Mobile Terrestrial Laser Scanner Research Articles

Mobile terrestrial laser scanner vs. UAV photogrammetry to estimate woody crop canopy parameters – Part 1: Methodology and comparison in vineyards

Characterizing crop canopies is especially important in the management of woody crops. In this article, two systems were compared to characterise a 50 m long vineyard row section. One of the systems was a mobile terrestrial laser scanner based on a light detection and ranging (LiDAR) sensor (MTLS-LiDAR). The other was an uncrewed aerial vehicle (UAV) based system using digital aerial photogrammetry (UAV-DAP). The resulting 3D point clouds were assessed qualitatively and quantitatively. Canopy heights, widths and volumes were obtained in 0.1 m long sections along the studied row. All the parameters derived from the two systems presented statistically significant differences. The coefficients of determination between systems were 0.619 for canopy maximum heights above ground level (agl), 0.686 for 90th percentile (P90) heights agl, and 0.283 and 0.274 for maximum and P90 vegetated heights, respectively. Coefficients of determination between averaged maximum canopy width and P90 canopy width were 0.328 and 0.317, respectively. Coefficients of determination between cross-sectional areas determined from maximum widths, P90 widths and from the occupancy grid method were 0.423, 0.409 and 0.334, respectively. Total canopy volume for the entire row obtained from the three cross section estimation methods differed between 19 m3 and 25 m3. The reasons found were that the MTLS-LiDAR-derived point cloud captured the canopy top and side variability but could be affected by occlusions, mixed pixels and tall grass-like weeds present in the surveyed area. For its part, the UAV-DAP-derived point cloud tended to miss top and side shoots and somewhat smoothed canopy variability. As neither of the systems is optimal, a balance needs to be found according to the specific requirements of the survey. For this purpose, a list of pros and cons is presented to support the selection of one of the two systems for canopy monitoring. The MTLS-LiDAR system should be chosen when high detail is required but small areas are to be scanned. Alternatively, the UAV-DAP system should be chosen when large areas are to be monitored and when canopy detail is not so important. Further results are presented in Part 2 for a larger area and including pear and peach orchards with different training systems. Future research is to be conducted on how the compared systems affect variability detection and support variable-rate prescriptions..

Mobile terrestrial laser scanner vs. UAV photogrammetry to estimate woody crop canopy parameters – Part 2: Comparison for different crops and training systems

The measurement of geometric canopy parameters in woody crops is an important task in Precision Agriculture because of their correlation with crop condition and productivity. In recent years, several technological approaches have been developed as an alternative to manual measurements, which are time- and labour-consuming. Two of the most commonly used 3D canopy characterization technologies are mobile terrestrial laser scanning (MTLS) based on light detection and ranging (LiDAR) sensors, and digital aerial photogrammetry (DAP) using imagery from uncrewed aerial vehicles (UAVs). Although both are state-of-the-art and have been fully tested and validated, a complete comparison between their geometric canopy parameter estimations in different woody crops and training systems has not been carried out. For this reason, a set of geometric parameters (canopy height, projected area, and volume) of a vineyard, an intensive peach orchard, and an intensive pear orchard were measured using UAV-DAP and MTLS-LiDAR. A comparison between both kinds of measurements was performed, accounting for the length of the sections in which the crop hedgerows were divided to extract the geometric parameters. Measurements from the UAV and the MTLS were highly correlated (R2 from 0.82 to 0.94) when considering the data from the three crops together, and the correlations were higher when analysing longer row sections. The canopy geometric parameters estimated using the MTLS-LiDAR always had higher values than those from the UAV-DAP. The results presented in this work provide useful data for a more informed selection of technological approaches for 3D crop characterization in Precision Fruticulture and high-throughput phenotyping.

Organic mulches as an alternative for under-vine weed management in Mediterranean irrigated vineyards: Impact on agronomic performance

One of the main challenges for organic vineyards is weed management. Weeds tend to compete for water and nutrients, and can cause large yield reductions. Traditional under-vine weed management in organic vineyards consists on mechanical cultivation along the season, which is associated to soil and young vine root damages, and to high fuel consumption. Thus, sustainable alternatives need to be found. Cover crops are becoming common in the last decades due to their multiple benefits in agroecosystems. Nevertheless, under-vine cover crop implementation in Mediterranean vineyards is limited as this competes for resources (water and nutrients), reducing the yield, vegetative development, and grape size of vines. The use of organic mulches could overcome all these problems, while benefitting vine performance. In the present work, the response of vines, soil and weeds to mulching was evaluated. An experiment was carried out in Raimat, Lleida (Catalonia, NE Spain) in a commercial vineyard from 2019 to 2021, and the following treatments applied: 1) mechanical cultivation with an in-row tiller; 2) mowing a permanent spontaneous cover with an in-row mower; 3) almond shell mulch; and 4) chopped pine wood mulch. Results showed lower weed cover along the three seasons in mulched treatments, as well as higher yield, better vine water status, and greater vegetative development from traditional measurement. The latter was confirmed by and analised with further detail with measurements acquired with a mobile terrestrial laser scanner (MTLS) based on light detection and ranging (LiDAR) sensors. Besides, petiole nutrient status was better in vines without a spontaneous living cover. Organic mulches improved vine performance and weed control, so these results allow to optimize water use efficiency in the Mediterranean basin with scarce water resources. Mulching can be considered as a useful strategy that enhances a more sustainable viticulture.

Stand Structural Characteristics Derived from Combined TLS and Landsat Data Support Predictions of Mushroom Yields in Mediterranean Forest

Forest fungi provide recreational and economic services, as well as ecosystem biodiversity. Wild mushroom yields are difficult to estimate; climatic conditions are known to trigger temporally localised yields, and forest structure also affects productivity. In this work, we analyse the capacity of remotely sensed variables to estimate wild mushroom biomass production in Mediterranean Pinus pinaster forests in Soria (Spain) using generalised additive mixed models (GAMMs). In addition to climate variables, multitemporal NDVI derived from Landsat data, as well as structural variables measured with mobile Terrestrial Laser Scanner (TLS), are considered. Models are built for all mushroom species as a single pool and for Lactarius deliciosus individually. Our results show that, in addition to autumn precipitation, the interaction of multitemporal NDVI and vegetation biomass are most explanatory of mushroom productivity in the models. When analysing the productivity models of Lactarius deliciosus, in addition to the interaction between canopy cover and autumn minimum temperature, basal area (BA) becomes relevant, indicating an optimal BA range for the development of this species. These findings contribute to the improvement of knowledge about wild mushroom productivity, helping to meet Goal 15 of the 2030 UN Agenda.

UTILITY POLES EXTRACTION FROM MOBILE LIDAR DATA IN URBAN AREA BASED ON DENSITY INFORMATION

Abstract. Utility poles located along roads play a key role in road safety and planning as well as communications and electricity distribution. In this regard, new sensing technologies such as Mobile Terrestrial Laser Scanner (MTLS) could be an efficient method to detect utility poles and other planimetric objects along roads. However, due to the vast amount of data collected by MTLS in the form of a point cloud, automated techniques are required to extract objects from this data. This study proposes a novel method for automatic extraction of utility poles from the MTLS point clouds. The proposed algorithm is composed of three consecutive steps of pre-processing, cable area detection, and poles extraction. The point cloud is first pre-processed and then candidate areas for utility poles are specified based on Hough Transform (HT). Utility poles are extracted by applying horizontal and vertical density information to these areas. The performance of the method was evaluated on a sample point cloud and 98% accuracy was achieved in extracting utility poles using the proposed method.

Fruit detection in an apple orchard using a mobile terrestrial laser scanner

The development of reliable fruit detection and localization systems provides an opportunity to improve the crop value and management by limiting fruit spoilage and optimised harvesting practices. Most proposed systems for fruit detection are based on RGB cameras and thus are affected by intrinsic constraints, such as variable lighting conditions. This work presents a new technique that uses a mobile terrestrial laser scanner (MTLS) to detect and localise Fuji apples. An experimental test focused on Fuji apple trees (Malus domestica Borkh. cv. Fuji) was carried out. A 3D point cloud of the scene was generated using an MTLS composed of a Velodyne VLP-16 LiDAR sensor synchronised with an RTK-GNSS satellite navigation receiver. A reflectance analysis of tree elements was performed, obtaining mean apparent reflectance values of 28.9%, 29.1%, and 44.3% for leaves, branches and trunks, and apples, respectively. These results suggest that the apparent reflectance parameter (at 905 nm wavelength) can be useful to detect apples. For that purpose, a four-step fruit detection algorithm was developed. By applying this algorithm, a localization success of 87.5%, an identification success of 82.4%, and an F1-score of 0.858 were obtained in relation to the total amount of fruits. These detection rates are similar to those obtained by RGB-based systems, but with the additional advantages of providing direct 3D fruit location information, which is not affected by sunlight variations. From the experimental results, it can be concluded that LiDAR-based technology and, particularly, its reflectance information, has potential for remote apple detection and 3D location.

Novel method for sphere target detection and center estimation from mobile terrestrial laser scanner data

Sphere targets have been widely used in mobile terrestrial laser scanner (MTLS) registration, using their center coordinates the multiple viewpoints clouds data can be transformed into a unified coordinate system. However, in practice, the task of sphere target detection and center estimation is still a labor intensive and time consuming for large surveying and mapping projects. For this reason, a novel method of fusion adaptive dynamic random sample consensus (AD-RANSAC) and nonlinear least squares (NLS) is proposed to automatically detect and optimally estimate center of sphere target. The basic procedure of the proposed method is that, firstly, a newly AD-RANSAC algorithm is developed to automatically and rapidly detect the sphere targets from MTLS point clouds data, which extends the traditional RANSAC algorithm to detect the sphere targets in dynamic states and recursively update the state estimations using sequential measurements, also improves the accuracy and speed of detection of sphere target. Secondly, the sphere target center coordinate is determined through the NLS optimal estimation algorithm, which has regions mathematical calculation and minimizes the errors in estimation of sphere targets center. Finally, twenty pre-surveyed sphere targets distributed in indoor field were conducted to check the validity of proposed method, experimental results are presented that validate this method and demonstrate our proposed method can automatically detect the sphere targets in more than 1 million points data within 1 min, the accuracy of the sphere center coordinate estimation is less than 2 cm.

Spatial variability in commercial orange groves. Part 1: canopy volume and height

Characterizing crop spatial variability is crucial for estimating the opportunities for site-specific management practices. In the context of tree crops, ranging sensor technology has been developed to assess tree canopy geometry and control real-time variable rate application of plant protection products and fertilizers. The objective of this study was to characterize the variability of canopy geometry attributes in commercial orange groves in Brazil and therefore estimate the potential impact of sensor-based site-specific management. Using a mobile terrestrial laser scanner, canopy volume and canopy height were measured in 0.25 m length transversal sections along the rows across five large scale commercial orange groves in Sao Paulo, Brazil. The coefficient of variation of canopy volume ranged from 30 to 40%. Canopy height was less variable, but closely related to canopy volume. Histograms of canopy volume and height were usually negatively skewed indicating regions of the groves with smaller plants and punctual plant resets. In scenarios where input application rates followed canopy volume variability, input savings were around 40% compared to constant rates based on the maximum canopy volume. Maps of canopy geometry derived from mobile terrestrial laser scanning revealed significant canopy spatial variability, suggesting that the groves would benefit from strategies based on management zones and other forms of site-specific management.

Kinect v2 Sensor-Based Mobile Terrestrial Laser Scanner for Agricultural Outdoor Applications

Mobile terrestrial laser scanners (MTLS), based on light detection and ranging sensors, are used worldwide in agricultural applications. MTLS are applied to characterize the geometry and the structure of plants and crops for technical and scientific purposes. Although MTLS exhibit outstanding performance, their high cost is still a drawback for most agricultural applications. This paper presents a low-cost alternative to MTLS based on the combination of a Kinect v2 depth sensor and a real time kinematic global navigation satellite system (GNSS) with extended color information capability. The theoretical foundations of this system are exposed along with some experimental results illustrating their performance and limitations. This study is focused on open-field agricultural applications, although most conclusions can also be extrapolated to similar outdoor uses. The developed Kinect-based MTLS system allows to select different acquisition frequencies and fields of view (FOV), from one to 512 vertical slices. The authors conclude that the better performance is obtained when a FOV of a single slice is used, but at the price of a very low measuring speed. With that particular configuration, plants, crops, and objects are reproduced accurately. Future efforts will be directed to increase the scanning efficiency by improving both the hardware and software components and to make it feasible using both partial and full FOV.

A Method to Obtain Orange Crop Geometry Information Using a Mobile Terrestrial Laser Scanner and 3D Modeling

LiDAR (Light Detection and Ranging) technology has been used to obtain geometrical attributes of tree crops in small field plots, sometimes using manual steps in data processing. The objective of this study was to develop a method for estimating canopy volume and height based on a mobile terrestrial laser scanner suited for large commercial orange groves. A 2D LiDAR sensor and a GNSS (Global Navigation Satellite System) receiver were mounted on a vehicle for data acquisition. A georeferenced point cloud representing the laser beam impacts on the crop was created and later classified into transversal sections along the row or into individual trees. The convex-hull and the alpha-shape reconstruction algorithms were used to reproduce the shape of the tree crowns. Maps of canopy volume and height were generated for a 25 ha orange grove. The different options of data processing resulted in different values of canopy volume. The alpha-shape algorithm was considered a good option to represent individual trees whereas the convex-hull was better when representing transversal sections of the row. Nevertheless, the canopy volume and height maps produced by those two methods were similar. The proposed system is useful for site-specific management in orange groves.

Accuracy assessment of a mobile terrestrial laser scanner for tree crops

LiDAR technology is one option to collect spatial data about canopy geometry in many crops. However, the method of data acquisition includes many errors related to the LiDAR sensor, the GNSS receiver and the data acquisition set up. Therefore, the objective of this study was to evaluate the errors involved in the data acquisition from a mobile terrestrial laser scanner (MTLS). Regular shaped objects were scanned with a developed MTLS in two different tests: i) with the system mounted on a vehicle and ii) with the system mounted on a platform running over a rail. The errors of area estimation varied between 0.001 and 0.071 m2 for the circle, square and triangle objects. The errors on volume estimations were between 0.0003 and 0.0017 m3, for cylinders and truncated cone.

Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds

LiDAR sensors are widely used in many areas and, in recent years, that includes agricultural tasks. In this work, a self-developed mobile terrestrial laser scanner based on a 2D light detection and ranging (LiDAR) sensor was used to scan an intensive olive orchard, and different algorithms were developed to estimate canopy volume. Canopy volume estimations derived from LiDAR sensor readings were compared to conventional estimations used in fruticulture/horticulture research and the results prove that they are equivalent with coefficients of correlation ranging from r = 0.56 to r = 0.82 depending on the algorithms used. Additionally, tools related to analysis of point cloud data from the LiDAR-based system are proposed to extract further geometrical and structural information from tree row crop canopies to be offered to farmers and technical advisors as digital raster maps. Having high spatial resolution information on canopy geometry (i.e., height, width and volume) and on canopy structure (i.e., light penetrability, leafiness and porosity) may result in better orchard management decisions. Easily obtainable, reliable information on canopy geometry and structure may favour the development of decision support systems either for irrigation, fertilization or canopy management, as well as for variable rate application of agricultural inputs in the framework of precision fruticulture/horticulture.

Multi-tree woody structure reconstruction from mobile terrestrial laser scanner point clouds based on a dual neighbourhood connectivity graph algorithm

A process is presented for the vector reconstruction of fruit plantations based on the model developed by Verroust and Lazarus. To solve occlusion problems, the use of a dual graph of local and extended connectivity is proposed. The process allows vegetation variables such as the length and volume of the ligneous structure to be measured, enabling studies such as intensity of pruning operations. The process has been tested against simulated models and real trees with different training systems: open-vase system (peach trees) and central leader hedgerow system (pear trees). The cost of the algorithm will be given by the cost of the implementation of Dijkstra's algorithm, which in its standard version is of potential ( O ( n 2 ) ). Algorithm accuracy was checked against point clouds of virtual trees. The reconstruction was also applied before and after a pruning operation of real trees to enable a study of the evolution of the vegetation indices. Results showed the algorithm to be suitable for multi-tree reconstruction of both central leader and open-vase training systems.

Mapping Vineyard Leaf Area Using Mobile Terrestrial Laser Scanners: Should Rows be Scanned On-the-Go or Discontinuously Sampled?

The leaf area index (LAI) is defined as the one-side leaf area per unit ground area, and is probably the most widely used index to characterize grapevine vigor. However, LAI varies spatially within vineyard plots. Mapping and quantifying this variability is very important for improving management decisions and agricultural practices. In this study, a mobile terrestrial laser scanner (MTLS) was used to map the LAI of a vineyard, and then to examine how different scanning methods (on-the-go or discontinuous systematic sampling) may affect the reliability of the resulting raster maps. The use of the MTLS allows calculating the enveloping vegetative area of the canopy, which is the sum of the leaf wall areas for both sides of the row (excluding gaps) and the projected upper area. Obtaining the enveloping areas requires scanning from both sides one meter length section along the row at each systematic sampling point. By converting the enveloping areas into LAI values, a raster map of the latter can be obtained by spatial interpolation (kriging). However, the user can opt for scanning on-the-go in a continuous way and compute 1-m LAI values along the rows, or instead, perform the scanning at discontinuous systematic sampling within the plot. An analysis of correlation between maps indicated that MTLS can be used discontinuously in specific sampling sections separated by up to 15 m along the rows. This capability significantly reduces the amount of data to be acquired at field level, the data storage capacity and the processing power of computers.

Deciduous tree reconstruction algorithm based on cylinder fitting from mobile terrestrial laser scanned point clouds

Vector reconstruction of objects from an unstructured point cloud obtained with a LiDAR-based system (light detection and ranging) is one of the most promising methods to build three dimensional models of orchards. The cylinder fitting method for woody structure reconstruction of leafless trees from point clouds obtained with a mobile terrestrial laser scanner (MTLS) has been analysed. The advantage of this method is that it performs reconstruction in a single step. The most time consuming part of the algorithm is generation of the cylinder direction, which must be recalculated at the inclusion of each point in the cylinder. The tree skeleton is obtained at the same time as the cluster of cylinders is formed. The method does not guarantee a unique convergence and the reconstruction parameter values must be carefully chosen. A balanced processing of clusters has also been defined which has proven to be very efficient in terms of processing time by following the hierarchy of branches, predecessors and successors. The algorithm was applied to simulated MTLS of virtual orchard models and to MTLS data of real orchards. The constraints applied in the method have been reviewed to ensure better convergence and simpler use of parameters. The results obtained show a correct reconstruction of the woody structure of the trees and the algorithm runs in linear logarithmic time.

Detecting road poles from mobile terrestrial laser scanning data

Detecting road poles from mobile terrestrial laser scanner (MTLS) point clouds is important for many geographical information system (GIS) applications such as right-of-way asset inventory compilation. The aim of this research is to automatically detect the road poles from unorganized 3D point clouds captured by an MTLS system named TITAN. The proposed pole detection pipeline consists of a sequence of five steps: organizing the 3D point clouds and nearest neighbor search, 2D density-based segmentation, vertical region growing, segment merging, and pole classification. The obtained average detection rate and accuracy for the three data sets tested were 86% and 97%, respectively.

DETECTION OF ROAD CURB FROM MOBILE TERRESTRIAL LASER SCANNER POINT CLOUD

Abstract. The detection of different road furniture such as curb, street floor and sidewalk from point clouds is important in many applications such as road maintenance and city planning. In this paper a pipeline for point cloud processing to detect the road curb from unorganized point clouds captured from a mobile terrestrial laser scanner is proposed. The proposed pipeline utilizes a covariance- based procedure to perform a 3D segmentation of point clouds. Features such as the road curb can be extracted by analyzing the local neighborhood of every point. This is done by computing the surface normal direction and the normalized eigenvalues. These parameters can be used to extract the ground objects, such as curb, street floor and sidewalk. The curb can be isolated from the rest of the ground objects based on the previous parameters in addition to elevation gradient within the local neighborhood. A 2D image processing scheme is also presented to find the curbs as edges in a generated 2D height image. The results show successful detection rates of 78% and 94% using 3D and 2D approaches respectively.