Low-cost Unmanned Aerial Systems Research Articles

Large-Scale 3D Terrain Reconstruction Using 3D Gaussian Splatting for Visualization and Simulation

Abstract. The fusion of low-cost unmanned aerial systems (UAS) with advanced photogrammetric techniques has revolutionized 3D terrain reconstruction, enabling the automated creation of detailed models. Concurrently, the advent of 3D Gaussian Splatting has introduced a paradigm shift in 3D data representation, offering visually realistic renditions distinct from traditional polygon-based models. Our research builds upon this foundation, aiming to integrate Gaussian Splatting into interactive simulations for immersive virtual environments. We address challenges such as collision detection by adopting a hybrid approach, combining Gaussian Splatting with photogrammetry-derived meshes. Through comprehensive experimentation covering varying terrain sizes and Gaussian densities, we evaluate scalability, performance, and limitations. Our findings contribute to advancing the use of advanced computer graphics techniques for enhanced 3D terrain visualization and simulation.

Efficiency Study of Combined UAS Photogrammetry and Terrestrial LiDAR in 3D Modeling for Maintenance and Management of Fill Dams

Low-cost unmanned aerial system (UAS) photogrammetry and terrestrial laser scanner (TLS, terrestrial LiDAR) technologies are being used as noncontact measurement methods for collecting unstructured data for the maintenance of construction infrastructure facilities. This study investigated the possibility of settlement, which is a maintenance condition evaluation item for fill-dam bodies, using point clouds based on the UAS (unmanned aerial system) structure from motion (UAS-SfM) and TLS (terrestrial laser scanner) point clouds. Specifically, the Z-axis RMSE of the point cloud improved to 0.012 m and the shape reproducibility rate to 98.53% by complementing the heterogeneous data of the UAS and TLS by combining the two systems with block coordination and ICP algorithms. The maximum settlement height and volume (heaving) of the dam crest and upstream and downstream slopes were derived from the combined UAS/TLS point-cloud-based 3D model. The quantitative values for the settlement of the fill-dam body were derived using the combined 3D model with high accuracy and density. This result verified the possibility of using the combined 3D model for evaluation of the maintenance condition.

Three-Dimensional Modelling of A Bridge by Integrating Terrestrial and Aerial Photogrammetry Applying an Adapted Capture Method

Three-dimensional (3D) photogrammetric modelling is a contemporary remote sensing method for generating digital models with their specific appearance and texture, which are used in various areas of life. The modelling of complex geometry objects, such as masonry bridges, is not an easy task, because of their specific features (the presence of arches and various niches). This determines the choice of an adapted capture method according to the individual characteristics of the object. The paper presents research aimed at generating a highly accurate three-dimensional model of a bridge, by combining terrestrial and aerial photogrammetry. A low-cost unmanned aerial system (UAS) was used for capturing the upper side of the bridge. This significantly optimised capture time. But its capabilities do not allow to capture the bridge arches from the bottom upwards, nor to obtain an accurate model of the bridge railing. Because of this, a terrestrial survey was made with a digital camera, thus complementing the information required to generate a comprehensive model of the bridge. The integration of aerial and terrestrial capturing using low-cost cameras and systems, along with the application of modern algorithms for processing, allow to create precise, accurate, and detailed digital models. It is all very important for future conservation, restoration, adaptation, and socialisation of such type of objects which are monuments of culture.

How the Small Object Detection via Machine Learning and UAS-Based Remote-Sensing Imagery Can Support the Achievement of SDG2: A Case Study of Vole Burrows

Small mammals, and particularly rodents, are common inhabitants of farmlands, where they play key roles in the ecosystem, but when overabundant, they can be major pests, able to reduce crop production and farmers’ incomes, with tangible effects on the achievement of Sustainable Development Goals no 2 (SDG2, Zero Hunger) of the United Nations. Farmers do not currently have a standardized, accurate method of detecting the presence, abundance, and locations of rodents in their fields, and hence do not have environmentally efficient methods of rodent control able to promote sustainable agriculture oriented to reduce the environmental impacts of cultivation. New developments in unmanned aerial system (UAS) platforms and sensor technology facilitate cost-effective data collection through simultaneous multimodal data collection approaches at very high spatial resolutions in environmental and agricultural contexts. Object detection from remote-sensing images has been an active research topic over the last decade. With recent increases in computational resources and data availability, deep learning-based object detection methods are beginning to play an important role in advancing remote-sensing commercial and scientific applications. However, the performance of current detectors on various UAS-based datasets, including multimodal spatial and physical datasets, remains limited in terms of small object detection. In particular, the ability to quickly detect small objects from a large observed scene (at field scale) is still an open question. In this paper, we compare the efficiencies of applying one- and two-stage detector models to a single UAS-based image and a processed (via Pix4D mapper photogrammetric program) UAS-based orthophoto product to detect rodent burrows, for agriculture/environmental applications as to support farmer activities in the achievements of SDG2. Our results indicate that the use of multimodal data from low-cost UASs within a self-training YOLOv3 model can provide relatively accurate and robust detection for small objects (mAP of 0.86 and an F1-score of 93.39%), and can deliver valuable insights for field management with high spatial precision able to reduce the environmental costs of crop production in the direction of precision agriculture management.

Quantifying Effects of Excess Water Stress at Early Soybean Growth Stages Using Unmanned Aerial Systems

Low-gradient agricultural areas prone to in-field flooding impact crop development and yield potential, resulting in financial losses. Early identification of the potential reduction in yield from excess water stress at the plot scale provides stakeholders with the high-throughput information needed to assess risk and make responsive economic management decisions as well as future investments. The objective of this study is to analyze and evaluate the application of proximal remote sensing from unmanned aerial systems (UAS) to detect excess water stress in soybean and predict the potential reduction in yield due to this excess water stress. A high-throughput data processing pipeline is developed to analyze multispectral images captured at the early development stages (R4–R5) from a low-cost UAS over two radiation use efficiency experiments in West–Central Indiana, USA. Above-ground biomass is estimated remotely to assess the soybean development by considering soybean genotype classes (High Yielding, High Yielding under Drought, Diversity, all classes) and transferring estimated parameters to a replicate experiment. Digital terrain analysis using the Topographic Wetness Index (TWI) is used to objectively compare plots more susceptible to inundation with replicate plots less susceptible to inundation. The results of the study indicate that proximal remote sensing estimates above-ground biomass at the R4–R5 stage using adaptable and transferable methods, with a calculated percent bias between 0.8% and 14% and root mean square error between 72 g/m2 and 77 g/m2 across all genetic classes. The estimated biomass is sensitive to excess water stress with distinguishable differences identified between the R4 and R5 development stages; this translates into a reduction in the percent of expected yield corresponding with observations of in-field flooding and high TWI. This study demonstrates transferable methods to estimate yield loss due to excess water stress at the plot level and increased potential to provide crop status assessments to stakeholders prior to harvest using low-cost UAS and a high-throughput data processing pipeline.

The AgriQ: A low-cost unmanned aerial system for precision agriculture

Precision agriculture currently presents significant growth thanks to the development of three main technologies: drones, vision-based systems, and embedded electronics. To take advantage of these technologies, services and products have launched cameras, drones, and algorithms in the cloud, from which the farmer can easily obtain valuable information for the optimization of their production. Notwithstanding the previous, in Latin America (and in general in the undeveloped countries), these technologies are costly and sometimes difficult to access for most farmers. This study proposes developing the low-cost Unmanned Aerial System (UAS) for precision agriculture tasks called AgriQ. It comprises three subsystems: (a) the drone; (b) the multispectral imaging system; and (c) the open-source software responsible for computing valuable information for the farmers. Intending to obtain a competitive UAS, we tackle the problem from four main points: (1) the construction of the drone; (2) the vision algorithms; (3) the autonomous trajectory considering all the parameters for properly recovering all the crops’ visual information; and (4) the construction of a low-cost multispectral imaging system with multiple bands. The multispectral imaging system is onboard the quadrotor and is composed of two low-cost cameras that have been modified to output multispectral imagery. With all this unmanned aerial system, we compute 8 vegetation indices that provide useful information to the farmers. To verify that the AgriQ is competitive versus commercial systems, we compared its performance to the professional multispectral camera RedEdge-M and popular Pix4D software. For this purpose, we have conducted experiments in two real environments, one of which is a real crop field. The experiments’ data show that our AgriQ UAS achieved prominent results at a fraction of the commercial system cost. Furthermore, to provide the user with a complete low-cost tool for precision agriculture, all the parts of the AgriQ are presented and explained in detail. The software is developed using open-source code where our contribution is available online on our GitHub site.

Can Low-Cost Unmanned Aerial Systems Describe the Forage Quality Heterogeneity? Insight from a Timothy Pasture Case Study in Southern Belgium

Applied to grazing management, unmanned aerial systems (UASs) allow for the monitoring of vegetation at the level of each individual on the pasture while covering a significant area (>10 ha per flight). Few studies have investigated the use of UASs to describe the forage quality in terms of nutritive value or chemical composition, while these parameters are essential in supporting the productive functions of animals and are known to change in space (i.e., sward species and structure) and time (i.e., sward phenology). Despite interest, these parameters are scarcely assessed by practitioners as they usually require important laboratory analyses. In this context, our study investigates the potential of off-the-shelf UAS systems in modeling essential parameters of pasture productivity in a precision livestock context: sward height, biomass, and forage quality. In order to develop a solution which is easily reproducible for the research community, we chose to avoid expensive solutions such as UAS LiDAR (light detection and ranging) or hyperspectral sensors, as well as comparing several UAS acquisition strategies (sensors and view angles). Despite their low cost, all tested strategies provide accurate height, biomass, and forage quality estimates of timothy pastures. Considering globally the three groups of parameters, the UAS strategy using the DJI Phantom 4 pro (Nadir view angle) provides the most satisfactory results. The UAS survey using the DJI Phantom 4 pro (Nadir view angle) provided R2 values of 0.48, 0.72, and 0.7, respectively, for individual sward height measurements, mean sward height, and sward biomass. In terms of forage quality modeling, this UAS survey strategy provides R2 values ranging from 0.33 (Acid Detergent Lignin) to 0.85 (fodder units for dairy and beef cattle and fermentable organic matter). Even if their performances are of lower order than state-of-art techniques such as LiDAR for sward height or hyperspectral sensors (for biomass and forage quality modeling), the important trade-off in terms of costs between UAS LiDAR (>100,000 €) or hyperspectral sensors (>50,000 €) promotes the use of such low-cost UAS solutions. This is particularly true for sward height modeling and biomass monitoring, where our low-cost solutions provide more accurate results than state-of-the-art field approaches, such as rising plate meters, with a broader extent and a finer spatial grain.

Upscaling UAS Paradigm to UltraLight Aircrafts: A Low-Cost Multi-Sensors System for Large Scale Aerial Photogrammetry

The use of unmanned aerial systems (UASs) has rapidly grown in many civil applications since the early 2010s. Nowadays, a large variety of reliable low-cost UAS sensors and controllers are available. However, contrary to ultralight aircrafts (ULAs), UASs have a too small operational range to efficiently cover large areas. Flight regulations prevailing in many countries further reduced this operational range; in particular, the “within visual line of sight” rule. This study presents a new system for image acquisition and high-quality photogrammetry of large scale areas (>10 km²). It was developed by upscaling the UAS paradigm, i.e., low-cost sensors and controllers, little (or no) on-board active stabilization, and adequate structure from motion photogrammetry, to an ULA platform. Because the system is low-cost (good quality-price ratio of UAS technologies), multi-sensors (large variety of available UAS sensors) and versatile (high ULA operational flexibility and more lenient regulation than for other platforms), the possible applications are numerous in miscellaneous research domains. The system was described in detail and illustrated from the flight and images acquisition to the photogrammetric routine. The system was successfully used to acquire high resolution and high quality RGB and multispectral images, and produced precisely georeferenced digital elevation model (DEM) and orthophotomosaics for a forested area of 1200 ha. The system can potentially carry any type of sensors. The system compatibility with any sensor can be tested, in terms of image quality and flight plan, with the proposed method. This study also highlighted a major technical limitation of the low-cost thermal infrared cameras: the too high integration time with respect to the flight speed of most UASs and ULAs. By providing the complete information required for reproducing the system, the authors seek to encourage its implementation in different geographical locations and scientific contexts, as well as, its combination with other sensors, in particular, laser imaging detection and ranging (LiDAR) and hyperspectral.

Determining the Suitable Number of Ground Control Points for UAS Images Georeferencing by Varying Number and Spatial Distribution

Currently, products that are obtained by Unmanned Aerial Systems (UAS) image processing based on structure-from-motion photogrammetry (SfM) are being investigated for use in high precision projects. Independent of the georeferencing process being done directly or indirectly, Ground Control Points (GCPs) are needed to increase the accuracy of the obtained products. A minimum of three GCPs is required to bring the results into a desired coordinate system through the indirect georeferencing process, but it is well known that increasing the number of GCPs will lead to a higher accuracy of the final results. The aim of this study is to find the suitable number of GCPs to derive high precision results and what is the effect of GCPs systematic or stratified random distribution on the accuracy of the georeferencing process and the final products, respectively. The case study involves an urban area of about 1 ha that was photographed with a low-cost UAS, namely, the DJI Phantom 3 Standard, at 28 m above ground. The camera was oriented in a nadiral position and 300 points were measured using a total station in a local coordinate system. The UAS images were processed using the 3DF Zephyr software performing a full BBA with a variable number of GCPs i.e., from four up to 150, while the number and the spatial location of check points (ChPs) was kept constant i.e., 150 for each independent distribution. In addition, the systematic and stratified random distribution of GCPs and ChPs spatial positions was analysed. Furthermore, the point clouds and the mesh surfaces that were automatically derived were compared with a terrestrial laser scanner (TLS) point cloud while also considering three test areas: two inside the area defined by GCPs and one outside the area. The results expressed a clear overview of the number of GCPs needed for the indirect georeferencing process with minimum influence on the final results. The RMSE can be reduced down to 50% when switching from four to 20 GCPs, whereas a higher number of GCPs only slightly improves the results.

A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS.

Small unmanned aerial systems (UASs) now have advanced waypoint-based navigation capabilities, which enable them to collect surveillance, wildlife ecology and air quality data in new ways. The ability to remotely sense and find a set of targets and descend and hover close to each target for an action is desirable in many applications, including inspection, search and rescue and spot spraying in agriculture. This paper proposes a robust framework for vision-based ground target finding and action using the high-level decision-making approach of Observe, Orient, Decide and Act (OODA). The proposed framework was implemented as a modular software system using the robotic operating system (ROS). The framework can be effectively deployed in different applications where single or multiple target detection and action is needed. The accuracy and precision of camera-based target position estimation from a low-cost UAS is not adequate for the task due to errors and uncertainties in low-cost sensors, sensor drift and target detection errors. External disturbances such as wind also pose further challenges. The implemented framework was tested using two different test cases. Overall, the results show that the proposed framework is robust to localization and target detection errors and able to perform the task.

Comparison of Pixel- and Object-Based Classification Methods of Unmanned Aerial Vehicle Data Applied to Coastal Dune Vegetation Communities: Casal Borsetti Case Study

Coastal dunes provide the hinterland with natural protection from marine dynamics. The specialized plant species that constitute dune vegetation communities are descriptive of the dune evolution status, which in turn reveals the ongoing coastal dynamics. The aims of this paper were to demonstrate the applicability of a low-cost unmanned aerial system for the classification of dune vegetation, in order to determine the level of detail achievable for the identification of vegetation communities and define the best-performing classification method for the dune environment according to pixel-based and object-based approaches. These goals were pursued by studying the north-Adriatic coastal dunes of Casal Borsetti (Ravenna, Italy). Four classification algorithms were applied to three-band orthoimages (red, green, and near-infrared). All classification maps were validated through ground truthing, and comparisons were performed for the three statistical methods, based on the k coefficient and on correctly and incorrectly classified pixel proportions of two maps. All classifications recognized the five vegetation classes considered, and high spatial resolution maps were produced (0.15 m). For both pixel-based and object-based methods, the support vector machine algorithm demonstrated a better accuracy for class recognition. The comparison revealed that an object approach is the better technique, although the required level of detail determines the final decision.

Vegetation Index Weighted Canopy Volume Model (CVMVI) for soybean biomass estimation from Unmanned Aerial System-based RGB imagery

Crop biomass estimation with high accuracy at low-cost is valuable for precision agriculture and high-throughput phenotyping. Recent technological advances in Unmanned Aerial Systems (UAS) significantly facilitate data acquisition at low-cost along with high spatial, spectral, and temporal resolution. The objective of this study was to explore the potential of UAS RGB imagery-derived spectral, structural, and volumetric information, as well as a proposed vegetation index weighted canopy volume model (CVMVI) for soybean [Glycine max (L.) Merr.] aboveground biomass (AGB) estimation. RGB images were collected from low-cost UAS throughout the growing season at a field site near Columbia, Missouri, USA. High-density point clouds were produced using the structure from motion (SfM) technique through a photogrammetric workflow based on UAS stereo images. Two-dimensional (2D) canopy structure metrics such as canopy height (CH) and canopy projected basal area (BA), as well as three-dimensional (3D) volumetric metrics such as canopy volume model (CVM) were derived from photogrammetric point clouds. A variety of vegetation indices (VIs) were also extracted from RGB orthomosaics. Then, CVMVI, which combines canopy spectral and volumetric information, was proposed. Commonly used regression models were established based on the UAS-derived information and field- measured AGB with a leave-one-out cross-validation. The results show that: (1) In general, canopy 2D structural metrics CH and BA yielded higher correlation with AGB than VIs. (2) Three-dimensional metrics, such as CVM, that encompass both horizontal and vertical properties of canopy provided better estimates for AGB compared to 2D structural metrics (R2 = 0.849; RRMSE = 18.7%; MPSE = 20.8%). (3) Optimized CVMVI, which incorporates both canopy spectral and 3D volumetric information outperformed the other indices and metrics, and was a better predictor for AGB estimation (R2 = 0.893; RRMSE = 16.3%; MPSE = 19.5%). In addition, CVMVI showed equal prediction power for different genotypes, which indicates its potential for high-throughput soybean biomass estimation. Moreover, a CVMVI based univariate regression model yielded AGB predicting capability comparable to multivariate complex regression models such as stepwise multilinear regression (SMR) and partial least squares regression (PLSR) that incorporate multiple canopy spectral indices and structural metrics. Overall, this study reveals the potential of canopy spectral, structural and volumetric information, and their combination (i.e., CVMVI) for estimations of soybean AGB. CVMVI was shown to be simple but effective in estimating AGB, and could be applied for high-throughput phenotyping and precision agro-ecological applications and management.

Mapping of River Terraces with Low-Cost UAS Based Structure-from-Motion Photogrammetry in a Complex Terrain Setting

River terraces are the principal geomorphic features for unraveling tectonics, sea level, and climate conditions during the evolutionary history of a river. The increasing availability of high-resolution topography data generated by low-cost Unmanned Aerial Systems (UAS) and modern photogrammetry offer an opportunity to identify and characterize these features. In this paper, we assessed the capabilities of UAS-based Structure-from-Motion (SfM) photogrammetry, coupled with a river terrace detection algorithm for mapping of river terraces over a 1.9 km2 valley of complex terrain setting, with a focus on the performance of this latest technology over such complex terrains. With the proposed image acquisition approach and SfM photogrammetry, we constructed a 3.8 cm resolution orthomosaic and digital surface model (DSM). The vertical accuracy of DSM was assessed against 196 independent checkpoints measured with a real-time kinematic (RTK) GPS. The results indicated that the root mean square error (RMSE) and mean absolute error (MAE) were 3.1 cm and 2.9 cm, respectively. These encouraging results suggest that this low-cost, logistically simple method can deliver high-quality terrain datasets even in the complex terrain, competitive with those obtained using more expensive laser scanning. A simple algorithm was then employed to detect river terraces from the generated DSM. The results showed that three levels of river terraces and a high-level floodplain were identified. Most of the detected river terraces were confirmed by field observations. Despite the highly erosive nature of fluvial systems, this work obtained good results, allowing fast analysis of fluvial valleys and their comparison. Overall, our results demonstrated that the low-cost UAS-based SfM technique could yield highly accurate ultrahigh-resolution topography data over complex terrain settings, making it particularly suitable for quick and cost-effective mapping of micro to medium-sized geomorphic features under such terrains in remote or poorly accessible areas. Methods discussed in this paper can also be applied to produce highly accurate digital terrain data over large spatial extents for some other places of complex terrains.

Assessing the Accuracy of Digital Surface Models Derived from Optical Imagery Acquired with Unmanned Aerial Systems

Small unmanned aerial systems (UASs) equipped with an optical camera are a cost-effective strategy for topographic surveys. These low-cost UASs can provide useful information for three-dimensional (3D) reconstruction even if they are equipped with a low-quality navigation system. To ensure the production of high-quality topographic models, careful consideration of the flight mode and proper distribution of ground control points are required. To this end, a commercial UAS was adopted to monitor a small earthen dam using different combinations of flight configurations and by adopting a variable number of ground control points (GCPs). The results highlight that optimization of both the choice and combination of flight plans can reduce the relative error of the 3D model to within two meters without the need to include GCPs. However, the use of GCPs greatly improved the quality of the topographic survey, reducing error to the order of a few centimeters. The combined use of images extracted from two flights, one with a camera mounted at nadir and the second with a 20° angle, was found to be beneficial for increasing the overall accuracy of the 3D model and especially the vertical precision.

The impact of number and spatial distribution of GCPs on the positional accuracy of geospatial products derived from low-cost UASs

ABSTRACTCurrently, orthophotos and Digital Terrain Models (DTMs) are commonly being generated from low-cost consumer-grade Unmanned Aerial Systems (UASs) using well-known workflows based on photogrammetric and structure-from-motion methods. To achieve high accuracy positioning standards Ground Control Points (GCPs) have to be used in the workflow. In this paper, we assess the impact of number and spatial distribution of GCPs on the positional accuracy of these two geospatial products. By using 3 well-known mapping accuracy standards and 170 checkpoints, the planimetric and altimetric accuracy is assessed for 13 cases of GCPs configuration. The results show that in general, the UAS workflow presents a better accuracy in planimetry than in altimetry. In addition, the application of the mapping standards shows that the two products are suitable for GIS and mapping applications requiring spatial details equivalent to scales less than or equal 1:3000. We also conclude that the insertion of GCPs in the central part of the block does not contribute significantly to an increase in the planimetric accuracy of the geospatial products. Finally, for the proposed minimum photogrammetric overlaps (70% and 35%), the ideal GCP distribution consists of a uniform distribution of GCPs. First, on the central part the block with a horizontal separation of 3 to 4 ground base units for the altimetric component. Second, on the periphery of the block with a separation of 7 to 8 ground base units for the planimetric component.

On the Use of Unmanned Aerial Vehicles for Antenna and Coverage Diagnostics in Mobile Networks

The capability of in-situ antenna measurement and diagnostics using a compact low-cost unmanned aerial system is presented. An onboard power sensor collects amplitude-only measurements, which are processed by means of an iterative phase retrieval technique. This technique allows recovering an equivalent currents distribution, which characterizes the antenna under test (AUT). These currents can be used for antenna diagnostics as well as to evaluate the field radiated by the AUT. In the field of mobile networks, the proposed system is of interest for rapid assessment of the antenna's operational conditions, providing radioelectric coverage within the mobile network cells and allowing the evaluation of antenna failures, such as incorrect tilt or beamforming/ feeding network malfunction. A proof of concept of the system is conducted for testing a commercial base transceiver station antenna under normal and malfunctioning operation conditions, discussing the extension of the system to allow measurements at millimeter-wave frequency bands.

Accuracy and effectiveness of low cost UASs and open source photogrammetric software for foredunes mapping

ABSTRACTThe recent use and availability of small and low cost Unmanned Aerial Systems (UAS) and the development of Open Source Photogrammetric (OSP) software opens new opportunities for their intensive use in topographic monitoring of foredune dynamics. This study assesses the accuracy and effectiveness of high resolution Digital Surface Models (DSM) generated by the combined use of low-altitude UAS imagery and OSP software. The Photogrammetric Workflow (PW) has been optimized to address the use an action camera and non-autonomous flight mode. To evaluate the accuracy of the DSMs derived from dense matching of UAS imagery we use a reference DSM obtained by a Terrestrial Laser Scanner in the same survey period. Moreover, we use robust statistical indicators for assessing the vertical accuracy of both DSMs: the raw DSM and the denoised DSM. The results shows that a detailed UAS-based DSM with a spatial resolution of 5 cm and vertical accuracy of 10 cm (robust root mean square error) can be obtained by using the proposed PW. The results also suggest that this low cost technology can be used effectively for 3D mapping of foredunes.

Application of Low-Cost UASs and Digital Photogrammetry for High-Resolution Snow Depth Mapping in the Arctic

The repeat acquisition of high-resolution snow depth measurements has important research and civil applications in the Arctic. Currently the surveying methods for capturing the high spatial and temporal variability of the snowpack are expensive, in particular for small areal extents. An alternative methodology based on Unmanned Aerial Systems (UASs) and digital photogrammetry was tested over varying surveying conditions in the Arctic employing two diverse and low-cost UAS-camera combinations (500 and 1700 USD, respectively). Six areas, two in Svalbard and four in Greenland, were mapped covering from 1386 to 38,410 m2. The sites presented diverse snow surface types, underlying topography and light conditions in order to test the method under potentially limiting conditions. The resulting snow depth maps achieved spatial resolutions between 0.06 and 0.09 m. The average difference between UAS-estimated and measured snow depth, checked with conventional snow probing, ranged from 0.015 to 0.16 m. The impact of image pre-processing was explored, improving point cloud density and accuracy for different image qualities and snow/light conditions. Our UAS photogrammetry results are expected to be scalable to larger areal extents. While further validation is needed, with the inclusion of extra validation points, the study showcases the potential of this cost-effective methodology for high-resolution monitoring of snow dynamics in the Arctic and beyond.

USE OF GREENDRONE UAS SYSTEM FOR MAIZE CROP MONITORING

Abstract. This research work presents the use of a low-cost Unmanned Aerial System (UAS) – GreenDrone for the monitoring of Maize crop. GreenDrone consist of a long endurance fixed wing air-frame equipped with a modified Canon camera for the calculation of Normalized Difference Vegetation Index (NDVI) and FLIR thermal camera for Water Stress Index (WSI) calculations. Several flights were conducted over the study site in order to acquire data during different phases of the crop growth. By the calculation of NDVI and NGB images we were able to identify areas with potential low yield, spatial variability in the plant counts, and irregularities in nitrogen application and water application related issues. Furthermore, some parameters which are important for the acquisition of good aerial images in order to create quality Orthomosaic image are also discussed.

Open-Source Processing and Analysis of Aerial Imagery Acquired with a Low-Cost Unmanned Aerial System to Support Invasive Plant Management

Remote sensing by Unmanned Aerial Systems (UAS) is a dynamic evolving technology. UAS are particularly useful in environmental monitoring and management because they have the capability to provide data at high temporal and spatial resolutions. Moreover, data acquisition costs are lower than those of conventional methods such as extensive ground sampling, manned airplanes, or satellites. Small fixed-wing UAS in particular offer further potential benefits as they extend the operational coverage of the area under study at lower operator risks and accelerate data deployment times. Taking these aspects into account, UAS might be an effective tool to support management of invasive plant based on early detection and regular monitoring. A straightforward UAS approach to map invasive plant species is presented in this study with the intention of providing ready-to-use field maps essential for action-oriented management. Our UAS utilizes low-cost sensors, free-of-charge software for mission planning and an affordable, commercial aerial platform to reduce operational costs, reducing expenses with personnel while increasing overall efficiency. We illustrate our approach using a real example of invasion by Acacia mangium in a Brazilian Savanna ecosystem. A. mangium was correctly identified with an overall accuracy of 82.7% from the analysis of imagery. This approach provides land management authorities and practitioners with new prospects for environmental restoration in areas where invasive plant species are present.