Digital Point Cloud Research Articles

Enhanced Estimation of Crown-Level Leaf Dry Biomass of Ginkgo Saplings Based on Multi-Height UAV Imagery and Digital Aerial Photogrammetry Point Cloud Data

Ginkgo is a multi-purpose economic tree species that plays a significant role in human production and daily life. The dry biomass of leaves serves as an accurate key indicator of the growth status of Ginkgo saplings and represents a direct source of economic yield. Given the characteristics of flexibility and high operational efficiency, affordable unmanned aerial vehicles (UAVs) have been utilized for estimating aboveground biomass in plantations, but not specifically for estimating leaf biomass at the individual sapling level. Furthermore, previous studies have primarily focused on image metrics while neglecting the potential of digital aerial photogrammetry (DAP) point cloud metrics. This study aims to investigate the estimation of crown-level leaf biomass in 3-year-old Ginkgo saplings subjected to different nitrogen treatments, using a synergistic approach that combines both image metrics and DAP metrics derived from UAV RGB images captured at varying flight heights (30 m, 60 m, and 90 m). In this study, image metrics (including the color and texture feature parameters) and DAP point cloud metrics (encompassing crown-level structural parameters, height-related and density-related metrics) were extracted and evaluated for modeling leaf biomass. The results indicated that models that utilized both image metrics and point cloud metrics generally outperformed those relying solely on image metrics. Notably, the combination of image metrics obtained from the 60 m flight height with DAP metrics derived from the 30 m height significantly enhanced the overall modeling performance, especially when optimal metrics were selected through a backward elimination approach. Among the regression methods employed, Gaussian process regression (GPR) models exhibited superior performance (CV-R2 = 0.79, rRMSE = 25.22% for the best model), compared to Partial Least Squares Regression (PLSR) models. The common critical image metrics for both GPR and PLSR models were found to be related to chlorophyll (including G, B, and their normalized indices such as NGI and NBI), while key common structural parameters from the DAP metrics included height-related and crown-related features (specifically, tree height and crown width). This approach of integrating optimal image metrics with DAP metrics derived from multi-height UAV imagery shows great promise for estimating crown-level leaf biomass in Ginkgo saplings and potentially other tree crops.

Solutions to enhance the accuracy of UAV’s images for creating extremely large-scale maps in urban area

Abstract Ho Chi Minh City, the largest urban agglomeration in Vietnam, is at the forefront of enabling the government’s efforts and advocating for smart city initiatives in the coming decades. To facilitate this advancement, the implementation of solutions to support the collection, construction, and updating of spatial databases is crucial. These databases serve as the foundational platform for effective urban management practices. Unmanned Aerial Vehicles (UAVs) have witnessed significant growth across various domains, particularly in spatial data collection. However, challenges arise when acquiring and processing UAV images in urban areas. These challenges include orthomosaic distortion in certain regions, insufficient accuracy for creating extremely large-scale maps (e.g., 1:200 or 1:500), and unclear boundaries between adjacent objects. The objective of this paper is to present flight tests conducted at varying altitudes, with different shooting angles, and varying layouts of Ground Control Points (GCPs), both with and without the UAV’s RTK receiver mode. In order to improve the accuracy of UAV’s images, the results suggest that the precision of orthomosaic, Digital Surface Model (DSM) and point clouds in the context of 3D mapping depends on various factors, including: the determination of the optimal flight altitude, optimizing number of GCPs, selecting an appropriate level of image processing, capturing images from varied angles, and using UAVs with RTK or PPK data acquisition modes to mitigate image distortion.

Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds

ABSTRACT Studies have shown that digital surface models and point clouds generated by the United States Department of Agriculture’s National Agriculture Imagery Program (NAIP) can measure basic forest parameters such as canopy height. However, all measured forest parameters from these studies are evaluated using the differences between NAIP digital surface models (DSMs) and available lidar digital terrain models (DTMs). A survey of NAIP point cloud classification and related ground point-generated DTMs has not yet been undertaken. This study applies a Support Vector Machine (SVM) to classifying ground and nonground points from NAIP point clouds for test sites in Wyoming and Arizona, USA. Light detection and ranging (lidar) data from the U.S. Geological Survey 3D Elevation Program (3DEP) are used to validate the classified NAIP ground points and their corresponding DTMs. Comparing height differences between filtered NAIP ground points and 3DEP ground points, the SVM classifier’s results show that the vertical root mean square error value is 1.87 m and 1.69 m for the Wyoming and Arizona sites, respectively. If NAIP point clouds were continuously measured, the resulting availability of medium-resolution DTMs would benefit the application of multitemporal forest health monitoring and DTM generation.

Airborne LiDAR Point Cloud Filtering Algorithm Based on Supervoxel Ground Saliency

Abstract. Airborne laser scanning (ALS) is able to penetrate sparse vegetation to obtain highly accurate height information on the ground surface. LiDAR point cloud filtering is an important prerequisite for downstream tasks such as digital terrain model (DTM) extraction and point cloud classification. Aiming at the problem that existing LiDAR point cloud filtering algorithms are prone to errors in complex terrain environments, an ALS point cloud filtering method based on supervoxel ground saliency (SGSF) is proposed in this paper. Firstly, a boundary-preserving TBBP supervoxel algorithm is utilized to perform supervoxel segmentation of ALS point clouds, and multi-directional scanning strip delineation and ground saliency computation are carried out for the clusters of supervoxel point clouds. Subsequently, the energy function is constructed by introducing the ground saliency and the optimal filtering plane of the supervoxel is solved using the semi-global optimization idea to realize the effective distinction between ground and non-ground points. Experimental results on the ALS point cloud filtering dataset openGF indicate that, compared to state-of-the-art surface-based filtering methods, the SGSF algorithm achieves the highest average values across various terrain conditions for multiple evaluation metrics. It also addresses the issue of recessed structures in buildings being prone to misclassification as ground points.

Deep learning-based 3D digital damage map of vertical-type tunnels using unmanned fusion data scanning

Expert-dependent visual inspection of vertical-type tunnels is often not only dangerous for workers, but also unreliable of the inspection results. To address the technical issues, a deep learning-based 3D digital damage mapping technique using an unmanned fusion data-scanning system is described in this article. The proposed system acquires raw digital images and point cloud data by moving vertically within the tunnel. Subsequently, optimal images are automatically selected and used for deep learning-based damage evaluation and fixed camera pose-based 3D digital modeling. Finally, a 3D digital damage map is generated by mapping the damage features onto the 3D digital map based on the predetermined camera pose. The proposed technique was experimentally validated in two different in-situ testbeds in South Korea, revealing that averaged precision and recall values are 90.89% and 98.18%, respectively. It is expected that the proposed technique can improve in-situ workability as well as data reliability.

Evaluation of Rock Mass Rating in Underground Mine Bukit Kachi Using UAV Photogrammetry Method

This paper describes the results of a field investigation with the objective of evaluating the possibility to produce drone-derived 3D digital point clouds sufficiently dense and accurate to determine the rock mass rating (RMR) in the underground mine in Bukit Kachi. Agisoft Photoscan software was used for producing the three-dimensional points cloud from the two-dimensional images' sequences. The rock mass rating was evaluated by using Discontinuity Set Extractor (DSE) and Dips 7.0 in Rockscience software. Results from this research show that 3D digital point clouds, derived from the processing of drone-flight images, were successfully used for reliable representation of discontinuity of the tunnel. According to the results of the analysis, both Tunnel A and Tunnel B are classified as "fair rock." Meanwhile, Tunnel A is failing due to geological conditions of feldspar decomposition that are classified as class I, which is "very poor rock." According to Dips 7.0 analysis, the major direction of discontinuity set of Tunnel A for Window right is N300 – N310, while for Window left is N350 – N360. The most dominant discontinuity direction for Tunnel B Window right is N340 – N350, and the orientation for Window left is N10 – N20. When manual mapping and DSE analysis are compared, both orientations of the discontinuity do not give the same direction due to less data reading in the field and high accuracy from the software.

Unlocking the full potential of reality capture for construction project cost monitoring: a mixed review

Purpose Digital data acquisition is crucial for operations in the digital transformation era. Reality capture (RC) has made an immeasurable contribution to various fields, especially in the built environment. This paper aims to review RC applications, potentials, limitations and the extent to which RC can be adopted for cost monitoring of construction projects. Design/methodology/approach A mixed-method approach, using Bibliometric analysis and the PRISMA framework, was used to review and analyse 112 peer-reviewed journal articles from the Scopus and Web of Science databases. Findings The study reveals RC has been applied in various areas in the built environment, but health and safety, cost and labour productivity monitoring have received little or no attention. It is proposed that RC can significantly support cost monitoring owing to its ability to acquire accurate and quick digital as-built 3D point cloud data, which contains rich measurement points for the valuation of work done. Research limitations/implications The study’s conclusions are based only on the Scopus and Web of Science data sets. Only English language documents were approved, whereas others may be in other languages. The research is a non-validation of findings using empirical data to confirm the data obtained from RC literature. Practical implications This paper highlights the importance of RC for cost monitoring in construction projects, filling knowledge gaps and enhancing project outcomes. Social implications The implementation of RC in the era of the digital revolution has the potential to improve project delivery around the world today. Every project’s success is largely determined by the availability of precise and detailed digital data. RC applications have pushed for more sustainable design, construction and operations in the built environment. Originality/value The study has given research trends on the extent of RC applications, potentials, limitations and future directions.

Meta-Universe Enabled Digital Modeling for Urban 3D Reality Visualization

Abstract The rapid acquisition of 3D data with high accuracy and efficiency, along with the reduction of production cycles and costs, are pressing challenges in the field of 3D reality modeling. This paper introduces a novel approach for urban 3D real-view modeling from a metacosmic perspective. Utilizing tilt-shift photography technology, this method captures three-dimensional data of urban scenes under the meta-universe framework. The data are then processed through an integration principle, combining DOM (Digital Orthophoto Map), DEM (Digital Elevation Model), and vector data to filter out interference. Subsequently, a high-density digital point cloud is generated using tilted imagery combined with aerial triangulation. This point cloud, along with a TIN (Triangulated Irregular Network) model, facilitates the construction of a comprehensive three-dimensional visualization model of urban environments, enabling detailed digital analysis. Data presentation demonstrates that with an increase in the number of image control points, the planimetric error decreases from 0.0536 m to 0.0388 m, reflecting a 27.61% improvement. Similarly, elevation accuracy improves from 0.0927 m to 0.0539 m, marking a 41.86% enhancement. This methodology supports the creation of highly precise and cost-effective three-dimensional realistic models of urban built-up areas, providing robust data support for the development and management of smart cities.

Photogrammetric Technique-Based Quantitative Measuring of Gravity Erosion on Steep Slopes in Laboratory: Accuracy and Application

Quantitative measuring of gravity erosion contributes to a better understanding of soil-mass failure occurrence and prediction. However, the measurement of gravity erosion requires the continuous monitoring of the objective terrain, due to its occurrence, usually within seconds, and combination with hydraulic erosion. The photogrammetric technique can quickly obtain terrain data and provide a new method for measuring gravity erosion. Based on a continuous high-overlapping image-acquisition equipment, a Structure-from-Motion-Multi-View-Stereo (SfM-MVS)-integrated workflow, and volume calculation, a new working methodology was established for measuring gravity erosion on steep granitic slopes in the laboratory. The results showed a good match between the digital point clouds derived from SfM-MVS-integrated workflow and terrestrial laser scanning (TLS), achieving millimeter-scale accuracy. The mean distance between the point clouds derived from TLS and SfM-MVS was 1.13 mm, with a standard deviation of 0.93 mm. The relative errors among the volumes calculated by SfM-MVS and TLS or the conventional oven-drying method were all within 10%, with a maximum error of 9.3% and a minimum error of 0.2%. A total of 213 gravitational erosion events were measured in the laboratory by using the SfM-MVS method, further confirming its feasibility.

A Multivariate Local Descriptor Registration Method for Surface Topography Evaluation

This paper illustrates a systematical surface topography measurement and evaluation method based on a 3D optical system. Firstly, the point cloud data of the workpiece are extracted by the use of a 3D structured light measurement system, and the STEP file of the design model is converted into point cloud data. Secondly, the local measurement point cloud (LMPC) and digital model point cloud (DMPC) are registered by a multivariate local descriptor registration scheme proposed in this study. Thirdly, the surface shapes extracted from the STEP file are applied as a reference to segment the measuring point cloud. Finally, an error analysis scheme is conducted on specific functional surfaces. An experiment was conducted to analyse the flatness, cylindricity and roughness to demonstrate the effectiveness and advantage of the method. The comparison results show that the proposed method outperforms other 3D optical surface topography analysis methods.

Investigation of estuarine mangrove ecosystem changes using unmanned aerial vehicle images: Case study in Xuan Thuy National Park (Vietnam)

This paper introduces an optimized approach for the integration of digital surface models (DSM), orthophotos, and point clouds constructed by unmanned aerial vehicle (UAV) photogrammetry for mapping topography and canopy height of mangrove forests. Object-oriented classification has been applied to orthophoto to accurately determine open mudflats. Elevation points of the point cloud that fall within open mudflats are used to linearly interpolate a digital terrain model (DTM) of mangroves, which is generally unavailable due to the great challenge for both remote sensing and field measurement methods. A canopy height model (CHM) of mangroves is generated by subtracting the DTM from the DSM. Consequently, the constructed CHM has higher accuracy compared to previous studies using only one fixed ground elevation or the water mean level to represent the irregular topography of mangrove forests. The optimized approach is applied to UAV images acquired in 2014 and 2020 to investigate topographical changes and vertical growth of mangroves in a 4 km 2 estuarine area of Xuan Thuy national park in Vietnam. Results show that the study area is vertically eroded with the rate of -0.015 m year −1 due to the groundwater flows, while the vertical growths of the two dominant mangrove species Kandelia obovata and the Sonneratia caseolaris are 0.242 m year −1 and 0.565 m year −1, respectively. A very weak correlation between topographical changes and vertical growth (R = -0.286) is the empirical evidence for the insignificant influence of erosion and/or deposition on the mangrove growth. This study used sole UAV-SfM method to provide comprehensive information on both the topography and canopy structure of fragmented mangrove forests. A combination of LiDAR data and UAV photogrammetry is recommended for dense mangrove inventory.

Extraction and Analysis of Spatial Feature Data of Traditional Villages Based on the Unmanned Aerial Vehicle (UAV) Image

In the process of the evolution of urban-rural spatial patterns, traditional village forms have been seriously eroded and destroyed, resulting in the gradual loss of garden feature elements and the blurring of spatial features. Facing these problems, accurate and efficient acquisition of relevant data becomes the key to traditional village conservation. We take the typical Huizhou Shuikou garden Tangmo village as an example and conduct a study on how to use UAV aerial images to extract spatial feature data. Firstly, the UAV was used to acquire aerial images of the study area through a predefined mission. Then, the image data are processed by using structure from motion (SfM) software to obtain digital surface model (DSM), digital orthophoto map (DOM), point cloud, and 3D model. Finally, the produced spatial data are applied to the spatial feature extraction analysis study. The results show the following: (1) the data produced by UAV aerial images have a horizontal accuracy of 0.034 m and a vertical accuracy of 0.039 m, which meet the requirements of traditional village spatial data collection. (2) The results of spatial feature elements and terrain feature extraction show that the 3D model and DSM can accurately extract and analyze the micro and macro spatial feature of traditional villages. (3) By using the cloth simulation filtering (CSF) to extract point clouds of buildings and streets, and after statistical analysis, we could quickly obtain their spatial features. Based on the spatial data obtained by using UAV, the study achieves the accurate collection of Tangmo DSM, DOM, 3D model, and spatial feature data and forms a method of data collection, processing and spatial feature extraction. The results of the study can provide a scientific basis for data collection on the spatial features of other cultural heritages and the conservation of traditional villages.

Assessing the utility of NAIP digital aerial photogrammetric point clouds for estimating canopy height of managed loblolly pine plantations in the southeastern United States

• A model was derived that adjusts NAIP heights to better align with field heights. • NAIP point clouds can be used to produce canopy height on a state-wide scale. • Stands that have had recent thinning cannot be reliably measured. Remote sensing offers many advantages to supplement traditional, ground-based forest measurements, such as limiting time in the field and fast spatial coverage. Data from airborne laser scanning (lidar) have provided accurate estimates of forest height, where, and when available. However, lidar is expensive to collect, and wall-to-wall coverage in the United States is lacking. Recent studies have investigated whether point clouds derived from digital aerial photogrammetry (DAP) can supplement lidar data for estimating forest height due to DAP’s lower costs and more frequent acquisitions. We estimated forest heights using point clouds derived from the National Agricultural Imagery Program (NAIP) DAP program in the United States to create a predicted height map for managed loblolly pine stands. For 534 plots in Virginia and North Carolina, with stand age ranging from 1 year to 42 years old, field-collected canopy heights were regressed against the 90th percentile of heights derived from NAIP point clouds. Model performance was good, with an R 2 of 0.93 and an RMSE of 1.44 m. However, heights in recent heavily thinned stands were consistently underestimated, likely due to between-row shadowing leading to a poor photogrammetric solution. The model was applied to non-thinned evergreen areas in Virginia, North Carolina, and Tennessee to produce a multi-state 5 m × 5 m canopy height map. NAIP-derived point clouds are a viable means of predicting canopy height in southern pine stands that have not been thinned recently.

ASSESSING RED PINE SEEDLINGS USING UAV POINT CLOUDS AND FIELD-VERIFIED DATA

Abstract. Accurate, reliable, and cost-efficient approaches to forest monitoring are critical for sustainable forest management. The use of digital photogrammetry for tree height estimation is well-known among forest managers and remote sensing researchers. Satellite remote sensing has not been very successful in providing detailed and reliable estimates of tree height. Unmanned Aerial Vehicles (UAVs) are one of the latest remote sensing platforms to get forest attributes information at very high temporal and spatial resolution. This study assessed the potential of using digital aerial photogrammetry point clouds and UAV acquired high-resolution imagery to estimate red pine seedlings' height in Adirondacks, New York. Seedling's location, height, crown width, and diameter were measured from 16 fixed area sample plots, and multispectral imagery was acquired with DJI Matrice 100- UAV fitted with Micasense RedEdge-M camera. UAV was flown under clear sky conditions at 93-meter height in a single grid pattern with 80% front and side overlap. PIX4D software was used to process UAV multispectral imagery and generate Digital Surface Model (DSM) and Orthomosiac at 6.08 cm/pixel resolution along with 3D Digital Terrain Model (DTM). 3D densified point cloud layers of regeneration canopy were generated at an average density of 1.54 per m3. Seedlings were differentiated from bare ground cover through supervised image classification methods. Preliminary results of this study highlight that multispectral imagery acquired from UAVs has the potential to characterize and provide detailed structural information to estimate red pine seedlings' height.

Estimation of mean dominant height using NAIP digital aerial photogrammetry and lidar over mixed deciduous forest in the southeastern USA

• Temperate deciduous tree heights estimated with point clouds from digital aerial photogrammetry. • Photogrammetrically-derived point clouds a possible surrogate for those derived from lidar data. • Lidar-derived DEM necessary to normalize point clouds derived from digital aerial photogrammetry. • A poor photogrammetric solution led to reduced estimation accuracy in some shadowed areas. In the absence of complete lidar coverage, digital surface models (DSMs) and point clouds produced from the United States Department of Agriculture National Agriculture Imagery Program (NAIP) are increasingly being analyzed for quality and application feasibility. This study compared canopy heights derived from NAIP DSMs (10 m) and point clouds to those derived from lidar data collected over Mountain Lake Biological Station and the Great Smoky Mountains Twin Creeks Site by the National Ecological Observatory Network (NEON) Airborne Observation Platform for 62 mixed deciduous tree plots. Mean dominant height (MDH) was estimated using lidar and the NAIP products using the 90th percentile of heights in a given plot as the independent variable for both the lidar- and NAIP-derived point clouds. The dependent variable was field-measured MDH, calculated using the four tallest trees for each 0.04-hectare plot based on the NEON woody vegetation structure dataset. All data (field and remotely sensed) were collected in 2018. Using maximum likelihood spatial error model for all analyses, the NAIP DSM (10 m resolution) resulted in a strong relationship with MDH (coefficient of determination ( R 2 ) = 0.90, standard error ( SE ) = 1.71 m). However, the 90th percentiles of heights derived from the point clouds were better at estimating MDH than was the comparatively coarse resolution DSM (NAIP point clouds: R 2 = 0.94, SE = 1.40 m; lidar: R 2 = 0.95, SE = 1.29 m, respectively) and are strongly correlated to each other ( R 2 = 0.99, SE = 0.68 m). The main limitation of the NAIP datasets was found to be where shadowing occurred due to steep terrain in the Great Smoky Mountain site. These areas resulted in erroneously high vegetation heights. Mean dominant heights estimated using NAIP DSMs and point clouds are thus comparable to those estimated using lidar data in these closed-canopy temperate deciduous forests where shadowing from steep terrain is not present. The utility of both the NAIP-derived 10 m DSM and the point clouds for estimating tree heights paves the way for statewide mapping of heights over the deciduous forests in Tennessee, Virginia, and possibly beyond.

TREE CANOPY HEIGHT ESTIMATION AND ACCURACY ANALYSIS BASED ON UAV REMOTE SENSING IMAGES

Abstract. The development of unmanned aerial vehicle (UAV) technology makes the traditional method of danger tree monitoring more digital and convenient. In order to explore the accuracy of tree height measurement by general consumer UAV, this paper takes a campus of University as the research area. The high-resolution images acquired by UAV were processed and compared the accuracy of extracting tree height based on the digital canopy height model (CHM) and point cloud information, respectively. The experimental results show that the mean absolute error of tree height based on point cloud extraction is 10.28cm, which is better than that based on CHM. The tree height extracted from CHM will be less than the measured height. In addition, the accuracy of extracting the height of round crown is better than that of cone crown. The correlation between the measured and true values of the two methods was 0.987 and 0.994, respectively, which indicates that the method of danger tree monitoring by UAV is feasible.

Documentación y análisis de un cepo de ancla romano y sus elementos iconográficos y epigráficos sellados

This paper aims to present the documentation and analysis methodology carried out on a lead trap from the ancient period, which belongs to the collection of traps in the Museum of Cádiz (Andalusia, Spain). The anchor stock had some interesting characteristics for this research. On the one hand, from the point of view of conservation and restoration, due to the alterations it presented. On the other hand, from a historical and archaeological point of view, it showed signs of reliefs on its surface hidden under the alteration products. The removal of the different layers of alteration that covered the surface during conservation and restoration treatments revealed an unpublished iconographic and epigraphic programme, as well as possible marks of use and manufacture. The poor state of conservation of the original surface made it impossible to visualise the details as a whole, so we applied photogrammetric methods, and subsequently processed models using various GIS analysis and point cloud processing softwares.Two photogrammetric models (in Agisoft PhotoScan) were made to document the trap in general: one prior to the conservation and restoration process; and a second three-dimensional (3D) model once the surface had been cleaned. The purpose of the second model was to visualise the reliefs programme in general, as well as the different surface details. The first complete 3D model of the object was used to perform a virtual reconstruction of the anchor including the elements that did not preserve, using a 3D modelling program (Blender).Nine areas of the stock surface were selected for the analyses of the various iconographic and epigraphic features, which were documented and processed in Agisoft PhotoScan. The Digital Elevation Model (DEM) and point cloud models were then processed with different analyses tools in Geographic Information System (GIS) (such as QGIS) and point cloud processing software (CloudCompare). Our results document a piece of highly interesting information from its surface consisting of reliefs of four dolphins; at least four rectangular stamps: two of them with possible inscriptions, and an anthropomorphic figure. Thanks to the comparative data, we conclude that the four dolphins were made with the same stamp during the stock manufacturing process. Further, we were able to reconstruct the dolphin stamp, partially preserved in each of the reliefs, by unifying the 3D models, thus revealing the original set. This system of stamping by means of reusable dies is well known in other elements such as amphorae but has not been studied in the specific case of lead traps.In the case of the epigraphic elements, the 3D documentation methodology revealed numerous micro-surface details, not visible under conventional documentation techniques, which could help specialists to interpret these inscriptions. Although they have not been analysed in this research, its documentation has promoted the appreciation of surface details that could refer to the manufacturing processes (moulds and tools) or the traces of use, providing historical information on this object. At the same time, the virtual reconstruction of the anchor has aided the formation of hypotheses on the dimensions and original appearance of the anchor. The different tools used, such as raster analysis using shadow mapping and point cloud alignment, proved to be very effective. They have fulfilled the established objectives and have helped to establish a possible analysis methodology for future lead traps with decorative elements. These types of artefacts recovered from underwater sites are very common in museum collections. In many cases, their state of conservation and the difficulty in handling them due to their size and weight make it difficult to document surface details. In this case, the multidisciplinary work of conservation and 3D documentation allows for high-quality documentation that is easy to access and exchange between researchers. The combined use of photogrammetric techniques with virtual RTI provides a non-invasive method for the object, low cost and easy processing compared to other conventional methods.

Mapping Unmanned Aerial System Data onto Building Information Modeling Parameters for Highway Construction Progress Monitoring

The transportation infrastructure management sector lacks automated procedures that can help it find and resolve the performance deviations. The objective of this research is to illustrate the mapping of Unmanned Aerial System (UAS) collected photogrammetric data to building information modeling (BIM) parameters, and their application for automated construction progress monitoring and the generation of as-built models. The goal is to support project managers to estimate project progress during highway construction. As a part of ongoing work, this paper takes into account 4D (3D + time) data that is acquired from 3D surface digital elevation models, point clouds, LiDAR data, and orthographic photos. It maps these 4D data onto BIM parameters to create as-built models of the project at different intervals. A comparison between as-planned and as-built models using the earned value management method is employed to develop metrics that can be used for indicating cost and schedule deviations during construction. The mapping methodology introduced in this paper is illustrated using an ongoing highway construction project case study. The main contribution of this paper is the organization, processing, and integration of UAS data with BIM data structures and project management workflows. The research outcomes will assist project managers in an easy and quick identification of potential performance problems and support the project management decision-making process.

Rice Height Monitoring between Different Estimation Models Using UAV Photogrammetry and Multispectral Technology

Unmanned aerial vehicle (UAV) photogrammetry was used to monitor crop height in a flooded paddy field. Three multi-rotor UAVs were utilized to conduct flight missions in order to capture RGB (RedGreenBlue) and multispectral images, and these images were analyzed using several different models to provide the best results. Two image sets taken by two UAVs, mounted with RGB cameras of the same resolution and Global Navigation Satellite System (GNSS) receivers of different accuracies, were applied to perform photogrammetry. Two methods were then proposed for creating crop height models (CHMs), one of which was denoted as the M1 method and was based on the Digital Surface Point Cloud (DSPC) and the Digital Terrain Point Cloud (DSPT). The other was denoted as the M2 method and was based on the DSPC and a bathymetric sensor. An image set taken by another UAV mounted with a multispectral camera was used for multispectral-based photogrammetry. A Normal Differential Vegetation Index (NDVI) and a Vegetation Fraction (VF) were then extracted. A new method based on multiple linear regression (MLR) combining the NDVI, the VF, and a Soil Plant Analysis Development (SPAD) value for estimating the measured height (MH) of rice was then proposed and denoted as the M3 method. The results show that the M1 method, the UAV with a GNSS receiver with a higher accuracy, obtained more reliable estimations, while the M2 method, the UAV with a GNSS receiver of moderate accuracy, was actually slightly better. The effect on the performance of CHMs created by the M1 and M2 methods is more negligible in different plots with different treatments; however, remarkably, the more uniform the distribution of vegetation over the water surface, the better the performance. The M3 method, which was created using only a SPAD value and a canopy NDVI value, showed the highest coefficient of determination (R2) for overall MH estimation, 0.838, compared with other combinations.

Detecting and Monitoring Early Post-Fire Sliding Phenomena Using UAV–SfM Photogrammetry and t-LiDAR-Derived Point Clouds

Soil changes, including landslides and erosion, are some of the most prominent post-fire effects in Mediterranean ecosystems. Landslide detection and monitoring play an essential role in mitigation measures. We tested two different methodologies in five burned sites with different characteristics in Central Greece. We compared Unmanned Aerial Vehicles (UAV)-derived high-resolution Digital Surface Models and point clouds with terrestrial Light Detection and Ranging (LiDAR)-derived point clouds to reveal new cracks and monitor scarps of pre-existing landslides. New cracks and scarps were revealed at two sites after the wildfire, measuring up to 27 m in length and up to 25 ± 5 cm in depth. Pre-existing scarps in both Kechries sites appeared to be active, with additional vertical displacements ranging from 5–15 ± 5 cm. In addition, the pre-existing landslide in Magoula expanded by 8%. Due to vegetation regrowth, no changes could be detected in the Agios Stefanos pre-existing landslide. This high-spatial-resolution mapping of slope deformations can be used as landslide precursor, assisting prevention measures. Considering the lack of vegetation after wildfires, UAV photogrammetry has great potential for tracing such early landslide indicators and is more efficient for accurately recording soil changes.