Photogrammetric Point Clouds Research Articles

Riparian ecosystems mapping at fine scale: a density approach based on multi‐temporal UAV photogrammetric point clouds

AbstractIn recent years, numerous directives worldwide have addressed the conservation and restoration of riparian corridors, activities that rely on continuous vegetation mapping to understand its volumetric features and health status. Mapping riparian corridors requires not only fine‐scale resolution but also the coverage of relatively large areas. The use of Unmanned Aerial Vehicles (UAV) allows for meeting both conditions, although the cost‐effectiveness of their use is highly influenced by the type of sensor mounted on them. Few works have so far investigated the use of photogrammetric sensors for individual tree crown detection, despite being cheaper than the most common Light Detection and Ranging (LiDAR) ones. This work aims to improve the individual crown detection from UAV‐photogrammetric datasets in a twofold way. Firstly, the effectiveness of a new approach that has already achieved interesting results in LiDAR applications was tested for photogrammetric point clouds. The test was carried out by comparing the accuracy achieved by the new approach, which is based on the point density features of the analysed dataset, with those related to the more common local maxima and textural methods. The results indicated the potentiality of the density‐based method, which achieved accuracy values (0.76 F‐score) consistent with the traditional methods (0.49–0.80 F‐score range) but was less affected by under‐ and over‐fitting. Secondly, the potential improvement of working on intra‐annual multi‐temporal datasets was assessed by applying the density‐based approach to seven different scenarios, three of which were constituted by single‐epoch datasets and the remaining given by the joining of the others. The F‐score increased from 0.67 to 0.76 when passing from single‐ to multi‐epoch datasets, aligning with the accuracy achieved by the new method when applied to LiDAR data. The results demonstrate the potential of multi‐temporal acquisitions when performing individual crown detection from photogrammetric data.

Estimating Tree Defects with Point Clouds Developed from Active and Passive Sensors

Traditional inventories require large investments of resources and a trained workforce to measure tree sizes and characteristics that affect wood quality and value, such as the presence of defects and damages. Handheld light detection and ranging (LiDAR) and photogrammetric point clouds developed using Structure from Motion (SfM) algorithms achieved promising results in tree detection and dimensional measurements. However, few studies have utilized handheld LiDAR or SfM to assess tree defects or damages. We used a Samsung Galaxy S7 smartphone camera to photograph trees and create digital models using SfM, and a handheld GeoSLAM Zeb Horizon to create LiDAR point cloud models of some of the main tree species from the Pacific Northwest. We compared measurements of damage count and damage length obtained from handheld LiDAR, SfM photogrammetry, and traditional field methods using linear mixed-effects models. The field method recorded nearly twice as many damages per tree as the handheld LiDAR and SfM methods, but there was no evidence that damage length measurements varied between the three survey methods. Lower damage counts derived from LiDAR and SfM were likely driven by the limited point cloud reconstructions of the upper stems, as usable tree heights were achieved, on average, at 13.6 m for LiDAR and 9.3 m for SfM, even though mean field-measured tree heights was 31.2 m. Our results suggest that handheld LiDAR and SfM approaches show potential for detection and measurement of tree damages, at least on the lower stem.

Effects of UAV-LiDAR and Photogrammetric Point Density on Tea Plucking Area Identification

High-cost data collection and processing are challenges for UAV LiDAR (light detection and ranging) mounted on unmanned aerial vehicles in crop monitoring. Reducing the point density can lower data collection costs and increase efficiency but may lead to a loss in mapping accuracy. It is necessary to determine the appropriate point cloud density for tea plucking area identification to maximize the cost–benefits. This study evaluated the performance of different LiDAR and photogrammetric point density data when mapping the tea plucking area in the Huashan Tea Garden, Wuhan City, China. The object-based metrics derived from UAV point clouds were used to classify tea plantations with the extreme learning machine (ELM) and random forest (RF) algorithms. The results indicated that the performance of different LiDAR point density data, from 0.25 (1%) to 25.44 pts/m2 (100%), changed obviously (overall classification accuracies: 90.65–94.39% for RF and 89.78–93.44% for ELM). For photogrammetric data, the point density was found to have little effect on the classification accuracy, with 10% of the initial point density (2.46 pts/m2), a similar accuracy level was obtained (difference of approximately 1%). LiDAR point cloud density had a significant influence on the DTM accuracy, with the RMSE for DTMs ranging from 0.060 to 2.253 m, while the photogrammetric point cloud density had a limited effect on the DTM accuracy, with the RMSE ranging from 0.256 to 0.477 m due to the high proportion of ground points in the photogrammetric point clouds. Moreover, important features for identifying the tea plucking area were summarized for the first time using a recursive feature elimination method and a novel hierarchical clustering-correlation method. The resultant architecture diagram can indicate the specific role of each feature/group in identifying the tea plucking area and could be used in other studies to prepare candidate features. This study demonstrates that low UAV point density data, such as 2.55 pts/m2 (10%), as used in this study, might be suitable for conducting finer-scale tea plucking area mapping without compromising the accuracy.

3D Modeling of Individual Trees from LiDAR and Photogrammetric Point Clouds by Explicit Parametric Representations for Green Open Space (GOS) Management

The development and management of green open spaces are essential in overcoming environmental problems such as air pollution and urban warming. 3D modeling and biomass calculation are the example efforts in managing green open spaces. In this study, 3D modeling was carried out on point clouds data acquired by the UAV photogrammetry and UAV LiDAR methods. 3D modeling is done explicitly using the point clouds fitting method. This study uses three fitting methods: the spherical fitting method, the ellipsoid fitting method, and the spherical harmonics fitting method. The spherical harmonics fitting method provides the best results and produces an R2 value between 0.324 to 0.945. In this study, Above-Ground Biomass (AGB) calculations were also carried out from the modeling results using three methods with UAV LiDAR and Photogrammetry data. AGB calculation using UAV LiDAR data gives better results than using photogrammetric data. AGB calculation using UAV LiDAR data gives an accuracy of 78% of the field validation results. However, for visualization purposes with a not-too-wide area, a 3D model of photogrammetric data using the spherical harmonics method can be used.

MACHINE LEARNING CLUSTERING FOR POINT CLOUDS OPTIMISATION VIA FEATURE ANALYSIS IN CULTURAL HERITAGE

Abstract. The paper presents an innovative approach that can assist survey methods by applying AI algorithms to improve the accuracy of point clouds generated from UAV images. Firstly, the work individually analyses several photogrammetric accuracy parameters, including reprojection error, angle of intersection between homologous points, number of cameras for single Tie Point calculation, verifying that a single parameter is not sufficient to filter noise from a photogrammetric point cloud. Therefore, some of the calculated parameters were analysed with the Self-Organizing Map (SOM) and a K-means, to check the impact of the precision parameters for reducing the noise associated with the definition of the 3D model. In the case study, in both machine learning clustering algorithms used, it was observed that the parameter that most influences noise in photogrammetric point clouds is the angle of intersection.

Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach

Accurate wall-to-wall estimation of forest crown cover is critical for a wide range of ecological studies. Notwithstanding the increasing use of UAVs in forest canopy mapping, the ultrahigh-resolution UAV imagery requires an appropriate procedure to separate the contribution of understorey from overstorey vegetation, which is complicated by the spectral similarity between the two forest components and the illumination environment. In this study, we investigated the integration of deep learning and the combined data of imagery and photogrammetric point clouds for boreal forest canopy mapping. The procedure enables the automatic creation of training sets of tree crown (overstorey) and background (understorey) data via the combination of UAV images and their associated photogrammetric point clouds and expands the applicability of deep learning models with self-supervision.Based on the UAV images with different overlap levels of 12 conifer forest plots that are categorized into “I”, “II” and “III” complexity levels according to illumination environment, we compared the self-supervised deep learning-predicted canopy maps from original images with manual delineation data and found an average intersection of union (IoU) larger than 0.9 for “complexity I” and “complexity II” plots and larger than 0.75 for “complexity III” plots. The proposed method was then compared with three classical image segmentation methods (i.e., maximum likelihood, Kmeans, and Otsu) in the plot-level crown cover estimation, showing outperformance in overstorey canopy extraction against other methods. The proposed method was also validated against wall-to-wall and pointwise crown cover estimates using UAV LiDAR and in situ digital cover photography (DCP) benchmarking methods. The results showed that the model-predicted crown cover was in line with the UAV LiDAR method (RMSE of 0.06) and deviate from the DCP method (RMSE of 0.18). We subsequently compared the new method and the commonly used UAV structure-from-motion (SfM) method at varying forward and lateral overlaps over all plots and a rugged terrain region, yielding results showing that the method-predicted crown cover was relatively insensitive to varying overlap (largest bias of less than 0.15), whereas the UAV SfM-estimated crown cover was seriously affected by overlap and decreased with decreasing overlap. In addition, canopy mapping over rugged terrain verified the merits of the new method, with no need for a detailed digital terrain model (DTM). The new method is recommended to be used in various image overlaps, illuminations, and terrains due to its robustness and high accuracy. This study offers opportunities to promote forest ecological applications (e.g., leaf area index estimation) and sustainable management (e.g., deforestation).

3D Modeling of Urban Area Based on Oblique UAS Images—An End-to-End Pipeline

3D modelling of urban areas is an attractive and active research topic, as 3D digital models of cities are becoming increasingly common for urban management as a consequence of the constantly growing number of people living in cities. Viewed as a digital representation of the Earth’s surface, an urban area modeled in 3D includes objects such as buildings, trees, vegetation and other anthropogenic structures, highlighting the buildings as the most prominent category. A city’s 3D model can be created based on different data sources, especially LiDAR or photogrammetric point clouds. This paper’s aim is to provide an end-to-end pipeline for 3D building modeling based on oblique UAS images only, the result being a parametrized 3D model with the Open Geospatial Consortium (OGC) CityGML standard, Level of Detail 2 (LOD2). For this purpose, a flight over an urban area of about 20.6 ha has been taken with a low-cost UAS, i.e., a DJI Phantom 4 Pro Professional (P4P), at 100 m height. The resulting UAS point cloud with the best scenario, i.e., 45 Ground Control Points (GCP), has been processed as follows: filtering to extract the ground points using two algorithms, CSF and terrain-mark; classification, using two methods, based on attributes only and a random forest machine learning algorithm; segmentation using local homogeneity implemented into Opals software; plane creation based on a region-growing algorithm; and plane editing and 3D model reconstruction based on piece-wise intersection of planar faces. The classification performed with ~35% training data and 31 attributes showed that the Visible-band difference vegetation index (VDVI) is a key attribute and 77% of the data was classified using only five attributes. The global accuracy for each modeled building through the workflow proposed in this study was around 0.15 m, so it can be concluded that the proposed pipeline is reliable.

SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds

With the recent availability and affordability of commercial depth sensors and 3D scanners, an increasing number of 3D (i.e., RGBD, point cloud) datasets have been publicized to facilitate research in 3D computer vision. However, existing datasets either cover relatively small areas or have limited semantic annotations. Fine-grained understanding of urban-scale 3D scenes is still in its infancy. In this paper, we introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km\(^2\). Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset. In addition to the more commonly encountered categories such as road and vegetation, urban-level categories including rail, bridge, and river are also included in our dataset. Based on this dataset, we further build a benchmark to evaluate the performance of state-of-the-art segmentation algorithms. In particular, we provide a comprehensive analysis and identify several key challenges limiting urban-scale point cloud understanding. The dataset is available at http://point-cloud-analysis.cs.ox.ac.uk/.

3D Urban Buildings Extraction Based on Airborne LiDAR and Photogrammetric Point Cloud Fusion According to U-Net Deep Learning Model Segmentation

The LiDAR and photogrammetric point clouds fusion procedure for building extraction according to U-Net deep learning model segmentation is provided and tested. Firstly, an initial geo-localization process is performed for photogrammetric point clouds generated using structure-from-motion and dense-matching methods. Then, point cloud segmentation is carried out based on U-Net deep learning model. The precision of the U-Net model for buildings extraction reachs 87%, with F-score of 0.89 and IoU of 0.80. It is shown that the U-Net method is effective for high-resolution image extraction. The detailed information can accurately be identified and extracted, such as vegetation located between buildings and roads. After segmentation, each chunk of the LiDAR and photogrammetric point clouds are finely registered and merged based on the iterative closest point algorithm. Finally, the fused point clouds are obtained. It shows that the structure and shape of the buildings could be delineated from the fused point clouds when both enough ground points and a higher point density are available. Furthermore, color information improves both visualization effect and properties identification. The experiments are conducted to extract individual buildings from three types of point clouds in three plots. A DoN (Difference of Normals) approach is used to isolate 3D buildings from other objects in densely built-up areas. It shows that most building extraction results have a Precision > 0.9 and favorable Recall and F-score values. Although the LiDAR extraction results have some advantages over the photogrammetric and fused ones in terms of Precision, the Recall and F-score results appear best for the fused point clouds. It shows that the fused data contains a high point density and RGB color information and could improve the building extraction.

Regional Sampling of Forest Canopy Covers Using UAV Visible Stereoscopic Imagery for Assessment of Satellite-Based Products in Northeast China

Canopy cover is an important parameter affecting forest succession, carbon fluxes, and wildlife habitats. Several global maps with different spatial resolutions have been produced based on satellite images, but facing the deficiency of reliable references for accuracy assessments. The rapid development of unmanned aerial vehicle (UAV) equipped with consumer-grade camera enables the acquisition of high-resolution images at low cost, which provides the research community a promising tool to collect reference data. However, it is still a challenge to distinguish tree crowns and understory green vegetation based on the UAV-based true color images (RGB) due to the limited spectral information. In addition, the canopy height model (CHM) derived from photogrammetric point clouds has also been used to identify tree crowns but limited by the unavailability of understory terrain elevations. This study proposed a simple method to distinguish tree crowns and understories based on UAV visible images, which was referred to as BAMOS for convenience. The central idea of the BAMOS was the synergy of spectral information from digital orthophoto map (DOM) and structural information from digital surface model (DSM). Samples of canopy covers were produced by applying the BAMOS method on the UAV images collected at 77 sites with a size of about 1.0 km 2 across Daxing’anling forested area in northeast of China. Results showed that canopy cover extracted by the BAMOS method was highly correlated to visually interpreted ones with correlation coefficient ( r ) of 0.96 and root mean square error (RMSE) of 5.7%. Then, the UAV-based canopy covers served as references for assessment of satellite-based maps, including MOD44B Version 6 Vegetation Continuous Fields (MODIS VCF), maps developed by the Global Land Cover Facility (GLCF) and by the Global Land Analysis and Discovery laboratory (GLAD). Results showed that both GLAD and GLCF canopy covers could capture the dominant spatial patterns, but GLAD canopy cover tended to miss scattered trees in highly heterogeneous areas, and GLCF failed to capture non-tree areas. Most important of all, obvious underestimations with RMSE about 20% were easily observed in all satellite-based maps, although the temporal inconsistency with references might have some contributions.

Assessment of Multispectral Vegetation Features for Digital Terrain Modeling in Forested Regions

Bare-earth extraction in forested regions has been considered challenging because of the lack of ground point information. In these regions, vision systems cannot capture any information about ground points under the canopy. Thus, the challenge of generating a digital terrain model by cameras increases. Nevertheless, one might alleviate ground filtering using vegetation's features (e.g., chlorophyll). In this regard, this article evaluated two machine-learning approaches [i.e., conditional random field (CRF), artificial neural network (ANN)] for generating digital terrain models when biophysical or biochemical features of vegetation are given. Terrain models were generated from multispectral image-based point clouds. A fivefold cross-validation methodology evaluated the CRF and ANN. The point clouds were retrieved from two study areas at different illumination and flight altitudes. Vegetation features were computed as vegetation indices from the multispectral point clouds. Results suggested that by using these indices, the classification of ground points could be enhanced. In particular, the vegetation indices that yielded the best outcomes were normalized difference vegetation index, green NDVI, and modified chlorophyll absorption reflectance index. Moreover, it was shown that CRF generates elevation models more smoothly than a triangular irregular network method. Thus, a CRF could be promising for classifying ground points in forested regions using geometric and vegetation features from a photogrammetric point cloud.

Automatic co-registration of photogrammetric point clouds with digital building models

Point clouds serve as a valuable resource for the modeling and updating of digital building models. For exploiting their full potential, they must be correctly registered within the building coordinate system. In this paper, we propose a highly automated co-registration algorithm for photogrammetric point clouds that works without the usage of control points. This drastically decreases the required interactive effort for the registration process. Instead, the building model serves as reference for the co-registration. The procedure extracts 3D line segments from the image data and associates them with bounding surfaces from the building model in order to calculate the necessary transformation parameters during an adjustment process. The validation shows that registration accuracies of ±3–5 cm can be achieved with our method. Since the stochastic properties of the input measurements are considered during the adjustment process, statements about the reached reliability of the transformation parameters can be made.

Align to locate: Registering photogrammetric point clouds to BIM for robust indoor localization

Indoor localization is critical for many smart applications in built environments such as service robot navigation and facility management. Building information models (BIMs) provide new streams of spatial and visual information about building interiors that can be exploited for robust indoor localization. However, previous localization methods that used BIM were unable to achieve high precision and accuracy, limiting their practical applications. To address this challenge, a new approach, “align-to-locate (A2L)”, is proposed in this study to leverage BIM as a reference to rectify and fine-tune coarse camera poses estimated by photogrammetry. The camera pose rectification is achieved using a new registration algorithm that aligns a photogrammetric point cloud with a BIM-referenced point cloud. The experiments demonstrated the effectiveness of the proposed A2L approach, which outperformed the state of the art with a localization error of 1.07 m and an orientation deviation of 3.7°. It was also found that query point clouds generated from photographs taken along the lateral or longitude directions are more conducive for registration. While increasing the number of data collection locations and images from each location can provide higher accuracy, this approach may compromise the computational speed. This study contributes to the challenging indoor localization problem by proposing the A2L approach and evaluating its applicability for more robust camera pose estimation through point-cloud-to-BIM registration. The developed A2L approach can be integrated as a post-processing module in existing vision-based localization methods to fine-tune their estimated camera poses.

POINT CLOUD REGISTRATION AND ACCURACY FOR 3D MODELLING - A REVIEW

Geoinformation is a surveying and mapping field where topography and details on the ground are spatially mapped. The point cloud is one of the data types that is used for measurement and visualisation of Earth features mapping. Point cloud could come from a different source such as terrestrial laser scanned or photogrammetry. The concepts of terrestrial laser scanning and photogrammetry surveying are elaborated in this paper. This paper also presents the method used for point cloud registration; Iterative Closest Point (ICP) and Feature Extraction and Matching (FEM) and the accuracy of laser scanned, and photogrammetric point cloud based on the previous experiments. Experimental analysis conducted in the previous study shows an impressive result on laser scanned point cloud with very mínimum errors compared to photogrammetric point cloud.

Semantics-aided 3D change detection on construction sites using UAV-based photogrammetric point clouds

As the key to the construction progress monitoring, methods and strategies for change detection using 3D point clouds from various sources have been investigated for years. However, how to achieve object-level change detection with uncertainty evaluation is still an unsolved topic. Occlusions and noise in 3D points and other attribute information, such as colors, lead to problems in the task of change detection. In this paper, we present a semantic-aided change detection method aimed at monitoring construction progress using UAV-based photogrammetric point clouds. Our framework consists of two key parts, which identify changes in a progressive manner: The first part consists of the detection of geometric changes using occupancy-based spatial difference identification, which indicates the changes of occupancy in 3D space, comprising changes in appearance or shape of building objects. In occupancy-based change detection, occupancy conflicts of occupied space and empty space along the viewing rays of cameras can be detected by considering the sensor positions. At the same time, occlusions can be handled implicitly. The second part involves changes in semantics, which are used to detect changes where the occupancy-based change detection is not sufficient for presenting changes, due to limitations of parameter settings or lack of attribute information. By utilizing semantic segmentation results presented by class probabilities, the uncertainty of the semantic changes can be estimated. For the detection of geometric and semantic changes, Dempster–Shafer theory is applied to fuse information from data acquired in different time epochs to detect changes. Using the two different types of changes, we can fully consider the changes that may happen at the construction sites and define the differences between the changes. By utilizing the proposed change detection methods, changes with different characteristics, including geometric changes and semantic changes, can be correctly identified. In a specific example, for a construction period from Dec 12, 2014 to Jan 16, 2015, 97.8% of the changed areas could be successfully detected. For the other construction period, from Jan 16, 2015 to Feb 26, 2015, 93.6% of changes were correctly detected.

Tree species diversity mapping using UAS-based digital aerial photogrammetry point clouds and multispectral imageries in a subtropical forest invaded by moso bamboo (Phyllostachys edulis)

Moso bamboo (Phyllostachys edulis) tends to invade any surrounding forest areas due to its aggressive characteristics (fast growth and clonal reproduction), where it changes the species composition and canopy structure of the forests, and has negative effects on forest diversity and ecosystem functions. Unmanned aerial system (UAS)-based remote sensing has the capacity to provide high-resolution, continuous spatial data that can be used to detect forest invasion dynamics. In this study, UAS-based RGB and multispectral image data and digital aerial photogrammetric point cloud (PPC) were acquired and used to detect areas of bamboo invasion in a subtropical forest of Southern China. First, a point cloud segmentation (PCS) method was applied for individual tree detection (ITD) using photogrammetric point clouds (PPCs). A random forest (RF) classifier was used to perform tree species classification based on PPC metrics, vegetation indices, and texture metrics. Finally, based on the results of the ITD and tree species classification, alpha-diversity (i.e., the species richness (S), Shannon-Wiener (H’), Simpson (D), and Pielou’s evenness index(J)) and the spatial variation in species composition along the altitude gradient (beta-diversity) in the invaded forests were assessed. Results demonstrated that PCS worked well for tree detection in invaded forests (F1-score = 80.63%), and the overall accuracy of tree species classification was 75.69%, with a kappa accuracy of 73.76%. The forest diversity analysis showed that all alpha-diversity values were generally predicted well (R2 = 0.84–0.91, RMSE = 0.05–0.84). The diversity showed a decreasing tendency with increasing bamboo invasion, and the predominantly broad-leaved invaded forests had higher diversity than the predominantly coniferous invaded forests. The human intervention had a significant impact on bamboo invasion. The ANOVA of the dispersion of the dissimilarities along the elevation gradient showed significant differences in abundance-weighted similarity among the altitude classes (ANOVA of the Bray-Curtis dissimilarity, F4,40 = 6.453, P = 0.0004***; ANOVA of the Jaccard dissimilarity, F4,40 = 5.20, P = 0.0017**). This study indicated the potential benefits of using UAS- based remote sensing data to identify tree species and predict forest diversity in bamboo-invaded forests. Our results suggested that tree species diversity can be directly estimated using individual tree detection results based on PPC data instead of modelling the relationship between field-measured indices and remote sensing data-derived metrics, and revealed the influence of human intervention on bamboo invasion.

Knowledge-based 3D reconstruction of bridge structures using UAV-based photogrammetric point cloud

Due to the rapid developments of smart cities, 3D modeling of urban infrastructures has become essential for making decisions in urban management and planning applications. 3D modeling of the bridge structure, as one of the most important civil structures in transportations, includes many challenges due to geometrical and structural complexities. A knowledge-based approach based on a 2D projection method is proposed for automatic 3D reconstruction of the main bridge elements including the railing, body, piers, and abutment using dense point clouds of unmanned aerial vehicle images. The knowledge is the geometric relations between the bridge elements to model different types of bridge structures. In the first step, the input point cloud is segmented into the major elements using a fuzzy c-means clustering algorithm. Next, the 2D model of each element is generated using a 2D projection-based reconstruction technique. Then 3D models of elements are created by extruding 2D models into the 3D space. Finally, an integrated 3D model of the bridge structure is formed by merging individual 3D models in a CAD format. In order to evaluate the performance of the proposed modeling framework, the length differences of the elements are compared with a reference model. In addition, the geometric distances between the final models and the input point cloud are calculated. Accordingly, a median error of about 3 cm between the length of the elements in the reference and reconstructed models and 3.8 cm between the reconstructed models and corresponding point clouds highlight the effectiveness of the proposed method to generate 3D models of different bridges.

Classification of Photogrammetric and Airborne LiDAR Point Clouds Using Machine Learning Algorithms

With the development of photogrammetry technologies, point clouds have found a wide range of use in academic and commercial areas. This situation has made it essential to extract information from point clouds. In particular, artificial intelligence applications have been used to extract information from point clouds to complex structures. Point cloud classification is also one of the leading areas where these applications are used. In this study, the classification of point clouds obtained by aerial photogrammetry and Light Detection and Ranging (LiDAR) technology belonging to the same region is performed by using machine learning. For this purpose, nine popular machine learning methods have been used. Geometric features obtained from point clouds were used for the feature spaces created for classification. Color information is also added to these in the photogrammetric point cloud. According to the LiDAR point cloud results, the highest overall accuracies were obtained as 0.96 with the Multilayer Perceptron (MLP) method. The lowest overall accuracies were obtained as 0.50 with the AdaBoost method. The method with the highest overall accuracy was achieved with the MLP (0.90) method. The lowest overall accuracy method is the GNB method with 0.25 overall accuracy.

Mapping Canopy Heights in Dense Tropical Forests Using Low-Cost UAV-Derived Photogrammetric Point Clouds and Machine Learning Approaches

Tropical forests are a key component of the global carbon cycle and climate change mitigation. Field- or LiDAR-based approaches enable reliable measurements of the structure and above-ground biomass (AGB) of tropical forests. Data derived from digital aerial photogrammetry (DAP) on the unmanned aerial vehicle (UAV) platform offer several advantages over field- and LiDAR-based approaches in terms of scale and efficiency, and DAP has been presented as a viable and economical alternative in boreal or deciduous forests. However, detecting with DAP the ground in dense tropical forests, which is required for the estimation of canopy height, is currently considered highly challenging. To address this issue, we present a generally applicable method that is based on machine learning methods to identify the forest floor in DAP-derived point clouds of dense tropical forests. We capitalize on the DAP-derived high-resolution vertical forest structure to inform ground detection. We conducted UAV-DAP surveys combined with field inventories in the tropical forest of the Congo Basin. Using airborne LiDAR (ALS) for ground truthing, we present a canopy height model (CHM) generation workflow that constitutes the detection, classification and interpolation of ground points using a combination of local minima filters, supervised machine learning algorithms and TIN densification for classifying ground points using spectral and geometrical features from the UAV-based 3D data. We demonstrate that our DAP-based method provides estimates of tree heights that are identical to LiDAR-based approaches (conservatively estimated NSE = 0.88, RMSE = 1.6 m). An external validation shows that our method is capable of providing accurate and precise estimates of tree heights and AGB in dense tropical forests (DAP vs. field inventories of old forest: r2 = 0.913, RMSE = 31.93 Mg ha−1). Overall, this study demonstrates that the application of cheap and easily deployable UAV-DAP platforms can be deployed without expert knowledge to generate biophysical information and advance the study and monitoring of dense tropical forests.

Evaluating the Capability of Unmanned Aerial System (UAS) Imagery to Detect and Measure the Effects of Edge Influence on Forest Canopy Cover in New England

Characterizing and measuring the extent of change at forest edges is important for making management decisions, especially in the face of climate change, but is difficult due to the large number of factors that can modify the response. Unmanned aerial systems (UAS) imagery may serve as a tool to detect and measure the forest response at the edge quickly and repeatedly, thus allowing a larger amount of area to be covered with less work. This study is a preliminary attempt to utilize UAS imagery to detect changes in canopy cover, known to exhibit changes due to edge influences, across forest edges in a New England forest. Changes in canopy cover with increasing distance from the forest edge were measured on the ground using digital cover photography and from photogrammetric point clouds and imagery-based maps of canopy gaps produced with UAS imagery. The imagery-based canopy gap products were significantly more similar to ground estimates for canopy cover (p value > 0.05) than the photogrammetric point clouds, but still suffered overestimation (RMSE of 0.088) due to the inability to detect small canopy openings. Both the ground and UAS data were able to detect a decrease in canopy cover to between 45–50 m from the edge, followed by an increase to 100 m. The UAS data had the advantage of a greater sampling intensity and was thus better able to detect a significant edge effect of minimal magnitude effect in the presence of heavy variability.