Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification

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Shezhou Luo

2015-12-01

Full Text Available Accurate land cover classification information is a critical variable for many applications. This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR (Light Detection and Ranging and CASI (Compact Airborne Spectrographic Imager hyperspectral data. Four LiDAR-derived images (DTM, DSM, nDSM, and intensity and CASI data (48 bands with 1 m spatial resolution were spatially resampled to 2, 4, 8, 10, 20 and 30 m resolutions using the nearest neighbor resampling method. These data were thereafter fused using the layer stacking and principal components analysis (PCA methods. Land cover was classified by commonly used supervised classifications in remote sensing images, i.e., the support vector machine (SVM and maximum likelihood (MLC classifiers. Each classifier was applied to four types of datasets (at seven different spatial resolutions: (1 the layer stacking fusion data; (2 the PCA fusion data; (3 the LiDAR data alone; and (4 the CASI data alone. In this study, the land cover category was classified into seven classes, i.e., buildings, road, water bodies, forests, grassland, cropland and barren land. A total of 56 classification results were produced, and the classification accuracies were assessed and compared. The results show that the classification accuracies produced from two fused datasets were higher than that of the single LiDAR and CASI data at all seven spatial resolutions. Moreover, we find that the layer stacking method produced higher overall classification accuracies than the PCA fusion method using both the SVM and MLC classifiers. The highest classification accuracy obtained (OA = 97.8%, kappa = 0.964 using the SVM classifier on the layer stacking fusion data at 1 m spatial resolution. Compared with the best classification results of the CASI and LiDAR data alone, the overall classification accuracies improved by 9.1% and 19.6%, respectively. Our findings also demonstrated that the

Fully Convolutional Networks for Ground Classification from LIDAR Point Clouds

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Rizaldy, A.; Persello, C.; Gevaert, C. M.; Oude Elberink, S. J.

2018-05-01

Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs). In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN), a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher). The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.

Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data

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Yanfei Zhong

2017-08-01

Full Text Available Hyperspectral images and light detection and ranging (LiDAR data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method based on adaptive differential evolution, namely ODF-ADE, for urban LULC classification. In the ODF-ADE framework the normalized difference vegetation index (NDVI, gray-level co-occurrence matrix (GLCM and digital surface model (DSM are extracted to form the feature map. The three different classifiers of the maximum likelihood classifier (MLC, support vector machine (SVM and multinomial logistic regression (MLR are used to classify the extracted features. To find the optimal weights for the different classification maps, weighted voting is used to obtain the classification result and the weights of each classification map are optimized by the differential evolution algorithm which uses a self-adaptive strategy to obtain the parameter adaptively. The final classification map is obtained after post-processing based on conditional random fields (CRF. The experimental results confirm that the proposed algorithm is very effective in urban LULC classification.

Buildings classification from airborne LiDAR point clouds through OBIA and ontology driven approach

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Tomljenovic, Ivan; Belgiu, Mariana; Lampoltshammer, Thomas J.

2013-04-01

In the last years, airborne Light Detection and Ranging (LiDAR) data proved to be a valuable information resource for a vast number of applications ranging from land cover mapping to individual surface feature extraction from complex urban environments. To extract information from LiDAR data, users apply prior knowledge. Unfortunately, there is no consistent initiative for structuring this knowledge into data models that can be shared and reused across different applications and domains. The absence of such models poses great challenges to data interpretation, data fusion and integration as well as information transferability. The intention of this work is to describe the design, development and deployment of an ontology-based system to classify buildings from airborne LiDAR data. The novelty of this approach consists of the development of a domain ontology that specifies explicitly the knowledge used to extract features from airborne LiDAR data. The overall goal of this approach is to investigate the possibility for classification of features of interest from LiDAR data by means of domain ontology. The proposed workflow is applied to the building extraction process for the region of "Biberach an der Riss" in South Germany. Strip-adjusted and georeferenced airborne LiDAR data is processed based on geometrical and radiometric signatures stored within the point cloud. Region-growing segmentation algorithms are applied and segmented regions are exported to the GeoJSON format. Subsequently, the data is imported into the ontology-based reasoning process used to automatically classify exported features of interest. Based on the ontology it becomes possible to define domain concepts, associated properties and relations. As a consequence, the resulting specific body of knowledge restricts possible interpretation variants. Moreover, ontologies are machinable and thus it is possible to run reasoning on top of them. Available reasoners (FACT++, JESS, Pellet) are used to check

FULLY CONVOLUTIONAL NETWORKS FOR GROUND CLASSIFICATION FROM LIDAR POINT CLOUDS

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A. Rizaldy

2018-05-01

Full Text Available Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs. In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN, a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher. The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.

Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data

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Jian Yang

2015-11-01

Full Text Available Delineating canopy gaps and quantifying gap characteristics (e.g., size, shape, and dynamics are essential for understanding regeneration dynamics and understory species diversity in structurally complex forests. Both high spatial resolution optical and light detection and ranging (LiDAR remote sensing data have been used to identify canopy gaps through object-based image analysis, but few studies have quantified the pros and cons of integrating optical and LiDAR for image segmentation and classification. In this study, we investigate whether the synergistic use of optical and LiDAR data improves segmentation quality and classification accuracy. The segmentation results indicate that the LiDAR-based segmentation best delineates canopy gaps, compared to segmentation with optical data alone, and even the integration of optical and LiDAR data. In contrast, the synergistic use of two datasets provides higher classification accuracy than the independent use of optical or LiDAR (overall accuracy of 80.28% ± 6.16% vs. 68.54% ± 9.03% and 64.51% ± 11.32%, separately. High correlations between segmentation quality and object-based classification accuracy indicate that classification accuracy is largely dependent on segmentation quality in the selected experimental area. The outcome of this study provides valuable insights of the usefulness of data integration into segmentation and classification not only for canopy gap identification but also for many other object-based applications.

A multiresolution hierarchical classification algorithm for filtering airborne LiDAR data

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Chen, Chuanfa; Li, Yanyan; Li, Wei; Dai, Honglei

2013-08-01

We presented a multiresolution hierarchical classification (MHC) algorithm for differentiating ground from non-ground LiDAR point cloud based on point residuals from the interpolated raster surface. MHC includes three levels of hierarchy, with the simultaneous increase of cell resolution and residual threshold from the low to the high level of the hierarchy. At each level, the surface is iteratively interpolated towards the ground using thin plate spline (TPS) until no ground points are classified, and the classified ground points are used to update the surface in the next iteration. 15 groups of benchmark dataset, provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) commission, were used to compare the performance of MHC with those of the 17 other publicized filtering methods. Results indicated that MHC with the average total error and average Cohen’s kappa coefficient of 4.11% and 86.27% performs better than all other filtering methods.

Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data

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Xiaolong Liu

2015-01-01

Full Text Available Identification of crop species is an important issue in agricultural management. In recent years, many studies have explored this topic using multi-spectral and hyperspectral remote sensing data. In this study, we perform dedicated research to propose a framework for mapping crop species by combining hyperspectral and Light Detection and Ranging (LiDAR data in an object-based image analysis (OBIA paradigm. The aims of this work were the following: (i to understand the performances of different spectral dimension-reduced features from hyperspectral data and their combination with LiDAR derived height information in image segmentation; (ii to understand what classification accuracies of crop species can be achieved by combining hyperspectral and LiDAR data in an OBIA paradigm, especially in regions that have fragmented agricultural landscape and complicated crop planting structure; and (iii to understand the contributions of the crop height that is derived from LiDAR data, as well as the geometric and textural features of image objects, to the crop species’ separabilities. The study region was an irrigated agricultural area in the central Heihe river basin, which is characterized by many crop species, complicated crop planting structures, and fragmented landscape. The airborne hyperspectral data acquired by the Compact Airborne Spectrographic Imager (CASI with a 1 m spatial resolution and the Canopy Height Model (CHM data derived from the LiDAR data acquired by the airborne Leica ALS70 LiDAR system were used for this study. The image segmentation accuracies of different feature combination schemes (very high-resolution imagery (VHR, VHR/CHM, and minimum noise fractional transformed data (MNF/CHM were evaluated and analyzed. The results showed that VHR/CHM outperformed the other two combination schemes with a segmentation accuracy of 84.8%. The object-based crop species classification results of different feature integrations indicated that

a Two-Step Classification Approach to Distinguishing Similar Objects in Mobile LIDAR Point Clouds

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He, H.; Khoshelham, K.; Fraser, C.

2017-09-01

Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

Tree species classification using within crown localization of waveform LiDAR attributes

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Blomley, Rosmarie; Hovi, Aarne; Weinmann, Martin; Hinz, Stefan; Korpela, Ilkka; Jutzi, Boris

2017-11-01

Since forest planning is increasingly taking an ecological, diversity-oriented perspective into account, remote sensing technologies are becoming ever more important in assessing existing resources with reduced manual effort. While the light detection and ranging (LiDAR) technology provides a good basis for predictions of tree height and biomass, tree species identification based on this type of data is particularly challenging in structurally heterogeneous forests. In this paper, we analyse existing approaches with respect to the geometrical scale of feature extraction (whole tree, within crown partitions or within laser footprint) and conclude that currently features are always extracted separately from the different scales. Since multi-scale approaches however have proven successful in other applications, we aim to utilize the within-tree-crown distribution of within-footprint signal characteristics as additional features. To do so, a spin image algorithm, originally devised for the extraction of 3D surface features in object recognition, is adapted. This algorithm relies on spinning an image plane around a defined axis, e.g. the tree stem, collecting the number of LiDAR returns or mean values of returns attributes per pixel as respective values. Based on this representation, spin image features are extracted that comprise only those components of highest variability among a given set of library trees. The relative performance and the combined improvement of these spin image features with respect to non-spatial statistical metrics of the waveform (WF) attributes are evaluated for the tree species classification of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.) and Silver/Downy birch (Betula pendula Roth/Betula pubescens Ehrh.) in a boreal forest environment. This evaluation is performed for two WF LiDAR datasets that differ in footprint size, pulse density at ground, laser wavelength and pulse width. Furthermore, we evaluate the

A TWO-STEP CLASSIFICATION APPROACH TO DISTINGUISHING SIMILAR OBJECTS IN MOBILE LIDAR POINT CLOUDS

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H. He

2017-09-01

Full Text Available Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

Atmospheric aerosol load morphological classification and retrieved visibility based on lidar backscatter measurements

CSIR Research Space (South Africa)

Tesfaye, M

2010-01-01

Full Text Available In this paper, the tropospheric aerosol load morphological classification and its impact on temporal variation of visibility are investigated using a continuous 23-hour single channel CSIR-NLC mobile LIDAR backscatter measurement. The trajectory...

Comparison of Aerosol Classification From Airborne High Spectral Resolution Lidar and the CALIPSO Vertical Feature Mask

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Burton, Sharon P.; Ferrare, Rich A.; Omar, Ali H.; Vaughan, Mark A.; Rogers, Raymond R.; Hostetler, Chris a.; Hair, Johnathan W.; Obland, Michael D.; Butler, Carolyn F.; Cook, Anthony L.;

Multi-Beam Surface Lidar for Lunar and Planetary Mapping

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Bufton, Jack L.; Garvin, James B.

1998-01-01

Surface lidar techniques are now being demonstrated in low Earth orbit with a single beam of pulsed laser radiation at 1064 nm that profiles the vertical structure of Earth surface landforms along the nadir track of a spacecraft. In addition, a profiling laser altimeter, called MOLA, is operating in elliptical Martian orbit and returning surface topography data. These instruments form the basis for suggesting an improved lidar instrument that employs multiple beams for extension of sensor capabilities toward the goal of true, 3-dimensional mapping of the Moon or other similar planetary surfaces. In general the lidar waveform acquired with digitization of a laser echo can be used for laser distance measurement (i.e. range-to-the-surface) by time-of-flight measurement and for surface slope and shape measurements by examining the detailed lidar waveform. This is particularly effective when the intended target is the lunar surface or another planetary body free of any atmosphere. The width of the distorted return pulse is a first order measure of the surface incidence angle, a combination of surface slope and laser beam pointing. Assuming an independent and absolute (with respect to inertial space) measurement of laser beam pointing on the spacecraft, it is possible to derive a surface slope with-respect-to the mean planetary surface or its equipotential gravity surface. Higher-order laser pulse distortions can be interpreted in terms of the vertical relief of the surface or reflectivity variations within the area of the laser beam footprint on the surface.

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data

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Gajda, Agnieszka; Wójtowicz-Nowakowska, Anna

2013-04-01

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data Land cover maps are generally produced on the basis of high resolution imagery. Recently, LiDAR (Light Detection and Ranging) data have been brought into use in diverse applications including land cover mapping. In this study we attempted to assess the accuracy of land cover classification using both high resolution aerial imagery and LiDAR data (airborne laser scanning, ALS), testing two classification approaches: a pixel-based classification and object-oriented image analysis (OBIA). The study was conducted on three test areas (3 km2 each) in the administrative area of Kraków, Poland, along the course of the Vistula River. They represent three different dominating land cover types of the Vistula River valley. Test site 1 had a semi-natural vegetation, with riparian forests and shrubs, test site 2 represented a densely built-up area, and test site 3 was an industrial site. Point clouds from ALS and ortophotomaps were both captured in November 2007. Point cloud density was on average 16 pt/m2 and it contained additional information about intensity and encoded RGB values. Ortophotomaps had a spatial resolution of 10 cm. From point clouds two raster maps were generated: intensity (1) and (2) normalised Digital Surface Model (nDSM), both with the spatial resolution of 50 cm. To classify the aerial data, a supervised classification approach was selected. Pixel based classification was carried out in ERDAS Imagine software. Ortophotomaps and intensity and nDSM rasters were used in classification. 15 homogenous training areas representing each cover class were chosen. Classified pixels were clumped to avoid salt and pepper effect. Object oriented image object classification was carried out in eCognition software, which implements both the optical and ALS data. Elevation layers (intensity, firs/last reflection, etc.) were used at segmentation stage due to

Detection and classification of pole-like road objects from mobile LiDAR data in motorway environment

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Yan, Li; Li, Zan; Liu, Hua; Tan, Junxiang; Zhao, Sainan; Chen, Changjun

2017-12-01

Mobile LiDAR Scanning (MLS) can collect 3-dimensional (3D) road and road-related geospatial information accurately and efficiently. Pole-like objects located in road environment are important street furniture and they are necessary information in road inventory and road mapping. The automatic detection and classification of pole-like road objects from mobile LiDAR data can greatly reduce the cost and improve the efficiency. This paper provides a complete workflow for the detection and classification of pole-like road objects from mobile LiDAR data in motorway environment. The major workflow includes three steps: data preprocessing, pole-like road objects detection and pole-like road objects classification. In data preprocessing step, ground points are removed by an automatic ground filtering algorithm, and then off-ground points are clustered into segments and the overlapped segments containing pole-like road objects are further separated through an iterative min-cut based segmentation approach. In detection step, filters utilizing both prior and shape information are used to detect the target objects. In classification step, features of objects are calculated and classified using Random Forest classifier. Our method was tested on two datasets scanned in motorway environment, and the results showed that the Matthews correlation coefficient of the two datasets in detection step was 93.7% and 95.9% respectively and the overall accuracy of the two datasets in classification step was 96.5% and 97.9% respectively.

Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification

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Wei Gong

2015-09-01

Full Text Available The abilities of multispectral LiDAR (MSL as a new high-potential active instrument for remote sensing have not been fully revealed. This study demonstrates the potential of using the spectral and spatial features derived from a novel MSL to discriminate surface objects. Data acquired with the MSL include distance information and the intensities of four wavelengths at 556, 670, 700, and 780 nm channels. A support vector machine was used to classify diverse objects in the experimental scene into seven types: wall, ceramic pots, Cactaceae, carton, plastic foam block, and healthy and dead leaves of E. aureum. Different features were used during classification to compare the performance of different detection systems. The spectral backscattered reflectance of one wavelength and distance represented the features from an equivalent single-wavelength LiDAR system; reflectance of the four wavelengths represented the features from an equivalent multispectral image with four bands. Results showed that the overall accuracy of using MSL data was as high as 88.7%, this value was 9.8%–39.2% higher than those obtained using a single-wavelength LiDAR, and 4.2% higher than for multispectral image.

Application of multiple signal classification algorithm to frequency estimation in coherent dual-frequency lidar

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Li, Ruixiao; Li, Kun; Zhao, Changming

2018-01-01

Coherent dual-frequency Lidar (CDFL) is a new development of Lidar which dramatically enhances the ability to decrease the influence of atmospheric interference by using dual-frequency laser to measure the range and velocity with high precision. Based on the nature of CDFL signals, we propose to apply the multiple signal classification (MUSIC) algorithm in place of the fast Fourier transform (FFT) to estimate the phase differences in dual-frequency Lidar. In the presence of Gaussian white noise, the simulation results show that the signal peaks are more evident when using MUSIC algorithm instead of FFT in condition of low signal-noise-ratio (SNR), which helps to improve the precision of detection on range and velocity, especially for the long distance measurement systems.

Fusion of LiDAR and aerial imagery for the estimation of downed tree volume using Support Vector Machines classification and region based object fitting

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Selvarajan, Sowmya

The study classifies 3D small footprint full waveform digitized LiDAR fused with aerial imagery to downed trees using Support Vector Machines (SVM) algorithm. Using small footprint waveform LiDAR, airborne LiDAR systems can provide better canopy penetration and very high spatial resolution. The small footprint waveform scanner system Riegl LMS-Q680 is addition with an UltraCamX aerial camera are used to measure and map downed trees in a forest. The various data preprocessing steps helped in the identification of ground points from the dense LiDAR dataset and segment the LiDAR data to help reduce the complexity of the algorithm. The haze filtering process helped to differentiate the spectral signatures of the various classes within the aerial image. Such processes, helped to better select the features from both sensor data. The six features: LiDAR height, LiDAR intensity, LiDAR echo, and three image intensities are utilized. To do so, LiDAR derived, aerial image derived and fused LiDAR-aerial image derived features are used to organize the data for the SVM hypothesis formulation. Several variations of the SVM algorithm with different kernels and soft margin parameter C are experimented. The algorithm is implemented to classify downed trees over a pine trees zone. The LiDAR derived features provided an overall accuracy of 98% of downed trees but with no classification error of 86%. The image derived features provided an overall accuracy of 65% and fusion derived features resulted in an overall accuracy of 88%. The results are observed to be stable and robust. The SVM accuracies were accompanied by high false alarm rates, with the LiDAR classification producing 58.45%, image classification producing 95.74% and finally the fused classification producing 93% false alarm rates The Canny edge correction filter helped control the LiDAR false alarm to 35.99%, image false alarm to 48.56% and fused false alarm to 37.69% The implemented classifiers provided a powerful tool for

Crown-Level Tree Species Classification Using Integrated Airborne Hyperspectral and LIDAR Remote Sensing Data

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Wang, Z.; Wu, J.; Wang, Y.; Kong, X.; Bao, H.; Ni, Y.; Ma, L.; Jin, J.

2018-05-01

Mapping tree species is essential for sustainable planning as well as to improve our understanding of the role of different trees as different ecological service. However, crown-level tree species automatic classification is a challenging task due to the spectral similarity among diversified tree species, fine-scale spatial variation, shadow, and underlying objects within a crown. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR) and hyperspectral imagery offer a great potential opportunity to derive crown spectral, structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, an innovative approach was developed for tree species classification at the crown level. The method utilized LiDAR data for individual tree crown delineation and morphological structure extraction, and Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery for pure crown-scale spectral extraction. Specifically, four steps were include: 1) A weighted mean filtering method was developed to improve the accuracy of the smoothed Canopy Height Model (CHM) derived from LiDAR data; 2) The marker-controlled watershed segmentation algorithm was, therefore, also employed to delineate the tree-level canopy from the CHM image in this study, and then individual tree height and tree crown were calculated according to the delineated crown; 3) Spectral features within 3 × 3 neighborhood regions centered on the treetops detected by the treetop detection algorithm were derived from the spectrally normalized CASI imagery; 4) The shape characteristics related to their crown diameters and heights were established, and different crown-level tree species were classified using the combination of spectral and shape characteristics. Analysis of results suggests that the developed classification strategy in this paper (OA = 85.12 %, Kc = 0.90) performed better than LiDAR-metrics method (OA = 79

Airborne Lidar Surface Topography (LIST) Simulator

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Yu, Anthony W.; Krainak, Michael A.; Harding, David J.; Abshire, James B.; Sun, Xiaoli; Cavanaugh, John; Valett, Susan; Ramos-Izquierdo, Luis; Winkert, Tom; Plants, Michael;

Airborne LIDAR Power Line Classification Based on Spatial Topological Structure Characteristics

Science.gov (United States)

Wang, Y.; Chen, Q.; Li, K.; Zheng, D.; Fang, J.

2017-09-01

Automatic extraction of power lines has become a topic of great importance in airborne LiDAR data processing for transmission line management. In this paper, we present a new, fully automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) neighbourhood selection, (iii) feature extraction based on spatial topology, and (iv) SVM classification. In a detailed evaluation involving seven neighbourhood definitions, 26 geometric features and two datasets, we demonstrated that the use of multi-scale neighbourhoods for individual 3D points significantly improved the power line classification. Additionally, we showed that the spatial topological features may even further improve the results while reducing data processing time.

Airborne Lidar Simulator for the Lidar Surface Topography (LIST) Mission

Science.gov (United States)

Yu, Anthony W.; Krainak, Michael A.; Abshire, James B.; Cavanaugh, John; Valett, Susan; Ramos-Izquierdo, Luis

2010-01-01

In 2007, the National Research Council (NRC) completed its first decadal survey for Earth science at the request of NASA, NOAA, and USGS. The Lidar Surface Topography (LIST) mission is one of fifteen missions recommended by NRC, whose primary objectives are to map global topography and vegetation structure at 5 m spatial resolution, and to acquire global surface height mapping within a few years. NASA Goddard conducted an initial mission concept study for the LIST mission in 2007, and developed the initial measurement requirements for the mission.

Empirical Radiometric Normalization of Road Points from Terrestrial Mobile Lidar System

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Tee-Ann Teo

2015-05-01

Full Text Available Lidar data provide both geometric and radiometric information. Radiometric information is influenced by sensor and target factors and should be calibrated to obtain consistent energy responses. The radiometric correction of airborne lidar system (ALS converts the amplitude into a backscatter cross-section with physical meaning value by applying a model-driven approach. The radiometric correction of terrestrial mobile lidar system (MLS is a challenging task because it does not completely follow the inverse square range function at near-range. This study proposed a radiometric normalization workflow for MLS using a data-driven approach. The scope of this study is to normalize amplitude of road points for road surface classification, assuming that road points from different scanners or strips should have similar responses in overlapped areas. The normalization parameters for range effect were obtained from crossroads. The experiment showed that the amplitude difference between scanners and strips decreased after radiometric normalization and improved the accuracy of road surface classification.

Water Mapping Using Multispectral Airborne LIDAR Data

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Yan, W. Y.; Shaker, A.; LaRocque, P. E.

2018-04-01

This study investigates the use of the world's first multispectral airborne LiDAR sensor, Optech Titan, manufactured by Teledyne Optech to serve the purpose of automatic land-water classification with a particular focus on near shore region and river environment. Although there exist recent studies utilizing airborne LiDAR data for shoreline detection and water surface mapping, the majority of them only perform experimental testing on clipped data subset or rely on data fusion with aerial/satellite image. In addition, most of the existing approaches require manual intervention or existing tidal/datum data for sample collection of training data. To tackle the drawbacks of previous approaches, we propose and develop an automatic data processing workflow for land-water classification using multispectral airborne LiDAR data. Depending on the nature of the study scene, two methods are proposed for automatic training data selection. The first method utilizes the elevation/intensity histogram fitted with Gaussian mixture model (GMM) to preliminarily split the land and water bodies. The second method mainly relies on the use of a newly developed scan line elevation intensity ratio (SLIER) to estimate the water surface data points. Regardless of the training methods being used, feature spaces can be constructed using the multispectral LiDAR intensity, elevation and other features derived from these parameters. The comprehensive workflow was tested with two datasets collected for different near shore region and river environment, where the overall accuracy yielded better than 96 %.

CROWN-LEVEL TREE SPECIES CLASSIFICATION USING INTEGRATED AIRBORNE HYPERSPECTRAL AND LIDAR REMOTE SENSING DATA

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Z. Wang

2018-05-01

Full Text Available Mapping tree species is essential for sustainable planning as well as to improve our understanding of the role of different trees as different ecological service. However, crown-level tree species automatic classification is a challenging task due to the spectral similarity among diversified tree species, fine-scale spatial variation, shadow, and underlying objects within a crown. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR and hyperspectral imagery offer a great potential opportunity to derive crown spectral, structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, an innovative approach was developed for tree species classification at the crown level. The method utilized LiDAR data for individual tree crown delineation and morphological structure extraction, and Compact Airborne Spectrographic Imager (CASI hyperspectral imagery for pure crown-scale spectral extraction. Specifically, four steps were include: 1 A weighted mean filtering method was developed to improve the accuracy of the smoothed Canopy Height Model (CHM derived from LiDAR data; 2 The marker-controlled watershed segmentation algorithm was, therefore, also employed to delineate the tree-level canopy from the CHM image in this study, and then individual tree height and tree crown were calculated according to the delineated crown; 3 Spectral features within 3 × 3 neighborhood regions centered on the treetops detected by the treetop detection algorithm were derived from the spectrally normalized CASI imagery; 4 The shape characteristics related to their crown diameters and heights were established, and different crown-level tree species were classified using the combination of spectral and shape characteristics. Analysis of results suggests that the developed classification strategy in this paper (OA = 85.12 %, Kc = 0.90 performed better than Li

CLASSIFICATION OF LIDAR DATA OVER BUILDING ROOFS USING K-MEANS AND PRINCIPAL COMPONENT ANALYSIS

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Renato César dos Santos

Full Text Available Abstract: The classification is an important step in the extraction of geometric primitives from LiDAR data. Normally, it is applied for the identification of points sampled on geometric primitives of interest. In the literature there are several studies that have explored the use of eigenvalues to classify LiDAR points into different classes or structures, such as corner, edge, and plane. However, in some works the classes are defined considering an ideal geometry, which can be affected by the inadequate sampling and/or by the presence of noise when using real data. To overcome this limitation, in this paper is proposed the use of metrics based on eigenvalues and the k-means method to carry out the classification. So, the concept of principal component analysis is used to obtain the eigenvalues and the derived metrics, while the k-means is applied to cluster the roof points in two classes: edge and non-edge. To evaluate the proposed method four test areas with different levels of complexity were selected. From the qualitative and quantitative analyses, it could be concluded that the proposed classification procedure gave satisfactory results, resulting in completeness and correctness above 92% for the non-edge class, and between 61% to 98% for the edge class.

Methods from Information Extraction from LIDAR Intensity Data and Multispectral LIDAR Technology

Science.gov (United States)

Scaioni, M.; Höfle, B.; Baungarten Kersting, A. P.; Barazzetti, L.; Previtali, M.; Wujanz, D.

2018-04-01

LiDAR is a consolidated technology for topographic mapping and 3D reconstruction, which is implemented in several platforms On the other hand, the exploitation of the geometric information has been coupled by the use of laser intensity, which may provide additional data for multiple purposes. This option has been emphasized by the availability of sensors working on different wavelength, thus able to provide additional information for classification of surfaces and objects. Several applications ofmonochromatic and multi-spectral LiDAR data have been already developed in different fields: geosciences, agriculture, forestry, building and cultural heritage. The use of intensity data to extract measures of point cloud quality has been also developed. The paper would like to give an overview on the state-of-the-art of these techniques, and to present the modern technologies for the acquisition of multispectral LiDAR data. In addition, the ISPRS WG III/5 on `Information Extraction from LiDAR Intensity Data' has collected and made available a few open data sets to support scholars to do research on this field. This service is presented and data sets delivered so far as are described.

METHODS FROM INFORMATION EXTRACTION FROM LIDAR INTENSITY DATA AND MULTISPECTRAL LIDAR TECHNOLOGY

Directory of Open Access Journals (Sweden)

M. Scaioni

2018-04-01

Full Text Available LiDAR is a consolidated technology for topographic mapping and 3D reconstruction, which is implemented in several platforms On the other hand, the exploitation of the geometric information has been coupled by the use of laser intensity, which may provide additional data for multiple purposes. This option has been emphasized by the availability of sensors working on different wavelength, thus able to provide additional information for classification of surfaces and objects. Several applications ofmonochromatic and multi-spectral LiDAR data have been already developed in different fields: geosciences, agriculture, forestry, building and cultural heritage. The use of intensity data to extract measures of point cloud quality has been also developed. The paper would like to give an overview on the state-of-the-art of these techniques, and to present the modern technologies for the acquisition of multispectral LiDAR data. In addition, the ISPRS WG III/5 on ‘Information Extraction from LiDAR Intensity Data’ has collected and made available a few open data sets to support scholars to do research on this field. This service is presented and data sets delivered so far as are described.

Processing LiDAR Data to Predict Natural Hazards

Science.gov (United States)

Fairweather, Ian; Crabtree, Robert; Hager, Stacey

2008-01-01

ELF-Base and ELF-Hazards (wherein 'ELF' signifies 'Extract LiDAR Features' and 'LiDAR' signifies 'light detection and ranging') are developmental software modules for processing remote-sensing LiDAR data to identify past natural hazards (principally, landslides) and predict future ones. ELF-Base processes raw LiDAR data, including LiDAR intensity data that are often ignored in other software, to create digital terrain models (DTMs) and digital feature models (DFMs) with sub-meter accuracy. ELF-Hazards fuses raw LiDAR data, data from multispectral and hyperspectral optical images, and DTMs and DFMs generated by ELF-Base to generate hazard risk maps. Advanced algorithms in these software modules include line-enhancement and edge-detection algorithms, surface-characterization algorithms, and algorithms that implement innovative data-fusion techniques. The line-extraction and edge-detection algorithms enable users to locate such features as faults and landslide headwall scarps. Also implemented in this software are improved methodologies for identification and mapping of past landslide events by use of (1) accurate, ELF-derived surface characterizations and (2) three LiDAR/optical-data-fusion techniques: post-classification data fusion, maximum-likelihood estimation modeling, and hierarchical within-class discrimination. This software is expected to enable faster, more accurate forecasting of natural hazards than has previously been possible.

Helicopter-based lidar system for monitoring the upper ocean and terrain surface

International Nuclear Information System (INIS)

Lee, Kwi Joo; Park, Youngsik; Bunkin, Alexey; Pershin, Serguei; Voliak, Konstantin; Nunes, Raul

2002-01-01

A compact helicopter-based lidar system is developed and tested under laboratory and field conditions. It is shown that the lidar can measure concentrations of chlorophyll a and dissolved organic matter at the surface of water bodies, detect fluorescence spectra of ground vegetation at a distance of up to 530 m, and determine the vertical profile of light-scattering particle concentration in the upper ocean. The possibilities of the lidar system are demonstrated by detection of polluted areas at the ocean surface, by online monitoring of three-dimensional distribution of light-scattering layers, and by recognition of plant types and physiological states

Maximizing the Diversity of Ensemble Random Forests for Tree Genera Classification Using High Density LiDAR Data

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Connie Ko

2016-08-01

Full Text Available Recent research into improving the effectiveness of forest inventory management using airborne LiDAR data has focused on developing advanced theories in data analytics. Furthermore, supervised learning as a predictive model for classifying tree genera (and species, where possible has been gaining popularity in order to minimize this labor-intensive task. However, bottlenecks remain that hinder the immediate adoption of supervised learning methods. With supervised classification, training samples are required for learning the parameters that govern the performance of a classifier, yet the selection of training data is often subjective and the quality of such samples is critically important. For LiDAR scanning in forest environments, the quantification of data quality is somewhat abstract, normally referring to some metric related to the completeness of individual tree crowns; however, this is not an issue that has received much attention in the literature. Intuitively the choice of training samples having varying quality will affect classification accuracy. In this paper a Diversity Index (DI is proposed that characterizes the diversity of data quality (Qi among selected training samples required for constructing a classification model of tree genera. The training sample is diversified in terms of data quality as opposed to the number of samples per class. The diversified training sample allows the classifier to better learn the positive and negative instances and; therefore; has a higher classification accuracy in discriminating the “unknown” class samples from the “known” samples. Our algorithm is implemented within the Random Forests base classifiers with six derived geometric features from LiDAR data. The training sample contains three tree genera (pine; poplar; and maple and the validation samples contains four labels (pine; poplar; maple; and “unknown”. Classification accuracy improved from 72.8%; when training samples were

A 3D convolutional neural network approach to land cover classification using LiDAR and multi-temporal Landsat imagery

Science.gov (United States)

Xu, Z.; Guan, K.; Peng, B.; Casler, N. P.; Wang, S. W.

2017-12-01

Landscape has complex three-dimensional features. These 3D features are difficult to extract using conventional methods. Small-footprint LiDAR provides an ideal way for capturing these features. Existing approaches, however, have been relegated to raster or metric-based (two-dimensional) feature extraction from the upper or bottom layer, and thus are not suitable for resolving morphological and intensity features that could be important to fine-scale land cover mapping. Therefore, this research combines airborne LiDAR and multi-temporal Landsat imagery to classify land cover types of Williamson County, Illinois that has diverse and mixed landscape features. Specifically, we applied a 3D convolutional neural network (CNN) method to extract features from LiDAR point clouds by (1) creating occupancy grid, intensity grid at 1-meter resolution, and then (2) normalizing and incorporating data into a 3D CNN feature extractor for many epochs of learning. The learned features (e.g., morphological features, intensity features, etc) were combined with multi-temporal spectral data to enhance the performance of land cover classification based on a Support Vector Machine classifier. We used photo interpretation for training and testing data generation. The classification results show that our approach outperforms traditional methods using LiDAR derived feature maps, and promises to serve as an effective methodology for creating high-quality land cover maps through fusion of complementary types of remote sensing data.

Ensemble classification of individual Pinus crowns from multispectral satellite imagery and airborne LiDAR

Science.gov (United States)

Kukunda, Collins B.; Duque-Lazo, Joaquín; González-Ferreiro, Eduardo; Thaden, Hauke; Kleinn, Christoph

2018-03-01

Distinguishing tree species is relevant in many contexts of remote sensing assisted forest inventory. Accurate tree species maps support management and conservation planning, pest and disease control and biomass estimation. This study evaluated the performance of applying ensemble techniques with the goal of automatically distinguishing Pinus sylvestris L. and Pinus uncinata Mill. Ex Mirb within a 1.3 km2 mountainous area in Barcelonnette (France). Three modelling schemes were examined, based on: (1) high-density LiDAR data (160 returns m-2), (2) Worldview-2 multispectral imagery, and (3) Worldview-2 and LiDAR in combination. Variables related to the crown structure and height of individual trees were extracted from the normalized LiDAR point cloud at individual-tree level, after performing individual tree crown (ITC) delineation. Vegetation indices and the Haralick texture indices were derived from Worldview-2 images and served as independent spectral variables. Selection of the best predictor subset was done after a comparison of three variable selection procedures: (1) Random Forests with cross validation (AUCRFcv), (2) Akaike Information Criterion (AIC) and (3) Bayesian Information Criterion (BIC). To classify the species, 9 regression techniques were combined using ensemble models. Predictions were evaluated using cross validation and an independent dataset. Integration of datasets and models improved individual tree species classification (True Skills Statistic, TSS; from 0.67 to 0.81) over individual techniques and maintained strong predictive power (Relative Operating Characteristic, ROC = 0.91). Assemblage of regression models and integration of the datasets provided more reliable species distribution maps and associated tree-scale mapping uncertainties. Our study highlights the potential of model and data assemblage at improving species classifications needed in present-day forest planning and management.

Airborne LIDAR borsight error calibration based on surface coincide

International Nuclear Information System (INIS)

Yuan, Fangyan; Li, Guoqing; Zuo, Zhengli; Li, Dong; Qi, Zengying; Qiu, Wen; Tan, Junxiang

2014-01-01

Light Detection and Ranging (LIDAR) is a system which can directly collect three-dimensional coordinate information of ground point and laser reflection strength information. With the wide application of LIDAR system, users hope to get more accurate results. Boresight error has an important effect on data accuracy and thus, it is thought that eliminating the error is very important. In recent years, many methods have been proposed to eliminate the error. Generally, they can be categorized into tie point method and surface matching method. In this paper, we propose another method called try value method based on surface coincide that is used in actual production by many companies. The method is simple and operable. Further, the efficacy of the method was demonstrated by analyzing the data from Zhangye city

Evaluation of Airborne Lidar Elevation Surfaces for Propagation of Coastal Inundation: The Importance of Hydrologic Connectivity

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Sandra Poppenga

2015-09-01

Full Text Available Detailed information about coastal inundation is vital to understanding dynamic and populated areas that are impacted by storm surge and flooding. To understand these natural hazard risks, lidar elevation surfaces are frequently used to model inundation in coastal areas. A single-value surface method is sometimes used to inundate areas in lidar elevation surfaces that are below a specified elevation value. However, such an approach does not take into consideration hydrologic connectivity between elevation grids cells resulting in inland areas that should be hydrologically connected to the ocean, but are not. Because inland areas that should drain to the ocean are hydrologically disconnected by raised features in a lidar elevation surface, simply raising the water level to propagate coastal inundation will lead to inundation uncertainties. We took advantage of this problem to identify hydrologically disconnected inland areas to point out that they should be considered for coastal inundation, and that a lidar-based hydrologic surface should be developed with hydrologic connectivity prior to inundation analysis. The process of achieving hydrologic connectivity with hydrologic-enforcement is not new, however, the application of hydrologically-enforced lidar elevation surfaces for improved coastal inundation mapping as approached in this research is innovative. In this article, we propagated a high-resolution lidar elevation surface in coastal Staten Island, New York to demonstrate that inland areas lacking hydrologic connectivity to the ocean could potentially be included in inundation delineations. For inland areas that were hydrologically disconnected, we evaluated if drainage to the ocean was evident, and calculated an area exceeding 11 ha (~0.11 km2 that could be considered in inundation delineations. We also assessed land cover for each inland area to determine the type of physical surfaces that would be potentially impacted if the inland areas

Evaluation of airborne lidar elevation surfaces for propagation of coastal inundation: the importance of hydrologic connectivity

Science.gov (United States)

Poppenga, Sandra K.; Worstell, Bruce B.

2015-01-01

Detailed information about coastal inundation is vital to understanding dynamic and populated areas that are impacted by storm surge and flooding. To understand these natural hazard risks, lidar elevation surfaces are frequently used to model inundation in coastal areas. A single-value surface method is sometimes used to inundate areas in lidar elevation surfaces that are below a specified elevation value. However, such an approach does not take into consideration hydrologic connectivity between elevation grids cells resulting in inland areas that should be hydrologically connected to the ocean, but are not. Because inland areas that should drain to the ocean are hydrologically disconnected by raised features in a lidar elevation surface, simply raising the water level to propagate coastal inundation will lead to inundation uncertainties. We took advantage of this problem to identify hydrologically disconnected inland areas to point out that they should be considered for coastal inundation, and that a lidar-based hydrologic surface should be developed with hydrologic connectivity prior to inundation analysis. The process of achieving hydrologic connectivity with hydrologic-enforcement is not new, however, the application of hydrologically-enforced lidar elevation surfaces for improved coastal inundation mapping as approached in this research is innovative. In this article, we propagated a high-resolution lidar elevation surface in coastal Staten Island, New York to demonstrate that inland areas lacking hydrologic connectivity to the ocean could potentially be included in inundation delineations. For inland areas that were hydrologically disconnected, we evaluated if drainage to the ocean was evident, and calculated an area exceeding 11 ha (~0.11 km2) that could be considered in inundation delineations. We also assessed land cover for each inland area to determine the type of physical surfaces that would be potentially impacted if the inland areas were considered as

Wall-to-wall tree type classification using airborne lidar data and CIR images

DEFF Research Database (Denmark)

Schumacher, Johannes; Nord-Larsen, Thomas

2014-01-01

analysed at the individual tree level (object-based). However, due to computational challenges, most object-based studies cover only smaller areas and experience of larger areas is lacking. We present an approach for an object-based, unsupervised classification of trees into broadleaf or conifer using......-based classification of the TST plots showed an overall accuracy of 84% and a kappa coefficient () of 0.61 when using all plots, and 92% and 0.79, respectively, when leaving out plots with larch. NFI plots were assigned to conifer- or broadleaf-dominated or mixed depending on the area covered by the segments...... of the two tree types. In areas where lidar data were collected specifically during leaf-off conditions, 71% of the NFI plots were assigned correctly into the three categories with = 0.53. Using only NFI plots dominated by one type (broadleaf or conifer), 78% were categorized correctly with = 0...

Tropospheric ozone seasonal and long-term variability as seen by lidar and surface measurements at the JPL-Table Mountain Facility, California

Directory of Open Access Journals (Sweden)

M. J. Granados-Muñoz

2016-07-01

Full Text Available A combined surface and tropospheric ozone climatology and interannual variability study was performed for the first time using co-located ozone photometer measurements (2013–2015 and tropospheric ozone differential absorption lidar measurements (2000–2015 at the Jet Propulsion Laboratory Table Mountain Facility (TMF; elev. 2285 m, in California. The surface time series were investigated both in terms of seasonal and diurnal variability. The observed surface ozone is typical of high-elevation remote sites, with small amplitude of the seasonal and diurnal cycles, and high ozone values, compared to neighboring lower altitude stations representative of urban boundary layer conditions. The ozone mixing ratio ranges from 45 ppbv in the winter morning hours to 65 ppbv in the spring and summer afternoon hours. At the time of the lidar measurements (early night, the seasonal cycle observed at the surface is similar to that observed by lidar between 3.5 and 9 km. Above 9 km, the local tropopause height variation with time and season impacts significantly the ozone lidar observations. The frequent tropopause folds found in the vicinity of TMF (27 % of the time, mostly in winter and spring produce a dual-peak vertical structure in ozone within the fold layer, characterized by higher-than-average values in the bottom half of the fold (12–14 km, and lower-than-averaged values in the top half of the fold (14–18 km. This structure is consistent with the expected origin of the air parcels within the fold, i.e., mid-latitude stratospheric air folding down below the upper tropospheric sub-tropical air. The influence of the tropopause folds extends down to 5 km, increasing the ozone content in the troposphere. No significant signature of interannual variability could be observed on the 2000–2015 de-seasonalized lidar time series, with only a statistically non-significant positive anomaly during the years 2003–2007. Our trend analysis

Automatic 3d Building Model Generations with Airborne LiDAR Data

Science.gov (United States)

Yastikli, N.; Cetin, Z.

2017-11-01

LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D) modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified that automatic 3D

AUTOMATIC 3D BUILDING MODEL GENERATIONS WITH AIRBORNE LiDAR DATA

Directory of Open Access Journals (Sweden)

N. Yastikli

2017-11-01

Full Text Available LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified

3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology

Science.gov (United States)

Brodu, N.; Lague, D.

2012-03-01

3D point clouds of natural environments relevant to problems in geomorphology (rivers, coastal environments, cliffs, …) often require classification of the data into elementary relevant classes. A typical example is the separation of riparian vegetation from ground in fluvial environments, the distinction between fresh surfaces and rockfall in cliff environments, or more generally the classification of surfaces according to their morphology (e.g. the presence of bedforms or by grain size). Natural surfaces are heterogeneous and their distinctive properties are seldom defined at a unique scale, prompting the use of multi-scale criteria to achieve a high degree of classification success. We have thus defined a multi-scale measure of the point cloud dimensionality around each point. The dimensionality characterizes the local 3D organization of the point cloud within spheres centered on the measured points and varies from being 1D (points set along a line), 2D (points forming a plane) to the full 3D volume. By varying the diameter of the sphere, we can thus monitor how the local cloud geometry behaves across scales. We present the technique and illustrate its efficiency in separating riparian vegetation from ground and classifying a mountain stream as vegetation, rock, gravel or water surface. In these two cases, separating the vegetation from ground or other classes achieve accuracy larger than 98%. Comparison with a single scale approach shows the superiority of the multi-scale analysis in enhancing class separability and spatial resolution of the classification. Scenes between 10 and one hundred million points can be classified on a common laptop in a reasonable time. The technique is robust to missing data, shadow zones and changes in point density within the scene. The classification is fast and accurate and can account for some degree of intra-class morphological variability such as different vegetation types. A probabilistic confidence in the classification

Lidar 2009 - All Returns

Data.gov (United States)

Kansas Data Access and Support Center — LIDAR-derived binary (.las) files containing classified points of all returns. We have 3 classifications Unclassified, Ground, Low points. The average Ground Sample...

DEM Development from Ground-Based LiDAR Data: A Method to Remove Non-Surface Objects

Directory of Open Access Journals (Sweden)

Maneesh Sharma

2010-11-01

Full Text Available Topography and land cover characteristics can have significant effects on infiltration, runoff, and erosion processes on watersheds. The ability to model the timing and routing of surface water and erosion is affected by the resolution of the digital elevation model (DEM. High resolution ground-based Light Detecting and Ranging (LiDAR technology can be used to collect detailed topographic and land cover characteristic data. In this study, a method was developed to remove vegetation from ground-based LiDAR data to create high resolution DEMs. Research was conducted on intensively studied rainfall–runoff plots on the USDA-ARS Walnut Gulch Experimental Watershed in Southeast Arizona. LiDAR data were used to generate 1 cm resolution digital surface models (DSM for 5 plots. DSMs created directly from LiDAR data contain non-surface objects such as vegetation cover. A vegetation removal method was developed which used a slope threshold and a focal mean filter method to remove vegetation and create bare earth DEMs. The method was validated on a synthetic plot, where rocks and vegetation were added incrementally. Results of the validation showed a vertical error of ±7.5 mm in the final DEM.

LiDAR Relative Reflectivity Surface (2011) for Coral Bay, St. John

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This image represents a LiDAR (Light Detection & Ranging) 0.3x0.3 meter resolution relative seafloor reflectivity surface for Coral Bay, St. John in the U.S....

LiDAR Relative Reflectivity Surface (2011) for Fish Bay, St. John

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This image represents a LiDAR (Light Detection & Ranging) 0.3x0.3 meter resolution relative seafloor reflectivity surface for Fish Bay, St. John in the U.S....

Search-Lidar Demonstrator for Detection of Small Sea-Surface Targets

NARCIS (Netherlands)

Heuvel, J.C. van den; Bekman, H.H.P.T.; Putten, F.J.M. van; Cohen, L.H.; Schleijpen, H.M.A.

2008-01-01

Coastal surveillance and naval operations in the littoral both have to deal with the threat of small sea-surface targets. These targets have a low radar cross-section and a low velocity that makes them hard to detect by radar. Typical threats include jet skis, FIAC’s, and speedboats. Lidar

Temporal Data Fusion Approaches to Remote Sensing-Based Wetland Classification

Science.gov (United States)

Montgomery, Joshua S. M.

This thesis investigates the ecology of wetlands and associated classification in prairie and boreal environments of Alberta, Canada, using remote sensing technology to enhance classification of wetlands in the province. Objectives of the thesis are divided into two case studies, 1) examining how satellite borne Synthetic Aperture Radar (SAR), optical (RapidEye & SPOT) can be used to evaluate surface water trends in a prairie pothole environment (Shepard Slough); and 2) investigating a data fusion methodology combining SAR, optical and Lidar data to characterize wetland vegetation and surface water attributes in a boreal environment (Utikuma Regional Study Area (URSA)). Surface water extent and hydroperiod products were derived from SAR data, and validated using optical imagery with high accuracies (76-97% overall) for both case studies. High resolution Lidar Digital Elevation Models (DEM), Digital Surface Models (DSM), and Canopy Height Model (CHM) products provided the means for data fusion to extract riparian vegetation communities and surface water; producing model accuracies of (R2 0.90) for URSA, and RMSE of 0.2m to 0.7m at Shepard Slough when compared to field and optical validation data. Integration of Alberta and Canadian wetland classifications systems used to classify and determine economic value of wetlands into the methodology produced thematic maps relevant for policy and decision makers for potential wetland monitoring and policy development.

RAPID INSPECTION OF PAVEMENT MARKINGS USING MOBILE LIDAR POINT CLOUDS

Directory of Open Access Journals (Sweden)

H. Zhang

2016-06-01

Full Text Available This study aims at building a robust semi-automated pavement marking extraction workflow based on the use of mobile LiDAR point clouds. The proposed workflow consists of three components: preprocessing, extraction, and classification. In preprocessing, the mobile LiDAR point clouds are converted into the radiometrically corrected intensity imagery of the road surface. Then the pavement markings are automatically extracted with the intensity using a set of algorithms, including Otsu’s thresholding, neighbor-counting filtering, and region growing. Finally, the extracted pavement markings are classified with the geometric parameters using a manually defined decision tree. Case studies are conducted using the mobile LiDAR dataset acquired in Xiamen (Fujian, China with different road environments by the RIEGL VMX-450 system. The results demonstrated that the proposed workflow and our software tool can achieve 93% in completeness, 95% in correctness, and 94% in F-score when using Xiamen dataset.

Automotive System for Remote Surface Classification.

Science.gov (United States)

Bystrov, Aleksandr; Hoare, Edward; Tran, Thuy-Yung; Clarke, Nigel; Gashinova, Marina; Cherniakov, Mikhail

2017-04-01

In this paper we shall discuss a novel approach to road surface recognition, based on the analysis of backscattered microwave and ultrasonic signals. The novelty of our method is sonar and polarimetric radar data fusion, extraction of features for separate swathes of illuminated surface (segmentation), and using of multi-stage artificial neural network for surface classification. The developed system consists of 24 GHz radar and 40 kHz ultrasonic sensor. The features are extracted from backscattered signals and then the procedures of principal component analysis and supervised classification are applied to feature data. The special attention is paid to multi-stage artificial neural network which allows an overall increase in classification accuracy. The proposed technique was tested for recognition of a large number of real surfaces in different weather conditions with the average accuracy of correct classification of 95%. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real conditions.

Comparison of Surface Flow Features from Lidar-Derived Digital Elevation Models with Historical Elevation and Hydrography Data for Minnehaha County, South Dakota

Science.gov (United States)

Poppenga, Sandra K.; Worstell, Bruce B.; Stoker, Jason M.; Greenlee, Susan K.

2009-01-01

The U.S. Geological Survey (USGS) has taken the lead in the creation of a valuable remote sensing product by incorporating digital elevation models (DEMs) derived from Light Detection and Ranging (lidar) into the National Elevation Dataset (NED), the elevation layer of 'The National Map'. High-resolution lidar-derived DEMs provide the accuracy needed to systematically quantify and fully integrate surface flow including flow direction, flow accumulation, sinks, slope, and a dense drainage network. In 2008, 1-meter resolution lidar data were acquired in Minnehaha County, South Dakota. The acquisition was a collaborative effort between Minnehaha County, the city of Sioux Falls, and the USGS Earth Resources Observation and Science (EROS) Center. With the newly acquired lidar data, USGS scientists generated high-resolution DEMs and surface flow features. This report compares lidar-derived surface flow features in Minnehaha County to 30- and 10-meter elevation data previously incorporated in the NED and ancillary hydrography datasets. Surface flow features generated from lidar-derived DEMs are consistently integrated with elevation and are important in understanding surface-water movement to better detect surface-water runoff, flood inundation, and erosion. Many topographic and hydrologic applications will benefit from the increased availability of accurate, high-quality, and high-resolution surface-water data. The remotely sensed data provide topographic information and data integration capabilities needed for meeting current and future human and environmental needs.

Mapping Urban Tree Canopy Cover Using Fused Airborne LIDAR and Satellite Imagery Data

Science.gov (United States)

Parmehr, Ebadat G.; Amati, Marco; Fraser, Clive S.

2016-06-01

Urban green spaces, particularly urban trees, play a key role in enhancing the liveability of cities. The availability of accurate and up-to-date maps of tree canopy cover is important for sustainable development of urban green spaces. LiDAR point clouds are widely used for the mapping of buildings and trees, and several LiDAR point cloud classification techniques have been proposed for automatic mapping. However, the effectiveness of point cloud classification techniques for automated tree extraction from LiDAR data can be impacted to the point of failure by the complexity of tree canopy shapes in urban areas. Multispectral imagery, which provides complementary information to LiDAR data, can improve point cloud classification quality. This paper proposes a reliable method for the extraction of tree canopy cover from fused LiDAR point cloud and multispectral satellite imagery data. The proposed method initially associates each LiDAR point with spectral information from the co-registered satellite imagery data. It calculates the normalised difference vegetation index (NDVI) value for each LiDAR point and corrects tree points which have been misclassified as buildings. Then, region growing of tree points, taking the NDVI value into account, is applied. Finally, the LiDAR points classified as tree points are utilised to generate a canopy cover map. The performance of the proposed tree canopy cover mapping method is experimentally evaluated on a data set of airborne LiDAR and WorldView 2 imagery covering a suburb in Melbourne, Australia.

Localized Segment Based Processing for Automatic Building Extraction from LiDAR Data

Science.gov (United States)

Parida, G.; Rajan, K. S.

2017-05-01

The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.

LOCALIZED SEGMENT BASED PROCESSING FOR AUTOMATIC BUILDING EXTRACTION FROM LiDAR DATA

Directory of Open Access Journals (Sweden)

G. Parida

2017-05-01

Full Text Available The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.

2009 SCDNR Charleston County Lidar

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Photoscience completed the original collection and classification of the multiple return LiDAR of Charleston County, South Carolina in the winter of 2006-2007. In...

2009 SCDNR Horry County Lidar

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Sanborn Map Company completed the original classification of the multiple return LiDAR of Horry County, South Carolina in 2009. In 2013, Dewberry was tasked with...

From LIDAR Scanning to 3d FEM Analysis for Complex Surface and Underground Excavations

Science.gov (United States)

Chun, K.; Kemeny, J.

2017-12-01

Light detection and ranging (LIDAR) has been a prevalent remote-sensing technology applied in the geological fields due to its high precision and ease to use. One of the major applications is to use the detailed geometrical information of underground structures as a basis for the generation of three-dimensional numerical model that can be used in FEM analysis. To date, however, straightforward techniques in reconstructing numerical model from the scanned data of underground structures have not been well established or tested. In this paper, we propose a comprehensive approach integrating from LIDAR scanning to finite element numerical analysis, specifically converting LIDAR 3D point clouds of object containing complex surface geometry into finite element model. This methodology has been applied to the Kartchner Caverns in Arizona for the stability analysis. Numerical simulations were performed using the finite element code ABAQUS. The results indicate that the highlights of our technologies obtained from LIDAR is effective and provide reference for other similar engineering project in practice.

SURFACE FITTING FILTERING OF LIDAR POINT CLOUD WITH WAVEFORM INFORMATION

Directory of Open Access Journals (Sweden)

S. Xing

2017-09-01

Full Text Available Full-waveform LiDAR is an active technology of photogrammetry and remote sensing. It provides more detailed information about objects along the path of a laser pulse than discrete-return topographic LiDAR. The point cloud and waveform information with high quality can be obtained by waveform decomposition, which could make contributions to accurate filtering. The surface fitting filtering method with waveform information is proposed to present such advantage. Firstly, discrete point cloud and waveform parameters are resolved by global convergent Levenberg Marquardt decomposition. Secondly, the ground seed points are selected, of which the abnormal ones are detected by waveform parameters and robust estimation. Thirdly, the terrain surface is fitted and the height difference threshold is determined in consideration of window size and mean square error. Finally, the points are classified gradually with the rising of window size. The filtering process is finished until window size is larger than threshold. The waveform data in urban, farmland and mountain areas from “WATER (Watershed Allied Telemetry Experimental Research” are selected for experiments. Results prove that compared with traditional method, the accuracy of point cloud filtering is further improved and the proposed method has highly practical value.

LiDAR Vegetation Investigation and Signature Analysis System (LVISA)

Science.gov (United States)

Höfle, Bernhard; Koenig, Kristina; Griesbaum, Luisa; Kiefer, Andreas; Hämmerle, Martin; Eitel, Jan; Koma, Zsófia

2015-04-01

Our physical environment undergoes constant changes in space and time with strongly varying triggers, frequencies, and magnitudes. Monitoring these environmental changes is crucial to improve our scientific understanding of complex human-environmental interactions and helps us to respond to environmental change by adaptation or mitigation. The three-dimensional (3D) description of the Earth surface features and the detailed monitoring of surface processes using 3D spatial data have gained increasing attention within the last decades, such as in climate change research (e.g., glacier retreat), carbon sequestration (e.g., forest biomass monitoring), precision agriculture and natural hazard management. In all those areas, 3D data have helped to improve our process understanding by allowing quantifying the structural properties of earth surface features and their changes over time. This advancement has been fostered by technological developments and increased availability of 3D sensing systems. In particular, LiDAR (light detection and ranging) technology, also referred to as laser scanning, has made significant progress and has evolved into an operational tool in environmental research and geosciences. The main result of LiDAR measurements is a highly spatially resolved 3D point cloud. Each point within the LiDAR point cloud has a XYZ coordinate associated with it and often additional information such as the strength of the returned backscatter. The point cloud provided by LiDAR contains rich geospatial, structural, and potentially biochemical information about the surveyed objects. To deal with the inherently unorganized datasets and the large data volume (frequently millions of XYZ coordinates) of LiDAR datasets, a multitude of algorithms for automatic 3D object detection (e.g., of single trees) and physical surface description (e.g., biomass) have been developed. However, so far the exchange of datasets and approaches (i.e., extraction algorithms) among LiDAR users

Remote Sensing of Aerosol Backscatter and Earth Surface Targets By Use of An Airborne Focused Continuous Wave CO2 Doppler Lidar Over Western North America

Science.gov (United States)

Jarzembski, Maurice A.; Srivastava, Vandana; Goodman, H. Michael (Technical Monitor)

2000-01-01

Airborne lidar systems are used to determine wind velocity and to measure aerosol or cloud backscatter variability. Atmospheric aerosols, being affected by local and regional sources, show tremendous variability. Continuous wave (cw) lidar can obtain detailed aerosol loading with unprecedented high resolution (3 sec) and sensitivity (1 mg/cubic meter) as was done during the 1995 NASA Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS) mission over western North America and the Pacific Ocean. Backscatter variability was measured at a 9.1 micron wavelength cw focused CO2 Doppler lidar for approximately 52 flight hours, covering an equivalent horizontal distance of approximately 30,000 km in the troposphere. Some quasi-vertical backscatter profiles were also obtained during various ascents and descents at altitudes that ranged from approximately 0.1 to 12 km. Similarities and differences for aerosol loading over land and ocean were observed. Mid-tropospheric aerosol backscatter background mode was approximately 6 x 10(exp -11)/ms/r, consistent with previous lidar datasets. While these atmospheric measurements were made, the lidar also retrieved a distinct backscatter signal from the Earth's surface from the unfocused part of the focused cw lidar beam during aircraft rolls. Atmospheric backscatter can be highly variable both spatially and temporally, whereas, Earth-surface backscatter is relatively much less variant and can be quite predictable. Therefore, routine atmospheric backscatter measurements by an airborne lidar also give Earth surface backscatter which can allow for investigating the Earth terrain. In the case where the Earth's surface backscatter is coming from a well-known and fairly uniform region, then it can potentially offer lidar calibration opportunities during flight. These Earth surface measurements over varying Californian terrain during the mission were compared with laboratory backscatter measurements using the same lidar of various

Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning

Directory of Open Access Journals (Sweden)

Victoria Plaza-Leiva

2017-03-01

Full Text Available Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM, Gaussian processes (GP, and Gaussian mixture models (GMM. A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl. Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning.

Science.gov (United States)

Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

2017-03-15

Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

Lidar to lidar calibration of Ground-based Lidar

DEFF Research Database (Denmark)

Fernandez Garcia, Sergio; Courtney, Michael

This report presents the result of the lidar to lidar calibration performed for ground-based lidar. Calibration is here understood as the establishment of a relation between the reference lidar wind speed measurements with measurement uncertainties provided by measurement standard and corresponding...... lidar wind speed indications with associated measurement uncertainties. The lidar calibration concerns the 10 minute mean wind speed measurements. The comparison of the lidar measurements of the wind direction with that from the reference lidar measurements are given for information only....

Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures

Directory of Open Access Journals (Sweden)

Tan Zhou

2017-12-01

Full Text Available A plethora of information contained in full-waveform (FW Light Detection and Ranging (LiDAR data offers prospects for characterizing vegetation structures. This study aims to investigate the capacity of FW LiDAR data alone for tree species identification through the integration of waveform metrics with machine learning methods and Bayesian inference. Specifically, we first conducted automatic tree segmentation based on the waveform-based canopy height model (CHM using three approaches including TreeVaW, watershed algorithms and the combination of TreeVaW and watershed (TW algorithms. Subsequently, the Random forests (RF and Conditional inference forests (CF models were employed to identify important tree-level waveform metrics derived from three distinct sources, such as raw waveforms, composite waveforms, the waveform-based point cloud and the combined variables from these three sources. Further, we discriminated tree (gray pine, blue oak, interior live oak and shrub species through the RF, CF and Bayesian multinomial logistic regression (BMLR using important waveform metrics identified in this study. Results of the tree segmentation demonstrated that the TW algorithms outperformed other algorithms for delineating individual tree crowns. The CF model overcomes waveform metrics selection bias caused by the RF model which favors correlated metrics and enhances the accuracy of subsequent classification. We also found that composite waveforms are more informative than raw waveforms and waveform-based point cloud for characterizing tree species in our study area. Both classical machine learning methods (the RF and CF and the BMLR generated satisfactory average overall accuracy (74% for the RF, 77% for the CF and 81% for the BMLR and the BMLR slightly outperformed the other two methods. However, these three methods suffered from low individual classification accuracy for the blue oak which is prone to being misclassified as the interior live oak due

Lidar to lidar calibration

DEFF Research Database (Denmark)

Fernandez Garcia, Sergio; Villanueva, Héctor

This report presents the result of the lidar to lidar calibration performed for ground-based lidar. Calibration is here understood as the establishment of a relation between the reference lidar wind speed measurements with measurement uncertainties provided by measurement standard and corresponding...... lidar wind speed indications with associated measurement uncertainties. The lidar calibration concerns the 10 minute mean wind speed measurements. The comparison of the lidar measurements of the wind direction with that from the reference lidar measurements are given for information only....

ALTERNATIVE METHODOLOGIES FOR THE ESTIMATION OF LOCAL POINT DENSITY INDEX: MOVING TOWARDS ADAPTIVE LIDAR DATA PROCESSING

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Z. Lari

2012-07-01

Full Text Available Over the past few years, LiDAR systems have been established as a leading technology for the acquisition of high density point clouds over physical surfaces. These point clouds will be processed for the extraction of geo-spatial information. Local point density is one of the most important properties of the point cloud that highly affects the performance of data processing techniques and the quality of extracted information from these data. Therefore, it is necessary to define a standard methodology for the estimation of local point density indices to be considered for the precise processing of LiDAR data. Current definitions of local point density indices, which only consider the 2D neighbourhood of individual points, are not appropriate for 3D LiDAR data and cannot be applied for laser scans from different platforms. In order to resolve the drawbacks of these methods, this paper proposes several approaches for the estimation of the local point density index which take the 3D relationship among the points and the physical properties of the surfaces they belong to into account. In the simplest approach, an approximate value of the local point density for each point is defined while considering the 3D relationship among the points. In the other approaches, the local point density is estimated by considering the 3D neighbourhood of the point in question and the physical properties of the surface which encloses this point. The physical properties of the surfaces enclosing the LiDAR points are assessed through eigen-value analysis of the 3D neighbourhood of individual points and adaptive cylinder methods. This paper will discuss these approaches and highlight their impact on various LiDAR data processing activities (i.e., neighbourhood definition, region growing, segmentation, boundary detection, and classification. Experimental results from airborne and terrestrial LiDAR data verify the efficacy of considering local point density variation for

Specular and diffuse object extraction from a LiDAR derived Digital Surface Model (DSM)

International Nuclear Information System (INIS)

Saraf, N M; Hamid, J R A; Kamaruddin, M H

2014-01-01

This paper intents to investigate the indifferent behaviour quantitatively of target objects of interest due to specular and diffuse reflectivity based on generated LiDAR DSM of the study site in Ampang, Kuala Lumpur. The LiDAR data to be used was initially checked for its reliability and accuracy. The point cloud LiDAR data was converted to raster to allow grid analysis of the next process of generating the DSM and DTM. Filtering and masking were made removing the features of interest (i.e. building and tree) and other unwanted above surface features. A normalised DSM and object segmentation approach were conducted on the trees and buildings separately. Error assessment and findings attained were highlighted and documented. The result of LiDAR verification certified that the data is reliable and useable. The RMSE obtained is within the tolerance value of horizontal and vertical accuracy (x, y, z) i.e. 0.159 m, 0.211 m 0.091 m respectively. Building extraction inclusive of roof top based on slope and contour analysis undertaken indicate the capability of the approach while single tree extraction through aspect analysis appears to preserve the accuracy of the extraction accordingly. The paper has evaluated the suitable methods of extracting non-ground features and the effective segmentation of the LiDAR data

Using LiDAR to Estimate Surface Erosion Volumes within the Post-storm 2012 Bagley Fire

Science.gov (United States)

Mikulovsky, R. P.; De La Fuente, J. A.; Mondry, Z. J.

2014-12-01

The total post-storm 2012 Bagley fire sediment budget of the Squaw Creek watershed in the Shasta-Trinity National Forest was estimated using many methods. A portion of the budget was quantitatively estimated using LiDAR. Simple workflows were designed to estimate the eroded volume's of debris slides, fill failures, gullies, altered channels and streams. LiDAR was also used to estimate depositional volumes. Thorough manual mapping of large erosional features using the ArcGIS 10.1 Geographic Information System was required as these mapped features determined the eroded volume boundaries in 3D space. The 3D pre-erosional surface for each mapped feature was interpolated based on the boundary elevations. A surface difference calculation was run using the estimated pre-erosional surfaces and LiDAR surfaces to determine volume of sediment potentially delivered into the stream system. In addition, cross sections of altered channels and streams were taken using stratified random selection based on channel gradient and stream order respectively. The original pre-storm surfaces of channel features were estimated using the cross sections and erosion depth criteria. Open source software Inkscape was used to estimate cross sectional areas for randomly selected channel features and then averaged for each channel gradient and stream order classes. The average areas were then multiplied by the length of each class to estimate total eroded altered channel and stream volume. Finally, reservoir and in-channel depositional volumes were estimated by mapping channel forms and generating specific reservoir elevation zones associated with depositional events. The in-channel areas and zones within the reservoir were multiplied by estimated and field observed sediment thicknesses to attain a best guess sediment volume. In channel estimates included re-occupying stream channel cross sections established before the fire. Once volumes were calculated, other erosion processes of the Bagley

Cloud Type Classification (cldtype) Value-Added Product

Energy Technology Data Exchange (ETDEWEB)

Flynn, Donna [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Shi, Yan [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lim, K-S [Korean Atomic Energy Research Inst., Daejeon (South Korea); Riihimaki, Laura [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2017-08-15

The Cloud Type (cldtype) value-added product (VAP) provides an automated cloud type classification based on macrophysical quantities derived from vertically pointing lidar and radar. Up to 10 layers of clouds are classified into seven cloud types based on predetermined and site-specific thresholds of cloud top, base and thickness. Examples of thresholds for selected U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility sites are provided in Tables 1 and 2. Inputs for the cldtype VAP include lidar and radar cloud boundaries obtained from the Active Remotely Sensed Cloud Location (ARSCL) and Surface Meteorological Systems (MET) data. Rain rates from MET are used to determine when radar signal attenuation precludes accurate cloud detection. Temporal resolution and vertical resolution for cldtype are 1 minute and 30 m respectively and match the resolution of ARSCL. The cldtype classification is an initial step for further categorization of clouds. It was developed for use by the Shallow Cumulus VAP to identify potential periods of interest to the LASSO model and is intended to find clouds of interest for a variety of users.

Calibration Methods for a Space Borne Backscatter Lidar

NARCIS (Netherlands)

Kunz, G.J.

1996-01-01

Lidar returns from cloud decks and from the Earth's surface are useful for calibrating single scatter lidar signals from space. To this end analytical methods (forward and backward) are presented for inverting lidar waveforms in terms of the path integrated lidar retum and the transmission losses

LiDAR Relative Reflectivity Surface (2011) for the St. Thomas East End Reserve, St. Thomas

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This image represents a LiDAR (Light Detection & Ranging) 0.3x0.3 meter resolution relative seafloor reflectivity surface for the St. Thomas East End Reserve...

Airborne Instrument Simulator for the Lidar Surface Topography (LIST) Mission

Science.gov (United States)

Yu, Anthony W.; Krainak, Michael A.; Harding, David J.; Abshire, James B.; Sun, Xiaoli; Cavanaugh, John; Valett, Susan; Ramos-Izquierdo, Luis

2010-01-01

In 2007, the National Research Council (NRC) completed its first decadal survey for Earth science at the request of NASA, NOAA, and USGS. The Lidar Surface Topography (LIST) mission is one of fifteen missions recommended by NRC, whose primary objectives are to map global topography and vegetation structure at 5 m spatial resolution, and to acquire global coverage with a few years. NASA Goddard conducted an initial mission concept study for the LIST mission 2007, and developed the initial measurement requirements for the mission.

PROCESSING UAV AND LIDAR POINT CLOUDS IN GRASS GIS

Directory of Open Access Journals (Sweden)

V. Petras

2016-06-01

Full Text Available Today’s methods of acquiring Earth surface data, namely lidar and unmanned aerial vehicle (UAV imagery, non-selectively collect or generate large amounts of points. Point clouds from different sources vary in their properties such as number of returns, density, or quality. We present a set of tools with applications for different types of points clouds obtained by a lidar scanner, structure from motion technique (SfM, and a low-cost 3D scanner. To take advantage of the vertical structure of multiple return lidar point clouds, we demonstrate tools to process them using 3D raster techniques which allow, for example, the development of custom vegetation classification methods. Dense point clouds obtained from UAV imagery, often containing redundant points, can be decimated using various techniques before further processing. We implemented and compared several decimation techniques in regard to their performance and the final digital surface model (DSM. Finally, we will describe the processing of a point cloud from a low-cost 3D scanner, namely Microsoft Kinect, and its application for interaction with physical models. All the presented tools are open source and integrated in GRASS GIS, a multi-purpose open source GIS with remote sensing capabilities. The tools integrate with other open source projects, specifically Point Data Abstraction Library (PDAL, Point Cloud Library (PCL, and OpenKinect libfreenect2 library to benefit from the open source point cloud ecosystem. The implementation in GRASS GIS ensures long term maintenance and reproducibility by the scientific community but also by the original authors themselves.

Measurement and Study of Lidar Ratio by Using a Raman Lidar in Central China

Directory of Open Access Journals (Sweden)

Wei Wang

2016-05-01

Full Text Available We comprehensively evaluated particle lidar ratios (i.e., particle extinction to backscatter ratio at 532 nm over Wuhan in Central China by using a Raman lidar from July 2013 to May 2015. We utilized the Raman lidar data to obtain homogeneous aerosol lidar ratios near the surface through the Raman method during no-rain nights. The lidar ratios were approximately 57 ± 7 sr, 50 ± 5 sr, and 22 ± 4 sr under the three cases with obviously different pollution levels. The haze layer below 1.8 km has a large particle extinction coefficient (from 5.4e-4 m−1 to 1.6e-4 m−1 and particle backscatter coefficient (between 1.1e-05 m−1sr−1 and 1.7e-06 m−1sr−1 in the heavily polluted case. Furthermore, the particle lidar ratios varied according to season, especially between winter (57 ± 13 sr and summer (33 ± 10 sr. The seasonal variation in lidar ratios at Wuhan suggests that the East Asian monsoon significantly affects the primary aerosol types and aerosol optical properties in this region. The relationships between particle lidar ratios and wind indicate that large lidar ratio values correspond well with weak winds and strong northerly winds, whereas significantly low lidar ratio values are associated with prevailing southwesterly and southerly wind.

Measurement and Study of Lidar Ratio by Using a Raman Lidar in Central China.

Science.gov (United States)

Wang, Wei; Gong, Wei; Mao, Feiyue; Pan, Zengxin; Liu, Boming

2016-05-18

We comprehensively evaluated particle lidar ratios (i.e., particle extinction to backscatter ratio) at 532 nm over Wuhan in Central China by using a Raman lidar from July 2013 to May 2015. We utilized the Raman lidar data to obtain homogeneous aerosol lidar ratios near the surface through the Raman method during no-rain nights. The lidar ratios were approximately 57 ± 7 sr, 50 ± 5 sr, and 22 ± 4 sr under the three cases with obviously different pollution levels. The haze layer below 1.8 km has a large particle extinction coefficient (from 5.4e-4 m(-1) to 1.6e-4 m(-1)) and particle backscatter coefficient (between 1.1e-05 m(-1)sr(-1) and 1.7e-06 m(-1)sr(-1)) in the heavily polluted case. Furthermore, the particle lidar ratios varied according to season, especially between winter (57 ± 13 sr) and summer (33 ± 10 sr). The seasonal variation in lidar ratios at Wuhan suggests that the East Asian monsoon significantly affects the primary aerosol types and aerosol optical properties in this region. The relationships between particle lidar ratios and wind indicate that large lidar ratio values correspond well with weak winds and strong northerly winds, whereas significantly low lidar ratio values are associated with prevailing southwesterly and southerly wind.

Automated lidar-derived canopy height estimates for the Upper Mississippi River System

Science.gov (United States)

Hlavacek, Enrika

2015-01-01

Land cover/land use (LCU) classifications serve as important decision support products for researchers and land managers. The LCU classifications produced by the U.S. Geological Survey’s Upper Midwest Environmental Sciences Center (UMESC) include canopy height estimates that are assigned through manual aerial photography interpretation techniques. In an effort to improve upon these techniques, this project investigated the use of high-density lidar data for the Upper Mississippi River System to determine canopy height. An ArcGIS tool was developed to automatically derive height modifier information based on the extent of land cover features for forest classes. The measurement of canopy height included a calculation of the average height from lidar point cloud data as well as the inclusion of a local maximum filter to identify individual tree canopies. Results were compared to original manually interpreted height modifiers and to field survey data from U.S. Forest Service Forest Inventory and Analysis plots. This project demonstrated the effectiveness of utilizing lidar data to more efficiently assign height modifier attributes to LCU classifications produced by the UMESC.

CLASSIFICATION BY USING MULTISPECTRAL POINT CLOUD DATA

Directory of Open Access Journals (Sweden)

C. T. Liao

2012-07-01

Full Text Available Remote sensing images are generally recorded in two-dimensional format containing multispectral information. Also, the semantic information is clearly visualized, which ground features can be better recognized and classified via supervised or unsupervised classification methods easily. Nevertheless, the shortcomings of multispectral images are highly depending on light conditions, and classification results lack of three-dimensional semantic information. On the other hand, LiDAR has become a main technology for acquiring high accuracy point cloud data. The advantages of LiDAR are high data acquisition rate, independent of light conditions and can directly produce three-dimensional coordinates. However, comparing with multispectral images, the disadvantage is multispectral information shortage, which remains a challenge in ground feature classification through massive point cloud data. Consequently, by combining the advantages of both LiDAR and multispectral images, point cloud data with three-dimensional coordinates and multispectral information can produce a integrate solution for point cloud classification. Therefore, this research acquires visible light and near infrared images, via close range photogrammetry, by matching images automatically through free online service for multispectral point cloud generation. Then, one can use three-dimensional affine coordinate transformation to compare the data increment. At last, the given threshold of height and color information is set as threshold in classification.

Classification by Using Multispectral Point Cloud Data

Science.gov (United States)

Liao, C. T.; Huang, H. H.

2012-07-01

Remote sensing images are generally recorded in two-dimensional format containing multispectral information. Also, the semantic information is clearly visualized, which ground features can be better recognized and classified via supervised or unsupervised classification methods easily. Nevertheless, the shortcomings of multispectral images are highly depending on light conditions, and classification results lack of three-dimensional semantic information. On the other hand, LiDAR has become a main technology for acquiring high accuracy point cloud data. The advantages of LiDAR are high data acquisition rate, independent of light conditions and can directly produce three-dimensional coordinates. However, comparing with multispectral images, the disadvantage is multispectral information shortage, which remains a challenge in ground feature classification through massive point cloud data. Consequently, by combining the advantages of both LiDAR and multispectral images, point cloud data with three-dimensional coordinates and multispectral information can produce a integrate solution for point cloud classification. Therefore, this research acquires visible light and near infrared images, via close range photogrammetry, by matching images automatically through free online service for multispectral point cloud generation. Then, one can use three-dimensional affine coordinate transformation to compare the data increment. At last, the given threshold of height and color information is set as threshold in classification.

COMPREHENSIVE COMPARISON OF TWO IMAGE-BASED POINT CLOUDS FROM AERIAL PHOTOS WITH AIRBORNE LIDAR FOR LARGE-SCALE MAPPING

Directory of Open Access Journals (Sweden)

E. Widyaningrum

2017-09-01

Full Text Available The integration of computer vision and photogrammetry to generate three-dimensional (3D information from images has contributed to a wider use of point clouds, for mapping purposes. Large-scale topographic map production requires 3D data with high precision and accuracy to represent the real conditions of the earth surface. Apart from LiDAR point clouds, the image-based matching is also believed to have the ability to generate reliable and detailed point clouds from multiple-view images. In order to examine and analyze possible fusion of LiDAR and image-based matching for large-scale detailed mapping purposes, point clouds are generated by Semi Global Matching (SGM and by Structure from Motion (SfM. In order to conduct comprehensive and fair comparison, this study uses aerial photos and LiDAR data that were acquired at the same time. Qualitative and quantitative assessments have been applied to evaluate LiDAR and image-matching point clouds data in terms of visualization, geometric accuracy, and classification result. The comparison results conclude that LiDAR is the best data for large-scale mapping.

Where’s the Ground Surface? – Elevation Bias in LIDAR-derived Digital Elevation Models Due to Dense Vegetation in Oregon Tidal Marshes

Science.gov (United States)

Light Detection and Ranging (LIDAR) is a powerful resource for coastal and wetland managers and its use is increasing. Vegetation density and other land cover characteristics influence the accuracy of LIDAR-derived ground surface digital elevation models; however the degree to wh...

REAL AND SIMULATED WAVEFORM RECORDING LIDAR DATA IN BOREAL JUVENILE FOREST VEGETATION

Directory of Open Access Journals (Sweden)

A. Hovi

2013-05-01

Full Text Available Airborne small-footprint LiDAR is replacing field measurements in regional-level forest inventories, but auxiliary field work is still required for the optimal management of young stands. Waveform (WF recording sensors can provide a more detailed description of the vegetation compared to discrete return (DR systems. Furthermore, knowing the shape of the signal facilitates comparisons between real data and those obtained with simulation tools. We performed a quantitative validation of a Monte Carlo ray tracing (MCRT -based LiDAR simulator against real data and used simulations and empirical data to study the WF recording LiDAR for the classification of boreal juvenile forest vegetation. Geometric-optical models of three common species were used as input for the MCRT model. Simulated radiometric and geometric WF features were in good agreement with the real data, and interspecies differences were preserved. We used the simulator to study the effects of sensor parameters on species classification performance. An increase in footprint size improved the classification accuracy up to a certain footprint size, while the emitted pulse width and the WF sampling rate had minor effects. Analyses on empirical data showed small improvement in performance compared to existing studies, when classifying seedling stand vegetation to four operational classes. The results on simulator validation serve as a basis for the future use of simulation models e.g. in LiDAR survey planning or in the simulation of synthetic training data, while the empirical findings clarify the potential of WF LiDAR data in the inventory chain for the operational forest management planning in Finland.

Results from the search-lidar demonstrator project for detection of small Sea-Surface targets

NARCIS (Netherlands)

Heuvel, J.C. van den; Putten, F.J.M. van; Cohen, L.H.; Kemp, R.A.W.; Franssen, G.C.

2009-01-01

Coastal surveillance and naval operations in the littoral both have to deal with the threat of small sea-surface targets. These targets have a low radar cross-section and a low velocity that makes them hard to detect by radar. Typical threats include jet skis, FIAC's, and speedboats. Previous lidar

3D Spatial and Spectral Fusion of Terrestrial Hyperspectral Imagery and Lidar for Hyperspectral Image Shadow Restoration Applied to a Geologic Outcrop

Science.gov (United States)

Hartzell, P. J.; Glennie, C. L.; Hauser, D. L.; Okyay, U.; Khan, S.; Finnegan, D. C.

2016-12-01

Recent advances in remote sensing technology have expanded the acquisition and fusion of active lidar and passive hyperspectral imagery (HSI) from an exclusively airborne technique to terrestrial modalities. This enables high resolution 3D spatial and spectral quantification of vertical geologic structures for applications such as virtual 3D rock outcrop models for hydrocarbon reservoir analog analysis and mineral quantification in open pit mining environments. In contrast to airborne observation geometry, the vertical surfaces observed by horizontal-viewing terrestrial HSI sensors are prone to extensive topography-induced solar shadowing, which leads to reduced pixel classification accuracy or outright removal of shadowed pixels from analysis tasks. Using a precisely calibrated and registered offset cylindrical linear array camera model, we demonstrate the use of 3D lidar data for sub-pixel HSI shadow detection and the restoration of the shadowed pixel spectra via empirical methods that utilize illuminated and shadowed pixels of similar material composition. We further introduce a new HSI shadow restoration technique that leverages collocated backscattered lidar intensity, which is resistant to solar conditions, obtained by projecting the 3D lidar points through the HSI camera model into HSI pixel space. Using ratios derived from the overlapping lidar laser and HSI wavelengths, restored shadow pixel spectra are approximated using a simple scale factor. Simulations of multiple lidar wavelengths, i.e., multi-spectral lidar, indicate the potential for robust HSI spectral restoration that is independent of the complexity and costs associated with rigorous radiometric transfer models, which have yet to be developed for horizontal-viewing terrestrial HSI sensors. The spectral restoration performance is quantified through HSI pixel classification consistency between full sun and partial sun exposures of a single geologic outcrop.

Remote Sensing of Sub-Surface Suspended Sediment Concentration by Using the Range Bias of Green Surface Point of Airborne LiDAR Bathymetry

Directory of Open Access Journals (Sweden)

Xinglei Zhao

2018-04-01

Full Text Available Suspended sediment concentrations (SSCs have been retrieved accurately and effectively through waveform methods by using green-pulse waveforms of airborne LiDAR bathymetry (ALB. However, the waveform data are commonly difficult to analyze. Thus, this paper proposes a 3D point-cloud method for remote sensing of SSCs in calm waters by using the range biases of green surface points of ALB. The near water surface penetrations (NWSPs of green lasers are calculated on the basis of the green and reference surface points. The range biases (ΔS are calculated by using the corresponding NWSPs and beam-scanning angles. In situ measured SSCs (C and range biases (ΔS are used to establish an empirical C-ΔS model at SSC sampling stations. The SSCs in calm waters are retrieved by using the established C-ΔS model. The proposed method is applied to a practical ALB measurement performed by Optech Coastal Zone Mapping and Imaging LiDAR. The standard deviations of the SSCs retrieved by the 3D point-cloud method are less than 20 mg/L.

2015 Oregon Department Forestry Lidar: Northwest OR

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — GeoTerra, Inc. was selected by Oregon Department of Forestry to provide Lidar remote sensing data including LAZ files of the classified Lidar points and surface...

2009 Bayfield County Lake Superior Lidar Survey

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The LIDAR survey presents digital elevation data sets of a bald earth surface model and 2ft interval contours covering Bayfield County, Wisconsin. The LIDAR data was...

A New Framework for Quantifying Lidar Uncertainty

Energy Technology Data Exchange (ETDEWEB)

Newman, Jennifer, F.; Clifton, Andrew; Bonin, Timothy A.; Churchfield, Matthew J.

2017-03-24

As wind turbine sizes increase and wind energy expands to more complex and remote sites, remote sensing devices such as lidars are expected to play a key role in wind resource assessment and power performance testing. The switch to remote sensing devices represents a paradigm shift in the way the wind industry typically obtains and interprets measurement data for wind energy. For example, the measurement techniques and sources of uncertainty for a remote sensing device are vastly different from those associated with a cup anemometer on a meteorological tower. Current IEC standards discuss uncertainty due to mounting, calibration, and classification of the remote sensing device, among other parameters. Values of the uncertainty are typically given as a function of the mean wind speed measured by a reference device. However, real-world experience has shown that lidar performance is highly dependent on atmospheric conditions, such as wind shear, turbulence, and aerosol content. At present, these conditions are not directly incorporated into the estimated uncertainty of a lidar device. In this presentation, we propose the development of a new lidar uncertainty framework that adapts to current flow conditions and more accurately represents the actual uncertainty inherent in lidar measurements under different conditions. In this new framework, sources of uncertainty are identified for estimation of the line-of-sight wind speed and reconstruction of the three-dimensional wind field. These sources are then related to physical processes caused by the atmosphere and lidar operating conditions. The framework is applied to lidar data from an operational wind farm to assess the ability of the framework to predict errors in lidar-measured wind speed.

Aerosol characteristics inversion based on the improved lidar ratio profile with the ground-based rotational Raman-Mie lidar

Science.gov (United States)

Ji, Hongzhu; Zhang, Yinchao; Chen, Siying; Chen, He; Guo, Pan

2018-06-01

An iterative method, based on a derived inverse relationship between atmospheric backscatter coefficient and aerosol lidar ratio, is proposed to invert the lidar ratio profile and aerosol extinction coefficient. The feasibility of this method is investigated theoretically and experimentally. Simulation results show the inversion accuracy of aerosol optical properties for iterative method can be improved in the near-surface aerosol layer and the optical thick layer. Experimentally, as a result of the reduced insufficiency error and incoherence error, the aerosol optical properties with higher accuracy can be obtained in the near-surface region and the region of numerical derivative distortion. In addition, the particle component can be distinguished roughly based on this improved lidar ratio profile.

Lidar to lidar calibration phase 1

DEFF Research Database (Denmark)

Yordanova, Ginka; Courtney, Michael

This report presents a feasibility study of a lidar to lidar (L2L) calibration procedure. Phase one of the project was conducted at Høvsøre, Denmark. Two windcubes were placed next to the 116m met mast and different methods were applied to obtain the sensing height error of the lidars. The purpose...... is to find the most consistent method and use it in a potential lidar to lidar calibration procedure....

Time series analysis of continuous-wave coherent Doppler Lidar wind measurements

DEFF Research Database (Denmark)

Sjöholm, Mikael; Mikkelsen, Torben; Mann, Jakob

2008-01-01

The influence of spatial volume averaging of a focused 1.55 mu m continuous-wave coherent Doppler Lidar on observed wind turbulence measured in the atmospheric surface layer over homogeneous terrain is described and analysed. Comparison of Lidar-measured turbulent spectra with spectra simultaneou......The influence of spatial volume averaging of a focused 1.55 mu m continuous-wave coherent Doppler Lidar on observed wind turbulence measured in the atmospheric surface layer over homogeneous terrain is described and analysed. Comparison of Lidar-measured turbulent spectra with spectra...

Automatic landslide detection from LiDAR DTM derivatives by geographic-object-based image analysis based on open-source software

Science.gov (United States)

Knevels, Raphael; Leopold, Philip; Petschko, Helene

2017-04-01

With high-resolution airborne Light Detection and Ranging (LiDAR) data more commonly available, many studies have been performed to facilitate the detailed information on the earth surface and to analyse its limitation. Specifically in the field of natural hazards, digital terrain models (DTM) have been used to map hazardous processes such as landslides mainly by visual interpretation of LiDAR DTM derivatives. However, new approaches are striving towards automatic detection of landslides to speed up the process of generating landslide inventories. These studies usually use a combination of optical imagery and terrain data, and are designed in commercial software packages such as ESRI ArcGIS, Definiens eCognition, or MathWorks MATLAB. The objective of this study was to investigate the potential of open-source software for automatic landslide detection based only on high-resolution LiDAR DTM derivatives in a study area within the federal state of Burgenland, Austria. The study area is very prone to landslides which have been mapped with different methodologies in recent years. The free development environment R was used to integrate open-source geographic information system (GIS) software, such as SAGA (System for Automated Geoscientific Analyses), GRASS (Geographic Resources Analysis Support System), or TauDEM (Terrain Analysis Using Digital Elevation Models). The implemented geographic-object-based image analysis (GEOBIA) consisted of (1) derivation of land surface parameters, such as slope, surface roughness, curvature, or flow direction, (2) finding optimal scale parameter by the use of an objective function, (3) multi-scale segmentation, (4) classification of landslide parts (main scarp, body, flanks) by k-mean thresholding, (5) assessment of the classification performance using a pre-existing landslide inventory, and (6) post-processing analysis for the further use in landslide inventories. The results of the developed open-source approach demonstrated good

Lidar to lidar calibration phase 2

DEFF Research Database (Denmark)

Yordanova, Ginka; Courtney, Michael

This report presents the results from phase 2 of a lidar to lidar (L2L) calibration procedure. Phase two of the project included two measurement campaigns conducted at given sites. The purpose was to find out if the lidar-to-lidar calibration procedure can be conducted with similar results...

2015 Oregon Department Forestry Lidar DEM: Northwest OR

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — GeoTerra, Inc. was selected by Oregon Department of Forestry to provide Lidar remote sensing data including LAZ files of the classified Lidar points and surface...

Lidar Remote Sensing for Industry and Environment Monitoring

Science.gov (United States)

Singh, Upendra N. (Editor); Itabe, Toshikazu (Editor); Sugimoto, Nobuo (Editor)

2000-01-01

Contents include the following: 1. Keynote paper: Overview of lidar technology for industrial and environmental monitoring in Japan. 2. lidar technology I: NASA's future active remote sensing mission for earth science. Geometrical detector consideration s in laser sensing application (invited paper). 3. Lidar technology II: High-power femtosecond light strings as novel atmospheric probes (invited paper). Design of a compact high-sensitivity aerosol profiling lidar. 4. Lasers for lidars: High-energy 2 microns laser for multiple lidar applications. New submount requirement of conductively cooled laser diodes for lidar applications. 5. Tropospheric aerosols and clouds I: Lidar monitoring of clouds and aerosols at the facility for atmospheric remote sensing (invited paper). Measurement of asian dust by using multiwavelength lidar. Global monitoring of clouds and aerosols using a network of micropulse lidar systems. 6. Troposphere aerosols and clouds II: Scanning lidar measurements of marine aerosol fields at a coastal site in Hawaii. 7. Tropospheric aerosols and clouds III: Formation of ice cloud from asian dust particles in the upper troposphere. Atmospheric boundary layer observation by ground-based lidar at KMITL, Thailand (13 deg N, 100 deg. E). 8. Boundary layer, urban pollution: Studies of the spatial correlation between urban aerosols and local traffic congestion using a slant angle scanning on the research vessel Mirai. 9. Middle atmosphere: Lidar-observed arctic PSC's over Svalbard (invited paper). Sodium temperature lidar measurements of the mesopause region over Syowa Station. 10. Differential absorption lidar (dIAL) and DOAS: Airborne UV DIAL measurements of ozone and aerosols (invited paper). Measurement of water vapor, surface ozone, and ethylene using differential absorption lidar. 12. Space lidar I: Lightweight lidar telescopes for space applications (invited paper). Coherent lidar development for Doppler wind measurement from the International Space

Optimization of eyesafe avalanche photodiode lidar for automobile safety and autonomous navigation systems

Science.gov (United States)

Williams, George M.

2017-03-01

Newly emerging accident-reducing, driver-assistance, and autonomous-navigation technology for automobiles is based on real-time three-dimensional mapping and object detection, tracking, and classification using lidar sensors. Yet, the lack of lidar sensors suitable for meeting application requirements appreciably limits practical widespread use of lidar in trucking, public livery, consumer cars, and fleet automobiles. To address this need, a system-engineering perspective to eyesafe lidar-system design for high-level advanced driver-assistance sensor systems and a design trade study including 1.5-μm spot-scanned, line-scanned, and flash-lidar systems are presented. A cost-effective lidar instrument design is then proposed based on high-repetition-rate diode-pumped solid-state lasers and high-gain, low-excess-noise InGaAs avalanche photodiode receivers and focal plane arrays. Using probabilistic receiver-operating-characteristic analysis, derived from measured component performance, a compact lidar system is proposed that is capable of 220 m ranging with 5-cm accuracy, which can be readily scaled to a 360-deg field of regard.

Lidar investigations of atmospheric aerosols over Sofia

International Nuclear Information System (INIS)

Dreischuh, T.; Deleva, A.; Peshev, Z.; Grigorov, I.; Kolarov, G.; Stoyanov, D.

2016-01-01

An overview is given of the laser remote sensing of atmospheric aerosols and related processes over the Sofia area performed in the Institute of Electronics, Bulgarian Academy of Sciences, during the last three years. Results from lidar investigations of the optical characteristics of atmospheric aerosols obtained in the frame of the European Aerosol Research Lidar Network, as well as from the lidar mapping of near-surface aerosol fields for remote monitoring of atmospheric pollutants are presented and discussed in this paper.

Typical Applications of Airborne LIDAR Technolagy in Geological Investigation

Science.gov (United States)

Zheng, X.; Xiao, C.

2018-05-01

The technology of airborne light detection and ranging (LiDAR), also referred to as Airborne Laser Scanning, is widely used for high-resolution topographic data acquisition (even under forest cover) with sub-meter planimetric and vertical accuracy. This contribution constructs the real digital terrain model to provide the direct observation data for the landscape analysis in geological domains. Based on the advantage of LiDAR, the authors mainly deal with the applications of LiDAR data to such fields as surface land collapse, landslide and fault structure extraction. The review conclusion shows that airborne LiDAR technology is becoming an indispensable tool for above mentioned issues, especially in the local and large scale investigations of micro-topography. The technology not only can identify the surface collapse, landslide boundary and subtle faulted landform, but also be able to extract the filling parameters of collapsed surface, the geomorphic parameters of landslide stability evaluation and cracks. This technology has extensive prospect of applications in geological investigation.

TYPICAL APPLICATIONS OF AIRBORNE LIDAR TECHNOLAGY IN GEOLOGICAL INVESTIGATION

Directory of Open Access Journals (Sweden)

X. Zheng

2018-05-01

Full Text Available The technology of airborne light detection and ranging (LiDAR, also referred to as Airborne Laser Scanning, is widely used for high-resolution topographic data acquisition (even under forest cover with sub-meter planimetric and vertical accuracy. This contribution constructs the real digital terrain model to provide the direct observation data for the landscape analysis in geological domains. Based on the advantage of LiDAR, the authors mainly deal with the applications of LiDAR data to such fields as surface land collapse, landslide and fault structure extraction. The review conclusion shows that airborne LiDAR technology is becoming an indispensable tool for above mentioned issues, especially in the local and large scale investigations of micro-topography. The technology not only can identify the surface collapse, landslide boundary and subtle faulted landform, but also be able to extract the filling parameters of collapsed surface, the geomorphic parameters of landslide stability evaluation and cracks. This technology has extensive prospect of applications in geological investigation.

TOLNet ozone lidar intercomparison during the discover-aq and frappé campaigns

Science.gov (United States)

Newchurch, Michael J.; Alvarez, Raul J.; Berkoff, Timothy A.; Carrion, William; DeYoung, Russell J.; Ganoe, Rene; Gronoff, Guillaume; Kirgis, Guillaume; Kuang, Shi; Langford, Andy O.; Leblanc, Thierry; McGee, Thomas J.; Pliutau, Denis; Senff, Christoph; Sullivan, John T.; Sumnicht, Grant; Twigg, Laurence W.; Wang, Lihua

2018-04-01

The Tropospheric Ozone Lidar Network (TOLNet) is a unique network of lidar systems that measure atmospheric profiles of ozone and aerosols, to contribute to air-quality studies, atmospheric modeling, and satellite validation efforts. The accurate characterization of these lidars is of critical interest, and is necessary to determine cross-instrument calibration uniformity. From July to August 2014, three lidars, the TROPospheric OZone (TROPOZ) lidar, the Tunable Optical Profiler for Aerosol and oZone (TOPAZ) lidar, and the Langley Mobile Ozone Lidar (LMOL), of TOLNet participated in the "Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality" (DISCOVER-AQ) mission and the "Front Range Air Pollution and Photochemistry Éxperiment" (FRAPPÉ) to measure sub-hourly ozone variations from near the surface to the top of the troposphere. Although large differences occur at few individual altitudes in the near field and far field range, the TOLNet lidars agree with each other within ±4%. These results indicate excellent measurement accuracy for the TOLNet lidars that is suitable for use in air-quality and ozone modeling efforts.

Parallel Landscape Driven Data Reduction & Spatial Interpolation Algorithm for Big LiDAR Data

Directory of Open Access Journals (Sweden)

Rahil Sharma

2016-06-01

Full Text Available Airborne Light Detection and Ranging (LiDAR topographic data provide highly accurate digital terrain information, which is used widely in applications like creating flood insurance rate maps, forest and tree studies, coastal change mapping, soil and landscape classification, 3D urban modeling, river bank management, agricultural crop studies, etc. In this paper, we focus mainly on the use of LiDAR data in terrain modeling/Digital Elevation Model (DEM generation. Technological advancements in building LiDAR sensors have enabled highly accurate and highly dense LiDAR point clouds, which have made possible high resolution modeling of terrain surfaces. However, high density data result in massive data volumes, which pose computing issues. Computational time required for dissemination, processing and storage of these data is directly proportional to the volume of the data. We describe a novel technique based on the slope map of the terrain, which addresses the challenging problem in the area of spatial data analysis, of reducing this dense LiDAR data without sacrificing its accuracy. To the best of our knowledge, this is the first ever landscape-driven data reduction algorithm. We also perform an empirical study, which shows that there is no significant loss in accuracy for the DEM generated from a 52% reduced LiDAR dataset generated by our algorithm, compared to the DEM generated from an original, complete LiDAR dataset. For the accuracy of our statistical analysis, we perform Root Mean Square Error (RMSE comparing all of the grid points of the original DEM to the DEM generated by reduced data, instead of comparing a few random control points. Besides, our multi-core data reduction algorithm is highly scalable. We also describe a modified parallel Inverse Distance Weighted (IDW spatial interpolation method and show that the DEMs it generates are time-efficient and have better accuracy than the one’s generated by the traditional IDW method.

Incremental and Enhanced Scanline-Based Segmentation Method for Surface Reconstruction of Sparse LiDAR Data

Directory of Open Access Journals (Sweden)

Weimin Wang

2016-11-01

Full Text Available The segmentation of point clouds is an important aspect of automated processing tasks such as semantic extraction. However, the sparsity and non-uniformity of the point clouds gathered by the popular 3D mobile LiDAR devices pose many challenges for existing segmentation methods. To improve the segmentation results of point clouds from mobile LiDAR devices, we propose an optimized segmentation method based on Scanline Continuity Constraint (SLCC in this work. Unlike conventional scanline-based segmentation methods, SLCC clusters scanlines using the continuity constraints in terms of the distance as well as the direction of two consecutive points. In addition, scanline clusters are agglomerated not only into primitive geometrical shapes but also irregular shapes. Another downside to existing segmentation methods is that they are not capable of incremental processing. This causes unnecessary memory and time consumption for applications that require frame-wise segmentation or when new point clouds are added. In order to address this, we propose an incremental scheme—the Incremental Recursive Segmentation (IRIS, that can be easily applied to any segmentation method. IRIS is achieved by combining the segments of newly added point clouds and the previously segmented results. Furthermore, as an example application, we construct a processing pipeline consisting of plane fitting and surface reconstruction using the segmentation results. Finally, we evaluate the proposed methods on three datasets acquired from a handheld Velodyne HDL-32E LiDAR device. The experimental results verify the efficiency of IRIS for any segmentation method and the advantages of SLCC for processing mobile LiDAR data.

AUTOMATED RECONSTRUCTION OF WALLS FROM AIRBORNE LIDAR DATA FOR COMPLETE 3D BUILDING MODELLING

Directory of Open Access Journals (Sweden)

Y. He

2012-07-01

Full Text Available Automated 3D building model generation continues to attract research interests in photogrammetry and computer vision. Airborne Light Detection and Ranging (LIDAR data with increasing point density and accuracy has been recognized as a valuable source for automated 3D building reconstruction. While considerable achievements have been made in roof extraction, limited research has been carried out in modelling and reconstruction of walls, which constitute important components of a full building model. Low point density and irregular point distribution of LIDAR observations on vertical walls render this task complex. This paper develops a novel approach for wall reconstruction from airborne LIDAR data. The developed method commences with point cloud segmentation using a region growing approach. Seed points for planar segments are selected through principle component analysis, and points in the neighbourhood are collected and examined to form planar segments. Afterwards, segment-based classification is performed to identify roofs, walls and planar ground surfaces. For walls with sparse LIDAR observations, a search is conducted in the neighbourhood of each individual roof segment to collect wall points, and the walls are then reconstructed using geometrical and topological constraints. Finally, walls which were not illuminated by the LIDAR sensor are determined via both reconstructed roof data and neighbouring walls. This leads to the generation of topologically consistent and geometrically accurate and complete 3D building models. Experiments have been conducted in two test sites in the Netherlands and Australia to evaluate the performance of the proposed method. Results show that planar segments can be reliably extracted in the two reported test sites, which have different point density, and the building walls can be correctly reconstructed if the walls are illuminated by the LIDAR sensor.

Active-passive data fusion algorithms for seafloor imaging and classification from CZMIL data

Science.gov (United States)

Park, Joong Yong; Ramnath, Vinod; Feygels, Viktor; Kim, Minsu; Mathur, Abhinav; Aitken, Jennifer; Tuell, Grady

2010-04-01

CZMIL will simultaneously acquire lidar and passive spectral data. These data will be fused to produce enhanced seafloor reflectance images from each sensor, and combined at a higher level to achieve seafloor classification. In the DPS software, the lidar data will first be processed to solve for depth, attenuation, and reflectance. The depth measurements will then be used to constrain the spectral optimization of the passive spectral data, and the resulting water column estimates will be used recursively to improve the estimates of seafloor reflectance from the lidar. Finally, the resulting seafloor reflectance cube will be combined with texture metrics estimated from the seafloor topography to produce classifications of the seafloor.

Derivation of Sky-View Factors from LIDAR Data

Science.gov (United States)

Kidd, Christopher; Chapman, Lee

2013-01-01

The use of Lidar (Light Detection and Ranging), an active light-emitting instrument, is becoming increasingly common for a range of potential applications. Its ability to provide fine resolution spatial and vertical resolution elevation data makes it ideal for a wide range of studies. This paper demonstrates the capability of Lidar data to measure sky view factors (SVF). The Lidar data is used to generate a spatial map of SVFs which are then compared against photographically-derived SVF at selected point locations. At each location three near-surface elevations measurements were taken and compared with collocated Lidar-derived estimated. It was found that there was generally good agreement between the two methodologies, although with decreasing SVF the Lidar-derived technique tended to overestimate the SVF: this can be attributed in part to the spatial resolution of the Lidar sampling. Nevertheless, airborne Lidar systems can map sky view factors over a large area easily, improving the utility of such data in atmospheric and meteorological models.

Aircraft Wake Vortex Measurement with Coherent Doppler Lidar

Directory of Open Access Journals (Sweden)

Wu Songhua

2016-01-01

Full Text Available Aircraft vortices are generated by the lift-producing surfaces of the aircraft. The variability of near-surface conditions can change the drop rate and cause the cell of the wake vortex to twist and contort unpredictably. The pulsed Coherent Doppler Lidar Detection and Ranging is an indispensable access to real aircraft vortices behavior which transmitting a laser beam and detecting the radiation backscattered by atmospheric aerosol particles. Experiments for Coherent Doppler Lidar measurement of aircraft wake vortices has been successfully carried out at the Beijing Capital International Airport (BCIA. In this paper, the authors discuss the Lidar system, the observation modes carried out in the measurements at BCIA and the characteristics of vortices.

Light Detection and Ranging (LIDAR) From Space - Laser Altimeters

Science.gov (United States)

Sun, Xiaoli

2016-01-01

Light detection and ranging, or lidar, is like radar but atoptical wavelengths. The principle of operation and theirapplications in remote sensing are similar. Lidars havemany advantages over radars in instrument designs andapplications because of the much shorter laser wavelengthsand narrower beams. The lidar transmitters and receiveroptics are much smaller than radar antenna dishes. Thespatial resolution of lidar measurement is much finer thanthat of radar because of the much smaller footprint size onground. Lidar measurements usually give a better temporalresolution because the laser pulses can be much narrowerthan radio frequency (RF) signals. The major limitation oflidar is the ability to penetrate clouds and ground surfaces.

Geometric Calibration and Radiometric Correction of LiDAR Data and Their Impact on the Quality of Derived Products

Directory of Open Access Journals (Sweden)

Wai-Yeung Yan

2011-09-01

Full Text Available LiDAR (Light Detection And Ranging systems are capable of providing 3D positional and spectral information (in the utilized spectrum range of the mapped surface. Due to systematic errors in the system parameters and measurements, LiDAR systems require geometric calibration and radiometric correction of the intensity data in order to maximize the benefit from the collected positional and spectral information. This paper presents a practical approach for the geometric calibration of LiDAR systems and radiometric correction of collected intensity data while investigating their impact on the quality of the derived products. The proposed approach includes the use of a quasi-rigorous geometric calibration and the radar equation for the radiometric correction of intensity data. The proposed quasi-rigorous calibration procedure requires time-tagged point cloud and trajectory position data, which are available to most of the data users. The paper presents a methodology for evaluating the impact of the geometric calibration on the relative and absolute accuracy of the LiDAR point cloud. Furthermore, the impact of the geometric calibration and radiometric correction on land cover classification accuracy is investigated. The feasibility of the proposed methods and their impact on the derived products are demonstrated through experimental results using real data.

The marbll experiment: towards a martian wind lidar

Directory of Open Access Journals (Sweden)

Määttänen Anni

2018-01-01

Full Text Available Operating a lidar on Mars would fulfill the need of accessing wind and aerosol profiles in the atmospheric boundary layer. This is the purpose of the MARs Boundary Layer Lidar (MARBLL instrument. We report recent developments of this compact direct-detection wind lidar designed to operate from the surface of Mars. A new laser source has been developed and an azimuthal scanning capability has been added. Preliminary results of a field campaign are presented.

MONITORING CONCEPTS FOR COASTAL AREAS USING LIDAR DATA

Directory of Open Access Journals (Sweden)

A. Schmidt

2013-05-01

Full Text Available Coastal areas are characterized by high spatial and temporal variability. In order to detect undesired changes at early stages, enabling rapid countermeasures to mitigate or minimize potential harm or hazard, a recurrent monitoring becomes necessary. In this paper, we focus on two monitoring task: the analysis of morphological changes and the classification and mapping of habitats. Our concepts are solely based on airborne lidar data which provide substantial information in coastal areas. For the first task, we generate a digital terrain model (DTM from the lidar point cloud and analyse the dynamic of an island by comparing the DTMs of different epochs with a time difference of six years. For the deeper understanding of the habitat composition in coastal areas, we classify the lidar point cloud by a supervised approach based on Conditional Random Fields. From the classified point cloud, water-land-boundaries as well as mussel bed objects are derived afterwards. We evaluate our approaches on two datasets of the German Wadden Sea.

Evaluating the potential for site-specific modification of LiDAR DEM derivatives to improve environmental planning-scale wetland identification using Random Forest classification

Science.gov (United States)

O'Neil, Gina L.; Goodall, Jonathan L.; Watson, Layne T.

2018-04-01

Wetlands are important ecosystems that provide many ecological benefits, and their quality and presence are protected by federal regulations. These regulations require wetland delineations, which can be costly and time-consuming to perform. Computer models can assist in this process, but lack the accuracy necessary for environmental planning-scale wetland identification. In this study, the potential for improvement of wetland identification models through modification of digital elevation model (DEM) derivatives, derived from high-resolution and increasingly available light detection and ranging (LiDAR) data, at a scale necessary for small-scale wetland delineations is evaluated. A novel approach of flow convergence modelling is presented where Topographic Wetness Index (TWI), curvature, and Cartographic Depth-to-Water index (DTW), are modified to better distinguish wetland from upland areas, combined with ancillary soil data, and used in a Random Forest classification. This approach is applied to four study sites in Virginia, implemented as an ArcGIS model. The model resulted in significant improvement in average wetland accuracy compared to the commonly used National Wetland Inventory (84.9% vs. 32.1%), at the expense of a moderately lower average non-wetland accuracy (85.6% vs. 98.0%) and average overall accuracy (85.6% vs. 92.0%). From this, we concluded that modifying TWI, curvature, and DTW provides more robust wetland and non-wetland signatures to the models by improving accuracy rates compared to classifications using the original indices. The resulting ArcGIS model is a general tool able to modify these local LiDAR DEM derivatives based on site characteristics to identify wetlands at a high resolution.

GLOBAL LAND COVER CLASSIFICATION USING MODIS SURFACE REFLECTANCE PROSUCTS

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K. Fukue

2016-06-01

Full Text Available The objective of this study is to develop high accuracy land cover classification algorithm for Global scale by using multi-temporal MODIS land reflectance products. In this study, time-domain co-occurrence matrix was introduced as a classification feature which provides time-series signature of land covers. Further, the non-parametric minimum distance classifier was introduced for timedomain co-occurrence matrix, which performs multi-dimensional pattern matching for time-domain co-occurrence matrices of a classification target pixel and each classification classes. The global land cover classification experiments have been conducted by applying the proposed classification method using 46 multi-temporal(in one year SR(Surface Reflectance and NBAR(Nadir BRDF-Adjusted Reflectance products, respectively. IGBP 17 land cover categories were used in our classification experiments. As the results, SR and NBAR products showed similar classification accuracy of 99%.

Reducing classification error of grassland overgrowth by combing low-density lidar acquisitions and optical remote sensing data

Science.gov (United States)

Pitkänen, T. P.; Käyhkö, N.

2017-08-01

Mapping structural changes in vegetation dynamics has, for a long time, been carried out using satellite images, orthophotos and, more recently, airborne lidar acquisitions. Lidar has established its position as providing accurate material for structure-based analyses but its limited availability, relatively short history, and lack of spectral information, however, are generally impeding the use of lidar data for change detection purposes. A potential solution in respect of detecting both contemporary vegetation structures and their previous trajectories is to combine lidar acquisitions with optical remote sensing data, which can substantially extend the coverage, span and spectral range needed for vegetation mapping. In this study, we tested the simultaneous use of a single low-density lidar data set, a series of Landsat satellite frames and two high-resolution orthophotos to detect vegetation succession related to grassland overgrowth, i.e. encroachment of woody plants into semi-natural grasslands. We built several alternative Random Forest models with different sets of variables and tested the applicability of respective data sources for change detection purposes, aiming at distinguishing unchanged grassland and woodland areas from overgrown grasslands. Our results show that while lidar alone provides a solid basis for indicating structural differences between grassland and woodland vegetation, and orthophoto-generated variables alone are better in detecting successional changes, their combination works considerably better than its respective parts. More specifically, a model combining all the used data sets reduces the total error from 17.0% to 11.0% and omission error of detecting overgrown grasslands from 56.9% to 31.2%, when compared to model constructed solely based on lidar data. This pinpoints the efficiency of the approach where lidar-generated structural metrics are combined with optical and multitemporal observations, providing a workable framework to

UAS-SfM for coastal research: Geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery

Science.gov (United States)

Sturdivant, Emily; Lentz, Erika; Thieler, E. Robert; Farris, Amy; Weber, Kathryn; Remsen, David P.; Miner, Simon; Henderson, Rachel

2017-01-01

The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.

UAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery

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Emily J. Sturdivant

2017-10-01

Full Text Available The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM photogrammetry applied to imagery acquired by unmanned aerial systems (UAS offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm have little influence on the classification accuracy.

Characterization of the horizontal structure of the tropical forest canopy using object-based LiDAR and multispectral image analysis

Science.gov (United States)

Dupuy, Stéphane; Lainé, Gérard; Tassin, Jacques; Sarrailh, Jean-Michel

2013-12-01

This article's goal is to explore the benefits of using Digital Surface Model (DSM) and Digital Terrain Model (DTM) derived from LiDAR acquisitions for characterizing the horizontal structure of different facies in forested areas (primary forests vs. secondary forests) within the framework of an object-oriented classification. The area under study is the island of Mayotte in the western Indian Ocean. The LiDAR data were the data originally acquired by an airborne small-footprint discrete-return LiDAR for the "Litto3D" coastline mapping project. They were used to create a Digital Elevation Model (DEM) at a spatial resolution of 1 m and a Digital Canopy Model (DCM) using median filtering. The use of two successive segmentations at different scales allowed us to adjust the segmentation parameters to the local structure of the landscape and of the cover. Working in object-oriented mode with LiDAR allowed us to discriminate six vegetation classes based on canopy height and horizontal heterogeneity. This heterogeneity was assessed using a texture index calculated from the height-transition co-occurrence matrix. Overall accuracy exceeds 90%. The resulting product is the first vegetation map of Mayotte which emphasizes the structure over the composition.

Laser safety in design of near-infrared scanning LIDARs

Science.gov (United States)

Zhu, X.; Elgin, D.

2015-05-01

3D LIDARs (Light Detection and Ranging) with 1.5μm nanosecond pulse lasers have been increasingly used in different applications. The main reason for their popularity is that these LIDARs have high performance while at the same time can be made eye-safe. Because the laser hazard effect on eyes or skin at this wavelength region (industrial mining applications. We have incorporated the laser safety requirements in the LIDAR design and conducted laser safety analysis for different operational scenarios. While 1.5μm is normally said to be the eye-safe wavelength, in reality a high performance 3D LIDAR needs high pulse energy, small beam size and high pulse repetition frequency (PRF) to achieve long range, high resolution and high density images. The resulting radiant exposure of its stationary beam could be many times higher than the limit for a Class 1 laser device. Without carefully choosing laser and scanning parameters, including field-of-view, scan speed and pattern, a scanning LIDAR can't be eye- or skin-safe based only on its wavelength. This paper discusses the laser safety considerations in the design of eye-safe scanning LIDARs, including laser pulse energy, PRF, beam size and scanning parameters in two basic designs of scanning mechanisms, i.e. galvanometer based scanner and Risley prism based scanner. The laser safety is discussed in terms of device classification, nominal ocular hazard distance (NOHD) and safety glasses optical density (OD).

Differential topology of complex surfaces elliptic surfaces with p g=1 smooth classification

CERN Document Server

Morgan, John W

1993-01-01

This book is about the smooth classification of a certain class of algebraicsurfaces, namely regular elliptic surfaces of geometric genus one, i.e. elliptic surfaces with b1 = 0 and b2+ = 3. The authors give a complete classification of these surfaces up to diffeomorphism. They achieve this result by partially computing one of Donalson's polynomial invariants. The computation is carried out using techniques from algebraic geometry. In these computations both thebasic facts about the Donaldson invariants and the relationship of the moduli space of ASD connections with the moduli space of stable bundles are assumed known. Some familiarity with the basic facts of the theory of moduliof sheaves and bundles on a surface is also assumed. This work gives a good and fairly comprehensive indication of how the methods of algebraic geometry can be used to compute Donaldson invariants.

Flood Modeling Using a Synthesis of Multi-Platform LiDAR Data

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Ryan M. Csontos

2013-09-01

Full Text Available This study examined the utility of a high resolution ground-based (mobile and terrestrial Light Detection and Ranging (LiDAR dataset (0.2 m point-spacing supplemented with a coarser resolution airborne LiDAR dataset (5 m point-spacing for use in a flood inundation analysis. The techniques for combining multi-platform LiDAR data into a composite dataset in the form of a triangulated irregular network (TIN are described, and quantitative comparisons were made to a TIN generated solely from the airborne LiDAR dataset. For example, a maximum land surface elevation difference of 1.677 m and a mean difference of 0.178 m were calculated between the datasets based on sample points. Utilizing the composite and airborne LiDAR-derived TINs, a flood inundation comparison was completed using a one-dimensional steady flow hydraulic modeling analysis. Quantitative comparisons of the water surface profiles and depth grids indicated an underestimation of flooding extent, volume, and maximum flood height using the airborne LiDAR data alone. A 35% increase in maximum flood height was observed using the composite LiDAR dataset. In addition, the extents of the water surface profiles generated from the two datasets were found to be statistically significantly different. The urban and mountainous characteristics of the study area as well as the density (file size of the high resolution ground based LiDAR data presented both opportunities and challenges for flood modeling analyses.

Three-dimension imaging lidar

Science.gov (United States)

Degnan, John J. (Inventor)

2007-01-01

This invention is directed to a 3-dimensional imaging lidar, which utilizes modest power kHz rate lasers, array detectors, photon-counting multi-channel timing receivers, and dual wedge optical scanners with transmitter point-ahead correction to provide contiguous high spatial resolution mapping of surface features including ground, water, man-made objects, vegetation and submerged surfaces from an aircraft or a spacecraft.

Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data

International Nuclear Information System (INIS)

Lukač, Niko; Žlaus, Danijel; Seme, Sebastijan; Žalik, Borut; Štumberger, Gorazd

2013-01-01

Highlights: ► A new method for estimating and rating buildings roofs’ solar potential is presented. ► Considering LiDAR geospatial data together with pyranometer measurements. ► Use of multi-resolution shadowing model with new heuristic vegetation shadowing. ► High correlation between estimated solar potential and onsite measurements. -- Abstract: The roof surfaces within urban areas are constantly attracting interest regarding the installation of photovoltaic systems. These systems can improve self-sufficiency of electricity supply, and can help to decrease the emissions of greenhouse gases throughout urban areas. Unfortunately, some roof surfaces are unsuitable for installing photovoltaic systems. This presented work deals with the rating of roof surfaces within urban areas regarding their solar potential and suitability for the installation of photovoltaic systems. The solar potential of a roof’s surface is determined by a new method that combines extracted urban topography from LiDAR data with the pyranometer measurements of global and diffuse solar irradiances. Heuristic annual vegetation shadowing and a multi-resolution shadowing model, complete the proposed method. The significance of different influential factors (e.g. shadowing) was analysed extensively. A comparison between the results obtained by the proposed method and measurements performed on an actual PV power plant showed a correlation agreement of 97.4%.

AN EFFICIENT METHOD FOR AUTOMATIC ROAD EXTRACTION BASED ON MULTIPLE FEATURES FROM LiDAR DATA

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Y. Li

2016-06-01

Full Text Available The road extraction in urban areas is difficult task due to the complicated patterns and many contextual objects. LiDAR data directly provides three dimensional (3D points with less occlusions and smaller shadows. The elevation information and surface roughness are distinguishing features to separate roads. However, LiDAR data has some disadvantages are not beneficial to object extraction, such as the irregular distribution of point clouds and lack of clear edges of roads. For these problems, this paper proposes an automatic road centerlines extraction method which has three major steps: (1 road center point detection based on multiple feature spatial clustering for separating road points from ground points, (2 local principal component analysis with least squares fitting for extracting the primitives of road centerlines, and (3 hierarchical grouping for connecting primitives into complete roads network. Compared with MTH (consist of Mean shift algorithm, Tensor voting, and Hough transform proposed in our previous article, this method greatly reduced the computational cost. To evaluate the proposed method, the Vaihingen data set, a benchmark testing data provided by ISPRS for “Urban Classification and 3D Building Reconstruction” project, was selected. The experimental results show that our method achieve the same performance by less time in road extraction using LiDAR data.

An Efficient Method for Automatic Road Extraction Based on Multiple Features from LiDAR Data

Science.gov (United States)

Li, Y.; Hu, X.; Guan, H.; Liu, P.

2016-06-01

The road extraction in urban areas is difficult task due to the complicated patterns and many contextual objects. LiDAR data directly provides three dimensional (3D) points with less occlusions and smaller shadows. The elevation information and surface roughness are distinguishing features to separate roads. However, LiDAR data has some disadvantages are not beneficial to object extraction, such as the irregular distribution of point clouds and lack of clear edges of roads. For these problems, this paper proposes an automatic road centerlines extraction method which has three major steps: (1) road center point detection based on multiple feature spatial clustering for separating road points from ground points, (2) local principal component analysis with least squares fitting for extracting the primitives of road centerlines, and (3) hierarchical grouping for connecting primitives into complete roads network. Compared with MTH (consist of Mean shift algorithm, Tensor voting, and Hough transform) proposed in our previous article, this method greatly reduced the computational cost. To evaluate the proposed method, the Vaihingen data set, a benchmark testing data provided by ISPRS for "Urban Classification and 3D Building Reconstruction" project, was selected. The experimental results show that our method achieve the same performance by less time in road extraction using LiDAR data.

Landslides Mapped from LIDAR Imagery, Kitsap County, Washington

Science.gov (United States)

McKenna, Jonathan P.; Lidke, David J.; Coe, Jeffrey A.

2008-01-01

Landslides are a recurring problem on hillslopes throughout the Puget Lowland, Washington, but can be difficult to identify in the densely forested terrain. However, digital terrain models of the bare-earth surface derived from LIght Detection And Ranging (LIDAR) data express topographic details sufficiently well to identify landslides. Landslides and escarpments were mapped using LIDAR imagery and field checked (when permissible and accessible) throughout Kitsap County. We relied almost entirely on derivatives of LIDAR data for our mapping, including topographic-contour, slope, and hill-shaded relief maps. Each mapped landslide was assigned a level of 'high' or 'moderate' confidence based on the LIDAR characteristics and on field observations. A total of 231 landslides were identified representing 0.8 percent of the land area of Kitsap County. Shallow debris topples along the coastal bluffs and large (>10,000 m2) landslide complexes are the most common types of landslides. The smallest deposit mapped covers an area of 252 m2, while the largest covers 0.5 km2. Previous mapping efforts that relied solely on field and photogrammetric methods identified only 57 percent of the landslides mapped by LIDAR (61 percent high confidence and 39 percent moderate confidence), although nine landslides previously identified were not mapped during this study. The remaining 43 percent identified using LIDAR have 13 percent high confidence and 87 percent moderate confidence. Coastal areas are especially susceptible to landsliding; 67 percent of the landslide area that we mapped lies within 500 meters of the present coastline. The remaining 33 percent are located along drainages farther inland. The LIDAR data we used for mapping have some limitations including (1) rounding of the interface area between low slope surfaces and vertical faces (that is, along the edges of steep escarpments) which results in scarps being mapped too far headward (one or two meters), (2) incorrect laser

Virtual Surveyor based Object Extraction from Airborne LiDAR data

Science.gov (United States)

Habib, Md. Ahsan

Topographic feature detection of land cover from LiDAR data is important in various fields - city planning, disaster response and prevention, soil conservation, infrastructure or forestry. In recent years, feature classification, compliant with Object-Based Image Analysis (OBIA) methodology has been gaining traction in remote sensing and geographic information science (GIS). In OBIA, the LiDAR image is first divided into meaningful segments called object candidates. This results, in addition to spectral values, in a plethora of new information such as aggregated spectral pixel values, morphology, texture, context as well as topology. Traditional nonparametric segmentation methods rely on segmentations at different scales to produce a hierarchy of semantically significant objects. Properly tuned scale parameters are, therefore, imperative in these methods for successful subsequent classification. Recently, some progress has been made in the development of methods for tuning the parameters for automatic segmentation. However, researchers found that it is very difficult to automatically refine the tuning with respect to each object class present in the scene. Moreover, due to the relative complexity of real-world objects, the intra-class heterogeneity is very high, which leads to over-segmentation. Therefore, the method fails to deliver correctly many of the new segment features. In this dissertation, a new hierarchical 3D object segmentation algorithm called Automatic Virtual Surveyor based Object Extracted (AVSOE) is presented. AVSOE segments objects based on their distinct geometric concavity/convexity. This is achieved by strategically mapping the sloping surface, which connects the object to its background. Further analysis produces hierarchical decomposition of objects to its sub-objects at a single scale level. Extensive qualitative and qualitative results are presented to demonstrate the efficacy of this hierarchical segmentation approach.

Development of a Dynamic Lidar Uncertainty Framework

Energy Technology Data Exchange (ETDEWEB)

Newman, Jennifer [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Clifton, Andrew [WindForS; Bonin, Timothy [CIRES/NOAA ESRL; Choukulkar, Aditya [CIRES/NOAA ESRL; Brewer, W. Alan [NOAA ESRL; Delgado, Ruben [University of Maryland Baltimore County

2017-08-07

As wind turbine sizes increase and wind energy expands to more complex and remote sites, remote-sensing devices such as lidars are expected to play a key role in wind resource assessment and power performance testing. The switch to remote-sensing devices represents a paradigm shift in the way the wind industry typically obtains and interprets measurement data for wind energy. For example, the measurement techniques and sources of uncertainty for a remote-sensing device are vastly different from those associated with a cup anemometer on a meteorological tower. Current IEC standards for quantifying remote sensing device uncertainty for power performance testing consider uncertainty due to mounting, calibration, and classification of the remote sensing device, among other parameters. Values of the uncertainty are typically given as a function of the mean wind speed measured by a reference device and are generally fixed, leading to climatic uncertainty values that apply to the entire measurement campaign. However, real-world experience and a consideration of the fundamentals of the measurement process have shown that lidar performance is highly dependent on atmospheric conditions, such as wind shear, turbulence, and aerosol content. At present, these conditions are not directly incorporated into the estimated uncertainty of a lidar device. In this presentation, we describe the development of a new dynamic lidar uncertainty framework that adapts to current flow conditions and more accurately represents the actual uncertainty inherent in lidar measurements under different conditions. In this new framework, sources of uncertainty are identified for estimation of the line-of-sight wind speed and reconstruction of the three-dimensional wind field. These sources are then related to physical processes caused by the atmosphere and lidar operating conditions. The framework is applied to lidar data from a field measurement site to assess the ability of the framework to predict

Small Imaging Depth LIDAR and DCNN-Based Localization for Automated Guided Vehicle.

Science.gov (United States)

Ito, Seigo; Hiratsuka, Shigeyoshi; Ohta, Mitsuhiko; Matsubara, Hiroyuki; Ogawa, Masaru

2018-01-10

We present our third prototype sensor and a localization method for Automated Guided Vehicles (AGVs), for which small imaging LIght Detection and Ranging (LIDAR) and fusion-based localization are fundamentally important. Our small imaging LIDAR, named the Single-Photon Avalanche Diode (SPAD) LIDAR, uses a time-of-flight method and SPAD arrays. A SPAD is a highly sensitive photodetector capable of detecting at the single-photon level, and the SPAD LIDAR has two SPAD arrays on the same chip for detection of laser light and environmental light. Therefore, the SPAD LIDAR simultaneously outputs range image data and monocular image data with the same coordinate system and does not require external calibration among outputs. As AGVs travel both indoors and outdoors with vibration, this calibration-less structure is particularly useful for AGV applications. We also introduce a fusion-based localization method, named SPAD DCNN, which uses the SPAD LIDAR and employs a Deep Convolutional Neural Network (DCNN). SPAD DCNN can fuse the outputs of the SPAD LIDAR: range image data, monocular image data and peak intensity image data. The SPAD DCNN has two outputs: the regression result of the position of the SPAD LIDAR and the classification result of the existence of a target to be approached. Our third prototype sensor and the localization method are evaluated in an indoor environment by assuming various AGV trajectories. The results show that the sensor and localization method improve the localization accuracy.

Extreme dust storm over the eastern Mediterranean in September 2015: satellite, lidar, and surface observations in the Cyprus region

Directory of Open Access Journals (Sweden)

R.-E. Mamouri

2016-11-01

Full Text Available A record-breaking dust storm originating from desert regions in northern Syria and Iraq occurred over the eastern Mediterranean in September 2015. In this contribution of a series of two articles (part 1, observations; part 2, atmospheric modeling, we provide a comprehensive overview of the aerosol conditions during this extreme dust outbreak in the Cyprus region. These observations are based on satellite observations (MODIS, moderate resolution imaging spectroradiometer of aerosol optical thickness (AOT and Ångström exponent, surface particle mass (PM10 concentrations measured at four sites in Cyprus, visibility observations at three airports in southern Cyprus and corresponding conversion products (particle extinction coefficient, dust mass concentrations, EARLINET (European Aerosol Research Lidar Network lidar observations of dust vertical layering over Limassol, particle optical properties (backscatter, extinction, lidar ratio, linear depolarization ratio, and derived profiles of dust mass concentrations. Maximum 550 nm AOT exceeded values of 5.0, according to MODIS, and the mass loads were correspondingly >  10 g m−2 over Larnaca and Limassol during the passage of an extremely dense dust front on 8 September 2015. Hourly mean PM10 values were close to 8000 µg m−3 and the observed meteorological optical range (visibility was reduced to 300–750 m at Larnaca and Limassol. The visibility observations suggest peak values of the near-surface total suspended particle (TSP extinction coefficients of 6000 Mm−1 and thus TSP mass concentrations of 10 000 µg m−3. The Raman polarization lidar observations mainly indicated a double layer structure of the dust plumes (reaching to about 4 km height, pointing to at least two different dust source regions. Dust particle extinction coefficients (532 nm already exceeded 1000 Mm−1 and the mass concentrations reached 2000 µg m−3 in the elevated dust layers on

Coastal and tidal landform detection from high resolution topobathymetric LiDAR data

DEFF Research Database (Denmark)

Andersen, Mikkel S.; Al-Hamdani, Zyad K.; Steinbacher, Frank

-resolution mapping of these land-water transition zones. We have carried out topobathymetric LiDAR surveys in the Knudedyb tidal inlet system, a coastal environment in the Danish Wadden Sea which is part of the Wadden Sea National Park and UNESCO World Heritage. Detailed digital elevation models (DEMs) with a grid...... to tides. Furthermore, we demonstrate the potential of morphometric analysis on high-resolution topobathymetric LiDAR data for automatic identification, characterisation and classification of different landforms present in coastal land-water transition zones. Acknowledgements This work was funded...

Basic Hand Gestures Classification Based on Surface Electromyography

Directory of Open Access Journals (Sweden)

Aleksander Palkowski

2016-01-01

Full Text Available This paper presents an innovative classification system for hand gestures using 2-channel surface electromyography analysis. The system developed uses the Support Vector Machine classifier, for which the kernel function and parameter optimisation are conducted additionally by the Cuckoo Search swarm algorithm. The system developed is compared with standard Support Vector Machine classifiers with various kernel functions. The average classification rate of 98.12% has been achieved for the proposed method.

Object-oriented identification of forested landslides with derivatives of single pulse LiDAR data

Science.gov (United States)

Van Den Eeckhaut, Miet; Kerle, Norman; Poesen, Jean; Hervás, Javier

2012-11-01

In contrast to the many studies that use expert-based analysis of LiDAR derivatives for landslide mapping in forested terrain, only few studies have attempted to develop (semi-)automatic methods for extracting landslides from LiDAR derivatives. While all these studies are pixel-based, it has not yet been tested whether object-oriented analysis (OOA) could be an alternative. This study investigates the potential of OOA using only single-pulse LiDAR derivatives, such as slope gradient, roughness and curvature to map landslides. More specifically, the focus is on both LiDAR data segmentation and classification of slow-moving landslides in densely vegetated areas, where spectral data do not allow accurate landslide identification. A multistage procedure has been developed and tested in the Flemish Ardennes (Belgium). The procedure consists of (1) image binarization and multiresolution segmentation, (2) classification of landslide parts (main scarps and landslide body segments) and non-landslide features (i.e. earth banks and cropland fields) with supervised support vector machines at the appropriate scale, (3) delineation of landslide flanks, (4) growing of a landslide body starting from its main scarp, and (5) final cleaning of the inventory map. The results obtained show that OOA using LiDAR derivatives allows recognition and characterization of profound morphologic properties of forested deep-seated landslides on soil-covered hillslopes, because more than 90% of the main scarps and 70% of the landslide bodies of an expert-based inventory were accurately identified with OOA. For mountainous areas with bedrock, on the other hand, creation of a transferable model is expected to be more difficult.

Current Research in Lidar Technology Used for the Remote Sensing of Atmospheric Aerosols

Science.gov (United States)

Comerón, Adolfo; Muñoz-Porcar, Constantino; Rocadenbosch, Francesc; Rodríguez-Gómez, Alejandro; Sicard, Michaël

2017-01-01

Lidars are active optical remote sensing instruments with unique capabilities for atmospheric sounding. A manifold of atmospheric variables can be profiled using different types of lidar: concentration of species, wind speed, temperature, etc. Among them, measurement of the properties of aerosol particles, whose influence in many atmospheric processes is important but is still poorly stated, stands as one of the main fields of application of current lidar systems. This paper presents a review on fundamentals, technology, methodologies and state-of-the art of the lidar systems used to obtain aerosol information. Retrieval of structural (aerosol layers profiling), optical (backscatter and extinction coefficients) and microphysical (size, shape and type) properties requires however different levels of instrumental complexity; this general outlook is structured following a classification that attends these criteria. Thus, elastic systems (detection only of emitted frequencies), Raman systems (detection also of Raman frequency-shifted spectral lines), high spectral resolution lidars, systems with depolarization measurement capabilities and multi-wavelength instruments are described, and the fundamentals in which the retrieval of aerosol parameters is based is in each case detailed. PMID:28632170

ASSESSMENT OF BOTTOM-OF-ATMOSPHERE REFLECTANCE IN LIDAR DATA AS REFERENCE FOR HYPERSPECTRAL IMAGERY

Directory of Open Access Journals (Sweden)

A. Roncat

2017-09-01

Full Text Available While airborne lidar has confirmed its leading role in delivering high-resolution 3D topographic information during the last decade, its radiometric potential has not yet been fully exploited. However, with the increasing availability of commercial lidar systems which (a make use of full-waveform information and (b operate at several wavelengths simultaneously, this potential is increasing as well. Radiometric calibration of the full-waveform information mentioned before allows for the derivation of physical target surface parameters such as the backscatter coefficient and a diffuse reflectance value at bottom of atmosphere (BOA, i.e. the target surface. With lidar being an active remote sensing technique, these parameters can be derived from lidar data itself, accompanied by the measurement or estimation of reference data for diffuse reflectance. In contrast to this, such a radiometric calibration for passive hyperspectral imagery (HSI requires the knowledge and/or estimation of much more unknowns. However, in case of corresponding wavelength(s radiometrically calibrated lidar datasets can deliver an areawide reference for BOA reflectance. This paper presents criteria to check where the assumption of diffuse BOA reflectance behaviour is fulfilled and how these criteria are assessed in lidar data; the assessment is illustrated by an extended lidar dataset. Moreover, for this lidar dataset and an HSI dataset recorded over the same area, the corresponding reflectance values are compared for different surface types.

Lidar signal-to-noise ratio improvements: Considerations and techniques

Science.gov (United States)

Hassebo, Yasser Y.

minimize detected sky background noise while maintaining maximum lidar signal throughput. Measurements, carried at 532 nm, show as much as a factor of 10 improvement in SNR and the attainable lidar range up to 34% over conventional un-polarized schemes. For vertically pointing lidars, the largest improvements are limited to the early morning and late afternoon hours, while for lidars scanning azimuthally and in elevation at angles other than vertical, significant improvements are achievable over more extended time periods. Observed changes in SNR improvements were also related to relative humidity and modification of underlying aerosol microphysics. A second, distinct objective of this research was to utilize multiwavelength lidar techniques to separate plume and cloud particles. Choice of the study location and time for this work was driven mainly by the availability of satellite data collected by NASA INTEX-NA and NOAA NEAQS experiment over New York City on July 21, 2004 in support of MODIS imagery. The lidar results identify smoke plumes over New York City and validate the plume source origin location using NOAA-HYSPLIT back trajectory analysis. Surface measurements, at the time, from in-situ particle counters are presented and show no enhanced PM2.5 loading. This result is supported by lidar measurements, which confirm that nearly all of the aerosol plumes are located above the normal aerosol boundary layer showing that satellite measurements are often incomplete and are not sufficient for assessing surface air quality.

Performance Simulations for a Spaceborne Methane Lidar Mission

Science.gov (United States)

Kiemle, C.; Kawa, Stephan Randolph; Quatrevalet, Mathieu; Browell, Edward V.

2014-01-01

Future spaceborne lidar measurements of key anthropogenic greenhouse gases are expected to close current observational gaps particularly over remote, polar, and aerosol-contaminated regions, where actual in situ and passive remote sensing observation techniques have difficulties. For methane, a "Methane Remote Lidar Mission" was proposed by Deutsches Zentrum fuer Luft- und Raumfahrt and Centre National d'Etudes Spatiales in the frame of a German-French climate monitoring initiative. Simulations assess the performance of this mission with the help of Moderate Resolution Imaging Spectroradiometer and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations of the earth's surface albedo and atmospheric optical depth. These are key environmental parameters for integrated path differential absorption lidar which uses the surface backscatter to measure the total atmospheric methane column. Results showthat a lidar with an average optical power of 0.45W at 1.6 µm wavelength and a telescope diameter of 0.55 m, installed on a low Earth orbit platform(506 km), will measure methane columns at precisions of 1.2%, 1.7%, and 2.1% over land, water, and snow or ice surfaces, respectively, for monthly aggregated measurement samples within areas of 50 × 50 km2. Globally, the mean precision for the simulated year 2007 is 1.6%, with a standard deviation of 0.7%. At high latitudes, a lower reflectance due to snow and ice is compensated by denser measurements, owing to the orbital pattern. Over key methane source regions such as densely populated areas, boreal and tropical wetlands, or permafrost, our simulations show that the measurement precision will be between 1 and 2%.

Characterizing the Vertical Distribution of Aerosols using Ground-based Multiwavelength Lidar Data

Science.gov (United States)

Ferrare, R. A.; Thorsen, T. J.; Clayton, M.; Mueller, D.; Chemyakin, E.; Burton, S. P.; Goldsmith, J.; Holz, R.; Kuehn, R.; Eloranta, E. W.; Marais, W.; Newsom, R. K.; Liu, X.; Sawamura, P.; Holben, B. N.; Hostetler, C. A.

2016-12-01

Observations of aerosol optical and microphysical properties are critical for developing and evaluating aerosol transport model parameterizations and assessing global aerosol-radiation impacts on climate. During the Combined HSRL And Raman lidar Measurement Study (CHARMS), we investigated the synergistic use of ground-based Raman lidar and High Spectral Resolution Lidar (HSRL) measurements to retrieve aerosol properties aloft. Continuous (24/7) operation of these co-located lidars during the ten-week CHARMS mission (mid-July through September 2015) allowed the acquisition of a unique, multiwavelength ground-based lidar dataset for studying aerosol properties above the Southern Great Plains (SGP) site. The ARM Raman lidar measured profiles of aerosol backscatter, extinction and depolarization at 355 nm as well as profiles of water vapor mixing ratio and temperature. The University of Wisconsin HSRL simultaneously measured profiles of aerosol backscatter, extinction and depolarization at 532 nm and aerosol backscatter at 1064 nm. Recent advances in both lidar retrieval theory and algorithm development demonstrate that vertically-resolved retrievals using such multiwavelength lidar measurements of aerosol backscatter and extinction can help constrain both the aerosol optical (e.g. complex refractive index, scattering, etc.) and microphysical properties (e.g. effective radius, concentrations) as well as provide qualitative aerosol classification. Based on this work, the NASA Langley Research Center (LaRC) HSRL group developed automated algorithms for classifying and retrieving aerosol optical and microphysical properties, demonstrated these retrievals using data from the unique NASA/LaRC airborne multiwavelength HSRL-2 system, and validated the results using coincident airborne in situ data. We apply these algorithms to the CHARMS multiwavelength (Raman+HSRL) lidar dataset to retrieve aerosol properties above the SGP site. We present some profiles of aerosol effective

Cirrus and aerosol lidar profilometer - analysis and results

Energy Technology Data Exchange (ETDEWEB)

Spinhirne, J.D.; Scott, V.S. [NASA Goddard Space Flight Center, Greenbelt, MD (United States); Reagan, J.A.; Galbraith, A. [Univ. of Arizona, Tucson, AZ (United States)

1996-04-01

A cloud and aerosol lidar set from over a year of near continuous operation of a micro pulse lidar (MPL) instrument at the Cloud and Radiation Testbed (CART) site has been established. MPL instruments are to be included in the Ames Research Center (ARC) instrument compliments for the SW Pacific and Arctic ARM sites. Operational processing algorithms are in development for the data sets. The derived products are to be cloud presence and classification, base height, cirrus thickness, cirrus optical thickness, cirrus extinction profile, aerosol optical thickness and profile, and planetary boundary layer (PBL) height. A cloud presence and base height algorithm is in use, and a data set from the CART site is available. The scientific basis for the algorithm development of the higher level data products and plans for implementation are discussed.

A Study on Factors Affecting Airborne LiDAR Penetration

Directory of Open Access Journals (Sweden)

Wei-Chen Hsu

2015-01-01

Full Text Available This study uses data from different periods, areas and parameters of airborne LiDAR (light detection and ranging surveys to understand the factors that influence airborne LiDAR penetration rate. A discussion is presented on the relationships between these factors and LiDAR penetration rate. The results show that the flight height above ground level (AGL does not have any relationship with the penetration rate. There are some factors that should have larger influence. For example, the laser is affected by a wet ground surface by reducing the number of return echoes. The field of view (FOV has a slightly negative correlation with the penetration rate, which indicates that the laser incidence angle close to zero should achieve the best penetration. The vegetation cover rate also shows a negative correlation with the penetration rate, thus bare ground and reduced vegetation in the aftermath of a typhoon also cause high penetration rate. More return echoes could be extracted from the full-waveform system, thereby effectively improving the penetration rate. This study shows that full-waveform LiDAR is an effective method for increasing the number of surface reflected echoes. This study suggests avoiding LiDAR survey employment directly following precipitation to prevent laser echo reduction.

LiDAR and Orthophoto Synergy to optimize Object-Based Landscape Change: Analysis of an Active Landslide

Directory of Open Access Journals (Sweden)

Martijn Kamps

2017-08-01

Full Text Available Active landslides have three major effects on landscapes: (1 land cover change, (2 topographical change, and (3 above ground biomass change. Data derived from multi-temporal Light Detection and Ranging technology (LiDAR are used in combination with multi-temporal orthophotos to quantify these changes between 2006 and 2012, caused by an active deep-seated landslide near the village of Doren in Austria. Land-cover is classified by applying membership-based classification and contextual improvements based on the synergy of orthophotos and LiDAR-based elevation data. Topographical change is calculated by differencing of LiDAR derived digital terrain models. The above ground biomass is quantified by applying a local-maximum algorithm for tree top detection, in combination with allometric equations. The land cover classification accuracies were improved from 65% (using only LiDAR and 76% (using only orthophotos to 90% (using data synergy for 2006. A similar increase from respectively 64% and 75% to 91% was established for 2012. The increased accuracies demonstrate the effectiveness of using data synergy of LiDAR and orthophotos using object-based image analysis to quantify landscape changes, caused by an active landslide. The method has great potential to be transferred to larger areas for use in landscape change analyses.

A novel approach to internal crown characterization for coniferous tree species classification

Science.gov (United States)

Harikumar, A.; Bovolo, F.; Bruzzone, L.

2016-10-01

The knowledge about individual trees in forest is highly beneficial in forest management. High density small foot- print multi-return airborne Light Detection and Ranging (LiDAR) data can provide a very accurate information about the structural properties of individual trees in forests. Every tree species has a unique set of crown structural characteristics that can be used for tree species classification. In this paper, we use both the internal and external crown structural information of a conifer tree crown, derived from a high density small foot-print multi-return LiDAR data acquisition for species classification. Considering the fact that branches are the major building blocks of a conifer tree crown, we obtain the internal crown structural information using a branch level analysis. The structure of each conifer branch is represented using clusters in the LiDAR point cloud. We propose the joint use of the k-means clustering and geometric shape fitting, on the LiDAR data projected onto a novel 3-dimensional space, to identify branch clusters. After mapping the identified clusters back to the original space, six internal geometric features are estimated using a branch-level analysis. The external crown characteristics are modeled by using six least correlated features based on cone fitting and convex hull. Species classification is performed using a sparse Support Vector Machines (sparse SVM) classifier.

A New 3D Object Pose Detection Method Using LIDAR Shape Set.

Science.gov (United States)

Kim, Jung-Un; Kang, Hang-Bong

2018-03-16

In object detection systems for autonomous driving, LIDAR sensors provide very useful information. However, problems occur because the object representation is greatly distorted by changes in distance. To solve this problem, we propose a LIDAR shape set that reconstructs the shape surrounding the object more clearly by using the LIDAR point information projected on the object. The LIDAR shape set restores object shape edges from a bird's eye view by filtering LIDAR points projected on a 2D pixel-based front view. In this study, we use this shape set for two purposes. The first is to supplement the shape set with a LIDAR Feature map, and the second is to divide the entire shape set according to the gradient of the depth and density to create a 2D and 3D bounding box proposal for each object. We present a multimodal fusion framework that classifies objects and restores the 3D pose of each object using enhanced feature maps and shape-based proposals. The network structure consists of a VGG -based object classifier that receives multiple inputs and a LIDAR-based Region Proposal Networks (RPN) that identifies object poses. It works in a very intuitive and efficient manner and can be extended to other classes other than vehicles. Our research has outperformed object classification accuracy (Average Precision, AP) and 3D pose restoration accuracy (3D bounding box recall rate) based on the latest studies conducted with KITTI data sets.

Near-surface and columnar measurements with a micro pulse lidar of atmospheric pollen in Barcelona, Spain

Directory of Open Access Journals (Sweden)

M. Sicard

2016-06-01

Full Text Available We present for the first time continuous hourly measurements of pollen near-surface concentration and lidar-derived profiles of particle backscatter coefficients and of volume and particle depolarization ratios during a 5-day pollination event observed in Barcelona, Spain, between 27 and 31 March 2015. Daily average concentrations ranged from 1082 to 2830 pollen m−3. Platanus and Pinus pollen types represented together more than 80 % of the total pollen. Maximum hourly pollen concentrations of 4700 and 1200 m−3 were found for Platanus and Pinus, respectively. Every day a clear diurnal cycle caused by the vertical transport of the airborne pollen was visible on the lidar-derived profiles with maxima usually reached between 12:00 and 15:00 UT. A method based on the lidar polarization capabilities was used to retrieve the contribution of the pollen to the total aerosol optical depth (AOD. On average the diurnal (09:00–17:00 UT pollen AOD was 0.05, which represented 29 % of the total AOD. Maximum values of the pollen AOD and its contribution to the total AOD reached 0.12 and 78 %, respectively. The diurnal means of the volume and particle depolarization ratios in the pollen plume were 0.08 and 0.14, with hourly maxima of 0.18 and 0.33, respectively. The diurnal mean of the height of the pollen plume was found at 1.24 km with maxima varying in the range of 1.47–1.78 km. A correlation study is performed (1 between the depolarization ratios and the pollen near-surface concentration to evaluate the ability of the former parameter to monitor pollen release and (2 between the depolarization ratios as well as pollen AOD and surface downward solar fluxes, which cause the atmospheric turbulences responsible for the particle vertical motion, to examine the dependency of the depolarization ratios and the pollen AOD upon solar fluxes. For the volume depolarization ratio the first correlation study yields to correlation

Forest Stand Segmentation Using Airborne LIDAR Data and Very High Resolution Multispectral Imagery

Science.gov (United States)

Dechesne, Clément; Mallet, Clément; Le Bris, Arnaud; Gouet, Valérie; Hervieu, Alexandre

2016-06-01

Forest stands are the basic units for forest inventory and mapping. Stands are large forested areas (e.g., ≥ 2 ha) of homogeneous tree species composition. The accurate delineation of forest stands is usually performed by visual analysis of human operators on very high resolution (VHR) optical images. This work is highly time consuming and should be automated for scalability purposes. In this paper, a method based on the fusion of airborne laser scanning data (or lidar) and very high resolution multispectral imagery for automatic forest stand delineation and forest land-cover database update is proposed. The multispectral images give access to the tree species whereas 3D lidar point clouds provide geometric information on the trees. Therefore, multi-modal features are computed, both at pixel and object levels. The objects are individual trees extracted from lidar data. A supervised classification is performed at the object level on the computed features in order to coarsely discriminate the existing tree species in the area of interest. The analysis at tree level is particularly relevant since it significantly improves the tree species classification. A probability map is generated through the tree species classification and inserted with the pixel-based features map in an energetical framework. The proposed energy is then minimized using a standard graph-cut method (namely QPBO with α-expansion) in order to produce a segmentation map with a controlled level of details. Comparison with an existing forest land cover database shows that our method provides satisfactory results both in terms of stand labelling and delineation (matching ranges between 94% and 99%).

Surface morphology of caldera-forming eruption deposits revealed by lidar mapping of Crater Lake National Park, Oregon- Implications for emplacement and surface modification

Science.gov (United States)

Robinson, Joel E.; Bacon, Charles R.; Major, Jon J.; Wright, Heather M.; Vallance, James W.

2017-01-01

Large explosive eruptions of silicic magma can produce widespread pumice fall, extensive ignimbrite sheets, and collapse calderas. The surfaces of voluminous ignimbrites are rarely preserved or documented because most terrestrial examples are heavily vegetated, or severely modified by post-depositional processes. Much research addresses the internal sedimentary characteristics, flow processes, and depositional mechanisms of ignimbrites, however, surface features of ignimbrites are less well documented and understood, except for comparatively small-volume deposits of historical eruptions. The ~7,700 calendar year B.P. climactic eruption of Mount Mazama, USA vented ~50 km3 of magma, deposited first as rhyodacite pumice fall and then as a zoned rhyodacite-to-andesite ignimbrite as Crater Lake caldera collapsed. Lidar collected during summer 2010 reveals the remarkably well-preserved surface of the Mazama ignimbrite and related deposits surrounding Crater Lake caldera in unprecedented detail despite forest cover. The ±1 m lateral and ±4 cm vertical resolution lidar allows surface morphologies to be classified. Surface morphologies are created by internal depositional processes and can point to the processes at work when pyroclastic flows come to rest. We describe nine surface features including furrow-ridge sets and wedge-shaped mounds in pumice fall eroded by high-energy pyroclastic surges, flow- parallel ridges that record the passage of multiple pyroclastic flows, perched benches of marginal deposits stranded by more-mobile pyroclastic-flow cores, hummocks of dense clasts interpreted as lag deposit, transverse ridges that mark the compression and imbrication of flows as they came to rest, scarps indicating ignimbrite remobilization, fields of pit craters caused by phreatic explosions, fractures and cracks caused by extensional processes resulting from ignimbrite volume loss, and stream channels eroded in the newly formed surface. The nine morphologies presented

New Generation Lidar Technology and Applications

Science.gov (United States)

Spinhirne, James D.

1999-01-01

atmospheric structure from space. The Geoscience Laser Altimeter System (GLAS) of the Earth Observing System is scheduled for deployment in the 2001 time frame. GLAS is both a cloud and aerosol lidar and a surface altimeter, principally for monitoring of polar ice sheets. The GLAS instrument is based on all solid state lasers operating at 40 Hz and high efficiency, solid state detectors. The design lifetime is three to five years. Data from the GLAS mission is expected to revolutionize some aspects of our understanding of the global distribution of cloud and aerosols for global climate prediction.

Foredune Classification and Storm Response: Automated Analysis of Terrestrial Lidar DEMs

Science.gov (United States)

2015-06-15

since Hurricane Sandy. 041 20/3/2015 4 Figure 1. A. The study site in Duck , NC showing the alongshore coordinates of the local coordinate...waves on March 10: Hs = 4.8 m at 16 sec Coastal Lidar and Radar Imaging System (CLARIS) Nor’easter Storm Conditions Study Site: Duck , NC...Engineer Research and Development Center, Coastal & Hydraulics Laboratory, Coastal Observation & Analysis Branch, 1261 Duck Rd, Duck , NC 27949, USA

Patterns of Canopy and Surface Layer Consumption in a Boreal Forest Fire from Repeat Airborne Lidar

Science.gov (United States)

Alonzo, Michael; Morton, Douglas C.; Cook, Bruce D.; Andersen, Hans-Erik; Babcock, Chad; Pattison, Robert

2017-01-01

Fire in the boreal region is the dominant agent of forest disturbance with direct impacts on ecosystem structure, carbon cycling, and global climate. Global and biome-scale impacts are mediated by burn severity, measured as loss of forest canopy and consumption of the soil organic layer. To date, knowledge of the spatial variability in burn severity has been limited by sparse field sampling and moderate resolution satellite data. Here, we used pre- and post-fire airborne lidar data to directly estimate changes in canopy vertical structure and surface elevation for a 2005 boreal forest fire on Alaskas Kenai Peninsula. We found that both canopy and surface losses were strongly linked to pre-fire species composition and exhibited important fine-scale spatial variability at sub-30m resolution. The fractional reduction in canopy volume ranged from 0.61 in lowland black spruce stands to 0.27 in mixed white spruce and broad leaf forest. Residual structure largely reflects standing dead trees, highlighting the influence of pre-fire forest structure on delayed carbon losses from above ground biomass, post-fire albedo, and variability in understory light environments. Median loss of surface elevation was highest in lowland black spruce stands (0.18 m) but much lower in mixed stands (0.02 m), consistent with differences in pre-fire organic layer accumulation. Spatially continuous depth-of-burn estimates from repeat lidar measurements provide novel information to constrain carbon emissions from the surface organic layer and may inform related research on post-fire successional trajectories. Spectral measures of burn severity from Landsat were correlated with canopy (r = 0.76) and surface (r = -0.71) removal in black spruce stands but captured less of the spatial variability in fire effects for mixed stands (canopy r = 0.56, surface r = -0.26), underscoring the difficulty in capturing fire effects in heterogeneous boreal forest landscapes using proxy measures of burn severity

Lidar calibration experiments

DEFF Research Database (Denmark)

Ejsing Jørgensen, Hans; Mikkelsen, T.; Streicher, J.

1997-01-01

detection to test the reproducibility and uncertainty of lidars. Lidar data were obtained from both single-ended and double-ended Lidar configurations. A backstop was introduced in one of the experiments and a new method was developed where information obtained from the backstop can be used in the inversion...... algorithm. Independent in-situ aerosol plume concentrations were obtained from a simultaneous tracer gas experiment with SF6, and comparisons with the two lidars were made. The study shows that the reproducibility of the lidars is within 15%, including measurements from both sides of a plume...

Airborne Lidar Measurements of Below-canopy Surface Water Height , Slope and Optical Properties in the Florida Everglades Shark River Slough

Science.gov (United States)

Dabney, P.; Harding, D. J.; Valett, S. R.; Yu, A. W.; Feliciano, E. A.; Neuenschwander, A. L.; Pitts, K.

2015-12-01

Determining the presence, persistence, optical properties and variation in height and slope of surface water beneath the dense canopies of flooded forests and mangrove stands could contribute to studies of the acquisition of water and nutrients by plant roots. NASA's airborne Slope Imaging Multi-polarization Photon-counting Lidar (SIMPL) provides unique capabilities that can identify below-canopy surface water, measure its height with respect to vegetation constituents with sub-decimeter precision and quantify its slope. It also provides information on canopy structure and closure, the water column extinction profile as a proxy for turbidity and water depth, with the penetration depth constrained by turbidity. It achieves this by using four laser beams operating at two wavelengths with measurements of water surface elevation at 1064 nm (near infrared) and water column properties at 532 nm (green), analogous to a bathymetric lidar. Importantly the instrument adds a polarimetry function, like some atmospheric lidars, which measures the amount of depolarization determined by the degree to which the plane-parallel transmitted laser pulse energy is converted to the perpendicular state. The degree of depolarization is sensitive to the number of photon multiple-scattering events. For the water surface, which is specular consisting only of single-scattering events, the near-infrared received signal retains the parallel polarization state. Absence of the perpendicular signal uniquely identifies surface water. Penetration of green light and the depth profile of photons converted to the perpendicular state compared to those in the parallel state is a measure of water-column multiple scattering, providing a relative measure of turbidity. The amount of photons reflected from the canopy versus the water provides a wavelength-dependent measure of canopy closure. By rapidly firing laser pulses (11,400 pulses per second) with a narrow width (1 nsec) and detecting single photons

Surface water classification and monitoring using polarimetric synthetic aperture radar

Science.gov (United States)

Irwin, Katherine Elizabeth

Surface water classification using synthetic aperture radar (SAR) is an established practice for monitoring flood hazards due to the high temporal and spatial resolution it provides. Surface water change is a dynamic process that varies both spatially and temporally, and can occur on various scales resulting in significant impacts on affected areas. Small-scale flooding hazards, caused by beaver dam failure, is an example of surface water change, which can impact nearby infrastructure and ecosystems. Assessing these hazards is essential to transportation and infrastructure maintenance. With current satellite missions operating in multiple polarizations, spatio-temporal resolutions, and frequencies, a comprehensive comparison between SAR products for surface water monitoring is necessary. In this thesis, surface water extent models derived from high resolution single-polarization TerraSAR-X (TSX) data, medium resolution dual-polarization TSX data and low resolution quad-polarization RADARSAT-2 (RS-2) data are compared. There exists a compromise between acquiring SAR data with a high resolution or high information content. Multi-polarization data provides additional phase and intensity information, which makes it possible to better classify areas of flooded vegetation and wetlands. These locations are often where fluctuations in surface water occur and are essential for understanding dynamic underlying processes. However, often multi-polarized data is acquired at a low resolution, which cannot image these zones effectively. High spatial resolution, single-polarization TSX data provides the best model of open water. However, these single-polarization observations have limited information content and are affected by shadow and layover errors. This often hinders the classification of other land cover types. The dual-polarization TSX data allows for the classification of flooded vegetation, but classification is less accurate compared to the quad-polarization RS-2 data

Object-based Dimensionality Reduction in Land Surface Phenology Classification

Directory of Open Access Journals (Sweden)

Brian E. Bunker

2016-11-01

Full Text Available Unsupervised classification or clustering of multi-decadal land surface phenology provides a spatio-temporal synopsis of natural and agricultural vegetation response to environmental variability and anthropogenic activities. Notwithstanding the detailed temporal information available in calibrated bi-monthly normalized difference vegetation index (NDVI and comparable time series, typical pre-classification workflows average a pixel’s bi-monthly index within the larger multi-decadal time series. While this process is one practical way to reduce the dimensionality of time series with many hundreds of image epochs, it effectively dampens temporal variation from both intra and inter-annual observations related to land surface phenology. Through a novel application of object-based segmentation aimed at spatial (not temporal dimensionality reduction, all 294 image epochs from a Moderate Resolution Imaging Spectroradiometer (MODIS bi-monthly NDVI time series covering the northern Fertile Crescent were retained (in homogenous landscape units as unsupervised classification inputs. Given the inherent challenges of in situ or manual image interpretation of land surface phenology classes, a cluster validation approach based on transformed divergence enabled comparison between traditional and novel techniques. Improved intra-annual contrast was clearly manifest in rain-fed agriculture and inter-annual trajectories showed increased cluster cohesion, reducing the overall number of classes identified in the Fertile Crescent study area from 24 to 10. Given careful segmentation parameters, this spatial dimensionality reduction technique augments the value of unsupervised learning to generate homogeneous land surface phenology units. By combining recent scalable computational approaches to image segmentation, future work can pursue new global land surface phenology products based on the high temporal resolution signatures of vegetation index time series.

Multistatic Aerosol Cloud Lidar in Space: A Theoretical Perspective

Science.gov (United States)

Mishchenko, Michael I.; Alexandrov, Mikhail D.; Cairns, Brian; Travis, Larry D.

2016-01-01

Accurate aerosol and cloud retrievals from space remain quite challenging and typically involve solving a severely ill-posed inverse scattering problem. In this Perspective, we formulate in general terms an aerosol and aerosol-cloud interaction space mission concept intended to provide detailed horizontal and vertical profiles of aerosol physical characteristics as well as identify mutually induced changes in the properties of aerosols and clouds. We argue that a natural and feasible way of addressing the ill-posedness of the inverse scattering problem while having an exquisite vertical-profiling capability is to fly a multistatic (including bistatic) lidar system. We analyze theoretically the capabilities of a formation-flying constellation of a primary satellite equipped with a conventional monostatic (backscattering) lidar and one or more additional platforms each hosting a receiver of the scattered laser light. If successfully implemented, this concept would combine the measurement capabilities of a passive multi-angle multi-spectral polarimeter with the vertical profiling capability of a lidar; address the ill-posedness of the inverse problem caused by the highly limited information content of monostatic lidar measurements; address the ill-posedness of the inverse problem caused by vertical integration and surface reflection in passive photopolarimetric measurements; relax polarization accuracy requirements; eliminate the need for exquisite radiative-transfer modeling of the atmosphere-surface system in data analyses; yield the day-and-night observation capability; provide direct characterization of ground-level aerosols as atmospheric pollutants; and yield direct measurements of polarized bidirectional surface reflectance. We demonstrate, in particular, that supplementing the conventional backscattering lidar with just one additional receiver flown in formation at a scattering angle close to 170deg can dramatically increase the information content of the

Automatic recognition of smoke-plume signatures in lidar signal

Science.gov (United States)

Utkin, Andrei B.; Lavrov, Alexander; Vilar, Rui

2008-10-01

A simple and robust algorithm for lidar-signal classification based on the fast extraction of sufficiently pronounced peaks and their recognition with a perceptron, whose efficiency is enhanced by a fast nonlinear preprocessing that increases the signal dimension, is reported. The method allows smoke-plume recognition with an error rate as small as 0.31% (19 misdetections and 4 false alarms in analyzing a test set of 7409 peaks).

Important LiDAR metrics for discriminating forest tree species in Central Europe

Science.gov (United States)

Shi, Yifang; Wang, Tiejun; Skidmore, Andrew K.; Heurich, Marco

2018-03-01

Numerous airborne LiDAR-derived metrics have been proposed for classifying tree species. Yet an in-depth ecological and biological understanding of the significance of these metrics for tree species mapping remains largely unexplored. In this paper, we evaluated the performance of 37 frequently used LiDAR metrics derived under leaf-on and leaf-off conditions, respectively, for discriminating six different tree species in a natural forest in Germany. We firstly assessed the correlation between these metrics. Then we applied a Random Forest algorithm to classify the tree species and evaluated the importance of the LiDAR metrics. Finally, we identified the most important LiDAR metrics and tested their robustness and transferability. Our results indicated that about 60% of LiDAR metrics were highly correlated to each other (|r| > 0.7). There was no statistically significant difference in tree species mapping accuracy between the use of leaf-on and leaf-off LiDAR metrics. However, combining leaf-on and leaf-off LiDAR metrics significantly increased the overall accuracy from 58.2% (leaf-on) and 62.0% (leaf-off) to 66.5% as well as the kappa coefficient from 0.47 (leaf-on) and 0.51 (leaf-off) to 0.58. Radiometric features, especially intensity related metrics, provided more consistent and significant contributions than geometric features for tree species discrimination. Specifically, the mean intensity of first-or-single returns as well as the mean value of echo width were identified as the most robust LiDAR metrics for tree species discrimination. These results indicate that metrics derived from airborne LiDAR data, especially radiometric metrics, can aid in discriminating tree species in a mixed temperate forest, and represent candidate metrics for tree species classification and monitoring in Central Europe.

Mapping of High Value Crops Through AN Object-Based Svm Model Using LIDAR Data and Orthophoto in Agusan del Norte Philippines

Science.gov (United States)

Candare, Rudolph Joshua; Japitana, Michelle; Cubillas, James Earl; Ramirez, Cherry Bryan

2016-06-01

This research describes the methods involved in the mapping of different high value crops in Agusan del Norte Philippines using LiDAR. This project is part of the Phil-LiDAR 2 Program which aims to conduct a nationwide resource assessment using LiDAR. Because of the high resolution data involved, the methodology described here utilizes object-based image analysis and the use of optimal features from LiDAR data and Orthophoto. Object-based classification was primarily done by developing rule-sets in eCognition. Several features from the LiDAR data and Orthophotos were used in the development of rule-sets for classification. Generally, classes of objects can't be separated by simple thresholds from different features making it difficult to develop a rule-set. To resolve this problem, the image-objects were subjected to Support Vector Machine learning. SVMs have gained popularity because of their ability to generalize well given a limited number of training samples. However, SVMs also suffer from parameter assignment issues that can significantly affect the classification results. More specifically, the regularization parameter C in linear SVM has to be optimized through cross validation to increase the overall accuracy. After performing the segmentation in eCognition, the optimization procedure as well as the extraction of the equations of the hyper-planes was done in Matlab. The learned hyper-planes separating one class from another in the multi-dimensional feature-space can be thought of as super-features which were then used in developing the classifier rule set in eCognition. In this study, we report an overall classification accuracy of greater than 90% in different areas.

Making lidar more photogenic: creating band combinations from lidar information

Science.gov (United States)

Stoker, Jason M.

2010-01-01

Over the past five to ten years the use and applicability of light detection and ranging (lidar) technology has increased dramatically. As a result, an almost exponential amount of lidar data is being collected across the country for a wide range of applications, and it is currently the technology of choice for high resolution terrain model creation, 3-dimensional city and infrastructure modeling, forestry and a wide range of scientific applications (Lin and Mills, 2010). The amount of data that is being delivered across the country is impressive. For example, the U.S. Geological Survey’s (USGS) Center for Lidar Information Coordination and Knowledge (CLICK), which is a National repository of USGS and partner lidar point cloud datasets (Stoker et al., 2006), currently has 3.5 percent of the United States covered by lidar, and has approximately another 5 percent in the processing queue. The majority of data being collected by the commercial sector are from discrete-return systems, which collect billions of lidar points in an average project. There are also a lot of discussions involving a potential National-scale Lidar effort (Stoker et al., 2008).

Defect detection and classification of machined surfaces under multiple illuminant directions

Science.gov (United States)

Liao, Yi; Weng, Xin; Swonger, C. W.; Ni, Jun

2010-08-01

Continuous improvement of product quality is crucial to the successful and competitive automotive manufacturing industry in the 21st century. The presence of surface porosity located on flat machined surfaces such as cylinder heads/blocks and transmission cases may allow leaks of coolant, oil, or combustion gas between critical mating surfaces, thus causing damage to the engine or transmission. Therefore 100% inline inspection plays an important role for improving product quality. Although the techniques of image processing and machine vision have been applied to machined surface inspection and well improved in the past 20 years, in today's automotive industry, surface porosity inspection is still done by skilled humans, which is costly, tedious, time consuming and not capable of reliably detecting small defects. In our study, an automated defect detection and classification system for flat machined surfaces has been designed and constructed. In this paper, the importance of the illuminant direction in a machine vision system was first emphasized and then the surface defect inspection system under multiple directional illuminations was designed and constructed. After that, image processing algorithms were developed to realize 5 types of 2D or 3D surface defects (pore, 2D blemish, residue dirt, scratch, and gouge) detection and classification. The steps of image processing include: (1) image acquisition and contrast enhancement (2) defect segmentation and feature extraction (3) defect classification. An artificial machined surface and an actual automotive part: cylinder head surface were tested and, as a result, microscopic surface defects can be accurately detected and assigned to a surface defect class. The cycle time of this system can be sufficiently fast that implementation of 100% inline inspection is feasible. The field of view of this system is 150mm×225mm and the surfaces larger than the field of view can be stitched together in software.

An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation

Directory of Open Access Journals (Sweden)

Wuming Zhang

2016-06-01

Full Text Available Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs from airborne LiDAR (light detection and ranging data. However, most filtering algorithms need to carefully set up a number of complicated parameters to achieve high accuracy. In this paper, we present a new filtering method which only needs a few easy-to-set integer and Boolean parameters. Within the proposed approach, a LiDAR point cloud is inverted, and then a rigid cloth is used to cover the inverted surface. By analyzing the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface. Finally, the ground points can be extracted from the LiDAR point cloud by comparing the original LiDAR points and the generated surface. Benchmark datasets provided by ISPRS (International Society for Photogrammetry and Remote Sensing working Group III/3 are used to validate the proposed filtering method, and the experimental results yield an average total error of 4.58%, which is comparable with most of the state-of-the-art filtering algorithms. The proposed easy-to-use filtering method may help the users without much experience to use LiDAR data and related technology in their own applications more easily.

Polar winter cloud depolarization measurements with the CANDAC Rayleigh-Mie-Raman Lidar

Science.gov (United States)

McCullough, E. M.; Nott, G. J.; Duck, T. J.; Sica, R. J.; Doyle, J. G.; Pike-thackray, C.; Drummond, J. R.

2011-12-01

), Evidence of liquid dependent ice nucleation in high-latitude stratiform clouds from surface remote sensors, Geophysical Research Letters, 38, L01803. Ebert, EE and J.A .Curry (1992), A parameterization of ice cloud optical properties for climate models, Journal of Geophysical Research 97:3831-3836. Intrieri JM, Fairall CW, Shupe MD, Persson POG, Andreas EL, Guest PS, Moritz RE. 2002. An annual cycle of Arctic surface cloud forcing at SHEBA. Journal of Geophysical Research 107 NO. C10, 8039 . Noel, V., H. Chepfer, M. Haeffelin, and Y. Morille (2006), Classification of ice crystal shapes in midlatitude ice clouds from three years of lidar observations over the SIRTA observatory. Journal of the Atmospheric Sciences, 63:2978 - 2991.

Quantifying TOLNet Ozone Lidar Accuracy During the 2014 DISCOVER-AQ and FRAPPE Campaigns

Science.gov (United States)

Wang, Lihua; Newchurch, Michael J.; Alvarez, Raul J., II; Berkoff, Timothy A.; Brown, Steven S.; Carrion, William; De Young, Russell J.; Johnson, Bryan J.; Ganoe, Rene; Gronoff, Guillaume;

Identification of Forested Landslides Using LiDar Data, Object-based Image Analysis, and Machine Learning Algorithms

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Xianju Li

2015-07-01

Full Text Available For identification of forested landslides, most studies focus on knowledge-based and pixel-based analysis (PBA of LiDar data, while few studies have examined (semi- automated methods and object-based image analysis (OBIA. Moreover, most of them are focused on soil-covered areas with gentle hillslopes. In bedrock-covered mountains with steep and rugged terrain, it is so difficult to identify landslides that there is currently no research on whether combining semi-automated methods and OBIA with only LiDar derivatives could be more effective. In this study, a semi-automatic object-based landslide identification approach was developed and implemented in a forested area, the Three Gorges of China. Comparisons of OBIA and PBA, two different machine learning algorithms and their respective sensitivity to feature selection (FS, were first investigated. Based on the classification result, the landslide inventory was finally obtained according to (1 inclusion of holes encircled by the landslide body; (2 removal of isolated segments, and (3 delineation of closed envelope curves for landslide objects by manual digitizing operation. The proposed method achieved the following: (1 the filter features of surface roughness were first applied for calculating object features, and proved useful; (2 FS improved classification accuracy and reduced features; (3 the random forest algorithm achieved higher accuracy and was less sensitive to FS than a support vector machine; (4 compared to PBA, OBIA was more sensitive to FS, remarkably reduced computing time, and depicted more contiguous terrain segments; (5 based on the classification result with an overall accuracy of 89.11% ± 0.03%, the obtained inventory map was consistent with the referenced landslide inventory map, with a position mismatch value of 9%. The outlined approach would be helpful for forested landslide identification in steep and rugged terrain.

Voxel-Based LIDAR Analysis and Applications

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Hagstrom, Shea T.

One of the greatest recent changes in the field of remote sensing is the addition of high-quality Light Detection and Ranging (LIDAR) instruments. In particular, the past few decades have been greatly beneficial to these systems because of increases in data collection speed and accuracy, as well as a reduction in the costs of components. These improvements allow modern airborne instruments to resolve sub-meter details, making them ideal for a wide variety of applications. Because LIDAR uses active illumination to capture 3D information, its output is fundamentally different from other modalities. Despite this difference, LIDAR datasets are often processed using methods appropriate for 2D images and that do not take advantage of its primary virtue of 3-dimensional data. It is this problem we explore by using volumetric voxel modeling. Voxel-based analysis has been used in many applications, especially medical imaging, but rarely in traditional remote sensing. In part this is because the memory requirements are substantial when handling large areas, but with modern computing and storage this is no longer a significant impediment. Our reason for using voxels to model scenes from LIDAR data is that there are several advantages over standard triangle-based models, including better handling of overlapping surfaces and complex shapes. We show how incorporating system position information from early in the LIDAR point cloud generation process allows radiometrically-correct transmission and other novel voxel properties to be recovered. This voxelization technique is validated on simulated data using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, a first-principles based ray-tracer developed at the Rochester Institute of Technology. Voxel-based modeling of LIDAR can be useful on its own, but we believe its primary advantage is when applied to problems where simpler surface-based 3D models conflict with the requirement of realistic geometry. To

Terrain Classification and Identification of Tree Stems Using Ground-Based Lidar

Science.gov (United States)

2012-12-01

diameters using lidar data (e.g. Henning & Radtke , 2006; Maas et al., 2008) with RMS errors of 1.0 cm, but this has been done with little or no understory...occlusion or data sparsity. Here, the range is selected to balance these two competing effects. In the literature (Aschoff et al., 2004; Henning & Radtke ...restrictions in the assumed fits. While the dbh estimates are not as accurate as some previously reported (e.g. Henning & Radtke (2006)), this is to be

Urban Classification Techniques Using the Fusion of LiDAR and Spectral Data

Science.gov (United States)

2012-09-01

37 D. MASK CREATION .......................................................................................39 viii 1. LiDAR-based Masks...in Quick Terrain Modeler 2. WorldView-2 The image used in this project was collected by WorldView-2 on November 8, 2011 at Zulu time 19:34:42...OBSERVATIONS A. PROCESS OVERVIEW The focus of this thesis was to create a robust technique for fusing LiDAR and spectral imagery for creation of a

PERFORMANCE EVALUATION OF A LIGHT-WEIGHT MULTI-ECHO LIDAR FOR UNMANNED ROTORCRAFT APPLICATIONS

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G. Conte

2013-08-01

Full Text Available The paper presents a light-weight and low-cost airborne terrain mapping system. The developed Airborne LiDAR Scanner (ALS system consists of a high-precision GNSS receiver, an inertial measurement unit and a magnetic compass which are used to complement a LiDAR sensor in order to compute a digital surface model. Evaluation of the accuracy of the generated surface model is presented. Additionally, a comparison is provided between the surface model generated from the developed ALS system and a model generated using a commercial photogrammetric software. Finally, the multi-echo capability of the used LiDAR sensor is evaluated in areas covered with dense vegetation. The ALS system and camera systems were mounted on-board an industrial unmanned helicopter of around 100 kilograms maximum take-off weight. Presented results are based on real flight-test data.

Lidar-Based Rock-Fall Hazard Characterization of Cliffs

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Collins, Brian D.; Greg M.Stock,

2017-01-01

Rock falls from cliffs and other steep slopes present numerous challenges for detailed geological characterization. In steep terrain, rock-fall source areas are both dangerous and difficult to access, severely limiting the ability to make detailed structural and volumetric measurements necessary for hazard assessment. Airborne and terrestrial lidar survey methods can provide high-resolution data needed for volumetric, structural, and deformation analyses of rock falls, potentially making these analyses straightforward and routine. However, specific methods to collect, process, and analyze lidar data of steep cliffs are needed to maximize analytical accuracy and efficiency. This paper presents observations showing how lidar data sets should be collected, filtered, registered, and georeferenced to tailor their use in rock fall characterization. Additional observations concerning surface model construction, volumetric calculations, and deformation analysis are also provided.

Retrieval method of aerosol extinction coefficient profile based on backscattering, side-scattering and Raman-scattering lidar

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Shan, Huihui; Zhang, Hui; Liu, Junjian; Tao, Zongming; Wang, Shenhao; Ma, Xiaomin; Zhou, Pucheng; Yao, Ling; Liu, Dong; Xie, Chenbo; Wang, Yingjian

2018-03-01

Aerosol extinction coefficient profile is an essential parameter for atmospheric radiation model. It is difficult to get higher signal to noise ratio (SNR) of backscattering lidar from the ground to the tropopause especially in near range. Higher SNR problem can be solved by combining side-scattering and backscattering lidar. Using Raman-scattering lidar, aerosol extinction to backscatter ratio (lidar ratio) can be got. Based on side-scattering, backscattering and Raman-scattering lidar system, aerosol extinction coefficient is retrieved precisely from the earth's surface to the tropopause. Case studies show this method is reasonable and feasible.

Lidar Cloud Detection with Fully Convolutional Networks

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Cromwell, E.; Flynn, D.

2017-12-01

The vertical distribution of clouds from active remote sensing instrumentation is a widely used data product from global atmospheric measuring sites. The presence of clouds can be expressed as a binary cloud mask and is a primary input for climate modeling efforts and cloud formation studies. Current cloud detection algorithms producing these masks do not accurately identify the cloud boundaries and tend to oversample or over-represent the cloud. This translates as uncertainty for assessing the radiative impact of clouds and tracking changes in cloud climatologies. The Atmospheric Radiation Measurement (ARM) program has over 20 years of micro-pulse lidar (MPL) and High Spectral Resolution Lidar (HSRL) instrument data and companion automated cloud mask product at the mid-latitude Southern Great Plains (SGP) and the polar North Slope of Alaska (NSA) atmospheric observatory. Using this data, we train a fully convolutional network (FCN) with semi-supervised learning to segment lidar imagery into geometric time-height cloud locations for the SGP site and MPL instrument. We then use transfer learning to train a FCN for (1) the MPL instrument at the NSA site and (2) for the HSRL. In our semi-supervised approach, we pre-train the classification layers of the FCN with weakly labeled lidar data. Then, we facilitate end-to-end unsupervised pre-training and transition to fully supervised learning with ground truth labeled data. Our goal is to improve the cloud mask accuracy and precision for the MPL instrument to 95% and 80%, respectively, compared to the current cloud mask algorithms of 89% and 50%. For the transfer learning based FCN for the HSRL instrument, our goal is to achieve a cloud mask accuracy of 90% and a precision of 80%.

Simulation of Satellite, Airborne and Terrestrial LiDAR with DART (I):Waveform Simulation with Quasi-Monte Carlo Ray Tracing

Science.gov (United States)

Gastellu-Etchegorry, Jean-Philippe; Yin, Tiangang; Lauret, Nicolas; Grau, Eloi; Rubio, Jeremy; Cook, Bruce D.; Morton, Douglas C.; Sun, Guoqing

2016-01-01

Light Detection And Ranging (LiDAR) provides unique data on the 3-D structure of atmosphere constituents and the Earth's surface. Simulating LiDAR returns for different laser technologies and Earth scenes is fundamental for evaluating and interpreting signal and noise in LiDAR data. Different types of models are capable of simulating LiDAR waveforms of Earth surfaces. Semi-empirical and geometric models can be imprecise because they rely on simplified simulations of Earth surfaces and light interaction mechanisms. On the other hand, Monte Carlo ray tracing (MCRT) models are potentially accurate but require long computational time. Here, we present a new LiDAR waveform simulation tool that is based on the introduction of a quasi-Monte Carlo ray tracing approach in the Discrete Anisotropic Radiative Transfer (DART) model. Two new approaches, the so-called "box method" and "Ray Carlo method", are implemented to provide robust and accurate simulations of LiDAR waveforms for any landscape, atmosphere and LiDAR sensor configuration (view direction, footprint size, pulse characteristics, etc.). The box method accelerates the selection of the scattering direction of a photon in the presence of scatterers with non-invertible phase function. The Ray Carlo method brings traditional ray-tracking into MCRT simulation, which makes computational time independent of LiDAR field of view (FOV) and reception solid angle. Both methods are fast enough for simulating multi-pulse acquisition. Sensitivity studies with various landscapes and atmosphere constituents are presented, and the simulated LiDAR signals compare favorably with their associated reflectance images and Laser Vegetation Imaging Sensor (LVIS) waveforms. The LiDAR module is fully integrated into DART, enabling more detailed simulations of LiDAR sensitivity to specific scene elements (e.g., atmospheric aerosols, leaf area, branches, or topography) and sensor configuration for airborne or satellite LiDAR sensors.

A new scheme for urban impervious surface classification from SAR images

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Zhang, Hongsheng; Lin, Hui; Wang, Yunpeng

2018-05-01

Urban impervious surfaces have been recognized as a significant indicator for various environmental and socio-economic studies. There is an increasingly urgent demand for timely and accurate monitoring of the impervious surfaces with satellite technology from local to global scales. In the past decades, optical remote sensing has been widely employed for this task with various techniques. However, there are still a range of challenges, e.g. handling cloud contamination on optical data. Therefore, the Synthetic Aperture Radar (SAR) was introduced for the challenging task because it is uniquely all-time- and all-weather-capable. Nevertheless, with an increasing number of SAR data applied, the methodology used for impervious surfaces classification remains unchanged from the methods used for optical datasets. This shortcoming has prevented the community from fully exploring the potential of using SAR data for impervious surfaces classification. We proposed a new scheme that is comparable to the well-known and fundamental Vegetation-Impervious surface-Soil (V-I-S) model for mapping urban impervious surfaces. Three scenes of fully polarimetric Radsarsat-2 data for the cities of Shenzhen, Hong Kong and Macau were employed to test and validate the proposed methodology. Experimental results indicated that the overall accuracy and Kappa coefficient were 96.00% and 0.8808 in Shenzhen, 93.87% and 0.8307 in Hong Kong and 97.48% and 0.9354 in Macau, indicating the applicability and great potential of the new scheme for impervious surfaces classification using polarimetric SAR data. Comparison with the traditional scheme indicated that this new scheme was able to improve the overall accuracy by up to 4.6% and Kappa coefficient by up to 0.18.

Range Information Characterization of the Hokuyo UST-20LX LIDAR Sensor

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Matthew A. Cooper

2018-05-01

Full Text Available This paper presents a study on the data measurements that the Hokuyo UST-20LX Laser Rangefinder produces, which compiles into an overall characterization of the LiDAR sensor relative to indoor environments. The range measurements, beam divergence, angular resolution, error effect due to some common painted and wooden surfaces, and the error due to target surface orientation are analyzed. It was shown that using a statistical average of sensor measurements provides a more accurate range measurement. It was also shown that the major source of errors for the Hokuyo UST-20LX sensor was caused by something that will be referred to as “mixed pixels”. Additional error sources are target surface material, and the range relative to the sensor. The purpose of this paper was twofold: (1 to describe a series of tests that can be performed to characterize various aspects of a LIDAR system from a user perspective, and (2 present a detailed characterization of the commonly-used Hokuyo UST-20LX LIDAR sensor.

Instantaneous Shoreline Extraction Utilizing Integrated Spectrum and Shadow Analysis From LiDAR Data and High-resolution Satellite Imagery

Science.gov (United States)

Lee, I.-Chieh

manually connected, for its length was less than 3% of the total shoreline length in our dataset. Secondly, the parameters for satellite image classification needed to be manually determined. The need for manpower was significantly less compared to the ground surveying or aerial photogrammetry. The first phase of shoreline extraction was to utilize Normalized Difference Vegetation Index (NDVI), Mean-Shift segmentation on the coordinate (X, Y, Z), and attributes (multispectral bands from satellite images) of the LiDAR points to classify each LiDAR point into land or water surface. Boundary of the land points were then traced to create the shoreline. The second phase of shoreline extraction solely from satellite images utilized spectrum, NDVI, and shadow analysis to classify the satellite images into classes. These classes were then refined by mean-shift segmentation on the panchromatic band. By tracing the boundary of the water surface, the shoreline can be created. Since these two shorelines may represent different shoreline instances in time, evaluating the changes of shoreline was the first to be done. Then an independent scenario analysis and a procedure are performed for the shoreline of each of the three conditions: in the process of erosion, in the process of accession, and remaining the same. With these three conditions, we could analysis the actual terrain type and correct the classification errors to obtain a more accurate shoreline. Meanwhile, methods of evaluating the quality of shorelines had also been discussed. The experiment showed that there were three indicators could best represent the quality of the shoreline. These indicators were: (1) shoreline accuracy, (2) land area difference between extracted shoreline and ground truth shoreline, and (3) bias factor from shoreline quality metrics.

Registration of vehicle based panoramic image and LiDAR point cloud

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Chen, Changjun; Cao, Liang; Xie, Hong; Zhuo, Xiangyu

2013-10-01

Higher quality surface information would be got when data from optical images and LiDAR were integrated, owing to the fact that optical images and LiDAR point cloud have unique characteristics that make them preferable in many applications. While most previous works focus on registration of pinhole perspective cameras to 2D or 3D LiDAR data. In this paper, a method for the registration of vehicle based panoramic image and LiDAR point cloud is proposed. Using the translation among panoramic image, single CCD image, laser scanner and Position and Orientation System (POS) along with the GPS/IMU data, precise co-registration between the panoramic image and the LiDAR point cloud in the world system is achieved. Results are presented under a real world data set collected by a new developed Mobile Mapping System (MMS) integrated with a high resolution panoramic camera, two laser scanners and a POS.

CO-REGISTRATION OF PHOTOGRAMMETRIC AND LIDAR DATA: METHODOLOGY AND CASE STUDY

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Mwafag Ghanma

2004-07-01

Full Text Available Registration activities combine data from different sources in order to attain higher accuracy and derive more information than available from one source. The increasing availability of a wide variety of sensors capable of capturing high quality and complementary data requires parallel efforts for developing accurate and robust registration techniques. Currently, photogrammetric and LIDAR systems are being incorporated in a wide spectrum of mapping applica¬tions such as city modeling, surface reconstruction, and object recognition. Photogrammetric processing of overlapping imagery provides accurate information regarding object space break-lines in addition to an explicit semantic description of the photographed objects. On the other hand, LIDAR systems supply dense geometric surface information in the form of non-selective points. Considering the properties of photogrammetric and LIDAR data, it is clear that the two technologies provide complementary information. However, the synergic characteristics of both systems can be fully utilized only after successful registration of the photogrammetric and LIDAR data relative to a common reference frame. The registration methodology has to deal with three issues: registration primitives, transformation function, and similarity measure. This paper presents two methodologies for utilizing straight-line features derived from both datasets as the registration primitives. The first methodology directly incorporates the LIDAR lines as control information in the photogrammetric triangulation. The second methodology starts by generating a photogrammetric model relative to an arbitrary datum. Then, LIDAR features are used as control information for the absolute orientation of the photogram¬metric model. In addition to the registration methodologies, the paper presents a comparative analysis between two approaches for extracting linear features from raw and processed/interpolated LIDAR data. Also, a comparative

Impact of varying lidar measurement and data processing techniques in evaluating cirrus cloud and aerosol direct radiative effects

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S. Lolli

2018-03-01

Full Text Available In the past 2 decades, ground-based lidar networks have drastically increased in scope and relevance, thanks primarily to the advent of lidar observations from space and their need for validation. Lidar observations of aerosol and cloud geometrical, optical and microphysical atmospheric properties are subsequently used to evaluate their direct radiative effects on climate. However, the retrievals are strongly dependent on the lidar instrument measurement technique and subsequent data processing methodologies. In this paper, we evaluate the discrepancies between the use of Raman and elastic lidar measurement techniques and corresponding data processing methods for two aerosol layers in the free troposphere and for two cirrus clouds with different optical depths. Results show that the different lidar techniques are responsible for discrepancies in the model-derived direct radiative effects for biomass burning (0.05 W m−2 at surface and 0.007 W m−2 at top of the atmosphere and dust aerosol layers (0.7 W m−2 at surface and 0.85 W m−2 at top of the atmosphere. Data processing is further responsible for discrepancies in both thin (0.55 W m−2 at surface and 2.7 W m−2 at top of the atmosphere and opaque (7.7 W m−2 at surface and 11.8 W m−2 at top of the atmosphere cirrus clouds. Direct radiative effect discrepancies can be attributed to the larger variability of the lidar ratio for aerosols (20–150 sr than for clouds (20–35 sr. For this reason, the influence of the applied lidar technique plays a more fundamental role in aerosol monitoring because the lidar ratio must be retrieved with relatively high accuracy. In contrast, for cirrus clouds, with the lidar ratio being much less variable, the data processing is critical because smoothing it modifies the aerosol and cloud vertically resolved extinction profile that is used as input to compute direct radiative effect calculations.

Impact of varying lidar measurement and data processing techniques in evaluating cirrus cloud and aerosol direct radiative effects

Science.gov (United States)

Lolli, Simone; Madonna, Fabio; Rosoldi, Marco; Campbell, James R.; Welton, Ellsworth J.; Lewis, Jasper R.; Gu, Yu; Pappalardo, Gelsomina

2018-03-01

In the past 2 decades, ground-based lidar networks have drastically increased in scope and relevance, thanks primarily to the advent of lidar observations from space and their need for validation. Lidar observations of aerosol and cloud geometrical, optical and microphysical atmospheric properties are subsequently used to evaluate their direct radiative effects on climate. However, the retrievals are strongly dependent on the lidar instrument measurement technique and subsequent data processing methodologies. In this paper, we evaluate the discrepancies between the use of Raman and elastic lidar measurement techniques and corresponding data processing methods for two aerosol layers in the free troposphere and for two cirrus clouds with different optical depths. Results show that the different lidar techniques are responsible for discrepancies in the model-derived direct radiative effects for biomass burning (0.05 W m-2 at surface and 0.007 W m-2 at top of the atmosphere) and dust aerosol layers (0.7 W m-2 at surface and 0.85 W m-2 at top of the atmosphere). Data processing is further responsible for discrepancies in both thin (0.55 W m-2 at surface and 2.7 W m-2 at top of the atmosphere) and opaque (7.7 W m-2 at surface and 11.8 W m-2 at top of the atmosphere) cirrus clouds. Direct radiative effect discrepancies can be attributed to the larger variability of the lidar ratio for aerosols (20-150 sr) than for clouds (20-35 sr). For this reason, the influence of the applied lidar technique plays a more fundamental role in aerosol monitoring because the lidar ratio must be retrieved with relatively high accuracy. In contrast, for cirrus clouds, with the lidar ratio being much less variable, the data processing is critical because smoothing it modifies the aerosol and cloud vertically resolved extinction profile that is used as input to compute direct radiative effect calculations.

A COMPARITIVE STUDY USING GEOMETRIC AND VERTICAL PROFILE FEATURES DERIVED FROM AIRBORNE LIDAR FOR CLASSIFYING TREE GENERA

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C. Ko

2012-07-01

Full Text Available We present a comparative study between two different approaches for tree genera classification using descriptors derived from tree geometry and those derived from the vertical profile analysis of LiDAR point data. The different methods provide two perspectives for processing LiDAR point clouds for tree genera identification. The geometric perspective analyzes individual tree crowns in relation to valuable information related to characteristics of clusters and line segments derived within crowns and overall tree shapes to highlight the spatial distribution of LiDAR points within the crown. Conversely, analyzing vertical profiles retrieves information about the point distributions with respect to height percentiles; this perspective emphasizes of the importance that point distributions at specific heights express, accommodating for the decreased point density with respect to depth of canopy penetration by LiDAR pulses. The targeted species include white birch, maple, oak, poplar, white pine and jack pine at a study site northeast of Sault Ste. Marie, Ontario, Canada.

2010 ARRA Lidar: California Coastal Project (Zone 3)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The primary purpose of this project was to develop a consistent and accurate surface elevation dataset derived from high-accuracy Light Detection and Ranging (LiDAR)...

2010 ARRA Lidar: California Coastal Project (Zone 4)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The primary purpose of this project was to develop a consistent and accurate surface elevation dataset derived from high-accuracy Light Detection and Ranging (LiDAR)...

FOREST STEM VOLUME CALCULATION USING AIRBORNE LIDAR DATA

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I. Büyüksalih

2017-05-01

Full Text Available Airborne LiDAR data have been collected for the city of Istanbul using Riegl laser scanner Q680i with 400 kHz and an average flight height of 600 m. The flight campaign was performed by a helicopter and covers an area of 5400 km2. According to a flight speed of 80 knot a point density of more than 16 points/m2 and a laser footprint size of 30 cm could be achieved. As a result of bundle adjustment, in total, approximately 17,000 LAS files with the file size of 500 m by 700 m have been generated for the whole city. The main object classes Ground, Building, Vegetation (medium, high were derived from these LAS files using the macros in Terrasolid software. The forest area under investigation is located northwest of the city of Istanbul, main tree species occurring in the test site are pine (pinus pinaster, oak (quercus and beech (fagus. In total, 120 LAS tiles covering the investigation area have been analysed using the software IMPACT of Joanneum Research Forschungsgesellschaft, Graz, Austria. First of all, the digital terrain model (DTM and the digital surface models (DSM were imported and converted into a raster file from the original laser point clouds with a spatial resolution of 50 cm. Then, a normalized digital surface model (nDSM was derived as the difference between DSM and the DTM. Tree top detection was performed by multi – resolution filter operations and tree crowns were segmented by a region growing algorithms develop specifically for this purpose. Breast Height Diameter (BHD was calculated on the base of tree height and crown areas derived from image segmentation applying allometric functions found in literature. The assessment of stem volume was then calculated as a function of tree height and BHD. A comparison of timber volume estimated from the LiDAR data and field plots measured by the Forest Department of Istanbul showed R2 of 0.46. The low correlation might arise either from the low quality of the field plots or

VERTICAL ACCURACY COMPARISON OF DIGITAL ELEVATION MODEL FROM LIDAR AND MULTITEMPORAL SATELLITE IMAGERY

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J. Octariady

2017-05-01

Full Text Available Digital elevation model serves to illustrate the appearance of the earth's surface. DEM can be produced from a wide variety of data sources including from radar data, LiDAR data, and stereo satellite imagery. Making the LiDAR DEM conducted using point cloud data from LiDAR sensor. Making a DEM from stereo satellite imagery can be done using same temporal or multitemporal stereo satellite imagery. How much the accuracy of DEM generated from multitemporal stereo stellite imagery and LiDAR data is not known with certainty. The study was conducted using LiDAR DEM data and multitemporal stereo satellite imagery DEM. Multitemporal stereo satellite imagery generated semi-automatically by using 3 scene stereo satellite imagery with acquisition 2013–2014. The high value given each of DEM serve as the basis for calculating high accuracy DEM respectively. The results showed the high value differences in the fraction of the meter between LiDAR DEM and multitemporal stereo satellite imagery DEM.

The design, development, and test of balloonborne and groundbased lidar systems. Volume 3: Groundbased lidar systems

Science.gov (United States)

Shepherd, O.; Aurilio, G.; Bucknam, R. D.; Hurd, A. G.; Robertie, N. F.

1991-06-01

This is Volume 3 of a three volume final report on the design, development and test of balloonborne and groundbased lidar systems. Volume 1 describes the design and fabrication of a balloonborne CO2 coherent payload to measure the 10.6 micrometers backscatter from atmospheric aerosols as a function of altitude. Volume 2 describes the August 1987 flight test of Atmospheric Balloonborne Lidar Experiment, ABLE 2. In this volume we describe groundbased lidar development and measurements. A design was developed for installation of the ABLE lidar in the GL rooftop dome. A transportable shed was designed to house the ABLE lidar at the various remote measurement sites. Refurbishment and modification of the ABLE lidar were completed to permit groundbased lidar measurements of clouds and aerosols. Lidar field measurements were made at Ascension Island during SABLE 89. Lidar field measurements were made at Terciera, Azores during GABLE 90. These tasks have been successfully completed, and recommendations for further lidar measurements and data analysis have been made.

QUALITY ASSESSMENT AND CONTROL OF OUTPUTS OF A NATIONWIDE AGRICULTURAL LAND COVER MAPPING PROGRAM USING LIDAR: PHIL-LIDAR 2 PARMAP EXPERIENCE

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H. M. Pagkalinawan

2017-11-01

Full Text Available The Agricultural Resources Extraction from LiDAR Surveys (PARMAP project component of the Nationwide Detailed Resources Assessment using LiDAR (Phil-LiDAR 2 Program aims to produce detailed agricultural maps using LiDAR. Agricultural land cover at crop level was classified through object based image analysis using Support Vector Machine as classifier and LiDAR derivatives from point cloud (2 points per sq.m. and orthophoto (0.5-meter resolution as inputs. An accuracy of at least 90 %, assessed using validation points from the field and through image interpretation, was required before proceeding to post-processing and map lay-out. Knowledge sharing and capacity development facilitated by the University of the Philippines Diliman (UPD enabled partner universities across the Philippines to produce outputs for their assigned region. Considering output layers were generated by multiple teams working on different landscape complexities with some degree of data quality variability, quality checking is crucial to ensure accuracy standards were met. UPD PARMap devised a centralized and end-to-end scheme divided into four steps – land classification, GIS post-processing, schema application, and map lay-out. At each step, a block is reviewed and, subsequently, either approved or returned with documentation on required revisions. Turnaround time of review is at least one block (area ranging from 10 to 580 sq. km. per day. For coastal municipalities, an additional integration process to incorporate mapped coastal features was applied. Common problems observed during quality checking include misclassifications, gaps between features, incomplete attributes and missing map elements. Some issues are particular to specific blocks such as problematic LiDAR derivatives. UPD addressed these problems through discussion and mentoring visits to partner universities. As of March 2017, a total of 336 municipal agricultural maps have been turned-over to various

Characterization and classification of vegetation canopy structure and distribution within the Great Smoky Mountains National Park using LiDAR

Energy Technology Data Exchange (ETDEWEB)

Kumar, Jitendra [ORNL; HargroveJr., William Walter [United States Department of Agriculture (USDA), United States Forest Service (USFS); Norman, Steven P [United States Department of Agriculture (USDA), United States Forest Service (USFS); Hoffman, Forrest M [ORNL; Newcomb, Doug [U.S. Fish and Wildlife Service

2015-01-01

Vegetation canopy structure is a critically important habit characteristic for many threatened and endangered birds and other animal species, and it is key information needed by forest and wildlife managers for monitoring and managing forest resources, conservation planning and fostering biodiversity. Advances in Light Detection and Ranging (LiDAR) technologies have enabled remote sensing-based studies of vegetation canopies by capturing three-dimensional structures, yielding information not available in two-dimensional images of the landscape pro- vided by traditional multi-spectral remote sensing platforms. However, the large volume data sets produced by airborne LiDAR instruments pose a significant computational challenge, requiring algorithms to identify and analyze patterns of interest buried within LiDAR point clouds in a computationally efficient manner, utilizing state-of-art computing infrastructure. We developed and applied a computationally efficient approach to analyze a large volume of LiDAR data and to characterize and map the vegetation canopy structures for 139,859 hectares (540 sq. miles) in the Great Smoky Mountains National Park. This study helps improve our understanding of the distribution of vegetation and animal habitats in this extremely diverse ecosystem.

Linear LIDAR versus Geiger-mode LIDAR: impact on data properties and data quality

Science.gov (United States)

Ullrich, A.; Pfennigbauer, M.

2016-05-01

LIDAR has become the inevitable technology to provide accurate 3D data fast and reliably even in adverse measurement situations and harsh environments. It provides highly accurate point clouds with a significant number of additional valuable attributes per point. LIDAR systems based on Geiger-mode avalanche photo diode arrays, also called single photon avalanche photo diode arrays, earlier employed for military applications, now seek to enter the commercial market of 3D data acquisition, advertising higher point acquisition speeds from longer ranges compared to conventional techniques. Publications pointing out the advantages of these new systems refer to the other category of LIDAR as "linear LIDAR", as the prime receiver element for detecting the laser echo pulses - avalanche photo diodes - are used in a linear mode of operation. We analyze the differences between the two LIDAR technologies and the fundamental differences in the data they provide. The limitations imposed by physics on both approaches to LIDAR are also addressed and advantages of linear LIDAR over the photon counting approach are discussed.

A Multi-temporal Analysis of Logging Impacts on Tropical Forest Structure Using Airborne Lidar Data

Science.gov (United States)

Keller, M. M.; Pinagé, E. R.; Duffy, P.; Longo, M.; dos-Santos, M. N.; Leitold, V.; Morton, D. C.

2017-12-01

The long-term impacts of selective logging on carbon cycling and ecosystem function in tropical-forests are still uncertain. Despite improvements in selective logging detection using satellite data, quantifying changes in forest structure from logging and recovery following logging is difficult using orbital data. We analyzed the dynamics of forest structure comparing logged and unlogged forests in the Eastern Brazilian Amazon (Paragominas Municipality, Pará State) using small footprint discrete return airborne lidar data acquired in 2012 and 2014. Logging operations were conducted at the 1200 ha study site from 2006 through 2013 using reduced impact logging techniques—management practices that minimize canopy and ground damage compared to more common conventional logging. Nevertheless, logging still reduced aboveground biomass by 10% to 20% in logged areas compared to intact forests. We aggregated lidar point-cloud data at spatial scales ranging from 50 m to 250 m and developed a binomial classification model based on the height distribution of lidar returns in 2012 and validated the model against the 2014 lidar acquisition. We accurately classified intact and logged forest classes compared with field data. Classification performance improved as spatial resolution increased (AUC = 0.974 at 250 m). We analyzed the differences in canopy gaps, understory damage (based on a relative density model), and biomass (estimated from total canopy height) of intact and logged classes. As expected, logging greatly increased both canopy gap formation and understory damage. However, while the area identified as canopy gap persisted for at least 8 years (from the oldest logging treatments in 2006 to the most recent lidar acquisition in 2014), the effects of ground damage were mostly erased by vigorous understory regrowth after about 5 years. The rate of new gap formation was 6 to 7 times greater in recently logged forests compared to undisturbed forests. New gaps opened at a

LIDAR Measurements of the Vertical Distribution of Aerosol Optical and Physical Properties over Central Asia

Science.gov (United States)

The vertical structure of aerosol optical and physical properties was measured by Lidar in Eastern Kyrgyzstan, Central Asia, from June 2008 to May 2009. Lidar measurements were supplemented with surface-based measurements of PM2.5 and PM10 mass and chemical ...

INTEGRATION OF LIDAR DATA INTO A MUNICIPAL GIS TO STUDY SOLAR RADIATION

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P. Africani

2013-04-01

Full Text Available Identifying the right roofs to install solar panels inside a urban area is crucial for both private citizens and the whole local population. The aim is not easy because a lot of consideration must be made: insolation, orientation of the surface, size of the surface, shading due to topography, shading due to taller buildings next the surface, shading due to taller vegetation and other possible problems typical of urban areas like the presence of chimneys. Accuracy of data related to the analyzed surfaces is indeed fundamental, and also the detail of geometric models used to represent buildings and their roofs. The complexity that these roofs can reach is elevated. This work uses LiDAR data to obtain, with a semi-automatic technique, the full geometry of each roof part complementing the pre-existing building data in the municipal cartography. With this data is possible to evaluate the placement of solar panels on roofs of a whole city analyzing the solar potential of each building in detail. Other traditional techniques, like photogrammetry, need strong manual editing effort in order to identify slopes and insert vector on surfaces at the right height. Regarding LiDAR data, in order to perform accurate modelling, it is necessary to obtain an high density point cloud. The method proposed can also be used as a fast and linear workflow process for an area where LiDAR data are available and a municipal cartography already exist: LiDAR data can be furthermore successfully used to cross-check errors in pre-existent digital cartography that can remain otherwise hidden.

Selection Algorithm for the CALIPSO Lidar Aerosol Extinction-to-Backscatter Ratio

Science.gov (United States)

Omar, Ali H.; Winker, David M.; Vaughan, Mark A.

2006-01-01

The extinction-to-backscatter ratio (S(sub a)) is an important parameter used in the determination of the aerosol extinction and subsequently the optical depth from lidar backscatter measurements. We outline the algorithm used to determine Sa for the Cloud and Aerosol Lidar and Infrared Pathfinder Spaceborne Observations (CALIPSO) lidar. S(sub a) for the CALIPSO lidar will either be selected from a look-up table or calculated using the lidar measurements depending on the characteristics of aerosol layer. Whenever suitable lofted layers are encountered, S(sub a) is computed directly from the integrated backscatter and transmittance. In all other cases, the CALIPSO observables: the depolarization ratio, delta, the layer integrated attenuated backscatter, beta, and the mean layer total attenuated color ratio, gamma, together with the surface type, are used to aid in aerosol typing. Once the type is identified, a look-up-table developed primarily from worldwide observations, is used to determine the S(sub a) value. The CALIPSO aerosol models include desert dust, biomass burning, background, polluted continental, polluted dust, and marine aerosols.

Doppler lidar investigation of wind turbine wake characteristics and atmospheric turbulence under different surface roughness.

Science.gov (United States)

Zhai, Xiaochun; Wu, Songhua; Liu, Bingyi

2017-06-12

Four field experiments based on Pulsed Coherent Doppler Lidar with different surface roughness have been carried out in 2013-2015 to study the turbulent wind field in the vicinity of operating wind turbine in the onshore and offshore wind parks. The turbulence characteristics in ambient atmosphere and wake area was analyzed using transverse structure function based on Plane Position Indicator scanning mode. An automatic wake processing procedure was developed to determine the wake velocity deficit by considering the effect of ambient velocity disturbance and wake meandering with the mean wind direction. It is found that the turbine wake obviously enhances the atmospheric turbulence mixing, and the difference in the correlation of turbulence parameters under different surface roughness is significant. The dependence of wake parameters including the wake velocity deficit and wake length on wind velocity and turbulence intensity are analyzed and compared with other studies, which validates the empirical model and simulation of a turbine wake for various atmosphere conditions.

Investigating the influence of LiDAR ground surface errors on the utility of derived forest inventories

Science.gov (United States)

Wade T. Tinkham; Alistair M. S. Smith; Chad Hoffman; Andrew T. Hudak; Michael J. Falkowski; Mark E. Swanson; Paul E. Gessler

2012-01-01

Light detection and ranging, or LiDAR, effectively produces products spatially characterizing both terrain and vegetation structure; however, development and use of those products has outpaced our understanding of the errors within them. LiDAR's ability to capture three-dimensional structure has led to interest in conducting or augmenting forest inventories with...

Feasibility study for airborne fluorescence/reflectivity lidar bathymetry

Science.gov (United States)

Steinvall, Ove; Kautsky, Hans; Tulldahl, Michael; Wollner, Erika

2012-06-01

There is a demand from the authorities to have good maps of the coastal environment for their exploitation and preservation of the coastal areas. The goal for environmental mapping and monitoring is to differentiate between vegetation and non-vegetated bottoms and, if possible, to differentiate between species. Airborne lidar bathymetry is an interesting method for mapping shallow underwater habitats. In general, the maximum depth range for airborne laser exceeds the possible depth range for passive sensors. Today, operational lidar systems are able to capture the bottom (or vegetation) topography as well as estimations of the bottom reflectivity using e.g. reflected bottom pulse power. In this paper we study the possibilities and advantages for environmental mapping, if laser sensing would be further developed from single wavelength depth sounding systems to include multiple emission wavelengths and fluorescence receiver channels. Our results show that an airborne fluorescence lidar has several interesting features which might be useful in mapping underwater habitats. An example is the laser induced fluorescence giving rise to the emission spectrum which could be used for classification together with the elastic lidar signal. In the first part of our study, vegetation and substrate samples were collected and their spectral reflectance and fluorescence were subsequently measured in laboratory. A laser wavelength of 532 nm was used for excitation of the samples. The choice of 532 nm as excitation wavelength is motivated by the fact that this wavelength is commonly used in bathymetric laser scanners and that the excitation wavelengths are limited to the visual region as e.g. ultraviolet radiation is highly attenuated in water. The second part of our work consisted of theoretical performance calculations for a potential real system, and comparison of separability between species and substrate signatures using selected wavelength regions for fluorescence sensing.

Sensitivity studies for a space-based methane lidar mission

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C. Kiemle

2011-10-01

Full Text Available Methane is the third most important greenhouse gas in the atmosphere after water vapour and carbon dioxide. A major handicap to quantify the emissions at the Earth's surface in order to better understand biosphere-atmosphere exchange processes and potential climate feedbacks is the lack of accurate and global observations of methane. Space-based integrated path differential absorption (IPDA lidar has potential to fill this gap, and a Methane Remote Lidar Mission (MERLIN on a small satellite in polar orbit was proposed by DLR and CNES in the frame of a German-French climate monitoring initiative. System simulations are used to identify key performance parameters and to find an advantageous instrument configuration, given the environmental, technological, and budget constraints. The sensitivity studies use representative averages of the atmospheric and surface state to estimate the measurement precision, i.e. the random uncertainty due to instrument noise. Key performance parameters for MERLIN are average laser power, telescope size, orbit height, surface reflectance, and detector noise. A modest-size lidar instrument with 0.45 W average laser power and 0.55 m telescope diameter on a 506 km orbit could provide 50-km averaged methane column measurement along the sub-satellite track with a precision of about 1% over vegetation. The use of a methane absorption trough at 1.65 μm improves the near-surface measurement sensitivity and vastly relaxes the wavelength stability requirement that was identified as one of the major technological risks in the pre-phase A studies for A-SCOPE, a space-based IPDA lidar for carbon dioxide at the European Space Agency. Minimal humidity and temperature sensitivity at this wavelength position will enable accurate measurements in tropical wetlands, key regions with largely uncertain methane emissions. In contrast to actual passive remote sensors, measurements in Polar Regions will be possible and biases due to aerosol

Time series analysis of continuous-wave coherent Doppler Lidar wind measurements

International Nuclear Information System (INIS)

Sjoeholm, M; Mikkelsen, T; Mann, J; Enevoldsen, K; Courtney, M

2008-01-01

The influence of spatial volume averaging of a focused 1.55 μm continuous-wave coherent Doppler Lidar on observed wind turbulence measured in the atmospheric surface layer over homogeneous terrain is described and analysed. Comparison of Lidar-measured turbulent spectra with spectra simultaneously obtained from a mast-mounted sonic anemometer at 78 meters height at the test station for large wind turbines at Hoevsoere in Western Jutland, Denmark is presented for the first time

Improving salt marsh digital elevation model accuracy with full-waveform lidar and nonparametric predictive modeling

Science.gov (United States)

Rogers, Jeffrey N.; Parrish, Christopher E.; Ward, Larry G.; Burdick, David M.

2018-03-01

Salt marsh vegetation tends to increase vertical uncertainty in light detection and ranging (lidar) derived elevation data, often causing the data to become ineffective for analysis of topographic features governing tidal inundation or vegetation zonation. Previous attempts at improving lidar data collected in salt marsh environments range from simply computing and subtracting the global elevation bias to more complex methods such as computing vegetation-specific, constant correction factors. The vegetation specific corrections can be used along with an existing habitat map to apply separate corrections to different areas within a study site. It is hypothesized here that correcting salt marsh lidar data by applying location-specific, point-by-point corrections, which are computed from lidar waveform-derived features, tidal-datum based elevation, distance from shoreline and other lidar digital elevation model based variables, using nonparametric regression will produce better results. The methods were developed and tested using full-waveform lidar and ground truth for three marshes in Cape Cod, Massachusetts, U.S.A. Five different model algorithms for nonparametric regression were evaluated, with TreeNet's stochastic gradient boosting algorithm consistently producing better regression and classification results. Additionally, models were constructed to predict the vegetative zone (high marsh and low marsh). The predictive modeling methods used in this study estimated ground elevation with a mean bias of 0.00 m and a standard deviation of 0.07 m (0.07 m root mean square error). These methods appear very promising for correction of salt marsh lidar data and, importantly, do not require an existing habitat map, biomass measurements, or image based remote sensing data such as multi/hyperspectral imagery.

VEHICLE LOCALIZATION BY LIDAR POINT CORRELATION IMPROVED BY CHANGE DETECTION

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A. Schlichting

2016-06-01

Full Text Available LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position and 0.06° (heading, and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.

Vehicle Localization by LIDAR Point Correlation Improved by Change Detection

Science.gov (United States)

Schlichting, A.; Brenner, C.

2016-06-01

LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.

Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar

Science.gov (United States)

Fedrigo, Melissa; Newnham, Glenn J.; Coops, Nicholas C.; Culvenor, Darius S.; Bolton, Douglas K.; Nitschke, Craig R.

2018-02-01

Light detection and ranging (lidar) data have been increasingly used for forest classification due to its ability to penetrate the forest canopy and provide detail about the structure of the lower strata. In this study we demonstrate forest classification approaches using airborne lidar data as inputs to random forest and linear unmixing classification algorithms. Our results demonstrated that both random forest and linear unmixing models identified a distribution of rainforest and eucalypt stands that was comparable to existing ecological vegetation class (EVC) maps based primarily on manual interpretation of high resolution aerial imagery. Rainforest stands were also identified in the region that have not previously been identified in the EVC maps. The transition between stand types was better characterised by the random forest modelling approach. In contrast, the linear unmixing model placed greater emphasis on field plots selected as endmembers which may not have captured the variability in stand structure within a single stand type. The random forest model had the highest overall accuracy (84%) and Cohen's kappa coefficient (0.62). However, the classification accuracy was only marginally better than linear unmixing. The random forest model was applied to a region in the Central Highlands of south-eastern Australia to produce maps of stand type probability, including areas of transition (the 'ecotone') between rainforest and eucalypt forest. The resulting map provided a detailed delineation of forest classes, which specifically recognised the coalescing of stand types at the landscape scale. This represents a key step towards mapping the structural and spatial complexity of these ecosystems, which is important for both their management and conservation.

LIDAR e Fotogrammetria Digitale verso una nuova integrazione

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Fulvio Rinaudo

2012-04-01

Full Text Available In tutte le applicazioni del rilevo metrico la tecnica LIDAR e la Fotogrammetria Digitale giocano ormai un ruoloquasi esclusivo. Gli efficienti servizi forniti dalle reti di stazioni permanenti e dalle piattaforme GNSS/IMU hannoconsentito una rapida diffusione e una affidabilità mai raggiunta per altre tecniche di rilievo in ambito territoriale,così come pure nelle applicazioni terrestri quali il rilievo architettonico o il monitoraggio di fenomeni naturali, ovesono stati sostituiti quasi integralmente gli approcci tradizionali del rilievo topografico terrestre.LIDAR and Digital Photogrammetry towards a new integration A new integration approach between digital photogrammetry and LiDAR is presented in this paper. Up to now LiDAR use some  photogrammetric  topics  to  manage  point  clouds  and digital photogrammetry extract useful information from LiDAR data to produce traditional and accurate orthophoto. Considering that in traditional photogrammetric plotting the surveyor define the 3D location of the break-lines and add points just in those parts of the object where smooth surfaces can be defined, the proposed solution foresees a continuous exchange of information between those techniques in order to automatically extract the break-lines of the surveyed object. The final results  is  the  complete  breack-lines  3D  description  and  the point clouds portions useful to define smooth surfaces.  At the end of the automatic procedure a complete 3D set of information is produced in order to build up the fine 3D model of the surveyed object.

LIDAR e Fotogrammetria Digitale verso una nuova integrazione

Directory of Open Access Journals (Sweden)

Fulvio Rinaudo

2012-04-01

Full Text Available In tutte le applicazioni del rilevo metrico la tecnica LIDAR e la Fotogrammetria Digitale giocano ormai un ruoloquasi esclusivo. Gli efficienti servizi forniti dalle reti di stazioni permanenti e dalle piattaforme GNSS/IMU hannoconsentito una rapida diffusione e una affidabilità mai raggiunta per altre tecniche di rilievo in ambito territoriale,così come pure nelle applicazioni terrestri quali il rilievo architettonico o il monitoraggio di fenomeni naturali, ovesono stati sostituiti quasi integralmente gli approcci tradizionali del rilievo topografico terrestre. LIDAR and Digital Photogrammetry towards a new integration A new integration approach between digital photogrammetry and LiDAR is presented in this paper. Up to now LiDAR use some  photogrammetric  topics  to  manage  point  clouds  and digital photogrammetry extract useful information from LiDAR data to produce traditional and accurate orthophoto. Considering that in traditional photogrammetric plotting the surveyor define the 3D location of the break-lines and add points just in those parts of the object where smooth surfaces can be defined, the proposed solution foresees a continuous exchange of information between those techniques in order to automatically extract the break-lines of the surveyed object. The final results  is  the  complete  breack-lines  3D  description  and  the point clouds portions useful to define smooth surfaces.  At the end of the automatic procedure a complete 3D set of information is produced in order to build up the fine 3D model of the surveyed object.

The Micro-Pulse Lidar Network (MPLNET): A Federated Network of Micro-pulse Lidars and AERONET Sunphotometers

Science.gov (United States)

Welton, Ellsworth J.; Campbell, James R.; Spinhirne, James D.; Berkoff, Timothy A.; Holben, Brent; Tsay, Si-Chee

2004-01-01

We present the formation of a new global-ground based eye-safe lidar network, the NASA Micro-Pulse Lidar Network (MPLNET). The aim of MPLNET is to acquire long- term observations of aerosol and cloud vertical profiles at unique geographic sites within the NASA Aerosol Robotic Network (AERONET). MPLNET utilizes standard instrumentation and data processing algorithms for efficient network operations and direct comparison of data between each site. The micro-pulse lidar is eye-safe, compact, and commercially available, and most easily allows growth of the network without sacrificing standardized instrumentation goals. Network growth follows a federated approach, pioneered by AERONET, wherein independent research groups may join MPLNET with their own instrument and site. MPLNET sites produce not only vertical profile data, but also column-averaged products already available from AERONET (aerosol optical depth, sky radiance, size distributions). Algorithms are presented for each MPLNET data product. Real-time Level 1 data products (next-day) include daily lidar signal images from the surface to -2Okm, and Level 1.5 aerosol extinction profiles at times co-incident with AERONET observations. Quality assured Level 2 aerosol extinction profiles are generated after screening the Level 1.5 results and removing bad data. Level 3 products include continuous day/night aerosol extinction profiles, and are produced using Level 2 calibration data. Rigorous uncertainty calculations are presented for all data products. Analysis of MPLNET data show the MPL and our analysis routines are capable of successfully retrieving aerosol profiles, with the strenuous accounting of uncertainty necessary for accurate interpretation of the results.

Unsupervised classification of lidar-based vegetation structure metrics at Jean Lafitte National Historical Park and Preserve

Science.gov (United States)

Kranenburg, Christine J.; Palaseanu-Lovejoy, Monica; Nayegandhi, Amar; Brock, John; Woodman, Robert

2012-01-01

Traditional vegetation maps capture the horizontal distribution of various vegetation properties, for example, type, species and age/senescence, across a landscape. Ecologists have long known, however, that many important forest properties, for example, interior microclimate, carbon capacity, biomass and habitat suitability, are also dependent on the vertical arrangement of branches and leaves within tree canopies. The objective of this study was to use a digital elevation model (DEM) along with tree canopy-structure metrics derived from a lidar survey conducted using the Experimental Advanced Airborne Research Lidar (EAARL) to capture a three-dimensional view of vegetation communities in the Barataria Preserve unit of Jean Lafitte National Historical Park and Preserve, Louisiana. The EAARL instrument is a raster-scanning, full waveform-resolving, small-footprint, green-wavelength (532-nanometer) lidar system designed to map coastal bathymetry, topography and vegetation structure simultaneously. An unsupervised clustering procedure was then applied to the 3-dimensional-based metrics and DEM to produce a vegetation map based on the vertical structure of the park's vegetation, which includes a flotant marsh, scrub-shrub wetland, bottomland hardwood forest, and baldcypress-tupelo swamp forest. This study was completed in collaboration with the National Park Service Inventory and Monitoring Program's Gulf Coast Network. The methods presented herein are intended to be used as part of a cost-effective monitoring tool to capture change in park resources.

Arrange and average algorithm for the retrieval of aerosol parameters from multiwavelength high-spectral-resolution lidar/Raman lidar data.

Science.gov (United States)

Chemyakin, Eduard; Müller, Detlef; Burton, Sharon; Kolgotin, Alexei; Hostetler, Chris; Ferrare, Richard

2014-11-01

We present the results of a feasibility study in which a simple, automated, and unsupervised algorithm, which we call the arrange and average algorithm, is used to infer microphysical parameters (complex refractive index, effective radius, total number, surface area, and volume concentrations) of atmospheric aerosol particles. The algorithm uses backscatter coefficients at 355, 532, and 1064 nm and extinction coefficients at 355 and 532 nm as input information. Testing of the algorithm is based on synthetic optical data that are computed from prescribed monomodal particle size distributions and complex refractive indices that describe spherical, primarily fine mode pollution particles. We tested the performance of the algorithm for the "3 backscatter (β)+2 extinction (α)" configuration of a multiwavelength aerosol high-spectral-resolution lidar (HSRL) or Raman lidar. We investigated the degree to which the microphysical results retrieved by this algorithm depends on the number of input backscatter and extinction coefficients. For example, we tested "3β+1α," "2β+1α," and "3β" lidar configurations. This arrange and average algorithm can be used in two ways. First, it can be applied for quick data processing of experimental data acquired with lidar. Fast automated retrievals of microphysical particle properties are needed in view of the enormous amount of data that can be acquired by the NASA Langley Research Center's airborne "3β+2α" High-Spectral-Resolution Lidar (HSRL-2). It would prove useful for the growing number of ground-based multiwavelength lidar networks, and it would provide an option for analyzing the vast amount of optical data acquired with a future spaceborne multiwavelength lidar. The second potential application is to improve the microphysical particle characterization with our existing inversion algorithm that uses Tikhonov's inversion with regularization. This advanced algorithm has recently undergone development to allow automated and

Assessment of vertically-resolved PM10 from mobile lidar observations

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J.-C. Raut

2009-11-01

Full Text Available We investigate in this study the vertical PM10 distributions from mobile measurements carried out from locations along the Paris Peripherique (highly trafficked beltway around Paris, examine distinctions in terms of aerosol concentrations between the outlying regions of Paris and the inner city and eventually discuss the influence of aerosol sources, meteorology, and dynamics on the retrieved PM10 distributions. To achieve these purposes, we combine in situ surface measurements with active remote sensing observations obtained from a great number of research programs in Paris area since 1999. Two approaches, devoted to the conversion of vertical profiles of lidar-derived extinction coefficients into PM10, have been set up. A very good agreement is found between the theoretical and empirical methods with a discrepancy of 3%. Hence, specific extinction cross-sections at 355 nm are provided with a reasonable relative uncertainty lower than 12% for urban (4.5 m2 g−1 and periurban (5.9 m2 g−1 aersols, lower than 26% for rural (7.1 m2 g−1 aerosols, biomass burning (2.6 m2 g−1 and dust (1.1 m2 g−1 aerosols The high spatial and temporal resolutions of the mobile lidar (respectively 1.5 m and 1 min enable to follow the spatiotemporal variability of various layers trapping aerosols in the troposphere. Appropriate specific extinction cross-sections are applied in each layer detected in the vertical heterogeneities from the lidar profiles. The standard deviation (rms between lidar-derived PM10 at 200 m above ground and surface network stations measurements was ~14μg m−3. This difference is particularly ascribed to a decorrelation of mass concentrations in the first meters of the boundary layer, as highlighted through multiangular lidar observations. Lidar signals can be used to follow mass concentrations with an uncertainty lower than 25% above urban areas and provide useful information on PM10 peak forecasting that affect air quality.

Detection of large above-ground biomass variability in lowland forest ecosystems by airborne LiDAR

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J. Jubanski

2013-06-01

Full Text Available Quantification of tropical forest above-ground biomass (AGB over large areas as input for Reduced Emissions from Deforestation and forest Degradation (REDD+ projects and climate change models is challenging. This is the first study which attempts to estimate AGB and its variability across large areas of tropical lowland forests in Central Kalimantan (Indonesia through correlating airborne light detection and ranging (LiDAR to forest inventory data. Two LiDAR height metrics were analysed, and regression models could be improved through the use of LiDAR point densities as input (R2 = 0.88; n = 52. Surveying with a LiDAR point density per square metre of about 4 resulted in the best cost / benefit ratio. We estimated AGB for 600 km of LiDAR tracks and showed that there exists a considerable variability of up to 140% within the same forest type due to varying environmental conditions. Impact from logging operations and the associated AGB losses dating back more than 10 yr could be assessed by LiDAR but not by multispectral satellite imagery. Comparison with a Landsat classification for a 1 million ha study area where AGB values were based on site-specific field inventory data, regional literature estimates, and default values by the Intergovernmental Panel on Climate Change (IPCC showed an overestimation of 43%, 102%, and 137%, respectively. The results show that AGB overestimation may lead to wrong greenhouse gas (GHG emission estimates due to deforestation in climate models. For REDD+ projects this leads to inaccurate carbon stock estimates and consequently to significantly wrong REDD+ based compensation payments.

State-of-the-Art: DTM Generation Using Airborne LIDAR Data

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Ziyue Chen

2017-01-01

Full Text Available Digital terrain model (DTM generation is the fundamental application of airborne Lidar data. In past decades, a large body of studies has been conducted to present and experiment a variety of DTM generation methods. Although great progress has been made, DTM generation, especially DTM generation in specific terrain situations, remains challenging. This research introduces the general principles of DTM generation and reviews diverse mainstream DTM generation methods. In accordance with the filtering strategy, these methods are classified into six categories: surface-based adjustment; morphology-based filtering, triangulated irregular network (TIN-based refinement, segmentation and classification, statistical analysis and multi-scale comparison. Typical methods for each category are briefly introduced and the merits and limitations of each category are discussed accordingly. Despite different categories of filtering strategies, these DTM generation methods present similar difficulties when implemented in sharply changing terrain, areas with dense non-ground features and complicated landscapes. This paper suggests that the fusion of multi-sources and integration of different methods can be effective ways for improving the performance of DTM generation.

State-of-the-Art: DTM Generation Using Airborne LIDAR Data

Science.gov (United States)

Chen, Ziyue; Gao, Bingbo; Devereux, Bernard

2017-01-01

Digital terrain model (DTM) generation is the fundamental application of airborne Lidar data. In past decades, a large body of studies has been conducted to present and experiment a variety of DTM generation methods. Although great progress has been made, DTM generation, especially DTM generation in specific terrain situations, remains challenging. This research introduces the general principles of DTM generation and reviews diverse mainstream DTM generation methods. In accordance with the filtering strategy, these methods are classified into six categories: surface-based adjustment; morphology-based filtering, triangulated irregular network (TIN)-based refinement, segmentation and classification, statistical analysis and multi-scale comparison. Typical methods for each category are briefly introduced and the merits and limitations of each category are discussed accordingly. Despite different categories of filtering strategies, these DTM generation methods present similar difficulties when implemented in sharply changing terrain, areas with dense non-ground features and complicated landscapes. This paper suggests that the fusion of multi-sources and integration of different methods can be effective ways for improving the performance of DTM generation. PMID:28098810

Enhancements to the CALIOP Aerosol Subtyping and Lidar Ratio Selection Algorithms for Level II Version 4

Science.gov (United States)

Omar, A. H.; Tackett, J. L.; Vaughan, M. A.; Kar, J.; Trepte, C. R.; Winker, D. M.

2016-12-01

This presentation describes several enhancements planned for the version 4 aerosol subtyping and lidar ratio selection algorithms of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The CALIOP subtyping algorithm determines the most likely aerosol type from CALIOP measurements (attenuated backscatter, estimated particulate depolarization ratios δe, layer altitude), and surface type. The aerosol type, so determined, is associated with a lidar ratio (LR) from a discrete set of values. Some of these lidar ratios have been updated in the version 4 algorithms. In particular, the dust and polluted dust will be adjusted to reflect the latest measurements and model studies of these types. Version 4 eliminates the confusion between smoke and clean marine aerosols seen in version 3 by modifications to the elevated layer flag definitions used to identify smoke aerosols over the ocean. In the subtyping algorithms pure dust is determined by high estimated particulate depolarization ratios [δe > 0.20]. Mixtures of dust and other aerosol types are determined by intermediate values of the estimated depolarization ratio [0.075limited to mixtures of dust and smoke, the so called polluted dust aerosol type. To differentiate between mixtures of dust and smoke, and dust and marine aerosols, a new aerosol type will be added in the version 4 data products. In the revised classification algorithms, polluted dust will still defined as dust + smoke/pollution but in the marine boundary layer instances of moderate depolarization will be typed as dusty marine aerosols with a lower lidar ratio than polluted dust. The dusty marine type introduced in version 4 is modeled as a mixture of dust + marine aerosol. To account for fringes, the version 4 Level 2 algorithms implement Subtype Coalescence Algorithm for AeRosol Fringes (SCAARF) routine to detect and classify fringe of aerosol plumes that are detected at 20 km or 80 km horizontal resolution at the plume base. These

BUILDING DAMAGE ASSESSMENT AFTER EARTHQUAKE USING POST-EVENT LiDAR DATA

Directory of Open Access Journals (Sweden)

H. Rastiveis

2015-12-01

Full Text Available After an earthquake, damage assessment plays an important role in leading rescue team to help people and decrease the number of mortality. Damage map is a map that demonstrates collapsed buildings with their degree of damage. With this map, finding destructive buildings can be quickly possible. In this paper, we propose an algorithm for automatic damage map generation after an earthquake using post-event LiDAR Data and pre-event vector map. The framework of the proposed approach has four main steps. To find the location of all buildings on LiDAR data, in the first step, LiDAR data and vector map are registered by using a few number of ground control points. Then, building layer, selected from vector map, are mapped on the LiDAR data and all pixels which belong to the buildings are extracted. After that, through a powerful classifier all the extracted pixels are classified into three classes of “debris”, “intact building” and “unclassified”. Since textural information make better difference between “debris” and “intact building” classes, different textural features are applied during the classification. After that, damage degree for each candidate building is estimated based on the relation between the numbers of pixels labelled as “debris” class to the whole building area. Calculating the damage degree for each candidate building, finally, building damage map is generated. To evaluate the ability proposed method in generating damage map, a data set from Port-au-Prince, Haiti’s capital after the 2010 Haiti earthquake was used. In this case, after calculating of all buildings in the test area using the proposed method, the results were compared to the damage degree which estimated through visual interpretation of post-event satellite image. Obtained results were proved the reliability of the proposed method in damage map generation using LiDAR data.

Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

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Xiangyun Hu

2015-08-01

Full Text Available Automatic extraction of ground points, called filtering, is an essential step in producing Digital Terrain Models from airborne LiDAR data. Scene complexity and computational performance are two major problems that should be addressed in filtering, especially when processing large point cloud data with diverse scenes. This paper proposes a fast and intelligent algorithm called Semi-Global Filtering (SGF. The SGF models the filtering as a labeling problem in which the labels correspond to possible height levels. A novel energy function balanced by adaptive ground saliency is employed to adapt to steep slopes, discontinuous terrains, and complex objects. Semi-global optimization is used to determine labels that minimize the energy. These labels form an optimal classification surface based on which the points are classified as either ground or non-ground. The experimental results show that the SGF algorithm is very efficient and able to produce high classification accuracy. Given that the major procedure of semi-global optimization using dynamic programming is conducted independently along eight directions, SGF can also be paralleled and sped up via Graphic Processing Unit computing, which runs at a speed of approximately 3 million points per second.

Optics of the ozone lidar ELSA

Science.gov (United States)

Porteneuve, J.

1992-01-01

In order to study the ozone layer in the Arctic, we have to define a new optical concept for a lidar. It was necessary to build a transportable system with a large collecting surface in a minimum of volume. It was too useful to have a multichannel receptor. A description of the Emettor Receptor System, collecting system, and analysis system is provided.

2015 Lowndes County (GA) Lidar

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TASK NAME: NOAA OCM Lidar for Lowndes County, GA with the option to Collect Lidar in Cook and Tift Counties, GA Lidar Data Acquisition and Processing Production Task...

2015 OLC Lidar: Wasco, WA

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — WSI collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Wasco County, WA, study area. The Oregon LiDAR Consortium's Wasco County...

Let’s agree on the casing of Lidar

Science.gov (United States)

Deering, Carol; Stoker, Jason M.

2014-01-01

Is it lidar, Lidar, LiDAR, LIDAR, LiDar, LiDaR, or liDAR? A comprehensive review of the scientific/technical literature reveals seven different casings of this short form for light detection and ranging. And there could be more.

A COMPARISON OF LIDAR REFLECTANCE AND RADIOMETRICALLY CALIBRATED HYPERSPECTRAL IMAGERY

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A. Roncat

2016-06-01

Full Text Available In order to retrieve results comparable under different flight parameters and among different flight campaigns, passive remote sensing data such as hyperspectral imagery need to undergo a radiometric calibration. While this calibration, aiming at the derivation of physically meaningful surface attributes such as a reflectance value, is quite cumbersome for passively sensed data and relies on a number of external parameters, the situation is by far less complicated for active remote sensing techniques such as lidar. This fact motivates the investigation of the suitability of full-waveform lidar as a “single-wavelength reflectometer” to support radiometric calibration of hyperspectral imagery. In this paper, this suitability was investigated by means of an airborne hyperspectral imagery campaign and an airborne lidar campaign recorded over the same area. Criteria are given to assess diffuse reflectance behaviour; the distribution of reflectance derived by the two techniques were found comparable in four test areas where these criteria were met. This is a promising result especially in the context of current developments of multi-spectral lidar systems.

2012 MEGIS Topographic Lidar: Statewide Lidar Project Area 1 (Aroostook), Maine

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — LiDAR data is a remotely sensed high resolution elevation data collected by an airborne platform. The LiDAR sensor uses a combination of laser range finding, GPS...

Volumetric visualization of multiple-return LIDAR data: Using voxels

Science.gov (United States)

Stoker, Jason M.

2009-01-01

Elevation data are an important component in the visualization and analysis of geographic information. The creation and display of 3D models representing bare earth, vegetation, and surface structures have become a major focus of light detection and ranging (lidar) remote sensing research in the past few years. Lidar is an active sensor that records the distance, or range, of a laser usually fi red from an airplane, helicopter, or satellite. By converting the millions of 3D lidar returns from a system into bare ground, vegetation, or structural elevation information, extremely accurate, high-resolution elevation models can be derived and produced to visualize and quantify scenes in three dimensions. These data can be used to produce high-resolution bare-earth digital elevation models; quantitative estimates of vegetative features such as canopy height, canopy closure, and biomass; and models of urban areas such as building footprints and 3D city models.

Retrieval of Methane Source Strengths in Europe Using a Simple Modeling Approach to Assess the Potential of Spaceborne Lidar Observations

Science.gov (United States)

Weaver, C.; Kiemle, C.; Kawa, S. R.; Aalto, T.; Necki, J.; Steinbacher, M.; Arduini, J.; Apadula, F.; Berkhout, H.; Hatakka, J.

2014-01-01

We investigate the sensitivity of future spaceborne lidar measurements to changes in surface methane emissions. We use surface methane observations from nine European ground stations and a Lagrangian transport model to infer surface methane emissions for 2010. Our inversion shows the strongest emissions from the Netherlands, the coal mines in Upper Silesia, Poland, and wetlands in southern Finland. The simulated methane surface concentrations capture at least half of the daily variability in the observations, suggesting that the transport model is correctly simulating the regional transport pathways over Europe. With this tool we can test whether proposed methane lidar instruments will be sensitive to changes in surface emissions. We show that future lidar instruments should be able to detect a 50% reduction in methane emissions from the Netherlands and Germany, at least during summer.

i-LOVE: ISS-JEM lidar for observation of vegetation environment

Science.gov (United States)

Asai, Kazuhiro; Sawada, Haruo; Sugimoto, Nobuo; Mizutani, Kohei; Ishii, Shoken; Nishizawa, Tomoaki; Shimoda, Haruhisa; Honda, Yoshiaki; Kajiwara, Koji; Takao, Gen; Hirata, Yasumasa; Saigusa, Nobuko; Hayashi, Masatomo; Oguma, Hiroyuki; Saito, Hideki; Awaya, Yoshio; Endo, Takahiro; Imai, Tadashi; Murooka, Jumpei; Kobatashi, Takashi; Suzuki, Keiko; Sato, Ryota

2012-11-01

It is very important to watch the spatial distribution of vegetation biomass and changes in biomass over time, representing invaluable information to improve present assessments and future projections of the terrestrial carbon cycle. A space lidar is well known as a powerful remote sensing technology for measuring the canopy height accurately. This paper describes the ISS(International Space Station)-JEM(Japanese Experimental Module)-EF(Exposed Facility) borne vegetation lidar using a two dimensional array detector in order to reduce the root mean square error (RMSE) of tree height due to sloped surface.

Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar

Directory of Open Access Journals (Sweden)

Juan Carlos Fernandez-Diaz

2016-11-01

Full Text Available In this paper we present a description of a new multispectral airborne mapping light detection and ranging (lidar along with performance results obtained from two years of data collection and test campaigns. The Titan multiwave lidar is manufactured by Teledyne Optech Inc. (Toronto, ON, Canada and emits laser pulses in the 1550, 1064 and 532 nm wavelengths simultaneously through a single oscillating mirror scanner at pulse repetition frequencies (PRF that range from 50 to 300 kHz per wavelength (max combined PRF of 900 kHz. The Titan system can perform simultaneous mapping in terrestrial and very shallow water environments and its multispectral capability enables new applications, such as the production of false color active imagery derived from the lidar return intensities and the automated classification of target and land covers. Field tests and mapping projects performed over the past two years demonstrate capabilities to classify five land covers in urban environments with an accuracy of 90%, map bathymetry under more than 15 m of water, and map thick vegetation canopies at sub-meter vertical resolutions. In addition to its multispectral and performance characteristics, the Titan system is designed with several redundancies and diversity schemes that have proven to be beneficial for both operations and the improvement of data quality.

MODELLING THE RELATIONSHIP BETWEEN LAND SURFACE TEMPERATURE AND LANDSCAPE PATTERNS OF LAND USE LAND COVER CLASSIFICATION USING MULTI LINEAR REGRESSION MODELS

Directory of Open Access Journals (Sweden)

A. M. Bernales

2016-06-01

Full Text Available The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC and land surface temperature (LST. Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric “Effective mesh size” was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas and looking for common predictors between LSTs of these two different farming periods.

Balloonborne lidar payloads for remote sensing

Science.gov (United States)

Shepherd, O.; Aurilio, G.; Hurd, A. G.; Rappaport, S. A.; Reidy, W. P.; Rieder, R. J.; Bedo, D. E.; Swirbalus, R. A.

1994-02-01

A series of lidar experiments has been conducted using the Atmospheric Balloonborne Lidar Experiment payload (ABLE). These experiments included the measurement of atmospheric Rayleigh and Mie backscatter from near space (approximately 30 km) and Raman backscatter measurements of atmospheric constituents as a function of altitude. The ABLE payload consisted of a frequency-tripled Nd:YAG laser transmitter, a 50 cm receiver telescope, and filtered photodetectors in various focal plane configurations. The payload for lidar pointing, thermal control, data handling, and remote control of the lidar system. Comparison of ABLE performance with that of a space lidar shows significant performance advantages and cost effectiveness for balloonborne lidar systems.

Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+

Science.gov (United States)

Leitold, Veronika; Keller, Michael; Morton, Douglas C; Cook, Bruce D; Shimabukuro, Yosio E

2015-12-01

Carbon stocks and fluxes in tropical forests remain large sources of uncertainty in the global carbon budget. Airborne lidar remote sensing is a powerful tool for estimating aboveground biomass, provided that lidar measurements penetrate dense forest vegetation to generate accurate estimates of surface topography and canopy heights. Tropical forest areas with complex topography present a challenge for lidar remote sensing. We compared digital terrain models (DTM) derived from airborne lidar data from a mountainous region of the Atlantic Forest in Brazil to 35 ground control points measured with survey grade GNSS receivers. The terrain model generated from full-density (~20 returns m -2 ) data was highly accurate (mean signed error of 0.19 ± 0.97 m), while those derived from reduced-density datasets (8 m -2 , 4 m -2 , 2 m -2 and 1 m -2 ) were increasingly less accurate. Canopy heights calculated from reduced-density lidar data declined as data density decreased due to the inability to accurately model the terrain surface. For lidar return densities below 4 m -2 , the bias in height estimates translated into errors of 80-125 Mg ha -1 in predicted aboveground biomass. Given the growing emphasis on the use of airborne lidar for forest management, carbon monitoring, and conservation efforts, the results of this study highlight the importance of careful survey planning and consistent sampling for accurate quantification of aboveground biomass stocks and dynamics. Approaches that rely primarily on canopy height to estimate aboveground biomass are sensitive to DTM errors from variability in lidar sampling density.

Uncovering archaeological landscapes at Angkor using lidar.

Science.gov (United States)

Evans, Damian H; Fletcher, Roland J; Pottier, Christophe; Chevance, Jean-Baptiste; Soutif, Dominique; Tan, Boun Suy; Im, Sokrithy; Ea, Darith; Tin, Tina; Kim, Samnang; Cromarty, Christopher; De Greef, Stéphane; Hanus, Kasper; Bâty, Pierre; Kuszinger, Robert; Shimoda, Ichita; Boornazian, Glenn

2013-07-30

Previous archaeological mapping work on the successive medieval capitals of the Khmer Empire located at Angkor, in northwest Cambodia (∼9th to 15th centuries in the Common Era, C.E.), has identified it as the largest settlement complex of the preindustrial world, and yet crucial areas have remained unmapped, in particular the ceremonial centers and their surroundings, where dense forest obscures the traces of the civilization that typically remain in evidence in surface topography. Here we describe the use of airborne laser scanning (lidar) technology to create high-precision digital elevation models of the ground surface beneath the vegetation cover. We identify an entire, previously undocumented, formally planned urban landscape into which the major temples such as Angkor Wat were integrated. Beyond these newly identified urban landscapes, the lidar data reveal anthropogenic changes to the landscape on a vast scale and lend further weight to an emerging consensus that infrastructural complexity, unsustainable modes of subsistence, and climate variation were crucial factors in the decline of the classical Khmer civilization.

Landslide stability analysis on basis of LIDAR data extraction

Science.gov (United States)

Hu, Hui; Fernandez-Steeger, Tomas M.; Dong, Mei; Azzam, Rafig

2010-05-01

Currently, existing contradictory between remediation and acquisition from natural resource induces a series of divergences. With regard to open pit mining, legal regulation requires human to fill back the open pit area with water or recreate new landscape by other materials; on the other hand, human can not help excavating the mining area due to the shortage of power resource. However, to engineering geologists, one coincident problem which takes place not only in filling but also in mining operation should be paid more attention to, i.e. the slope stability analysis within these areas. There are a number of construction activities during remediation or mining process which can directly or indirectly cause slope failure. Lives can be endangered since local failure either while or after remediation; for mining process, slope failure in a bench, which carries a main haul road or is adjacent to human activity area, would be significant catastrophe to the whole mining program. The stability of an individual bench or slope is controlled by several factors, which are geological condition, morphology, climate, excavation techniques and transportation approach. The task which takes the longest time is to collect the morphological data. Consequently, it is one of the most dangerous tasks due to the time consuming in mining field. LIDAR scanning for morphological data collecting can help to skip this obstacle since advantages of LIDAR techniques as follows: • Dynamic range available on the market: from 3 m to beyond 1 km, • Ruggedly designed for demanding field applications, • Compact, easily hand-carried and deployed by a single operator. In 2009, scanning campaigns for 2 open pit quarry have been carried out. The aim for these LIDAR detections is to construct a detailed 3D quarry model and analyze the bench stability to support the filling planning. The 3D quarry surface was built up by using PolyWorks 10.1 on basis of LIDAR data. LIDAR data refining takes an

Development of a pulsed 9.5 micron lidar for regional scale O3 measurement

Science.gov (United States)

Stewart, R. W.

1980-01-01

A pulsed infrared lidar system designed for application to the remote sensing of atmospheric trace gases from an airborne platform is described. The system is also capable of measuring the infrared backscatter characteristics of the ocean surface, terrain, cloud, and aerosol targets. The lidar employed is based on dual wavelength pulse energy measurements in the 9-11 micrometer wavelength region.

Leveraging North Carolina's QL2 Lidar to Quantify Sensitivity of National Water Model Derived Flood Inundation Extent to DEM Resolution

Science.gov (United States)

Lovette, J. P.; Lenhardt, W. C.; Blanton, B.; Duncan, J. M.; Stillwell, L.

2017-12-01

The National Water Model (NWM) has provided a novel framework for near real time flood inundation mapping across CONUS at a 10m resolution. In many regions, this spatial scale is quickly being surpassed through the collection of high resolution lidar (1 - 3m). As one of the leading states in data collection for flood inundation mapping, North Carolina is currently improving their previously available 20 ft statewide elevation product to a Quality Level 2 (QL2) product with a nominal point spacing of 0.7 meters. This QL2 elevation product increases the ground points by roughly ten times over the previous statewide lidar product, and by over 250 times when compared to the 10m NED elevation grid. When combining these new lidar data with the discharge estimates from the NWM, we can further improve statewide flood inundation maps and predictions of at-risk areas. In the context of flood risk management, these improved predictions with higher resolution elevation models consistently represent an improvement on coarser products. Additionally, the QL2 lidar also includes coarse land cover classification data for each point return, opening the possibility for expanding analysis beyond the use of only digital elevation models (e.g. improving estimates of surface roughness, identifying anthropogenic features in floodplains, characterizing riparian zones, etc.). Using the NWM Height Above Nearest Drainage approach, we compare flood inundation extents derived from multiple lidar-derived grid resolutions to assess the tradeoff between precision and computational load in North Carolina's coastal river basins. The elevation data distributed through the state's new lidar collection program provide spatial resolutions ranging from 5-50 feet, with most inland areas also including a 3 ft product. Data storage increases by almost two orders of magnitude across this range, as does processing load. In order to further assess the validity of the higher resolution elevation products on

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System.

Science.gov (United States)

de Moura, Karina de O A; Balbinot, Alexandre

2018-05-01

A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method

Lidar configurations for wind turbine control

DEFF Research Database (Denmark)

Mirzaei, Mahmood; Mann, Jakob

2016-01-01

Lidar sensors have proved to be very beneficial in the wind energy industry. They can be used for yaw correction, feed-forward pitch control and load verification. However, the current lidars are expensive. One way to reduce the price is to use lidars with few measurement points. Finding the best...... by the lidar is compared against the effective wind speed on a wind turbine rotor both theoretically and through simulations. The study provides some results to choose the best configuration of the lidar with few measurement points....

Qualitative internal surface roughness classification using acoustic emission

International Nuclear Information System (INIS)

Mohd Hafizi Zohari; Mohd Hanif Saad

2009-04-01

This paper describes a novel new nondestructive method of qualitative internal surface roughness classification for pipes utilizing Acoustic Emission (AE) signal. Two different flowrate are introduced in a pipe obstructed using normally available components (e.g.: valve). The AE signal at suitable location from the obstruction are obtained and the peak amplitudes, RMS amplitude and energy of the AE signal are obtained. A dimensionless number, the Bangi Number, AB, is then calculated as a ratio of the AE parameters (peak amplitude, RMS amplitude or energy) in low flowrate measurement compared to the AE parameters in high flowrate measurement. It was observed that the Bangi Number, AB obtained can then be used to successfully discriminate between rough and smooth internal surface roughness. (author)

Performance Assessment of High Resolution Airborne Full Waveform LiDAR for Shallow River Bathymetry

Directory of Open Access Journals (Sweden)

Zhigang Pan

2015-04-01

Full Text Available We evaluate the performance of full waveform LiDAR decomposition algorithms with a high-resolution single band airborne LiDAR bathymetry system in shallow rivers. A continuous wavelet transformation (CWT is proposed and applied in two fluvial environments, and the results are compared to existing echo retrieval methods. LiDAR water depths are also compared to independent field measurements. In both clear and turbid water, the CWT algorithm outperforms the other methods if only green LiDAR observations are available. However, both the definition of the water surface, and the turbidity of the water significantly influence the performance of the LiDAR bathymetry observations. The results suggest that there is no single best full waveform processing algorithm for all bathymetric situations. Overall, the optimal processing strategies resulted in a determination of water depths with a 6 cm mean at 14 cm standard deviation for clear water, and a 16 cm mean and 27 cm standard deviation in more turbid water.

IEA Wind Task 32: Wind lidar identifying and mitigating barriers to the adoption of wind lidar

DEFF Research Database (Denmark)

Clifton, Andrew; Clive, Peter; Gottschall, Julia

2018-01-01

IEA Wind Task 32 exists to identify and mitigate barriers to the adoption of lidar for wind energy applications. It leverages ongoing international research and development activities in academia and industry to investigate site assessment, power performance testing, controls and loads, and complex...... flows. Since its initiation in 2011, Task 32 has been responsible for several recommended practices and expert reports that have contributed to the adoption of ground-based, nacelle-based, and floating lidar by the wind industry. Future challenges include the development of lidar uncertainty models......, best practices for data management, and developing community-based tools for data analysis, planning of lidar measurements and lidar configuration. This paper describes the barriers that Task 32 identified to the deployment of wind lidar in each of these application areas, and the steps that have been...

LIDAR Research & Development Lab

Data.gov (United States)

Federal Laboratory Consortium — The LIDAR Research and Development labs are used to investigate and improve LIDAR components such as laser sources, optical signal detectors and optical filters. The...

Model of the Correlation between Lidar Systems and Wind Turbines for Lidar-Assisted Control

DEFF Research Database (Denmark)

Schlipf, David; Cheng, Po Wen; Mann, Jakob

2013-01-01

- or spinner-based lidar system. If on the one hand, the assumed correlation is overestimated, then the uncorrelated frequencies of the preview will cause unnecessary control action, inducing undesired loads. On the other hand, the benefits of the lidar-assisted controller will not be fully exhausted......, if correlated frequencies are filtered out. To avoid these miscalculations, this work presents a method to model the correlation between lidar systems and wind turbines using Kaimal wind spectra. The derived model accounts for different measurement configurations and spatial averaging of the lidar system......Investigations of lidar-assisted control to optimize the energy yield and to reduce loads of wind turbines have increased significantly in recent years. For this kind of control, it is crucial to know the correlation between the rotor effective wind speed and the wind preview provided by a nacelle...

Investigating the size, shape and surface roughness dependence of polarization lidars with light-scattering computations on real mineral dust particles: Application to dust particles' external mixtures and dust mass concentration retrievals

Science.gov (United States)

Mehri, Tahar; Kemppinen, Osku; David, Grégory; Lindqvist, Hannakaisa; Tyynelä, Jani; Nousiainen, Timo; Rairoux, Patrick; Miffre, Alain

2018-05-01

Our understanding of the contribution of mineral dust to the Earth's radiative budget is limited by the complexity of these particles, which present a wide range of sizes, are highly-irregularly shaped, and are present in the atmosphere in the form of particle mixtures. To address the spatial distribution of mineral dust and atmospheric dust mass concentrations, polarization lidars are nowadays frequently used, with partitioning algorithms allowing to discern the contribution of mineral dust in two or three-component particle external mixtures. In this paper, we investigate the dependence of the retrieved dust backscattering (βd) vertical profiles with the dust particle size and shape. For that, new light-scattering numerical simulations are performed on real atmospheric mineral dust particles, having determined mineralogy (CAL, DOL, AGG, SIL), derived from stereogrammetry (stereo-particles), with potential surface roughness, which are compared to the widely-used spheroidal mathematical shape model. For each dust shape model (smooth stereo-particles, rough stereo-particles, spheroids), the dust depolarization, backscattering Ångström exponent, lidar ratio are computed for two size distributions representative of mineral dust after long-range transport. As an output, two Saharan dust outbreaks involving mineral dust in two, then three-component particle mixtures are studied with Lyon (France) UV-VIS polarization lidar. If the dust size matters most, under certain circumstances, βd can vary by approximately 67% when real dust stereo-particles are used instead of spheroids, corresponding to variations in the dust backscattering coefficient as large as 2 Mm- 1·sr- 1. Moreover, the influence of surface roughness in polarization lidar retrievals is for the first time discussed. Finally, dust mass-extinction conversion factors (ηd) are evaluated for each assigned shape model and dust mass concentrations are retrieved from polarization lidar measurements. From

Atmospheric Boundary Layer temperature and humidity from new-generation Raman lidar

Science.gov (United States)

Froidevaux, Martin; Higgins, Chad; Simeonov, Valentin; Pardyjak, Eric R.; Parlange, Marc B.

2010-05-01

Mixing ratio and temperature data, obtained with EPFL Raman lidar during the TABLE-08 experiment are presented. The processing methods will be discussed along with fundamental physics. An independent calibration is performed at different distances along the laser beam, demonstrating that the multi-telescopes design of the lidar system is reliable for field application. The maximum achievable distance as a function of time and/or space averaging will also be discussed. During the TABLE-08 experiment, different type of lidar measurements have been obtained including: horizontal and vertical time series, as well as boundary layer "cuts", during day and night. The high resolution data, 1s in time and 1.25 m in space, are used to understand the response of the atmosphere to variations in surface variability.

Calibration of Ground-based Lidar instrument

DEFF Research Database (Denmark)

Yordanova, Ginka; Gómez Arranz, Paula

This report presents the result of the lidar calibration performed for the given Ground-based Lidar at DTU’s test site for large wind turbines at Høvsøre, Denmark. Calibration is here understood as the establishment of a relation between the reference wind speed measurements with measurement...... uncertainties provided by measurement standard and corresponding lidar wind speed indications with associated measurement uncertainties. The lidar calibration concerns the 10 minute mean wind speed measurements. The comparison of the lidar measurements of the wind direction with that from wind vanes...

Using LiDAR to as a Potential Method for Detection Plastics in Water

Science.gov (United States)

Lee, G.; Neal, A.; Mielke, R.; Bookhagen, B.

2010-12-01

We conducted a series of experiments using Light Detection and Range (LiDAR) technology as an innovative way to detect the presence of plastics in water. The purpose of this study was to determine if LiDAR technology is a feasible, non-intrusive alternative to dredging in the ocean to determine the amount of plastics in the ocean. We used a tripod mounted RIEGL LMS-Z420i terrestrial LiDAR 3-D scanner and the associated operating software RiSCAN Pro. The terrestrial LiDAR is an optical remote sensing technology that measures the reflection of near infared light to find the range of a distant target that is most commonly used to create high precision digital elevation models of terrestrial surfaces. In theory, water should absorb the near infared light, while the plastics should reflect the light. The experiments consisted of different scale models of plastic pellets in water, ranging from a small plastic dish to a large tank to test the range of the LiDAR in different salt and fresh water mediums.

2013 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Tulalip Partnership

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In October 2012, WSI (Watershed Sciences, Inc.) was contracted by the Puget Sound LiDAR Consortium (PSLC)to collect Light Detection and Ranging (LiDAR) data on a...

2013 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Saddle Mountain

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In October 2013, WSI, a Quantum Spatial Company (QSI), was contracted by the Puget Sound LiDAR Consortium (PSLC) to collect Light Detection and Ranging (LiDAR) data...

Lidar and Hyperspectral Remote Sensing for the Analysis of Coniferous Biomass Stocks and Fluxes

Science.gov (United States)

Halligan, K. Q.; Roberts, D. A.

2006-12-01

Airborne lidar and hyperspectral data can improve estimates of aboveground carbon stocks and fluxes through their complimentary responses to vegetation structure and biochemistry. While strong relationships have been demonstrated between lidar-estimated vegetation structural parameters and field data, research is needed to explore the portability of these methods across a range of topographic conditions, disturbance histories, vegetation type and climate. Additionally, research is needed to evaluate contributions of hyperspectral data in refining biomass estimates and determination of fluxes. To address these questions we are a conducting study of lidar and hyperspectral remote sensing data across sites including coniferous forests, broadleaf deciduous forests and a tropical rainforest. Here we focus on a single study site, Yellowstone National Park, where tree heights, stem locations, above ground biomass and basal area were mapped using first-return small-footprint lidar data. A new method using lidar intensity data was developed for separating the terrain and vegetation components in lidar data using a two-scale iterative local minima filter. Resulting Digital Terrain Models (DTM) and Digital Canopy Models (DCM) were then processed to retrieve a diversity of vertical and horizontal structure metrics. Univariate linear models were used to estimate individual tree heights while stepwise linear regression was used to estimate aboveground biomass and basal area. Three small-area field datasets were compared for their utility in model building and validation of vegetation structure parameters. All structural parameters were linearly correlated with lidar-derived metrics, with higher accuracies obtained where field and imagery data were precisely collocated . Initial analysis of hyperspectral data suggests that vegetation health metrics including measures of live and dead vegetation and stress indices may provide good indicators of carbon flux by mapping vegetation

CALIPSO-Inferred Aerosol Direct Radiative Effects: Bias Estimates Using Ground-Based Raman Lidars

Science.gov (United States)

Thorsen, Tyler; Fu, Qiang

2016-01-01

Observational constraints on the change in the radiative energy budget caused by the presence of aerosols, i.e. the aerosol direct radiative effect (DRE), have recently been made using observations from the Cloud- Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO). CALIPSO observations have the potential to provide improved global estimates of aerosol DRE compared to passive sensor-derived estimates due to CALIPSO's ability to perform vertically-resolved aerosol retrievals over all surface types and over cloud. In this study we estimate the uncertainties in CALIPSO-inferred aerosol DRE using multiple years of observations from the Atmospheric Radiation Measurement (ARM) program's Raman lidars (RL) at midlatitude and tropical sites. Examined are assumptions about the ratio of extinction-to-backscatter (i.e. the lidar ratio) made by the CALIPSO retrievals, which are needed to retrieve the aerosol extinction profile. The lidar ratio is shown to introduce minimal error in the mean aerosol DRE at the top-of-atmosphere and surface. It is also shown that CALIPSO is unable to detect all radiatively-significant aerosol, resulting in an underestimate in the magnitude of the aerosol DRE by 30-50%. Therefore, global estimates of the aerosol DRE inferred from CALIPSO observations are likely too weak.

An innovative rotational Raman lidar to measure the temperature profile from the surface to 30 km altitude

Science.gov (United States)

Hauchecorne, Alain; Keckhut, Philippe; Mariscal, Jean-François; d'Almeida, Eric; Dahoo, Pierre-Richard; Porteneuve, Jacques

2016-06-01

A concept of innovative rotational Raman lidar with daylight measurement capability is proposed to measure the vertical profile of temperature from the ground to the middle stratosphere. The optical filtering is made using a Fabry-Pérot Interferometer with line spacing equal to the line spacing of the Raman spectrum. The detection is made using a linear PMT array operated in photon counting mode. We plan to build a prototype and to test it at the Haute-Provence Observatory lidar facility. to achieve a time resolution permitting the observation of small-scale atmospheric processes playing a role in the troposphere-stratosphere interaction as gravity waves. If successful, this project will open the possibility to consider a Raman space lidar for the global observation of atmospheric temperature profiles.

IEA Wind Task 32: Wind Lidar Identifying and Mitigating Barriers to the Adoption of Wind Lidar

Directory of Open Access Journals (Sweden)

Andrew Clifton

2018-03-01

Full Text Available IEA Wind Task 32 exists to identify and mitigate barriers to the adoption of lidar for wind energy applications. It leverages ongoing international research and development activities in academia and industry to investigate site assessment, power performance testing, controls and loads, and complex flows. Since its initiation in 2011, Task 32 has been responsible for several recommended practices and expert reports that have contributed to the adoption of ground-based, nacelle-based, and floating lidar by the wind industry. Future challenges include the development of lidar uncertainty models, best practices for data management, and developing community-based tools for data analysis, planning of lidar measurements and lidar configuration. This paper describes the barriers that Task 32 identified to the deployment of wind lidar in each of these application areas, and the steps that have been taken to confirm or mitigate the barriers. Task 32 will continue to be a meeting point for the international wind lidar community until at least 2020 and welcomes old and new participants.

Classification of Forest Vertical Structure in South Korea from Aerial Orthophoto and Lidar Data Using an Artificial Neural Network

Directory of Open Access Journals (Sweden)

Soo-Kyung Kwon

2017-10-01

Full Text Available Every vegetation colony has its own vertical structure. Forest vertical structure is considered as an important indicator of a forest’s diversity and vitality. The vertical structure of a forest has typically been investigated by field survey, which is the traditional method of forest inventory. However, this method is very time- and cost-consuming due to poor accessibility. Remote sensing data such as satellite imagery, aerial photography, and lidar data can be a viable alternative to the traditional field-based forestry survey. In this study, we classified forest vertical structures from red-green-blue (RGB aerial orthophotos and lidar data using an artificial neural network (ANN, which is a powerful machine learning technique. The test site was Gongju province in South Korea, which contains single-, double-, and triple-layered forest structures. The performance of the proposed method was evaluated by comparing the results with field survey data. The overall accuracy achieved was about 70%. It means that the proposed approach can classify the forest vertical structures from the aerial orthophotos and lidar data.

Opo lidar sounding of trace atmospheric gases in the 3 - 4 μm spectral range

Science.gov (United States)

Romanovskii, Oleg A.; Sadovnikov, Sergey A.; Kharchenko, Olga V.; Yakovlev, Semen V.

2018-04-01

The applicability of a KTA crystal-based laser system with optical parametric oscillators (OPO) generation to lidar sounding of the atmosphere in the spectral range 3-4 μm is studied in this work. A technique developed for lidar sounding of trace atmospheric gases (TAG) is based on differential absorption lidar (DIAL) method and differential optical absorption spectroscopy (DOAS). The DIAL-DOAS technique is tested to estimate its efficiency for lidar sounding of atmospheric trace gases. The numerical simulation performed shows that a KTA-based OPO laser is a promising source of radiation for remote DIAL-DOAS sounding of the TAGs under study along surface tropospheric paths. A possibility of using a PD38-03-PR photodiode for the DIAL gas analysis of the atmosphere is shown.

Retrieval method of aerosol extinction coefficient profile by an integral lidar system and case study

Science.gov (United States)

Shan, Huihui; Zhang, Hui; Liu, Junjian; Wang, Shenhao; Ma, Xiaomin; Zhang, Lianqing; Liu, Dong; Xie, Chenbo; Tao, Zongming

2018-02-01

Aerosol extinction coefficient profile is an essential parameter for atmospheric radiation model. But it is difficult to get the full aerosol extinction profile from the ground to the tropopause especially in near ground precisely using backscattering lidar. A combined measurement of side-scattering, backscattering and Raman-scattering lidar is proposed to retrieve the aerosol extinction coefficient profile from the surface to the tropopause which covered a dynamic range of 5 orders. The side-scattering technique solves the dead zone and the overlap problem caused by the traditional lidar in the near range. Using the Raman-scattering the aerosol lidar ratio (extinction to backscatter ratio) can be obtained. The cases studies in this paper show the proposed method is reasonable and feasible.

Person detection and tracking with a 360° lidar system

Science.gov (United States)

Hammer, Marcus; Hebel, Marcus; Arens, Michael

2017-10-01

Today it is easily possible to generate dense point clouds of the sensor environment using 360° LiDAR (Light Detection and Ranging) sensors which are available since a number of years. The interpretation of these data is much more challenging. For the automated data evaluation the detection and classification of objects is a fundamental task. Especially in urban scenarios moving objects like persons or vehicles are of particular interest, for instance in automatic collision avoidance, for mobile sensor platforms or surveillance tasks. In literature there are several approaches for automated person detection in point clouds. While most techniques show acceptable results in object detection, the computation time is often crucial. The runtime can be problematic, especially due to the amount of data in the panoramic 360° point clouds. On the other hand, for most applications an object detection and classification in real time is needed. The paper presents a proposal for a fast, real-time capable algorithm for person detection, classification and tracking in panoramic point clouds.

USDA soil classification system dictates site surface management

International Nuclear Information System (INIS)

Bowmer, W.J.

1985-01-01

Success or failure of site surface management practices greatly affects long-term site stability. The US Department of Agriculture (USDA) soil classification system best documents those parameters which control the success of installed practices for managing both erosion and surface drainage. The USDA system concentrates on soil characteristics in the upper three meters of the surface that support the associated flora both physically and physiologically. The USDA soil survey first identifies soil series based on detailed characteristics that are related to production potential. Using the production potential, land use capability classes are developed. Capability classes reveal the highest and best agronomic use for the site. Lower number classes are considered arable while higher number classes are best suited for grazing agriculture. Application of ecological principles based on the USDA soil survey reveals the current state of the site relative to its ecological potential. To assure success, site management practices must be chosen that are compatible with both production capability and current state of the site

Ocean subsurface particulate backscatter estimation from CALIPSO spaceborne lidar measurements

Science.gov (United States)

Chen, Peng; Pan, Delu; Wang, Tianyu; Mao, Zhihua

2017-10-01

A method for ocean subsurface particulate backscatter estimation from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite was demonstrated. The effects of the CALIOP receiver's transient response on the attenuated backscatter profile were first removed. The two-way transmittance of the overlying atmosphere was then estimated as the ratio of the measured ocean surface attenuated backscatter to the theoretical value computed from wind driven wave slope variance. Finally, particulate backscatter was estimated from the depolarization ratio as the ratio of the column-integrated cross-polarized and co-polarized channels. Statistical results show that the derived particulate backscatter by the method based on CALIOP data agree reasonably well with chlorophyll-a concentration using MODIS data. It indicates a potential use of space-borne lidar to estimate global primary productivity and particulate carbon stock.

Fluorescence lidar measurements at the archaeological site House of Augustus at Palatino, Rome

Science.gov (United States)

Raimondi, Valentina; Alisi, Chiara; Barup, Kerstin; Bracciale, Maria Paola; Broggi, Alessandra; Conti, Cinzia; Hällström, Jenny; Lognoli, David; Palombi, Lorenzo; Santarelli, Maria Laura; Sprocati, Anna Rosa

2013-10-01

Early diagnostics and documentation fulfill an essential role for an effective planning of conservation and restoration of cultural heritage assets. In particular, remote sensing techniques that do not require the use of scaffolds or lifts, such as fluoresence lidar, can provide useful information to obtain an overall assessment of the status of the investigated surfaces and can be exploited to address analytical studies in selected areas. Here we present the results of a joint Italian-Swedish project focused on documenting and recording the status of some sections of the part closed to the public by using fluorescence hyperspectral imaging lidar. The lidar used a tripled-frequency Nd:YAG laser emitting at 355 nm as excitation source and an intensified, gated 512x512-pixel CCD as detector. The lidar had imaging capabilities thanks to a computer-controlled scanning mirror. The fluorescence characteristics of fresco wall paintings were compared to those of fresco fragments found at the same archaeological site and separately examined in the lab using FT-IR and Raman techniques for the identification of pigments. The fluorescence lidar was also used to remotely detect the growth of phototrophic biodeteriogens on the walls. The fluorescence lidar data were compared with results from biological sampling, cultivation and laboratory analysis by molecular techniques.

Remote mapping of vegetation and geological features by lidar in the 9-11-μm region

International Nuclear Information System (INIS)

Foy, Bernard R.; McVey, Brian D.; Petrin, Roger R.; Tiee, Joe J.; Wilson, Carl W.

2001-01-01

We report examples of the use of a scanning tunable CO 2 laser lidar system in the 9-11-μm region to construct images of vegetation and rocks at ranges as far as 5 km from the instrument. Range information is combined with horizontal and vertical distances to yield an image with three spatial dimensions simultaneous with the classification of target type. Object classification is based on reflectance spectra, which are sufficiently distinct to allow discrimination between several tree species, between trees and scrub vegetation, and between natural and artificial targets. Limitations imposed by laser speckle noise are discussed

Lidar Inter-Comparison Exercise Final Campaign Report

Energy Technology Data Exchange (ETDEWEB)

Protat, A [Australian Bureau of Meterology; Young, S

2015-02-01

The objective of this field campaign was to evaluate the performance of the new Leosphere R-MAN 510 lidar, procured by the Australian Bureau of Meteorology, by testing it against the MicroPulse Lidar (MPL) and Raman lidars, at the Darwin Atmospheric Radiation Measurement (ARM) site. This lidar is an eye-safe (355 nm), turn-key mini Raman lidar, which allows for the detection of aerosols and cloud properties, and the retrieval of particulate extinction profiles. To accomplish this evaluation, the R-MAN 510 lidar has been operated at the Darwin ARM site, next to the MPL, Raman lidar, and Vaisala ceilometer (VCEIL) for three months (from 20 January 2013 to 20 April 2013) in order to collect a sufficient sample size for statistical comparisons.

Innovative High-Accuracy Lidar Bathymetric Technique for the Frequent Measurement of River Systems

Science.gov (United States)

Gisler, A.; Crowley, G.; Thayer, J. P.; Thompson, G. S.; Barton-Grimley, R. A.

2015-12-01

Lidar (light detection and ranging) provides absolute depth and topographic mapping capability compared to other remote sensing methods, which is useful for mapping rapidly changing environments such as riverine systems. Effectiveness of current lidar bathymetric systems is limited by the difficulty in unambiguously identifying backscattered lidar signals from the water surface versus the bottom, limiting their depth resolution to 0.3-0.5 m. Additionally these are large, bulky systems that are constrained to expensive aircraft-mounted platforms and use waveform-processing techniques requiring substantial computation time. These restrictions are prohibitive for many potential users. A novel lidar device has been developed that allows for non-contact measurements of water depth down to 1 cm with an accuracy and precision of shallow to deep water allowing for shoreline charting, measuring water volume, mapping bottom topology, and identifying submerged objects. The scalability of the technique opens up the ability for handheld or UAS-mounted lidar bathymetric systems, which provides for potential applications currently unavailable to the community. The high laser pulse repetition rate allows for very fine horizontal resolution while the photon-counting technique permits real-time depth measurement and object detection. The enhanced measurement capability, portability, scalability, and relatively low-cost creates the opportunity to perform frequent high-accuracy monitoring and measuring of aquatic environments which is crucial for understanding how rivers evolve over many timescales. Results from recent campaigns measuring water depth in flowing creeks and murky ponds will be presented which demonstrate that the method is not limited by rough water surfaces and can map underwater topology through moderately turbid water.

The Extraction of Vegetation Points from LiDAR Using 3D Fractal Dimension Analyses

Directory of Open Access Journals (Sweden)

Haiquan Yang

2015-08-01

Full Text Available Light Detection and Ranging (LiDAR, a high-precision technique used for acquiring three-dimensional (3D surface information, is widely used to study surface vegetation information. Moreover, the extraction of a vegetation point set from the LiDAR point cloud is a basic starting-point for vegetation information analysis, and an important part of its further processing. To extract the vegetation point set completely and to describe the different spatial morphological characteristics of various features in a LiDAR point cloud, we have used 3D fractal dimensions. We discovered that every feature has its own distinctive 3D fractal dimension interval. Based on the 3D fractal dimensions of tall trees, we propose a new method for the extraction of vegetation using airborne LiDAR. According to this method, target features can be distinguished based on their morphological characteristics. The non-ground points acquired by filtering are processed by region growing segmentation and the morphological characteristics are evaluated by 3D fractal dimensions to determine the features required for the determination of the point set for tall trees. Avon, New York, USA was selected as the study area to test the method and the result proves the method’s efficiency. Thus, this approach is feasible. Additionally, the method uses the 3D coordinate properties of the LiDAR point cloud and does not require additional information, such as return intensity, giving it a larger scope of application.

An innovative rotational Raman lidar to measure the temperature profile from the surface to 30 km altitude

Directory of Open Access Journals (Sweden)

Hauchecorne Alain

2016-01-01

Full Text Available A concept of innovative rotational Raman lidar with daylight measurement capability is proposed to measure the vertical profile of temperature from the ground to the middle stratosphere. The optical filtering is made using a Fabry-Pérot Interferometer with line spacing equal to the line spacing of the Raman spectrum. The detection is made using a linear PMT array operated in photon counting mode. We plan to build a prototype and to test it at the Haute-Provence Observatory lidar facility. to achieve a time resolution permitting the observation of small-scale atmospheric processes playing a role in the troposphere-stratosphere interaction as gravity waves. If successful, this project will open the possibility to consider a Raman space lidar for the global observation of atmospheric temperature profiles.

2015 OLC Lidar DEM: Wasco, WA

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — WSI collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Wasco County, WA, study area. The Oregon LiDAR Consortium's Wasco County...

Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+

Science.gov (United States)

Veronika Leitold; Michael Keller; Douglas C Morton; Bruce D Cook; Yosio E Shimabukuro

2015-01-01

Background: Carbon stocks and fluxes in tropical forests remain large sources of uncertainty in the global carbon budget. Airborne lidar remote sensing is a powerful tool for estimating aboveground biomass, provided that lidar measurements penetrate dense forest vegetation to generate accurate estimates of surface topography and canopy heights. Tropical forest areas...

Adaptive Data Processing Technique for Lidar-Assisted Control to Bridge the Gap between Lidar Systems and Wind Turbines: Preprint

Energy Technology Data Exchange (ETDEWEB)

Schlipf, David; Raach, Steffen; Haizmann, Florian; Cheng, Po Wen; Fleming, Paul; Scholbrock, Andrew, Krishnamurthy, Raghu; Boquet, Mathieu

2015-12-14

This paper presents first steps toward an adaptive lidar data processing technique crucial for lidar-assisted control in wind turbines. The prediction time and the quality of the wind preview from lidar measurements depend on several factors and are not constant. If the data processing is not continually adjusted, the benefit of lidar-assisted control cannot be fully exploited, or can even result in harmful control action. An online analysis of the lidar and turbine data are necessary to continually reassess the prediction time and lidar data quality. In this work, a structured process to develop an analysis tool for the prediction time and a new hardware setup for lidar-assisted control are presented. The tool consists of an online estimation of the rotor effective wind speed from lidar and turbine data and the implementation of an online cross correlation to determine the time shift between both signals. Further, initial results from an ongoing campaign in which this system was employed for providing lidar preview for feed-forward pitch control are presented.

2015 OLC Lidar: Chelan

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Quantum Spatial has collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Chelan FEMA study area. This study area is located in...

2009 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Lewis County, Washington

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Watershed Sciences, Inc. (WSI) collected Light Detection and Ranging (LiDAR) data for the Lewis County survey area for the Puget Sound LiDAR Consortium. This data...

Crown-level tree species classification from AISA hyperspectral imagery using an innovative pixel-weighting approach

Science.gov (United States)

Liu, Haijian; Wu, Changshan

2018-06-01

Crown-level tree species classification is a challenging task due to the spectral similarity among different tree species. Shadow, underlying objects, and other materials within a crown may decrease the purity of extracted crown spectra and further reduce classification accuracy. To address this problem, an innovative pixel-weighting approach was developed for tree species classification at the crown level. The method utilized high density discrete LiDAR data for individual tree delineation and Airborne Imaging Spectrometer for Applications (AISA) hyperspectral imagery for pure crown-scale spectra extraction. Specifically, three steps were included: 1) individual tree identification using LiDAR data, 2) pixel-weighted representative crown spectra calculation using hyperspectral imagery, with which pixel-based illuminated-leaf fractions estimated using a linear spectral mixture analysis (LSMA) were employed as weighted factors, and 3) representative spectra based tree species classification was performed through applying a support vector machine (SVM) approach. Analysis of results suggests that the developed pixel-weighting approach (OA = 82.12%, Kc = 0.74) performed better than treetop-based (OA = 70.86%, Kc = 0.58) and pixel-majority methods (OA = 72.26, Kc = 0.62) in terms of classification accuracy. McNemar tests indicated the differences in accuracy between pixel-weighting and treetop-based approaches as well as that between pixel-weighting and pixel-majority approaches were statistically significant.

Comparative study of building footprint estimation methods from LiDAR point clouds

Science.gov (United States)

Rozas, E.; Rivera, F. F.; Cabaleiro, J. C.; Pena, T. F.; Vilariño, D. L.

2017-10-01

Building area calculation from LiDAR points is still a difficult task with no clear solution. Their different characteristics, such as shape or size, have made the process too complex to automate. However, several algorithms and techniques have been used in order to obtain an approximated hull. 3D-building reconstruction or urban planning are examples of important applications that benefit of accurate building footprint estimations. In this paper, we have carried out a study of accuracy in the estimation of the footprint of buildings from LiDAR points. The analysis focuses on the processing steps following the object recognition and classification, assuming that labeling of building points have been previously performed. Then, we perform an in-depth analysis of the influence of the point density over the accuracy of the building area estimation. In addition, a set of buildings with different size and shape were manually classified, in such a way that they can be used as benchmark.

Lidar Remote Sensing for Characterizing Forest Vegetation - Special Issue. Foreword

Science.gov (United States)

Popescu, Sorin C.; Nelson, Ross F.

2011-01-01

The Silvilaser 2009 conference held in College Station, Texas, USA, was the ninth conference in the Silvilaser series, which started in 2002 with the international workshop on using lidar (Light Detection and Ranging) for analyzing forest structure, held in Victoria, British Columbia, Canada. Following the Canadian workshop, subsequent forestry-lidar conferences took place in Australia, Sweden, Germany, USA, Japan, Finland, and the United Kingdom (UK). By the time this Silvilaser 2009 special issue of PE&RS is published, the 10th international conference will have been held in Freiburg, Germany, and planning will be ongoing for the 11th meeting to take place in Tasmania, Australia, in October 2011. Papers presented at the 2005 conference held in Blacksburg, Virginia, USA, were assembled in a special issue of PE&RS published in December 2006. Other special issues resulting from previous conferences were published in journals such as the Canadian Journal of Remote Sensing (2003), the Scandinavian Journal of Forest Research (2004), and Japan s Journal of Forest Planning (2008). Given the conference history and the much longer record of publications on lidar applications for estimating forest biophysical parameters, which dates back to the early 1980s, we may consider lidar an established remote sensing technology for characterizing forest canopy structure and estimating forest biophysical parameters. Randy Wynne, a professor at Virginia Tech and the final keynote speaker at Silvilaser 2009, made the case that it was time to push 30 years of research into operations, along the lines of what has already been done to good effect in the Scandinavian countries. In Randy s words, it s time to "Just do it!" This special issue includes a selection of papers presented during the 2009 Silvilaser conference, which consisted of eight sections as follows: (1) biomass and carbon stock estimates, (2) tree species and forest type classification, (3) data fusion and integration, (4, 5

Charactering lidar optical subsystem using four quadrants method

Science.gov (United States)

Tian, Xiaomin; Liu, Dong; Xu, Jiwei; Wang, Zhenzhu; Wang, Bangxin; Wu, Decheng; Zhong, Zhiqing; Xie, Chenbo; Wang, Yingjian

2018-02-01

Lidar is a kind of active optical remote sensing instruments , can be applied to sound atmosphere with a high spatial and temporal resolution. Many parameter of atmosphere can be get by using different inverse algorithm with lidar backscatter signal. The basic setup of a lidar consist of a transmitter and a receiver. To make sure the quality of lidar signal data, the lidar must be calibrated before being used to measure the atmospheric variables. It is really significant to character and analyze lidar optical subsystem because a well equiped lidar optical subsystem contributes to high quality lidar signal data. we pay close attention to telecover test to character and analyze lidar optical subsystem.The telecover test is called four quadrants method consisting in dividing the telescope aperture in four quarants. when a lidar is well configured with lidar optical subsystem, the normalized signal from four qudrants will agree with each other on some level. Testing our WARL-II lidar by four quadrants method ,we find the signals of the four basically consistent with each other both in near range and in far range. But in detail, the signals in near range have some slight distinctions resulting from overlap function, some signals distinctions are induced by atmospheric instability.

EXTRACTION OF BUILDING BOUNDARY LINES FROM AIRBORNE LIDAR POINT CLOUDS

Directory of Open Access Journals (Sweden)

Y.-H. Tseng

2016-10-01

Full Text Available Building boundary lines are important spatial features that characterize the topographic maps and three-dimensional (3D city models. Airborne LiDAR Point clouds provide adequate 3D spatial information for building boundary mapping. However, information of boundary features contained in point clouds is implicit. This study focuses on developing an automatic algorithm of building boundary line extraction from airborne LiDAR data. In an airborne LiDAR dataset, top surfaces of buildings, such as roofs, tend to have densely distributed points, but vertical surfaces, such as walls, usually have sparsely distributed points or even no points. The intersection lines of roof and wall planes are, therefore, not clearly defined in point clouds. This paper proposes a novel method to extract those boundary lines of building edges. The extracted line features can be used as fundamental data to generate topographic maps of 3D city model for an urban area. The proposed method includes two major process steps. The first step is to extract building boundary points from point clouds. Then the second step is followed to form building boundary line features based on the extracted boundary points. In this step, a line fitting algorithm is developed to improve the edge extraction from LiDAR data. Eight test objects, including 4 simple low buildings and 4 complicated tall buildings, were selected from the buildings in NCKU campus. The test results demonstrate the feasibility of the proposed method in extracting complicate building boundary lines. Some results which are not as good as expected suggest the need of further improvement of the method.

Employing lidar to detail vegetation canopy architecture for prediction of aeolian transport

Science.gov (United States)

Sankey, Joel B.; Law, Darin J.; Breshears, David D.; Munson, Seth M.; Webb, Robert H.

2013-01-01

The diverse and fundamental effects that aeolian processes have on the biosphere and geosphere are commonly generated by horizontal sediment transport at the land surface. However, predicting horizontal sediment transport depends on vegetation architecture, which is difficult to quantify in a rapid but accurate manner. We demonstrate an approach to measure vegetation canopy architecture at high resolution using lidar along a gradient of dryland sites ranging from 2% to 73% woody plant canopy cover. Lidar-derived canopy height, distance (gaps) between vegetation elements (e.g., trunks, limbs, leaves), and the distribution of gaps scaled by vegetation height were correlated with canopy cover and highlight potentially improved horizontal dust flux estimation than with cover alone. Employing lidar to estimate detailed vegetation canopy architecture offers promise for improved predictions of horizontal sediment transport across heterogeneous plant assemblages.

Insect remote sensing using a polarization sensitive cw lidar system in chinese rice fields

Science.gov (United States)

Zhu, Shiming; Malmqvist, Elin; Li, Yiyun; Jansson, Samuel; Li, Wansha; Duan, Zheng; Fu, Wei; Svanberg, Katarina; Bood, Joakim; Feng, Hongqiang; Åkesson, Susanne; Song, Ziwei; Zhang, Baoxin; Zhao, Guangyu; Li, Dunsong; Brydegaard, Mikkel; Svanberg, Sune

2018-04-01

A joint Chinese-Swedish field campaign of Scheimpflug continuous-wave lidar monitoring of rice-field flying pest insects was pursued in very hot July weather conditions close to Guangzhou, China. The occurrence of insects, birds and bats with almost 200 hours of round-the-clock polarization-sensitive recordings was studied. Wing-beat frequency recordings and depolarization properties were used for target classification. Influence of weather conditions on the flying fauna was also investigated.

The design, development, and test of balloonborne and groundbased lidar systems. Volume 1: Balloonborne coherent CO2 lidar system

Science.gov (United States)

Shepherd, O.; Aurilio, G.; Bucknam, R. D.; Hurd, A. G.; Rappaport, S. A.

1991-06-01

This is Volume 1 of a three volume final report on the design, development, and test of balloonborne and groundbased lidar systems. Volume 2 describes the flight test of Atmospheric Balloonborne Lidar Experiment, ABLE 2, which successfully made atmospheric density backscatter measurements during a flight over White Sands Missile Range. Volume 3 describes groundbased lidar development and measurements, including the design of a telescope dome lidar installation, the design of a transportable lidar shed for remote field sites, and field measurements of atmospheric and cloud backscatter from Ascension Island during SABLE 89 and Terciera, Azores during GABLE 90. In this volume, Volume 1, the design and fabrication of a balloonborne CO2 coherent lidar payload are described. The purpose of this payload is to measure, from altitudes greater than 20 km, the 10.6 micrometers backscatter from atmospheric aerosols as a function of altitude. Minor modifications to the lidar would provide for aerosol velocity measurements to be made. The lidar and payload system design was completed, and major components were fabricated and assembled. These tasks have been successfully completed, and recommendations for further lidar measurements and data analysis have been made.

Semantic segmentation of forest stands of pure species combining airborne lidar data and very high resolution multispectral imagery

Science.gov (United States)

Dechesne, Clément; Mallet, Clément; Le Bris, Arnaud; Gouet-Brunet, Valérie

2017-04-01

Forest stands are the basic units for forest inventory and mapping. Stands are defined as large forested areas (e.g., ⩾ 2 ha) of homogeneous tree species composition and age. Their accurate delineation is usually performed by human operators through visual analysis of very high resolution (VHR) infra-red images. This task is tedious, highly time consuming, and should be automated for scalability and efficient updating purposes. In this paper, a method based on the fusion of airborne lidar data and VHR multispectral images is proposed for the automatic delineation of forest stands containing one dominant species (purity superior to 75%). This is the key preliminary task for forest land-cover database update. The multispectral images give information about the tree species whereas 3D lidar point clouds provide geometric information on the trees and allow their individual extraction. Multi-modal features are computed, both at pixel and object levels: the objects are individual trees extracted from lidar data. A supervised classification is then performed at the object level in order to coarsely discriminate the existing tree species in each area of interest. The classification results are further processed to obtain homogeneous areas with smooth borders by employing an energy minimum framework, where additional constraints are joined to form the energy function. The experimental results show that the proposed method provides very satisfactory results both in terms of stand labeling and delineation (overall accuracy ranges between 84 % and 99 %).

Semiconductor Laser Wind Lidar for Turbine Control

DEFF Research Database (Denmark)

Hu, Qi

This thesis describes an experimentally oriented study of continuous wave (CW) coherent Doppler lidar system design. The main application is remote wind sensing for active wind turbine control using nacelle mounted lidar systems; and the primary focus is to devise an industrial instrument that can...... historical overview within the topic of wind lidar systems. Both the potential and the challenges of an industrialized wind lidar has been addressed here. Furthermore, the basic concept behind the heterodyne detection and a brief overview of the lidar signal processing is explained; and a simple...... investigation of the telescope truncation and lens aberrations is conducted, both numerically and experimentally. It is shown that these parameters dictate the spatial resolution of the lidar system, and have profound impact on the SNR. In this work, an all-semiconductor light source is used in the lidar design...

Computational modeling of river flow using bathymetry collected with an experimental, water-penetrating, green LiDAR

Science.gov (United States)

Kinzel, P. J.; Legleiter, C. J.; Nelson, J. M.

2009-12-01

Airborne bathymetric Light Detection and Ranging (LiDAR) systems designed for coastal and marine surveys are increasingly being deployed in fluvial environments. While the adaptation of this technology to rivers and streams would appear to be straightforward, currently technical challenges remain with regard to achieving high levels of vertical accuracy and precision when mapping bathymetry in shallow fluvial settings. Collectively these mapping errors have a direct bearing on hydraulic model predictions made using these data. We compared channel surveys conducted along the Platte River, Nebraska, and the Trinity River, California, using conventional ground-based methods with those made with the hybrid topographic/bathymetric Experimental Advanced Airborne Research LiDAR (EAARL). In the turbid and braided Platte River, a bathymetric-waveform processing algorithm was shown to enhance the definition of thalweg channels over a more simplified, first-surface waveform processing algorithm. Consequently flow simulations using data processed with the shallow bathymetric algorithm resulted in improved prediction of wetted area relative to the first-surface algorithm, when compared to the wetted area in concurrent aerial imagery. However, when compared to using conventionally collected data for flow modeling, the inundation extent was over predicted with the EAARL topography due to higher bed elevations measured by the LiDAR. In the relatively clear, meandering Trinity River, bathymetric processing algorithms were capable of defining a 3 meter deep pool. However, a similar bias in depth measurement was observed, with the LiDAR measuring the elevation of the river bottom above its actual position, resulting in a predicted water surface higher than that measured by field data. This contribution addresses the challenge of making bathymetric measurements with the EAARL in different environmental conditions encountered in fluvial settings, explores technical issues related to

Development of lidar techniques for environmental studies

Energy Technology Data Exchange (ETDEWEB)

Andersson, Mats

1996-09-01

The lidar group in Lund has performed many DIAL measurements with a mobile lidar system that was first described in 1987. The lidar system is based on a Nd:YAG-pumped dye laser. During the last few years the lidar group has focused on fluorescence imaging and mercury measurements in the troposphere. In 1994 we performed two campaigns: one fluorescence imaging measurement campaign outside Avignon, France and one unique lidar campaign at a mercury mine in Almaden, Spain. Both campaigns are described in this thesis. This thesis also describes how the mobile lidar system was updated with the graphical programming language LabVIEW to obtain a user friendly lidar system. The software controls the lidar system and analyses measured data. The measurement results are shown as maps of species concentration. All electronics and the major parts of the program are described. A new graphical technique to estimate wind speed from plumes is also discussed. First measurements have been performed with the new system. 31 refs, 19 figs, 1 tab

Pointing Knowledge for SPARCLE and Space-Based Doppler Wind Lidars in General

Science.gov (United States)

Emmitt, G. D.; Miller, T.; Spiers, G.

1999-01-01

The SPAce Readiness Coherent Lidar Experiment (SPARCLE) will fly on a space shuttle to demonstrate the use of a coherent Doppler wind lidar to accurately measure global tropospheric winds. To achieve the LOS (Line of Sight) accuracy goal of approx. m/s, the lidar system must be able to account for the orbiter's velocity (approx. 7750 m/s) and the rotational component of the earth's surface motion (approx. 450 m/s). For SPARCLE this requires knowledge of the attitude (roll, pitch and yaw) of the laser beam axis within an accuracy of 80 microradians. (approx. 15 arcsec). Since SPARCLE can not use a dedicated star tracker from its earth-viewing orbiter bay location, a dedicated GPS/INS (Global Positioning System/Inertial Navigation System) will be attached to the lidar instrument rack. Since even the GPS/INS has unacceptable drifts in attitude information, the SPARCLE team has developed a way to periodically scan the instrument itself to obtain less than 10 microradian (2 arcsec) attitude knowledge accuracy that can then be used to correct the GPS/INS output on a 30 minute basis.

An Innovative Concept for Spacebased Lidar Measurement of Ocean Carbon Biomass

Science.gov (United States)

Hu, Yongxiang; Behrenfeld, Michael; Hostetler, Chris; Pelon, Jacques; Trepte, Charles; Hair, John; Slade, Wayne; Cetinic, Ivona; Vaughan, Mark; Lu, Xiaomei;

Retrievals of Aerosol Microphysics from Simulations of Spaceborne Multiwavelength Lidar Measurements

Science.gov (United States)

Whiteman, David N.; Perez-Ramírez, Daniel; Veselovskii, Igor; Colarco, Peter; Buchard, Virginie

2017-01-01

In support of the Aerosol, Clouds, Ecosystems mission, simulations of a spaceborne multiwavelength lidar are performed based on global model simulations of the atmosphere along a satellite orbit track. The yield for aerosol microphysical inversions is quantified and comparisons are made between the aerosol microphysics inherent in the global model and those inverted from both the model's optical data and the simulated three backscatter and two extinction lidar measurements, which are based on the model's optical data. We find that yield can be significantly increased if inversions based on a reduced optical dataset of three backscatter and one extinction are acceptable. In general, retrieval performance is better for cases where the aerosol fine mode dominates although a lack of sensitivity to particles with sizes less than 0.1 microns is found. Lack of sensitivity to coarse mode cases is also found, in agreement with earlier studies. Surface area is generally the most robustly retrieved quantity. The work here points toward the need for ancillary data to aid in the constraints of the lidar inversions and also for joint inversions involving lidar and polarimeter measurements.

Installation report - Lidar

DEFF Research Database (Denmark)

Georgieva Yankova, Ginka; Villanueva, Héctor

The report describes the installation, configuration and data transfer for the ground-based lidar. The unit is provided by a customer but is installed and operated by DTU while in this project.......The report describes the installation, configuration and data transfer for the ground-based lidar. The unit is provided by a customer but is installed and operated by DTU while in this project....

Aerosol Retrievals from Proposed Satellite Bistatic Lidar Observations: Algorithm and Information Content

Science.gov (United States)

Alexandrov, M. D.; Mishchenko, M. I.

2017-12-01

Accurate aerosol retrievals from space remain quite challenging and typically involve solving a severely ill-posed inverse scattering problem. We suggested to address this ill-posedness by flying a bistatic lidar system. Such a system would consist of formation flying constellation of a primary satellite equipped with a conventional monostatic (backscattering) lidar and an additional platform hosting a receiver of the scattered laser light. If successfully implemented, this concept would combine the measurement capabilities of a passive multi-angle multi-spectral polarimeter with the vertical profiling capability of a lidar. Thus, bistatic lidar observations will be free of deficiencies affecting both monostatic lidar measurements (caused by the highly limited information content) and passive photopolarimetric measurements (caused by vertical integration and surface reflection).We present a preliminary aerosol retrieval algorithm for a bistatic lidar system consisting of a high spectral resolution lidar (HSRL) and an additional receiver flown in formation with it at a scattering angle of 165 degrees. This algorithm was applied to synthetic data generated using Mie-theory computations. The model/retrieval parameters in our tests were the effective radius and variance of the aerosol size distribution, complex refractive index of the particles, and their number concentration. Both mono- and bimodal aerosol mixtures were considered. Our algorithm allowed for definitive evaluation of error propagation from measurements to retrievals using a Monte Carlo technique, which involves random distortion of the observations and statistical characterization of the resulting retrieval errors. Our tests demonstrated that supplementing a conventional monostatic HSRL with an additional receiver dramatically increases the information content of the measurements and allows for a sufficiently accurate characterization of tropospheric aerosols.

Frequency Stepped Pulse Train Modulated Wind Sensing Lidar

DEFF Research Database (Denmark)

Olesen, Anders Sig; Pedersen, Anders Tegtmeier; Rottwitt, Karsten

2011-01-01

of frequency shifts corresponding to a specific distance. The spatial resolution depends on the repetition rate of the pulses in the pulse train. Directional wind measurements are shown and compared to a CW lidar measurement. The carrier to noise ratio of the FSPT lidar compared to a CW lidar is discussed......In this paper a wind sensing lidar utilizing a Frequency Stepped Pulse Train (FSPT) is demonstrated. One of the advantages in the FSTP lidar is that it enables direct measurement of wind speed as a function of distance from the lidar. Theoretically the FSPT lidar continuously produces measurements...... as is the case with a CW lidar, but at the same time with a spatial resolution, and without the range ambiguity originating from e.g. clouds. The FSPT lidar utilizes a frequency sweeping source for generation of the FSPT. The source generates a pulse train where each pulse has an optical carrier frequency...

CHARACTERIZING URBAN VOLUMETRY USING LIDAR DATA

Directory of Open Access Journals (Sweden)

T. Santos

2013-05-01

Most common metrics are based on area (2D measurements. These include indicators like impervious area per capita or surface occupied by green areas, having usually as primary source a spectral image obtained through a satellite or airborne camera. More recently, laser scanning data has become available for large-scale applications. Such sensors acquire altimetric information and are used to produce Digital Surface Models (DSM. In this context, LiDAR data available for the city is explored along with demographic information, and a framework to produce volumetric (3D urban indexes is proposed, and measures like Built Volume per capita, Volumetric Density and Volumetric Homogeneity are computed.

Lidar-Observed Stress Vectors and Veer in the Atmospheric Boundary Layer

DEFF Research Database (Denmark)

Berg, Jacob; Mann, Jakob; Patton, Edward G.

2013-01-01

This study demonstrates that a pulsed wind lidar is a reliable instrument for measuring angles between horizontal vectors of significance in the atmospheric boundary layer. Three different angles are considered: the wind turning, the angle between the stress vector and the mean wind direction......, and the angle between the stress vector and the vertical gradient of the mean velocity vector. The latter is assumed to be zero by the often applied turbulent-viscosity hypothesis, so that the stress vector can be described through the vertical gradient of velocity. In the atmospheric surface layer, where...... the Coriolis force is negligible, this is supposedly a good approximation. High-resolution large-eddy simulation data show that this is indeed the case even beyond the surface layer. In contrast, through analysis of WindCube lidar measurements supported by sonic measurements, the study shows that it is only...

Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations.

Science.gov (United States)

Huang, Rongyong; Zheng, Shunyi; Hu, Kun

2018-06-01

Registration of large-scale optical images with airborne LiDAR data is the basis of the integration of photogrammetry and LiDAR. However, geometric misalignments still exist between some aerial optical images and airborne LiDAR point clouds. To eliminate such misalignments, we extended a method for registering close-range optical images with terrestrial LiDAR data to a variety of large-scale aerial optical images and airborne LiDAR data. The fundamental principle is to minimize the distances from the photogrammetric matching points to the terrestrial LiDAR data surface. Except for the satisfactory efficiency of about 79 s per 6732 × 8984 image, the experimental results also show that the unit weighted root mean square (RMS) of the image points is able to reach a sub-pixel level (0.45 to 0.62 pixel), and the actual horizontal and vertical accuracy can be greatly improved to a high level of 1/4⁻1/2 (0.17⁻0.27 m) and 1/8⁻1/4 (0.10⁻0.15 m) of the average LiDAR point distance respectively. Finally, the method is proved to be more accurate, feasible, efficient, and practical in variety of large-scale aerial optical image and LiDAR data.

Lidar Technology at the Goddard Laser and Electro-Optics Branch

Science.gov (United States)

Heaps, William S.; Obenschain, Arthur F. (Technical Monitor)

2000-01-01

The Laser and Electro-Optics Branch at Goddard Space flight Center was established about three years ago to provide a focused center of engineering support and technology development in these disciplines with an emphasis on spaced based instruments for Earth and Space Science. The Branch has approximately 15 engineers and technicians with backgrounds in physics, optics, and electrical engineering. Members of the Branch are currently supporting a number of space based lidar efforts as well as several technology efforts aimed at enabling future missions. The largest effort within the Branch is support of the Ice, Cloud, and land Elevation Satellite (ICESAT) carrying the Geoscience Laser Altimeter System (GLAS) instrument. The ICESAT/GLAS primary science objectives are: 1) To determine the mass balance of the polar ice sheets and their contributions to global sea level change; and 2) To obtain essential data for prediction of future changes in ice volume and sea-level. The secondary science objectives are: 1) To measure cloud heights and the vertical structure of clouds and aerosols in the atmosphere; 2) To map the topography of land surfaces; and 3) To measure roughness, reflectivity, vegetation heights, snow-cover, and sea-ice surface characteristics. Our efforts have concentrated on the GLAS receiver component development, the Laser Reference Sensor for the Stellar Reference System, the GLAS fiber optics subsystems, and the prelaunch calibration facilities. We will report on our efforts in the development of the space qualified interference filter [Allan], etalon filter, photon counting detectors, etalor/laser tracking system, and instrument fiber optics, as well as specification and selection of the star tracker and development of the calibration test bed. We are also engaged in development work on lidar sounders for chemical species. We are developing new lidar technology to enable a new class of miniature lidar instruments that are compatible with small

The design, development, and test of balloonborne and groundbased lidar systems. Volume 2: Flight test of Atmospheric Balloon Lidar Experiment, ABLE 2

Science.gov (United States)

Shepherd, O.; Bucknam, R. D.; Hurd, A. G.; Sheehan, W. H.

1991-06-01

This is Volume 3 of a three volume final report on the design, development, and test of balloonborne and groundbased lidar systems. Volume 1 describes the design and fabrication of a balloonborne CO2 coherent payload to measure the 10.6 micrometers backscatter from atmospheric aerosols as a function of altitude. Volume 2 describes the Aug. 1987 flight test of Atmospheric Balloonborne Lidar Experiment, ABLE 2. In this volume we describe groundbased lidar development and measurements. A design was developed for installation of the ABLE lidar in the GL rooftop dome. A transportable shed was designed to house the ABLE lidar at the various remote measurement sites. Refurbishment and modification of the ABLE lidar were completed to permit groundbased lidar measurements of clouds and aerosols. Lidar field measurements were made at Ascension Island during SABLE 89. Lidar field measurements were made at Terciera, Azores during GABLE 90. These tasks were successfully completed, and recommendations for further lidar measurements and data analysis were made.

Systematic variations in multi-spectral lidar representations of canopy height profiles and gap probability

Science.gov (United States)

Chasmer, L.; Hopkinson, C.; Gynan, C.; Mahoney, C.; Sitar, M.

2015-12-01

Airborne and terrestrial lidar are increasingly used in forest attribute modeling for carbon, ecosystem and resource monitoring. The near infra-red wavelength at 1064nm has been utilised most in airborne applications due to, for example, diode manufacture costs, surface reflectance and eye safety. Foliage reflects well at 1064nm and most of the literature on airborne lidar forest structure is based on data from this wavelength. However, lidar systems also operate at wavelengths further from the visible spectrum (e.g. 1550nm) for eye safety reasons. This corresponds to a water absorption band and can be sensitive to attenuation if surfaces contain moisture. Alternatively, some systems operate in the visible range (e.g. 532nm) for specialised applications requiring simultaneous mapping of terrestrial and bathymetric surfaces. All these wavelengths provide analogous 3D canopy structure reconstructions and thus offer the potential to be combined for spatial comparisons or temporal monitoring. However, a systematic comparison of wavelength-dependent foliage profile and gap probability (index of transmittance) is needed. Here we report on two multispectral lidar missions carried out in 2013 and 2015 over conifer, deciduous and mixed stands in Ontario, Canada. The first used separate lidar sensors acquiring comparable data at three wavelengths, while the second used a single sensor with 3 integrated laser systems. In both cases, wavelenegths sampled were 532nm, 1064nm and 1550nm. The experiment revealed significant differences in proportions of returns at ground level, the vertical foliage distribution and gap probability across wavelengths. Canopy attenuation was greatest at 532nm due to photosynthetic plant tissue absorption. Relative to 1064nm, foliage was systematically undersampled at the 10% to 60% height percentiles at both 1550nm and 532nm (this was confirmed with coincident terrestrial lidar data). When using all returns to calculate gap probability, all

Opo lidar sounding of trace atmospheric gases in the 3 – 4 μm spectral range

Directory of Open Access Journals (Sweden)

Romanovskii Oleg A.

2018-01-01

Full Text Available The applicability of a KTA crystal-based laser system with optical parametric oscillators (OPO generation to lidar sounding of the atmosphere in the spectral range 3–4 μm is studied in this work. A technique developed for lidar sounding of trace atmospheric gases (TAG is based on differential absorption lidar (DIAL method and differential optical absorption spectroscopy (DOAS. The DIAL-DOAS technique is tested to estimate its efficiency for lidar sounding of atmospheric trace gases. The numerical simulation performed shows that a KTA-based OPO laser is a promising source of radiation for remote DIAL-DOAS sounding of the TAGs under study along surface tropospheric paths. A possibility of using a PD38-03-PR photodiode for the DIAL gas analysis of the atmosphere is shown.

2006 MDEQ Camp Shelby, MS Lidar Survey

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This metadata record describes the acquisition and processing of bare earth lidar data, raw point cloud lidar data, lidar intensity data, and floodmap breaklines...

2012-2013 U.S. Geological Survey LiDAR: Territory of Guam

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Territory of Guam, LiDAR Task G11PD01189 This task order is for production of surface model products of The Territory of Guam. The models are produced from data...

Insect remote sensing using a polarization sensitive cw lidar system in chinese rice fields

Directory of Open Access Journals (Sweden)

Zhu Shiming

2018-01-01

Full Text Available A joint Chinese-Swedish field campaign of Scheimpflug continuous-wave lidar monitoring of rice-field flying pest insects was pursued in very hot July weather conditions close to Guangzhou, China. The occurrence of insects, birds and bats with almost 200 hours of round-the-clock polarization-sensitive recordings was studied. Wing-beat frequency recordings and depolarization properties were used for target classification. Influence of weather conditions on the flying fauna was also investigated.

2015 Puget Sound LiDAR Consortium (PSLC) LiDAR: WA DNR Lands (P1)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In June 2014, WSI, a Quantum Spatial Inc. (QSI) company, was contracted by the Puget Sound LiDAR Consortium (PSLC) to collect Light Detection and Ranging (LiDAR)...

2014 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Willapa Valley (Delivery 1)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In January, 2014 WSI, a Quantum Spatial (QSI) company, was contracted by the Puget Sound LiDAR Consortium (PSLC) to collect Light Detection and Ranging (LiDAR) data...

2015 Puget Sound LiDAR Consortium (PSLC) LiDAR: WA DNR Lands (P2)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In June 2014, WSI, a Quantum Spatial Inc. (QSI) company, was contracted by the Puget Sound LiDAR Consortium (PSLC) to collect Light Detection and Ranging (LiDAR)...

Mapping Above- and Below-Ground Carbon Pools in Boreal Forests: The Case for Airborne Lidar.

Science.gov (United States)

Kristensen, Terje; Næsset, Erik; Ohlson, Mikael; Bolstad, Paul V; Kolka, Randall

2015-01-01

A large and growing body of evidence has demonstrated that airborne scanning light detection and ranging (lidar) systems can be an effective tool in measuring and monitoring above-ground forest tree biomass. However, the potential of lidar as an all-round tool for assisting in assessment of carbon (C) stocks in soil and non-tree vegetation components of the forest ecosystem has been given much less attention. Here we combine the use airborne small footprint scanning lidar with fine-scale spatial C data relating to vegetation and the soil surface to describe and contrast the size and spatial distribution of C pools within and among multilayered Norway spruce (Picea abies) stands. Predictor variables from lidar derived metrics delivered precise models of above- and below-ground tree C, which comprised the largest C pool in our study stands. We also found evidence that lidar canopy data correlated well with the variation in field layer C stock, consisting mainly of ericaceous dwarf shrubs and herbaceous plants. However, lidar metrics derived directly from understory echoes did not yield significant models. Furthermore, our results indicate that the variation in both the mosses and soil organic layer C stock plots appears less influenced by differences in stand structure properties than topographical gradients. By using topographical models from lidar ground returns we were able to establish a strong correlation between lidar data and the organic layer C stock at a stand level. Increasing the topographical resolution from plot averages (~2000 m2) towards individual grid cells (1 m2) did not yield consistent models. Our study demonstrates a connection between the size and distribution of different forest C pools and models derived from airborne lidar data, providing a foundation for future research concerning the use of lidar for assessing and monitoring boreal forest C.

A GLOBAL SOLUTION TO TOPOLOGICAL RECONSTRUCTION OF BUILDING ROOF MODELS FROM AIRBORNE LIDAR POINT CLOUDS

Directory of Open Access Journals (Sweden)

J. Yan

2016-06-01

Full Text Available This paper presents a global solution to building roof topological reconstruction from LiDAR point clouds. Starting with segmented roof planes from building LiDAR points, a BSP (binary space partitioning algorithm is used to partition the bounding box of the building into volumetric cells, whose geometric features and their topology are simultaneously determined. To resolve the inside/outside labelling problem of cells, a global energy function considering surface visibility and spatial regularization between adjacent cells is constructed and minimized via graph cuts. As a result, the cells are labelled as either inside or outside, where the planar surfaces between the inside and outside form the reconstructed building model. Two LiDAR data sets of Yangjiang (China and Wuhan University (China are used in the study. Experimental results show that the completeness of reconstructed roof planes is 87.5%. Comparing with existing data-driven approaches, the proposed approach is global. Roof faces and edges as well as their topology can be determined at one time via minimization of an energy function. Besides, this approach is robust to partial absence of roof planes and tends to reconstruct roof models with visibility-consistent surfaces.

UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA

Science.gov (United States)

Sankey, Temuulen T.; Donager, Jonathon; McVay, Jason L.; Sankey, Joel B.

2017-01-01

Forest vegetation classification and structure measurements are fundamental steps for planning, monitoring, and evaluating large-scale forest changes including restoration treatments. High spatial and spectral resolution remote sensing data are critically needed to classify vegetation and measure their 3-dimensional (3D) canopy structure at the level of individual species. Here we test high-resolution lidar, hyperspectral, and multispectral data collected from unmanned aerial vehicles (UAV) and demonstrate a lidar-hyperspectral image fusion method in treated and control forests with varying tree density and canopy cover as well as in an ecotone environment to represent a gradient of vegetation and topography in northern Arizona, U.S.A. The fusion performs better (88% overall accuracy) than either data type alone, particularly for species with similar spectral signatures, but different canopy sizes. The lidar data provides estimates of individual tree height (R2 = 0.90; RMSE = 2.3 m) and crown diameter (R2 = 0.72; RMSE = 0.71 m) as well as total tree canopy cover (R2 = 0.87; RMSE = 9.5%) and tree density (R2 = 0.77; RMSE = 0.69 trees/cell) in 10 m cells across thin only, burn only, thin-and-burn, and control treatments, where tree cover and density ranged between 22 and 50% and 1–3.5 trees/cell, respectively. The lidar data also produces highly accurate digital elevation model (DEM) (R2 = 0.92; RMSE = 0.75 m). In comparison, 3D data derived from the multispectral data via structure-from-motion produced lower correlations with field-measured variables, especially in dense and structurally complex forests. The lidar, hyperspectral, and multispectral sensors, and the methods demonstrated here can be widely applied across a gradient of vegetation and topography for monitoring landscapes undergoing large-scale changes such as the forests in the southwestern U.S.A.

Mapping wetlands in Nova Scotia with multi-beam RADARSAT-2 Polarimetric SAR, optical satellite imagery, and Lidar data

Science.gov (United States)

Jahncke, Raymond; Leblon, Brigitte; Bush, Peter; LaRocque, Armand

2018-06-01

Wetland maps currently in use by the Province of Nova Scotia, namely the Department of Natural Resources (DNR) wetland inventory map and the swamp wetland classes of the DNR forest map, need to be updated. In this study, wetlands were mapped in an area southwest of Halifax, Nova Scotia by classifying a combination of multi-date and multi-beam RADARSAT-2 C-band polarimetric SAR (polSAR) images with spring Lidar, and fall QuickBird optical data using the Random Forests (RF) classifier. The resulting map has five wetland classes (open-water/marsh complex, open bog, open fen, shrub/treed fen/bog, swamp), plus lakes and various upland classes. Its accuracy was assessed using data from 156 GPS wetland sites collected in 2012 and compared to the one obtained with the current wetland map of Nova Scotia. The best overall classification was obtained using a combination of Lidar, RADARSAT-2 HH, HV, VH, VV intensity with polarimetric variables, and QuickBird multispectral (89.2%). The classified image was compared to GPS validation sites to assess the mapping accuracy of the wetlands. It was first done considering a group consisting of all wetland classes including lakes. This showed that only 69.9% of the wetland sites were correctly identified when only the QuickBird classified image was used in the classification. With the addition of variables derived from lidar, the number of correctly identified wetlands increased to 88.5%. The accuracy remained the same with the addition of RADARSAT-2 (88.5%). When we tested the accuracy for identifying wetland classes (e.g. marsh complex vs. open bog) instead of grouped wetlands, the resulting wetland map performed best with either QuickBird and Lidar, or QuickBird, Lidar, and RADARSAT-2 (66%). The Province of Nova Scotia's current wetland inventory and its associated wetland classes (aerial-photo interpreted) were also assessed against the GPS wetland sites. This provincial inventory correctly identified 62.2% of the grouped wetlands

Classification of surface types using SIR-C/X-SAR, Mount Everest Area, Tibet

Science.gov (United States)

Albright, Thomas P.; Painter, Thomas H.; Roberts, Dar A.; Shi, Jiancheng; Dozier, Jeff; Fielding, Eric

1998-01-01

Imaging radar is a promising tool for mapping snow and ice cover in alpine regions. It combines a high-resolution, day or night, all-weather imaging capability with sensitivity to hydrologic and climatic snow and ice parameters. We use the spaceborne imaging radar-C/X-band synthetic aperture radar (SIR-C/X-SAR) to map snow and glacial ice on the rugged north slope of Mount Everest. From interferometrically derived digital elevation data, we compute the terrain calibration factor and cosine of the local illumination angle. We then process and terrain-correct radar data sets acquired on April 16, 1994. In addition to the spectral data, we include surface slope to improve discrimination among several surface types. These data sets are then used in a decision tree to generate an image classification. This method is successful in identifying and mapping scree/talus, dry snow, dry snow-covered glacier, wet snow-covered glacier, and rock-covered glacier, as corroborated by comparison with existing surface cover maps and other ancillary information. Application of the classification scheme to data acquired on October 7 of the same year yields accurate results for most surface types but underreports the extent of dry snow cover.

Analysis and validation of ozone variability observed by lidar during the ESCOMPTE-2001 campaign

Science.gov (United States)

Ancellet, G.; Ravetta, F.

2005-03-01

An ozone lidar was successfully operated as a ground-based instrument during the ESCOMPTE experiment in June/July 2001. Ozone profiles were measured between 0.5 and 5 km. Moreover, simultaneous measurements of the lidar scattering ratio (SR) at 316 nm diagnosed the diurnal evolution of the PBL top. Comparison of this data set with in-situ measurements by ultralight aircraft (ULM) and balloon soundings supports the existence of well-defined layers over the whole altitude range. Differences between measurements techniques are not due to instrumental inaccuracies but point towards the existence of ozone plumes with sharp horizontal gradients. This is indeed supported by aircraft horizontal cross-section available twice a day at two different levels in the planetary boundary layer (PBL) and the free troposphere. Analysis of the ozone data set has shown a good correlation between surface meteorological conditions, surface ozone measurements and lidar ozone profiles in the PBL. Observed ozone maxima or minima are linked either to sea breeze circulation bringing polluted air masses over the lidar or synoptic flows bringing air with background O 3 values into the region. The observed variability of the ozone field is very large over the whole altitude range. Although it is the result of local temporal variability and advection of spatial inhomogenities, the latter proved to be an important contribution.

Advances and perspectives in bathymetry by airborne lidar

Science.gov (United States)

Zhou, Guoqing; Wang, Chenxi; Li, Mingyan; Wang, Yuefeng; Ye, Siqi; Han, Caiyun

2015-12-01

In this paper, the history of the airborne lidar and the development stages of the technology are reviewed. The basic principle of airborne lidar and the method of processing point-cloud data were discussed. At present, single point laser scanning method is widely used in bathymetric survey. Although the method has high ranging accuracy, the data processing and hardware system is too much complicated and expensive. For this reason, this paper present a kind of improved dual-frequency method for bathymetric and sea surface survey, in this method 176 units of 1064nm wavelength laser has been used by push-broom scanning and due to the airborne power limits still use 532nm wavelength single point for bathymetric survey by zigzag scanning. We establish a spatial coordinates for obtaining the WGS-84 of point cloud by using airborne POS system.

Canopy wake measurements using multiple scanning wind LiDARs

Science.gov (United States)

Markfort, C. D.; Carbajo Fuertes, F.; Iungo, V.; Stefan, H. G.; Porte-Agel, F.

2014-12-01

Canopy wakes have been shown, in controlled wind tunnel experiments, to significantly affect the fluxes of momentum, heat and other scalars at the land and water surface over distances of ˜O(1 km), see Markfort et al. (EFM, 2013). However, there are currently no measurements of the velocity field downwind of a full-scale forest canopy. Point-based anemometer measurements of wake turbulence provide limited insight into the extent and details of the wake structure, whereas scanning Doppler wind LiDARs can provide information on how the wake evolves in space and varies over time. For the first time, we present measurements of the velocity field in the wake of a tall patch of forest canopy. The patch consists of two uniform rows of 40-meter tall deciduous, plane trees, which border either side of the Allée de Dorigny, near the EPFL campus. The canopy is approximately 250 m long, and it is approximately 40 m wide, along the direction of the wind. A challenge faced while making field measurements is that the wind rarely intersects a canopy normal to the edge. The resulting wake flow may be deflected relative to the mean inflow. Using multiple LiDARs, we measure the evolution of the wake due to an oblique wind blowing over the canopy. One LiDAR is positioned directly downwind of the canopy to measure the flow along the mean wind direction and the other is positioned near the canopy to evaluate the transversal component of the wind and how it varies with downwind distance from the canopy. Preliminary results show that the open trunk space near the base of the canopy results in a surface jet that can be detected just downwind of the canopy and farther downwind dissipates as it mixes with the wake flow above. A time-varying recirculation zone can be detected by the periodic reversal of the velocity near the surface, downwind of the canopy. The implications of canopy wakes for measurement and modeling of surface fluxes will be discussed.

Comparison of Aerosol optical depth (AOD) derived from AERONET sunphotometer and Lidar system

International Nuclear Information System (INIS)

Khor, Wei Ying; Hee, Wan Shen; Tan, Fuyi; Lim, Hwee San; Jafri, Mohamad Zubir Mat; Holben, Brent

2014-01-01

Aerosol optical depth (AOD) is the measure of aerosols distributed within a column of air from the instrument or Earth's surface to the top of the atmosphere. In this paper, we compared the AOD measured by the Raymetrics Lidar system and AERONET sunphotometer. A total of 6 days data which was collected by both instruments were compiled and compared. Generally, AOD value calculated from Lidar data are higher than that calculated from AERONET data. Differences and similarities in the AOD data trend were observed and the corresponding explanations were done. Level 1.5 data of AERONET is estimated to have an accuracy of ±0.03, thus the Lidar data should follow the trend of the AERONET. But in this regards, this study was conducted less than one month and was very difficult to justify the differences and similarities between AOD measured by the Raymetrics Lidar system and AERONET sunphotometer. So further studies for an extended period will be needed and performed with more comprehensive LIDAR measurements. The slope of the best-fit straight line for the data points between the AOD values retrieved from LIDAR and the AERONET measurements is the closest to unity and the coefficient of determination is high (above 0. 6692). Factors which affect AOD data were discussed. As a conclusion, the trends of the AOD of both systems are similar. Yet due to some external factors, the trend will be slightly different

2012 USGS Lidar: Juneau (AK)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This task order is for planning, acquisition, processing, and derivative products of LiDAR data to be collected for Juneau, Alaska. LiDAR data, and derivative...

Generic methodology for calibrating profiling nacelle lidars

DEFF Research Database (Denmark)

Borraccino, Antoine; Courtney, Michael; Wagner, Rozenn

Improving power performance assessment by measuring at different heights has been demonstrated using ground-based profiling LIDARs. More recently, nacelle-mounted lidars studies have shown promising capabilities to assess power performance. Using nacelle lidars avoids the erection of expensive me...

Iowa LiDAR Mapping Project

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — This is collection level metadata for LAS and ASCII data files from the statewide Iowa Lidar Project. The Iowa Light Detection and Ranging (LiDAR) Project collects...

Clear-air lidar dark band

Science.gov (United States)

Girolamo, Paolo Di; Scoccione, Andrea; Cacciani, Marco; Summa, Donato; Schween, Jan H.

2018-04-01

This paper illustrates measurements carried out by the Raman lidar BASIL in the frame of HOPE, revealing the presence of a clear-air dark band phenomenon (i.e. the appearance of a minimum in lidar backscatter echoes) in the upper portion of the convective boundary layer. The phenomenon is clearly distinguishable in the lidar backscatter echoes at 1064 nm. This phenomenon is attributed to the presence of lignite aerosol particles advected from the surrounding open pit mines in the vicinity of the measuring site.

Classification Order of Surface-Confined Intermixing at Epitaxial Interface

Science.gov (United States)

Michailov, M.

The self-organization phenomena at epitaxial interface hold special attention in contemporary material science. Being relevant to the fundamental physical problem of competing, long-range and short-range atomic interactions in systems with reduced dimensionality, these phenomena have found exacting academic interest. They are also of great technological importance for their ability to bring spontaneous formation of regular nanoscale surface patterns and superlattices with exotic properties. The basic phenomenon involved in this process is surface diffusion. That is the motivation behind the present study which deals with important details of diffusion scenarios that control the fine atomic structure of epitaxial interface. Consisting surface imperfections (terraces, steps, kinks, and vacancies), the interface offers variety of barriers for surface diffusion. Therefore, the adatoms and clusters need a certain critical energy to overcome the corresponding diffusion barriers. In the most general case the critical energies can be attained by variation of the system temperature. Hence, their values define temperature limits of system energy gaps associated with different diffusion scenarios. This systematization imply classification order of surface alloying: blocked, incomplete, and complete. On that background, two diffusion problems, related to the atomic-scale surface morphology, will be discussed. The first problem deals with diffusion of atomic clusters on atomically smooth interface. On flat domains, far from terraces and steps, we analyzed the impact of size, shape, and cluster/substrate lattice misfit on the diffusion behavior of atomic clusters (islands). We found that the lattice constant of small clusters depends on the number N of building atoms at 1 < N ≤ 10. In heteroepitaxy, this effect of variable lattice constant originates from the enhanced charge transfer and the strong influence of the surface potential on cluster atomic arrangement. At constant

Lidar Ice nuclei estimates and how they relate with airborne in-situ measurements

Science.gov (United States)

Marinou, Eleni; Amiridis, Vassilis; Ansmann, Albert; Nenes, Athanasios; Balis, Dimitris; Schrod, Jann; Binietoglou, Ioannis; Solomos, Stavros; Mamali, Dimitra; Engelmann, Ronny; Baars, Holger; Kottas, Michael; Tsekeri, Alexandra; Proestakis, Emmanouil; Kokkalis, Panagiotis; Goloub, Philippe; Cvetkovic, Bojan; Nichovic, Slobodan; Mamouri, Rodanthi; Pikridas, Michael; Stavroulas, Iasonas; Keleshis, Christos; Sciare, Jean

2018-04-01

By means of available ice nucleating particle (INP) parameterization schemes we compute profiles of dust INP number concentration utilizing Polly-XT and CALIPSO lidar observations during the INUIT-BACCHUS-ACTRIS 2016 campaign. The polarization-lidar photometer networking (POLIPHON) method is used to separate dust and non-dust aerosol backscatter, extinction, mass concentration, particle number concentration (for particles with radius > 250 nm) and surface area concentration. The INP final products are compared with aerosol samples collected from unmanned aircraft systems (UAS) and analyzed using the ice nucleus counter FRIDGE.

Lidar Ice nuclei estimates and how they relate with airborne in-situ measurements

Directory of Open Access Journals (Sweden)

Marinou Eleni

2018-01-01

Full Text Available By means of available ice nucleating particle (INP parameterization schemes we compute profiles of dust INP number concentration utilizing Polly-XT and CALIPSO lidar observations during the INUIT-BACCHUS-ACTRIS 2016 campaign. The polarization-lidar photometer networking (POLIPHON method is used to separate dust and non-dust aerosol backscatter, extinction, mass concentration, particle number concentration (for particles with radius > 250 nm and surface area concentration. The INP final products are compared with aerosol samples collected from unmanned aircraft systems (UAS and analyzed using the ice nucleus counter FRIDGE.

Advancing Lidar Sensors Technologies for Next Generation Landing Missions

Science.gov (United States)

Amzajerdian, Farzin; Hines, Glenn D.; Roback, Vincent E.; Petway, Larry B.; Barnes, Bruce W.; Brewster, Paul F.; Pierrottet, Diego F.; Bulyshev, Alexander

2015-01-01

Missions to solar systems bodies must meet increasingly ambitious objectives requiring highly reliable "precision landing", and "hazard avoidance" capabilities. Robotic missions to the Moon and Mars demand landing at pre-designated sites of high scientific value near hazardous terrain features, such as escarpments, craters, slopes, and rocks. Missions aimed at paving the path for colonization of the Moon and human landing on Mars need to execute onboard hazard detection and precision maneuvering to ensure safe landing near previously deployed assets. Asteroid missions require precision rendezvous, identification of the landing or sampling site location, and navigation to the highly dynamic object that may be tumbling at a fast rate. To meet these needs, NASA Langley Research Center (LaRC) has developed a set of advanced lidar sensors under the Autonomous Landing and Hazard Avoidance Technology (ALHAT) project. These lidar sensors can provide precision measurement of vehicle relative proximity, velocity, and orientation, and high resolution elevation maps of the surface during the descent to the targeted body. Recent flights onboard Morpheus free-flyer vehicle have demonstrated the viability of ALHAT lidar sensors for future landing missions to solar system bodies.

2014 OLC Lidar: Colville, WA

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — WSI, a Quantum Spatial company, has collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Colville study area. This study area is...

2015 OLC Lidar DEM: Chelan

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Quantum Spatial has collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Chelan FEMA study area. This study area is located in...

2015 OLC Lidar: Okanogan WA

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Quantum Spatial has collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Okanogan FEMA study area. This study area is located in...

Occurrence and characteristics of mutual interference between LIDAR scanners

Science.gov (United States)

Kim, Gunzung; Eom, Jeongsook; Park, Seonghyeon; Park, Yongwan

2015-05-01

The LIDAR scanner is at the heart of object detection of the self-driving car. Mutual interference between LIDAR scanners has not been regarded as a problem because the percentage of vehicles equipped with LIDAR scanners was very rare. With the growing number of autonomous vehicle equipped with LIDAR scanner operated close to each other at the same time, the LIDAR scanner may receive laser pulses from other LIDAR scanners. In this paper, three types of experiments and their results are shown, according to the arrangement of two LIDAR scanners. We will show the probability that any LIDAR scanner will interfere mutually by considering spatial and temporal overlaps. It will present some typical mutual interference scenario and report an analysis of the interference mechanism.

OpenTopography: Enabling Online Access to High-Resolution Lidar Topography Data and Processing Tools

Science.gov (United States)

Crosby, Christopher; Nandigam, Viswanath; Baru, Chaitan; Arrowsmith, J. Ramon

2013-04-01

High-resolution topography data acquired with lidar (light detection and ranging) technology are revolutionizing the way we study the Earth's surface and overlying vegetation. These data, collected from airborne, tripod, or mobile-mounted scanners have emerged as a fundamental tool for research on topics ranging from earthquake hazards to hillslope processes. Lidar data provide a digital representation of the earth's surface at a resolution sufficient to appropriately capture the processes that contribute to landscape evolution. The U.S. National Science Foundation-funded OpenTopography Facility (http://www.opentopography.org) is a web-based system designed to democratize access to earth science-oriented lidar topography data. OpenTopography provides free, online access to lidar data in a number of forms, including the raw point cloud and associated geospatial-processing tools for customized analysis. The point cloud data are co-located with on-demand processing tools to generate digital elevation models, and derived products and visualizations which allow users to quickly access data in a format appropriate for their scientific application. The OpenTopography system is built using a service-oriented architecture (SOA) that leverages cyberinfrastructure resources at the San Diego Supercomputer Center at the University of California San Diego to allow users, regardless of expertise level, to access these massive lidar datasets and derived products for use in research and teaching. OpenTopography hosts over 500 billion lidar returns covering 85,000 km2. These data are all in the public domain and are provided by a variety of partners under joint agreements and memoranda of understanding with OpenTopography. Partners include national facilities such as the NSF-funded National Center for Airborne Lidar Mapping (NCALM), as well as non-governmental organizations and local, state, and federal agencies. OpenTopography has become a hub for high-resolution topography

Comparison of lidar-derived PM10 with regional modeling and ground-based observations in the frame of MEGAPOLI experiment

Directory of Open Access Journals (Sweden)

J.-C. Raut

2011-10-01

have been compared and have shown similar results with a RMSE (and MAPE of 6.3 mg m−2 (30.1% and 5.2 mg m−2 (22.3% with POLYPHEMUS and CHIMERE when comparing with lidar-derived PM10 with periurban relationship. The values are of the same order of magnitude than other comparisons realized in previous studies. The discrepancies observed between models and measured PM10 can be explained by difficulties to accurately model the background conditions, the positions and strengths of the plume, the vertical turbulent diffusion (as well as the limited vertical model resolutions and chemical processes as the formation of secondary aerosols. The major advantage of using vertically resolved lidar observations in addition to surface concentrations is to overcome the problem of limited spatial representativity of surface measurements. Even for the case of a well-mixed boundary layer, vertical mixing is not complete, especially in the surface layer and near source regions. Also a bad estimation of the mixing layer height would introduce errors in simulated surface concentrations, which can be detected using lidar measurements. In addition, horizontal spatial representativity is larger for altitude integrated measurements than for surface measurements, because horizontal inhomogeneities occurring near surface sources are dampened.

2006 Fulton County Georgia Lidar

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Light Detection and Ranging (LiDAR) LAS dataset is a survey of Fulton County. The Fulton County LiDAR Survey project area consists of approximately 690.5 square...

2014 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Cedar River Watershed (Delivery 2)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In September 2013, WSI, a Quantum Spatial company (QSI), was contracted by the Puget Sound LiDAR Consortium (PSLC) to collect Light Detection and Ranging (LiDAR)...

2014 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Cedar River Watershed (Delivery 1)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In September 2013, WSI, a Quantum Spatial company (QSI), was contracted by the Puget Sound LiDAR Consortium (PSLC) to collect Light Detection and Ranging (LiDAR)...

SAR and LIDAR fusion: experiments and applications

Science.gov (United States)

Edwards, Matthew C.; Zaugg, Evan C.; Bradley, Joshua P.; Bowden, Ryan D.

2013-05-01

In recent years ARTEMIS, Inc. has developed a series of compact, versatile Synthetic Aperture Radar (SAR) systems which have been operated on a variety of small manned and unmanned aircraft. The multi-frequency-band SlimSAR has demonstrated a variety of capabilities including maritime and littoral target detection, ground moving target indication, polarimetry, interferometry, change detection, and foliage penetration. ARTEMIS also continues to build upon the radar's capabilities through fusion with other sensors, such as electro-optical and infrared camera gimbals and light detection and ranging (LIDAR) devices. In this paper we focus on experiments and applications employing SAR and LIDAR fusion. LIDAR is similar to radar in that it transmits a signal which, after being reflected or scattered by a target area, is recorded by the sensor. The differences are that a LIDAR uses a laser as a transmitter and optical sensors as a receiver, and the wavelengths used exhibit a very different scattering phenomenology than the microwaves used in radar, making SAR and LIDAR good complementary technologies. LIDAR is used in many applications including agriculture, archeology, geo-science, and surveying. Some typical data products include digital elevation maps of a target area and features and shapes extracted from the data. A set of experiments conducted to demonstrate the fusion of SAR and LIDAR data include a LIDAR DEM used in accurately processing the SAR data of a high relief area (mountainous, urban). Also, feature extraction is used in improving geolocation accuracy of the SAR and LIDAR data.

Topography of the Moon from the Clementine Lidar

Science.gov (United States)

Smith, David E.; Zuber, Maria T.; Neumann, Gregory A.; Lemoine, Frank G.

1997-01-01

Range measurements from the lidar instrument carried aboard the Clementine spacecraft have been used to produce an accurate global topographic model of the Moon. This paper discusses the function of the lidar; the acquisition, processing, and filtering of observations to produce a global topographic model; and the determination of parameters that define the fundamental shape of the Moon. Our topographic model: a 72nd degree and order spherical harmonic expansion of lunar radii, is designated Goddard Lunar Topography Model 2 (GLTM 2). This topographic field has an absolute vertical accuracy of approximately 100 m and a spatial resolution of 2.5 deg. The field shows that the Moon can be described as a sphere with maximum positive and negative deviations of approx. 8 km, both occurring on the farside, in the areas of the Korolev and South Pole-Aitken (S.P.-Aitken) basins. The amplitude spectrum of the topography shows more power at longer wavelengths as compared to previous models, owing to more complete sampling of the surface, particularly the farside. A comparison of elevations derived from the Clementine lidar to control point elevations from the Apollo laser altimeters indicates that measured relative topographic heights generally agree to within approx. 200 in over the maria. While the major axis of the lunar gravity field is aligned in the Earth-Moon direction, the major axis of topography is displaced from this line by approximately 10 deg to the cast and intersects the farside 24 deg north of the equator. The magnitude of impact basin topography is greater than the lunar flattening (approx. 2 km) and equatorial ellipticity (approx. 800 m), which imposes a significant challenge to interpreting the lunar figure. The floors of mare basins are shown to lie close to an equipotential surface, while the floors of unflooded large basins, except for S.P.-Aitken, lie above this equipotential. The radii of basin floors are thus consistent with a hydrostatic mechanism

A cloud masking algorithm for EARLINET lidar systems

Science.gov (United States)

Binietoglou, Ioannis; Baars, Holger; D'Amico, Giuseppe; Nicolae, Doina

2015-04-01

Cloud masking is an important first step in any aerosol lidar processing chain as most data processing algorithms can only be applied on cloud free observations. Up to now, the selection of a cloud-free time interval for data processing is typically performed manually, and this is one of the outstanding problems for automatic processing of lidar data in networks such as EARLINET. In this contribution we present initial developments of a cloud masking algorithm that permits the selection of the appropriate time intervals for lidar data processing based on uncalibrated lidar signals. The algorithm is based on a signal normalization procedure using the range of observed values of lidar returns, designed to work with different lidar systems with minimal user input. This normalization procedure can be applied to measurement periods of only few hours, even if no suitable cloud-free interval exists, and thus can be used even when only a short period of lidar measurements is available. Clouds are detected based on a combination of criteria including the magnitude of the normalized lidar signal and time-space edge detection performed using the Sobel operator. In this way the algorithm avoids misclassification of strong aerosol layers as clouds. Cloud detection is performed using the highest available time and vertical resolution of the lidar signals, allowing the effective detection of low-level clouds (e.g. cumulus humilis). Special attention is given to suppress false cloud detection due to signal noise that can affect the algorithm's performance, especially during day-time. In this contribution we present the details of algorithm, the effect of lidar characteristics (space-time resolution, available wavelengths, signal-to-noise ratio) to detection performance, and highlight the current strengths and limitations of the algorithm using lidar scenes from different lidar systems in different locations across Europe.

Detection and classification of orange peel on polished steel surfaces by interferometric microscopy

International Nuclear Information System (INIS)

2T Research GmbH, Viktor Kaplan Strasse 2, Wiener Neustadt 2700 (Austria))" data-affiliation=" (AC2T Research GmbH, Viktor Kaplan Strasse 2, Wiener Neustadt 2700 (Austria))" >Miranda-Medina, M L; 2T Research GmbH, Viktor Kaplan Strasse 2, Wiener Neustadt 2700 (Austria))" data-affiliation=" (AC2T Research GmbH, Viktor Kaplan Strasse 2, Wiener Neustadt 2700 (Austria))" >Somkuti, P; 2T Research GmbH, Viktor Kaplan Strasse 2, Wiener Neustadt 2700 (Austria))" data-affiliation=" (AC2T Research GmbH, Viktor Kaplan Strasse 2, Wiener Neustadt 2700 (Austria))" >Steiger, B

2013-01-01

In this work, we provide a general description of the so-called orange peel defect produced on polished steel surfaces. By characterizing a prototype set of samples with various degrees orange peel, we attempt to create a simple model that allows the classification of additional samples through the study of surface parameters. On those surfaces, the orange peel structure has roughness amplitudes in the nanometer range. Detecting surface features on that range requires the implementation of a high-precision technique, such as phase shifting interferometry (PSI). Therefore, we can contribute to the improvement of the manufacturing of polished steel surfaces as well as to the quality control by using optical techniques.

2012 MEGIS Topographic Lidar: Statewide Lidar Project Areas 2 and 3 (Mid-Coastal Cleanup), Maine

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — LiDAR data is a remotely sensed high resolution elevation data collected by an airborne platform. The LiDAR sensor uses a combination of laser range finding, GPS...

Standards – An Important Step for the (Public Use of Lidars

Directory of Open Access Journals (Sweden)

Althausen Dietrich

2016-01-01

Full Text Available Lidar standards are needed to ensure quality and lidar product control at the interface between lidar manufacturers and lidar users. Meanwhile three lidar standards have been published by German and international standardization organizations. This paper describes the cooperation between the lidar technique inventors, lidar instrument constructors, and lidar product users to establish useful standards. Presently a backscatter lidar standard is elaborated in Germany. Key points of this standard are presented here. Two German standards were already accepted as international standards by the International Organization for Standardization (ISO. Hence, German and international organizations for the establishment of lidar standards are introduced to encourage a cooperative work on lidar standards by lidar scientists.

On Feature Extraction from Large Scale Linear LiDAR Data

Science.gov (United States)

Acharjee, Partha Pratim

Airborne light detection and ranging (LiDAR) can generate co-registered elevation and intensity map over large terrain. The co-registered 3D map and intensity information can be used efficiently for different feature extraction application. In this dissertation, we developed two algorithms for feature extraction, and usages of features for practical applications. One of the developed algorithms can map still and flowing waterbody features, and another one can extract building feature and estimate solar potential on rooftops and facades. Remote sensing capabilities, distinguishing characteristics of laser returns from water surface and specific data collection procedures provide LiDAR data an edge in this application domain. Furthermore, water surface mapping solutions must work on extremely large datasets, from a thousand square miles, to hundreds of thousands of square miles. National and state-wide map generation/upgradation and hydro-flattening of LiDAR data for many other applications are two leading needs of water surface mapping. These call for as much automation as possible. Researchers have developed many semi-automated algorithms using multiple semi-automated tools and human interventions. This reported work describes a consolidated algorithm and toolbox developed for large scale, automated water surface mapping. Geometric features such as flatness of water surface, higher elevation change in water-land interface and, optical properties such as dropouts caused by specular reflection, bimodal intensity distributions were some of the linear LiDAR features exploited for water surface mapping. Large-scale data handling capabilities are incorporated by automated and intelligent windowing, by resolving boundary issues and integrating all results to a single output. This whole algorithm is developed as an ArcGIS toolbox using Python libraries. Testing and validation are performed on a large datasets to determine the effectiveness of the toolbox and results are

A geomorphologist's dream come true: synoptic high resolution river bathymetry with the latest generation of airborne dual wavelength lidar

Science.gov (United States)

Lague, Dimitri; Launeau, Patrick; Michon, Cyril; Gouraud, Emmanuel; Juge, Cyril; Gentile, William; Hubert-Moy, Laurence; Crave, Alain

2016-04-01

challenge lies in the post-processing of the massive amount of data generated by the instrument (typically 10 billions points for 60 km of rivers). Yet the very high density of the raw point cloud data (40 pts/m² on topography, 20 pts/m² on bathymetry) and the full waveform nature of the signal offers new opportunities to develop classification and change detection algorithms. In this context, we present a new automated workflow to extract automatically the water surface (a critical aspect for refraction correction) and submerged data in highly complex fluvial environments based on a combined analysis of the 1064 nm and 532 nm channels. We conclude that topo-bathymetric lidar is getting close to being an operational technique for fluvial bathymetry offering a vast range of applications in hydrology, ecohydrology, geomorphology and river management.

A Concealed Car Extraction Method Based on Full-Waveform LiDAR Data

Directory of Open Access Journals (Sweden)

Chuanrong Li

2016-01-01

Full Text Available Concealed cars extraction from point clouds data acquired by airborne laser scanning has gained its popularity in recent years. However, due to the occlusion effect, the number of laser points for concealed cars under trees is not enough. Thus, the concealed cars extraction is difficult and unreliable. In this paper, 3D point cloud segmentation and classification approach based on full-waveform LiDAR was presented. This approach first employed the autocorrelation G coefficient and the echo ratio to determine concealed cars areas. Then the points in the concealed cars areas were segmented with regard to elevation distribution of concealed cars. Based on the previous steps, a strategy integrating backscattered waveform features and the view histogram descriptor was developed to train sample data of concealed cars and generate the feature pattern. Finally concealed cars were classified by pattern matching. The approach was validated by full-waveform LiDAR data and experimental results demonstrated that the presented approach can extract concealed cars with accuracy more than 78.6% in the experiment areas.

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography

Science.gov (United States)

Siu, Ho Chit; Shah, Julie A.; Stirling, Leia A.

2016-01-01

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces. PMID:27792155

Gas analysis within remote porous targets using LIDAR multi-scatter techniques

Science.gov (United States)

Guan, Z. G.; Lewander, M.; Grönlund, R.; Lundberg, H.; Svanberg, S.

2008-11-01

Light detection and ranging (LIDAR) experiments are normally pursued for range resolved atmospheric gas measurements or for analysis of solid target surfaces using fluorescence of laser-induced breakdown spectroscopy. In contrast, we now demonstrate the monitoring of free gas enclosed in pores of materials, subject to impinging laser radiation, employing the photons emerging back to the surface laterally of the injection point after penetrating the medium in heavy multiple scattering processes. The directly reflected light is blocked by a beam stop. The technique presented is a remote version of the newly introduced gas in scattering media absorption spectroscopy (GASMAS) technique, which so far was pursued with the injection optics and the detector in close contact with the sample. Feasibility measurements of LIDAR-GASMAS on oxygen in polystyrene foam were performed at a distance of 6 m. Multiple-scattering induced delays of the order of 50 ns, which corresponds to 15 m optical path length, were observed. First extensions to a range of 60 m are discussed. Remote observation of gas composition anomalies in snow using differential absorption LIDAR (DIAL) may find application in avalanche victim localization or for leak detection in snow-covered natural gas pipelines. Further, the techniques may be even more useful for short-range, non-intrusive GASMAS measurements, e.g., on packed food products.

Turbulence estimation from a continuous-wave scanning lidar (SpinnerLidar)

DEFF Research Database (Denmark)

Barnhoorn, J.G.; Sjöholm, Mikael; Mikkelsen, Torben Krogh

2017-01-01

out, and 2) the mixing of velocity covariances from other components into the line-of-sight variance measurements. However, turbulence measurements based on upwind horizontal rotor plane scanning of the line-of-sight variance measurements combined with ensemble-averaged Doppler spectra width...... deviations averaged over 10-min sampling periods are compared. Lidar variances are inherently more prone to noise which always yields a positive bias. The 5.3 % higher turbulence level measured by the SpinnerLidar relative to the cup anemometer may equally well be attributed to truncation of turbulent...

Saginaw Bay, MI LiDAR

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TASK NAME:(NRCS) Saginaw Bay, MI LiDAR LiDAR Data Acquisition and Processing Production Task USGS Contract No. G10PC00057 Task Order No. G11PD01254 Woolpert Order...

Detection of Wind Evolution and Lidar Trajectory Optimization for Lidar-Assisted Wind Turbine Control

Directory of Open Access Journals (Sweden)

David Schlipf

2015-11-01

Full Text Available Recent developments in remote sensing are offering a promising opportunity to rethink conventional control strategies of wind turbines. With technologies such as lidar, the information about the incoming wind field - the main disturbance to the system - can be made available ahead of time. Initial field testing of collective pitch feedforward control shows, that lidar measurements are only beneficial if they are filtered properly to avoid harmful control action. However, commercial lidar systems developed for site assessment are usually unable to provide a usable signal for real time control. Recent research shows, that the correlation between the measurement of rotor effective wind speed and the turbine reaction can be modeled and that the model can be used to optimize a scan pattern. This correlation depends on several criteria such as turbine size, position of the measurements, measurement volume, and how the wind evolves on its way towards the rotor. In this work the longitudinal wind evolution is identified with the line-of-sight measurements of a pulsed lidar system installed on a large commercial wind turbine. This is done by staring directly into the inflowing wind during operation of the turbine and fitting the coherence between the wind at different measurement distances to an exponential model taking into account the yaw misalignment, limitation to line-of-sight measurements and the pulse volume. The identified wind evolution is then used to optimize the scan trajectory of a scanning lidar for lidar-assisted feedforward control in order to get the best correlation possible within the constraints of the system. Further, an adaptive filer is fitted to the modeled correlation to avoid negative impact of feedforward control because of uncorrelated frequencies of the wind measurement. The main results of the presented work are a first estimate of the wind evolution in front of operating wind turbines and an approach which manufacturers of

Holographic Raman lidar

International Nuclear Information System (INIS)

Andersen, G.

2000-01-01

Full text: We have constructed a Raman lidar system that incorporates a holographic optical element. By resolving just 3 nitrogen lines in the Resonance Raman spectroscopy (RRS) spectrum, temperature fits as good as 1% at altitudes of 20km can be made in 30 minutes. Due to the narrowband selectivity of the HOE, the lidar provides measurements over a continuous 24hr period. By adding a 4th channel to capture the Rayleigh backscattered light, temperature profiles can be extended to 80km

Pointing Verification Method for Spaceborne Lidars

Directory of Open Access Journals (Sweden)

Axel Amediek

2017-01-01

Full Text Available High precision acquisition of atmospheric parameters from the air or space by means of lidar requires accurate knowledge of laser pointing. Discrepancies between the assumed and actual pointing can introduce large errors due to the Doppler effect or a wrongly assumed air pressure at ground level. In this paper, a method for precisely quantifying these discrepancies for airborne and spaceborne lidar systems is presented. The method is based on the comparison of ground elevations derived from the lidar ranging data with high-resolution topography data obtained from a digital elevation model and allows for the derivation of the lateral and longitudinal deviation of the laser beam propagation direction. The applicability of the technique is demonstrated by using experimental data from an airborne lidar system, confirming that geo-referencing of the lidar ground spot trace with an uncertainty of less than 10 m with respect to the used digital elevation model (DEM can be obtained.

Lidar-Radiometer Inversion Code (LIRIC) for the Retrieval of Vertical Aerosol Properties from Combined Lidar Radiometer Data: Development and Distribution in EARLINET

Science.gov (United States)

Chaikovsky, A.; Dubovik, O.; Holben, Brent N.; Bril, A.; Goloub, P.; Tanre, D.; Pappalardo, G.; Wandinger, U.; Chaikovskaya, L.; Denisov, S.;

2010 U.S. Geological Survey (USGS) Topographic LiDAR: San Francisco Bay, California

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The primary purpose of this project was to develop a consistent and accurate surface elevation dataset derived from high-accuracy Light Detection and Ranging (LiDAR)...

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

Directory of Open Access Journals (Sweden)

Alireza G. Kashani

2015-11-01

Full Text Available 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.

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

Science.gov (United States)

Kashani, Alireza G; Olsen, Michael J; Parrish, Christopher E; Wilson, Nicholas

2015-11-06

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.

Application of Lidar Data to the Performance Evaluations of CMAQ Model

Science.gov (United States)

The Tropospheric Ozone (O3) Lidar Network (TOLNet) provides time/height O3 measurements from near the surface to the top of the troposphere to describe in high-fidelity spatial-temporal distributions, which is uniquely useful to evaluate the temporal evolution of O3 profiles in a...

Atmospheric lidar: legislative, scientific and technological aspects; Lidar atmosferico. Aspetti legislativi, scientifici e tecnologici

Energy Technology Data Exchange (ETDEWEB)

Barbini, R.; Colao, F.; Fiorani, L.; Palucci, A. [ENEA, Divisione Fisica Applicata, Centro Ricerche Frascati, Frascati, RM (Italy)

2000-07-01

The Atmospheric Lidar is one of the systems of the Mobile Laboratory of Laser Remote Sensing under development at the ENEA Research Center of Frascati. This technical report addresses the legislative, scientific and technological aspects that are the basis for the identification of the requirements, the definition of the architecture and the fixation of the specifications of the Atmospheric Lidar. The problems of air pollution are introduced in section 2. A summary of the Italian laws on that topic is then given. Section 4 provides a survey of the atmospheric measurements that can be achieved with the lidar. The sensitivity in the monitoring of pollutants is discussed in section 5. The other systems of the Mobile Laboratory of Laser Remote Sensing are shortly described in section 6. The last section is devoted to conclusions and perspectives. [Italian] Il lidar atmosferico e' uno dei sistemi del Laboratorio Mobile di Telerilevamento Laser in corso di realizzazione presso il Centro Ricerche di Frascati dell'ENEA. Questo rapporto tecnico discute gli aspetti legislativi, scientifici, tecnologici che sono alla base dell'individuazione dei requisiti, della definizione dell'architettura e della fissazione delle specifiche del Lidar atmosferico. La problematica dell'inquinamento dell'aria e' introdotta nella sezione 2. Segue un riassunto della legislazione italiana su tale tematica. La sezione 4 offre una panoramica delle misure atmosferiche realizzabili con il Lidar. La sensibilita' nel monitoraggio di inquinanti e' discussa nella sezione 5. Gli altri sistemi del Laboratorio Mobile di Telerilevamento Laser sono descritti brevemente nella sezione 6. L'ultima sezione e' dedicata alle conclusioni e alle prospettive.

Nonlinear filtering for LIDAR signal processing

Directory of Open Access Journals (Sweden)

D. G. Lainiotis

1996-01-01

Full Text Available LIDAR (Laser Integrated Radar is an engineering problem of great practical importance in environmental monitoring sciences. Signal processing for LIDAR applications involves highly nonlinear models and consequently nonlinear filtering. Optimal nonlinear filters, however, are practically unrealizable. In this paper, the Lainiotis's multi-model partitioning methodology and the related approximate but effective nonlinear filtering algorithms are reviewed and applied to LIDAR signal processing. Extensive simulation and performance evaluation of the multi-model partitioning approach and its application to LIDAR signal processing shows that the nonlinear partitioning methods are very effective and significantly superior to the nonlinear extended Kalman filter (EKF, which has been the standard nonlinear filter in past engineering applications.

Towards 4d Virtual City Reconstruction from LIDAR Point Cloud Sequences

Science.gov (United States)

Józsa, O.; Börcs, A.; Benedek, C.

2013-05-01

In this paper we propose a joint approach on virtual city reconstruction and dynamic scene analysis based on point cloud sequences of a single car-mounted Rotating Multi-Beam (RMB) Lidar sensor. The aim of the addressed work is to create 4D spatio-temporal models of large dynamic urban scenes containing various moving and static objects. Standalone RMB Lidar devices have been frequently applied in robot navigation tasks and proved to be efficient in moving object detection and recognition. However, they have not been widely exploited yet for geometric approximation of ground surfaces and building facades due to the sparseness and inhomogeneous density of the individual point cloud scans. In our approach we propose an automatic registration method of the consecutive scans without any additional sensor information such as IMU, and introduce a process for simultaneously extracting reconstructed surfaces, motion information and objects from the registered dense point cloud completed with point time stamp information.

Classification of grass pollen through the quantitative analysis of surface ornamentation and texture.

Science.gov (United States)

Mander, Luke; Li, Mao; Mio, Washington; Fowlkes, Charless C; Punyasena, Surangi W

2013-11-07

Taxonomic identification of pollen and spores uses inherently qualitative descriptions of morphology. Consequently, identifications are restricted to categories that can be reliably classified by multiple analysts, resulting in the coarse taxonomic resolution of the pollen and spore record. Grass pollen represents an archetypal example; it is not routinely identified below family level. To address this issue, we developed quantitative morphometric methods to characterize surface ornamentation and classify grass pollen grains. This produces a means of quantifying morphological features that are traditionally described qualitatively. We used scanning electron microscopy to image 240 specimens of pollen from 12 species within the grass family (Poaceae). We classified these species by developing algorithmic features that quantify the size and density of sculptural elements on the pollen surface, and measure the complexity of the ornamentation they form. These features yielded a classification accuracy of 77.5%. In comparison, a texture descriptor based on modelling the statistical distribution of brightness values in image patches yielded a classification accuracy of 85.8%, and seven human subjects achieved accuracies between 68.33 and 81.67%. The algorithmic features we developed directly relate to biologically meaningful features of grass pollen morphology, and could facilitate direct interpretation of unsupervised classification results from fossil material.

Coherent Lidar Turbulence Measurement for Gust Load Alleviation

Science.gov (United States)

Bogue, Rodney K.; Ehernberger, L. J.; Soreide, David; Bagley, Hal

1996-01-01

Atmospheric turbulence adversely affects operation of commercial and military aircraft and is a design constraint. The airplane structure must be designed to survive the loads imposed by turbulence. Reducing these loads allows the airplane structure to be lighter, a substantial advantage for a commercial airplane. Gust alleviation systems based on accelerometers mounted in the airplane can reduce the maximum gust loads by a small fraction. These systems still represent an economic advantage. The ability to reduce the gust load increases tremendously if the turbulent gust can be measured before the airplane encounters it. A lidar system can make measurements of turbulent gusts ahead of the airplane, and the NASA Airborne Coherent Lidar for Advanced In-Flight Measurements (ACLAIM) program is developing such a lidar. The ACLAIM program is intended to develop a prototype lidar system for use in feasibility testing of gust load alleviation systems and other airborne lidar applications, to define applications of lidar with the potential for improving airplane performance, and to determine the feasibility and benefits of these applications. This paper gives an overview of the ACLAIM program, describes the lidar architecture for a gust alleviation system, and describes the prototype ACLAIM lidar system.

Lidar instruments for ESA Earth observation missions

Science.gov (United States)

Hélière, Arnaud; Armandillo, Errico; Durand, Yannig; Culoma, Alain; Meynart, Roland

2017-11-01

The idea of deploying a lidar system on an Earthorbiting satellite stems from the need for continuously providing profiles of our atmospheric structure with high accuracy and resolution and global coverage. Interest in this information for climatology, meteorology and the atmospheric sciences in general is huge. Areas of application range from the determination of global warming and greenhouse effects, to monitoring the transport and accumulation of pollutants in the different atmospheric regions (such as the recent fires in Southeast Asia), to the assessment of the largely unknown microphysical properties and the structural dynamics of the atmosphere itself. Spaceborne lidar systems have been the subject of extensive investigations by the European Space Agency since mid 1970's, resulting in mission and instrument concepts, such as ATLID, the cloud backscatter lidar payload of the EarthCARE mission, ALADIN, the Doppler wind lidar of the Atmospheric Dynamics Mission (ADM) and more recently a water vapour Differential Absorption Lidar considered for the WALES mission. These studies have shown the basic scientific and technical feasibility of spaceborne lidars, but they have also demonstrated their complexity from the instrument viewpoint. As a result, the Agency undertook technology development in order to strengthen the instrument maturity. This is the case for ATLID, which benefited from a decade of technology development and supporting studies and is now studied in the frame of the EarthCARE mission. ALADIN, a Direct Detection Doppler Wind Lidar operating in the Ultra -Violet, will be the 1st European lidar to fly in 2007 as payload of the Earth Explorer Core Mission ADM. WALES currently studied at the level of a phase A, is based upon a lidar operating at 4 wavelengths in near infrared and aims to profile the water vapour in the lower part of the atmosphere with high accuracy and low bias. Lastly, the European Space Agency is extending the lidar instrument field

Aerodynamic roughness length estimation from very high-resolution imaging LIDAR observations over the Heihe basin in China

Directory of Open Access Journals (Sweden)

J. Colin

2010-12-01

Full Text Available Roughness length of land surfaces is an essential variable for the parameterisation of momentum and heat exchanges. The growing interest in the estimation of the surface turbulent flux parameterisation from passive remote sensing leads to an increasing development of models, and the common use of simple semi-empirical formulations to estimate surface roughness. Over complex surface land cover, these approaches would benefit from the combined use of passive remote sensing and land surface structure measurements from Light Detection And Ranging (LIDAR techniques. Following early studies based on LIDAR profile data, this paper explores the use of imaging LIDAR measurements for the estimation of the aerodynamic roughness length over a heterogeneous landscape of the Heihe river basin, a typical inland river basin in the northwest of China. The point cloud obtained from multiple flight passes over an irrigated farmland area were used to separate the land surface topography and the vegetation canopy into a Digital Elevation Model (DEM and a Digital Surface Model (DSM respectively. These two models were then incorporated in two approaches: (i a strictly geometrical approach based on the calculation of the plan surface density and the frontal surface density to derive a geometrical surface roughness; (ii a more aerodynamic approach where both the DEM and DSM are introduced in a Computational Fluid Dynamics model (CFD. The inversion of the resulting 3-D wind field leads to a fine representation of the aerodynamic surface roughness. Examples of the use of these three approaches are presented for various wind directions together with a cross-comparison of results on heterogeneous land cover and complex roughness element structures.

IEA Task 32: Wind Lidar Systems for Wind Energy Deployment (LIDAR)

Energy Technology Data Exchange (ETDEWEB)

Kuhn, Martin; Trabucchi, Davide; Clifton, Andrew; Courtney, Mike; Rettenmeier, Andreas

2016-05-25

Under the International Energy Agency Wind Implementing Agreement (IEA Wind) Task 11, researchers started examining novel applications for remote sensing and the issues around them during the 51st topical expert meeting about remote sensing in January 2007. The 59th topical expert meeting organized by Task 11 in October 2009 was also dedicated to remote sensing, and the first draft of the Task's recommended practices on remote sensing was published in January 2013. The results of the Task 11 topical expert meetings provided solid groundwork for a new IEA Wind Task 32 on wind lidar technologies. Members of the wind community identified the need to consolidate the knowledge about wind lidar systems to facilitate their use, and to investigate how to exploit the advantages offered by this technology. This was the motivation that led to the start of IEA Wind Task 32 'Lidar Application for Wind Energy Deployment' in November 2011. The kick-off was meeting was held in May 2012.

A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification

Science.gov (United States)

Liu, Tao; Im, Jungho; Quackenbush, Lindi J.

2015-12-01

This study provides a novel approach to individual tree crown delineation (ITCD) using airborne Light Detection and Ranging (LiDAR) data in dense natural forests using two main steps: crown boundary refinement based on a proposed Fishing Net Dragging (FiND) method, and segment merging based on boundary classification. FiND starts with approximate tree crown boundaries derived using a traditional watershed method with Gaussian filtering and refines these boundaries using an algorithm that mimics how a fisherman drags a fishing net. Random forest machine learning is then used to classify boundary segments into two classes: boundaries between trees and boundaries between branches that belong to a single tree. Three groups of LiDAR-derived features-two from the pseudo waveform generated along with crown boundaries and one from a canopy height model (CHM)-were used in the classification. The proposed ITCD approach was tested using LiDAR data collected over a mountainous region in the Adirondack Park, NY, USA. Overall accuracy of boundary classification was 82.4%. Features derived from the CHM were generally more important in the classification than the features extracted from the pseudo waveform. A comprehensive accuracy assessment scheme for ITCD was also introduced by considering both area of crown overlap and crown centroids. Accuracy assessment using this new scheme shows the proposed ITCD achieved 74% and 78% as overall accuracy, respectively, for deciduous and mixed forest.

Lidar extinction measurement in the mid infrared

Science.gov (United States)

Mitev, Valentin; Babichenko, S.; Borelli, R.; Fiorani, L.; Grigorov, I.; Nuvoli, M.; Palucci, A.; Pistilli, M.; Puiu, Ad.; Rebane, Ott; Santoro, S.

2014-11-01

We present a lidar measurement of atmospheric extinction coefficient. The measurement is performed by inversion of the backscatter lidar signal at wavelengths 3'000nm and 3'500nm. The inversion of the backscatter lidar signal was performed with constant extinction-to-backscatter ration values of 104 and exponential factor 0.1.

A generalized adaptive mathematical morphological filter for LIDAR data

Science.gov (United States)

Cui, Zheng

Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth's surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in "cut-off" errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain, a trend analysis method was developed to adaptively estimate the slope-related thresholds of the PM filter based on changes of topographic slopes and the characteristics of non-terrain objects. The comparison of the PM and generalized adaptive PM (GAPM) filters for selected study areas indicates that the GAPM filter preserves the most "cut-off" points removed incorrectly by the PM filter. The application of the GAPM filter to seven ISPRS benchmark datasets shows that the GAPM filter reduces the filtering error by 20% on average, compared with the method used by the popular commercial software TerraScan. The combination of the cluster method, adaptive trend analysis, and the PM filter allows users without much experience in

2012 Puget Sound LiDAR Consortium (PSLC) Topographic LiDAR: Quinault River Watershed, Washington (Delivery 1)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Watershed Sciences, Inc. (WSI) collected Light Detection and Ranging (LiDAR) data on the Quinault watershed survey area for the Puget Sound LiDAR Consortium. This...

Tropospheric Ozone Source Attribution in Southern California during Summer 2014 Based on Lidar Measurements and Model Simulations

Science.gov (United States)

Granados Munoz, Maria Jose; Johnson, Matthew S.; Leblanc, Thierry

2016-01-01

In the past decades, significant efforts have been made to increase tropospheric ozone long-term monitoring. A large number of ground-based, airborne and space-borne instruments are currently providing valuable data to contribute to better understand tropospheric ozone budget and variability. Nonetheless, most of these instruments provide in-situ surface and column-integrated data, whereas vertically resolved measurements are still scarce. Besides ozonesondes and aircraft, lidar measurements have proven to be valuable tropospheric ozone profilers. Using the measurements from the tropospheric ozone differential absorption lidar (DIAL) located at the JPL Table Mountain Facility, California, and the GEOS-Chem and GEOS-5 model outputs, the impact of the North American monsoon on tropospheric ozone during summer 2014 is investigated. The influence of the Monsoon lightning-induced NOx will be evaluated against other sources (e.g. local anthropogenic emissions and the stratosphere) using also complementary data such as backward-trajectories analysis, coincident water vapor lidar measurements, and surface ozone in-situ measurements.

Calibrating nacelle lidars

Energy Technology Data Exchange (ETDEWEB)

Courtney, M.

2013-01-15

Nacelle mounted, forward looking wind lidars are beginning to be used to provide reference wind speed measurements for the power performance testing of wind turbines. In such applications, a formal calibration procedure with a corresponding uncertainty assessment will be necessary. This report presents four concepts for performing such a nacelle lidar calibration. Of the four methods, two are found to be immediately relevant and are pursued in some detail. The first of these is a line of sight calibration method in which both lines of sight (for a two beam lidar) are individually calibrated by accurately aligning the beam to pass close to a reference wind speed sensor. A testing procedure is presented, reporting requirements outlined and the uncertainty of the method analysed. It is seen that the main limitation of the line of sight calibration method is the time required to obtain a representative distribution of radial wind speeds. An alternative method is to place the nacelle lidar on the ground and incline the beams upwards to bisect a mast equipped with reference instrumentation at a known height and range. This method will be easier and faster to implement and execute but the beam inclination introduces extra uncertainties. A procedure for conducting such a calibration is presented and initial indications of the uncertainties given. A discussion of the merits and weaknesses of the two methods is given together with some proposals for the next important steps to be taken in this work. (Author)

Aerosol backscatter measurements at 10.6 microns with airborne and ground-based CO2 Doppler lidars over the Colorado High Plains. I - Lidar intercomparison

Science.gov (United States)

Bowdle, David A.; Rothermel, Jeffry; Vaughan, J. Michael; Brown, Derek W.; Post, Madison J.

1991-01-01

An airborne continuous-wave (CW) focused CO2 Doppler lidar and a ground-based pulsed CO2 Doppler lidar were to obtain seven pairs of comparative measurements of tropospheric aerosol backscatter profiles at 10.6-micron wavelength, near Denver, Colorado, during a 20-day period in July 1982. In regions of uniform backscatter, the two lidars show good agreement, with differences usually less than about 50 percent near 8-km altitude and less than a factor of 2 or 3 elsewhere but with the pulsed lidar often lower than the CW lidar. Near sharp backscatter gradients, the two lidars show poorer agreement, with the pulsed lidar usually higher than the CW lidar. Most discrepancies arise from a combination of atmospheric factors and instrument factors, particularly small-scale areal and temporal backscatter heterogeneity above the planetary boundary layer, unusual large-scale vertical backscatter structure in the upper troposphere and lower stratosphere, and differences in the spatial resolution, detection threshold, and noise estimation for the two lidars.

Can Wind Lidars Measure Turbulence?

DEFF Research Database (Denmark)

Sathe, Ameya; Mann, Jakob; Gottschall, Julia

2011-01-01

Modeling of the systematic errors in the second-order moments of wind speeds measured by continuous-wave (ZephIR) and pulsed (WindCube) lidars is presented. These lidars use the conical scanning technique to measure the velocity field. The model captures the effect of volume illumination and coni...

4D Near Real-Time Environmental Monitoring Using Highly Temporal LiDAR

Science.gov (United States)

Höfle, Bernhard; Canli, Ekrem; Schmitz, Evelyn; Crommelinck, Sophie; Hoffmeister, Dirk; Glade, Thomas

2016-04-01

The last decade has witnessed extensive applications of 3D environmental monitoring with the LiDAR technology, also referred to as laser scanning. Although several automatic methods were developed to extract environmental parameters from LiDAR point clouds, only little research has focused on highly multitemporal near real-time LiDAR (4D-LiDAR) for environmental monitoring. Large potential of applying 4D-LiDAR is given for landscape objects with high and varying rates of change (e.g. plant growth) and also for phenomena with sudden unpredictable changes (e.g. geomorphological processes). In this presentation we will report on the most recent findings of the research projects 4DEMON (http://uni-heidelberg.de/4demon) and NoeSLIDE (https://geomorph.univie.ac.at/forschung/projekte/aktuell/noeslide/). The method development in both projects is based on two real-world use cases: i) Surface parameter derivation of agricultural crops (e.g. crop height) and ii) change detection of landslides. Both projects exploit the "full history" contained in the LiDAR point cloud time series. One crucial initial step of 4D-LiDAR analysis is the co-registration over time, 3D-georeferencing and time-dependent quality assessment of the LiDAR point cloud time series. Due to the high amount of datasets (e.g. one full LiDAR scan per day), the procedure needs to be performed fully automatically. Furthermore, the online near real-time 4D monitoring system requires to set triggers that can detect removal or moving of tie reflectors (used for co-registration) or the scanner itself. This guarantees long-term data acquisition with high quality. We will present results from a georeferencing experiment for 4D-LiDAR monitoring, which performs benchmarking of co-registration, 3D-georeferencing and also fully automatic detection of events (e.g. removal/moving of reflectors or scanner). Secondly, we will show our empirical findings of an ongoing permanent LiDAR observation of a landslide (Gresten

Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor

Science.gov (United States)

Rahman, Husna Abdul; Harun, Sulaiman Wadi; Arof, Hamzah; Irawati, Ninik; Musirin, Ismail; Ibrahim, Fatimah; Ahmad, Harith

2014-05-01

An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.

2011 NOAA Bathymetric Lidar: U.S. Virgin Islands - St. Thomas, St. John, St. Croix (Salt River Bay, Buck Island)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data represents a LiDAR (Light Detection & Ranging) gridded bathymetric surface and a gridded relative seafloor reflectivity surface (incorporated into the...

The OptD-multi method in LiDAR processing

International Nuclear Information System (INIS)

Błaszczak-Bąk, Wioleta; Sobieraj-Żłobińska, Anna; Kowalik, Michał

2017-01-01

New and constantly developing technology for acquiring spatial data, such as LiDAR (light detection and ranging), is a source for large volume of data. However, such amount of data is not always needed for developing the most popular LiDAR products: digital terrain model (DTM) or digital surface model. Therefore, in many cases, the number of contained points are reduced in the pre-processing stage. The degree of reduction is determined by the algorithm used, which should enable the user to obtain a dataset appropriate and optimal for the planned purpose. The aim of this article is to propose a new Optimum Dataset method (OptD method) in the processing of LiDAR point clouds. The OptD method can reduce the number of points in a dataset for the specified optimization criteria concerning the characteristics of generated DTM. The OptD method can be used in two variants: OptD-single (one criterion for optimization) and OptD-multi (two or more optimization criteria). The OptD-single method has been thoroughly tested and presented by Błaszczak-Bąk (2016 Acta Geodyn. Geomater . 13/4 379–86). In this paper the authors discussed the OptD-multi method. (paper)

Multiangle lidar observations of the Atmosphere

Science.gov (United States)

Lalitkumar Prakash, Pawar; Choukiker, Yogesh Kumar; Raghunath, K.

2018-04-01

Atmospheric Lidars are used extensively to get aerosol parameters like backscatter coefficient, backscatter ratio etc. National Atmospheric Research Laboratory, Gadanki (13°N, 79°E), India has a powerful lidar which has alt-azimuth capability. Inversion method is applied to data from observations of lidar system at different azimuth and elevation angles. Data Analysis is described and Observations in 2D and 3D format are discussed. Presence of Cloud and the variation of backscatter parameters are seen in an interesting manner.

LIDAR for atmosphere research over Africa

CSIR Research Space (South Africa)

Sivakumar, V

2008-11-01

Full Text Available d’aéronomie, CNRS, Paris, France 1Email: SVenkataraman@csir.co.za – www.csir.co.za K-6665 [www.kashangroup.com] Lidar for atmospheric studies: The CSIR’s laser research into monitoring various pollutants in the lower atmosphere via... to lidar applications for atmosphere studies including pollutant monitoring. The following salient features emanated from the survey: • Around 80% of the lidars are in the northern hemisphere • Of the 20% in the southern hemisphere region...

2012 Oregon Lidar Consortium (OLC) Lidar: Keno (OR)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Watershed Sciences, Inc. (WSI) collected Light Detection and Ranging (LiDAR) data of the Oregon Keno Study Area for the Oregon Department of Geology and Mineral...

Unveiling topographical changes using LiDAR mapping capability: case study of Belaga in Sarawak, East-Malaysia

Science.gov (United States)

Ganendra, T. R.; Khan, N. M.; Razak, W. J.; Kouame, Y.; Mobarakeh, E. T.

2016-06-01

The use of Light Detection and Ranging (LiDAR) remote sensing technology to scan and map landscapes has proven to be one of the most popular techniques to accurately map topography. Thus, LiDAR technology is the ultimate method of unveiling the surface feature under dense vegetation, and, this paper intends to emphasize the diverse techniques that can be utilized to elucidate topographical changes over the study area, using multi-temporal airborne full waveform LiDAR datasets collected in 2012 and 2014. Full waveform LiDAR data offers access to an almost unlimited number of returns per shot, which enables the user to explore in detail topographical changes, such as vegetation growth measurement. The study also found out topography changes at the study area due to earthwork activities contributing to soil consolidation, soil erosion and runoff, requiring cautious monitoring. The implications of this study not only concurs with numerous investigations undertaken by prominent researchers to improve decision making, but also corroborates once again that investigations employing multi-temporal LiDAR data to unveil topography changes in vegetated terrains, produce more detailed and accurate results than most other remote sensing data.

Laser remote sensing of water vapor: Raman lidar development

International Nuclear Information System (INIS)

Goldsmith, J.E.M.; Lapp, M.; Bisson, S.E.; Melfi, S.H.; Whiteman, D.N.; Ferrare, R.A.; Evans, K.D.

1994-01-01

The goal of this research is the development of a critical design for a Raman lidar system optimized to match ARM Program needs for profiling atmospheric water vapor at CART sites. This work has emphasized the development of enhanced daytime capabilities using Raman lidar techniques. This abstract touches briefly on the main components of the research program, summarizing results of the efforts. A detailed Raman lidar instrument model has been developed to predict the daytime and nighttime performance capabilities of Raman lidar systems. The model simulates key characteristics of the lidar system, using realistic atmospheric profiles, modeled background sky radiance, and lidar system parameters based on current instrument capabilities. The model is used to guide development of lidar systems based on both the solar-blind concept and the narrowband, narrow field-of-view concept for daytime optimization

Three-dimensional mapping of light transmittance and foliage distribution using lidar

International Nuclear Information System (INIS)

Todd, K.W.; Csillag, F.; Atkinson, P.M.

2003-01-01

The horizontal and vertical distributions of light transmittance were evaluated as a function of foliage distribution using lidar (light detection and ranging) observations for a sugar maple (Acer saccharum) stand in the Turkey Lakes Watershed. Along the vertical profile of vegetation, horizontal slices of probability of light transmittance were derived from an Optech ALTM 1225 instrument's return pulses (two discrete, 15-cm diameter returns) using indicator kriging. These predictions were compared with (i) below canopy (1-cm spatial resolution) transect measurements of the fraction of photosynthetically active radiation (FPAR) and (ii) measurements of tree height. A first-order trend was initially removed from the lidar returns. The vertical distribution of vegetation height was then sliced into nine percentiles and indicator variograms were fitted to them. Variogram parameters were found to vary as a function of foliage height above ground. In this paper, we show that the relationship between ground measurements of FPAR and kriged estimates of vegetation cover becomes stronger and tighter at coarser spatial resolutions. Three-dimensional maps of foliage distribution were computed as stacks of the percentile probability surfaces. These probability surfaces showed correspondence with individual tree-based observations and provided a much more detailed characterization of quasi-continuous foliage distribution. These results suggest that discrete-return lidar provides a promising technology to capture variations of foliage characteristics in forests to support the development of functional linkages between biophysical and ecological studies. (author)

Sensitivity analysis of nacelle lidar free stream wind speed measurements to wind-induction reconstruction model and lidar range configuration

DEFF Research Database (Denmark)

Svensson, Elin; Borraccino, Antoine; Meyer Forsting, Alexander Raul

The sensitivity of nacelle lidar wind speed measurements to wind-induction models and lidar range configurations is studied using experimental data from the Nørrekær Enge (NKE) measurement campaign and simulated lidar data from Reynold-Averaged Navier Stokes (RANS) aerodynamic computational fluid...... the ZDM was configured to measure at five distances. From the configured distances, a large number of range configurations were created and systematically tested to determine the sensitivity of the reconstructed wind speeds to the number of ranges, minimum range and maximum range in the range......) of the fitting residuals. The results demonstrate that it is not possible to use RANS CFD simulated lidar data to determine optimal range configurations for real-time nacelle lidars due to their perfect (unrealistic) representation of the simulated flow field. The recommended range configurations are therefore...

2007 USGS Lidar: Canyon Fire (CA)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This Southern California Light Detection and Ranging (LiDAR) data is to provide high accuracy LIDAR data. These datasets will be the initial acquisition to support...

Spaceborne Lidar in the Study of Marine Systems.

Science.gov (United States)

Hostetler, Chris A; Behrenfeld, Michael J; Hu, Yongxiang; Hair, Johnathan W; Schulien, Jennifer A

2018-01-03

Satellite passive ocean color instruments have provided an unbroken ∼20-year record of global ocean plankton properties, but this measurement approach has inherent limitations in terms of spatial-temporal sampling and ability to resolve vertical structure within the water column. These limitations can be addressed by coupling ocean color data with measurements from a spaceborne lidar. Airborne lidars have been used for decades to study ocean subsurface properties, but recent breakthroughs have now demonstrated that plankton properties can be measured with a satellite lidar. The satellite lidar era in oceanography has arrived. Here, we present a review of the lidar technique, its applications in marine systems, a perspective on what can be accomplished in the near future with an ocean- and atmosphere-optimized satellite lidar, and a vision for a multiplatform virtual constellation of observational assets that would enable a three-dimensional reconstruction of global ocean ecosystems.

Spaceborne Lidar in the Study of Marine Systems

Science.gov (United States)

Hostetler, Chris A.; Behrenfeld, Michael J.; Hu, Yongxiang; Hair, Johnathan W.; Schulien, Jennifer A.

2018-01-01

Satellite passive ocean color instruments have provided an unbroken ˜20-year record of global ocean plankton properties, but this measurement approach has inherent limitations in terms of spatial-temporal sampling and ability to resolve vertical structure within the water column. These limitations can be addressed by coupling ocean color data with measurements from a spaceborne lidar. Airborne lidars have been used for decades to study ocean subsurface properties, but recent breakthroughs have now demonstrated that plankton properties can be measured with a satellite lidar. The satellite lidar era in oceanography has arrived. Here, we present a review of the lidar technique, its applications in marine systems, a perspective on what can be accomplished in the near future with an ocean- and atmosphere-optimized satellite lidar, and a vision for a multiplatform virtual constellation of observational assets that would enable a three-dimensional reconstruction of global ocean ecosystems.

Extraction of Features from High-resolution 3D LiDaR Point-cloud Data

Science.gov (United States)

Keller, P.; Kreylos, O.; Hamann, B.; Kellogg, L. H.; Cowgill, E. S.; Yikilmaz, M. B.; Hering-Bertram, M.; Hagen, H.

2008-12-01

Airborne and tripod-based LiDaR scans are capable of producing new insight into geologic features by providing high-quality 3D measurements of the landscape. High-resolution LiDaR is a promising method for studying slip on faults, erosion, and other landscape-altering processes. LiDaR scans can produce up to several billion individual point returns associated with the reflection of a laser from natural and engineered surfaces; these point clouds are typically used to derive a high-resolution digital elevation model (DEM). Currently, there exist only few methods that can support the analysis of the data at full resolution and in the natural 3D perspective in which it was collected by working directly with the points. We are developing new algorithms for extracting features from LiDaR scans, and present method for determining the local curvature of a LiDaR data set, working directly with the individual point returns of a scan. Computing the curvature enables us to rapidly and automatically identify key features such as ridge-lines, stream beds, and edges of terraces. We fit polynomial surface patches via a moving least squares (MLS) approach to local point neighborhoods, determining curvature values for each point. The size of the local point neighborhood is defined by a user. Since both terrestrial and airborne LiDaR scans suffer from high noise, we apply additional pre- and post-processing smoothing steps to eliminate unwanted features. LiDaR data also captures objects like buildings and trees complicating greatly the task of extracting reliable curvature values. Hence, we use a stochastic approach to determine whether a point can be reliably used to estimate curvature or not. Additionally, we have developed a graph-based approach to establish connectivities among points that correspond to regions of high curvature. The result is an explicit description of ridge-lines, for example. We have applied our method to the raw point cloud data collected as part of the Geo

Cyclops: single-pixel imaging lidar system based on compressive sensing

Science.gov (United States)

Magalhães, F.; Correia, M. V.; Farahi, F.; Pereira do Carmo, J.; Araújo, F. M.

2017-11-01

Mars and the Moon are envisaged as major destinations of future space exploration missions in the upcoming decades. Imaging LIDARs are seen as a key enabling technology in the support of autonomous guidance, navigation and control operations, as they can provide very accurate, wide range, high-resolution distance measurements as required for the exploration missions. Imaging LIDARs can be used at critical stages of these exploration missions, such as descent and selection of safe landing sites, rendezvous and docking manoeuvres, or robotic surface navigation and exploration. Despite these devices have been commercially available and used for long in diverse metrology and ranging applications, their size, mass and power consumption are still far from being suitable and attractive for space exploratory missions. Here, we describe a compact Single-Pixel Imaging LIDAR System that is based on a compressive sensing technique. The application of the compressive codes to a DMD array enables compression of the spatial information, while the collection of timing histograms correlated to the pulsed laser source ensures image reconstruction at the ranged distances. Single-pixel cameras have been compared with raster scanning and array based counterparts in terms of noise performance, and proved to be superior. Since a single photodetector is used, a better SNR and higher reliability is expected in contrast with systems using large format photodetector arrays. Furthermore, the event of failure of one or more micromirror elements in the DMD does not prevent full reconstruction of the images. This brings additional robustness to the proposed 3D imaging LIDAR. The prototype that was implemented has three modes of operation. Range Finder: outputs the average distance between the system and the area of the target under illumination; Attitude Meter: provides the slope of the target surface based on distance measurements in three areas of the target; 3D Imager: produces 3D ranged

Development of Prototype Micro-Lidar using Narrow Linewidth Semiconductor Lasers for Mars Boundary Layer Wind and Dust Opacity Profiles

Science.gov (United States)

Menzies, Robert T.; Cardell, Greg; Chiao, Meng; Esproles, Carlos; Forouhar, Siamak; Hemmati, Hamid; Tratt, David

1999-01-01

We have developed a compact Doppler lidar concept which utilizes recent developments in semiconductor diode laser technology in order to be considered suitable for wind and dust opacity profiling in the Mars lower atmosphere from a surface location. The current understanding of the Mars global climate and meteorology is very limited, with only sparse, near-surface data available from the Viking and Mars Pathfinder landers, supplemented by long-range remote sensing of the Martian atmosphere. The in situ measurements from a lander-based Doppler lidar would provide a unique dataset particularly for the boundary layer. The coupling of the radiative properties of the lower atmosphere with the dynamics involves the radiative absorption and scattering effects of the wind-driven dust. Variability in solar irradiance, on diurnal and seasonal time scales, drives vertical mixing and PBL (planetary boundary layer) thickness. The lidar data will also contribute to an understanding of the impact of wind-driven dust on lander and rover operations and lifetime through an improvement in our understanding of Mars climatology. In this paper we discuss the Mars lidar concept, and the development of a laboratory prototype for performance studies, using, local boundary layer and topographic target measurements.

Multiangle lidar observations of the Atmosphere

Directory of Open Access Journals (Sweden)

Lalitkumar Prakash Pawar

2018-01-01

Full Text Available Atmospheric Lidars are used extensively to get aerosol parameters like backscatter coefficient, backscatter ratio etc. National Atmospheric Research Laboratory, Gadanki (13°N, 79°E, India has a powerful lidar which has alt-azimuth capability. Inversion method is applied to data from observations of lidar system at different azimuth and elevation angles. Data Analysis is described and Observations in 2D and 3D format are discussed. Presence of Cloud and the variation of backscatter parameters are seen in an interesting manner.

Wind measurement via direct detection lidar

Science.gov (United States)

Afek, I.; Sela, N.; Narkiss, N.; Shamai, G.; Tsadka, S.

2013-10-01

Wind sensing Lidar is considered a promising technology for high quality wind measurements required for various applications such as hub height wind resource assessment, power curve measurements and advanced, real time, forward looking turbine control. Until recently, the only available Lidar technology was based on coherent Doppler shift detection, whose market acceptance has been slow primarily due to its exuberant price. Direct detection Lidar technology provides an alternative to remote sensing of wind by incorporating high precision measurement, a robust design and an affordable price tag.

Eye safety report 1 (dual wavelength). Human risk analysis simulator for space lidars

International Nuclear Information System (INIS)

Schulmeister, K.; Mellerio, J.; Sonneck, G.

2001-09-01

This report contains the results of a risk study for a satellite based lidar mission that uses two different laser wavelengths to measure atmospheric properties. A lidar can be considered as a laser radar and is an acronym for light detection and ranging. The lidar measures properties of the atmosphere by analysis of laser radiation that is directed back to the lidar. As only part of the laser radiation is scattered ir absorbed by the atmosphere, the remaining laser radiation emitted from the spacecraft is incident on the earth's surface, where it might lead to injuries, especially to the eye, if biological thresholds are exceeded. For the analysed mission there is no hazard to the skin, only a potential one to the eye. Because the footprint of the satellite's laser beam on the surface of the earth is so small and it moves so fast, the chance of the naked eye being exposed to the laser is small. Because of the magnification provided by an optical instrument, and the concomitant reduction in the field of view, the probability of exposure of an eye that is using such an instrument decreases with increasing optical power. However, because an increased optical power implies increased diameter of the light gathering optics, the laser energy delivered to an eye increases with instrument size so that if exposure did occur, the probability of delivering energy to the eye that exceeds the threshold for damage increases. There are thus two conflicting processes at work for viewing with optical instruments: an increase in diameter increases the energy delivered but reduces the probability of lidar beam interception. Energy delivered to the eye is such that damage thresholds will not be exceeded for naked eye viewing or for the use of small optical instruments. Exposure via telescopes with diameter larger than 12 cm could result in retinal damage of the exposed eye. (author)

Accuracy Assessment of Lidar-Derived Digital Terrain Model (dtm) with Different Slope and Canopy Cover in Tropical Forest Region

Science.gov (United States)

Salleh, M. R. M.; Ismail, Z.; Rahman, M. Z. A.

2015-10-01

Airborne Light Detection and Ranging (LiDAR) technology has been widely used recent years especially in generating high accuracy of Digital Terrain Model (DTM). High density and good quality of airborne LiDAR data promises a high quality of DTM. This study focussing on the analysing the error associated with the density of vegetation cover (canopy cover) and terrain slope in a LiDAR derived-DTM value in a tropical forest environment in Bentong, State of Pahang, Malaysia. Airborne LiDAR data were collected can be consider as low density captured by Reigl system mounted on an aircraft. The ground filtering procedure use adaptive triangulation irregular network (ATIN) algorithm technique in producing ground points. Next, the ground control points (GCPs) used in generating the reference DTM and these DTM was used for slope classification and the point clouds belong to non-ground are then used in determining the relative percentage of canopy cover. The results show that terrain slope has high correlation for both study area (0.993 and 0.870) with the RMSE of the LiDAR-derived DTM. This is similar to canopy cover where high value of correlation (0.989 and 0.924) obtained. This indicates that the accuracy of airborne LiDAR-derived DTM is significantly affected by terrain slope and canopy caver of study area.

ACCURACY ASSESSMENT OF LIDAR-DERIVED DIGITAL TERRAIN MODEL (DTM WITH DIFFERENT SLOPE AND CANOPY COVER IN TROPICAL FOREST REGION

Directory of Open Access Journals (Sweden)

M. R. M. Salleh

2015-10-01

Full Text Available Airborne Light Detection and Ranging (LiDAR technology has been widely used recent years especially in generating high accuracy of Digital Terrain Model (DTM. High density and good quality of airborne LiDAR data promises a high quality of DTM. This study focussing on the analysing the error associated with the density of vegetation cover (canopy cover and terrain slope in a LiDAR derived-DTM value in a tropical forest environment in Bentong, State of Pahang, Malaysia. Airborne LiDAR data were collected can be consider as low density captured by Reigl system mounted on an aircraft. The ground filtering procedure use adaptive triangulation irregular network (ATIN algorithm technique in producing ground points. Next, the ground control points (GCPs used in generating the reference DTM and these DTM was used for slope classification and the point clouds belong to non-ground are then used in determining the relative percentage of canopy cover. The results show that terrain slope has high correlation for both study area (0.993 and 0.870 with the RMSE of the LiDAR-derived DTM. This is similar to canopy cover where high value of correlation (0.989 and 0.924 obtained. This indicates that the accuracy of airborne LiDAR-derived DTM is significantly affected by terrain slope and canopy caver of study area.

Connecting meteorology to surface transport in aeolian landscapes: Peering into the boundary layer with Doppler lidar

Science.gov (United States)

Gunn, A.; Jerolmack, D. J.; Edmonds, D. A.; Ewing, R. C.; Wanker, M.; David, S. R.

2017-12-01

Aolian sand dunes grow to 100s or 1000s of meters in wavelength by sand saltation, which also produces dust plumes that feed cloud formation and may spread around the world. The relations among sediment transport, landscape dynamics and wind are typically observed at the limiting ends of the relevant range: highly resolved and localized ground observations of turbulence and relevant fluxes; or regional and synoptic-scale meteorology and satellite imagery. Between the geostrophic winds aloft and shearing stress on the Earth's surface is the boundary layer, whose stability and structure determines how momentum is transferred and ultimately entrains sediment. Although the literature on atmospheric boundary layer flows is mature, this understanding is rarely applied to aeolian landscape dynamics. Moreover, there are few vertically and time-resolved datasets of atmospheric boundary layer flows in desert sand seas, where buoyancy effects are most pronounced. Here we employ a ground-based upward-looking doppler lidar to examine atmospheric boundary layer flow at the upwind margin of the White Sands (New Mexico) dune field, providing continuous 3D wind velocity data from the surface to 300-m aloft over 70 days of the characteristically windy spring season. Data show highly resolved daily cyles of convective instabilty due to daytime heating and stable stratification due to nightime cooling which act to enhance or depress, respectively, the surface wind stresses for a given free-stream velocity. Our data implicate convective instability in driving strong saltation and dust emission, because enhanced mixing flattens the vertical velocity profile (raising surface wind speed) while upward advection helps to deliver dust to the high atmosphere. We also find evidence for Ekman spiralling, with a magnitude that depends on atmospheric stability. This spiralling gives rise to a deflection in the direction between geostrophic and surface winds, that is significant for the

2014 OLC Lidar DEM: Colville, WA

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — WSI, a Quantum Spatial company, has collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Colville study area. This study area is...

2014 PSLC Lidar: City of Redmond

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — In February 2014, Quantum Spatial (QSI) was contracted by the Puget Sound LiDAR Consortium (PSLC) to collect Light Detection and Ranging (LiDAR) data for the City of...

2014 Horry County, South Carolina Lidar

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set is comprised of lidar point cloud data. This project required lidar data to be acquired over Horry County, South Carolina. The total area of the Horry...

Elevation - LIDAR Survey - Roseau County, Minnesota

Data.gov (United States)

Army Corps of Engineers, Department of the Army, Department of Defense — LIDAR Data for Roseau County Minnesota. This project consists of approximately 87 square miles of LIDAR mapping in Roseau County, Minnesota at two sites: area 1,...

2006 Volusia County Florida LiDAR

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This dataset is the lidar data for Volusia County, Florida, approximately 1,432 square miles, acquired in early March of 2006. A total of 143 flight lines of Lidar...

Urban forest topographical mapping using UAV LIDAR

Science.gov (United States)

Putut Ash Shidiq, Iqbal; Wibowo, Adi; Kusratmoko, Eko; Indratmoko, Satria; Ardhianto, Ronni; Prasetyo Nugroho, Budi

2017-12-01

Topographical data is highly needed by many parties, such as government institution, mining companies and agricultural sectors. It is not just about the precision, the acquisition time and data processing are also carefully considered. In relation with forest management, a high accuracy topographic map is necessary for planning, close monitoring and evaluating forest changes. One of the solution to quickly and precisely mapped topography is using remote sensing system. In this study, we test high-resolution data using Light Detection and Ranging (LiDAR) collected from unmanned aerial vehicles (UAV) to map topography and differentiate vegetation classes based on height in urban forest area of University of Indonesia (UI). The semi-automatic and manual classifications were applied to divide point clouds into two main classes, namely ground and vegetation. There were 15,806,380 point clouds obtained during the post-process, in which 2.39% of it were detected as ground.

3D pulsed chaos lidar system.

Science.gov (United States)

Cheng, Chih-Hao; Chen, Chih-Ying; Chen, Jun-Da; Pan, Da-Kung; Ting, Kai-Ting; Lin, Fan-Yi

2018-04-30

We develop an unprecedented 3D pulsed chaos lidar system for potential intelligent machinery applications. Benefited from the random nature of the chaos, conventional CW chaos lidars already possess excellent anti-jamming and anti-interference capabilities and have no range ambiguity. In our system, we further employ self-homodyning and time gating to generate a pulsed homodyned chaos to boost the energy-utilization efficiency. Compared to the original chaos, we show that the pulsed homodyned chaos improves the detection SNR by more than 20 dB. With a sampling rate of just 1.25 GS/s that has a native sampling spacing of 12 cm, we successfully achieve millimeter-level accuracy and precision in ranging. Compared with two commercial lidars tested side-by-side, namely the pulsed Spectroscan and the random-modulation continuous-wave Lidar-lite, the pulsed chaos lidar that is in compliance with the class-1 eye-safe regulation shows significantly better precision and a much longer detection range up to 100 m. Moreover, by employing a 2-axis MEMS mirror for active laser scanning, we also demonstrate real-time 3D imaging with errors of less than 4 mm in depth.

HiRes camera and LIDAR ranging system for the Clementine mission

Energy Technology Data Exchange (ETDEWEB)

Ledebuhr, A.G.; Kordas, J.F.; Lewis, I.T. [and others

1995-04-01

Lawrence Livermore National Laboratory developed a space-qualified High Resolution (HiRes) imaging LIDAR (Light Detection And Ranging) system for use on the DoD Clementine mission. The Clementine mission provided more than 1.7 million images of the moon, earth, and stars, including the first ever complete systematic surface mapping of the moon from the ultra-violet to near-infrared spectral regions. This article describes the Clementine HiRes/LIDAR system, discusses design goals and preliminary estimates of on-orbit performance, and summarizes lessons learned in building and using the sensor. The LIDAR receiver system consists of a High Resolution (HiRes) imaging channel which incorporates an intensified multi-spectral visible camera combined with a Laser ranging channel which uses an avalanche photo-diode for laser pulse detection and timing. The receiver was bore sighted to a light-weight McDonnell-Douglas diode-pumped ND:YAG laser transmitter that emmitted 1.06 {micro}m wavelength pulses of 200 mJ/pulse and 10 ns pulse-width, The LIDAR receiver uses a common F/9.5 Cassegrain telescope assembly. The optical path of the telescope is split using a color-separating beamsplitter. The imaging channel incorporates a filter wheel assembly which spectrally selects the light which is imaged onto a custom 12 mm gated image intensifier fiber-optically-coupled into a 384 x 276 pixel frame transfer CCD FPA. The image intensifier was spectrally sensitive over the 0.4 to 0.8 {micro}m wavelength region. The six-position filter wheel contained 4 narrow spectral filters, one broadband and one blocking filter. At periselene (400 km) the HiRes/LIDAR imaged a 2.8 km swath width at 20-meter resolution. The LIDAR function detected differential signal return with a 40-meter range accuracy, with a maximum range capability of 640 km, limited by the bit counter in the range return counting clock.

Site Classification using Multichannel Channel Analysis of Surface Wave (MASW) method on Soft and Hard Ground

Science.gov (United States)

Ashraf, M. A. M.; Kumar, N. S.; Yusoh, R.; Hazreek, Z. A. M.; Aziman, M.

2018-04-01

Site classification utilizing average shear wave velocity (Vs(30) up to 30 meters depth is a typical parameter. Numerous geophysical methods have been proposed for estimation of shear wave velocity by utilizing assortment of testing configuration, processing method, and inversion algorithm. Multichannel Analysis of Surface Wave (MASW) method is been rehearsed by numerous specialist and professional to geotechnical engineering for local site characterization and classification. This study aims to determine the site classification on soft and hard ground using MASW method. The subsurface classification was made utilizing National Earthquake Hazards Reduction Program (NERHP) and international Building Code (IBC) classification. Two sites are chosen to acquire the shear wave velocity which is in the state of Pulau Pinang for soft soil and Perlis for hard rock. Results recommend that MASW technique can be utilized to spatially calculate the distribution of shear wave velocity (Vs(30)) in soil and rock to characterize areas.

Complex terrain and wind lidars

Energy Technology Data Exchange (ETDEWEB)

Bingoel, F.

2009-08-15

This thesis includes the results of a PhD study about complex terrain and wind lidars. The study mostly focuses on hilly and forested areas. Lidars have been used in combination with cups, sonics and vanes, to reach the desired vertical measurement heights. Several experiments are performed in complex terrain sites and the measurements are compared with two different flow models; a linearised flow model LINCOM and specialised forest model SCADIS. In respect to the lidar performance in complex terrain, the results showed that horizontal wind speed errors measured by a conically scanning lidar can be of the order of 3-4% in moderately-complex terrain and up to 10% in complex terrain. The findings were based on experiments involving collocated lidars and meteorological masts, together with flow calculations over the same terrains. The lidar performance was also simulated with the commercial software WAsP Engineering 2.0 and was well predicted except for some sectors where the terrain is particularly steep. Subsequently, two experiments were performed in forested areas; where the measurements are recorded at a location deep-in forest and at the forest edge. Both sites were modelled with flow models and the comparison of the measurement data with the flow model outputs showed that the mean wind speed calculated by LINCOM model was only reliable between 1 and 2 tree height (h) above canopy. The SCADIS model reported better correlation with the measurements in forest up to approx6h. At the forest edge, LINCOM model was used by allocating a slope half-in half out of the forest based on the suggestions of previous studies. The optimum slope angle was reported as 17 deg.. Thus, a suggestion was made to use WAsP Engineering 2.0 for forest edge modelling with known limitations and the applied method. The SCADIS model worked better than the LINCOM model at the forest edge but the model reported closer results to the measurements at upwind than the downwind and this should be

2012 USGS Lidar: Elwha River (WA)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TASK NAME: Elwha River, WA LiDAR LiDAR Data Acquisition and Processing Production Task USGS Contract No. G10PC00057 Task Order No. G11PD01088 Woolpert Order No....

Lidar data used in the COFIN project

DEFF Research Database (Denmark)

Ejsing Jørgensen, Hans; Nielsen, Morten

1999-01-01

This report presents the Lidar data used in the COFIN project. The Lidar data have been obtained from several ground level dispersion experiments over flat and complex terrain. The method for treating the data and the conditons under which the data wereobtained are described in detail. Finally we...... describe the Tools to extract and visualize the Lidar data. Data, report, and visualisation tools are available on the Risø FTP server....

Modelling the Carbon Stocks Estimation of the Tropical Lowland Dipterocarp Forest Using LIDAR and Remotely Sensed Data

Science.gov (United States)

Zaki, N. A. M.; Latif, Z. A.; Suratman, M. N.; Zainal, M. Z.

2016-06-01

Tropical forest embraces a large stock of carbon in the global carbon cycle and contributes to the enormous amount of above and below ground biomass. The carbon kept in the aboveground living biomass of trees is typically the largest pool and the most directly impacted by the anthropogenic factor such as deforestation and forest degradation. However, fewer studies had been proposed to model the carbon for tropical rain forest and the quantification still remain uncertainties. A multiple linear regression (MLR) is one of the methods to define the relationship between the field inventory measurements and the statistical extracted from the remotely sensed data which is LiDAR and WorldView-3 imagery (WV-3). This paper highlight the model development from fusion of multispectral WV-3 with the LIDAR metrics to model the carbon estimation of the tropical lowland Dipterocarp forest of the study area. The result shown the over segmentation and under segmentation value for this output is 0.19 and 0.11 respectively, thus D-value for the classification is 0.19 which is 81%. Overall, this study produce a significant correlation coefficient (r) between Crown projection area (CPA) and Carbon stocks (CS); height from LiDAR (H_LDR) and Carbon stocks (CS); and Crown projection area (CPA) and height from LiDAR (H_LDR) were shown 0.671, 0.709 and 0.549 respectively. The CPA of the segmentation found to be representative spatially with higher correlation of relationship between diameter at the breast height (DBH) and carbon stocks which is Pearson Correlation p = 0.000 (p using multiple linear regression method. The study concluded that the integration of WV-3 imagery with the CHM raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the Lowland Dipterocarp forest.

INTEGRATION OF HETEROGENOUS DIGITAL SURFACE MODELS

Directory of Open Access Journals (Sweden)

R. Boesch

2012-08-01

Full Text Available The application of extended digital surface models often reveals, that despite an acceptable global accuracy for a given dataset, the local accuracy of the model can vary in a wide range. For high resolution applications which cover the spatial extent of a whole country, this can be a major drawback. Within the Swiss National Forest Inventory (NFI, two digital surface models are available, one derived from LiDAR point data and the other from aerial images. Automatic photogrammetric image matching with ADS80 aerial infrared images with 25cm and 50cm resolution is used to generate a surface model (ADS-DSM with 1m resolution covering whole switzerland (approx. 41000 km2. The spatially corresponding LiDAR dataset has a global point density of 0.5 points per m2 and is mainly used in applications as interpolated grid with 2m resolution (LiDAR-DSM. Although both surface models seem to offer a comparable accuracy from a global view, local analysis shows significant differences. Both datasets have been acquired over several years. Concerning LiDAR-DSM, different flight patterns and inconsistent quality control result in a significantly varying point density. The image acquisition of the ADS-DSM is also stretched over several years and the model generation is hampered by clouds, varying illumination and shadow effects. Nevertheless many classification and feature extraction applications requiring high resolution data depend on the local accuracy of the used surface model, therefore precise knowledge of the local data quality is essential. The commercial photogrammetric software NGATE (part of SOCET SET generates the image based surface model (ADS-DSM and delivers also a map with figures of merit (FOM of the matching process for each calculated height pixel. The FOM-map contains matching codes like high slope, excessive shift or low correlation. For the generation of the LiDAR-DSM only first- and last-pulse data was available. Therefore only the point

Deploying scanning lidars at coastal sites

DEFF Research Database (Denmark)

Courtney, Michael; Simon, Elliot

that the most desirable sites are away from sand dunes and with some significant elevation above the sea surface, such as at the top of a cliff. Coastal planning restrictions in Denmark are quite restrictive and it was important to allow sufficient time to obtain permission from the relevant authorities....... At the same time, with our particular application, the authorities and land owners were quite favourably inclined to give permission to temporary installations in support of wind energy research. The report concludes with the final positions and a pictorial description of the three RUNE scanning lidars....

Leveraging Open Standards and Technologies to Enhance Community Access to Earth Science Lidar Data

Science.gov (United States)

Crosby, C. J.; Nandigam, V.; Krishnan, S.; Cowart, C.; Baru, C.; Arrowsmith, R.

2011-12-01

Lidar (Light Detection and Ranging) data, collected from space, airborne and terrestrial platforms, have emerged as an invaluable tool for a variety of Earth science applications ranging from ice sheet monitoring to modeling of earth surface processes. However, lidar present a unique suite of challenges from the perspective of building cyberinfrastructure systems that enable the scientific community to access these valuable research datasets. Lidar data are typically characterized by millions to billions of individual measurements of x,y,z position plus attributes; these "raw" data are also often accompanied by derived raster products and are frequently terabytes in size. As a relatively new and rapidly evolving data collection technology, relevant open data standards and software projects are immature compared to those for other remote sensing platforms. The NSF-funded OpenTopography Facility project has developed an online lidar data access and processing system that co-locates data with on-demand processing tools to enable users to access both raw point cloud data as well as custom derived products and visualizations. OpenTopography is built on a Service Oriented Architecture (SOA) in which applications and data resources are deployed as standards compliant (XML and SOAP) Web services with the open source Opal Toolkit. To develop the underlying applications for data access, filtering and conversion, and various processing tasks, OpenTopography has heavily leveraged existing open source software efforts for both lidar and raster data. Operating on the de facto LAS binary point cloud format (maintained by ASPRS), open source libLAS and LASlib libraries provide OpenTopography data ingestion, query and translation capabilities. Similarly, raster data manipulation is performed through a suite of services built on the Geospatial Data Abstraction Library (GDAL). OpenTopography has also developed our own algorithm for high-performance gridding of lidar point cloud data

Optimizing Lidar Scanning Strategies for Wind Energy Measurements (Invited)

Science.gov (United States)

Newman, J. F.; Bonin, T. A.; Klein, P.; Wharton, S.; Chilson, P. B.

2013-12-01

Environmental concerns and rising fossil fuel prices have prompted rapid development in the renewable energy sector. Wind energy, in particular, has become increasingly popular in the United States. However, the intermittency of available wind energy makes it difficult to integrate wind energy into the power grid. Thus, the expansion and successful implementation of wind energy requires accurate wind resource assessments and wind power forecasts. The actual power produced by a turbine is affected by the wind speeds and turbulence levels experienced across the turbine rotor disk. Because of the range of measurement heights required for wind power estimation, remote sensing devices (e.g., lidar) are ideally suited for these purposes. However, the volume averaging inherent in remote sensing technology produces turbulence estimates that are different from those estimated by a sonic anemometer mounted on a standard meteorological tower. In addition, most lidars intended for wind energy purposes utilize a standard Doppler beam-swinging or Velocity-Azimuth Display technique to estimate the three-dimensional wind vector. These scanning strategies are ideal for measuring mean wind speeds but are likely inadequate for measuring turbulence. In order to examine the impact of different lidar scanning strategies on turbulence measurements, a WindCube lidar, a scanning Halo lidar, and a scanning Galion lidar were deployed at the Southern Great Plains Atmospheric Radiation Measurement (ARM) site in Summer 2013. Existing instrumentation at the ARM site, including a 60-m meteorological tower and an additional scanning Halo lidar, were used in conjunction with the deployed lidars to evaluate several user-defined scanning strategies. For part of the experiment, all three scanning lidars were pointed at approximately the same point in space and a tri-Doppler analysis was completed to calculate the three-dimensional wind vector every 1 second. In another part of the experiment, one of

Infrastructure Investment Protection with LiDAR

Science.gov (United States)

2012-10-15

The primary goal of this research effort was to explore the wide variety of uses of LiDAR technology and to evaluate their : applicability to NCDOT practices. NCDOT can use this information about LiDAR in determining how and when the : technology can...

2013 NRCS-USGS Lidar: Lauderdale (MS)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TASK NAME:NRCS LAUDERDALE MS 0.7M NPS LIDAR. LiDAR Data Acquisition and Processing Production Task. USGS Contract No. G10PC00057. Task Order No. G12PD000125 Woolpert...

2014 USGS/NRCS Lidar: Central MS

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TASK NAME: USGS-NRCS Laurel MS 0.7m NPS LIDAR Lidar Data Acquisition and Processing Production Task USGS Contract No. G10PC00057 Task Order No. G13PD01086 Woolpert...

Prediction of topographic and bathymetric measurement performance of airborne low-SNR lidar systems

Science.gov (United States)

Cossio, Tristan

Low signal-to-noise ratio (LSNR) lidar (light detection and ranging) is an alternative paradigm to traditional lidar based on the detection of return signals at the single photoelectron level. The objective of this work was to predict low altitude (600 m) LSNR lidar system performance with regards to elevation measurement and target detection capability in topographic (dry land) and bathymetric (shallow water) scenarios. A modular numerical sensor model has been developed to provide data for further analysis due to the dearth of operational low altitude LSNR lidar systems. This simulator tool is described in detail, with consideration given to atmospheric effects, surface conditions, and the effects of laser phenomenology. Measurement performance analysis of the simulated topographic data showed results comparable to commercially available lidar systems, with a standard deviation of less than 12 cm for calculated elevation values. Bathymetric results, although dependent largely on water turbidity, were indicative of meter-scale horizontal data spacing for sea depths less than 5 m. The high prevalence of noise in LSNR lidar data introduces significant difficulties in data analysis. Novel algorithms to reduce noise are described, with particular focus on their integration into an end-to-end target detection classifier for both dry and submerged targets (cube blocks, 0.5 m to 1.0 m on a side). The key characteristic exploited to discriminate signal and noise is the temporal coherence of signal events versus the random distribution of noise events. Target detection performance over dry earth was observed to be robust, reliably detecting over 90% of targets with a minimal false alarm rate. Comparable results were observed in waters of high clarity, where the investigated system was generally able to detect more than 70% of targets to a depth of 5 m. The results of the study show that CATS, the University of Florida's LSNR lidar prototype, is capable of high fidelity

Lidar-based mapping of flood control levees in south Louisiana

Science.gov (United States)

Thatcher, Cindy A.; Lim, Samsung; Palaseanu-Lovejoy, Monica; Danielson, Jeffrey J.; Kimbrow, Dustin R.

2016-01-01

Flood protection in south Louisiana is largely dependent on earthen levees, and in the aftermath of Hurricane Katrina the state’s levee system has received intense scrutiny. Accurate elevation data along the levees are critical to local levee district managers responsible for monitoring and maintaining the extensive system of non-federal levees in coastal Louisiana. In 2012, high resolution airborne lidar data were acquired over levees in Lafourche Parish, Louisiana, and a mobile terrestrial lidar survey was conducted for selected levee segments using a terrestrial lidar scanner mounted on a truck. The mobile terrestrial lidar data were collected to test the feasibility of using this relatively new technology to map flood control levees and to compare the accuracy of the terrestrial and airborne lidar. Metrics assessing levee geometry derived from the two lidar surveys are also presented as an efficient, comprehensive method to quantify levee height and stability. The vertical root mean square error values of the terrestrial lidar and airborne lidar digital-derived digital terrain models were 0.038 m and 0.055 m, respectively. The comparison of levee metrics derived from the airborne and terrestrial lidar-based digital terrain models showed that both types of lidar yielded similar results, indicating that either or both surveying techniques could be used to monitor geomorphic change over time. Because airborne lidar is costly, many parts of the USA and other countries have never been mapped with airborne lidar, and repeat surveys are often not available for change detection studies. Terrestrial lidar provides a practical option for conducting repeat surveys of levees and other terrain features that cover a relatively small area, such as eroding cliffs or stream banks, and dunes.

Utilizing LiDAR Datasets From Experimental Watersheds to Advance Ecohydrological Understanding in Seasonally Snow-Covered Forests

Science.gov (United States)

Harpold, A. A.; Broxton, P. D.; Guo, Q.; Barlage, M. J.; Gochis, D. J.

2014-12-01

The Western U.S. is strongly reliant on snowmelt from forested areas for ecosystem services and downstream populations. The ability to manage water resources from snow-covered forests faces major challenges from drought, disturbance, and regional changes in climate. An exciting avenue for improving ecohydrological process understanding is Light Detection and Ranging (LiDAR) because the technology simultaneously observes topography, forest properties, and snow/ice at high-resolution (100 km2). The availability and quality of LiDAR datasets is increasing rapidly, however they remain under-utilized for process-based ecohydrology investigations. This presentation will illustrate how LiDAR datasets from the Critical Zone Observatory (CZO) network have been applied to advance ecohydrological understanding through direct empirical analysis, as well as model parameterization and verification. Direct analysis of the datasets has proved fruitful for pre- and post-disturbance snow distribution estimates and interpreting in-situ snow depth measurements across sites. In addition, we illustrate the potential value of LiDAR to parameterize and verify of physical models with two examples. First, we use LiDAR to parameterize a land surface model, Noah multi-parameterization (Noah-MP), to investigate the sensitivity of modeled water and energy fluxes to high-resolution forest information. Second, we present a Snow Physics and Laser Mapping (SnowPALM) model that is parameterized with LiDAR information at its native 1-m scale. Both modeling studies demonstrate the value of LiDAR for representing processes with greater fidelity. More importantly, the increased model fidelity led to different estimates of water and energy fluxes at larger, watershed scales. Creating a network of experimental watersheds with LiDAR datasets offers the potential to test theories and models in previously unexplored ways.

Telescope aperture optimization for spacebased coherent wind lidar

Science.gov (United States)

Ge, Xian-ying; Zhu, Jun; Cao, Qipeng; Zhang, Yinchao; Yin, Huan; Dong, Xiaojing; Wang, Chao; Zhang, Yongchao; Zhang, Ning

2015-08-01

Many studies have indicated that the optimum measurement approach for winds from space is a pulsed coherent wind lidar, which is an active remote sensing tool with the characteristics that high spatial and temporal resolutions, real-time detection, high mobility, facilitated control and so on. Because of the significant eye safety, efficiency, size, and lifetime advantage, 2μm wavelength solid-state laser lidar systems have attracted much attention in spacebased wind lidar plans. In this paper, the theory of coherent detection is presented and a 2μm wavelength solid-state laser lidar system is introduced, then the ideal aperture is calculated from signal-to-noise(SNR) view at orbit 400km. However, considering real application, even if the lidar hardware is perfectly aligned, the directional jitter of laser beam, the attitude change of the lidar in the long round trip time of the light from the atmosphere and other factors can bring misalignment angle. So the influence of misalignment angle is considered and calculated, and the optimum telescope diameter(0.45m) is obtained as the misalignment angle is 4 μrad. By the analysis of the optimum aperture required for spacebased coherent wind lidar system, we try to present the design guidance for the telescope.

Applications of KHZ-CW Lidar in Ecological Entomology

Science.gov (United States)

Malmqvist, Elin; Brydegaard, Mikkel

2016-06-01

The benefits of kHz lidar in ecological entomology are explained. Results from kHz-measurements on insects, carried out with a CW-lidar system, employing the Scheimpflug principle to obtain range resolution, are presented. A method to extract insect events and analyze the large amount of lidar data is also described.

INTERACT-II campaign:comparison of commercial lidars and ceilometers with advanced multi-wavelength Raman lidars

Science.gov (United States)

Rosoldi, Marco; Madonna, Fabio; Pappalardo, Gelsomina; Vande Hey, Joshua; Zheng, Yunhui; Vaisala Team

2017-04-01

Knowledge of aerosol spatio-temporal distribution in troposphere is essential for the study of climate and air quality. For this purpose, global scale high resolution continuous measurements of tropospheric aerosols are needed. Global coverage high resolution networks of ground-based low-cost and low-maintenance remote sensing instruments, such as commercial automatic lidars and ceilometers, can strongly contribute to this scientific mission. Therefore, it is very interesting for scientific community to understand to which extent these instruments are able to provide reliable aerosol measurements and fill in the geographical gaps of existing networks of the advanced lidars, like EARLINET (European Aerosol Research LIdar NETwork). The INTERACT-II (INTERcomparison of Aerosol and Cloud Tracking) campaign, carried out at CIAO (CNR-IMAA Atmospheric Observatory) in Tito Scalo, Potenza, Italy (760m a.s.l., 40.60°N, 15.72°E), aims to evaluate the performances of commercial automatic lidars and ceilometers for tropospheric aerosol profiling. The campaign has been performed in the period from July 2016 to January 2017 in the framework of ACTRIS-2 (Aerosol Clouds Trace gases Research InfraStructure) H2020 research infrastructure project. Besides the commercial ceilometers operational at CIAO (VAISALA CT25K and Luftt CHM15k), the performance of a CL51 VAISALA ceilometer, a Campbell CS135 ceilometer and a mini-Micro Pulse Lidar (MPL) have been assessed using the EARLINET multi-wavelengths Raman lidars operative at CIAO as reference. Following a similar approach used in the first INTERACT campaign (Madonna et al., AMT 2015), attenuated backscatter coefficient profiles and signals obtained from all the instruments have been compared, over a vertical resolution of 60 meters and a temporal integration ranging between 1 and 2 hours, depending on the observed atmospheric scenario. CIAO lidars signals have been processed using the EARLINET Single Calculus Chain (SCC) also with the

Advances in High Energy Solid-State Pulsed 2-Micron Lidar Development for Ground and Airborne Wind, Water Vapor and CO2 Measurements

Science.gov (United States)

Singh, Upendra N.; Yu, Jirong; Petros, Mulugeta; Refaat, Tamer; Kavaya, Michael J.; Remus, Ruben

2015-01-01

NASA Langley Research Center has a long history of developing 2-micron lasers. From fundamental spectroscopy research, theoretical prediction of new materials, laser demonstration and engineering of lidar systems, it has been a very successful program spanning around two decades. Successful development of 2-micron lasers has led to development of a state-of-the-art compact lidar transceiver for a pulsed coherent Doppler lidar system for wind measurement with an unprecedented laser pulse energy of 250 millijoules in a rugged package. This high pulse energy is produced by a Ho:Tm:LuLiF laser with an optical amplifier. While the lidar is meant for use as an airborne instrument, ground-based tests were carried out to characterize performance of the lidar. Atmospheric measurements will be presented, showing the lidar's capability for wind measurement in the atmospheric boundary layer and free troposphere. Lidar wind measurements are compared to a balloon sonde, showing good agreement between the two sensors. Similar architecture has been used to develop a high energy, Ho:Tm:YLF double-pulsed 2-micron Integrated Differential Absorption Lidar (IPDA) instrument based on direct detection technique that provides atmospheric column CO2 measurements. This instrument has been successfully used to measure atmospheric CO2 column density initially from a ground mobile lidar trailer, and then it was integrated on B-200 plane and 20 hours of flight measurement were made from an altitude ranging 1500 meters to 8000 meters. These measurements were compared to in-situ measurements and National Oceanic and Atmospheric Administration (NOAA) airborne flask measurement to derive the dry mixing ratio of the column CO2 by reflecting the signal by various reflecting surfaces such as land, vegetation, ocean surface, snow and sand. The lidar measurements when compared showed a very agreement with in-situ and airborne flask measurement. NASA Langley Research Center is currently developing a

Software design of control system of CCD side-scatter lidar

Science.gov (United States)

Kuang, Zhiqiang; Liu, Dong; Deng, Qian; Zhang, Zhanye; Wang, Zhenzhu; Yu, Siqi; Tao, Zongming; Xie, Chenbo; Wang, Yingjian

2018-03-01

Because of the existence of blind zone and transition zone, the application of backscattering lidar in near-ground is limited. The side-scatter lidar equipped with the Charge Coupled Devices (CCD) can separate the transmitting and receiving devices to avoid the impact of the geometric factors which is exited in the backscattering lidar and, detect the more precise near-ground aerosol signals continuously. Theories of CCD side-scatter lidar and the design of control system are introduced. The visible control of laser and CCD and automatic data processing method of the side-scatter lidar are developed by using the software of Visual C #. The results which are compared with the calibration of the atmospheric aerosol lidar data show that signals from the CCD side- scatter lidar are convincible.

An Aerosol Extinction-to-Backscatter Ratio Database Derived from the NASA Micro-Pulse Lidar Network: Applications for Space-based Lidar Observations

Science.gov (United States)

Welton, Ellsworth J.; Campbell, James R.; Spinhime, James D.; Berkoff, Timothy A.; Holben, Brent; Tsay, Si-Chee; Bucholtz, Anthony

2004-01-01

Backscatter lidar signals are a function of both backscatter and extinction. Hence, these lidar observations alone cannot separate the two quantities. The aerosol extinction-to-backscatter ratio, S, is the key parameter required to accurately retrieve extinction and optical depth from backscatter lidar observations of aerosol layers. S is commonly defined as 4*pi divided by the product of the single scatter albedo and the phase function at 180-degree scattering angle. Values of S for different aerosol types are not well known, and are even more difficult to determine when aerosols become mixed. Here we present a new lidar-sunphotometer S database derived from Observations of the NASA Micro-Pulse Lidar Network (MPLNET). MPLNET is a growing worldwide network of eye-safe backscatter lidars co-located with sunphotometers in the NASA Aerosol Robotic Network (AERONET). Values of S for different aerosol species and geographic regions will be presented. A framework for constructing an S look-up table will be shown. Look-up tables of S are needed to calculate aerosol extinction and optical depth from space-based lidar observations in the absence of co-located AOD data. Applications for using the new S look-up table to reprocess aerosol products from NASA's Geoscience Laser Altimeter System (GLAS) will be discussed.

Multi-wavelength Ocean Profiling and Atmospheric Lidar

Data.gov (United States)

National Aeronautics and Space Administration — We propose to build and demonstrate the world's first multi-wavelength ocean-profiling high spectral resolution lidar (HSRL). The lidar will provide profiles of...

2012 Oregon Lidar Consortium (OLC) Lidar DEM: Keno (OR)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Watershed Sciences, Inc. (WSI) collected Light Detection and Ranging (LiDAR) data of the Oregon Keno Study Area for the Oregon Department of Geology and Mineral...

Shipborne LiDAR system for coastal change monitoring

Science.gov (United States)

Kim, chang hwan; Park, chang hong; Kim, hyun wook; hyuck Kim, won; Lee, myoung hoon; Park, hyeon yeong

2016-04-01

Coastal areas, used as human utilization areas like leisure space, medical care, ports and power plants, etc., are regions that are continuously changing and interconnected with oceans and land and the sea level has risen by about 8cm (1.9mm / yr) due to global warming from 1964 year to 2006 year in Korea. Coastal erosion due to sea-level rise has caused the problem of marine ecosystems and loss of tourism resources, etc. Regular monitoring of coastal erosion is essential at key locations with such volatility. But the survey method of land mobile LiDAR (light detection and ranging) system has much time consuming and many restrictions. For effective monitoring beach erosion, KIOST (Korea Institute of Ocean Science & Technology) has constructed a shipborne mobile LiDAR system. The shipborne mobile LiDAR system comprised a land mobile LiDAR (RIEGL LMS-420i), an INS (inertial navigation system, MAGUS Inertial+), a RTKGPS (LEICA GS15 GS25), and a fixed platform. The shipborne mobile LiDAR system is much more effective than a land mobile LiDAR system in the measuring of fore shore areas without shadow zone. Because the vessel with the shipborne mobile LiDAR system is continuously moved along the shoreline, it is possible to efficiently survey a large area in a relatively short time. Effective monitoring of the changes using the constructed shipborne mobile LiDAR system for seriously eroded coastal areas will be able to contribute to coastal erosion management and response.

The Effect of Wind-Turbine Wakes on Summertime US Midwest Atmospheric Wind Profiles as Observed with Ground-Based Doppler Lidar

Science.gov (United States)

Rhodes, Michael E.; Lundquist, Julie K.

2013-07-01

We examine the influence of a modern multi-megawatt wind turbine on wind and turbulence profiles three rotor diameters (D) downwind of the turbine. Light detection and ranging (lidar) wind-profile observations were collected during summer 2011 in an operating wind farm in central Iowa at 20-m vertical intervals from 40 to 220 m above the surface. After a calibration period during which two lidars were operated next to each other, one lidar was located approximately 2D directly south of a wind turbine; the other lidar was moved approximately 3D north of the same wind turbine. Data from the two lidars during southerly flow conditions enabled the simultaneous capture of inflow and wake conditions. The inflow wind and turbulence profiles exhibit strong variability with atmospheric stability: daytime profiles are well-mixed with little shear and strong turbulence, while nighttime profiles exhibit minimal turbulence and considerable shear across the rotor disk region and above. Consistent with the observations available from other studies and with wind-tunnel and large-eddy simulation studies, measurable reductions in wake wind-speeds occur at heights spanning the wind turbine rotor (43-117 m), and turbulent quantities increase in the wake. In generalizing these results as a function of inflow wind speed, we find the wind-speed deficit in the wake is largest at hub height or just above, and the maximum deficit occurs when wind speeds are below the rated speed for the turbine. Similarly, the maximum enhancement of turbulence kinetic energy and turbulence intensity occurs at hub height, although observations at the top of the rotor disk do not allow assessment of turbulence in that region. The wind shear below turbine hub height (quantified here with the power-law coefficient) is found to be a useful parameter to identify whether a downwind lidar observes turbine wake or free-flow conditions. These field observations provide data for validating turbine-wake models and wind

Quantifying soil carbon loss and uncertainty from a peatland wildfire using multi-temporal LiDAR

Science.gov (United States)

Reddy, Ashwan D.; Hawbaker, Todd J.; Wurster, F.; Zhu, Zhiliang; Ward, S.; Newcomb, Doug; Murray, R.

2015-01-01

Peatlands are a major reservoir of global soil carbon, yet account for just 3% of global land cover. Human impacts like draining can hinder the ability of peatlands to sequester carbon and expose their soils to fire under dry conditions. Estimating soil carbon loss from peat fires can be challenging due to uncertainty about pre-fire surface elevations. This study uses multi-temporal LiDAR to obtain pre- and post-fire elevations and estimate soil carbon loss caused by the 2011 Lateral West fire in the Great Dismal Swamp National Wildlife Refuge, VA, USA. We also determine how LiDAR elevation error affects uncertainty in our carbon loss estimate by randomly perturbing the LiDAR point elevations and recalculating elevation change and carbon loss, iterating this process 1000 times. We calculated a total loss using LiDAR of 1.10 Tg C across the 25 km2 burned area. The fire burned an average of 47 cm deep, equivalent to 44 kg C/m2, a value larger than the 1997 Indonesian peat fires (29 kg C/m2). Carbon loss via the First-Order Fire Effects Model (FOFEM) was estimated to be 0.06 Tg C. Propagating the LiDAR elevation error to the carbon loss estimates, we calculated a standard deviation of 0.00009 Tg C, equivalent to 0.008% of total carbon loss. We conclude that LiDAR elevation error is not a significant contributor to uncertainty in soil carbon loss under severe fire conditions with substantial peat consumption. However, uncertainties may be more substantial when soil elevation loss is of a similar or smaller magnitude than the reported LiDAR error.

Characterization and classification of vegetation canopy structure and distribution within the Great Smoky Mountains National Park using LiDAR

Science.gov (United States)

Jitendra Kumar; Jon Weiner; William W. Hargrove; Steve Norman; Forrest M. Hoffman; Doug Newcomb

2016-01-01

Vegetation canopy structure is a critically important habitat characteristic for many threatened and endangered birds and other animal species, and it is key information needed by forest and wildlife managers for monitoring and managing forest resources, conservation planning and fostering biodiversity. Advances in Light Detection and Ranging (LiDAR) technologies have...

Recruiting Conventional Tree Architecture Models into State-of-the-Art LiDAR Mapping for Investigating Tree Growth Habits in Structure.

Science.gov (United States)

Lin, Yi; Jiang, Miao; Pellikka, Petri; Heiskanen, Janne

2018-01-01

Mensuration of tree growth habits is of considerable importance for understanding forest ecosystem processes and forest biophysical responses to climate changes. However, the complexity of tree crown morphology that is typically formed after many years of growth tends to render it a non-trivial task, even for the state-of-the-art 3D forest mapping technology-light detection and ranging (LiDAR). Fortunately, botanists have deduced the large structural diversity of tree forms into only a limited number of tree architecture models, which can present a-priori knowledge about tree structure, growth, and other attributes for different species. This study attempted to recruit Hallé architecture models (HAMs) into LiDAR mapping to investigate tree growth habits in structure. First, following the HAM-characterized tree structure organization rules, we run the kernel procedure of tree species classification based on the LiDAR-collected point clouds using a support vector machine classifier in the leave-one-out-for-cross-validation mode. Then, the HAM corresponding to each of the classified tree species was identified based on expert knowledge, assisted by the comparison of the LiDAR-derived feature parameters. Next, the tree growth habits in structure for each of the tree species were derived from the determined HAM. In the case of four tree species growing in the boreal environment, the tests indicated that the classification accuracy reached 85.0%, and their growth habits could be derived by qualitative and quantitative means. Overall, the strategy of recruiting conventional HAMs into LiDAR mapping for investigating tree growth habits in structure was validated, thereby paving a new way for efficiently reflecting tree growth habits and projecting forest structure dynamics.

Systematic Relationships Between Lidar Observables and Sizes And Mineral Composition Of Dust Aerosols

Science.gov (United States)

Van Diedenhoven, Bastiaan; Stangl, Alexander; Perlwitz, Jan; Fridlind, Ann M.; Chowdhary, Jacek; Cairns, Brian

2015-01-01

The physical and chemical properties of soil dust aerosol particles fundamentally affect their interaction with climate, including shortwave absorption and radiative forcing, nucleation of cloud droplets and ice crystals, heterogeneous formation of sulfates and nitrates on the surface of dust particles, and atmospheric processing of iron into bioavailable forms that increase the productivity of marine phytoplankton. Lidar measurements, such as extinction-to-backscatter, color and depolarization ratios, are frequently used to distinguish between aerosol types with different physical and chemical properties. The chemical composition of aerosol particles determines their complex refractive index, hence affecting their backscattering properties. Here we present a study on how dust aerosol backscattering and depolarization properties at wavelengths of 355, 532 and 1064 nm are related to size and complex refractive index, which varies with the mineral composition of the dust. Dust aerosols are represented by collections of spheroids with a range of prolate and oblate aspect ratios and their optical properties are obtained using T-matrix calculations. We find simple, systematic relationships between lidar observables and the dust size and complex refractive index that may aid the use of space-based or airborne lidars for direct retrieval of dust properties or for the evaluation of chemical transport models using forward simulated lidar variables. In addition, we present first results on the spatial variation of forward-simulated lidar variables based on a dust model that accounts for the atmospheric cycle of eight different mineral types plus internal mixtures of seven mineral types with iron oxides, which was recently implemented in the NASA GISS Earth System ModelE2.

2010 ARRA Lidar: 4 Southeast Counties (MI)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — TASK NAME: Southeast Michigan LiDAR LiDAR Data Acquisition and Processing Production Task- Monroe, St. Clair, Macomb, and Livingston Counties SEMCOG CONTRACT:...