Light Detection And Ranging Technique Research Articles

Subsurface fluorescence time-of-flight imaging using a large-format single-photon avalanche diode sensor for tumor depth assessment.

Fluorescence guidance is used clinically by surgeons to visualize anatomical and/or physiological phenomena in the surgical field that are difficult or impossible to detect by the naked eye. Such phenomena include tissue perfusion or molecular phenotypic information about the disease being resected. Conventional fluorescence-guided surgery relies on long, microsecond scale laser pulses to excite fluorescent probes. However, this technique only provides two-dimensional information; crucial depth information, such as the location of malignancy below the tissue surface, is not provided. We developed a depth sensing imaging technique using light detection and ranging (LiDAR) time-of-flight (TOF) technology to sense the depth of target tissue while overcoming the influence of tissue optical properties and fluorescent probe concentration. The technology is based on a large-format (), binary, gated, single-photon avalanche diode (SPAD) sensor with an 18ps time-gate step, synchronized with a picosecond pulsed laser. The fast response of the sensor was developed and tested for its ability to quantify fluorescent inclusions at depth and optical properties in tissue-like phantoms through analytical model fitting of the fast temporal remission data. After calibration and algorithmic extraction of the data, the SPAD LiDAR technique allowed for sub-mm resolution depth sensing of fluorescent inclusions embedded in tissue-like phantoms, up to a maximum of 5mm in depth. The approach provides robust depth sensing even in the presence of variable tissue optical properties and separates the effects of fluorescence depth from absorption and scattering variations. LiDAR TOF fluorescence imaging using an SPAD camera provides both fluorescence intensity images and the temporal profile of fluorescence, which can be used to determine the depth at which the signal is emitted over a wide field of view. The proposed tool enables fluorescence imaging at a higher depth in tissue and with higher spatial precision than standard, steady-state fluorescence imaging tools, such as intensity-based near-infrared fluorescence imaging, optical coherence tomography, Raman spectroscopy, or confocal microscopy. Integration of this technique into a standard surgical tool could enable rapid, more accurate estimation of resection boundaries, thereby improving the surgeon's efficacy and efficiency, and ultimately improving patient outcomes.

Enhancing LiDAR performance using threshold photon-number-resolving detection.

Single-photon light detection and ranging (LiDAR) is widely used to reconstruct 3D scenes. Nevertheless, depth and reflectivity maps obtained by single-photon detection usually suffer from noise problems. Threshold LiDAR techniques using photon-number-resolving detectors were proposed to suppress noise by filtering low photon numbers, but these techniques renounce multiple levels of information and could not be compatible when it comes to high-noise low-signal regime. In this manuscript, we propose a detection scheme which combines the noise suppression of threshold detection with the signal amplification of photon-number-resolving detectors to further enhance LiDAR performance. The enhancement attained is compared to single-photon and threshold detection schemes under a wide range of signal and noise conditions, in terms of signal-to-noise-ratio (SNR), detection rate and false alarm rate, which are key metrics for LiDAR. Extensive simulations and real-world experiments show that the proposed scheme can reconstruct better depth and reflectivity maps. These results enable the development of high-efficient and low-noise LiDAR systems.

Smartphone-Based LiDAR Application for Easy and Accurate Wound Size Measurement.

The accurate assessment of wound size is a critical step in advanced wound care management. This study aims to introduce and validate a Light Detection and Ranging (LiDAR) technique for measuring wound size. Twenty-eight wounds treated from December 2022 to April 2023 at the Chungnam National University Hospital were analyzed. All the wounds were measured using three techniques: conventional ruler methods, the LiDAR technique, and ImageJ analysis. Correlation analysis, linear regression, and Bland-Altman plot analysis were performed to validate the accuracy of the novel method. The measurement results (mean ± standard deviation) obtained using the ruler method, LiDAR technique, and ImageJ analysis were 112.99 ± 110.07 cm2, 73.59 ± 72.97 cm2, and 74.29 ± 72.15 cm2, respectively. The Pearson correlation coefficient was higher for the LiDAR application (0.995) than for the conventional ruler methods (mean difference, -5.0000 cm2), as was the degree of agreement (mean difference, 38.6933 cm2). Wound size measurement using LiDAR is a simple and reliable method that will enable practitioners to conveniently assess wounds with a flattened and irregular shape with higher accuracy. However, non-flattened wounds cannot be assessed owing to the technical limitations of LiDAR.

LiDAR reveals a preference for intermediate visibility by a forest-dwelling ungulate species.

Visibility (viewshed) plays a significant and diverse role in animals' behaviour and fitness. Understanding how visibility influences animal behaviour requires the measurement of habitat visibility at spatial scales commensurate to individual animal choices. However, measuring habitat visibility at a fine spatial scale over a landscape is a challenge, particularly in highly heterogeneous landscapes (e.g. forests). As a result, our ability to model the influence of fine-scale visibility on animal behaviour has been impeded or limited. In this study, we demonstrate the application of the concept of three-dimensional (3D) cumulative viewshed in the study of animal spatial behaviour at a landscape level. Specifically, we employed a newly described approach that combines terrestrial and airborne light detection and ranging (LiDAR) to measure fine-scale habitat visibility (3D cumulative viewshed) on a continuous scale in forested landscapes. We applied the LiDAR-derived visibility to investigate how visibility in forests affects the summer habitat selection and the movement of 20 GPS-collared female red deer Cervus elaphus in a temperate forest in Germany. We used integrated step selection analysis to determine whether red deer show any preference for fine-scale habitat visibility and whether visibility is related to the rate of movement of red deer. We found that red deer selected intermediate habitat visibility. Their preferred level of visibility during the day was substantially lower than that of night and twilight, whereas the preference was not significantly different between night and twilight. In addition, red deer moved faster in high-visibility areas, possibly mainly to avoid predation and anthropogenic risk. Furthermore, red deer moved most rapidly between locations in the twilight. For the first time, the preference for intermediate habitat visibility and the adaption of movement rate to fine-scale visibility by a forest-dwelling ungulate species at a landscape scale was revealed. The LiDAR technique used in this study offers fine-scale habitat visibility at the landscape level in forest ecosystems, which would be of broader interest in the fields of animal ecology and behaviour.

Using LiDAR technique and modified Community Land Model for calculating water interception of cherry tree canopy

In precision agriculture, methods for analysing 3D point clouds of plants have been introduced, particularly pointing to the high accuracy of light detection and ranging (LiDAR) laser scanning under field conditions. In the present work, LiDAR-based 3D point clouds of cherry trees (n = 255) were analysed for estimating the leaf area as the main factor for water interception. Canopies were scanned for segmenting leaf area pointing to a high variability of canopy surface. The derived tree-specific data of leaf area index (LAI) were implemented into the Community Land Model (CLM), which takes into account canopy interception processes during rainfall events. During canopy development of perennial trees the LAI increased resulting in increased water interception. Events with low rain fall the interception reached 38–100 % capturing LAI of 0.76 – 2.11 m2/m2, respectively. In high rainfall events, interception varied 10–14 % capturing the same LAI range. An equation for describing the varying effects of rainfall intensity and LAI is proposed. The evapotranspiration and water interception data point to a substantial decrease of effective water supply that varies tree-individually during the season. In commercial fruit production, the proposed method can support precise irrigation management.

Laser-Based Systems for Standoff Detection of CWA: A Short Review

In recent times, the use of Chemical Warfare (CW) agents against civilian and military by terrorists and rogue countries are recurrent. The examples of such attacks are available in open literature. They are highly dangerous due to their acute toxicity nature. They tend to stimulate and paralyze nerve system of the body and cause other toxic side effects. Once these toxic chemicals are released into the atmosphere, timely detection and identification of the same at standoff distances will help the defensive forces to take proper counter measures. Detection techniques such as ion mobility spectrometry, infrared spectrometry, Raman spectrometry, FTIR, LIDAR, etc. have been used widely. Among all these techniques, Raman spectroscopy techniques and LIDAR (LIght Detection and Ranging) is the only active remote detection method that can detect and discriminate the chemical agents at standoff distances. Infrared DIAL (Differential Absorption Lidar) is a versatile technique for remote detection of various chemicals. DIAL technique can provide spatially resolved measurements of these agents with sufficient sensitivity (at few ppm levels) at ranges of several kilometers by exploiting the concept such as scattering and absorption from the atmosphere. The main objective of this review paper is to bring out the details of highly potential chemical warfare agents, their properties and laser based remote detection sensors. LIDAR technique is emphasized in more detail because of their long range detection capability, sensitivity and selectivity. An overview of existing R&D based and commercial systems is also discussed.

When a definition makes the difference: operative issues about tree height measures from RPAS-derived CHMs

Tree height (H) survey is a fundamental step in forest mensuration. The error affecting tree height measure, necessarily influences the correspondent tree estimates. The remotely survey of vegetation using PHODAR (PHOtogrammetric Detection And Ranging) or LiDAR (Light Detection And Ranging) techniques generates very high-density point clouds, that result into Canopy Height Models (CHMs) having GSD (Ground Sampling Distance) of few centimetres. This GSD value potentially allows to survey single crown apexes, which, from a forestry point of view, do not represent the actual tree height. Apex height value, in fact, does not represent the prevailing dendrometric height (PDH) but the maximum tree value. In this study we propose a new approach aimed at measuring dendrometric height by PHODAR derived CHM, taking care about this issue. The proposed method defines a correcting factor (found equal to 95% percentile of CHM values distribution within a given crown) for the tree height extraction from CHM based on the PDH concept. The method could be implemented to single crown approach in forest parameters extraction algorithms permitting more reliable results, especially in terms of tree volume and related estimations (e.g., carbon stock quantification, allometric models).

Influence of voxel size on forest canopy height estimates using full-waveform airborne LiDAR data

BackgroundForest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR (Light Detection and Ranging), small-footprint full-waveform airborne LiDAR (FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates.MethodsA range of voxel sizes (from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxel-based LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest (RF) regression method.Results and conclusionsThe results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies (R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m (R2 = 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement (33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.

Analyzing the Angle Effect of Leaf Reflectance Measured by Indoor Hyperspectral Light Detection and Ranging (LiDAR)

Hyperspectral light detection and ranging (LiDAR) (HSL) combines the characteristics of hyperspectral imaging and LiDAR techniques into a single instrument without any data registration. It provides more information than hyperspectral imaging or LiDAR alone in the extraction of vegetation physiological and biochemical parameters. However, the laser pulse intensity is affected by the incident angle, and its effect on HSL has not yet been fully explored. It is important for employing HSL to investigate vegetation properties. The aim of this paper is to study the incident angle effect of leaf reflectance with HSL and build a model about this impact. In this paper, we studied the angle effect of leaf reflectance from indoor HSL measurements of individual leaves from four typical tree species in Beijing. We observed that (a) the increasing of incident angle decreases the leaf reflectance; (b) the leaf spectrum observed by HSL from 650 to 1000 nm with 10 nm spectral resolution (36 channels) are consistent with those that measured by Analytica Spectra Devices (ASD) spectrometer (R2 = 0.9472 ~ 0.9897); (c) the specular reflection is significant in the red bands, and clear non-Lambertian characteristics are observed. In the near-infrared, there is little specular reflection, but it follows the Lambert-scattering law. We divided the whole band (650–1000 nm) into six bands and established an empirical model to correct the influence of angle effect on the reflectance of the leaf for HSL applications. In the future, the calibration of HSL measurements applied for other targets will be studied by rigorous experiments and modelling.

On-Ground Vineyard Reconstruction Using a LiDAR-Based Automated System.

Crop 3D modeling allows site-specific management at different crop stages. In recent years, light detection and ranging (LiDAR) sensors have been widely used for gathering information about plant architecture to extract biophysical parameters for decision-making programs. The study reconstructed vineyard crops using light detection and ranging (LiDAR) technology. Its accuracy and performance were assessed for vineyard crop characterization using distance measurements, aiming to obtain a 3D reconstruction. A LiDAR sensor was installed on-board a mobile platform equipped with an RTK-GNSS receiver for crop 2D scanning. The LiDAR system consisted of a 2D time-of-flight sensor, a gimbal connecting the device to the structure, and an RTK-GPS to record the sensor data position. The LiDAR sensor was facing downwards installed on-board an electric platform. It scans in planes perpendicular to the travel direction. Measurements of distance between the LiDAR and the vineyards had a high spatial resolution, providing high-density 3D point clouds. The 3D point cloud was obtained containing all the points where the laser beam impacted. The fusion of LiDAR impacts and the positions of each associated to the RTK-GPS allowed the creation of the 3D structure. Although point clouds were already filtered, discarding points out of the study area, the branch volume cannot be directly calculated, since it turns into a 3D solid cluster that encloses a volume. To obtain the 3D object surface, and therefore to be able to calculate the volume enclosed by this surface, a suitable alpha shape was generated as an outline that envelops the outer points of the point cloud. The 3D scenes were obtained during the winter season when only branches were present and defoliated. The models were used to extract information related to height and branch volume. These models might be used for automatic pruning or relating this parameter to evaluate the future yield at each location. The 3D map was correlated with ground truth, which was manually determined, pruning the remaining weight. The number of scans by LiDAR influenced the relationship with the actual biomass measurements and had a significant effect on the treatments. A positive linear fit was obtained for the comparison between actual dry biomass and LiDAR volume. The influence of individual treatments was of low significance. The results showed strong correlations with actual values of biomass and volume with R2 = 0.75, and when comparing LiDAR scans with weight, the R2 rose up to 0.85. The obtained values show that this LiDAR technique is also valid for branch reconstruction with great advantages over other types of non-contact ranging sensors, regarding a high sampling resolution and high sampling rates. Even narrow branches were properly detected, which demonstrates the accuracy of the system working on difficult scenarios such as defoliated crops.

A COMPARATIVE ASSESSMENT OF REMOTE SENSING IMAGING TECHNIQUES: OPTICAL, SAR AND LIDAR

Abstract. Remote sensing is a popular technique that is using in the mapping and monitoring of earth features. Early remote sensing was predominantly passive, i.e. it depends upon the sun for energy. Till the last decade, optical imaging is mainly used in remote sensing works. Over the time, significant innovations and improvements have been made in the active remote sensing which resulted in the form of sophisticated imaging techniques like Synthetic Aperture RADAR (SAR) and Light Detection And Ranging (LiDAR). These advancements promoted the use of multi-sensor and multi-source data in remote sensing projects. This highlights the need to review these imaging techniques. Therefore, this review work is carried out. In this review, different imaging techniques are discussed and compared. Optical, SAR and LiDAR techniques are first summarized and then they are compared on the basis of technical specification and working. Finally, an in-depth assessment is made on the applicability of these imaging techniques in different fields.

A Review of Progress and Applications of Pulsed Doppler Wind LiDARs

Doppler wind LiDAR (Light Detection And Ranging) makes use of the principle of optical Doppler shift between the reference and backscattered radiations to measure radial velocities at distances up to several kilometers above the ground. Such instruments promise some advantages, including its large scan volume, movability and provision of 3-dimensional wind measurements, as well as its relatively higher temporal and spatial resolution comparing with other measurement devices. In recent decades, Doppler LiDARs developed by scientific institutes and commercial companies have been well adopted in several real-life applications. Doppler LiDARs are installed in about a dozen airports to study aircraft-induced vortices and detect wind shears. In the wind energy industry, the Doppler LiDAR technique provides a promising alternative to in-situ techniques in wind energy assessment, turbine wake analysis and turbine control. Doppler LiDARs have also been applied in meteorological studies, such as observing boundary layers and tracking tropical cyclones. These applications demonstrate the capability of Doppler LiDARs for measuring backscatter coefficients and wind profiles. In addition, Doppler LiDAR measurements show considerable potential for validating and improving numerical models. It is expected that future development of the Doppler LiDAR technique and data processing algorithms will provide accurate measurements with high spatial and temporal resolutions under different environmental conditions.

Bioaerosol detection over Athens, Greece using the laser induced fluorescence technique

The challenge in today's bioaerosol monitoring is to retrieve real-time information on the qualitative and quantitative composition of the ambient air in bioparticles implicated to human health. A pilot study was conducted during March–May 2018 in Athens, Greece in order to detect bioparticles within the Planetary Boundary Layer (PBL) by implementing the LIF LiDAR (Laser-Induced Fluorescence Light Detection and Ranging) technique at an excitation wavelength of 266 nm in order to determine the major components' contribution on the total fluorescence LiDAR signals aloft (30–100 m above our site). The laboratory characterization of the prevalent pollen grains and fungal spores fluorescence signatures enabled through deconvolution the breaking down of the retrieved LIF LiDAR signals and unravelled each bioparticle's contribution. The bioaerosol occurrence and concentration, as determined by the concurrent sampling with a volumetric particle sampler, verified that the detected fluorescence is related to the fungal and pollen aerosol concentration. The results of this study are very promising for the implementation of remote sensing technology in routine detection and quantification of airborne bioparticles in real-time which is important for allergy sufferers and physicians.

Determination of Dangerous Parts of the Energy Transmission Line by the Canopy Height Model Produced from LiDAR Data

LiDAR (Light Detection and Ranging) technique is  preferable method to determine vertical metric values of  High Voltage Transmission Lines (HVTL) in global regions and steep land. The importance of these facilities is increasing day by day because of the necessity of electricity energy in our daily life. Determination  of vertical heights is an important problem especially in forested areas after construction. In this study, metric values of vegetation which violate vertical safety limits under voltage wires were determined by using Canopy Height Model (CHM) using LiDAR data. As a study area 450x150 m2 was selected the existing voltage line in the region between Beykoz District and Çekmeköy located on the Anatolian side of Istanbul. Firstly, it has been made conductive classification of HVTL that is using LiDAR classification techniques.  Also it is determined the dangerous limits of trees and vegetation in HVTL vertical oscillation. As a result the comparison of the values of the vertical elevation  with the local methods and  obtained from the LiDAR data yielded a Root Mean Square Error (RMSE) value of 0.36m was found.

Assessment of spray drift potential reduction for hollow-cone nozzles: Part 2. LiDAR technique

Pesticide spray drift poses health hazards to humans and causes a significant impact on the environment. In this work the capacity of an ad hoc light detection and ranging (LiDAR) system to differentiate spray nozzles according to their potential drift risk is evaluated for the first time. A total of 23 drift potential tests using 10 hollow-cone nozzles were carried out with the sprayer kept in a static position. Drift potential reduction (DPR) values of between 88.6% and 93.6% were obtained when comparing standard and drift reduction nozzle types. It was also possible to order different standard nozzle sizes according to their DPR. The LiDAR signal was correlated with several droplet size parameters measured by a phase Doppler particle analyzer (PDPA), being V100 the best indicator. In the four field tests that were performed, the LiDAR system was also able to differentiate between standard and drift reduction nozzles under real application conditions, obtaining a DPR of 56.7%. The results of this work demonstrate that the developed LiDAR system is an advantageous alternative for the assessment of drift potential reduction.

Combining hyperspectral imagery and LiDAR pseudo-waveform for predicting crop LAI, canopy height and above-ground biomass

Crop Leaf area index (LAI), canopy height and above-ground biomass (AGB) are important structural parameters. Accurate predictions of these three parameters are required for improving the applications of crop growth monitoring, health status assessment and yield prediction. Airborne Light Detection and Ranging (LiDAR) system is the most reliable technique for accurately predicting vegetation structure parameters. LiDAR technique has been broadly applied to estimate vegetation LAI, height and biomass, and reliable prediction results have been obtained. However, LiDAR data lack the spectral information of vegetation. The combination of LiDAR data and hyperspectral imagery can achieve complementary advantages of two data sources and improve the prediction accuracies of vegetation parameters. In this research, we aim to estimate maize LAI, canopy height and AGB using the combined hyperspectral imagery and LiDAR pseudo-waveforms. We constructed the LiDAR pseudo-waveforms through discrete-return point clouds and extracted pseudo-waveform variables. The prediction models of maize LAI, canopy height and AGB were established with a random forest (RF) regression algorithm using the traditional statistical variables derived from discrete-return point clouds, the pseudo-waveform variables, the combined hyperspectral vegetation indices and pseudo-waveform variables, respectively. Moreover, the comparative analyses of the three prediction models were conducted to determine the optimal prediction model and explore the potential of the combined hyperspectral vegetation indices and pseudo-waveform variables for predicting maize crop structural parameters. The results showed the strong relationships between LiDAR pseudo-waveform variables and maize LAI, height, and biomass (R2 = 0.799, 0.832 and 0.871, respectively). Moreover, the pseudo-waveform variables produced better results than the results estimated from traditional statistical variables of discrete-return LiDAR (R2 = 0.772, 0.812 and 0.811, respectively). Therefore, it is a viable method for predicting maize LAI, canopy height and AGB using the LiDAR pseudo-waveforms created from discrete-return LiDAR data. Nevertheless, we found that the combined pseudo-waveform variables and vegetation indices derived from hyperspectral imagery produced a better prediction result (R2 = 0.829, 0.892 and 0.909, respectively) when compared to LiDAR pseudo-waveform data alone, and the prediction accuracies improved by 3.8%, 7.2% and 4.4%, respectively. The combined hyperspectral imagery and LiDAR pseudo-waveform data provided complementary information and therefore improved prediction accuracies of these parameters. Although small improvements were observed, the combined data have potential for improving predictions of crop parameters. Our study will provide valuable information for predicting vegetation LAI, canopy height and AGB using the combined hyperspectral imagery and pseudo-waveform constructed from discrete-return LiDAR data.

Comparison of Mobile LIDAR Technique with Terrestrial Methods in Digital Map Production

Terrestrial methods and photogrammetric method are generally used in the production of maps. In recent years, along with these two methods, the Mobile LIDAR (Light Detection and Ranging) technique has begun to be employed in the production of maps. The Mobile LIDAR technique has some advantages and disadvantages. The most important advantages of the system is it’s speed and it’s capability of scanning millions of points called point cloud in a very short time. Topographic structure and superstructure of the project area should be suitable for Mobile LIDAR technique. Besides, the cost of the system is very high and data storage problem can not be ignored. These can be listed as the disadvantages of the system. In this study, the maps of the same region produced by terrestrial methods and Mobile LIDAR technique are compared in terms of spatial accuracy and the accuracy of the Mobile LIDAR technique was examined. Approximately 30,000 detail points were measured by both methods and horizontal coordinates and orthometric heights were calculated. The differences in the positions of these 30.000 detail points obtained from both methods were lower than the limit values specified in the Large Scale Map and Map Information Production Regulation of Turkey. This means that the accuracy of the detail point locations measured by Mobile LIDAR is acceptable. Keywords : Mobile LIDAR, Digital map production, GNSS, Terrestrial methods DOI : 10.7176/JSTR/5-3-21

Feasibility Study of Ore Classification Using Active Hyperspectral LiDAR

Recently, a major effort has been made to develop methods or tools for rock characterization and mineral content mapping. Light detection and ranging (LiDAR) is an efficient active remote sensing technique for collecting geometry information about rock surfaces. However, traditional LiDAR sensors work with a single-wavelength laser source, and it is unfeasible to obtain spectral information using one LiDAR sensor. The combination of hyperspectral imaging and LiDAR techniques is an emerging method for acquiring spatial and spectral information simultaneously that allows remote mapping of high-resolution mineral content and distributions and identifies subtle chemical variations. Unfortunately, spatial and spectral data registration, which introduces additional complicated data processing, is an inevitable and essential issue for this method. In this letter, first, we investigate the feasibility of ore classification applications with hyperspectral LiDAR (HSL). HSL consists of 17 spectral channels covering the visible–shortwave infrared (SWIR) spectral range. Spatial and spectral information about seven different ore samples is obtained under a controlled laboratory environment using HSL. The standard deviation of the distance measurements is less than 1.1 cm for different spectral channels, and the classification accuracy can reach 100% if all 17 spectral measurements are used. To optimize the system design with lower cost and system complexity, a spectral band selection criterion is built based on the feature contribution degree (FCD), which is calculated using the normalized variance of the reflectance values for different ore samples at each wavelength. Two different strategies of FCD selection are tested to generate vectors: ascending sequences and descending sequences. Feature vectors with descending sequences have better classification accuracy. In addition, the results show that the classification accuracy can reach 100% with the feature vector of the seven largest FCD values compared to 59.57% for the feature vector with the seven smallest FCD values. Moreover, we find that the channels with high FCD values are primarily centered in SWIR bands. This result could be a reference for optimizing the hardware design of HSL for ore classification or mineral identification.

Laser Scanning Systems and Techniques in Rockfall Source Identification and Risk Assessment: A Critical Review

Rockfall poses risk to people, their properties and to transportation ways in mountainous and hilly regions. This catastrophe shows various characteristics such as vast distribution, sudden occurrence, variable magnitude, strong fatalness and randomicity. Therefore, prediction of rockfall phenomenon both spatially and temporally is a challenging task. Digital Terrain model (DTM) is one of the most significant elements in rockfall source identification and risk assessment. Light detection and ranging (LiDAR) is the most advanced effective technique to derive high-resolution and accurate DTM. This paper presents a critical overview of rockfall phenomenon (definition, triggering factors, motion modes and modeling) and LiDAR technique in terms of data pre-processing, DTM generation and the factors that can be obtained from this technique for rockfall source identification and risk assessment. It also reviews the existing methods that are utilized for the evaluation of the rockfall trajectories and their characteristics (frequency, velocity, bouncing height and kinetic energy), probability, susceptibility, hazard and risk. Detail consideration is given on quantitative methodologies in addition to the qualitative ones. Various methods are demonstrated with respect to their application scales (local and regional). Additionally, attention is given to the latest improvement, particularly including the consideration of the intensity of the phenomena and the magnitude of the events at chosen sites.

Estimation of hydromorphological attributes of a small forested catchment by applying the Structure from Motion (SfM) approach

Structure from Motion (SfM) represents a good low-cost alternative to generate high resolution topography where LiDAR (Light Detection and Ranging) data is scarce or unaffordable. In this work, we demonstrate the advantages of high resolution elevation models (DEM) obtained using the SfM technique to delineate catchment boundaries and the stream network. The SfM-based DEM was compared with LiDAR data, distributed by the Mexican Government, and a previous high resolution topographic map generated by a RTK-GPS system. Aerial images were collected on a forested ecohydrological monitoring site in northwest Mexico using a commercial grade digital camera attached to a tethered helium balloon. Here we applied the SfM method with the removal of the vegetation, similarly to the more advance LiDAR methods. This was achieved by adjusting the point cloud classification parameters (maximum angle, maximum distance and cell size), which to our knowledge, has not has not been reported in the available SfM literature. The SfM terrain model showed minimal differences in ground elevation in the center of the image domain (0-0.5 m) while errors increased on the edges of the domain. The SfM model generated the largest catchment area, main and total channel length (1.07 ha, 106.1 and 223 m, respectively) while LiDAR model obtained the smallest area and main channel length (0.77 ha and 92.9 m, respectively). On the other hand, the SfM model had a better and accurate representation of the river network among all models evaluated due to its closest proximity to the observed GPS-tracked main channel. We concluded that the integration of low cost unmanned aerial vehicles and the SfM method is a good alternative to estimate hydro-morphological attributes in small catchments. Furthermore, we found that high resolution SfM-based terrain models had a fairly good representation of small catchments which is useful in regions with limited data availability. The main findings of this research provide scientific value within the field of hydrological remote sensing in particular in the acquisition of high resolution topography in remote areas without access to more expensive LiDAR or survey techniques. High resolution DEMs allow for a better characterization of catchment area size and stream network delineation which influence hydrological processes (i.e. soil moisture redistribution, runoff, ET).