Airborne Laser Scanning Systems Research Articles

Overview and Benchmark on Multi-Modal Lidar Point Cloud Registration for Forest Applications

Abstract. Light Detection and Ranging (LIDAR) is widely acknowledged as a robust tool for monitoring forest structure, dynamics, and changes. To achieve a high-complete forest structural model, LiDAR data acquisition from both aerial (above-canopy) and terrestrial (below-canopy) platforms is commonplace. Consequently, in such multi-modal LiDAR cases, robust data registration is required for accurate forest analysis, such as biomass and canopy growth. Yet, multi-modal LiDAR registration remains a significant challenge due to differences in observation perspectives, deficient data overlap, and often inhomogeneity in point distributions and densities. The challenge increases in complex forest environments due to the abundance of unstable features (e.g., leaves) and occlusions. Thus, the dynamic nature of forest scenes needs to be considered when applying registration methods on forest point clouds. In this paper, we overview the latest advancements in registering forest point clouds from multi-modal data acquisitions, aiming to discuss the strengths and weaknesses of the most used LiDAR registration methods for forest applications. To support our investigations, we benchmark two multi-modal registration methods especially designed for forest mapping against traditional global and feature-based approaches. Experiment assessments were conducted using two point clouds acquired from a permanent laser scanning and airborne laser scanning systems at a boreal forest plot.

SUITABLE LANDING SITE SELECTION FOR UNMANNED AERIAL VEHICLES USING AIRBORNE LASER SCANNING POINT CLOUD

Abstract. In the context of autonomous landing for unmanned aerial vehicles (UAVs), selecting a suitable landing site is crucial. This research presents a new method for automatically identifying safe landing sites based on point cloud data acquired through an airborne laser scanning (ALS) system. The proposed approach begins by detecting flat regions using principal component analysis (PCA) and region-growing algorithms. Subsequently, a terrain complexity assessment is conducted through plane fitting using an enhanced progressive sample consensus (PROSAC) algorithm. This assessment assists in identifying the most suitable landing site within a specified landing zone. The method's effectiveness is demonstrated through experiments conducted on two distinct natural terrains from the DALE dataset. The results show that the proposed approach can accurately classify landing zones and identify preferred sites that meet safety criteria. This study's findings underscore the proposed method's effectiveness and feasibility in improving the safety and reliability of autonomous UAV landings.

Old-Growth Forests in Urban Nature Reserves: Balancing Risks for Visitors and Biodiversity Protection in Warsaw, Poland

Urban nature reserves in Poland are precious relics of ancient nature with preserved biodiversity. They consist of valuable trees several 100 years old, are biodiverse, and are valuable recreational spaces right in and around cities. It is therefore critical to manage tradeoffs between visitor safety due to, e.g., falling dead branches and the need for old-grown trees for biodiversity conservation. This study aimed to determine whether airborne laser scanning data (LiDAR) can confirm that trees exhibiting the worst crown defoliation are the first to be damaged in storms. Our results show that during Storm Eunice in 2022, the detected defoliated trees, in fact, were damaged the most. Despite such evidence available to the city, no targeted changes to the management of the reserves were taken after the storm. One of the forests was completely closed to visitors; in the other forest, areas with damaged trees were fenced off, and then, the remaining branches and fallen trees were removed to make the forest available for recreation. Using available evidence such as LiDAR data, we propose more targeted and nuanced forms of managing biodiversity conservation in conjunction with visitor safety. This includes the establishment of priority areas, visitor information, and visitor management. This way, airborne laser scanning and Geographic Information Systems can be used to balance management needs accounting for both biodiverse old-grown forest structures while at the same time providing added safety for visitors.

机载激光扫描系统在盘锦沿海滩涂测绘中的应用

机载激光扫描系统在盘锦沿海滩涂测绘中的应用

Modelling internal tree attributes for breeding applications in Douglas-fir progeny trials using RPAS-ALS

Coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) is one of the most commercially important softwood species in North America. In British Columbia, Canada, breeding has increased volume gains between 20 and 30%, while 97% of seedlings come from improved seed sources. Branching traits in particular, have a strong influence on strength and stiffness of Douglas-fir wood; however, they are rarely measured. Remotely Piloted Aerial Systems and Airborne Laser Scanning Systems (RPAS-LS) produce high-density three-dimensional point clouds that can be used for the creation of internal geometric features describing individual tree branching structures. We analyzed a Coastal Douglas-fir progeny test trial located in British Columbia, Canada, and developed a new method to estimate branch attributes from RPAS-LS data for inclusion as selection criteria in tree improvement programs. Branch length, angle, width, and volume were estimated for each tree. Narrow-sense heritability (the proportion of variation due to genetics) and genetic correlations were also estimated. The method extracted branch length with a correlation (r) of 0.93 compared to manual measurements. Using these branch attributes, results then show that branch angle had the highest heritability (0.277), while tree height and branch length had the highest genetic correlation (0.668). These findings are encouraging for forest managers as they indicate that branch level metrics should be considered when selecting trees in breeding programs.

Forest Inventory Assessment Using Integrated Light Detection and Ranging (LiDAR) Systems: Merged Point Cloud of Airborne and Mobile Laser Scanning Systems

Forest Inventory Assessment Using Integrated Light Detection and Ranging (LiDAR) Systems: Merged Point Cloud of Airborne and Mobile Laser Scanning Systems

Predicting Growth of Individual Trees Directly and Indirectly Using 20-Year Bitemporal Airborne Laser Scanning Point Cloud Data

Reviewing forest carbon sinks is of the utmost importance in efforts to control climate change. This study focuses on reporting the 20-year boreal forest growth values acquired with airborne laser scanning (ALS). The growth was examined on the Kalkkinen research site in southern Finland as a continuation of several earlier growth studies performed in the same area. The data for the study were gathered with three totally different airborne laser scanning systems, namely using Toposys-I Falcon in June 2000 and Riegl VUX-1HA and miniVUX-3UAV in June 2021 with approximate point densities of 11, 1360, and 460 points/m2, respectively. The ALS point cloud was preprocessed to identify individual trees, from each of which different features were extracted either for direct or indirect growth measurement. In the direct method, the growth value is predicted based on differences of features, whereas in the indirect method, the growth value is obtained by subtracting the results of two independent predictions of different years. The growth in individual tree attributes, such as growth in height, diameter at breast height (DBH), and stem volume, were calculated for direct estimation. Field reference campaigns were performed in the summer of 2001 and in November 2021 to validate the obtained growth values. The study showed that long-term series growth of height, DBH, and stem volume are possible to record with a high-to-moderate coefficient of determination (R2) of 0.90, 0.48, and 0.45 in the best-case scenarios. The respective root-mean-squared errors (RMSE) values were 0.98 m, 0.02 m, and 0.17 m3, and the biases were −0.06 m, 0.00 m, and 0.17 m3. The direct method produced better metrics in terms of RMSE-% and bias, but the indirect method produced better best-fit lines. Additionally, the mean growth values for height, diameter, and stem volume intervals were compared, and they are presumed to be usable even for forest modelling.

USE OF TREE HEIGHT FOR MANGROVE SPECIES CLASSIFICATION

Abstract. Mangrove forests play an important role in the balance of biodiversity. However, they are threatened by agriculture, aquaculture, urbanization and global warming. That’s why it is imperative to monitor this ecosystem and understand how it evolves in the face of these threats in order to better preserve it. The traditional methods are invasive and time consuming. Besides, it is often difficult to get into mangroves because of the particular structure of some species, so measurements cannot be taken in those areas. That’s why it is very interesting to use aerial data provided by unmanned aerial vehicles (UAVs) photos or airborne laser scanning systems (ALS). Moreover, some representative elements of mangroves are only a few tens of centimeters high. This is the case of pneumatophores. Traditional measurements would be much too long. In this case, it is interesting to use terrestrial laser scanning systems (TLS) to make measurements and to follow them. A research project began in 2021 to try to understand how urban mangroves develop in semi-arid regions, using remote sensing techniques (photogrammetry, airborne and terrestrial laser scanning). The purpose of this paper is first to present the project and the issues of monitoring mangrove forests. Then, it proposes a state of the art of the methodologies used to record mangrove. Finally, it presents the different acquisitions made as well as the first results of species classification based on photogrammetric point cloud processing. The assessment based on ground truth shows already promising results.

Direct and automatic measurements of stem curve and volume using a high-resolution airborne laser scanning system

Today, high-quality reference tree measurements, including the position, diameter, height and volume, are cumbersome and slow to carry out, but highly needed for forest inventories based on airborne laser scanning. Mobile laser scanning technologies hold the promise for collecting reference data for forest inventories with an extremely high efficiency. Perhaps, the most efficient approach for reference data collection would be to mount a high-resolution laser scanning system on board an airborne vehicle flying at a low altitude above the forest canopy since this would allow recording reference samples of individual trees with the speed of flight. To demonstrate the potential of this technology, we mounted an in-house developed HeliALS-DW laser scanning system on board a helicopter and collected point cloud data in a boreal forest on three test sites containing a total of 1469 trees. The obtained point clouds incorporated sufficiently many high-quality stem hits for estimating the stem curves and stem volumes of individual trees since the point clouds had a relatively high point density of 2200–3800 echoes/m2, and the scanner had been tilted by 15° from the nadir to increase the possibility of recording stem hits. To automatically estimate the diameters at breast height (DBH) and stem curves of individual trees, we used algorithms designed to tolerate moderate drifts in the trajectory of the laser scanner. Furthermore, the stem volumes of individual trees were computed by using the estimated stem curves and tree heights without any allometric models. Using the proposed methods, we were able to estimate the stem curves with a root-mean-square error (RMSE) of 1.7–2.6 cm (6–9%) while detecting 42–71% of the trees. The RMSE of stem volume estimates was 0.1–0.15 m3 (12–21%). We also showed that the tree detection rate could be improved up to 87–96% for trees with a DBH exceeding 20 cm if slightly larger average errors for the stem attributes were allowed. Our results pave the way for using high-resolution airborne laser scanning for field reference data collection by conducting direct measurements of tree stems with a high efficiency.

An Automated Process of Creating 3D City Model for Monitoring Urban Infrastructures

This paper describes the process of designing models and tools for an automated way of creating 3D city model based on a raw point cloud.Also, making and forming 3D models of buildings. Models and tools for creating tools made in the model builder application within the ArcGIS Pro software. An unclassified point cloud obtained by the LiDAR system was used for the model input data. The point cloud, collected by the airborne laser scanning system (ALS), is classified into several classes: ground, high and low noise, and buildings. Based on the created DEMs, points classified as buildings and formed prints of buildings, realistic 3D city models were created. Created 3D models of cities can be used as a basis for monitoring the infrastructure of settlements and other analyzes that are important for further development and architecture of cities.

A Comparison of Three Airborne Laser Scanner Types for Species Identification of Individual Trees.

Species identification is a critical factor for obtaining accurate forest inventories. This paper compares the same method of tree species identification (at the individual crown level) across three different types of airborne laser scanning systems (ALS): two linear lidar systems (monospectral and multispectral) and one single-photon lidar (SPL) system to ascertain whether current individual tree crown (ITC) species classification methods are applicable across all sensors. SPL is a new type of sensor that promises comparable point densities from higher flight altitudes, thereby increasing lidar coverage. Initial results indicate that the methods are indeed applicable across all of the three sensor types with broadly similar overall accuracies (Hardwood/Softwood, 83–90%; 12 species, 46–54%; 4 species, 68–79%), with SPL being slightly lower in all cases. The additional intensity features that are provided by multispectral ALS appear to be more beneficial to overall accuracy than the higher point density of SPL. We also demonstrate the potential contribution of lidar time-series data in improving classification accuracy (Hardwood/Softwood, 91%; 12 species, 58%; 4 species, 84%). Possible causes for lower SPL accuracy are (a) differences in the nature of the intensity features and (b) differences in first and second return distributions between the two linear systems and SPL. We also show that segmentation (and field-identified training crowns deriving from segmentation) that is performed on an initial dataset can be used on subsequent datasets with similar overall accuracy. To our knowledge, this is the first study to compare these three types of ALS systems for species identification at the individual tree level.

UAV-BASED LIDAR HIGH-RESOLUTION SNOW DEPTH MAPPING IN THE SWISS ALPS: COMPARING FLAT AND STEEP FORESTS

Abstract. Snow depth mapping in Alpine forests is of high importance for hydrogeology, ecology, tourism, and natural hazards prevention. Different remote sensing approaches have been employed for the precise mapping of snow depth within forests. However, optical sensors cannot provide below-canopy information. While Airborne Laser Scanning (ALS) systems have been used successfully in this context and allow obtaining data below canopies, the costs of acquisitions are very high, not allowing frequent data acquisitions. UAV-based Lidar technology potentially can provide the critical below-canopy information at lower cost and allows for frequent acquisitions.First attempts to employ a UAV-based Lidar system in forests have proven promising, but they are limited to flat forests and to grid-level snow depth calculations. In this study, we present UAV-based Lidar data of both flat and steep forests. Different Lidar processing workflows are analyzed and compared, and snow depth algorithms are used both at the point and the grid level. Whereas the UAV-Lidar system proved capable of mapping snow in both environments, the steep forests' data processing comes with greater challenges, especially for the 3D registration, ground classification, and point-to-point snow depth calculations.

Classification of tree species classes in a hemi-boreal forest from multispectral airborne laser scanning data using a mini raster cell method

Classification of tree species or species classes is still a challenge for remote sensing-based forest inventory. Operational use of Airborne Laser Scanning (ALS) data for prediction of forest variables has this far been dominated by area-based methods where laser scanning data have been used for estimation of forest variables within raster cells. Classification of tree species has however not been achieved with sufficient accuracy with area-based methods using only ALS data. Furthermore, analysis of tree species at the level of raster cells with typical size of 15 m × 15 m is not ideal in the case of mixed species stands. Most ALS systems for terrestrial mapping use only one wavelength of light. New multispectral ALS systems for terrestrial mapping have recently become operational, such as the Optech Titan system with wavelengths 1550 nm, 1064 nm, and 532 nm. This study presents an alternative type of area-based method for classification of tree species classes where multispectral ALS data are used in combination with small raster cells. In this “mini raster cell method” features for classification are derived from the intensity of the different wavelengths in small raster cells using a moving window average approach to allow for a heterogeneous tree species composition. The most common tree species in the Nordic countries are Pinus sylvestris and Picea abies, constituting about 80% of the growing stock volume. The remaining 20% consists of several deciduous species, mainly Betula pendula and Betula pubescens, and often grow in mixed forest stands. Classification was done for pine (Pinus sylvestris), spruce (Picea abies), deciduous species and mixed species in middle-aged and mature stands in a study area located in hemi-boreal forest in the southwest of Sweden (N 58°27’, E 13°39’). The results were validated at plot level with the tree species composition defined as proportion of basal area of the tree species classes. The mini raster cell classification method was slightly more accurate (75% overall accuracy) than classification with a plot level area-based method (68% overall accuracy). The explanation is most likely that the mini raster cell method is successful at classifying homogenous patches of tree species classes within a field plot, while classification based on plot level analysis requires one or several heterogeneous classes of mixed species forest. The mini raster cell method also results in a high-resolution tree species map. The small raster cells can be aggregated to estimate tree species composition for arbitrary areas, for example forest stands or area units corresponding to field plots.

Fusion of crown and trunk detections from airborne UAS based laser scanning for small area forest inventories

High density point clouds from unmanned airborne laser scanning (UALS) systems have great potential for small area forest inventories. We propose an UALS-based tree level inventory method that takes advantage of both the segmented crowns and segmented trunks: hybrid tree detection (HTD). The method is tested at twenty 30 m × 30 m validation plots of varying maturity, tree species distribution and stocking density. With the traditional individual tree crown delineation (ITC) approach, tree attributes are only predicted for crown segmented trees. Here, we assume that a segmented crown can contain more than one tree. Our idea is to identify segmented crowns that contain more than one segmented trunk. One of the trunks is linked to a segmented crown (upper most tree) and the remainders are treated as understory trunks. Heights of the crown segmented trees and the diameters of the understory trunks are used as predictor variables in nonlinear mixed-effects models of tree volume. The %RMSE and %MD values of volume predictions at the 30 m × 30 m validation plot level were 22.2%, −13.3% and 18.8%, −8.3% for ITC and HTD, respectively. We conclude that the proposed HTD approach improves the accuracy of ITC in managed boreal forests when using UALS data.

A Convex Hull-Based Feature Descriptor for Learning Tree Species Classification From ALS Point Clouds

Classifying tree species from point clouds acquired by light detection and ranging (LiDAR) scanning systems is important in many applications, including remote sensing, virtual reality, and forestry inventory. Compared with terrestrial laser scanning systems, airborne laser scanning (ALS) systems can acquire large-scale tree point clouds from only a single scan. However, ALS point clouds have the disadvantages of low density, uneven distribution, and unclear branch structure, making the classification of tree species from ALS point clouds a challenging task. Recently, deep learning-based classification approaches, such as PointNet++, which can operate directly on 3-D point sets, have been intensively studied in scene classification. However, the classification precision of learning-based approaches for point clouds relies on point coordinates and features, such as normals. Unlike the face normals of regular objects, trees have complex branch structures and detailed leaves, which are difficult to capture using ALS systems. Hence, it might be inappropriate to use the normals of ALS tree points for classification. In this letter, we propose a novel convex hull-based feature descriptor for tree species classification using the deep learning network PointNet++. To evaluate the effectiveness of our approach, three additional feature descriptors (normal descriptor, alpha shape-based descriptor, and covariance descriptor) are also investigated with PointNet++. The results show that the convex hull-based feature descriptor can achieve 86.6% overall accuracy in tree species classification, which is notably higher than the other three descriptors.

Using Leaf-Off and Leaf-On Multispectral Airborne Laser Scanning Data to Characterize Seedling Stands

Information from seedling stands in time and space is essential for sustainable forest management. To fulfil these informational needs with limited resources, remote sensing is seen as an intriguing alternative for forest inventorying. The structure and tree species composition in seedling stands have created challenges for capturing this information using sensors providing sparse point densities that do not have the ability to penetrate canopy gaps or provide spectral information. Therefore, multispectral airborne laser scanning (mALS) systems providing dense point clouds coupled with multispectral intensity data theoretically offer advantages for the characterization of seedling stands. The aim of this study was to investigate the capability of Optech Titan mALS data to characterize seedling stands in leaf-off and leaf-on conditions, as well as to retrieve the most important forest inventory attributes, such as distinguishing deciduous from coniferous trees, and estimating tree density and height. First, single-tree detection approaches were used to derive crown boundaries and tree heights from which forest structural attributes were aggregated for sample plots. To predict tree species, a random forests classifier was trained using features from two single-channel intensities (SCIs) with wavelengths of 1550 (SCI-Ch1) and 1064 nm (SCI-Ch2), and multichannel intensity (MCI) data composed of three mALS channels. The most important and uncorrelated features were analyzed and selected from 208 features. The highest overall accuracies in classification of Norway spruce, birch, and nontree class in leaf-off and leaf-on conditions obtained using SCI-Ch1 and SCI-Ch2 were 87.36% and 69.47%, respectively. The use of MCI data improved classification by up to 96.55% and 92.54% in leaf-off and leaf-on conditions, respectively. Overall, leaf-off data were favorable for distinguishing deciduous from coniferous trees and tree density estimation with a relative root mean square error (RMSE) of 37.9%, whereas leaf-on data provided more accurate height estimations, with a relative RMSE of 10.76%. Determining the canopy threshold for separating ground returns from vegetation returns was found to be critical, as mapped trees might have a height below one meter. The results showed that mALS data provided benefits for characterizing seedling stands compared to single-channel ALS systems.

MAPPING OF WATER SURFACE LEVELS AND SLOPES WITH SINGLE PHOTON LIDAR – A CASE STUDY AT THE RIVER RHINE

Abstract. Precise knowledge of water surface level heights is crucial for safe ship navigation and as basis for calibration of hydrodynamic-numerical models. While Airborne Laser Scanning (ALS) is a well established technique for topographic mapping, ALS-based water surface mapping using conventional infrared lasers suffers from the high degree of specular reflection which leads to data voids for off-nadir angles beyond 5–7 degrees. The advent of single photon sensitive ALS systems using green laser sources presents the prospect of large-area, high-resolution water surface mapping due to the high receiver sensitivity and measurement rate of such systems. Building on previous studies on subject matters, we present the results of a pilot project initiated and conducted by the German Federal Institute of Hydrology (BfG, Koblenz) at the Rhine River. Three specific test sites with varying water surface and flow velocity properties were captured on October 30th and 31th, 2019 with the Leica SPL100 from flying altitudes of 3000 m, 2500 m, 1600 m, and 800 m, respectively. As anticipated, the water surface laser pulse density was high and exhibited 20–145 points/m2 depending on flying altitude. After quality control, strip adjustment, and point cloud analysis, three water surface classification methods were implemented based on: (i) height quantiles, (ii) point cloud segmentation, and (iii) inverse DTM filtering. All approaches featured relative and absolute water level height accuracies better than 10 cm. We conclude that Single Photon LiDAR based high resolution mapping of water surface levels and tilts is feasible when employing application specific data acquisition parameters, i.e., off-nadir angle ≤10° and flying altitude ≤3000 m.

Forest Inventory and Diversity Attribute Modelling Using Structural and Intensity Metrics from Multi-Spectral Airborne Laser Scanning Data

Airborne laser scanning (ALS) systems tuned to the near-infrared (NIR; 1064 nm) wavelength have become the best available data source for characterizing vegetation structure. Proliferation of multi-spectral ALS (M-ALS) data with lasers tuned at two additional wavelengths (commonly 532 nm; green, and 1550 nm; short-wave infrared (SWIR)) has promoted interest in the benefit of additional wavelengths for forest inventory modelling. In this study, structural and intensity based M-ALS metrics were derived from wavelengths independently and combined to assess their value for modelling forest inventory attributes (Lorey’s height (HL), gross volume (V), and basal area (BA)) and overstorey species diversity (Shannon index (H), Simpson index (D), and species richness (R)) in a diverse mixed-wood forest in Ontario, Canada. The area-based approach (ABA) to forest attribute modelling was used, where structural- and intensity-based metrics were calculated and used as inputs for random forest models. Structural metrics from the SWIR channel (SWIRstruc) were found to be the most accurate for H and R (%RMSE = 14.3 and 14.9), and NIRstruc were most accurate for V (%RMSE = 20.4). The addition of intensity metrics marginally increased the accuracy of HL models for SWIR and combined channels (%RMSE = 7.5). Additionally, a multi-resolution (0.5, 1, 2 m) voxel analysis was performed, where intensity data were used to calculate a suite of spectral indices. Plot-level summaries of spectral indices from each voxel resolution alone, as well as combined with structural metrics from the NIR wavelength, were used as random forest predictors. The addition of structural metrics from the NIR band reduced %RMSE for all models with HL, BA, and V realizing the largest improvements. Intensity metrics were found to be important variables in the 1 m and 2 m voxel models for D and H. Overall, results indicated that structural metrics were the most appropriate. However, the inclusion of intensity metrics, and continued testing of their potential for modelling diversity indices is warranted, given minor improvements when included. Continued analyses using M-ALS intensity metrics and voxel-based indices would help to better understand the value of these data, and their future role in forest management.

Suitability of Airborne and Terrestrial Laser Scanning for Mapping Tree Crop Structural Metrics for Improved Orchard Management

Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) systems are useful tools for deriving horticultural tree structure estimates. However, there are limited studies to guide growers and agronomists on different applications of the two technologies for horticultural tree crops, despite the importance of measuring tree structure for pruning practices, yield forecasting, tree condition assessment, irrigation and fertilization optimization. Here, we evaluated ALS data against near coincident TLS data in avocado, macadamia and mango orchards to demonstrate and assess their accuracies and potential application for mapping crown area, fractional cover, maximum crown height, and crown volume. ALS and TLS measurements were similar for crown area, fractional cover and maximum crown height (coefficient of determination (R2) ≥ 0.94, relative root mean square error (rRMSE) ≤ 4.47%). Due to the limited ability of ALS data to measure lower branches and within crown structure, crown volume estimates from ALS and TLS data were less correlated (R2 = 0.81, rRMSE = 42.66%) with the ALS data found to consistently underestimate crown volume. To illustrate the effects of different spatial resolution, capacity and coverage of ALS and TLS data, we also calculated leaf area, leaf area density and vertical leaf area profile from the TLS data, while canopy height, tree row dimensions and tree counts) at the orchard level were calculated from ALS data. Our results showed that ALS data have the ability to accurately measure horticultural crown structural parameters, which mainly rely on top of crown information, and measurements of hedgerow width, length and tree counts at the orchard scale is also achievable. While the use of TLS data to map crown structure can only cover a limited number of trees, the assessment of all crown strata is achievable, allowing measurements of crown volume, leaf area density and vertical leaf area profile to be derived for individual trees. This study provides information for growers and horticultural industries on the capacities and achievable mapping accuracies of standard ALS data for calculating crown structural attributes of horticultural tree crops.

UAV Laser scanning technology: a potential cost-effective tool for micro-topography detection over wooded areas for archaeological prospection

ABSTRACT Airborne laser scanning (ALS) has recently been identified as a potential tool in topographic mapping for archaeological prospection. However, most existing applications in this field refers to manned ALS systems, for which the high operation and maintenance costs limits its application in small-scale archaeological investigation. In this paper, we conducted an exploratory study on the application of the unmanned aerial vehicle (UAV) laser scanning (ULS) system in ancient micro-topography detection over wooded areas. Compared with manned ALS technology, we analyzed the advantages and potentials of ULS technology for archaeological applications. Then we outlined existing mainstream survey-grade UAV-based laser scanners, data processing and visualization approaches. Furthermore, we performed case studies in three cultural heritage sites in Zhejiang Province, China using two representative mainstream survey-grade ULS systems. Results were then verified by an in-site investigation. Finally, the correct selection of ULS devices, the planning of data acquisition missions and the use of appropriate data processing methods specifically for archaeological prospection were discussed. This paper provides a cost-effective and flexible solution for micro-topography detection in wooded areas. ULS technology, as demonstrated here, can be an important supplement to existing archaeological investigation methods, particularly for small-scale areas, and has promising prospects in archaeological applications.