Light Detection And Ranging Intensity Research Articles

Combining LiDAR and Spaceborne Multispectral Data for Mapping Successional Forest Stages in Subtropical Forests

The Brazilian Atlantic Rainforest presents great diversity of flora and stand structures, making it difficult for traditional forest inventories to collect reliable and recurrent information to classify forest succession stages. In recent years, remote sensing data have been explored to save time and effort in classifying successional forest stages. However, there is a need to understand if any of these sensors stand out for this purpose. Here, we evaluate the use of multispectral satellite data from four different platforms (CBERS-4A, Landsat-8/OLI, PlanetScope, and Sentinel-2) and airborne light detection and ranging (LiDAR) to classify three forest succession stages in a subtropical ombrophilous mixed forest located in southern Brazil. Different features extracted from multispectral and LiDAR data, such as spectral bands, vegetation indices, texture features, and the canopy height model (CHM) and LiDAR intensity, were explored using two conventional machine learning methods such as random trees (RT) and support vector machine (SVM). The statistically based maximum likelihood (MLC) algorithm was also compared. The classification accuracy was evaluated by generating a confusion matrix and calculating the kappa index and standard deviation based on field measurements and unmanned aerial vehicle (UAV) data. Our results show that the kappa index ranged from 0.48 to 0.95, depending on the chosen dataset and method. The best result was obtained using the SVM algorithm associated with spectral bands, CHM, LiDAR intensity, and vegetation indices, regardless of the sensor. Datasets with Landsat-8 or Sentinel-2 information performed better results than other optical sensors, which may be due to the higher intraclass variability and less spectral bands in CBERS-4A and PlanetScope data. We found that the height information derived from airborne LiDAR and its intensity combined with the multispectral data increased the classification accuracy. However, the results were also satisfactory when using only multispectral data. These results highlight the potential of using freely available satellite information and open-source software to optimize forest inventories and monitoring, enabling a better understanding of forest structure and potentially supporting forest management initiatives and environmental licensing programs.

Longitudinal Degradation of Pavement Marking Detectability for Mobile LiDAR Sensing Technology in Real-World Use

Recent advancements in vehicle automation and driver-assistance systems that detect pavement markings has increased the importance of the detectability of pavement markings through various sensor modalities across weather and road conditions. Among the sensing techniques, light detection and ranging (LiDAR) sensors have become popular for vehicle-automation applications. This study used low-cost mobile multi-beam LiDAR to assess the performance of several types of pavement marking materials installed on a limited-access highway in various conditions, and quantified the degradation in detection performance over three years. Four marking materials, HPS-8, polyurea, cold plastic, and sprayable thermoplastic, were analyzed in the current study. LiDAR reflectivity data extracted from a total of 210 passes through the test sections were analyzed. A new detectability score based on LiDAR intensity data was proposed to quantify the marking detectability. The results showed that the pavement marking detectability varied across the material types over the years. The results provide guidance for selecting materials and developing maintenance schedules when marking detectability by LiDAR is a concern.

Marine Habitat Mapping Using Bathymetric LiDAR Data: A Case Study from Bonne Bay, Newfoundland

Marine habitats provide various benefits to the environment and humans. In this regard, an accurate marine habitat map is an important component of effective marine management. Newfoundland’s coastal area is covered by different marine habitats, which should be correctly mapped using advanced technologies, such as remote sensing methods. In this study, bathymetric Light Detection and Ranging (LiDAR) data were applied to accurately discriminate different habitat types in Bonne Bay, Newfoundland. To this end, the LiDAR intensity image was employed along with an object-based Random Forest (RF) algorithm. Two types of habitat classifications were produced: a two-class map (i.e., Vegetation and Non-Vegetation) and a five-class map (i.e., Eelgrass, Macroalgae, Rockweed, Fine Sediment, and Gravel/Cobble). It was observed that the accuracies of the produced habitat maps were reasonable considering the existing challenges, such as the error of the LiDAR data and lacking enough in situ samples for some of the classes such as macroalgae. The overall classification accuracies for the two-class and five-class maps were 87% and 80%, respectively, indicating the high capability of the developed machine learning model for future marine habitat mapping studies. The results also showed that Eelgrass, Fine Sediment, Gravel/Cobble, Macroalgae, and Rockweed cover 22.4% (3.66 km2), 51.4% (8.39 km2), 13.5% (2.21 km2), 6.9% (1.12 km2), and 5.8% (0.95 km2) of the study area, respectively.

Recognition of ecological vegetation fairy circles in intertidal salt marshes from UAV LiDAR point clouds

• Fairy circles are detected from 3D point clouds of UAV LiDAR for the first time. • A new model is proposed to correct the intensity data of UAV LiDAR. • Merely corrected intensity data can reliably be used for point cloud filtering. • Absent instantaneous UAV LiDAR scanning distances and scan angles are recovered. • The proposed method is promising for spatial patterns recognition in other scenes. Fairy circles (FC) are a type of spatial self-organized patterns that widely exist in various vegetation ecosystems and the accurate detection and quantitative characterization of these mysterious circles remain a technical challenge. In this study, vegetation FC in intertidal salt marshes are recognized from the derived reflectance information (backscattered intensity) and geometric quantities of light detection and ranging (LiDAR) carried on unmanned aerial vehicle (UAV). The specular effect on the UAV LiDAR intensity data over nadir regions of wet salt marshes is eliminated using the laser radar equation and Phong model where the absent distances and incidence angles are approximately retrieved on the basis of geometric and temporal relations in data collection. The FC are progressively recovered through three interconnected procedures. First, the retrieved reflectance information is used to discriminate the mudflat and vegetation points. Second, a spatial connectivity clustering algorithm is utilized on the extracted vegetation points to form individual spatially disconnected clusters. Finally, FC and regular vegetation are successfully recognized by jointly using the salient, size, and circularity features of the generated clusters. A multi-echo UAV LiDAR system is used for data collection at an intertidal salt marsh to assess the feasibility and prospects of the proposed method. Taking the manual detection results from the orthophoto generated by images of a UAV camera system as a reference, the missing detection rate, false detection rate, and area detection error of the proposed method are 6%, 9%, and 5%, respectively. Results suggest that UAV LiDAR is an extremely promising technique to characterize the geometric properties (e.g., location, size, and quantity) of FC from a holistic perspective.

LiDAR Intensity Completion: Fully Exploiting the Message from LiDAR Sensors.

Light Detection and Ranging (LiDAR) systems are novel sensors that provide robust distance and reflection strength by active pulsed laser beams. They have significant advantages over visual cameras by providing active depth and intensity measurements that are robust to ambient illumination. However, the systemsstill pay limited attention to intensity measurements since the output intensity maps of LiDAR sensors are different from conventional cameras and are too sparse. In this work, we propose exploiting the information from both intensity and depth measurements simultaneously to complete the LiDAR intensity maps. With the completed intensity maps, mature computer vision techniques can work well on the LiDAR data without any specific adjustment. We propose an end-to-end convolutional neural network named LiDAR-Net to jointly complete the sparse intensity and depth measurements by exploiting their correlations. For network training, an intensity fusion method is proposed to generate the ground truth. Experiment results indicate that intensity–depth fusion can benefit the task and improve performance. We further apply an off-the-shelf object (lane) segmentation algorithm to the completed intensity maps, which delivers consistent robust to ambient illumination performance. We believe that the intensity completion method allows LiDAR sensors to cope with a broader range of practice applications.

Generalized LiDAR Intensity Normalization and Its Positive Impact on Geometric and Learning-Based Lane Marking Detection

Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS) have been utilized to detect lane markings through intensity-based approaches. As LiDAR data continue to be used for lane marking extraction, greater emphasis is being placed on enhancing the utility of the intensity values. Typically, intensity correction/normalization approaches are conducted prior to lane marking extraction. The goal of intensity correction is to adjust the intensity values of a LiDAR unit using geometric scanning parameters (i.e., range or incidence angle). Intensity normalization aims at adjusting the intensity readings of a LiDAR unit based on the assumption that intensity values across laser beams/LiDAR units/MMS should be similar for the same object. As MMS technology develops, correcting/normalizing intensity values across different LiDAR units on the same system and/or different MMS is necessary for lane marking extraction. This study proposes a generalized correction/normalization approach for handling single-beam/multi-beam LiDAR scanners onboard single or multiple MMS. The generalized approach is developed while considering the intensity values of asphalt and concrete pavement. For a performance evaluation of the proposed approach, geometric/morphological and deep/transfer-learning-based lane marking extraction with and without intensity correction/normalization is conducted. The evaluation shows that the proposed approach improves the performance of lane marking extraction (e.g., the F1-score of a U-net model can change from 0.1% to 86.2%).

LiDAR intensity correction for road marking detection

The intensity data recorded by light detection and ranging (LiDAR) contains the spectral characteristics of a scanned target, making it an essential source of information in mobile laser scanning (MLS) related applications. However, the raw intensity can be distorted by distance and incidence angle, obstructing its use in autonomous driving. To correct the intensity distortion, an automatic demarcation selection, based on distance effect function, and a Blinn-Phong model-based angle effect correction, have been proposed to correct the raw intensity, establishing the relationship between intensity correction performance and the influencing factors, including modeling data selection, incidence angle span (IAS) and point cloud density. Separability (SEP) and balanced F score (BFS) were used to evaluate the separability, accuracy and totality of road marking detection. Experimental results showed that the background data-driven angle effect function (BDAF) used was beneficial for enhancing marking and road classification. IAS has a major influence on correcting intensity performance. SEP was improved by 35% when IAS was increased from 3.17° to 13.5°, with point density of 535 pt/m2. The IAS threshold could be adjusted according to the instrument performance used. It offers a meaningful investigation of detection and classification of planar target using corrected MLS intensity.

Robust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features

Co-registration of aerial imagery and Light Detection and Ranging (LiDAR) data is quite challenging because the different imaging mechanisms produce significant geometric and radiometric distortions between the two multimodal data sources. To address this problem, we propose a robust and effective coarse-to-fine registration method that is conducted in two stages utilizing spatial constraints and Gabor structural features. In the first stage, the LiDAR point cloud data is transformed into an intensity map that is used as the reference image. Then, coarse registration is completed by designing a partition-based Features from Accelerated Segment Test (FAST) operator to extract the uniformly distributed interest points in the aerial images and thereafter performing a local geometric correction based on the collinearity equations using the exterior orientation parameters (EoPs). The coarse registration aims to provide a reliable spatial geometry relationship for the subsequent fine registration and is designed to eliminate rotation and scale changes, as well as making only a few translation differences exist between the images. In the second stage, a novel feature descriptor called multi-Scale and multi-Directional Features of odd Gabor (SDFG) is first built to capture the multi-scale and multi-directional structural properties of the images. Then, the three-dimensional (3D) phase correlation (PC) of the SDFG descriptor is established to detect the control points (CPs) between the aerial and LiDAR intensity image in the frequency domain, where the image matching is accelerated by the 3D Fast Fourier Transform (FFT) technique. Finally, the obtained CPs not only are employed to refine the EoPs, but also are used to achieve the fine registration of the aerial images and LiDAR data. We conduct experiments to verify the robustness of the proposed registration method using three sets of aerial images and LiDAR data with different scene coverage. Experimental results show that the proposed method is robust to geometric distortions and radiometric changes. Moreover, it achieves the registration accuracy of less than 2 pixels for all cases, which outperforms the current four state-of-the-art methods, demonstrating its superior registration performance.

MULTISPECTRAL AND MOBILE MAPPING ISPRS WG III/5 DATA SET: FIRST ANALYSIS OF THE DATASET IMPACT

Abstract. Light Detection and Ranging (LiDAR) technology is playing a major role in different applications. Indeed, the possibility of exploiting either 3D geometric features and radiometric information makes LiDAR suitable for a wide range of practical domains. LiDAR proved also being quite flexible in terms of platforms where it can be implemented for the acquisition, spanning from airborne sensors up to car-based and hand-held instruments. Due to the rapid expansion of research concerning LiDAR intensity, the ISPRS WG III/5 launched in 2017 an initiative aimed at providing free access to LiDAR data acquired by modern multispectral ALS sensors as well as MLS data. The two datasets, MLS Data Set 1 – (“Sun Prairie”), and Multispectral LiDAR Data Set 2 – (“Tobermory”), were provided by Teledyne Optech Company (Canada) and were made freely available to researchers upon request. This paper is presenting the first results of this initiative in terms of applications, application domains and topics tackled by applicants. The relevance of this data set is also evaluated through a bibliometric analysis considering both Scopus and Web of Science indexed databases to analyse the main directions where the scientific research, the technical development and the application interest is moving to.

Characterizing building materials using multispectral imagery and LiDAR intensity data

This paper addresses the underlying bottleneck of unknown materials and material characteristics for assessing the life cycle of an existing structure or considering interventions. This is done by classifying and characterizing common building materials with two readily accessible, remote sensing technologies: multispectral imaging and Light Detection and Ranging (LiDAR). A total of 142 samples, including concrete of 3 different water/cement ratios, 2 mortar types, and 2 brick types (each type fired at 3 different temperatures) were scanned using a 5-band multispectral camera in the visible, RedEdge, and Near Infrared range and 2 laser scanners with different wavelengths. A Partial Least Squares Discriminant Analysis model was developed to classify the main materials and then the subcategories within each material type. With multispectral data, an 82.75% average correct classification rate was achieved (improving to 83.07% when combined with LiDAR intensity data), but the effect was not uniformly positive. This paper demonstrates the potential to identify building materials in a non-destructive, non-contact manner to aid in in-situ facade material labelling.

Automatic Detection of Shield Tunnel Leakages Based on Terrestrial Mobile LiDAR Intensity Images Using Deep Learning

Water leakages are very important signals that characterize the serious potential structural damages or flaws in shield tunnels. Automatic, timely, and accurate detection of water leakages is of great significance to the safe operation and maintenance for shield tunnels. However, existing methods (e.g., passive optical images) are highly limited by the confined spaces and dim light conditions in shield tunnels. This paper proposes a new method that uses the intensity images of terrestrial mobile LiDAR (Light Detection and Ranging) for automatic leakage detection in shield tunnels based on deep learning. A self-developed terrestrial mobile LiDAR system (Faro Focus X330) are used to simultaneously obtain the point clouds and intensity information of the shield tunnels. The original intensity data are corrected for the distance effect to generate intensity images. A improved Fully Convolutional Network (FCN) based VGG-19 that is a network structure in deep learning is constructed to achieve accurate tunnel leakage detection based on intensity images. The results show that the proposed method can rapidly and accurately detect leakages in shield tunnels and the leakage detection results are not affected by the tunnel attachments (e.g., bolt holes, cables, and metal facilities). The error rate and segmentation speed are 4.8% and 0.6 s, respectively.

A practical method utilizing multi-spectral LiDAR to aid points cloud matching in SLAM

Light Detection and Ranging (LiDAR) sensors are popular in Simultaneous Localization and Mapping (SLAM) owing to their capability of obtaining ranging information actively. Researchers have attempted to use the intensity information that accompanies each range measurement to enhance LiDAR SLAM positioning accuracy. However, before employing LiDAR intensities in SLAM, a calibration operation is usually carried out so that the intensity is independent of the incident angle and range. The range is determined from the laser beam transmitting time. Therefore, the key to using LiDAR intensities in SLAM is to obtain the incident angle between the laser beam and target surface. In a complex environment, it is difficult to obtain the incident angle robustly. This procedure also complicates the data processing in SLAM and as a result, further application of the LiDAR intensity in SLAM is hampered. Motivated by this problem, in the present study, we propose a Hyperspectral LiDAR (HSL)-based-intensity calibration-free method to aid point cloud matching in SLAM. HSL employed in this study can obtain an eight-channel range accompanied by corresponding intensity measurements. Owing to the design of the laser, the eight-channel range and intensity were collected with the same incident angle and range. According to the laser beam radiation model, the ratio values between two randomly selected channels’ intensities at an identical target are independent of the range information and incident angle. To test the proposed method, the HSL was employed to scan a wall with different coloured papers pasted on it (white, red, yellow, pink, and green) at four distinct positions along a corridor (with an interval of 60 cm in between two consecutive positions). Then, a ratio value vector was constructed for each scan. The ratio value vectors between consecutive laser scans were employed to match the point cloud. A classic Iterative Closest Point (ICP) algorithm was employed to estimate the HSL motion using the range information from the matched point clouds. According to the test results, we found that pink and green papers were distinctive at 650, 690, and 720 nm. A ratio value vector was constructed using 650-nm spectral information against the reference channel. Furthermore, compared with the classic ICP using range information only, the proposed method that matched ratio value vectors presented an improved performance in heading angle estimation. For the best case in the field test, the proposed method enhanced the heading angle estimation by 72%, and showed an average 25.5% improvement in a featureless spatial testing environment. The results of the primary test indicated that the proposed method has the potential to aid point cloud matching in typical SLAM of real scenarios.

Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification

Point cloud classification plays an important role in a wide range of airborne light detection and ranging (LiDAR) applications, such as topographic mapping, forest monitoring, power line detection, and road detection. However, due to the sensor noise, high redundancy, incompleteness, and complexity of airborne LiDAR systems, point cloud classification is challenging. Traditional point cloud classification methods mostly focus on the development of handcrafted point geometry features and employ machine learning-based classification models to conduct point classification. In recent years, the advances of deep learning models have caused researchers to shift their focus towards machine learning-based models, specifically deep neural networks, to classify airborne LiDAR point clouds. These learning-based methods start by transforming the unstructured 3D point sets to regular 2D representations, such as collections of feature images, and then employ a 2D CNN for point classification. Moreover, these methods usually need to calculate additional local geometry features, such as planarity, sphericity and roughness, to make use of the local structural information in the original 3D space. Nonetheless, the 3D to 2D conversion results in information loss. In this paper, we propose a directionally constrained fully convolutional neural network (D-FCN) that can take the original 3D coordinates and LiDAR intensity as input; thus, it can directly apply to unstructured 3D point clouds for semantic labeling. Specifically, we first introduce a novel directionally constrained point convolution (D-Conv) module to extract locally representative features of 3D point sets from the projected 2D receptive fields. To make full use of the orientation information of neighborhood points, the proposed D-Conv module performs convolution in an orientation-aware manner by using a directionally constrained nearest neighborhood search. Then, we design a multiscale fully convolutional neural network with downsampling and upsampling blocks to enable multiscale point feature learning. The proposed D-FCN model can therefore process input point cloud with arbitrary sizes and directly predict the semantic labels for all the input points in an end-to-end manner. Without involving additional geometry features as input, the proposed method demonstrates superior performance on the International Society for Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark dataset. The results show that our model achieves a new state-of-the-art performance on powerline, car, and facade categories. Moreover, to demonstrate the generalization abilities of the proposed method, we conduct further experiments on the 2019 Data Fusion Contest Dataset. Our proposed method achieves superior performance than the comparing methods and accomplishes an overall accuracy of 95.6% and an average F1 score of 0.810.

Supervised spatial classification of multispectral LiDAR data in urban areas.

Multispectral LiDAR (light detection and ranging) data have been initially used for land cover classification. However, there are still high classification uncertainties, especially in urban areas, where objects are often mixed and confounded. This study investigated the efficiency of combining advanced statistical methods and LiDAR metrics derived from multispectral LiDAR data for improving land cover classification accuracy in urban areas. The study area is located in Oshawa, Ontario, Canada, on the Lake Ontario shoreline. Multispectral Optech Titan LiDAR data over the study area were acquired on 3 September 2014 in a single strip of 3 km2. Using the channels at 1,550 nm (C1), 1,064 nm (C2) and 532 nm (C3), LiDAR intensity data, normalized digital surface model (nDSM), pseudo normalized difference vegetation index (PseudoNDVI), morphological profiles (MP), and a novel hierarchical morphological profiles (HMP) were derived and used as features for the classification. A support vector machine classifier with a radial basis function (RBF) kernel was applied in the classification stage, where the optimal parameters for the classifier were selected by a grid search procedure. The combination of intensity, pseudoNDVI, nDSM and HMP resulted in the best land cover classification, with an overall accuracy of 93.28%.

Comparative Performances of Airborne LiDAR Height and Intensity Data for Leaf Area Index Estimation

Leaf area index (LAI) estimation based on remote sensing data has often relied on the use of spectral vegetation indices from optical data. However, it is difficult to accurately estimate LAI due to saturation of spectral signals. Light detection and ranging (LiDAR) systems have emerged as promising technologies for overcoming the saturation problem, and an increasing number of studies have been conducted on LAI estimation using LiDAR data. In this study, we compared the performance of LAI estimation using LiDAR height and intensity data, and explored the potential for estimating forest LAI using combined LiDAR height and intensity data. LAI estimation models were established using LiDAR height, intensity, and a combination of LiDAR height and intensity metrics based on a random forest regression algorithm. Our results show that the laser intercept index derived from LiDAR height or intensity data was the most important predictor for LAI. Field measurements of LAI at 64 sites were used to assess the power of various LiDAR metrics in predicting LAI. The results show that both LiDAR height and intensity metrics alone could reliably estimate forest LAI. However, compared to LiDAR intensity metrics [ $R^{{\rm 2}}\,= \,0.610$ with root mean squared error (RMSE) of 0.664], LiDAR height metrics had a better predictive power ( $R^{{\rm 2}}\,= \,0.765$ with RMSE of 0.562). Moreover, the combined LiDAR height and intensity metrics resulted in the highest LAI estimation accuracy ( $R^{{\rm 2}}\,= \,0.809$ with RMSE of 0.501). Therefore, the combination of LiDAR height and intensity data has a great potential for improving the LAI estimation accuracy.

Evaporite karst geohazards in the Delaware Basin, Texas: review of traditional karst studies coupled with geophysical and remote sensing characterization

Evaporite karst throughout the Gypsum Plain of west Texas is complex and extensive, including manifestations ranging from intrastratal brecciation and hypogene caves to epigene features and suffosion caves. Recent advances in hydrocarbon exploration and extraction has resulted in increased infrastructure development and utilization in the area; as a result, delineation and characterization of potential karst geohazards throughout the region have become a greater concern. While traditional karst surveys are essential for delineating the subsurface extent and morphology of individual caves for speleogenetic interpretation, these methods tend to underestimate the total extent of karst development and require surficial manifestation of karst phenomena. Therefore, this study utilizes a composite suite of remote sensing and traditional field studies for improved karst delineation and detection of potential karst geohazards within gypsum karst. Color InfraRed (CIR) imagery were utilized for delineation of lineaments associated with fractures, while Normalized Density Vegetation Index (NDVI) analyses were used to delineate regions of increased moisture flux and probable zones of shallow karst development. Digital Elevation Models (DEM) constructed from high-resolution LiDAR (Light Detection and Ranging) data were used to spatially interpret sinkholes, while analyses of LiDAR intensity data were used in a novel way to categorize local variations in surface geology. Resistivity data, including both direct current (DC) and capacitively coupled (CC) resistivity analyses, were acquired and interpreted throughout the study area to delineate potential shallow karst geohazards specifically associated with roadways of geohazard concern; however, detailed knowledge of the surrounding geology and local karst development proved essential for proper interpretation of resistivity inversions. The composite suite of traditional field investigations and remotely sensed karst delineations used in this study illustrate how complex gypsum karst terrains can be characterized with greater detail through the utilization of rapidly advancing technologies, especially in arid environments with low vegetation densities.

Temperature Compensation for Radiometric Correction of Terrestrial LiDAR Intensity Data

Correction of terrestrial Light Detection and Ranging (LiDAR) intensity data has been increasingly studied in recent years. The purpose is to obtain additional insight into the scanned environment that is not available from the geometric information alone. Radiometric correction, as the name implies, corrects the received intensity to standard reflectance values in the range of ( 0 , 1 ) . This correction typically compensates for the dependence of angle and distance. This paper presents an additional compensation for temperature that may be necessary for some LiDAR instruments such as the Faro Focus 3 D X 330 laser scanner. It is also shown that temperature compensation is not necessary for the Riegl VZ–400. Another important contribution of this work is the verification of a previously published radiometric correction in different environments. The correction was applied to two different Terrestrial Laser Scanner (TLS) instruments: a Faro Focus 3 D X 330 and Riegl VZ-400. Overall, the VZ-400, without temperature compensation, produced better results with a Root Mean Square (RMS) of the standard deviation of error being 0.053 and a RMS of the mean error of 0.036 compared to 0.069 and 0.046 for the Faro Focus 3 D X 330. It was found, for the case of the Faro device, that the temperature of the instrument played an important role in the accuracy of the results. The proposed temperature compensation method improved the RMS standard deviation of the error by 1.4 times and the RMS of the error by 2.6 times, compared to the uncompensated results.

Estimation of Forest Topsoil Properties Using Airborne LiDAR-Derived Intensity and Topographic Factors

Forest topsoil supports vegetation growth and contains the majority of soil nutrients that are important indices of soil fertility and quality. Therefore, estimating forest topsoil properties, such as soil organic matter (SOM), total nitrogen (Total N), pH, litter-organic (O-A) horizon depth (Depth) and available phosphorous (AvaP), is of particular importance for forest development and management. As an emerging technology, light detection and ranging (LiDAR) can capture the three-dimensional structure and intensity information of scanned objects, and can generate high resolution digital elevation models (DEM) using ground echoes. Moreover, great power for estimating forest topsoil properties is enclosed in the intensity information of ground echoes. However, the intensity has not been well explored for this purpose. In this study, we collected soil samples from 62 plots and the coincident airborne LiDAR data in a Korean pine forest in Northeast China, and assessed the effectiveness of both multi-scale intensity data and LiDAR-derived topographic factors for estimating forest topsoil properties. The results showed that LiDAR-derived variables could be robust predictors of four topsoil properties (SOM, Total N, pH, and Depth), with coefficients of determination (R2) ranging from 0.46 to 0.66. Ground-returned intensity was identified as the most effective predictor for three topsoil properties (SOM, Total N, and Depth) with R2 values of 0.17–0.64. Meanwhile, LiDAR-derived topographic factors, except elevation and sediment transport index, had weak explanatory power, with R2 no more than 0.10. These findings suggest that the LiDAR intensity of ground echoes is effective for estimating several topsoil properties in forests with complicated topography and dense canopy cover. Furthermore, combining intensity and multi-scale LiDAR-derived topographic factors, the prediction accuracies (R2) were enhanced by negligible amounts up to 0.40, relative to using intensity only for topsoil properties. Moreover, the prediction accuracy for Depth increased by 0.20, while for other topsoil properties, the prediction accuracies increased negligibly, when the scale dependency of soil–topography relationship was taken into consideration.

A Linear Feature-Based Approach for the Registration of Unmanned Aerial Vehicle Remotely-Sensed Images and Airborne LiDAR Data

Compared with traditional manned airborne photogrammetry, unmanned aerial vehicle remote sensing (UAVRS) has the advantages of lower cost and higher flexibility in data acquisition. It has, therefore, found various applications in fields such as three-dimensional (3D) mapping, emergency management, and so on. However, due to the instability of the UAVRS platforms and the low accuracy of the onboard exterior orientation (EO) observations, the use of direct georeferencing image data leads to large location errors. Light detection and ranging (LiDAR) data, which is highly accurate 3D information, is treated as a complementary data source to the optical images. This paper presents a semi-automatic approach for the registration of UAVRS images and airborne LiDAR data based on linear control features. The presented approach consists of three main components, as follows. (1) Buildings are first separated from the point cloud by the integrated use of height and size filtering and RANdom SAmple Consensus (RANSAC) plane fitting, and the 3D line segments of the building ridges and boundaries are semi-automatically extracted through plane intersection and boundary regularization with manual selections; (2) the 3D line segments are projected to the image space using the initial EO parameters to obtain the approximate locations, and all the corresponding 2D line segments are semi-automatically extracted from the UAVRS images. Meanwhile, the tie points of the UAVRS images are generated using a Förstner operator and least-squares image matching; and (3) by use of the equations derived from the coplanarity constraints of the linear control features and the colinear constraints of the tie points, block bundle adjustment is carried out to update the EO parameters of the UAVRS images in the coordinate framework of the LiDAR data, achieving the co-registration of the two datasets. Experiments were performed to demonstrate the validity and effectiveness of the presented method, and a comparison with the traditional registration method based on LiDAR intensity images showed that the presented method is more accurate, and a sub-pixel accuracy level can be achieved.

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration.

In addition to precise 3D coordinates, most light detection and ranging (LIDAR) systems also record “intensity”, loosely defined as the strength of the backscattered echo for each measured point. To date, LIDAR intensity data have proven beneficial in a wide range of applications because they are related to surface parameters, such as reflectance. While numerous procedures have been introduced in the scientific literature, and even commercial software, to enhance the utility of intensity data through a variety of “normalization”, “correction”, or “calibration” techniques, the current situation is complicated by a lack of standardization, as well as confusing, inconsistent use of terminology. In this paper, we first provide an overview of basic principles of LIDAR intensity measurements and applications utilizing intensity information from terrestrial, airborne topographic, and airborne bathymetric LIDAR. Next, we review effective parameters on intensity measurements, basic theory, and current intensity processing methods. We define terminology adopted from the most commonly-used conventions based on a review of current literature. Finally, we identify topics in need of further research. Ultimately, the presented information helps lay the foundation for future standards and specifications for LIDAR radiometric calibration.