Automated analytic pipeline for field-based plot level plant phenotyping with remotely sensed data

The volcanic Trindade Island is a remote Brazilian offshore territory in the South Atlantic, located ca. 1.140 kilometers east of the southeast coast of Brazil. The island’s permanent exposure to geological hazards requires assessment. However, the lack of erosion and landslides temporal data impedes predictive geohazard analyses. Therefore, we compiled pre-existing data from nautical charts and surveyed the surface terrain on Trindade Island to generate Digital Terrain Models (DTMs) and comparative accuracy analyses. The DTM based on pre-existing data shows the lowest accuracy (root mean square error - RMSE: 12.3 m) yet is adequate for regional studies. In contrast, the DTM developed from real-time kinematic global navigation satellite systems (RTK-GNSS) has the highest vertical accuracy (RMSE: 0.48 m), but spatial variability of ground elements was underestimated and limited to meter-sized (and larger) elements. The DTM obtained using the unmanned aerial vehicle (UAV) with ground control points (GCP), on the other hand, presented lower accuracy (RMSE: 2.37 m) than the RTK-GNSS model but still allowed observation of centimetric (and larger) ground features. For geohazard assessment on Trindade Island, models that allow fine-scale studies are needed. A UAV with GCP provides such standards and proved to be the most viable option in remote and complex sites as well. Hence, this study, the first to allow multi-temporal analysis of geohazard assessment on Trindade Island, offers a viable solution for similar analyses in other remote locations.

High-resolution (approximately 1 m) flood modelling is increasingly recognised as essential for resolving flow pathways and hydraulic connectivity in complex urban environments. At this scale, flood dynamics are strongly controlled by micro-topographic features, including hydraulically permeable elements beneath vegetation and bridge structures, as well as small-scale obstructions such as kerbs and surface discontinuities. However, conventional Digital Terrain Model (DTM) generation approaches struggle to reliably represent such features due to vegetation occlusion and sensor-specific terrain acquisition limitations, often necessitating extensive manual intervention.This study presents a semi-automated terrain reconstruction framework that integrates Unmanned Aerial Vehicle-borne LiDAR, Unmanned Aerial Vehicle （UAV）oblique photogrammetry, handheld LiDAR Simultaneous Localization and Mapping (SLAM), and Real-Time Kinematic Global Navigation Satellite Systems (RTK-GNSS) measurements to generate flood-ready DTMs for high-resolution hydrodynamic modelling. Rather than treating multi-source datasets as interchangeable inputs, the framework explicitly exploits their differing information characteristics and spatial sensitivities to occlusion and ground accessibility. UAV LiDAR provides spatially continuous but occlusion-prone surface measurements, handheld LiDAR SLAM offers dense ground-level observations in vegetated and structurally complex areas, and RTK-GNSS provides sparse but high-accuracy elevation control.An initial DTM is established through adaptive fusion of morphologically filtered UAV-derived DTMs and SLAM-derived ground observations, supported by vegetation indices extracted from digital orthophoto maps and SLAM point-density metrics. To address residual elevation errors arising from partial occlusion and sensor limitations, a residual learning strategy based on a U-Net architecture is employed to predict local elevation corrections relative to RTK-GNSS ground truth. The learning component is explicitly constrained to operate as a local correction mechanism rather than an end-to-end terrain predictor, thereby preserving physical plausibility and spatial consistency of the reconstructed terrain.The framework is demonstrated over the Zhengzhou University campus (approximately 1 km²), encompassing diverse building typologies, vegetation densities, and pedestrian and vehicular infrastructure. The hydrodynamic relevance of the reconstructed DTM is evaluated using the High-Performance Integrated Hydrodynamic Modelling System（HiPIMS） through comparative two-dimensional simulations of historical extreme flood events. Results demonstrate that improved representation of micro-topographic controls significantly enhances simulated flow connectivity, inundation extent, and inundation timing relative to conventional terrain products, and provides a transferable workflow for campus- and neighbourhood-scale flood modelling and risk assessment and urban digital twin applications.

<p>Detailed information on rock formations and assessment of their geological structures, such as joints, faults, shears and bedding planes, are required for evaluation of rock integrity and stability. Our research demonstrates a comprehensive approach for producing high spatial resolution 3D information of rock formations from unmanned aerial vehicle (UAV) imagery for rock joint identification, and presents an innovative technique for using Terrestrial Laser Scanning (TLS) data to derive ground control points (GCPs) for geo-referencing of UAV imagery of vertical rock walls. UAV imagery was collected from a freestanding 90 m tall rock formation with a 3.5 km perimeter, via oblique rock façade scans and also at-nadir, covering a ground and rock facade area of approximately 0.32 and 0.25 km<sup>2</sup>, respectively. Seventy-two GCPs were distributed around the rock for geo-referencing of the UAV imagery. As GCPs could not be deployed on vertical rock walls, a TLS system was used for identification of 93 distinct natural features as pseudo-GCPs on the rock walls. Forty scans were collected and geo-referenced from triplets of GCPs placed on the ground near the TLS system for each scan. A Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) survey was performed on the GCPs, using a base station and a rover. Continuously Operating Reference Stations (CORS) data were used to fix the position of the base station and improve the absolute geometric position to an average and lowest accuracy of 3.3 and 18.6 mm for 177 GCPs. A total of 44 façade scans, as well as 14 low and 5 high altitude nadir-viewing UAV flights, were undertaken with a Zenmuse X3 RGB camera mounted to a DJI Matrice 100 platform, resulting in a collection of nearly 17,000 photos. The five high altitude flights were designed to include a larger area around the rock to incorporate additional GCPs, while the low altitude flights were to increase the spatial resolution of the imaged rock. Flight planning was undertaken with the Universal Ground Control Station Client application. Façade scans were flown horizontally and parallel to the rock walls at a distance of 20-30 m and at heights between 10-80 m with sidelaps >70% between horizontal flight lines and >80% forward overlap along flight lines. Façade scans were collected with a 4° viewing angle to ensure the base of the rock was included. The Agisoft MetaShape software was initially used to generate a sparse point cloud using all façade scans and nadir-viewing imagery. Geo-referencing of the UAV imagery was based on 136 GCPs, which produced an accuracy of 0.1558 m, with an addition 100 control points kept aside for independent evaluation, yielding an accuracy of 0.2018 m. Subsequent image processing was split into 14 evenly sized “chunks” to enable more efficient processing of a dense point cloud (4.33 billion points) and 3D model (mesh with 129.5 million faces and texture layer) for the entire area. The produced 3D model was found suitable for identification of rock joints larger than 1 m in length.</p>

Dynamic monitoring of an offshore jacket platform based on RTK-GNSS measurement by CF-CEEMDAN method

Better methods are needed for mapping agricultural drainage pipe systems. Prior research on small test plots indicates that ground penetrating radar (GPR) is oftentimes capable of detecting buried drainage pipes; however, the feasibility of employing this geophysical technique in larger field areas has not been adequately evaluated. Ground penetrating radar integrated with a Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) may be an effective and efficient means of mapping drain lines within agricultural fields. Therefore, GPR-RTK/GNSS was tested in three agricultural settings; with Site 1 and Site 2 located in Beltsville, MD, USA and Site 3 near Columbus, OH, USA. Soils at the three sites ranged from silty clay loam to loamy sand. A GPR unit with 250 MHz antennas was used to detect drainage pipes, and at Sites 1 and 2, a physical GNSS base station was utilized, while a virtual base station was employed at Site 3. The GPR-RTK/GNSS configurations used in this study delineated a complex rectangular drainage pipe system at Site 1, with one set of drainage pipes oriented southwest-northeast and a second oriented southeast-northwest. At Site 2, a herringbone drain line pattern was outlined, and at Site 3, random drain lines were found. When integrated with RTK/GNSS, spiral or serpentine GPR transects (or spiral/serpentine segments of a GPR transects) were utilized to provide insight on drain line directional trends. Consequently, given suitable field conditions, GPR integrated with RTK/GNSS can be a valuable tool for farmers and drainage contractors needing to map subsurface drainage systems.

The launch of the very high resolution (VHR) sensor satellites has paved the way for further exploitation of the capabilities of stereo satellite imagery for many applications and most importantly in the extraction of topographical details. Advances in digital technologies in the photogrammetric technique provide more accurate and up to date topographic representation, while traditional methods of topographic mapping are time-consuming and require large technical support. This paper explores the methods of production and an analysis of the capabilities of the high-resolution stereo imaging system for the extraction of features (topographic map) and generation of digital elevation model (DEM). This study uses high-resolution GeoEye-1 stereo pair images. For qualitative assessment, topographic features were extracted by digitizing the georeferenced stereo pair images and overlapped to form a DEM and orthophoto within the Summit Evolution software. Georeferencing was carried out using the existing Ground Control Points (GCPs) and Check Points (CPs) established using Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) technique. For quantitative assessment, a total of 7 existing GCPs were considered suitable for use and 5 CPs were established as a check for accuracy of the output. The accuracy of the CPs was evaluated by computing Root Mean Square Error (RMSE). The average horizontal RMSE for the left image and right image were 1.17150098m and 0.819334672m respectively and the average vertical RMSE was 1.605898565m. Additionally, the standard deviation for elevation data from generated DEM using photogrammetric technique and existing elevation data of RTK-GNSS technique were computed, which resulted in 15.91286667 and 16.59529228 respectively. The accuracy was evaluated by comparing the results of the data extracted using stereo satellite images and those extracted from ground measurement techniques (Total station and RTK-GNSS). The comparative analysis showed that stereo pair images provide a viable alternative to other ground mapping techniques providing results within the acceptable range. Based on error estimation and analysis, it concludes that, if strict photogrammetric processing model and ground control points are employed, high-resolution satellite imagery can be used for the accurate generation and update of the topographic maps, DEM and digital orthophotos, which can be further be used as a reference for comparative analysis applications.

In order to quantitatively analyze the roughness of the bench floor during open-pit mine blasting, this study proposes a real-time measuring method for the three-dimensional terrain of the bench floor during the excavation process. Real-time monitoring is conducted at the boundary and discrete internal points of the workbench floor during electric shovel operation, utilizing real-time kinematic global navigation satellite system (RTK-GNSS) positioning technology. An improved convex hull algorithm is introduced to automatically extract the optimal boundary of discrete point clouds based on their spatial distribution characteristics. This study establishes a digital elevation model (DEM) using five interpolation algorithms for 3D terrain visualization simulation. Through cross-validation, a comparative analysis of the DEM accuracy, the simulation results of the ordinary kriging interpolation algorithm were found to be optimized. The optimized interpolation algorithm is applied to simulate the 3D terrain in the Dexing open-pit copper mine, and the relevant terrain parameters were calculated. This dataset can serve as a precise foundation for the real-time path planning of elevation blasting design and ground leveling operations.

Autonomous landing system is an important part of unmanned aerial vehicle (UAV) autonomous flight. Large fixed‐wing UAVs often consume more onboard space and computing resources. With the improvement of system integration and the reduction of high‐performance global navigation satellite system (GNSS) costs, real‐time kinematic (RTK) GNSS has been widely used in small‐scale and low‐cost fixed‐wing UAVs. This article uses less onboard hardware and ground facility requirements to design an autonomous taking off and landing system suitable for general fixed‐wing UAVs. A modified gradient descent attitude estimator, based on microelectromechanical system (MEMS) inertial measurement unit (IMU) and RTK GNSS, and a guidance method based on a tracking differentiator for using in a high dynamic environment are proposed, and circular path following and vertical transient simulations are performed. The simulation results show that this method can improve attitude estimation accuracy, motion control accuracy, and altitudinal transient quality. Finally, the actual flight test verifies its feasibility in the actual environment. The flight test results prove that it is feasible to implement the autonomous landing process of fixed‐wing UAV with the proposed system and method.

This study evaluated the applicability of the DJI Phantom4 RTK Unmanned Aerial Vehicle (UAV) with a built-in Real Time Kinematic-Global Navigation Satellite System (RTK-GNSS) for field management. The accuracies of the positional data on the x-, y-, and z-axes were 78, 73, and 292 mm, respectively, on average. The precisions of the positional data were 134, 171, and 89 mm, respectively, on average. The labor-saving effect of the RTK-UAV was 81% compared to the conventional method. RTK-UAV estimates of snow depth distribution corresponded with actual measured values, although there was an average error of 76 mm.

Commercial Global Positioning System (GPS) receivers, such as the ones in cell phones, usually can provide meter‐level accuracy in the best case. However, this is not enough for autonomous vehicles, which require centimeter‐level localization. One technology to achieve centimeter accuracy is the real‐time kinematic (RTK) Global Navigation Satellite System (GNSS). This chapter introduces localization with RTK GNSS and its role in an autonomous driving technology stack. It provides an overview of GNSS technologies and presents a step‐to‐step detailed guide on setting up one's own RTK GNSS system for high‐precision localization. The chapter discusses RTK‐GNSS architecture and demonstrates how to setup a cloud server to broadcast base station correction signal, or the Radio Technical Commission for Maritime Services (RTCM) signal. When a cloud server is not a good option, it also demonstrates how to set up FreeWave radio to broadcast RTCM signals.

Coastal erosion poses a significant risk to India’s 11,098.81 km shoreline, particularly in regions where coastal economies rely heavily on fishing. Karaikal, Puducherry, has experienced erosion along 37% of its coastline (NCCR, 2018), underscoring the need for robust sediment volume and shoreline change assessments. This study quantifies sediment dynamics by integrating Unmanned Aerial Vehicle (UAV) photogrammetry with Real-Time Kinematic Global Navigation Satellite System (RTK GNSS) surveys across TR Pattinam and Vanjiur beaches. Shoreline positions were mapped using RTK GNSS by delineating the centre line between high-water (HWL) and low-water (LWL) marks, while UAV-derived edges were extracted via the Canny detection algorithm. UAV flights were conducted at 50 m altitude with 85% image overlap. The Canny method achieved an average SSIM of ~0.8 and positional accuracy within one pixel.Sediment volumes were estimated by generating UAV-based digital elevation models (DEMs) using Structure from Motion and comparing them with centimetre-accurate GNSS transects spaced at 30 m intervals. Elevation differences were integrated alongshore to compute volumetric changes, with UAV elevations cross-checked against GNSS profiles for bias assessment. Results indicate strong agreement between UAV and GNSS datasets, demonstrating the reliability of this integrate.Key words: Shoreline change, Erosion, GNSS, Edge detection ,Sediment volume

Unmanned aerial vehicle (UAV) imagery is considered the best approach to acquire high-resolution data over a large area in a short time, such as in the case of coastal wetlands comprising small-sized patches of perennial plants. However, to the best of our knowledge, no previous studies have used UAV imagery to quantitatively estimate blue carbon in coastal wetlands. Therefore, this study aimed to estimate the area-wide carbon stocks accumulated in halophyte habitats through the case studies of the southern coastal wetlands in South Korea using UAV imagery. Accordingly, object-based image analysis was used to identify the spatial distribution of major halophytes based on the spectral reflectance derived from multispectral UAV images. Elemental analysis (EA) was performed to estimate the content of organic carbon accumulated in individual halophytes, and the halophyte communities identified in the case study area were confirmed to have accumulated 20.80 MgC of blue carbon. These results demonstrate that applying EA to UAV images can be an innovative method to solve the spatial uncertainty encountered in the monitoring of blue carbon in existing field-based surveys and traditional remote sensing.

Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because unmanned aerial vehicle imagery may be acquired quickly during critical periods of rapid crop growth. The objective of this study was to evaluate the use of unmanned aerial vehicle for the monitoring barley growth. Unmanned aerial vehicle imagery obtained from middle February to late June in Wanju, Jeollabuk-do. Unmanned aerial vehicle imagery corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). We analyzed the relationships between NDVI UAV of barley and biophysical measurements such as plant height, number of tiller and shoot dry weight over an entire barley growth period. The similar trend between NDVI UAV and growth parameters was shown. Correlation analysis between NDVI UAV and barley growth parameters revealed that NDVI UAV was highly correlated with shoot dry weight (r=0.932) and plant height (r=0.879). According to the relationship among growth parameters and NDVI UAV , the temporal variation of NDVIUAV was significant to interpret barley growth. The spatial distribution map of barley growth was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when NDVI UAV was applied to power function. From these results, NDVI UAV can be used as a new tool for monitoring barley growth.

Tree height is one of the key parameters in forest plantations that plays a crucial role in estimation of above-ground biomass (AGB) of trees and stands. The parameter may be obtained by different methods from airborne remotely sensed datasets such shadow length of each tree individual or crown height models (CHMs). However, tree height estimation based on shadow length might be biased considering diverse topography of forest sites. Therefore, this study aims to develop a reliable method to estimate tree height in a plantation forest using shadow length on UAV imagery. First, heights of 151 pine (Pinus eldarica) trees were precisely measured in Pardisan Park, North Khorasan province, Iran. Additionally, a collection of images was captured by a Phantom 4Pro UAV in order to illustrate the study area. Then, two different approaches were considered to estimate the height of trees. In the first approach, tree heights were estimated based on shadow length on the UAV orthomosaic and correcting the effect of slope. The second approach considered the UAV-based CHM and height estimation using CHM segmentation and local maximum filtering. The results showed that tree heights estimated by the first approach were not significantly different from the in-situ data (p=0.298). Furthermore, the heights estimated by the slope corrected shadow length showed higher precision compared to the heights estimated by the shadow length without slope correction (Relative root mean squared error, RRMSE 5.6% and 8.2% respectively). However, the heights obtained from the first approach were less precise than the second approach (RRMSE 5.6% and 4.2% respectively). In general, it was concluded that height estimation of pine trees based on shadow length after correction of slope effects can be considered as a reliable approach, although CHM is more efficient in estimating the tree heights. The findings of this study are applicable for height estimation of pine trees within plantation forests on UAV imagery.

Multi-scale adversarial network for vehicle detection in UAV imagery