Lidar Data Acquisition Research Articles

Land morphology analysis with LiDAR technology to increase oil palm production

Abstract The morphological condition of the land plays an important role in determining the quality of growth of oil palm plants. Integrated management of oil palm plantations is a key factor in increasing productivity. Analysis of land morphology in oil palm plantations is a crucial first step. The objectives of this study were (1) to characterize land morphology with LiDAR implementation, (2) to explain the LiDAR mechanism in the NDVI class classification used for determining the oil palm Vegetation Index, and (3) to provide ideas to optimize oil palm productivity. The study was conducted using the image interpretation method from the acquisition of LiDAR data which has a resolution of 3 x 3 cm in the form of DEM and orthophoto to be able to perform land surface morphology analysis (MPL) and NDVI flatfoot. The data needed in this study is Orthophoto, which is used to crosscheck field conditions, plant canopy conditions, and populations of plants per block. NDVI processing is used to determine the Vegetation Index to interpret the health of oil palm plants. The results showed that LiDAR technology can be used to determine the health of oil palm plants. The overall accuracy and reliability value of NDVI reached 88.33% and 88.13%, respectively. This shows that the value of NDVI can predict the health of oil palm plants and can be used to monitor them effectively.

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.

Linking High-Resolution UAV-Based Remote Sensing Data to Long-Term Vegetation Sampling—A Novel Workflow to Study Slow Ecotone Dynamics

Unravelling slow ecosystem migration patterns requires a fundamental understanding of the broad-scale climatic drivers, which are further modulated by fine-scale heterogeneities just outside established ecosystem boundaries. While modern Unoccupied Aerial Vehicle (UAV) remote sensing approaches enable us to monitor local scale ecotone dynamics in unprecedented detail, they are often underutilised as a temporal snapshot of the conditions on site. In this study in the Southern Alps of New Zealand, we demonstrate how the combination of multispectral and thermal data, as well as LiDAR data (2019), supplemented by three decades (1991–2021) of treeline transect data can add great value to field monitoring campaigns by putting seedling regeneration patterns at treeline into a spatially explicit context. Orthorectification and mosaicking of RGB and multispectral imagery produced spatially extensive maps of the subalpine area (~4 ha) with low spatial offset (Craigieburn: 6.14 ± 4.03 cm; Mt Faust: 5.11 ± 2.88 cm, mean ± standard error). The seven multispectral bands enabled a highly detailed delineation of six ground cover classes at treeline. Subalpine shrubs were detected with high accuracy (up to 90%), and a clear identification of the closed forest canopy (Fuscospora cliffortioides, >95%) was achieved. Two thermal imaging flights revealed the effect of existing vegetation classes on ground-level thermal conditions. UAV LiDAR data acquisition at the Craigieburn site allowed us to model vegetation height profiles for ~6000 previously classified objects and calculate annual fine-scale variation in the local solar radiation budget (20 cm resolution). At the heart of the proposed framework, an easy-to-use extrapolation procedure was used for the vegetation monitoring datasets with minimal georeferencing effort. The proposed method can satisfy the rapidly increasing demand for high spatiotemporal resolution mapping and shed further light on current treeline recruitment bottlenecks. This low-budget framework can readily be expanded to other ecotones, allowing us to gain further insights into slow ecotone dynamics in a drastically changing climate.

A study of annual tree-wise LiDAR intensity patterns of boreal species observed using a hyper-temporal laser scanning time series

This study introduces the annual tree-wise intensity patterns of three boreal tree species, silver birch (Betula pendula Roth.), Scots pine (Pinus sylvestris L.), and Norway spruce (P,icea abies H. Karst.), observed from a long-term hyper-temporal point cloud dataset collected with a permanent laser scanning (LiDAR) station. An annual LiDAR intensity pattern refers to the trend of variations of tree-wise calibrated LiDAR intensity values over the course of a year, primarily linked to species-specific phenological characteristics. Such pattern was discovered from hyper-temporal (76 observations between April 2020 and April 2021) high resolution (0.01 m 3D point spacing at 100 m distance) point cloud observations acquired using a single wavelength (1550 nm) static LiDAR system installed on a climate monitoring tower at the Hyytiälä forest research station in central Finland. A set of experiments was designed to explore the interactions among tree-wise calibrated LiDAR intensity, species, data quality, and observation dates. Thus, to provide practical suggestions for expectable accuracies of tree species classification using diverse types of LiDAR systems and data acquisition strategies. It was revealed that by combining high spatial resolution (up to 100,000 points/m2) with bi-weekly observations (two scans per week except winter period), the average tree species classification accuracy reaches 96.8% for the three boreal tree species in the study area, where the few misclassified cases are attributed to inaccurate individual tree segmentation. This suggests that the annual tree-wise LiDAR intensity patterns, which reflects the tree-wise phenological dynamics, are robust and species-specific. When the spatial resolution was reduced to 5 points/ m2, the average accuracy of species classification among the three studied species reached 85% when using bi-temporal data from spring and autumn. Our analysis further indicates that arranging data acquisition with a two-week variability in late autumn and late spring could yield higher accuracy in species classification. Furthermore, our study revealed the importance of subcanopy information for species classification, specifically in distinguishing different coniferous species. When limited to upper canopy data (as in simulated airborne LiDAR scenarios), achieving a tree species classification accuracy of over 80% required either high point density (e.g. 8000 points/m2) combined with bi-temporal collection in a year, or low point density (5 points/ m2) with a dense time series (e.g., 76 time points within a year), which showcased the possibility to compensate low spatial resolution with high temporal resolution, and vice versa, in capturing species-specific phenological characteristics of trees.

Image to Image Deep Learning for Enhanced Vegetation Height Modeling in Texas

Vegetation canopy height mapping is vital for forest monitoring. However, the high cost and inefficiency of manual tree measurements, coupled with the irregular and limited local-scale acquisition of airborne LiDAR data, continue to impede its widespread application. The increasing availability of high spatial resolution imagery is creating opportunities to characterize forest attributes at finer resolutions over large regions. In this study, we investigate the synergy of airborne lidar and high spatial resolution USDA-NAIP imagery for detailed canopy height mapping using an image-to-image deep learning approach. Our main inputs were 1 m NAIP image patches which served as predictor layers and corresponding 1 m canopy height models derived from airborne lidar data, which served as output layers. We adapted a U-Net model architecture for canopy height regression, training and validating the models with 10,000 256-by-256 pixel image patches. We evaluated three settings for the U-Net encoder depth and used both 1 m and 2 m datasets to assess their impact on model performance. Canopy height predictions from the fitted models were highly correlated (R2 = 0.70 to 0.89), precise (MAE = 1.37–2.21 m), and virtually unbiased (Bias = −0.20–0.07 m) with respect to validation data. The trained models also performed adequately well on the independent test data (R2 = 0.62–0.78, MAE = 3.06–4.1 m). Models with higher encoder depths (3,4) and trained with 2 m data provide better predictions than models with encoder depth 2 and trained on 1 m data. Inter-comparisons with existing canopy height products also showed our canopy height map provided better agreement with reference airborne lidar canopy height estimates. This study shows the potential of developing regional canopy height products using airborne lidar and NAIP imagery to support forest productivity and carbon modeling at spatially detailed scales. The 30 m canopy height map generated over Texas holds promise in advancing economic and sustainable forest management goals and enhancing decision-making in natural resource management across the state.

A Method for the Precise Coordinate Determination of an Inaccessible Location.

Surveyors are occasionally tasked to with determining the coordinates of inaccessible locations or points in civil engineering applications, ground control points for photogrammetry or LiDAR data acquisition, among others. The present work outlines and investigates a novel method for estimating the GNSS coordinates of an inaccessible location where a surveying instrument cannot be set up. The procedure is based on the well-known surveying intersection method and data extracted from an Earth Gravity Model (e.g., EGM 2008). The location's coordinates are obtained from the least-squares adjustment of the angles and distances measured from at least two sites to the unknown point using a total station, within the framework of the Gauss-Helmert method. Field tests confirmed that the accuracy of the determined coordinates of the inaccessible point is at the level of 1 cm. The proposed method bypasses standard coordinate transformation steps performed with the traditional approach, directly producing geocentric coordinates of the unknown points.

A Scalable Method to Improve Large-Scale Lidar Topographic Differencing Results

Differencing digital terrain models (DTMs) generated from multitemporal airborne light detection and ranging (lidar) data provide accurate and detailed information about three-dimensional (3D) changes on the Earth. However, noticeable spurious errors along flight paths are often included in the differencing results, hindering the accurate analysis of the topographic changes. This paper proposes a new scalable method to alleviate the problematic systematic errors with a high degree of automation in consideration of the practical limitations raised when processing the rapidly increasing amount of large-scale lidar datasets. The proposed method focused on estimating the displacements caused by vertical positioning errors, which are the most critical error source, and adjusting the DTMs already produced as basic lidar products without access to the point cloud and raw data from the laser scanner. The feasibility and effectiveness of the proposed method were evaluated with experiments with county-level multitemporal airborne lidar datasets in Indiana, USA. The experimental results demonstrated that the proposed method could estimate the vertical displacement reasonably along the flight paths and improve the county-level lidar differencing results by reducing the problematic errors and increasing consistency across the flight paths. The improved differencing results presented in this paper are expected to provide more consistent information about topographic changes in Indiana. In addition, the proposed method can be a feasible solution to upcoming problems induced by rapidly increasing large-scale multitemporal lidar given recent active government-driven lidar data acquisition programs, such as the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP).

Structural and Tectonic Evolution of the Porgera Gold Mine; Highlands of Papua New Guinea

The Porgera Transfer Zone (PTZ) is a major crustal and probably lithospheric structure across Papua New Guinea recording >50 km offset of ophiolites and very different patterns of geology and topography on either side. In the Late Jurassic, the PTZ probably separated oceanic crust and thick Jurassic Om shales to the west from a continental promontory to the east. During the Late Miocene to Recent orogenesis, the differential compression of these features is interpreted to have created a dextral strike slip fault across the fold belt with pull-apart basins at sites of fault relays. This facilitated the ascent of intrusions and mineralization at Porgera. The acquisition of high-resolution LIDAR data semi-regionally around the Porgera Gold Mine greatly improved interpretation of the regional geology and particularly the recognition of normal faults. By correlating with sparse dip data and paly-dated samples, it was possible to create stratigraphic sections and interpret structural cross-sections using the LIDAR data. As the area involved strike–slip offsets, it was important to construct sections in multiple orientations in order to interpret the 3D geology. Both dips and fault orientation could be directly inferred from the LIDAR data such that sections could be constructed orthogonally to them. A balanced, restored and forward-modelled cross-section illustrates the interaction between thrust faults and normal faults during compression and that it was synchronous with the development of a pull-apart basin. A semi-regional 3D geological model, which was developed mainly from the LIDAR data, supports the hypothesis of inversion of the thick Om beds to the west before or during compression of the continental promontory to the east resulting in dextral strike–slip offsets across the PTZ. A jog or relay in the faults occurred and caused a pull-apart collapse basin to develop in the area of the Porgera mine. Similar pull-apart graben, or negative flower structures, were detected nearby and may be areas for future exploration.

A METHODOLOGY FOR CAVE FLOOR BASEMAP SYNTHESIS FROM POINT CLOUD DATA: A CASE STUDY OF SLAM-BASED LIDAR AT LAS CUEVAS, BELIZE

Abstract. Creating cave maps is an essential part of cave research. Traditional cartographic efforts are extremely time consuming and subjective, motivating the development of new techniques using terrestrial lidar scanners and mobile lidar systems. However, processing the large point clouds from these scanners to produce detailed, yet manageable “maps” remains a challenge. In this work, we present a methodology for synthesizing a basemap representing the cave floor from large scale point clouds, based on a case study of a SLAM-based lidar data acquisition from a cave system in the archaeological site of Las Cuevas, Belize. In 4 days of fieldwork, the 335 m length of the cave system was scanned, resulting in a point cloud of 4.1 billion points, with 1.6 billion points classified as part of the cave floor. This point cloud was processed to produce a basemap that can be used in GIS, where natural and anthropogenic features are clearly visible and can be traced to create accurate 2D maps similar to traditional cartography.

Literature Review of Unmanned Aerial Systems and LIDAR with Application to Distribution Utility Vegetation Management

Standardized tree risk assessment protocols are beneficial to utility vegetation management (UVM) in that they provide the most consistent qualitative assessment of a tree’s likelihood of failure, likelihood of impact, and overall risk. Yet, utility foresters do not often inspect off-right-of-way (ROW) vegetation due to constraints such as accessibility and time, which leaves many off-ROW trees unmonitored or with limited monitoring. This review focuses on the key studies addressing the application of unmanned aerial systems (UAS)-based LIDAR systems, especially in terms of UVM along electrical distribution systems. We reviewed the scientific literature in terms of the acquisition of UAS-based LIDAR data and the processing of the data. The incorporation of UAS-based LIDAR will allow utility foresters to better manage both on- and off-ROW trees to better identify tree risks and thus reduce outages and increase resiliency of distribution power lines.

Differential absorption lidar data acquisition and control system for remote detection of trace chemicals including methane and thiodiglycol.

We report the design and development of a data acquisition and control system for high-speed acquisition of weak, backscattered differential signals and synchronized sequential operation of all subsystems of a tunable, mid-infrared Differential Absorption Lidar (DIAL) system. The presence of a low-level concentration of chemical species results in weak differential return signals. The differential signal also varies dynamically with respect to background atmospheric conditions. The challenge is to measure this low level of differential signal with high resolution and also control the sequence of operation of subsystems such as lasers and scanners for real time testing and evaluation in open field conditions. The concentration spread of the chemical species varies rapidly with distance. In order to capture this spatial variation, the lidar signal should be sampled and digitized at a high sampling rate. A customized Peripheral Component Interconnect based data acquisition of a 12-bit resolution, 30 Mega samples per second sampling rate, and an industrial personal computer-based control system has been realized. Detection algorithms and the firing sequence of the laser have been developed indigenously and implemented in the LabVIEW platform. The developed graphical user interface has various modes of operation as per user requirement and is capable of executing automatic operations for the developed DIAL system in order to detect and identify the chemicals. The performance has been evaluated by detecting the chemical species thiodiglycol at 800m using 3190 and 3300nm (online and offline wavelengths) with a differential cross-section of 2.5 × 10-23m2. Similarly, methane has been detected and quantified with concentration of 2.2 ppm up to 300m using 3316 and 3326nm (online and offline wavelengths, respectively).

UAV LiDAR Metrics for Monitoring Crop Height, Biomass and Nitrogen Uptake: A Case Study on a Winter Wheat Field Trial

Efficient monitoring of crop traits such as biomass and nitrogen uptake is essential for an optimal application of nitrogen fertilisers. However, currently available remote sensing approaches suffer from technical shortcomings, such as poor area efficiency, long postprocessing requirements and the inability to capture ground and canopy from a single acquisition. To overcome such shortcomings, LiDAR scanners mounted on unmanned aerial vehicles (UAV LiDAR) represent a promising sensor technology. To test the potential of this technology for crop monitoring, we used a RIEGL Mini-VUX-1 LiDAR scanner mounted on a DJI Matrice 600 pro UAV to acquire a point cloud from a winter wheat field trial. To analyse the UAV-derived LiDAR point cloud, we adopted LiDAR metrics, widely used for monitoring forests based on LiDAR data acquisition approaches. Of the 57 investigated UAV LiDAR metrics, the 95th percentile of the height of normalised LiDAR points was strongly correlated with manually measured crop heights (R2 = 0.88) and with crop heights derived by monitoring using a UAV system with optical imaging (R2 = 0.92). In addition, we applied existing models that employ crop height to approximate dry biomass (DBM) and nitrogen uptake. Analysis of 18 destructively sampled areas further demonstrated the high potential of the UAV LiDAR metrics for estimating crop traits. We found that the bincentile 60 and the 90th percentile of the reflectance best revealed the relevant characteristics of the vertical structure of the winter wheat plants to be used as proxies for nitrogen uptake and DBM. We conclude that UAV LiDAR metrics provide relevant characteristics not only of the vertical structure of winter wheat plants, but also of crops in general and are, therefore, promising proxies for monitoring crop traits, with potential use in the context of Precision Agriculture.

Design of Lidar Data Acquisition and Control System in High Repetition Rate and Photon-Counting Mode: Providing Testing for Space-Borne Lidar

For ground-based lidars in atmospheric observation, their data acquisition unit and control unit usually work independently. They usually require the cooperation of large-volume, high-power-consumption Industrial Personal Computer (IPC). However, the space-borne lidar has high requirements on the stability and integration of the acquisition control system. In this paper, a new data acquisition and lidar control system (DALCS) was developed based on System-on-Chip Field-Programmable Gate Array (SoC FPGA) technology. It can be used in lidar systems with high repetition rate and photon-counting mode and has functions such as data storage, laser control, automatic collimation, wireless communication, and fault self-test. DALCS has two working modes: in online mode, the echo data collected by DALCS are transmitted to the computer for display in real-time and then stored with the current time as the file name; in offline mode, the data are stored in local non-volatile memory, which can be read remotely and can work autonomously when there is no IPC. The test results showed that in the frequency range of 0–70 M, the counting linearity of DALCS reached 0.9999, and the maximum relative error between the DALCS card and the standard signal source was 0.211%. The comparison results showed that the correlation coefficient between DALCS and MCS-PCI was as high as 0.99768. The DALCS was placed in a self-developed lidar sensor system for continuous observation, and the system worked stably under different weather conditions. The range-squared-corrected signal profiles obtained from the observations reflect the spatial and temporal distribution characteristics of aerosols and clouds well. This provides scheme verification and experimental support for the development of space-borne lidar data acquisition and control system.

High-Resolution Canopy Height Model Generation and Validation Using USGS 3DEP LiDAR Data in Indiana, USA

Forest canopy height model (CHM) is useful for analyzing forest stocking and its spatiotemporal variations. However, high-resolution CHM with regional coverage is commonly unavailable due to the high cost of LiDAR data acquisition and computational cost associated with data processing. We present a CHM generation method using U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) LiDAR data for tree height measurement capabilities for entire state of Indiana, USA. The accuracy of height measurement was investigated in relation to LiDAR point density, inventory height, and the timing of data collection. A simple data exploratory analysis (DEA) was conducted to identify problematic input data. Our CHM model has high accuracy compared to field-based height measurement (R2 = 0.85) on plots with relatively accurate GPS locations. Our study provides an easy-to-follow workflow for 3DEP LiDAR based CHM generation in a parallel processing environment for a large geographic area. In addition, the resulting CHM can serve as critical baseline information for monitoring and management decisions, as well as the calculation of other key forest metrics such as biomass and carbon storage.

Fast Task-Based Adaptive Sampling for 3D Single-Photon Multispectral Lidar Data

3D single-photon LiDAR imaging plays an important role in numerous applications. However, long acquisition times and significant data volumes present a challenge for LiDAR imaging. This paper proposes a task-optimized adaptive sampling framework that enables fast acquisition and processing of high-dimensional single-photon LiDAR data. Given a task of interest, the iterative sampling strategy targets the most informative regions of a scene which are defined as those minimizing parameter uncertainties. The task is performed by considering a Bayesian model that is carefully built to allow fast per-pixel computations while delivering parameter estimates with quantified uncertainties. The framework is demonstrated on multispectral 3D single-photon LiDAR imaging when considering object classification and/or target detection as tasks. It is also analysed for both sequential and parallel scanning modes for different detector array sizes. Results on simulated and real data show the benefit of the proposed optimized sampling strategy when compared to state-of-the-art sampling strategies.

Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case Study.

Collecting precise as-built data is essential for tracking construction progress. Three-dimensional models generated from such data capture the as-is conditions of the structures, providing valuable information for monitoring existing infrastructure over time. As-built data can be acquired using a wide range of remote sensing technologies, among which mobile LiDAR is gaining increasing attention due to its ability to collect high-resolution data over a relatively large area in a short time. The quality of mobile LiDAR data depends not only on the grade of onboard LiDAR scanners but also on the accuracy of direct georeferencing information and system calibration. Consequently, millimeter-level accuracy is difficult to achieve. In this study, the performance of mapping-grade and surveying-grade mobile LiDAR systems for bridge monitoring is evaluated against static laser scanners. Field surveys were conducted over a concrete bridge where grinding was required to achieve desired smoothness. A semi-automated, feature-based fine registration strategy is proposed to compensate for the impact of georeferencing and system calibration errors on mobile LiDAR data. Bridge deck thickness is evaluated using surface segments to minimize the impact of inherent noise in the point cloud. The results show that the two grades of mobile LiDAR delivered thickness estimates that are in agreement with those derived from static laser scanning in the 1 cm range. The mobile LiDAR data acquisition took roughly five minutes without having a significant impact on traffic, while the static laser scanning required more than three hours.

An assessment of large‐scale flood modelling based on LiDAR data

AbstractLarge‐scale flood modelling approaches designed for regional to continental scales usually rely on relatively simple assumptions to represent the potentially highly complex river bathymetry at the watershed scale based on digital elevation models (DEMs) with a resolution in the range of 25–30 m. Here, high‐resolution (1 m) LiDAR DEMs are employed to present a novel large‐scale methodology using a more realistic estimation of bathymetry based on hydrogeomorphological GIS tools to extract water surface slope. The large‐scale 1D/2D flood model LISFLOOD‐FP is applied to validate the simulated flood levels using detailed water level data in four different watersheds in Quebec (Canada), including continuous profiles over extensive distances measured with the HydroBall technology. A GIS‐automated procedure allows to obtain the average width required to run LISFLOOD‐FP. The GIS‐automated procedure to estimate bathymetry from LiDAR water surface data uses a hydraulic inverse problem based on discharge at the time of acquisition of LiDAR data. A tiling approach, allowing several small independent hydraulic simulations to cover an entire watershed, greatly improves processing time to simulate large watersheds with a 10‐m resampled LiDAR DEM. Results show significant improvements to large‐scale flood modelling at the watershed scale with standard deviation in the range of 0.30 m and an average fit of around 90%. The main advantage of the proposed approach is to avoid the need to collect expensive bathymetry data to efficiently and accurately simulate flood levels over extensive areas.

Design of multi-channel data acquisition system for real-time perception lidar

Lidar is the most effective means for real-time environmental awareness. Based on the demand of multi-channel perception lidar with large amount of data, real-time data transmission and miniaturization, a multi-channel parallel signal acquisition and processing system was designed based on FPGA and DSP. A self-developed three-dimensional lidar was designed, including mechanical scanning, linear array detection, data acquisition and control system. The system was implemented and verified in the three-dimensional point cloud imaging experiments. In this system, FPGA was responsible for multi-channel lidar data acquisition, control and transmission. DSP was used for data calculation and uploading the point cloud data to the upper computer through the network to realize the real-time display of point cloud. The experiments of lidar show that the proposed real-time data acquisition system could meet the requirements of point cloud calculation at 2 Mpts/s, with real time data acquisition and procession at 20 fps. The rapid calculation of lidar point cloud of the environment and obstacles is realized. It can be applied to automatic driving, navigation obstacle avoidance, perimeter security and etc.

Between Land and Sea: An Airborne LiDAR Field Survey to Detect Ancient Sites in the Chekka Region/Lebanon Using Spatial Analyses

AbstractThe interdisciplinary project “Between Land and Sea” combines geological, geomorphological and paleo-environmental approaches to identify archaeological remains of the Chekka region (Lebanon). In order to record the topography of this area, the first ever scientific airborne LiDAR data acquisition in Lebanon was conducted in autumn 2018. This work describes not only the acquisition and processing of the LiDAR data, but also the attempt to derive possible archaeological sites from the generated elevation model based on methods for spatial analysis. Using an “inverted mound” (iMound) algorithm, areas of possible settlement structures could be identified, which were classified regarding their probability of a possible ancient site using a deductive predictive model. A preliminary validation of some of the detected favoured areas using high-resolution aerial images has shown that the methods applied can provide hints to previously undiscovered sites. It was possible to identify probable ancient wall remains at several detected locations. In addition, least-cost path analyses were performed to reconstruct possible trade and transport routes from the Lebanon Mountains to the Mediterranean coast. The combination of the results of the iMound detection and classification as well as the calculated path system could point to the strategic location of the modern village of Kfar Hazir as a kind of traffic junction. Moreover, reconstructed main transport routes provide indications of heavily frequented roads and may form the basis for further investigations. To validate the results, upcoming field surveys will be realized on site.

A Combined Radar & Lidar System Based on Integrated Photonics in Silicon-on-Insulator

This work reports the first realization of a combined radar&lidar system based on silicon-on-insulator photonic integrated circuit (PIC). The software-defined architecture comprises a frequency-flexible and simultaneous multi-band radar operation and an advanced lidar with coherent detection for range and velocity measurements. Both systems are implemented within a single chip, allowing a coherent radar and lidar parallel data acquisition in order to take advantage of their complementary characteristics. This feature potentializes the study of complex environments providing a more complete cognition of the target or scene under observation. Here, we present the first sets of results of the radar and lidar subsystems characterization without any auxiliary frontend that confirms the large frequency flexibility of the radar system and the great potentiality for high resolution range and velocity measurements of the lidar system. These results open the way to an innovative generation of transceivers in the field of microwave photonics, potentially outperforming current systems thanks to its multiband and multifunction capability owing to a unique hybrid radar&lidar coherent architecture. Moreover, by operating in the photonic domain, radar and lidar subsystems are able to share the same optical source and digital electronics unit, allowing for an ultimate reduction of size, weight, and power consumption, thus making the proposed architecture suitable for the most stringent applications.