Low-cost Unmanned Aerial Vehicle Research Articles

Unmanned aerial vehicles equipped with sensor packages to study spatiotemporal variations of air pollutants in industry parks.

Unmanned aerial vehicles (UAVs) equipped with a miniaturized sensor package were developed for aerial observations, which realizes aerial observations affordable to scientists in atmospheric science and achieves aerial measurements in high spatial resolution. UAVs are deployed to a variety of aerial detecting tasks in different scientific scenarios including chemical industry parks (CIPs) with hazardous gases emissions, and some places difficult for humans to reach. In this study, UAV sensing technology was deployed to detect air pollutants in a suburb, a CIP and a natural gas plant, respectively. The effects of atmospheric conditions such as the atmospheric boundary layer height, long-distance transport and atmospheric stability on the spatiotemporal variations of the air pollutants vertical profiles were investigated by the UAV. The UAV with the sensor package was deployed to capture the methane (CH4) leakages in a natural gas plant. The spatiotemporal variations of CH4 in both vertical and horizontal directions studied by UAV were employed to calculate accurate CH4 emissions, which is crucial to reducing the emissions of greenhouse gases. The low-cost UAV sensing technology for air pollutants was developed by Dr. Xiaobing Pang, who was funded by the Newton Fellowship in 2009 and worked in the University of York. This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'.

Photogrammetric Measurement of Grassland Fire Spread: Techniques and Challenges with Low-Cost Unmanned Aerial Vehicles

The spread of natural fires is a complex issue, as its mathematical modeling needs to consider many parameters. Therefore, the results of such modeling always need to be validated by comparison with experimental measurements under real-world conditions. Remote sensing with the support of satellite or aerial sensors has long been used for this purpose. In this article, we focused on data collection with an unmanned aerial vehicle (UAV), which was used both for creating a digital surface model and for dynamic monitoring of the spread of controlled grassland fires in the visible spectrum. We subsequently tested the impact of various processing settings on the accuracy of the digital elevation model (DEM) and orthophotos, which are commonly used as a basis for analyzing fire spread. For the DEM generated from images taken during the final flight after the fire, deviations did not exceed 0.1 m compared to the reference model from LiDAR. Scale errors in the model with only approximal WGS84 exterior orientation parameters did not exceed a relative accuracy of 1:500, and possible deformations of the DEM up to 0.5 m in height had a minimal impact on determining the rate of fire spread, even with oblique images taken at an angle of 45°. The results of the experiments highlight the advantages of using low-cost SfM photogrammetry and provide an overview of potential issues encountered in measuring and performing photogrammetric processing of fire spread.

Combining Low-Cost UAV Imagery with Machine Learning Classifiers for Accurate Land Use/Land Cover Mapping

Land use/land cover (LULC) is a fundamental concept of the Earth’s system intimately connected to many phases of the human and physical environment. LULC mappings has been recently revolutionized by the use of high-resolution imagery from unmanned aerial vehicles (UAVs). The present study proposes an innovative approach for obtaining LULC maps using consumer-grade UAV imagery combined with two machine learning classification techniques, namely RF and SVM. The methodology presented herein is tested at a Mediterranean agricultural site located in Greece. The emphasis has been placed on the use of a commercially available, low-cost RGB camera which is a typical consumer’s option available today almost worldwide. The results evidenced the capability of the SVM when combined with low-cost UAV data in obtaining LULC maps at very high spatial resolution. Such information can be of practical value to both farmers and decision-makers in reaching the most appropriate decisions in this regard.

Low-cost UAV coordinated carbon observation network: Carbon dioxide measurement with multiple UAVs

Improving the mitigation of global greenhouse effects requires drastic reductions in carbon emissions. Effective carbon management at different spatial scales is therefore crucial to a more sustainable path of economic development. Regional and local carbon management currently rely on the traditional bottom-up method of emission reports, which are known for their opaque and complex procedures. Novel top-down carbon monitoring methods including space-based, aerial and ground-based observations are tantalising alternatives to more reliable data sources of carbon emissions as well as sinks. Unmanned Aerial Vehicles (UAVs) based carbon monitoring is an upcoming field because of its lower costs, higher spatial-temporal resolution and operability at a wider range of weather conditions compared to satellite observations. UAVs equipped with Non-dispersive Infrared (NDIR) carbon dioxide (CO2) sensors can measure in the air with faster response and can intelligently navigate themselves within areas of greater interest. With the help of lighter NDIR payloads and multiple UAVs flying in synchrony, larger cross sections or areas for maximising information acquisition of emission plumes can be covered. In this article, we first introduce the design of a miniaturised NDIR payload, which is compatible with a lightweight UAV to achieve a single system weighing less than 4 kg. Then, we test our design via comparisons against another NDIR payload to determine its accuracy (−0.82 ppm and −0.44 ppm) and precision (0.12 ppm and −0.03 ppm) in measurements of two vertical concentration profiles. Finally, we deployed four UAVs on two major anthropogenic emission source monitoring campaigns with coordinated flight patterns. To fully understand the benefit of multi-UAV carbon monitoring, we attempted two flight patterns and computed power plant emission rates via the cross-sectional flux method. While all flights successfully captured the concentration enhancements downwind of the power plants, the single-sectional flight pattern measured an emission rate of 10ktCO2/day which is 44% less than that reported monthly and the multi-sectional flight pattern yielded relatively varied results (8.5ktCO2/day on average, which is 54% less) for the respective sections due to wind underestimations, plume section undersampling and wind fluctuations.

Transfer learning achieves high recall for object classification in fluvial environments with limited data

Field surveys to collect data from fluvial ecosystems traditionally focus on specific phenomena related to geomorphology or hydrology. Low-cost unmanned aerial vehicles (UAVs) additionally empower the fast and massive collection of airborne photogrammetry, providing geospatially explicit information. This remote sensing data complements field surveys by offering contextual information on geomorphological conditions, including digital terrain models. AI-based image recognition can augment contextual information to extrapolate archetypal object classes through name labels, such as “gravel”, “sand”, “plant”, or “large wood”. However, obtaining sufficient ground truth data for these classifications, particularly in morphodynamic fluvial environments, is challenging and induces high costs. This study introduces a transfer learning approach to address the challenge of low data availability, enabling AI-based mapping of complex objects in fluvial landscapes. We leverage the learned general structure of a deep convolutional neural network (CNN) pre-trained on a broad range of images. The fixed latent features of the pre-trained CNN stem from GoogLeNet. A fixed feature extractor serves to classify objects with limited data amounts. Satisfactory performance is measured with a recall rate, expressing the ability of a model to find all occurrences of a class on an image. High spatial heterogeneity in the locations of measurements on the x-y plane improves model performance. With a minimum of 400 labeled instances, the model achieves a satisfactory 93.75-% recall for a “large wood” target class, providing evidence of the effectiveness of transfer learning in remote sensing for geomorphological studies. This ability to detect large woods in river environments is critical to restoration efforts as it helps create fish habitat, which is essential to supporting biodiversity.

Performance Analysis of Forest Canopy Height Model Generated from UAV and InSAR

Forest canopy height is a crucial parameter in ecosystem process modelling, yet research on generating canopy height models (CHMs) using photogrammetry method from UAV data remains limited compared to methods such as light detection and ranging (LiDAR). This study investigates the performance of accuracy and effectiveness of CHM generated from low-cost Unmanned Aerial Vehicle (UAV) via the photogrammetry method together with the well-known long-established Interferometric Synthetic Aperture Radar (InSAR). Leveraging advancements in UAV technology for three-dimensional land surface modelling, this research focuses on the Pasoh Research Station (nearby the Arboretum area) of Pasoh Forest Reserve in Negeri Sembilan, Malaysia. A series of images were captured at ten different UAV flying altitudes ranging from 120m to 500m above the mean sea level. Subsequently, CHMs were derived from both UAV and InSAR data, and compared against field measurements of tree height. The results indicate that UAV with a flying height below 200m can generate much more accurate results (with average height difference less than 10m) than InSAR (average height difference of 10.532m). It is noticeable that when comparing the InSAR-generated CHM to field measurements at lower altitudes (below 150m), the canopy height obtained from InSAR data is less accurate (more than 10m difference with field measurement) than the CHM obtained via UAV photogrammetry processing (only 5.353 m difference with field measurement). However, when UAV flying height is above 200m, InSAR data is performed more reliably. A 120m UAV flying height has a good potential to generate canopy height that is closer to the field measurement.

UAV Photogrammetric Surveys for Tree Height Estimation

In the context of precision agriculture (PA), geomatic surveys exploiting UAV (unmanned aerial vehicle) platforms allow the dimensional characterization of trees. This paper focuses on the use of low-cost UAV photogrammetry to estimate tree height, as part of a project for the phytoremediation of contaminated soils. Two study areas with different characteristics in terms of mean tree height (5 m; 0.7 m) are chosen to test the procedure even in a challenging context. Three campaigns are performed in an olive grove (Area 1) at different flying altitudes (30 m, 40 m, and 50 m), and one UAV flight is available for Area 2 (42 m of altitude), where three species are present: oleander, lentisk, and poplar. The workflow involves the elaboration of the UAV point clouds through the SfM (structure from motion) approach, digital surface models (DSMs), vegetation filtering, and a GIS-based analysis to obtain canopy height models (CHMs) for height extraction based on a local maxima approach. UAV-derived heights are compared with in-field measurements, and promising results are obtained for Area 1, confirming the applicability of the procedure for tree height extraction, while the application in Area 2 (shorter tree seedlings) is more problematic.

Estimating Winter Wheat Plant Nitrogen Content by Combining Spectral and Texture Features Based on a Low-Cost UAV RGB System throughout the Growing Season

As prior information for precise nitrogen fertilization management, plant nitrogen content (PNC), which is obtained timely and accurately through a low-cost method, is of great significance for national grain security and sustainable social development. In this study, the potential of the low-cost unmanned aerial vehicle (UAV) RGB system was investigated for the rapid and accurate estimation of winter wheat PNC across the growing season. Specifically, texture features were utilized as complements to the commonly used spectral information. Five machine learning regression algorithms, including support vector machines (SVMs), classification and regression trees, artificial neural networks, K-nearest neighbors, and random forests, were employed to establish the bridge between UAV RGB image-derived features and ground-truth PNC, with multivariate linear regression serving as the reference. The results show that both spectral and texture features had significant correlations with ground-truth PNC, indicating the potential of low-cost UAV RGB images to estimate winter wheat PNC. The H channel, S4O6, and R_SE and R_EN had the highest correlation among the spectral indices, Gabor texture features, and grey level co-occurrence matrix texture features, with absolute Pearson’s correlation coefficient values of 0.63, 0.54, and 0.69, respectively. When the texture features were used together with spectral indices, the PNC estimation accuracy was enhanced, with the root mean square error (RMSE) decreasing from 2.56 to 2.24 g/kg, for instance, when using the SVM regression algorithm. The SVM regression algorithm with validation achieved the highest estimation accuracy, with a coefficient of determination (R2) of 0.62 and an RMSE of 2.15 g/kg based on the optimal feature combination of B_CON, B_M, G_DIS, H, NGBDI, R_EN, R_M, R_SE, S3O7, and VEG. Overall, this study demonstrated that the low-cost UAV RGB system could be successfully used to map the PNC of winter wheat across the growing season.

Integrating Unmanned Aerial Vehicle-Derived Vegetation and Texture Indices for the Estimation of Leaf Nitrogen Concentration in Drip-Irrigated Cotton under Reduced Nitrogen Treatment and Different Plant Densities

The accurate assessment of nitrogen (N) status is important for N management and yield improvement. The N status in plants is affected by plant densities and N application rates, while the methods for assessing the N status in drip-irrigated cotton under reduced nitrogen treatment and different plant densities are lacking. Therefore, this study was conducted with four different N treatments (195.5, 299, 402.5, and 506 kg N ha−1) and three sowing densities (6.9 × 104, 13.8 × 104, and 24 × 104 plants ha−1) by using a low-cost Unmanned Aerial Vehicle (UAV) system to acquire RGB imagery at a 10 m flight altitude at cotton main growth stages. We evaluated the performance of different ground resolutions (1.3, 2.6, 5.2, 10.4, 20.8, 41.6, 83.2, and 166.4 cm) for image textures, vegetation indices (VIs), and their combination for leaf N concentration (LNC) estimation using four regression methods (stepwise multiple linear regression, SMLR; support vector regression, SVR; extreme learning machine, ELM; random forest, RF). The results showed that combining VIs (ExGR, GRVI, GBRI, GRRI, MGRVI, RGBVI) and textures (VAR, HOM, CON, DIS) yielded higher estimation accuracy than using either alone. Specifically, the RF regression models had a higher accuracy and stability than SMLR and the other two machine learning algorithms. The best accuracy (R2 = 0.87, RMSE = 3.14 g kg−1, rRMSE = 7.00%) was obtained when RF was applied in combination with VIs and texture. Thus, the combination of VIs and textures from UAV images using RF could improve the estimation accuracy of drip-irrigated cotton LNC and may have a potential contribution in the rapid and non-destructive nutrition monitoring and diagnosis of other crops or other growth parameters.

Corn Plant Counting Using Deep Learning and UAV Images

The adoption of new technologies, such as unmanned aerial vehicles (UAVs), image processing, and machine learning, is disrupting traditional concepts in agriculture, with a new range of possibilities opening in its fields of research. Plant density is one of the most important corn (Zea mays L.) yield factors, yet its precise measurement after the emergence of plants is impractical in large-scale production fields due to the amount of labor required. This letter aims to develop techniques that enable corn plant counting and the automation of this process through deep learning and computational vision, using images of several corn crops obtained using a low-cost unmanned aerial vehicle (UAV) platform assembled with an RGB sensor.

Evaluation of Fissures and Cracks in Bridges by Applying Digital Image Capture Techniques Using an Unmanned Aerial Vehicle

The evaluation of cracks and fissures in bridge structures is essential to ensure the long-term safety, durability, and functionality of these infrastructures. In this sense, processing grayscale images and adjusting brightness and contrast levels can improve the visibility of cracks and fissures in bridge structures. These techniques, complemented by professional expertise and efficient inspection tools such as Unmanned Aerial Vehicles (UAVs), allow for a comprehensive and accurate structural integrity assessment. This study used the edge detection technique to analyze photographs obtained with a low-cost UAV as a means of image capture. This tool was used to reach hard-to-reach areas where there could be damage, thus making it easier to detect fissures or cracks. To capture the failures, two case studies, a small bridge and a large bridge, were selected, both located in Concepción City in southern Chile. During both inspections, cracks were detected that could affect the structure of the bridges in the future. To analyze these findings, ImageJ software 1.54h was used, which allowed the length and thickness of the cracks to be measured and evaluated. In addition, to validate the procedure proposed, real values manually measured on-site were compared with those delivered by the software analyses, where no statistically significant differences were found. With the method presented in this study, it was possible to quantify the damage, following the bridge maintenance standards established by the Ministry of Public Works of Chile, whose inspection criteria can be applied to other projects worldwide.

Accurate leaf area index estimation in sorghum using high-resolution UAV data and machine learning models

Accurate estimation of leaf area index (LAI) is essential for precision agriculture, yet traditional ground-based measurements are destructive, time-consuming, and limited in scale. This study aimed to address the need for rapid, non-destructive LAI monitoring over large areas by evaluating unmanned aerial vehicle (UAV) data and machine learning (ML) models. A field experiment with four irrigation treatments was conducted to obtain wide range of LAI values over two years. Multispectral and thermal UAV images were acquired throughout the growing season along with destructive LAI measurements. Five ML algorithms, including K-Nearest Neighbors (K-NN), Extra Trees Regressor (ETR), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR) were tested. Also, feature selection procedures were implemented to obtain useful information among the features used for the ML model. Results showed the K-NN model achieved the highest accuracy (R2 = 0.97, RMSE = 0.46, MAE = 0.197), followed by ETR. The analysis of feature selection revealed that the combination of Normalized Difference Vegetation Index (NDVI) and canopy height (NDVI×Hc) product had the highest importance, followed by Soil-Adjusted Vegetation Index (SAVI) and Green Normalized Difference Vegetation Index (GNDVI). Also, utilizing vegetation indices calculated from multiple spectral bands proved to be more effective than using individual bands alone. Overall, the study demonstrates that UAV data and ML techniques can estimate sorghum LAI precisely to support precision agriculture applications. Moreover, using low-cost UAVs equipped with multispectral sensors presents a cost-effective and reliable method for LAI estimation using ML models.

Aero Mapping Application for Affected Area Detection by the Semeru Volcano Eruption in 2022

The development of geospatial technology, especially Unmanned Aerial vehicles (UAV) or drones, has been growing rapidly. One of the uses of UAV is to detect disaster hazards, the extent of the affected area, and the element of risks. The latest UAV technology in the form of Aero Mapping is beneficial for mapping disaster-affected areas, in this case, post-volcanic eruption lava flows. Aero mapping with the rotary wing type is low-cost (<$ 2000/Rp.29.500.000) but a potent tool for collecting spatial data quickly and accurately. This application is applied to detect areas affected by volcanic eruptions. The photo image produced by the UAV can be used to analyze lava flows, lava deposits, changes in landforms, and changes in land cover due to volcanic eruptions. This study aims to detect the area affected by the eruption of Semeru Volcano post-eruption in 2022. In the research study area, there is no detailed scale mapping can detect the direction/path of post-eruptive lahars. This research seeks to detail the description of the affected areas. The results of aerial photographs show that low-cost UAVs are capable of producing high-resolution data of up to 2.86 cm/px (Orthomosaic), 6.94 cm/px (DSM), and 3.94 cm/px (GSD) with a total area of up to 755 Ha. This area is obtained from 34 flight processes at an altitude of 120 meters with 70% front and side overlap. These findings prove that aero mapping is a low-cost technique that can be used to map new lava flows after the 2022 eruption at Semeru Volcano.

Low-cost UAV detection via WiFi traffic analysis and machine learning

In recent years, unmanned aerial vehicles (UAVs) have undergoing experienced remarkable advancements. Nevertheless, the growing utilization of UAVs brings forth potential security threats to the public, particularly in private and sensitive locales. To address these emerging hazards, we introduce a low-cost, three-stage UAV detection framework for monitoring invading UAVs in vulnerable zones. This framework devised through an exhaustive investigation of the Chinese UAV market. Various scenarios were examined to evaluate the effectiveness of the framework, and it was subsequently implemented on a portable board. Experiments demonstrated that the proposed framework can detect invading UAVs at an early stage, even in stealthy mode. As such, the framework has the potential to be applied in the formulation of a portable surveillance system for a UAV-restricted region.

UAV-based plant height estimation of maize cultivated using different varieties and sowing spacing

This study aimed at estimating plant height of maize using low-cost unmanned aerial vehicle (UAV) based imagery. The experiment was laid out in a factorial arrangement in randomized complete block design. The factors were maize varieties (honampa, ahooden, ahoofe and abontem) and intra-row sowing spacing (20 cm, 30 cm and 40 cm). A constant inter-row spacing of 80 cm was used for all sub-plots. Data (both UAV-based imagery and manual data) were taken 21 days after sowing (DAS) -seedling/establishment stage-, 42 DAS (vegetative stage), 63 DAS (tasseling stage), and 84 DAS (physiological maturity stage). The results showed a high correlation between the UAV-based plant height and manual measurement with R2 (adjust) &gt; 0.92 in all cases. For the intra-row sowing spacing, the 20 cm plant spacing was found to have had the best overall R2 (adjust) of 0.949, showing the strongest regression of UAV with the manual data among the treatments. Also, it was found that the best stage to estimate maize plant height is dependent on the variety being used since the honampa gave the best correlation coefficient (R = 0.85) at the tasseling stage while the ahooden, ahoofe and abontem gave optimum correlation coefficients (R) of 0.70, 0.90 and 0.69 respectively at the vegetative stage. Overall, the study shows potency of using UAV-based imagery for estimating plant height of maize. The findings of the study could be useful to agronomists and smallholder farmers in Ghana and other developing countries regarding the usefulness of the technology for in-field data collection and advisory services.

Low-Cost LiDAR-GNSS-UAV Technology Development for PT Garam’s Three-Dimensional Stockpile Modelling Needs

Unmanned Aerial Vehicles (UAV), Global Navigation Satellite Systems (GNSS), and LiDAR will be combined into one of the newest technologies to cover each other's deficiencies. Surveyors can use UAV, GNSS, and LiDAR multi-sensors to map the stockpile of salt PT Garam, whereas the previous process used manual calculations. LiDAR is a survey tool with a low price, around 999 USD. To minimise operational costs, surveyors can use Low-Cost LiDAR, GNSS, and UAV at around 638 USD. The results of the data obtained are calibrated with pitch, roll, and yaw to get the vertical height of the existing contours.

Close-Range Transmission Line Inspection Method for Low-Cost UAV: Design and Implementation

With the rapid development of microelectronics, unmanned aerial vehicles (UAVs) for electric inspection tasks have become popular. Among these tasks, transmission line inspections are more complicated than component and tower inspections owing to the small size, poor functionality, and severe magnetic field interference of transmission lines. Existing solutions invariably use high-precision devices and maintain safety distances during inspections. However, capturing detailed transmission line information over long distances is challenging. Moreover, sophisticated equipment implies high costs and considerable value risks. This work proposes a method using RGB cameras and mm-wave radar to accomplish close-range transmission line inspections. A heading correction and two correction modules address waypoint mission mismatch and wind interference during tracking. In addition, adaptive complementary fusion is designed to solve anomaly identification. Finally, the proposed method validated in a 10 kV transmission line environment demonstrates successful close-range inspection while acquiring high-definition (HD) images. The validation results prove the practical feasibility of the proposed low-cost transmission line inspection method, which is of great significance for reducing inspection costs and promoting the popularization of UAV inspections.

Ultrasonic- and IMU-Based High-Precision UAV Localization for the Low-Cost Autonomous Inspection in Oil and Gas Pressure Vessels

With the increasing demands for unmanned aerial vehicle (UAV) based autonomous inspections in the oil and gas industry, one of the challenging issues for 3D UAV positioning has emerged due to the satellite signal blocking. Considering the existing characteristics of the ultrasonic based technique, such as the low cost, extremely lightweight and high positioning accuracy, it can be promising as the potential solution. Nevertheless, the low position update rate and vulnerable positioning performance to the changing environment still limit its applications on UAV. Therefore, in this article, an ultrasonic and inertial measurement unit (IMU) based localisation algorithm and low cost UAV autonomous inspection system are presented. With the incorporation of the IMU, the position update rate, accuracy and stability of the algorithm can all be significantly improved. This is done by the adaptively estimated noise covariance matrices through the proposed adaptive extended Kalman filter (AEKF) algorithm and the added weighting factors. Followed by, an additional virtual observation process is presented to overcome the unavailability of the observation information for further performance improvement. Finally, extensive numerical results and field tests demonstrate that the proposed algorithm and system can achieve the high update rate, reliable, accurate and precision UAV positioning in oil and gas pressure vessels and are feasible for the UAV autonomous inspection in these environments.

Enhancing Research Outcomes through Drone-Generated Imagery and Photogrammetry Software Analysis

Drones have gained popularity in numerous fields, including education, where they are employed as valuable tools for enhancing students' knowledge. In particular, the use of drones in land surveying and mapping has revolutionized the industry, replacing manual and time-consuming methods from the 19th century. Unmanned aerial vehicles (UAVs) enable surveyors to cover vast areas easily, even in challenging terrains, while producing high-resolution maps and collecting extensive data efficiently. With the aid of automated software, quality control can be achieved with minimal training. The benefits of drone surveying encompass terrain modeling, surveying, and 3D mapping for GIS purposes, such as topography and volume measurements. The return on investment (ROI) from utilizing drones in mapping and surveying is substantial. Furthermore, UAVs find practical application in close-range mapping, particularly in engineering survey works that traditionally incur significant costs, labor, and time. Low-cost UAVs offer reliable information for various applications, including road design, bridges, and land surveys, matching the accuracy of conventional engineering surveys and policies, particularly for small-scale mapping. UAVs present a stable and rapidly advancing technology, positioning them as competitive alternatives to other surveying methods. This study consists of five phases: preliminary study, data collection, data processing, post-processing, and analysis, with a focus on UAVs as a data processing tool. The project demonstrates the utilization of UAV images and photogrammetry software, which involve capturing overlapping images of the Earth's surface using cameras mounted on drones or airplanes. These images are then processed with photogrammetry software to generate geo-referenced maps, ortho mosaic images, and other valuable outputs. The software is capable of processing both images and lidar points, offering versatility in its applications, including artistic modeling and surveying. The increasing popularity of UAV images and photogrammetry software extends to various fields, such as agriculture, military operations, disaster management, and artistic modeling. As UAVs become more affordable, their accessibility expands to a broader range of users. Aerial mapping with UAVs necessitates mission planning and ground control points, making drones valuable in mapping, search and rescue missions, transportation, and other disaster management applications. Multiple photogrammetry software options are available, each with distinct features and advantages. By leveraging UAV images and photogrammetry software, previously challenging images can be acquired, opening new possibilities for diverse applications.

Remote sensing of salmonid spawning sites in freshwater ecosystems: The potential of low-cost UAV data.

Salmonids are especially vulnerable during their embryonic development, but monitoring of their spawning grounds is rare and often relies on manual counting of their nests (redds). This method, however, is prone to sampling errors resulting in over- or underestimations of redd counts. Salmonid spawning habitat in shallow water areas can be distinguished by their visible reflection which makes the use of standard unmanned aerial vehicles (UAV) a viable option for their mapping. Here, we aimed to develop a standardised approach to detect salmonid spawning habitat that is easy and low-cost. We used a semi-automated approach by applying supervised classification techniques to UAV derived RGB imagery from two contrasting lakes in Iceland. For both lakes six endmember classes were obtained with high accuracies. Most importantly, producer's and user's accuracy for classifying spawning redds was >90% after applying post-classification improvements for both study areas. What we are proposing here is an entirely new approach for monitoring spawning habitats which will address some the major shortcomings of the widely used redd count method e.g. collecting and analysing large amounts of data cost and time efficiently, limiting observer bias, and allowing for precise quantification over different temporal and spatial scales.