Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

Efficient Drone-Based Rare Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection

Monitoring rare plant species is used to confirm presence, assess health, and verify population trends. Unmanned aerial systems (UAS) are ideal tools for monitoring rare plants because they can efficiently collect data without impacting the plant or endangering personnel. However, UAS flight planning can be subjective, resulting in ineffective use of flight time and overcollection of imagery. This study used a Maxent machine-learning predictive model to create targeted flight areas to monitor Geum radiatum, an endangered plant endemic to the Blue Ridge Mountains in North Carolina. The Maxent model was developed with ten environmental layers as predictors and known plant locations as training data. UAS flight areas were derived from the resulting probability raster as isolines delineated from a probability threshold based on flight parameters. Visual analysis of UAS imagery verified the locations of 33 known plants and discovered four previously undocumented occurrences. Semi-automated detection of plant species was explored using a neural network object detector. Although the approach was successful in detecting plants in on-ground images, no plants were identified in the UAS aerial imagery, indicating that further improvements are needed in both data acquisition and computer vision techniques. Despite this limitation, the presented research provides a data-driven approach to plan targeted UAS flight areas from predictive modeling, improving UAS data collection for rare plant monitoring.

Concept of an Innovative Autonomous Unmanned System for Bathymetric Monitoring of Shallow Waterbodies (INNOBAT System)

Bathymetry is a subset of hydrography, aimed at measuring the depth of waterbodies and waterways. Measurements are taken inter alia to detect natural obstacles or other navigational obstacles that endanger the safety of navigation, to examine the navigability conditions, anchorages, waterways and other commercial waterbodies, and to determine the parameters of the safe depth of waterbodies in the vicinity of ports, etc. Therefore, it is necessary to produce precise and reliable seabed maps, so that any hazards that may occur, particularly in shallow waterbodies, can be prevented, including the high dynamics of hydromorphological changes. This publication is aimed at developing a concept of an innovative autonomous unmanned system for bathymetric monitoring of shallow waterbodies. A bathymetric and topographic system will use autonomous unmanned aerial and surface vehicles to study the seabed relief in the littoral zone (even at depths of less than 1 m), in line with the requirements set out for the most stringent International Hydrographic Organization (IHO) order—exclusive. Unlike other existing solutions, the INNOBAT system will enable the coverage of the entire surveyed area with measurements, which will allow a comprehensive assessment of the hydrographic and navigation situation in the waterbody to be conducted.

UAV Photogrammetry under Poor Lighting Conditions-Accuracy Considerations

The use of low-level photogrammetry is very broad, and studies in this field are conducted in many aspects. Most research and applications are based on image data acquired during the day, which seems natural and obvious. However, the authors of this paper draw attention to the potential and possible use of UAV photogrammetry during the darker time of the day. The potential of night-time images has not been yet widely recognized, since correct scenery lighting or lack of scenery light sources is an obvious issue. The authors have developed typical day- and night-time photogrammetric models. They have also presented an extensive analysis of the geometry, indicated which process element had the greatest impact on degrading night-time photogrammetric product, as well as which measurable factor directly correlated with image accuracy. The reduction in geometry during night-time tests was greatly impacted by the non-uniform distribution of GCPs within the study area. The calibration of non-metric cameras is sensitive to poor lighting conditions, which leads to the generation of a higher determination error for each intrinsic orientation and distortion parameter. As evidenced, uniformly illuminated photos can be used to construct a model with lower reprojection error, and each tie point exhibits greater precision. Furthermore, they have evaluated whether commercial photogrammetric software enabled reaching acceptable image quality and whether the digital camera type impacted interpretative quality. The research paper is concluded with an extended discussion, conclusions, and recommendation on night-time studies.

Determining Peak Altitude on Maps, Books and Cartographic Materials: Multidisciplinary Implications

Mountain peaks and their altitude have been of interest to researchers across disciplines. Measurement methods and techniques have changed and developed over the years, leading to more accurate measurements and, consequently, more accurate determination of peak altitudes. This research transpired due to the frequency of misstatements found in existing sources including books, maps, guidebooks and the Internet. Such inaccuracies have the potential to create controversy, especially among peak-baggers in pursuit of climbing the highest summits. The Polish Sudetes Mountains were selected for this study; 24 summits in the 14 mesoregions were measured. Measurements were obtained employing the global navigation satellite system (GNSS) and light detection and ranging (LiDAR), both modern and highly precise techniques. Moreover, to determine the accuracy of measurements, several of the summits were measured using a mobile phone as an additional method. We compare GNSS vs. LiDAR and verify the level of confidence of peak heights obtained by automatic methods from LiDAR data alone. The GNSS receiver results showed a discrepancy of approximately 10 m compared with other information sources examined. Findings indicate that the heights of peaks presented in cartographic materials are inaccurate, especially in lesser-known mountain ranges. Furthermore, among all the mountain ranges examined, the results demonstrated that five of the summits were no longer classed as the highest, potentially impacting tourist perceptions and subsequent visitation. Overall, due to the topographical relief characteristics and varying vegetation cover of mountains, we argue that the re-measuring procedure should comprise two steps: (1) develop high-resolution digital elevation models (DEMs) based on LiDAR; (2) assumed heights should be measured using precise GNSS receivers. Unfortunately, due to the time constraints and the prohibitive costs of GNSS, LiDAR continues to be the most common source of new altitude data.

Analysis of the Suitability of High-Resolution DEM Obtained Using ALS and UAS (SfM) for the Identification of Changes and Monitoring the Development of Selected Geohazards in the Alpine Environment—A Case Study in High Tatras, Slovakia

The current trend in the use of remote sensing technologies is their use as a tool for monitoring hard-to-reach areas, objects or phenomena in the alpine environment. Remote sensing technology is also effectively used to monitor geohazards and the development of human-made changes in the country. Research presented in this study demonstrates the results for the usability of the publicly available national digital elevation model DEM 5.0 obtained by utilizing the airborne laser scanning (ALS) survey to monitor the development of erosion, morphological changes of talus cones, or the dynamics of movement of rock blocks between stages of measurement in the alpine environment of the High Tatras mountains. The reference methods for this study are the terrestrial laser scanning (TLS) and structure-from-motion (SfM) photogrammetric approach using unmanned aerial systems (UASs). By comparing the created DEMs, the ALS point cloud’s accuracy on mostly rocky areas of different sizes was verified. The results show that the standard deviation of the ALS point cloud ranges from 19 to 46 mm depending on the area’s size and characteristics. The maximum difference ranges from 100 to 741 mm. The value of systematic displacement of data obtained by different technologies ranges from 1 to 29 mm. This research confirms the suitability of the ALS method with its advantages and limits for the detection of movement of rock blocks or change of position of any natural or anthropogenic objects with a size from approximately 1 m2.

Color and Laser Data as a Complementary Approach for Heritage Documentation

Heritage recording has received much attention and benefits from recent developments in the field of range and imaging sensors. While these methods have often been viewed as two different methodologies, data integration can achieve different products, which are not always found in a single technique. Data integration in this paper can be divided into two levels: laser scanner data aided by photogrammetry and photogrammetry aided by scanner data. At the first level, superior radiometric information, mobility and accessibility of imagery can be actively used to add texture information and allow for new possibilities in terms of data interpretation and completeness of complex site documentation. In the second level, true orthophoto is generated based on laser data, the results are rectified images with a uniform scale representing all objects at their planimetric position. The proposed approaches enable flexible data fusion and allow images to be taken at an optimum time and position for radiometric information. Data fusion usually involves serious distortions in the form of a double mapping of occluded objects that affect the product quality. In order to enhance the efficiency of visibility analysis in complex structures, a proposed visibility algorithm is implemented into the developed methods of texture mapping and true orthophoto generation. The algorithm filters occluded areas based on a patch processing using a grid square unit set around the projected vertices. The depth of the mapped triangular vertices within the patch neighborhood is calculated to assign the visible one. In this contribution, experimental results from different historical sites in Jordan are presented as a validation of the proposed algorithms. Algorithms show satisfactory performance in terms of completeness and correctness of occlusion detection and spectral information mapping. The results indicate that hybrid methods could be used efficiently in the representation of heritage structures.

Multiplatform-SfM and TLS Data Fusion for Monitoring Agricultural Terraces in Complex Topographic and Landcover Conditions

Agricultural terraced landscapes, which are important historical heritage sites (e.g., UNESCO or Globally Important Agricultural Heritage Systems (GIAHS) sites) are under threat from increased soil degradation due to climate change and land abandonment. Remote sensing can assist in the assessment and monitoring of such cultural ecosystem services. However, due to the limitations imposed by rugged topography and the occurrence of vegetation, the application of a single high-resolution topography (HRT) technique is challenging in these particular agricultural environments. Therefore, data fusion of HRT techniques (terrestrial laser scanning (TLS) and aerial/terrestrial structure from motion (SfM)) was tested for the first time in this context (terraces), to the best of our knowledge, to overcome specific detection problems such as the complex topographic and landcover conditions of the terrace systems. SfM–TLS data fusion methodology was trialed in order to produce very high-resolution digital terrain models (DTMs) of two agricultural terrace areas, both characterized by the presence of vegetation that covers parts of the subvertical surfaces, complex morphology, and inaccessible areas. In the unreachable areas, it was necessary to find effective solutions to carry out HRT surveys; therefore, we tested the direct georeferencing (DG) method, exploiting onboard multifrequency GNSS receivers for unmanned aerial vehicles (UAVs) and postprocessing kinematic (PPK) data. The results showed that the fusion of data based on different methods and acquisition platforms is required to obtain accurate DTMs that reflect the real surface roughness of terrace systems without gaps in data. Moreover, in inaccessible or hazardous terrains, a combination of direct and indirect georeferencing was a useful solution to reduce the substantial inconvenience and cost of ground control point (GCP) placement. We show that in order to obtain a precise data fusion in these complex conditions, it is essential to utilize a complete and specific workflow. This workflow must incorporate all data merging issues and landcover condition problems, encompassing the survey planning step, the coregistration process, and the error analysis of the outputs. The high-resolution DTMs realized can provide a starting point for land degradation process assessment of these agriculture environments and supplies useful information to stakeholders for better management and protection of such important heritage landscapes.

Increasing the Geometrical and Interpretation Quality of Unmanned Aerial Vehicle Photogrammetry Products using Super-Resolution Algorithms

Unmanned aerial vehicles (UAVs) have now become very popular in photogrammetric and remote-sensing applications. Every day, these vehicles are used in new applications, new terrains, and new tasks, facing new problems. One of these problems is connected with flight altitude and the determined ground sample distance in a specific area, especially within cities and industrial and construction areas. The problem is that a safe flight altitude and camera parameters do not meet the required or demanded ground sampling distance or the geometrical and texture quality. In the cases where the flight level cannot be reduced and there is no technical ability to change the UAV camera or lens, the author proposes the use of a super-resolution algorithm for enhancing images acquired by UAVs and, consequently, increase the geometrical and interpretation quality of the final photogrammetric product. The main study objective was to utilize super-resolution (SR) algorithms to improve the geometric and interpretative quality of the final photogrammetric product, assess its impact on the accuracy of the photogrammetric processing and on the traditional digital photogrammetry workflow. The research concept assumes a comparative analysis of photogrammetric products obtained on the basis of data collected from small, commercial UAVs and products obtained from the same data but additionally processed by the super-resolution algorithm. As the study concludes, the photogrammetric products that are created as a result of the algorithms’ operation on high-altitude images show a comparable quality to the reference products from low altitudes and, in some cases, even improve their quality.

Using Small Unmanned Aerial Vehicle in 3D Modeling of Highways with Tree-Covered Roadsides to Estimate Sight Distance

The safe and efficient operation of highways largely depends on the adequate provision of sight distance. Small unmanned aerial vehicles (UAVs) can be utilized to efficiently complete data acquisition very soon after identifying an issue when searching for potential highway safety risks. A double grid flight is proposed to obtain an adequate three-dimensional (3D) recreation of the road environment, ensuring an unbiased sight distance output. Then, a dense cloud point is derived through a Structure from Motion Multi-View Stereo process. The point cloud is classified to produce both a terrain model, characterized by its resolution, and a 3D-object model, characterized by the maximum edge length of the entities. The resulting road environment model is utilized to calculate sight distance in a geographic information system. The results enabled the detection of accident-prone locations caused by sight distance limitations. Moreover, the impact of the 3D modeling parameters on the results was assessed.