Unmanned Aerial Vehicle Data Research Articles

Comparison of UAV and WorldView-3 Imagery for Mangrove Species Mapping in Mundupesisir Coastal, Cirebon

Abstract One of the efforts for monitoring and managing mangrove areas is mapping the mangrove areas at the species level. This aims to explore the potential and efforts for rehabilitation, as well as an inventory of the mangrove areas biodiversity. The study area was located in the mangrove Kasih Sayang area, Mundupesisir Village, Cirebon Regency, West Java. Remote sensing image data which have high-resolution can be utilized to more efficiently map mangrove species. The approach that can be used is the Geographic Object-Based Imagery Analysis (GEOBIA) method. This research aims to (1) map the mangrove species in Mundupesisir Village, Cirebon using Unmanned Aerial Vehicle (UAV) data and WorldView-3 imagery, and (2) compare the results and accuracy assessment values of mangrove species from UAV data and WorldView-3 imagery. UAV data (0.0179 m) has a visible image (RGB) band, while WorldView-3 is a multispectral image that has 8 bands (2 m) and a panchromatic band (0.5 m). Field data collection was obtained and collected at purposive random sampling to identify different species based on their physical characteristics. The GEOBIA approach used includes segmentation and classification processes. Multiresolution segmentation algorithm was used in the segmentation process. The classification used the Nearest Neighbor algorithm based on segmentation results. The results of the GEOBIA approach successfully mapped three dominant species in the mangrove Kasih Sayang area, Mundupesisir, Cirebon Regency, namely Avicennia marina, Rhizophora mucronata, and Acrostichum aureum.

Rare plants detection using a YOLOv3 neural network

Rare plant species restoration (reintroduction) is one of the main biodiversity conservation activities. Reintroduced plants need constant monitoring in order to study features of their development and control the population state. To reduce the human impact on the natural habitat of plants and simplify the monitoring process, we propose the use of automatic analysis of unmanned aerial vehicles (UAVs) data using the Yolov3 neural network. The article discusses neural network parameters for detecting Paeonia Tenuifolia, reintroduced in the Samara region by ecologists from the Department of Ecology, Botany and Nature Conservation of Samara University. The main issue under research is the possibility of training a neural network from peony images collected in an artificial habitat with a subsequent application to images collected in a natural habitat and the possibilities of using multi-temporal data to improve the network training quality. The experiments have shown that training a neural network exclusively using images collected in the natural habitat makes it possible to achieve a probability of correct detection of peonies of 0.93, while using data obtained at different years allows increasing the probability of correct detection to 0.95.

3D Modeling of rural environments from multiscale aerial imagery

Given the increasing attention to environmental preservation and sustainable development, the digitization of rural landscapes stands out as a pivotal strategy for effective environmental management and sustainability, land use planning, and preservation of cultural heritage. This work proposes a novel methodology for generating 3D models of rural landscapes by integrating multiscale data sources. Although Unmanned Aerial Vehicles (UAV) simplify the acquisition of multi-source data, their coverage is typically restricted to small landscapes due to their limited range and flight time. On the other hand, although the use of aerial images provides a broader view of the terrain, it is important to note that the low resolution of these images interferes with the task of accurate 3D modeling. Given these challenges, we propose a methodology that combines UAV data and high-resolution aerial imagery provided by the Spanish National Orthophoto Program (PNOA). This multi-source data integration is crucial to generating detailed and accurate 3D models of rural environments. The proposed methodology involves three steps: (1) semantic segmentation of aerial images identifying features such as vegetation, ground, and human-made structures, (2) estimation of the Digital Elevation Model (DEM), and (3) 3D modeling of rural environments using the point clouds generated from UAV images. The conducted experiments demonstrate the effectiveness of our approach identifying and representing previously mentioned features. Thus, this work presents advances in 3D representation techniques for real scenarios, contributing to the coordination of land utilization and environmental sustainability in rural landscapes.

Comparison Utilization of UAV Images & Satellite Images for Geospatial Analysis

The rapid advancements in remote sensing technologies have opened new avenues for geospatial analysis by providing high-quality imagery and data. Unmanned Aerial Vehicles (UAVs) and satellites have emerged as prominent tools for capturing Earth's surface information, each offering unique advantages and limitations. This paper presents a comprehensive comparison of the utilisation of UAV images and satellite images in geospatial analysis. The practical implications of employing UAVs and satellites in diverse scenarios include environmental monitoring, disaster management, urban planning, and agriculture. The benefits of UAVs, such as their ability to capture high- resolution images with precision and on-demand deployment, are contrasted with satellites' global coverage and consistent monitoring capabilities. Integrating UAV and satellite data is emphasized as a complementary approach to geospatial analysis. Techniques for fusing data from these sources are discussed, enabling the creation of comprehensive datasets that combine detailed local information from UAVs with broader context from satellite imagery.

Review of Crop Phenotyping in Field Plot Experiments Using UAV-Mounted Sensors and Algorithms

The phenotyping of field crops quantifies a plant’s structural and physiological characteristics to facilitate crop breeding. High-throughput unmanned aerial vehicle (UAV)-based remote sensing platforms have been extensively researched as replacements for more laborious and time-consuming manual field phenotyping. This review aims to elucidate the advantages and challenges of UAV-based phenotyping techniques. This is a comprehensive overview summarizing the UAV platforms, sensors, and data processing while also introducing recent technological developments. Recently developed software and sensors greatly enhance the accessibility of UAV-based phenotyping, and a summary of recent research (publications 2019–2024) provides implications for future research. Researchers have focused on integrating multiple sensing data or utilizing machine learning algorithms, such as ensemble learning and deep learning, to enhance the prediction accuracies of crop physiological traits. However, this approach will require big data alongside laborious destructive measurements in the fields. Future research directions will involve standardizing the process of merging data from multiple field experiments and data repositories. Previous studies have focused mainly on UAV technology in major crops, but there is a high potential in minor crops or cropping systems for future sustainable crop production. This review can guide new practitioners who aim to implement and utilize UAV-based phenotyping.

Estimation of Rice Plant Coverage Using Sentinel-2 Based on UAV-Observed Data

Vegetation coverage is a crucial parameter in agriculture, as it offers essential insight into crop growth and health conditions. The spatial resolution of spaceborne sensors is limited, hindering the precise measurement of vegetation coverage. Consequently, fine-resolution ground observation data are indispensable for establishing correlations between remotely sensed reflectance and plant coverage. We estimated rice plant coverage per pixel using time-series Sentinel-2 Multispectral Instrument (MSI) data, enabling the monitoring of rice growth conditions over a wide area. Coverage was calculated using unmanned aerial vehicle (UAV) data with a spatial resolution of 3 cm with the spectral unmixing method. Coverage maps were generated every 2–3 weeks throughout the rice-growing season. Subsequently, crop growth was estimated at 10 m resolution through multiple linear regression utilizing Sentinel-2 MSI reflectance data and coverage maps. In this process, a geometric registration of MSI and UAV data was conducted to improve their spatial agreement. The coefficients of determination (R2) of the multiple linear regression models were 0.92 and 0.94 for the Level-1C and Level-2A products of Sentinel-2 MSI, respectively. The root mean square errors of estimated rice plant coverage were 10.77% and 9.34%, respectively. This study highlights the promise of satellite time-series models for accurate estimation of rice plant coverage.

Research on dynamic prediction method of temperature environment of unmanned aerial vehicle platform based on physical information and neural network

During the flight of an unmanned aerial vehicle, the temperature environment inside the cabin is very harsh, resulting in frequent malfunctions of airborne equipment. Therefore, it is necessary to predict the temperature environment experienced by airborne equipment during its life cycle in the early stages of design to support the formulation of design requirements, determine test verification conditions, used and maintained. In order to accurately predict the temperature environment of the unmanned aerial vehicle platform, this paper proposes a prediction method that integrates physical information and neural networks. In view of the thermal environment characteristics of unmanned aerial vehicle platform, the physical mechanism of the formation and transfer of the unmanned aerial vehicle thermal environment was analyzed. Based on the physical mechanism equations and combined with the measured data of unmanned aerial vehicles, an Elman neural network model was established to achieve high flight speed, speed, etc. parameters are characterized as a function of the temperature inside the unmanned aerial vehicle cabin, and finally the model is verified. The results show that the temperature environment prediction model integrates physical information and neural networks can accurately predict the dynamic changes in the temperature inside the unmanned aircraft cabin. The maximum error of the model is 2.76°C, which meets engineering needs.

Use of remote sensing and image processing for identification of wild orchids

A novel multi-technique approach has been applied for the identification and mapping of wild orchids using a combination of remote sensing and spectral image analysis. The five orchid species identified were the common spotted-orchid (Dactylorhiza fuchsia), heath spotted-orchid (Dactylorhiza maculata), pyramidal orchid (Anacamptis pyramidalis), heath fragrant-orchid (Gymnadenia borealis), and the dark-red helleborine (Epipactis atrorubens). Field studies have been done using a hand-held spectrometer operating in the 400–700 nm visible spectrum, photogrammetry using a digital camera as well as a multispectral image camera operating at the specific spectral bands of 450 nm (blue), 560 nm (green), 650 nm (red), 730 nm (red edge) and 840 nm (near-infrared) attached to an unmanned aerial vehicle Data analysis, using the hand-held spectrometer, followed by pattern recognition using principal component analysis and partial least squares-discriminant analysis, have identified the key distinguishing wavelengths for identification of the 5 orchid types as 400, 410, 420 and 560 nm. The use of remote sensing, using the UAV-MSI, and application of a dedicated spectral index has enabled field identification of the orchids. Finally, object-based image analysis of field gathered photogrammetry imagery, has enabled use of shape, size, and color to identify and distinguish orchid species. The developed data analytic tool, using random forest classification, can be used to identify and characterize wild orchids across multiple sites within their short lifespan with an accuracy of 86%. Any longer-term study would provide invaluable information on the diversity and complexity of orchid habitat, population variation both intra- and inter-site location, as well as the impact of climate change.

Combining LiDAR and Spaceborne Multispectral Data for Mapping Successional Forest Stages in Subtropical Forests

The Brazilian Atlantic Rainforest presents great diversity of flora and stand structures, making it difficult for traditional forest inventories to collect reliable and recurrent information to classify forest succession stages. In recent years, remote sensing data have been explored to save time and effort in classifying successional forest stages. However, there is a need to understand if any of these sensors stand out for this purpose. Here, we evaluate the use of multispectral satellite data from four different platforms (CBERS-4A, Landsat-8/OLI, PlanetScope, and Sentinel-2) and airborne light detection and ranging (LiDAR) to classify three forest succession stages in a subtropical ombrophilous mixed forest located in southern Brazil. Different features extracted from multispectral and LiDAR data, such as spectral bands, vegetation indices, texture features, and the canopy height model (CHM) and LiDAR intensity, were explored using two conventional machine learning methods such as random trees (RT) and support vector machine (SVM). The statistically based maximum likelihood (MLC) algorithm was also compared. The classification accuracy was evaluated by generating a confusion matrix and calculating the kappa index and standard deviation based on field measurements and unmanned aerial vehicle (UAV) data. Our results show that the kappa index ranged from 0.48 to 0.95, depending on the chosen dataset and method. The best result was obtained using the SVM algorithm associated with spectral bands, CHM, LiDAR intensity, and vegetation indices, regardless of the sensor. Datasets with Landsat-8 or Sentinel-2 information performed better results than other optical sensors, which may be due to the higher intraclass variability and less spectral bands in CBERS-4A and PlanetScope data. We found that the height information derived from airborne LiDAR and its intensity combined with the multispectral data increased the classification accuracy. However, the results were also satisfactory when using only multispectral data. These results highlight the potential of using freely available satellite information and open-source software to optimize forest inventories and monitoring, enabling a better understanding of forest structure and potentially supporting forest management initiatives and environmental licensing programs.

CHEKG: a collaborative and hybrid methodology for engineering modular and fair domain-specific knowledge graphs

Ontologies constitute the semantic model of Knowledge Graphs (KGs). This structural association indicates the potential existence of methodological analogies in the development of ontologies and KGs. The deployment of fully and well-defined methodologies for KG development based on existing ontology engineering methodologies (OEMs) has been suggested and efficiently applied. However, most of the modern/recent OEMs may not include tasks that (i) empower knowledge workers and domain experts to closely collaborate with ontology engineers and KG specialists for the development and maintenance of KGs, (ii) satisfy special requirements of KG development, such as (a) ensuring modularity and agility of KGs, (b) assessing and mitigating bias at schema and data levels. Toward this aim, the paper presents a methodology for the Collaborative and Hybrid Engineering of Knowledge Graphs (CHEKG), which constitutes a hybrid (schema-centric/top-down and data-driven/bottom-up), collaborative, agile, and iterative approach for developing modular and fair domain-specific KGs. CHEKG contributes to all phases of the KG engineering lifecycle: from the specification of a KG to its exploitation, evaluation, and refinement. The CHEKG methodology is based on the main phases of the extended Human-Centered Collaborative Ontology Engineering Methodology (ext-HCOME), while it adjusts and expands the individual processes and tasks of each phase according to the specialized requirements of KG development. Apart from the presentation of the methodology per se, the paper presents recent work regarding the deployment and evaluation of the CHEKG methodology for the engineering of semantic trajectories as KGs generated from unmanned aerial vehicles (UAVs) data during real cultural heritage documentation scenarios.

Relationships of Fire Rate of Spread with Spectral and Geometric Features Derived from UAV-Based Photogrammetric Point Clouds

Unmanned aerial vehicles (UAVs) equipped with RGB, multispectral, or thermal cameras have demonstrated their potential to provide high-resolution data before, during, and after wildfires and prescribed burns. Pre-burn point clouds generated through the photogrammetric processing of UAV images contain geometrical and spectral information of vegetation, while active fire imagery allows for deriving fire behavior metrics. This paper focuses on characterizing the relationship between the fire rate of spread (RoS) in prescribed burns and a set of independent geometrical, spectral, and neighborhood variables extracted from UAV-derived point clouds. For this purpose, different flights were performed before and during the prescribed burning in seven grasslands and open forest plots. Variables extracted from the point cloud were interpolated to a grid, which was sized according to the RoS semivariogram. Random Forest regressions were applied, obtaining up to 0.56 of R2 in the different plots studied. Geometric variables from the point clouds, such as planarity and the spectral normalized blue–red difference index (NBRDI), are related to fire RoS. In analyzing the results, the minimum value of the eigenentropy (Eigenentropy_MIN), the mean value of the planarity (Planarity_MEAN), and percentile 75 of the NBRDI (NBRDI_P75) obtained the highest feature importance. Plot-specific analyses unveiled distinct combinations of geometric and spectral features, although certain features, such as Planarity_MEAN and the mean value of the grid obtained from the standard deviation of the distance between points (Dist_std_MEAN), consistently held high importance across all plots. The relationships between pre-burning UAV data and fire RoS can complement meteorological and topographic variables, enhancing wildfire and prescribed burn models.

Preliminary Design and Optimization of Primary Structures for a Tilt-Duct UAV

Tilt-duct Unmanned Aerial Vehicles (UAV) combine the high-speed efficiency of fixed-wing aircrafts with vertical takeoff and landing (VTOL) and the hover capabilities of rotary-wing aircrafts while maximizing the advantages of ducted fans in terms of noise reduction, efficiency, and safety, making it a pivotal direction for the future of aviation such as urban air mobility. This paper concentrates on the design and optimization of the primary structures of a laboratory-designed reference tilt-duct UAV. Firstly, the general data of the reference tilt-duct UAV are presented. According to the load conditions, the overall structural layout design for the wing, fuselage, and empennage is carried out, where special attention has been paid to account for the requirements of VTOL/hover and cruise flight modes. Based on the structural layout, finite element models (FEM) are established and static analyses are performed. The results indicate that the design can fulfill the structural requirements during a flight mission. Furthermore, based on the Method of Feasible Directions (MFD) algorithm, we have carried out the optimization of the composite wing box that incorporates manufacturing constraints. Via optimization, the total mass of the wing box is reduced by 38.6%, i.e., from 3.73 kg to 2.29 kg. The results indicate that the combination of composite materials with a tilt-duct configuration holds significant potential for future high-efficiency and environmentally friendly aviation.

Drone‐based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review

AbstractOver the last decade, the use of unmanned aerial vehicles (UAVs) for plant phenotyping and field crop monitoring has significantly evolved and expanded. These technologies have been particularly valuable for monitoring crop growth and health and for managing abiotic and biotic stresses such as drought, fertilization deficiencies, disease, and bioaggressors. This paper provides a comprehensive review of the progress in UAV‐based plant phenotyping, with a focus on the current use and application of drone technology to gain information on plant growth, development, adaptation, and yield. We reviewed over 200 research articles and discuss the best tools and methodologies for different research purposes, the challenges that need to be overcome, and the major research gaps that remain. First, the review offers a critical focus on elucidating the distinct characteristics of UAV platforms, highlighting the diverse sensor technologies employed and shedding light on the nuances of UAV data acquisition and processing methodologies. Second, it presents a comprehensive analysis of the multiple applications of UAVs in field phenotyping, underscoring the transformative potential of integrating machine learning techniques for plant analysis. Third, it delves into the realm of machine learning applications for plant phenotyping, emphasizing its role in enhancing data analysis and interpretation. Furthermore, the paper extensively examines the open issues and research challenges within the domain, addressing the complexities and limitations faced during data acquisition, processing, and interpretation. Finally, it outlines the future trends and emerging technologies in the field of UAV‐based plant phenotyping, paving the way for innovative advancements and methodologies.

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.

Response of road erosion to hydrological connectivity under a heavy rainstorm in an agricultural watershed on the Loess Plateau

Road network extension is closely associated with soil erosion in watersheds during frequent extreme rainfall events. Identifying potential risk areas that actively deliver surface runoff to road structures and channel networks is critical for comprehending hydrological processes and controlling road erosion. In this study, the road erosion (landslides, collapses, scour pits, and gullies on monitored roads) on the Loess Plateau during a heavy rainstorm was analyzed using field survey and unmanned aerial vehicle (UAV) data. We defined hydrological connections as the intersections of roads and channel networks, and the activated hydrological connections represent hydrological connectivity. The results of this study highlight the exacerbating effect of hydrological connectivity on road erosion during a rainstorm in an agricultural watershed in the Loess Plateau region. The results showed that the road erosion intensity ranged from 2.16 × 102 to 1.02 × 104 t/ha. Strong hydrological connectivity resulted in average soil erosion amounts of landslides, collapses, scour pits, and gullies in the downslope area that were 2.16, 1.78, 5.46, and 6.98 times greater than those without hydrological connectivity, respectively. The presence of hydrological connectivity generated a 1.88 times higher road surface erosion intensity than that without hydrological connectivity. The Upslope-Road connections created the most significant enhancement effect on road erosion, and up to 65.37 % landslide erosion and 41.97 % collapse erosion amounts were influenced by these connections.The enhancement effect of Upslope–Road connections on road erosion varied with different road types, this impact of Upslope–Road connections on road erosion were only observed on the landslide erosion of petroleum transport roads and the collapse erosion of agricultural roads. Conversely, Road–Downslope connections did not significantly impact road erosion (p > 0.05). To effectively control the negative effect of road erosion, hydrological connections should be identified and regulated to impede hydrological connectivity activation during rainstorms by modifying tillage activities, improving road maintenance, and strengthening drainage systems.

Enhancing in-season yield forecast accuracy for film-mulched wheat: A hybrid approach coupling crop model and UAV remote-sensing data by ensemble learning technique

Accurate in-season yield forecasts for field-scale crops are crucial for both farmers and decision-makers. Common methods for yield prediction are limited by the availability of unknown weather data (process-based crop models) and the failure to consider yield formation processes (statistical models based on unmanned aerial vehicle (UAV) images), respectively. Furthermore, previous studies focused only on crops without mulching, yet mulching is an important agronomic approach to increase grain yield in the arid areas of northwest China. We aim to develop a hybrid approach coupling crop model and UAV data through ensemble learning to achieve in-season yield forecasts for film-mulched wheat. A four-year field experiment was constructed (2018–2020 and 2021–2023). We first calibrated AquaCrop using data from 2018 to 2020, and historical weather data were employed to drive AquaCrop for predicting yields in 2021–2023. Next, statistical models were constructed to predict yields based on spectral and textural indices calculated from UAV images. Finally, a hybrid approach coupling the AquaCrop model and remote-sensing data was developed using ensemble learning technique. Quantifying the relative contribution of features used SHapley Additive exPlanations values. The results indicated that AquaCrop yield forecasts exhibited considerable uncertainties (R2: 0.53–0.63; NRMSE: 16.54%–14.83%). The interpretation of yield for remote-sensing data was influenced by background and saturation effects, reaching its highest accuracy at the heading stage (R2 was 0.80, NRMSE was 11.88%). Ensemble learning demonstrated strong performance compared to machine learning algorithms. The coupling model combined the advantages of crop and statistical models by the ensemble learning algorithm, achieving accurate yield predictions more than 40 days before harvest (heading stage) based on AdaBoost regression (R2 was 0.88, NRMSE was 8.40%). The most important forecasting factors affecting yield prediction were the textural indices, followed by the AquaCrop simulated values. Overall, the coupled model showed good performance in predicting the in-season yield of film-mulched wheat, which provided new insights into farm-scale yield prediction. Further validation of the generalizability of the coupled model in different scenarios is required in the future to improve the applicability of the model in actual production practice.

Enhancing Coastal Risk Recognition: Assessing UAVs for Monitoring Accuracy and Implementation in a Digital Twin Framework

This paper addresses the intricate challenges of coastal management, particularly in rapidly forming tidal flats, emphasizing the need for innovative monitoring strategies. The dynamic coastal topography, exemplified by a newly formed tidal flat in Shanghai, underscores the urgency of advancements in coastal risk recognition. By utilizing a digital twin framework integrated with state-of-the-art unmanned aerial vehicles (UAVs), we systematically evaluate three configurations and identify the optimal setup incorporating real-time kinematics (RTK) and light detection and ranging (LiDAR). This UAV configuration excels in efficiently mapping the 3D coastal terrain. It has an error of less than 0.1 m when mapping mudflats at an altitude of 100 m. The integration of UAV data with a precise numerical ocean model forms the foundation of our dynamic risk assessment framework. The results showcase the transformative potential of the digital twin framework, providing unparalleled accuracy and efficiency in coastal risk recognition. Visualization through Unity Engine or Unreal Engine enhances accessibility, fostering community engagement and awareness. By predicting and simulating potential risks in real-time, this study offers a forward-thinking strategy for mitigating coastal dangers. This research not only contributes a comprehensive strategy for coastal risk management but also sets a precedent for the integration of cutting-edge technologies in safeguarding coastal ecosystems. The findings are significant in paving the way for a more resilient and sustainable approach to coastal management, addressing the evolving environmental pressures on our coastlines.

Analysis of UAV data communication stability method in extreme environment

In order to get in touch with the affected area smoothly after the disaster, this paper uses drones as air base stations. Their high mobility and sensitivity make them apt for such critical roles. However, during extreme weather conditions, these unmanned aerial vehicles (UAVs) encounter various challenges that can impair their stability and, consequently, their ability to serve as reliable air base stations. As a result, signal transmission can be interrupted or severely limited. Among the common factors affecting UAV stability are strong winds, temperature fluctuations, heavy rainfall, and electromagnetic interference. These factors can cause the UAVs to deviate from their intended flight paths, drop in altitude, or even lose connection with the ground control. As a consequence, the reliability of communication, so critical in emergency situations, gets compromised. To address the problem of degraded UAV stability, several solutions have been proposed and implemented. One approach involves enhancing the drone's onboard stabilization systems, incorporating advanced algorithms that allow it to autonomously adjust to environmental changes. Another strategy is the deployment of a network of drones, ensuring redundancy. If one drone faces difficulties, another can take over, maintaining the communication link. Furthermore, advances in materials science have led to the development of UAVs with more robust structures, capable of withstanding the rigors of adverse weather conditions. Lastly, the use of ground-based boosters or repeaters can amplify signals, ensuring that even if a UAV's transmission is weak, the communication remains uninterrupted.

Capabılıtıes of usıng UAVs to determıne forest road excavatıon volumes ın mountaınous areas

Decision support systems based on spatial data are widely used in some countries for planning production activitiesin forestry and designing forest roads. In recent years, Unmanned Aerial Vehicles (UAVs) have been playingan active role in the production of high resolution and accurate digital elevation models (DEMs) of forested areas.In the preparation of progress payments for forest road construction works, the workload is high, while the feethat has to be paid to the contractor company after the completion of the road construction is determined accordingto the final cost in the progress payment schedules. In this study, the applicability of UAV technology for determiningforest road cut volume and land classification was investigated. A 640-meter section of the forest roadcoded 001 of Taşlıyayla Forest Management Sub-Directorate (Bolu/Türkiye) was examined in the study. Flightswith an UAV (drone) were carried out in autonomous mode before and after the construction of the road examinedin the study. In addition, in order to determine the amount of cut, measurements of the geometric characteristicsof the road were made in the field after the road construction. As a result of the study, 2189 m³ cut volumewas found with the UAV data with an average error of 3 cm. According to the field measurement results, theamount of cut was determined as 2080 m³. A difference of +5% was found between the UAV data and the fieldmeasurement results. In addition, using the JavaScript coding language on the Google Earth Engine platform, soil(46%) and loose soil (54%) land classification was identified from the orthophoto images. The obtained resultsreveal the usability of UAVs in determining forest road volumes.

Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches.

Precise and timely leaf area index (LAI) estimation for winter wheat is crucial for precision agriculture. The emergence of high-resolution unmanned aerial vehicle (UAV) data and machine learning techniques offers a revolutionary approach for fine-scale estimation of wheat LAI at the low cost. While machine learning has proven valuable for LAI estimation, there are still model limitations and variations that impede accurate and efficient LAI inversion. This study explores the potential of classical machine learning models and deep learning model for estimating winter wheat LAI using multispectral images acquired by drones. Initially, the texture features and vegetation indices served as inputs for the partial least squares regression (PLSR) model and random forest (RF) model. Then, the ground-measured LAI data were combined to invert winter wheat LAI. In contrast, this study also employed a convolutional neural network (CNN) model that solely utilizes the cropped original image for LAI estimation. The results show that vegetation indices outperform the texture features in terms of correlation analysis with LAI and estimation accuracy. However, the highest accuracy is achieved by combining both vegetation indices and texture features to invert LAI in both conventional machine learning methods. Among the three models, the CNN approach yielded the highest LAI estimation accuracy (R 2 = 0.83), followed by the RF model (R 2 = 0.82), with the PLSR model exhibited the lowest accuracy (R 2 = 0.78). The spatial distribution and values of the estimated results for the RF and CNN models are similar, whereas the PLSR model differs significantly from the first two models. This study achieves rapid and accurate winter wheat LAI estimation using classical machine learning and deep learning methods. The findings can serve as a reference for real-time wheat growth monitoring and field management practices.