DJI Phantom Research Articles

INTELLIGENT MONITORING OF THE PHYSIOLOGICAL STATE OF AGRICULTURAL PRODUCTS USING UAV

The article discusses the technology for automated neural network monitoring of the vineyard’s physiological condition. Images of leaves, obtained using an unmanned aerial vehicle (UAV), are the main indicator of the physiological vineyard’s condition. The proposed solution is based on the integrated use of convolutional neural network method (CNN) and machine vision technologies. To determine the optimal neural network (NN) model, a variant analysis was carried out. In accordance with its results, the YOLOv7 model was chosen, which satisfies the introduced time limit and provides the required detection quality. The training of the YOLOv7 neural network was implemented in the Python environment using the PyTorch framework and the OpenCV computer vision library. The dataset consisting of 6320 images of grape leaves (including healthy and diseased ones) has been used for neural network training. The obtained results showed that the detection accuracy is at least 91%. Visualization of monitoring results has been carried out using heatmap, allowing to obtain information about vineyard physiological condition in dynamics. The proposed mathematical model allows to calculate the monitored vineyard’s area made by one complex per day. The obtained results showed that effective monitoring area using one DJI Phantom 4 UAV per day is 2.5 hectares.

Characteristics of the micropseudokarst landforms in Pâclele Mari mud volcanic area (Romania)

In this paper, we present the results of the geomorphological study of the micro pseudokarst landfoms developed on Pâclele Mari mud volcanic site that belongs to the famous mud volcanic area Berca-Arbănași, Buzău Subcarpathians, Romania. Different types of pseudokarstic cavity formation can be observed in the area, especially in the sloping periphery of the mud volcanic area, where badlands developed (badland pseudokarst), and rheogene pseudokarst in the mud flows. The liquid mud material gets denser and wimple on the surface of the mud that flows in the trough, it compiles and then covers the liquid mud channel. These micro-size covered tunnels are similarly formed as lava tubes at the lava flows of real volcanic areas. In order to complement the field measurements, we carried out surveys with a DJI Phantom 3 and 4, and Mavic Pro quadcopter to determine the landforms for the photogrammetry. Granular composition tests were carried out on sediment samples collected in the mud volcanic area by a laser diffraction particle analyser.

Estimating Summer Maize Biomass by Integrating UAV Multispectral Imagery with Crop Physiological Parameters.

The aboveground biomass (AGB) of summer maize is an important indicator for assessing crop growth status and predicting yield, playing a significant role in agricultural management and decision-making. Traditional on-site measurements of AGB are limited, due to low efficiency and a lack of spatial information. The development of unmanned aerial vehicle (UAV) technology in agriculture offers a rapid and cost-effective method for obtaining crop growth information, but currently, the prediction accuracy of summer maize AGB based on UAVs is limited. This study focuses on the entire growth period of summer maize. Multispectral images of six key growth stages of maize were captured using a DJI Phantom 4 Pro, and color indices and elevation data (DEM) were extracted from these growth stage images. Combining measured data such as summer maize AGB and plant height, which were collected on the ground, and based on the three machine learning algorithms of partial least squares regression (PLSR), random forest (RF), and long short-term memory (LSTM), an input feature analysis of PH was carried out, and a prediction model of summer maize AGB was constructed. The results show that: (1) using unmanned aerial vehicle spectral data (CIS) alone to predict the biomass of summer maize has relatively poor prediction accuracy. Among the three models, the LSTM (CIS) model has the best simulation effect, with a coefficient of determination (R2) ranging from 0.516 to 0.649. The R2 of the RF (CIS) model is 0.446-0.537. The R2 of the PLSR (CIS) model is 0.323-0.401. (2) After adding plant height (PH) data, the accuracy and stability of model prediction significantly improved. R2 increased by about 25%, and both RMSE and NRSME decreased by about 20%. Among the three prediction models, the LSTM (PH + CIS) model had the best performance, with R2 = 0.744, root mean square error (RSME) = 4.833 g, and normalized root mean square error (NRSME) = 0.107. Compared to using only color indices (CIS) as the model input, adding plant height (PH) significantly enhances the prediction effect of AGB (aboveground biomass) prediction in key growth periods of summer maize. This method can serve as a reference for the precise monitoring of crop biomass status through remote sensing with unmanned aerial vehicles.

Assessing the Impact of Environmental Conditions on Reflectance Values in Inland Waters Using Multispectral UAS Imagery

This study investigates the impact of environmental conditions on reflectance values obtained from multispectral Unmanned Aerial System (UAS) imagery in inland waters, focusing on sun glint, cloud glint, wind-generated waves, and cloud shading projections. Conducted in two reservoirs with differing water qualities, UAS platforms equipped with MicaSense Altum and DJI Phantom 4 Multispectral sensors were used to collect multispectral images. The results show that sun glint significantly increases reflectance variability as solar elevation rises, particularly beyond 54°, compromising data quality. Optimal flight operations should occur within a solar elevation angle range of 25° to 47° to minimize these effects. Cloud shading introduces complex variability, reducing median reflectance. Wind-generated waves enhance sun glint, increasing variability across all spectral bands, while cloud glints amplify reflectance non-uniformly, leading to inconsistent data variability. These findings underscore the need for precise correction techniques and strategic UAS deployment to mitigate environmental interferences. This study offers valuable insights for improving UAS-based monitoring and guiding future research in diverse aquatic environments.

Model for Estimating Soil Chemical Properties with RGB Drone Images

Precision agriculture optimizes crop management by providing accurate data on soil chemical properties, thereby improving agricultural productivity and sustainability. This study aims to develop models to estimate soil chemical properties, such as pH, electrical conductivity (EC), and organic matter (OM), by analyzing drone-captured RGB images. The methodology included photogrammetric flights with a DJI Phantom 4 Pro drone equipped with a 20 Mpx camera and simultaneous sampling, laboratory analysis and on-site measurements, with Royal Eijkelkamp EC meter set voor grond multiparameter sensors and pH meter set for soil and water. The aerial images were processed with the PIX4Dmapper software, to generate the orthophoto and spectral bands. With the resulting orthophoto of 1.6 cm/pixel, eight spectral indices were calculated, using the spatial analysis tools of ArcGIS software. The in situ results showed an average pH value of 5.83, indicating a slightly acidic soil, and an EC of 1.09 dS/m, suggesting a soil with a low concentration of dissolved salts. Laboratory analyses showed a medium-high content of OM, with an average of 5.19 %. A strong correlation was found between OM and pH_index with coefficients of determination R2=0.55, while moderate correlations were also observed between pH with pH_index and EC with sal_index6 with coefficients of determination R2=-0.39 and R2=0.42 respectively. The aforementioned results allowed the generation of two models for the estimation of these variables from RGB images.

Enhancing safety feedback to the design of small, unmanned aircraft by joint assessment of impact area and human fatality.

Advantages of commercial UAS-based services come with the disadvantage of posing third party risk (TPR) to overflown population on the ground. Especially challenging is that the imposed level of ground TPR tends to increase linearly with the density of potential customers of UAS services. This challenge asks for the development of complementary directions in reducing ground TPR. The first direction is to reduce the rate of a UAS crash to the ground. The second direction is to reduce overflying in more densely populated areas by developing risk-aware UAS path planning strategies. The third direction is to develop UAS designs that reduce the product in case of a crashing UAS, where is the size of the crash impact area on the ground, and is the probability of fatality for a person in the crash impact area. Because small UAS accident and incident data are scarce, each of these three developments is in need of predictive models regarding their contribution to ground TPR. Such models have been well developed for UAS crash event rate and risk-aware UAS path planning. The objective of this article is to develop an improved model and assessment method for the product In literature, the model development and assessment of the latter two terms is accomplished along separate routes. The objective of this article is to develop an integrated approach. The first step is the development of an integrated model for the product . The second step is to show that this integrated model can be assessed by conducting dynamical simulations of Finite Element (FE) or Multi-Body System (MBS) models of collision between a UAS and a human body. Application of this novel method is illustrated and compared to existing methods for a DJI Phantom III UAS crashing to the ground.

A low-cost UAV survey can unravel Baird's Tapir (Tapirus bairdii) trail network dynamics in a neotropical highland forest

UAV technology has transformed spatial data collection and high-spatial resolution analysis of diverse environments around the world. Its role within geosciences, ecology, engineering, and other sciences has been increasing withing the last decade. In ecological and geomorphological aspects, it offers a powerful tool for studying landforms, surface processes, vegetation, and animal behavior globally. Our study employs high-resolution UAV surveys to analyze Tapirus bairdii trail networks and their connection to the landscape in a Costa Rican highland paramo-forest national park. Using a DJI Phantom 4 V.2 UAV, we conducted a photogrammetric flight over 3.19 ha, producing datasets with a spatial resolution of 2.99 cm/pixel. These datasets facilitated geostatistical analysis of Tapirus bairdii trails, revealing their complexity and hotspots near lagoons. We investigated lagoon characteristics, finding that finer sediments spatially correspond with higher trail activity. This suggests that sediment types in lagoons influence Tapirus bairdii navigation, impacting trail network composition. Our findings highlight UAV technology's role in advancing ecological and geomorphological research in protected areas, providing insights into Tapirus bairdii behavior within highland ecosystems. Overall, to the date this is the first high-resolution UAV survey of Tapirus bairdii within these types of highland tropical environments.

3D LoD2 modelling of Halong City based on UAV point cloud

3D urban building models are crucial for linking, converging, and integrating economic and social urban data. They are extensively utilized in numerous domains such as smart city development, comprehensive social management, and emergency decision-making. Advanced technologies like affordable UAV (Unmanned Aerial Systems) imagery enable a greater level of automation in data acquisition compared to traditional digital photogrammetry methods. The main objective of this research is to build a 3D LoD2 CityGML model with dense point clouds from UAV images. This study presents a completed workflow for generating 3D CityGML models of the city at LoD2 using UAV data. The use of dense point cloud data from UAV technology in the experimental area has been performed using a DJI Phantom 4 Pro. The original point clouds should be denoised using the statistical outlier remover (SOR), the main goal is to reduce noise while preserving the building's geometry. After that, the point clouds of object features were vectored for the level of detail 2 (LoD2) of the object's 3D volume corresponding to its actual height, and objects belonging to the Feature Class 3D layer will represent LoD2 on SketchUp Pro 2021 software to generate a highly accurate 3D model. The evaluation results show that the square errors calculated from the test points for the three axes X, Y, Z are 1.4 cm, 1.6 cm, 1.7 cm, respectively. Conducting research to choose the UAV photography method aims to offer an efficient and cost-effective solution, saving time and human resources, to address the 3D mapping challenges in urban areas across Vietnam.

Utilization of the DJI Phantom 3 Advance Quadcopter Type Drone Vehicle for Area Mapping of the Samarinda State Agricultural Polytechnic Campus

This research is motivated by the fact that currently remote sensing technology continues to develop, both in terms of data collection and processing. This is characterized by the existence of data collection techniques using unmanned aerial vehicles for aerial photo mapping. The advantage of using this technology is that it is effective and efficient both in terms of time and human resources for mapping in areas that are not too large. Another advantage is that it can produce clearer photos, because the plane's flying height is below 400 meters above ground level so that during the shooting process there is no cloud interference. The aim of this research is to create an aerial photo map at the Samarinda State Agricultural Polytechnic, validate objects in aerial photos and in the field, and provide information about the area, boundaries and topography of the Samarinda State Agricultural Polytechnic campus area. The implementation of activities and research objects is the Samarinda State Agricultural Polytechnic campus area. This research uses a small format aerial photo mapping method using a Quadcopter, making it easier to determine where to start taking off and landing. With a Quadcopter vehicle, grounding problems are no longer an obstacle. The research results showed that photography carried out with a Quadcopter at a height of 200 m above the ground produced an area of 28.17 Ha and a spatial resolution of 8.40 cm/pixel. The validation results in aerial photos and in the field using drones have a small difference between 0-4 cm with an average of 1.625 cm/photo. From the digitization results of campus objects, there are 30 objects on campus with a total area of 2.67 Ha. As for the topography results in the campus area, the highest point is 107.5 meters above sea level and the lowest point is 60 meters above sea level.

An improved mining image segmentation with K-Means and morphology using drone dataset

The mining industry faces the challenge of incorporating advanced technology to explore new ways of increasing productivity and reducing costs. Our focus is on integrating drone technology to revolutionize mining tasks like inspection, mapping, and surveying. Drones offer a precision advantage over traditional satellite methods. To this end, we have created a dataset consisting of 373 aerial images captured by a DJI Phantom 4 drone, which depict a mining site in the Benslimane region of Western Morocco. These images, with a ground resolution of 2.5 cm per pixel, are the basis of our research. Our study aims to address the challenges posed by traditional mining techniques and to leverage technological innovations to improve segmentation and classification. The proposed approach includes new methodologies, particularly the combination of K-Means clustering and mathematical morphology, to overcome limitations and deliver better segmentation results. Our findings represent a significant step forward in advancing mining operations through the effective use of modern technologies.

Principles and methods of creating artificial vegetation cover on disturbed industrial lands

Biological reclamation is used to restore technogenically disturbed landscapes. Plant characteristics are important when choosing a plant assortment for the restoration of degraded areas. The purpose of this work was to study the principles and methods of creating artificial vegetation cover on disturbed industrial lands. Before carrying out biological reclamation, the surface was finished and a layer of potentially fertile soil layers with a capacity of at least 1 m and a fertile soil layer with a capacity of at least 0,5 m was applied. Before starting work, the quality of the seed material and its value were determined. Soil improvers (sapropel, latex, microorganisms, cellulose mulch) and biologics (TOP organic, complex of rhizospheric bacteria, hydrogel) were used for planting herbaceous and woody shrubby plants. After carrying out the hydraulic sowing activities, field germination of the seed was monitored every 10 days. Aerial photography was performed with an RGB camera using unmanned aerial vehicles of the DJI Phantom 4 Pro+ model with an on-board Global Navigation Satellite System receiver installed. It was found that the use of a biological product based on a complex of rhizospheric bacteria activated the processes of seed germination in experimental plots. Such activation made it possible to obtain productive bushiness of perennial crops in the early stages, due to which the area of vegetation covering the experimental site increased. The addition of hydrogel and TOP organic with a weight of 2 kg also had a positive effect on seed germination and plant survival. A comparative analysis of all experimental parameters revealed that the most effective projective coating of the site was obtained with the combined use of hydrogel and the preparation TOR-organic. At the same time, a further increase in the mass fraction of hydrogel in the substrate does not significantly affect the result of the creation of artificial vegetation cover on disturbed industrial lands.

An Advanced IBVS-Flatness Approach for Real-Time Quadrotor Navigation: A Full Control Scheme in the Image Plane

This article presents an innovative method for planning and tracking the trajectory in the image plane for the visual control of a quadrotor. The community of researchers working on 2D control widely recognizes this challenge as complex, because a trajectory defined in image space can lead to unpredictable movements of the robot in Cartesian space. While researchers have addressed this problem for mobile robots, quadrotors continue to face significant challenges. To tackle this issue, the adopted approach involves considering the separation of altitude control from the other variables, thus reducing the workspace. Furthermore, the movements of the quadrotor (pitch, roll, and yaw) are interdependent. Consequently, the connection between the inputs and outputs cannot be reversed. The task complexity becomes significant. To address this issue, we propose the following scenario: When the quadrotor is equipped with a downward-facing camera, flying at high altitude is sensible to spot a target. However, to minimize disturbances and conserve energy, the quadrotor needs to descend in altitude. This can result in the target being lost. The solution to this problem is a new methodology based on the principle of differential flatness, allowing the separation of altitude control from the other variables. The system first detects the target at high altitude, then plots a trajectory in the image coordinate system between the acquired image and the desired image. It is crucial to emphasize that this step is performed offline, ensuring that the image processing time does not affect the control frequency. Through the proposed trajectory planning, complying with the constraints of differential flatness, the quadrotor can follow the imposed dynamics. To ensure the tracking of the target while following the generated trajectory, the proposed control law takes the form of an Image Based Visual Servoing (IBVS) scheme. We validated this method using the RVCTOOLS environment in MATLAB. The DJI Phantom 1 quadrotor served as a testbed to evaluate, under real conditions, the effectiveness of the proposed control law. We specifically designed an electronic card to transfer calculated commands to the DJI Phantom 1 control joystick via Bluetooth. This card integrates a PIC18F2520 microcontroller, a DAC8564 digital-to-analogue converter, and an RN42 Bluetooth module. The experimental results demonstrate the effectiveness of this method, ensuring the precise tracking of the target as well as the accurate tracking of the path generated in the image coordinate system.

Accuracy Assessment of UAV Photogrammetry System with RTK Measurements for Direct Georeferencing

Abstract. The direct georeferencing accuracy of unmanned aerial vehicle (UAV) images with real-time kinematic (RTK) measurements is a concerned topic in the community of photogrammetry. This study assesses the capabilities of a multi-rotor platform equipped with RTK technology, specifically a DJI Phantom 4 RTK UAV, for robust direct georeferencing. The UAV surveyed a square and a building at Wuhan University to assess the accuracy and spatial consistency of direct georeferencing in close-range photography. We tested checkpoint errors under various ground control points (GCPs) configurations. The results show that without GCP, an analysis of 71 spatially distributed checkpoints produced a root mean square error (RMSE) of 5.58 cm in the Z direction. This finding indicates that RTK-equipped UAVs can achieve acceptable error margins even without using GCPs, thereby fulfilling the precision requirements for large-scale mapping.

COMPARATIVE ANALYSIS OF LOW-COST CONSUMER-GRADE AND COMMERCIAL-GRADE DRONES FOR LAND SURVEYING APPLICATIONS

Abstract. This study investigates the feasibility of using low-cost consumer drones, specifically the DJI Mini 2, for land surveys compared to commercial-grade drones like the DJI Phantom 4 Pro V2. The research, conducted within the University of the Philippines - Diliman campus, evaluates the horizontal accuracy of resulting ortho-images and explores the advantages and disadvantages of each platform. Initiated by the recognition of aerial drones' transformative impact on land surveying, particularly acknowledged by the Land Management Bureau in the Philippines, the study addresses concerns about the use of consumer-grade drones for surveying. While some advocate for commercial-grade drones, citing concerns about accuracy, a prior study in India demonstrated the suitability of consumer-grade drones for geomatics applications. The methodology involves creating a flight plan with specified parameters for both drones, establishing Ground Control Points (GCPs), and conducting two flights. The results, processed using UAS Mapping Workflow, indicate comparable errors between the consumer-grade DJI Mini 2 and commercial-grade DJI Phantom 4 Pro V2. Despite slight differences, both drones meet the accuracy standards for land surveys. The study contributes to the discourse on affordable means of secure land tenure, envisioning consumer-grade drones as viable alternatives for cost-effective land surveys. The DJI Mini 2, at a fraction of the cost, demonstrates the capability to achieve accuracy levels comparable to its commercial counterpart. While acknowledging the need for further testing and optimization, the findings suggest the potential of consumer-grade drones in advancing sustainable urban development and poverty reduction through accessible land survey methods.

Real-Time UAV Trajectory Prediction for UTM Surveillance Using Machine Learning

The Unmanned Aerial System (UAS) Traffic Management (UTM) surveillance plays an important role in monitoring the safety compliance of UAV flights within specific operational zones. However, the quality of data reception from UAVs depends on transmission signal quality, leading to variable data latency during flights. As a means of addressing this issue and improving UAV operational safety, trajectory prediction emerges as a promising solution. This study aims to implement real-time trajectory prediction through the integration of machine learning techniques within the UTM surveillance system using Broadcast Remote ID. To facilitate this, a homemade receiver for broadcast Remote ID is developed using the ESP32 microcontroller. Subsequently, two distinctive flight tests are executed using a DJI Phantom 4 UAV. In the first flight, UAV trajectory data are used to serve as training input for the machine learning algorithms. The second flight focuses on the implementation of real-time trajectory prediction. The trajectory prediction model uses inputs such as latitude, longitude, height, speed, direction, latency time, and the Received Signal Strength Indicator (RSSI). Through an analysis of the first flight’s offline data, the Gated Recurrent Unit (GRU) algorithm is preferred to the Long Short-Term Memory (LSTM) algorithm. The outcomes of the second flight test prove the feasibility of the GRU-based trajectory prediction. The GRU model successfully produces real-time predictions during UAV flight, showcasing accuracy levels similar to those derived from offline prediction analyses with short processing time. This real-time trajectory prediction could improve the safety of UAV operation by providing the estimated position when the latency time is higher than the threshold.

Smart Cities and The UAVs Applications. A Review Of The Ongoing Research in The UAVs Unit

Nowadays, drones are one of the most important and valuable sources for acquiring aerial imagery and data related to inspection, monitoring, mapping, smart city organization, disaster monitoring and prevention, and 3D modeling. Drones are a low-cost alternative to aerial and space photography because they provide users with images with high spatial resolution down to centimeters. These images enable the production of accurate 3D models such as Digital Surface and Terrain Models (DTM/DSM), contour lines, vector information, etc. This research provides a brief overview of the studies conducted in the Unmanned Aircraft Unit - Remote Sensing Center - University of Mosul. The programs, sequence of operations, and treatments that were followed on the drone images taken by these aircraft were reviewed to prepare them for use in remote sensing and aerial survey operations. This flows towards the use of this technology in the development of smart cities. Specialized GIS software was also used to analyze and process the aerial images that were taken using the DJI Phantom 4 aircraft to study several areas in the city of Mosul, where several sorties were conducted at different periods and times of time.

INVESTIGATION OF THE PROCESSES OF ECOLOGICAL AND ECOSYSTEM CHANGES IN WATER BODIES USING UAV DATA

In recent years, the water landscapes of inland rivers have been subjected to significant an- thropogenic impacts exceeding the limits of their self-healing ability. Traditional methods of monitoring ge- oecological parameters - sampling of water in the field with subsequent laboratory analysis - require signif- icant financial and time costs. Traditional methods allow assessing the ecological state of the entire water- course not spatially, but pointwise. In this research, sensors on board the UAV measured solar radiation reflected from the surface of the water and used image data from different camera ranges. The combina- tion of images used was based on a common methodology for identifying key features, and a geographic information system (GIS) modelling approach was used for spatially analyze the distribution and interaction of environmental features. In addition, research the ecological condition of the Yertis River and the quality of water discharged after the municipal wastewater treatment facilities of Ust-Kamenogorsk city into the Yertis River, the data from the DJI Phantom 4 RTK/multispectral drone was processed and the normalized vegetation index NDVI, normalised water level difference index NDWI, water chromaticness index CI, tur- bidity index NDTI and chlorophyll concentration index "a" were calculated. The results obtained can serve as a basis for scientific analysis of the environmental conditions of the researching area, including for learning the processes of eutrophication and other changes in the environment.

BUILT-IN LENS CORRECTION PROFILES IN LOW-COST CAMERAS: AN ISSUE FOR PHOTOGRAMMETRIC APPLICATIONS?

Abstract. Photogrammetric applications nowadays envisage the use of more and more low-cost cameras such as those equipped on commercial UAV platforms. Typically, these low-grade cameras suffer from extreme radial distortion and strong vignetting among other defects. This, initiated a trend among the low-cost cameras’ manufacturers to try to hide the camera defects by applying software pre-corrections to the images. These Built-In Correction Profiles gets applied to both the JPG files, directly in-camera, and usually to the raw files as well, through the opcode functions of the DNG standard. In this paper we rise this issue that is still under-reported in the literature and further assess the accuracy implication of applying or discarding the Built-In Correction Profile in the scenario of UAV mapping. We tested the commercial UAV DJI Phantom 4 Pro v2 in a calibration environment and a field test to compare the performance of pre-corrected versus uncorrected images. In our tests, processing the original uncorrected images led to improved IO calibration and reduced bowing effect in the field test.

Validation of Unmanned Aerial Vehicle Photogrammetry for Landslide Mapping

Unmanned Aerial Vehicle (UAV) photogrammetry offers quick and effective landslide monitoring. UAV named Polines 01-GD has been developed for photogrammetry. The UAV is designed flexibly in its gimbal to change the camera accordingly. However, UAV validation is needed to evaluate the quality of the device. This study aims to validate the performance of UAV Polines 01-GD for photogrammetry from quantitative and qualitative analysis. The quantitative analysis was performed by RMSE and area accuracy in m2 with the reference DJI Phantom 4 Pro. Meanwhile, the qualitative analysis was done using the DEM (Digital Elevation Model) result. 3D Ground Control Points (GCPs) size 2 m x 2 m were used and placed in the landslide area, UNNES park, Semarang. The camera assembled for an experiment in Polines 01-GD is GoPro Hero 3. The results show that the RMSE of Polines 01-GD is 0,0000234193, and the area accuracy to the real measurement of GCP is 97,8%. The results of landslide indicated by the data was taken in March 2022 compared to the first flight on August 2021, showing landslides in 79.4 m and 60.3 m. Even so, the DEM of DJI Phantom 4 Pro result is clearer than Polines 01-GD. In conclusion, UAV Polines 01-GD can be used for photogrammetry. However, the flight time can be a challenge for future research as Polines 01-GD can only fly in 15 minutes, which is half of DJI Phantom 4 Pro.

Detecting Floral Resource Availability Using Small Unmanned Aircraft Systems

Floral resources for native pollinators that live in wildland settings are diverse and vary across and within growing seasons. Understanding floral resource dynamics and management is becoming increasingly important as honeybee farms seek public land for summer pasture. Small Unmanned Aircraft Systems (sUASs) present a viable approach for accurate broad floristic surveys and present an additional solution to more traditional alternative methods of vegetation assessment. This methodology was designed as a simplified approach using tools frequently available to land managers. The images of three subalpine meadows were captured from a DJI Phantom 4 Pro drone platform three times over the growing season in 2019 in Sanpete County, Utah. The images were composited using Pix4D software 4.5.6 and classified using a simple supervised approach in ENVI 4.8 and ArcGIS Pro 2.4.3 These same meadows were assessed using two traditional ocular methods of vegetation cover–meter-squared quadrats and macroplot estimation. The areas assessed with these methods were compared side by side with their classified counterparts from drone imagery. Classified images were not only found to be highly accurate when detecting overall floral cover and floral color groups (76–100%), but they were also strongly correlated with quadrat estimations, suggesting that these methods used in tandem may be a conducive strategy toward increased accuracy and efficiency when determining floral cover at broad spatial scales.