Light Detection And Ranging Research Articles

Machine learning-based prediction approach for ranging resolution enhancement of FMCW LiDAR system with LSTM networks

Ranging resolution is one of the most important factors of frequency-modulated continuous wave (FMCW) light detection and ranging (LiDAR) systems, which is determined by the effective sweep bandwidth and observation time of the ranging signal used for Fourier transform. Conventional methods for resolution enhancement either require expensive devices or complicated operation with human intervention, which is high-cost and inefficient. For the first time, a machine learning-based prediction approach is proposed for resolution improvement. A long short-term memory (LSTM) network with a special architecture, which is driven by a theoretical model of FMCW ranging signals, is established for signal prediction. A resampling-based preprocessing block is then designed to denoise the original ranging data and enhance its feature. Finally, multi-step prediction of the input time-series signal is performed using the well-trained LSTM network to increase effective data length so as to enhance the resolution. The effectiveness of the proposed method is verified through several experiments. The results show that the proposed method not only has the advantages of low-cost and high-efficiency, but also enables the FMCW LiDAR system to possess larger equivalent bandwidth, better ranging resolution, and higher signal-to-noise ratio (SNR), enlightening the development of machine learning in high-precision ranging systems.

Exploitation of the Number of Return Echoes for DTM Extraction from Point Clouds Acquired by LiDAR UAS DJI Zenmuse L1

Abstract. Following the enormous technological developments of LiDAR (Light Detection And Ranging) sensors, it is currently easier to find them commercially in the UA (Uncrewed Aerial Systems) sector. In particular, with the Zenmuse L1 by DJI (Dà-Jiāng Innovations) the market has grown globally, mainly due to the compactness of the product that is easily compatible with UAS. The L1 sensor can record up to three returns of the emanating signal, so it can acquire a larger amount of points, such as those below the vegetation. Therefore, in addition to the geometric information of the points, the Zenmuse L1 point clouds also provide other information, such as the number of echo returns from 1 to 3. This data could be exploited to improve the automatic extraction of the digital terrain model (DTM) from the point clouds, hopefully leading to the avoidance of manual correction. This research aims to focus on evaluating whether the addition of the return number feature can affect the identification of the ground points through different computational methods and can improve the time efficiency of state-of-the-art algorithms.

Development of a Precise Tree Structure from LiDAR Point Clouds

Abstract. A precise tree structure that represents the distribution of tree stem, branches, and leaves is crucial for accurately capturing the full representation of a tree. Light Detection and Ranging (LiDAR)-based three-dimensional (3D) point clouds (PCs) capture the geometry of scanned objects including forests stands and individual trees. PCs are irregular, unstructured, often noisy, and contaminated by outliers. Researchers have struggled to develop methods to separate leaves and wood without losing the tree geometry. This paper proposes a solution that employs only the spatial coordinates (x, y, z) of the PC. The new algorithm works as a filtering approach, utilizing multi-scale neighborhood-based geometric features (GFs) e.g., linearity, planarity, and verticality to classify linear (wood) and non-linear (leaf) points. This involves finding potential wood points and coupling them with an octree-based segmentation to develop a tree architecture. The main contributions of this paper are (i) investigating the potential of different GFs to split linear and non-linear points, (ii) introducing a novel method that pointwise classifies leaf and wood points, and (iii) developing a precise 3D tree structure. The performance of the new algorithm has been demonstrated through terrestrial laser scanning PCs. For a Scots pine tree, the new method classifies leaf and wood points with an overall accuracy of 97.9%.

Comparison of Deep and Machine Learning Approaches for Quebec Tree Species Classification Using a Combination of Multispectral and LiDAR Data

Accessing tree species information is required for making appropriate decisions in forest management. 3D photo-interpretation using high spatial resolution aerial imagery is used to provide information on tree species in the province of Quebec. However, the shortage of qualified interpreters and the increasing costs of 3D photo-interpretation have affected the production of the forest inventory. In this study, we employed deep and machine learning models to classify nine tree species (i.e., paper birch, yellow birch, red maple, poplar, black spruce, white spruce, tamarack, jack pine, and balsam fir). We used a combination of spectral and vertical structural information extracted from 30-cm aerial imagery and airborne light detection and ranging (LiDAR) data in a 9 , 100 km 2 forested area in Quebec, Canada. The results indicated that Dense Convolution Network (DenseNet) achieved the best overall accuracy of 78%, outperforming machine learning methods by 5%. In addition, the models’ performance was independently assessed using permanent and temporary ground sample plots, acknowledging the superiority of DenseNet in terms of overall accuracy (73%) in predicting the dominant species. Our results suggest that the combination of aerial imagery and airborne LiDAR data, using deep learning approaches, can be applied to accurately map tree species.

Lightweight environment sensing algorithm for intelligent driving based on improved YOLOv7

AbstractAccurately and quickly detecting obstacles ahead is a prerequisite for intelligent driving. The combined detection scheme of light detection and ranging (LiDAR) and the camera is far more capable of coping with complex road conditions than a single sensor. However, immediately afterward, ensuring the real‐time performance of the sensing algorithms through a significantly increased amount of computation has become a new challenge. For this purpose, the paper introduces an improved dynamic obstacle detection algorithm based on YOLOv7 (You Only Look Once version 7) to overcome the drawbacks of slow and unstable detection of traditional methods. Concretely, Mobilenetv3 supplants the backbone network utilized in the original YOLOv7 architecture, thereby achieving a reduction in computational overhead. It integrates a specialized layer for the detection of small‐scale targets and incorporates a convolutional block attention module to enhance detection efficacy for diminutive obstacles. Furthermore, the framework adopts the Efficient Intersection over Union Loss function, which is specifically designed to mitigate the issue of mutual occlusion among detected objects. On a dataset consisting of 27,362 labelled KITTI data samples, the improved YOLOv7 algorithm achieves 92.6% mean average precision and 82 frames per second, which reduces the Model_size by 85.9% and loses only 1.5% accuracy compared with the traditional YOLOv7 algorithm. In addition, this paper builds a virtual scene to test the improved algorithm and fuses LiDAR and camera data. Experimental results conducted on a test vehicle equipped with a camera and LiDAR sensor demonstrate the effectiveness and significant performance of the method. The improved obstacle detection algorithm proposed in this research can significantly reduce the computational cost of the environment perception task, meet the requirements of real‐world applications, and is crucial for achieving safer and smarter driving.

APPLICATION OF LASER SCANNING DATA IN SOLVING TERRITORIAL PLANNING PROBLEMS

The article aims to thoroughly investigate and analyse the possibilities of using laser scanning data in spatial planning. The main goal is to identify the potential of this technology to improve the efficiency and accuracy of planning processes, particularly in the urban environment. The LiDAR (Light Detection and Ranging) data integration into territorial planning is fundamental to modern urban management frameworks. Using the precise and comprehensive geospatial data obtained through LiDAR scanning provides a reliable basis for conducting in-depth analysis and strategic decision-making in territorial development initiatives. LiDAR technology offers unparalleled accuracy and detail, enabling urban planners to effectively map and visualise various aspects of urban environments, including topography, land use patterns, infrastructure, and natural features. With this extensive information, stakeholders can gain valuable insights into existing spatial dynamics, identify potential areas for development or conservation, and assess the impact of proposed projects on the surrounding landscape. Moreover, integrating LiDAR data facilitates the optimisation of urban resource management processes. City authorities can streamline land use planning, infrastructure development, and environmental management efforts by accurately delineating land plots and infrastructure networks. In turn, it promotes more efficient allocation of resources, minimises operational costs, and enhances overall urban sustainability. Furthermore, LiDAR technology is crucial in enhancing urban resilience and adaptability. Generating high-resolution elevation models and floodplain maps allows urban planners to identify areas prone to natural hazards such as floods, landslides, or coastal erosion. With this knowledge, city planners can implement proactive measures to mitigate risks, improve disaster preparedness, and safeguard community well-being. In contemporary urban challenges, such as climate change and rapid urbanisation, LiDAR technology is becoming increasingly indispensable. By embracing LiDAR technology, urban planners can develop more resilient and sustainable cities better equipped to withstand and adapt to evolving environmental, social, and economic pressures. In essence, the effective utilisation of LiDAR data in territorial planning enhances spatial planning accuracy and fosters innovation, resilience, and sustainability within urban environments. By harnessing the power of LiDAR technology, cities can pave the way toward a more prosperous, equitable, and resilient future for generations to come. Keywords: data, laser scanning, territorial planning, potential, accuracy, efficiency, analysis, prospects.

Reconstruction algorithm of Gm-APD LiDAR based on synchronized pseudo-random time coding

Efficient photon counting imaging of long-range targets is challenging, especially in low signal-to-back ratio (SBR) situations. In this paper, a synchronized pseudo-random time coding detection method for Geiger-mode avalanche photodiode (Gm-APD) light detection and ranging (LiDAR) is proposed, which realizes Gm-APD LiDAR long-range detection by combining pseudo-random time coding and time of flight, and the multiple peaks are used to de-noise the image at the image level. Scenes of building with different SBR are simulated with Monte Carlo methods, and the effect of coding length on algorithm performance is discussed and compared with the performance of non-coding algorithms. The high performance of the proposed algorithm is verified by field experiments in low SBR scenes. Moreover, the proposed algorithm improves the target reconstruction correctness ratio by 70% over the other non-coding algorithms to 83% in the scene of building with SBR of 0.015, and has the smallest root mean square error (RMSE) of the distance. It improves the target integrity and provides the possibility of practical application of LiDAR target detection in very low SBR and long-range conditions, and even more support for target feature extraction and target recognition.

Coaxial transceiving LiDAR based on a silicon photonic optical phased array.

A high performance optical phased array (OPA) combined with frequency-modulated continuous-wave (FMCW) technology is essential for coherent all-solid-state light detection and ranging (LiDAR). In this work, we propose and experimentally demonstrate a coaxial transceiver based on a single OPA for a LiDAR system, which releases the off-chip circulator and collimator. The proposed scheme is demonstrated on the commonly used silicon-on-insulator (SOI) platform. For realizing the long optical grating antenna with only one-step etching, the bound state in the continuum is harnessed to simplify the fabrication process and ease the fabrication precision. Experimental results indicate that the OPA is with 0.076° vertical beam divergence under a 1.5 mm-long grating antenna. The measured field of view (FOV) is 40° × 8° without grating lobes under a wavelength band of 60 nm. The coaxial transceiver of the single OPA is also demonstrated with the FMCW method for ranging measurement at different angles.

Indicators for monitoring reduced impact logging in the Brazilian amazon derived from airborne laser scanning technology

Monitoring reduced impact logging (RIL) activities in sustainably managed forest areas in the Amazon is a costly and complex, yet crucial endeavor. One viable monitoring option is the use of airborne laser scanning (LiDAR), which enables estimating forest structure parameters over large areas in a reduced timeframe with high accuracy. In this study, we propose and assess the applicability of five monitoring indicators for RIL based on Light Detection and Ranging (LiDAR) data acquisition in areas under forest concession. Five Annual Production Units (APUs) were investigated within the Forest Management Unit (FMU) III of the Jamari National Forest, located in the Southwest of the Brazilian Amazon. These sites were surveyed by LiDAR in 2014 and 2015 (one year after the exploration). Digital Terrain Models (DTMs), Surface Models (DSMs), and Canopy Height Models (CHMs) were generated for each APU. The proposed indicators were: i. Detection and identification of crown removal in dominant and co-dominant trees above 30 m; ii. Gap detection in the forest canopy; iii. Impacts of Reduced Impact Logging on the Understory; iv. Changes in the vertical canopy profile; and v. Affected areas within Permanent Preservation Areas (PPAs) and restricted areas. There was a 3.95% reduction in the occurrence of taller canopies after RIL, and a higher occurrence of small gaps (λ > 1), with λ values (2.34) being higher in the area with the oldest logging history (APU 1). Gini coefficient values in all APUs were below 0.5, indicating a low intensity of disturbances in the forest canopy. The shape (γ) and scale (β) parameters of the understory and canopy were not significantly correlated with variables related to selective logging. Restricted areas were considered for the allocation of roads, trails, log landings, and places with slopes equal to or >15%, and the indices of areas affected by RIL in PPAs and restricted areas were <2%. The proposed indicators using LiDAR data show great potential for monitoring managed areas in the Amazon and can be utilized by concession companies and government oversight.

Efficient Convex-Hull-Based Vehicle Pose Estimation Method for 3D LiDAR

Vehicle pose estimation with light detection and ranging (LiDAR) is essential in the perception technology of autonomous driving. However, because of incomplete observation measurements and sparsity of the LiDAR point cloud, it is challenging to achieve satisfactory pose extraction based on 3D LiDAR with the existing pose estimation methods. In addition, the demand for real-time performance further increases the difficulty of the pose estimation task. In this paper, we propose a novel vehicle pose estimation method based on the convex hull. The extracted 3D cluster is reduced to the convex hull, reducing the subsequent computation burden while preserving essential contour information. Subsequently, a novel criterion based on the minimum occlusion area is developed for the search-based algorithm, enabling accurate pose estimation. Additionally, this criterion renders the proposed algorithm particularly well-suited for obstacle avoidance. The proposed algorithm is validated on the KITTI dataset and a manually labeled dataset acquired at an industrial park. The results demonstrate that our proposed method can achieve better accuracy than the classical pose estimation method while maintaining real-time speed.

Insulator Extraction from UAV LiDAR Point Cloud Based on Multi-Type and Multi-Scale Feature Histogram

Insulators are key components to ensure the normal operation of power facilities in transmission corridors. Existing insulator identification methods mainly use image data and lack the acquisition of three-dimensional information. This paper proposes an efficient insulator extraction method based on UAV (unmanned aerial vehicle) LiDAR (light detection and ranging) point cloud, using five histogram features: horizontal density (HD), horizontal void (HV), horizontal width (HW), vertical width (VW) and vertical void (VV). Firstly, a voxel-based method is employed to roughly extract power lines and pylons from the original point cloud. Secondly, the VV histogram is used to categorize the pylons into suspension and tension types, and the HD histogram is used to locate the tower crossarm and further refine the roughly extracted powerlines. Then, for the suspension tower, insulators are segmented based on the HV histogram and HD difference histogram. For the tension tower, the HW histogram is used to recognize the jumper conductor (JC) and transmission conductor (TC) from the power line. The HW histogram and VW histogram are used to extract the tension insulator in the TC and suspension insulator in the JC, respectively. Finally, considering the problem of setting a suitable grid width when constructing the feature histogram, an adaptive method of multi-scale histograms is proposed to refine the extraction result. Two 220 kV long transmission lines are used for the validation, and the overall object-based accuracy for suspension and tension towers are 100% and 97.3%, respectively. Compared with the point feature-based method, the mean F1 score of the proposed method improved by 0.3, and the runtime for each tower is within 2 s.

Forest canopy height modelling based on photogrammetric data and machine learning methods

AbstractForest topographic survey is a problem that photogrammetry has not solved for a long time. Forest canopy height is a crucial forest biophysical parameter which is used to derive essential information about forest ecosystems. In order to construct a canopy height model in forest areas, this study extracts spectral feature factors from digital orthophoto map and geometric feature factors from digital surface model, which are generated through aerial photogrammetry and LiDAR (light detection and ranging). The maximum information coefficient, Pearson, Kendall, Spearman correlation coefficients, and a new proposed index of relative importance are employed to assess the correlation between each feature factor and forest vertical heights. Gradient boosting decision tree regression is introduced and utilised to construct a canopy height model, which enables the prediction of unknown canopy height in forest areas. Two additional machine learning techniques, namely random forest regression and support vector machine regression, are employed to construct canopy height model for comparative analysis. The data sets from two study areas have been processed for model training and prediction, yielding encouraging experimental results that demonstrate the potential of canopy height model to achieve prediction accuracies of 0.3 m in forested areas with 50% vegetation coverage and 0.8 m in areas with 99% vegetation coverage, even when only a mere 10% of the available data sets are selected as model training data. The above approaches present techniques for modelling canopy height in forested areas with varying conditions, which have been shown to be both feasible and reliable.

Synergy of remote sensing data collected with low-cost mobile mapping platform for detection and prediction of damages: a park alley case study

Data synergy involves acquiring and combining data from different sensors to achieve better problem analysis and research results. For more comprehensive data analysis, the sensors are not only mounted on one platform. Still, they should also be compatible with software and hardware, e.g. for the same timestamp registration by different sensors. The aim of this article is to propose the synergy between various remote sensing sensors including the ground penetrating radar (GPR), LiDAR (Light Detection and Ranging) sensor and three photogrammetric RGB cameras for damage detection in a pavement in a park alley. The data were acquired with a low-cost platform, in the Pole Mokotowskie Park in Warsaw, Poland. Three drives were made along the same path with the platform, so it was possible to assess the repeatability of the data. Based on the GPR data, orthophotomap, and digital terrain model (DTM) from images, an analysis of the cracks in the pavement was done. The paper proves additive value from the synergy of data collected for the alley also in the form of a common visualization of acquired data. Results presented in the article showed that using mobile mapping platform and technologies describing the situation above and below the ground level enable a more detailed analysis and inspection of the damages in the park alley.

CFD analysis of local influences in offshore wind measurements employing a floating LiDAR system

In the present work, the CFD modeling of neutral and stable atmospheric boundary layers regarding the horizontal homogeneity condition is discussed to assess the impact of local influences in wind measurements made by a Light Detection and Ranging (LiDAR) remote sensor installed at an offshore site. The LiDAR will be mounted on a real floating, production, storage, and offloading (FPSO) vessel, 200 km far from the coast, and the simulations serve as preliminary study for the impact of the geometry presence, in the air flow and in the measurements taken by the LiDAR. The reproduction of the homogeneity condition is discussed and the impacts of the presence of the ship structure are quantified considering the scanning scheme of the LiDAR. Turbulence is modeled with the k-ε and k-ω models for the neutral case, and k-ε with a modification following the Monin-Obukhov Similarity Theory for the stable boundary laye. By carrying out simulations with empty and blocked (i.e., with the FPSO installed) domains, we show that the effect of the platform is local and only significant at small heights. The neutral and stable cases show similar deviations (≤ 2 %) between the velocity fields in the blocked and empty domains. The neutral case shows locally a little more impact than the stable case, and the reason for that is discussed. We also found that the LiDAR scanning scheme could attenuate the impacts of the flow distortion in comparison with direct punctual measurements.

Incorporating multi-source remote sensing in the detection of earthquake-damaged buildings based on logistic regression modelling

After an earthquake, efficiently and accurately acquiring information about damaged buildings can help reduce casualties. Earth observation data have been widely used to map affected areas after earthquakes. However, fine post-earthquake assessment results are needed to manage recovery and reconstruction and to estimate economic losses. In this paper, for quantification and precision purposes, a method of earthquake-induced building damage information extraction incorporating multi-source remote sensing data is proposed. The method consists of three steps： (1) Analysis of multisource features that describe texture, colour, and geometry, (2) rough set theory is carried out to further determine the feature parameters, (3) Logistic regression model (LRM) was built to describe the relationship between the occurrence and absence of destroyed buildings within an individual object. Old Beichuan County (centered at approximately 31.833︒N, 104.459° E), China, the area most devastated by the Wenchuan earthquake on May 12, 2008, is used to test the proposed hypothesis. Multi-source remote sensing imagery include optical data, synthetic aperture radar (SAR) data, and digital surface model (DSM) data generated by interpolating light detection and ranging (LiDAR) point cloud data. Through comparison with the ground survey, the experimental results show that the detection accuracy of the proposed method is 94.2 %; the area under the receiver operating characteristic (ROC) curve is 0.827. The efficiency of the proposed method is demonstrated using 6 modes of data combination acquired from the same area in old Beichuan County. The approach is one of the first attempts to extract damaged buildings through the fusion of three types of data with different features. The approach addresses multivariate regression methodologies and compares the potential of features for application in the damage detection field.

Mengekstrak Parameter Fungsi Skala Kanopi di Kawasan Konservasi Lembah Danum, Sabah Menggunakan Data Pesawat Udara LiDAR

Forest restoration is an effort to restore the growth and balance degraded forest ecosystems, through various approaches such as reforestation, land restoration, endangered species conservation, and forest fire prevention. Conventional field data collection challenges, such as limited spatial coverage, tall tree measurement difficulties, and human observation errors, hinder canopy-scale functional traits monitoring and extraction across forest treatments. The study addresses the importance of forest restoration and the challenges associated with conventional field measurements in extracting canopy scale functional traits by utilizing Airborne light detection and ranging (LiDAR) technology for more accurate information involving the old growth, natural regeneration, and active restored forests in Danum Valley Conservation Area and INFAPRO Sabah. The study utilized diverse processing algorithms, allometric equations, and correlative modeling methods. At the grid level, various LiDAR functional traits were calculated, leaf area index, gap fraction, and canopy density, alongside assessment of multiple correlative modeling strategies. Linear regression and analysis of variance analyzed relationships between LiDAR-derived and field-derived canopy scale functional traits between different forest treatments. The results showed high R-squared values ranging from 0.73 to 0.91, Anova F-statistics and probability values (p) showed that there is a strong relationship between the field data and predicted LiDAR values for three-canopy scale functional traits in different forest treatment types. These results indicate that LiDAR technology is effective in predicting canopy scale functional traits and has the potential to provide accurate and detailed information on forest restoration areas for future conservation and management efforts.

Mapping global mangrove canopy height by integrating Ice, Cloud, and Land Elevation Satellite-2 photon-counting LiDAR data with multi-source images

Large-scale and precise measurement of mangrove canopy height is crucial for understanding and evaluating wetland ecosystems' condition, health, and productivity. This study generates a global mangrove canopy height map with a 30 m resolution by integrating Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) photon-counting light detection and ranging (LiDAR) data with multi-source imagery. Initially, high-quality mangrove canopy height samples were extracted using meticulous processing and filtering of ICESat-2 data. Subsequently, mangrove canopy height models were established using the random forest (RF) algorithm, incorporating ICESat-2 canopy height samples, Sentinel-2 data, TanDEM-X DEM data and WorldClim data. Furthermore, a global 30 m mangrove canopy height map was generated utilizing the Google Earth Engine platform. Finally, the global map's accuracy was evaluated by comparing it with reference canopy heights derived from both space-borne and airborne LiDAR data. Results indicate that the global 30 m resolution mangrove height map was found to be consistent with canopy heights obtained from space-borne (r = 0.88, Bisa = −0.07 m, RMSE = 3.66 m, RMSE% = 29.86 %) and airborne LiDAR (r = 0.52, Bisa = −1.08 m, RMSE = 3.39 m, RMSE% = 39.05 %). Additionally, our findings reveal that mangroves worldwide exhibit an average height of 12.65 m, with the tallest mangrove reaching a height of 44.94 m. These results demonstrate the feasibility and effectiveness of using ICESat-2 data integrated with multi-source imagery to generate a global mangrove canopy height map. This dataset offers reliable information that can significantly support government and organizational efforts to protect and conserve mangrove ecosystems.

Multi-source remote sensing-based landslide investigation: the case of the August 7, 2020, Gokseong landslide in South Korea

Landslides pose a growing concern worldwide, emphasizing the need for accurate prediction and assessment to mitigate their impact. Recent advancements in remote sensing technology offer unprecedented datasets at various scales, yet practical applications demand further case studies to fully integrate these technologies into landslide analysis. This study presents a case study approach to fully leverage variety of multi-source remote sensing technologies for analyzing the characteristics of a landslide. The selected case is a landslide with a long runout debris flow that occurred in Gokseong County, South Korea, on August 7, 2020. The chosen multi-source technologies encompass digital photogrammetry using RGB and multi-spectral imageries, 3D point clouds acquired by light detection and ranging (LiDAR) mounted on an unmanned aerial vehicle (UAV), and satellite interferometric synthetic aperture radar (InSAR). The satellite InSAR analysis identifies the initial displacement, triggered by rainfall and later transforming into a debris flow. The utilization of digital photogrammetry, employing UAV-RGB and multi-spectral image data, precisely delineates the extent affected by the landslide. The landslide encompassed a runout distance of 678 m, featuring an initiation zone characterized by an average slope of 35°. Notably, the eroded and deposited areas measured 2.55 × 104 m2 and 1.72 × 104 m2, respectively. The acquired UAV-LiDAR data further reveal the eroded and deposited landslide volumes approximately measuring 5.60 × 104 m3 and 1.58 × 104 m3, respectively. This study contributes a valuable dataset on a rainfall-induced landslide with a long runout debris flow, underscoring the effectiveness of multi-source remote sensing technology in monitoring and comprehending complex landslide events.

Improving the Accuracy of Digital Terrain Models Using Drone-Based LiDAR for the Morpho-Structural Analysis of Active Calderas: The Case of Ischia Island, Italy

Over the past two decades, the airborne Light Detection and Ranging (LiDAR) system has become a useful tool for acquiring high-resolution topographic data, especially in active tectonics studies. Analyzing Digital Terrain Models (DTMs) from LiDAR exposes morpho-structural elements, aiding in the understanding of fault zones, among other applications. Despite its effectiveness, challenges persist in regions with rapid deformation, dense vegetation, and human impact. We propose an adapted workflow transitioning from the conventional airborne LiDAR system to the usage of drone-based LiDAR technology for higher-resolution data acquisition. Additionally, drones offer a more cost-effective solution, both in an initial investment and ongoing operational expenses. Our goal is to demonstrate how drone-based LiDAR enhances the identification of active deformation features, particularly for earthquake-induced surface faulting. To evaluate the potential of our technique, we conducted a drone-based LiDAR survey in the Casamicciola Terme area, north of Ischia Island, Italy, known for the occurrence of destructive shallow earthquakes, including the 2017 Md = 4 event. We assessed the quality of our acquired DTM by comparing it with existing elevation datasets for the same area. We discuss the advantages and limitations of each DTM product in relation to our results, particularly when applied to fault mapping. By analyzing derivative DTM products, we identified the fault scarps within the Casamicciola Holocene Graben (CHG) and mapped its structural geometry in detail. The analysis of both linear and areal geomorphic features allowed us to identify the primary factors influencing the current morphological arrangement of the CHG area. Our detailed map depicts a nested graben formed by two main structures (the Maio and Sentinella faults) and minor internal faults (the Purgatorio and Nizzola faults). High-resolution DEMs acquired by drone-based LiDAR facilitated detailed studies of the geomorphology and fault activity. A similar approach can be applied in regions where the evidence of high slip-rate faults is difficult to identify due to vegetation cover and inaccessibility.

Impact of long-term application of biosolids to surface water nitrogen in a working forest plantation in King County, Washington

Forest fertilization with municipal biosolids has been shown to increase tree growth and enhance forest soils. However, there are concerns that nitrogen from the biosolids could impact surface waters through movement from subsurface flow. Here we analyzed data on soil and surface water nitrogen from a working tree plantation that has used biosolids for over three decades to see if there was evidence of N movement through the soil to surface waters. GIS (Geographic Information System) was used to map application units over time and LiDAR (Light Detection and Ranging) was used to delineate watersheds. The program is located in King County Washington with biosolids provided by the King County Wastewater Treatment program. We assembled records to determine if there is any evidence of movement of NO3− through soils or any enrichment in surface waters. While soils show evidence of NO3− enrichment following biosolids application with cumulative loading rates up to 26 Mg ha−1, this is generally limited to the ’A’ soil horizon and does not increase linearly with increased biosolids loading rates. There was no indication of increased surface water NO3− concentration relative to biosolids application rates, with a small trend of decreasing water NO3− over time. Surface water NO3− concentration was not correlated with the fraction of the watershed area that had been amended with biosolids, and there was no observable increase in surface water NO3− with increased frequency of biosolids applications to the tree plantations. These results suggest that the current biosolids program is sufficiently protective of ground and surface waters. These observations suggest that biosolids application can be conducted on a large scale with multiple benefits and no discernible impact to surface waters.