Search Results (611)

This study proposes a UAV-based remote measurement method for accurately locating pedestrians and other small targets within small reservoir dams. To address the imprecise coordinate information in reservoir areas after prolonged operations, a transformation method for converting UAV coordinates into the local coordinate system without relying on preset parameters is introduced, accomplished by integrating the Structure from Motion (SfM) algorithm to calculate the transformation parameters. An improved YOLOv8 network is introduced for the high-precision detection of small pedestrian targets, complemented by a laser rangefinder to facilitate accurate 3D locating of targets from varying postures and positions. Furthermore, the integration of a thermal infrared camera facilitates the detection and localization of potential seepage. The experimental validation and application across two real small reservoir dams confirm the accuracy and applicability of the proposed approach, demonstrating the efficiency of the proposed routine UAV surveillance strategy and proving its potential to establish electronic fences and enhance maintenance operations. Full article

Accurate and timely estimation of oat biomass is crucial for the development of sustainable and efficient agricultural practices. This research focused on estimating and predicting forage oat biomass using UAV and agronomic variables. A Matrice 300 equipped with a multispectral camera was used for 14 flights, capturing 21 spectral indices per flight. Concurrently, agronomic data were collected at six stages synchronized with UAV flights. Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). The Random Forest model showed the best performance, with a coefficient of determination R² of 0.52 on the test set, followed by Support Vector Machines with an R² of 0.50. Differences in root mean square error (RMSE) and mean absolute error (MAE) among the models highlighted variations in prediction accuracy. This study underscores the effectiveness of photogrammetry, UAV, and machine learning in estimating forage biomass, demonstrating that the proposed approach can provide relatively accurate estimations for this purpose. Full article

In developing nations, such as Ekiti State, Nigeria, the utilization of remotely sensed data, particularly satellite and UAV imagery, remains significantly underexploited in land administration. This limits multi-resolution imagery’s potential in land governance and socio-economic development. This study examines factors influencing UAV adoption for land administration in Nigeria, mapping seven rural, peri-urban, and urban sites with orthomosaics (2.2 cm to 3.39 cm resolution). Boundaries were manually delineated, and parcel areas were calculated. Using the 0.05 m orthomosaic as a reference, the Horizontal Radial Root Mean Square Error (RMSEr) and Normalized Parcel Area Error (NPAE) were computed. Results showed a consistent increase in error with increasing resolution (0.1 m to 1 m), with RMSEr ranging from 0.053 m (formal peri-urban) to 2.572 m (informal rural settlement). Formal settlements with physical demarcations exhibited more consistent values. A comparison with GNSS data revealed that RMSEr values conformed to the American Society for Photogrammetry and Remote Sensing (ASPRS) Class II and III standards. The research demonstrates physical demarcations’ role in facilitating cadastral mapping, with formal settlements showing the highest suitability. This study recommends context-specific imagery resolution to enhance land governance. Key implications include promoting settlement typology awareness and addressing UAV regulatory challenges. NPAE values can serve as a metric for assessing imagery resolution fitness for cadastral mapping. Full article

Ginkgo is a multi-purpose economic tree species that plays a significant role in human production and daily life. The dry biomass of leaves serves as an accurate key indicator of the growth status of Ginkgo saplings and represents a direct source of economic yield. Given the characteristics of flexibility and high operational efficiency, affordable unmanned aerial vehicles (UAVs) have been utilized for estimating aboveground biomass in plantations, but not specifically for estimating leaf biomass at the individual sapling level. Furthermore, previous studies have primarily focused on image metrics while neglecting the potential of digital aerial photogrammetry (DAP) point cloud metrics. This study aims to investigate the estimation of crown-level leaf biomass in 3-year-old Ginkgo saplings subjected to different nitrogen treatments, using a synergistic approach that combines both image metrics and DAP metrics derived from UAV RGB images captured at varying flight heights (30 m, 60 m, and 90 m). In this study, image metrics (including the color and texture feature parameters) and DAP point cloud metrics (encompassing crown-level structural parameters, height-related and density-related metrics) were extracted and evaluated for modeling leaf biomass. The results indicated that models that utilized both image metrics and point cloud metrics generally outperformed those relying solely on image metrics. Notably, the combination of image metrics obtained from the 60 m flight height with DAP metrics derived from the 30 m height significantly enhanced the overall modeling performance, especially when optimal metrics were selected through a backward elimination approach. Among the regression methods employed, Gaussian process regression (GPR) models exhibited superior performance (CV-R² = 0.79, rRMSE = 25.22% for the best model), compared to Partial Least Squares Regression (PLSR) models. The common critical image metrics for both GPR and PLSR models were found to be related to chlorophyll (including G, B, and their normalized indices such as NGI and NBI), while key common structural parameters from the DAP metrics included height-related and crown-related features (specifically, tree height and crown width). This approach of integrating optimal image metrics with DAP metrics derived from multi-height UAV imagery shows great promise for estimating crown-level leaf biomass in Ginkgo saplings and potentially other tree crops. Full article

Earth hummocks are periglacial landforms that are widely distributed in arctic and alpine regions. This study employed an uncrewed aerial vehicle (UAV) and a structure from motion (SfM) framework to map and analyze the spatial distribution and morphological characteristics of earth hummocks across an extensive area in Daisetsuzan National Park, Japan. The UAV-captured images were processed using SfM photogrammetry to create orthomosaic images and high-resolution DEMs. We identified the distribution and morphological characteristics of earth hummocks using orthoimages, hillshade maps, and DEMs and analyzed how their morphological parameters relate to topographical conditions. A total of 18,838 individual earth hummocks in an area of approximately 82,599 m² were mapped and analyzed across the two study areas, surpassing the scale of existing studies. The average length, width, and height of these earth hummocks are 1.22 m, 1.03 m, and 0.15 m, respectively, and topographical features such as slope, aspect, and landforms are demonstrated to have an essential influence on the morphology of the earth hummocks. These findings enhance our understanding of topographical features. Furthermore, this study demonstrates the efficacy of utilizing the UAV-SfM framework with multi-directional hillshade mapping as an alternative to manual field measurements in studying periglacial landforms in mountainous regions. Full article

The study of light pollution is a relatively new and specific field of measurement. The current literature is dominated by articles that describe the use of ground and satellite data as a source of information on light pollution. However, there is a need to study the phenomenon on a microscale, i.e., locally within small locations such as housing estates, parks, buildings, or even inside buildings. Therefore, there is an important need to measure light pollution at a lower level, at the low level of the skyline. In this paper, the authors present a new drone design for light pollution measurement. A completely new original design for an unmanned platform for light pollution measurement is presented, which is adapted to mount custom sensors (not originally designed to be mounted on a unmanned aerial vehicles) allowing registration in the nadir and zenith directions. The application and use of traditional photometric sensors in the new configuration, such as the spectrometer and the sky quality meter (SQM), is presented. A multispectral camera for nighttime measurements, a calibrated visible-light camera, is used. The results of the unmanned aerial vehicle (UAV) are generated products that allow the visualisation of multimodal photometric data together with the presence of a geographic coordinate system. This paper also presents the results from field experiments during which the light spectrum is measured with the installed sensors. As the results show, measurements at night, especially with multispectral cameras, allow the assessment of the spectrum emitted by street lamps, while the measurement of the sky quality depends on the flight height only up to a 10 m above ground level. Full article

Underground coal mining induces surface subsidence, leading to disasters such as damage to buildings and infrastructure, landslides, and surface water accumulation. Preventing and controlling disasters in subsidence areas and reutilizing land depend on understanding subsidence regularity and obtaining surface subsidence monitoring data. These data are crucial for the reutilization of regional land resources and disaster prevention and control. Subsidence hazards are also a key constraint to mine development. Recently, with the rapid advancement of UAV technology, the use of UAV photogrammetry for surface subsidence monitoring has become a significant trend in this field. The periodic imagery data quickly acquired by UAV are used to construct DEM through point cloud filtering. Then, surface subsidence information is obtained by differencing DEM from different periods. However, due to the accuracy limitations inherent in UAV photogrammetry, the subsidence data obtained through this method are characterized by errors, making it challenging to achieve high-precision ground surface subsidence monitoring. Therefore, this paper proposes a spatial domain filtering algorithm for UAV photogrammetry combined with surface deformation caused by coal mining based on the surface subsidence induced by coal mining and combined with the characteristics of the surface change. This algorithm significantly reduces random error in the differential DEM, achieving high-precision ground subsidence monitoring using UAV. Simulation and field test results show that the surface subsidence elevation errors obtained in the simulation tests are reduced by more than 50% compared to conventional methods. In field tests, this method reduced surface subsidence elevation errors by 39%. The monitoring error for surface subsidence was as low as 8 mm compared to leveling survey data. This method offers a new technical pathway for high-precision surface subsidence monitoring in mining areas using UAV photogrammetry. Full article

Tailings ponds are recognized as significant sources of potential man-made debris flow and major environmental disasters. Recent frequent tailings dam failures and growing trends in fine tailings outputs underscore the critical need for innovative monitoring and safety management techniques. Here, we propose an approach that integrates UAV photogrammetry with convolutional neural networks (CNNs) to extract beach line indicators (BLIs) and conduct enhanced dam safety evaluations. The significance of real 3D geometry construction in numerical analysis is investigated. The results demonstrate that the optimized You Only Look At CoefficienTs (YOLACT) model outperforms in recognizing the beach boundary line, achieving a mean Intersection over Union (mIoU) of 72.63% and a mean Pixel Accuracy (mPA) of 76.2%. This approach shows promise for future integration with autonomously charging UAVs, enabling comprehensive coverage and automated monitoring of BLIs. Additionally, the anti-slide and seepage stability evaluations are impacted by the geometry shape and water condition configuration. The proposed approach provides more conservative seepage calculations, suggesting that simplified 2D modeling may underestimate tailings dam stability, potentially affecting dam designs and regulatory decisions. Multiple numerical methods are suggested for cross-validation. This approach is crucial for balancing safety regulations with economic feasibility, helping to prevent excessive and unsustainable burdens on enterprises and advancing towards the goal of zero harm to people and the environment in tailings management. Full article

Unmanned aerial vehicles (UAVs) are now widely preferred systems that are capable of rapid mapping and generating topographic models with relatively high positional accuracy. Since the integrated GNSS receivers of UAVs do not allow for sufficiently accurate outcomes either horizontally or vertically, a conventional method is to use ground control points (GCPs) to perform bundle block adjustment (BBA) of the outcomes. Since the number of GCPs to be installed limits the process in UAV operations, there is an important research question whether the precise point positioning (PPP) method can be an alternative when the real-time kinematic (RTK), network RTK, and post-process kinematic (PPK) techniques cannot be used to measure GCPs. This study introduces a novel approach using precise point positioning with ambiguity resolution (PPP-AR) for ground control point (GCP) positioning in UAV photogrammetry. For this purpose, the results are evaluated by comparing the horizontal and vertical coordinates obtained from the 24 h GNSS sessions of six calibration pillars in the field and the horizontal length differences obtained by electronic distance measurement (EDM). Bartlett’s test is applied to statistically determine the accuracy of the results. The results indicate that the coordinates obtained from a two-hour PPP-AR session show no significant difference from those acquired in a 30 min session, demonstrating PPP-AR to be a viable alternative for GCP positioning. Therefore, the PPP technique can be used for the BBA of GCPs to be established for UAVs in large-scale map generation. However, the number of GCPs to be selected should be four or more, which should be homogeneously distributed over the study area. Full article

Extensive livestock farming plays a crucial role in the economy of agrosilvopastoral systems of the southwestern Iberian Peninsula (known as dehesas and montados in Spanish and Portuguese, respectively) as well as providing essential ecosystem services. The existence of livestock in these areas heavily relies on the effective management of natural resources (annual pastures and water stored in ponds built ad hoc). The present work aims to assess the water availability in these ponds by developing equations to estimate the water volume based on the surface area, which can be quantified by means of remote sensing techniques. For this purpose, field surveys were carried out in September 2021, 2022 and 2023 at ponds located in representative farms, using unmanned aerial vehicles (UAVs) equipped with RGB sensors and survey-grade global navigation satellite systems and inertial measurement units (GNSS-IMU). These datasets were used to produce high-resolution 3D models by means of Structure-from-Motion and Multi-View Stereo photogrammetry, facilitating the estimation of the stored water volume within a Geographic Information System (GIS). The Volume–Area–Height relationships were calibrated to allow conversions between these parameters. Regression analyses were performed using the maximum volume and area data to derive mathematical models (power and quadratic functions) that resulted in significant statistical relationships (r² > 0.90, p < 0.0001). The root mean square error (RMSE) varied from 1.59 to 17.06 m³ and 0.16 to 3.93 m³ for the power and quadratic function, respectively. Both obtained equations (i.e., power and quadratic general functions) were applied to the estimated water storage in similar water bodies using available aerial or satellite imagery for the period from 1984 to 2021. Full article

We propose a novel mono-temporal framework with a physical basis and ecological consistency to retrieve fire severity at very high spatial resolution. First, we sampled the Composite Burn Index (CBI) in 108 field plots that were subsequently surveyed through unmanned aerial vehicle (UAV) flights. Then, we mimicked the field methodology for CBI assessment in the remote sensing framework. CBI strata were identified through individual tree segmentation and geographic object-based image analysis (GEOBIA). In each stratum, wildfire ecological effects were estimated through the following methods: (i) the vertical structural complexity of vegetation legacies was computed from 3D-point clouds, as a proxy for biomass consumption; and (ii) the vegetation biophysical variables were retrieved from multispectral data by the inversion of the PROSAIL radiative transfer model, with a direct physical link with the vegetation legacies remaining after canopy scorch and torch. The CBI scores predicted from UAV ecologically related metrics at the strata level featured high fit with respect to the field-measured CBI scores (R² > 0.81 and RMSE < 0.26). Conversely, the conventional retrieval of fire effects using a battery of UAV structural and spectral predictors (point height distribution metrics and spectral indices) computed at the plot level provided a much worse performance (R² = 0.677 and RMSE = 0.349). Full article

The analysis and preservation of the cultural heritage sites are critical for maintaining their historical and architectural integrity, as they can be damaged by various factors, including climatic, geological, geomorphological, and human actions. Based on this, the present study proposes a semi-automatic and non-learning-based method for detecting degraded surfaces within cultural heritage sites by integrating UAV, photogrammetry, and 3D data analysis. A 20th-century fortification from Romania was chosen as the case study due to its physical characteristics and state of degradation, making it ideal for testing the methodology. Images were collected using UAV and terrestrial sensors and processed to create a detailed 3D point cloud of the site. The developed pipeline effectively identified degraded areas, including cracks and material loss, with high accuracy. The classification and segmentation algorithms, including K-means clustering, geometrical features, RANSAC, and FACETS, improved the detection of destructured areas. The combined use of these algorithms facilitated a detailed assessment of the structural condition. This integrated approach demonstrated that the algorithms have the potential to support each other in minimizing individual limitations and accurately identifying degraded surfaces. Even though some limitations were observed, such as the potential for the overestimation of false negatives and positives areas, the damaged surfaces were extracted with high precision. The methodology proved to be a practical and economical solution for cultural heritage monitoring and conservation, offering high accuracy and flexibility. One of the greatest advantages of the method is its ease of implementation, its execution speed, and the potential of using entirely open-source software. This approach can be easily adapted to various heritage sites, significantly contributing to their protection and valorization. Full article

Low-altitude aerial photogrammetry can be an alternative source of forest inventory data and a practical tool for rapid forest attribute updates. The availability of low-cost unmanned aerial systems (UASs) and continuous technological advances in terms of their flight duration and automation capabilities makes these solutions interesting tools for supporting various forest management needs. However, any practical application requires a priori empirical validation and optimization steps, especially if it is to be used under different forest conditions. This study investigates the influence of the main flight parameters, i.e., ground sampling distance and photo overlap, on the performance of individual tree detection (ITD) stand-level forest inventories, based on photogrammetric data obtained from budget unmanned aerial systems. The investigated sites represented the most common forest conditions in the Polish lowlands. The results showed no direct influence of the investigated factors on growing stock volume predictions within the analyzed range, i.e., overlap from 80 × 80 to 90 × 90% and GSD from 2 to 6 cm. However, we found that the tree detection ratio had an influence on estimation errors, which ranged from 0.6 to 15.3%. The estimates were generally coherent across repeated flights and were not susceptible to the weather conditions encountered. The study demonstrates the suitability of the ITD method for small-area forest inventories using photogrammetric UAV data, as well as its potential optimization for larger-scale surveys. Full article

This study documented the contribution of precise positioning involving a global navigation satellite system (GNSS) and a real-time kinematic (RTK) system in unmanned aerial vehicle (UAV) photogrammetry, particularly for establishing the coordinate data of ground control points (GCPs). Without augmentation, GNSS positioning solutions are inaccurate and pose a high degree of uncertainty if such data are used in UAV data processing for mapping. The evaluation included a comparative assessment of sample coordinates involving RTK and an ordinary GPS device and the application of precise GCP data for UAV photogrammetry in field crop research, monitoring nitrogen deficiency stress in maize. This study confirmed the superior performance of the RTK system in providing positional data, with 4 cm bias as compared to 311 cm with the non-augmented GNSS technique, making it suitable for use in agronomic research involving row crops. Precise GCP data in this study allow the UAV-based Normalized Difference Red-Edge Index (NDRE) data to effectively characterize maize crop responses to N nutrition during the growing season, with detailed analyses revealing the causal relationship in that a compromised optimum canopy chlorophyll content under limiting nitrogen environment was the reason for reduced canopy cover under an N-deficiency environment. Without RTK-based GCPs, different and, to some degree, misleading results were evident, and therefore, this study warrants the requirement of precise GCP data for scientific research investigations attempting to use UAV photogrammetry for agronomic field crop study. Full article

This paper investigates the impact of flood-induced hydrodynamic forces and high discharge on the masonry arch “Hannibal Bridge” (called “Ponte di Annibale” in Italy) using the Hydraulic Engineering Center’s River Analysis Simulation (HEC-RAS) v6.5.0. hydraulic numerical method, incorporating Unmanned Aerial Vehicle (UAV) photogrammetry and aerial Light Detection and Ranging (LIDAR) data for visual analysis. The research highlights the highly transient behavior of fast flood flows, particularly when carrying debris, and their effect on bridge superstructures. Utilizing a Digital Elevation Model to extract cross-sectional and elevation data, the research examined 23 profiles over 800 m of the river. The results indicate that the maximum allowable water depth in front of the bridge is 4.73 m, with a Manning’s coefficient of 0.03 and a longitudinal slope of 9 m per kilometer. Therefore, a novel method to identify the risks through HEC-RAS modeling significantly improves the conservation of masonry bridges by providing precise topographical and hydrological data for accurate simulations. Moreover, the detailed information obtained from LIDAR and UAV photogrammetry about the bridge’s materials and structures can be incorporated into the conservation models. This comprehensive approach ensures that preservation efforts are not only addressing the immediate hydrodynamic threats but are also informed by a thorough understanding of the bridge’s structural and material conditions. Understanding rating curves is essential for water management and flood forecasting, with the study confirming a Manning roughness coefficient of 0.03 as suitable for smooth open-channel flows and emphasizing the importance of geomorphological conditions in hydraulic simulation. Full article