a challenge for the automated generation of 3D models. To extract building heights, a range of data sources were utilized, including OSM attribute data, as well as georeferenced and classified point clouds and a digital elevation model (DEM) obtained from openly available LiDAR survey data of the Slovenian Environment Agency. A digital surface model (DSM) and digital terrain model (DTM) were derived from the processed LiDAR data. Building outlines and attributes were extracted from OSM and processed using QGIS. Spatial coverage of OSM data for buildings in the study area is excellent, whereas only 18% have attributes describing external appearance of the building and 6% describing roof type. LASTools software (rapidlasso GmbH, Friedrichshafener Straße 1, 82205 Gilching, GERMANY) was used to derive and assign building heights from 3D coordinates of the segmented point clouds. Various software options for procedural modeling were compared and Blender was selected due to the ability to process OSM data, availability of documentation, and low computing requirements. Using procedural modeling, a 3D model with level of detail (LOD) 1 was created fully automated. After analyzing roof types, a 3D model with LOD2 was created fully automated for 87.64% of buildings. For the remaining buildings, a comparison of procedural roof modeling and manual roof editing was performed. Finally, a visual comparison between the resulting 3D model and Google Earth’s model was performed. The main objective of this study is to demonstrate the efficient modeling process using open data and free software and resulting in an enhanced accuracy of the 3D building models compared to previous LOD2 iterations. Full article

The greatest challenges encountered in geospatial studies are related to the availability, accuracy, relevance and cost of the data used. The main mapping techniques currently employed are based on digital data, which are used to create digital elevation models (DEMs). The aim of the present study is to devise and apply methodologies for the generation and validation of high-resolution mapping materials, usable both for local, large-scale analyses, and for the calculation of certain morphometric parameters based on structure from motion (SFM) techniques, applied to images acquired by means of a drone at low cost. As a case study, the ruins of the Belci dam, located in Romania, were analysed, where, with the help of a drone, GIS measurements were performed on the arborescent vegetation of the study area, and a digital terrain model (DTM) of the dam was generated. The costs of such a methodological endeavour are low, which allows for the repetition of the steps involved in devising the maps necessary for such studies on a weekly, seasonal, or annual basis, or after extreme events (floods, landslides etc.). The cartographic materials created in the present study allowed us to calculate the active section of the left earthfill dike of the Belci dam, as well as the volume of material removed by the flood of 1991. Full article

ArcticDEM provides the public with an unprecedented opportunity to access very high-spatial resolution digital elevation models (DEMs) covering the pan-Arctic surfaces. As it is generated from stereo-pairs of optical satellite imagery, ArcticDEM represents a mixture of a digital surface model (DSM) over a non-ground areas and digital terrain model (DTM) at bare grounds. Reconstructing DTM from ArcticDEM is thus needed in studies requiring bare ground elevation, such as modeling hydrological processes, tracking surface change dynamics, and estimating vegetation canopy height and associated forest attributes. Here we proposed an automated approach for estimating DTM from ArcticDEM in two steps: (1) identifying ground pixels from WorldView-2 imagery using a Gaussian mixture model (GMM) with local refinement by morphological operation, and (2) generating a continuous DTM surface using ArcticDEMs at ground locations and spatial interpolation methods (ordinary kriging (OK) and natural neighbor (NN)). We evaluated our method at three forested study sites characterized by different canopy cover and topographic conditions in Livengood, Alaska, where airborne lidar data is available for validation. Our results demonstrate that (1) the proposed ground identification method can effectively identify ground pixels with much lower root mean square errors (RMSEs) (<0.35 m) to the reference data than the comparative state-of-the-art approaches; (2) NN performs more robustly in DTM interpolation than OK; (3) the DTMs generated from NN interpolation with GMM-based ground masks decrease the RMSEs of ArcticDEM to 0.648 m, 1.677 m, and 0.521 m for Site-1, Site-2, and Site-3, respectively. This study provides a viable means of deriving high-resolution DTM from ArcticDEM that will be of great value to studies focusing on the Arctic ecosystems, forest change dynamics, and earth surface processes. Full article

Imaging and measuring the Earth’s relief with sensors mounted upon unmanned aerial vehicles is an increasingly frequently used and promising method of remote sensing. In the context of fluvial geomorphology and its applications, e.g., landform mapping or flood modelling, the reliable representation of the land surface on digital elevation models is crucial. The main objective of the study was to assess and compare the accuracy of state-of-the-art remote sensing technologies in generating DEMs for riverscape characterization and fluvial monitoring applications. In particular, we were interested in DAP and LiDAR techniques comparison, and UAV applicability. We carried out field surveys, i.e., GNSS-RTK measurements, UAV and aircraft flights, on islands and sandbars within a nature reserve on a braided section of the Vistula River downstream from the city of Warsaw, Poland. We then processed the data into DSMs and DTMs based on four sources: ULS (laser scanning from UAV), UAV-DAP (digital aerial photogrammetry), ALS (airborne laser scanning), and satellite Pléiades imagery processed with DAP. The magnitudes of errors are represented by the cross-reference of values generated on DEMs with GNSS-RTK measurements. Results are presented for exposed sediment bars, riverine islands covered by low vegetation and shrubs, or covered by riparian forest. While the average absolute height error of the laser scanning DTMs oscillates around 8–11 cm for most surfaces, photogrammetric DTMs from UAV and satellite data gave errors averaging more than 30 cm. Airborne and UAV LiDAR measurements brought almost the perfect match. We showed that the UAV-based LiDAR sensors prove to be useful for geomorphological mapping, especially for geomorphic analysis of the river channel at a large scale, because they reach similar accuracies to ALS and better than DAP-based image processing. Full article