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Sample records for dar bimaran-e mobtala

  1. Eleanor Roosevelt Resigns from the DAR: A Study in Conscience.

    ERIC Educational Resources Information Center

    Freeman, Elsie T.; And Others

    1984-01-01

    Because the Daughters of the American Revolution's (DAR) Black exclusion rule prevented Black singer Marion Anderson from performing in the DAR auditorium in 1939, Eleanor Roosevelt resigned from the organization. Primary source materials regarding this incident and learning activities for secondary level students are presented. (RM)

  2. Using LiDAR to characterize logjams in lowland rivers

    NASA Astrophysics Data System (ADS)

    Abalharth, Mahdi; Hassan, Marwan A.; Klinkenberg, Brian; Leung, Vivian; McCleary, Richard

    2015-10-01

    Logjams significantly influence watershed hydrology, flow regime, channel morphology and stability, and processes in lowland rivers. Consequently, logjams play a major role in the existence and conservation of the riparian and aquatic ecosystems along major waterways. In this paper, we attempt to detect and quantify logjams in river channels using LiDAR technology in conjunction with traditional fieldwork. To the best of our knowledge, LiDAR-based analysis has not been used to characterize logjams in streams. Overall, when applied in a lowland river environment, LiDAR-based analysis demonstrates a comprehensive solution for detecting logjams in relation to the fieldwork, with a low rate of omission. A filtered approach predicted the presence of 95% of fieldwork-reported logjams (a 5% rate of omission), but also identified six logjams not identified in the field (a 10% rate of commission). A nonfiltered approach identified 87% of field-reported logjams, producing a 13% rate of omission and a 6.7% rate of commission. Dimension measurements were more consistent in the filtered LiDAR approach, showing 53%, 34%, and 90% of R2 improvements for the length, width, and height, respectively, over the unfiltered LiDAR values. As vegetation cover hindered accurate delineation of logjam boundaries by LiDAR, field and LiDAR measurements of nonvegetation-obstructed logjams were more highly correlated than the field and LiDAR measurements of partially and completely vegetation-obstructed logjams.

  3. Lava flow texture LiDAR signatures

    NASA Astrophysics Data System (ADS)

    Whelley, P.; Garry, W. B.; Scheidt, S. P.; Irwin, R. P., III; Fox, J.; Bleacher, J. E.; Hamilton, C. W.

    2014-12-01

    High-resolution point clouds and digital elevation models (DEMs) are used to investigate lava textures on the Big Island of Hawaii. An experienced geologist can distinguish fresh or degraded lava textures (e.g., blocky, a'a and pahoehoe) visually in the field. Lava texture depends significantly on eruption conditions, and it is therefore instructive, if accurately determined. In places where field investigations are prohibitive (e.g., Mercury, Venus, the Moon, Mars, Io and remote regions on Earth) lava texture must be assessed from remote sensing data. A reliable method for differentiating lava textures in remote sensing data remains elusive. We present preliminary results comparing properties of lava textures observed in airborne and terrestrial Light Detection and Ranging (LiDAR) data. Airborne data, in this study, were collected in 2011 by Airborne 1 Corporation and have a ~1m point spacing. The authors collected the terrestrial data during a May 2014 field season. The terrestrial scans have a heterogeneous point density. Points close to the scanner are 1 mm apart while 200 m in the distance points are 10 cm apart. Both platforms offer advantages and disadvantages beyond the differences in scale. Terrestrial scans are a quantitative representation of what a geologist sees "on the ground". Airborne scans are a point of view routinely imaged by other remote sensing tools, and can therefore be quickly compared to complimentary data sets (e.g., spectral scans or image data). Preliminary results indicate that LiDAR-derived surface roughness, from both platforms, is useful for differentiating lava textures, but at different spatial scales. As all lava types are quite rough, it is not simply roughness that is the most advantageous parameter; rather patterns in surface roughness can be used to differentiate lava surfaces of varied textures. This work will lead to faster and more reliable volcanic mapping efforts for planetary exploration as well as terrestrial

  4. Processing LiDAR Data to Predict Natural Hazards

    NASA Technical Reports Server (NTRS)

    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.

  5. Exploring tree species signature using waveform LiDAR data

    NASA Astrophysics Data System (ADS)

    Zhou, T.; Popescu, S. C.; Krause, K.

    2015-12-01

    Successful classification of tree species with waveform LiDAR data would be of considerable value to estimate the biomass stocks and changes in forests. Current approaches emphasize converting the full waveform data into discrete points to get larger amount of parameters and identify tree species using several discrete-points variables. However, ignores intensity values and waveform shapes which convey important structural characteristics. The overall goal of this study was to employ the intensity and waveform shape of individual tree as the waveform signature to detect tree species. The data was acquired by the National Ecological Observatory Network (NEON) within 250*250 m study area located in San Joaquin Experimental Range. Specific objectives were to: (1) segment individual trees using the smoothed canopy height model (CHM) derived from discrete LiDAR points; (2) link waveform LiDAR with above individual tree boundaries to derive sample signatures of three tree species and use these signatures to discriminate tree species in a large area; and (3) compare tree species detection results from discrete LiDAR data and waveform LiDAR data. An overall accuracy of the segmented individual tree of more than 80% was obtained. The preliminary results show that compared with the discrete LiDAR data, the waveform LiDAR signature has a higher potential for accurate tree species classification.

  6. Tensor Modeling Based for Airborne LiDAR Data Classification

    NASA Astrophysics Data System (ADS)

    Li, N.; Liu, C.; Pfeifer, N.; Yin, J. F.; Liao, Z. Y.; Zhou, Y.

    2016-06-01

    Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the "raw" data attributes. Then, the feature rasters of LiDAR data are stored as a tensor, and tensor decomposition is used to select component features. This tensor representation could keep the initial spatial structure and insure the consideration of the neighborhood. Based on a small number of component features a k nearest neighborhood classification is applied.

  7. Shipborne LiDAR system for coastal change monitoring

    NASA Astrophysics Data System (ADS)

    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.

  8. Modelling rating curves using remotely sensed LiDAR data

    USGS Publications Warehouse

    Nathanson, Marcus; Kean, Jason W.; Grabs, Thomas J.; Seibert, Jan; Laudon, Hjalmar; Lyon, Steve W.

    2012-01-01

    Accurate stream discharge measurements are important for many hydrological studies. In remote locations, however, it is often difficult to obtain stream flow information because of the difficulty in making the discharge measurements necessary to define stage-discharge relationships (rating curves). This study investigates the feasibility of defining rating curves by using a fluid mechanics-based model constrained with topographic data from an airborne LiDAR scanning. The study was carried out for an 8m-wide channel in the boreal landscape of northern Sweden. LiDAR data were used to define channel geometry above a low flow water surface along the 90-m surveyed reach. The channel topography below the water surface was estimated using the simple assumption of a flat streambed. The roughness for the modelled reach was back calculated from a single measurment of discharge. The topographic and roughness information was then used to model a rating curve. To isolate the potential influence of the flat bed assumption, a 'hybrid model' rating curve was developed on the basis of data combined from the LiDAR scan and a detailed ground survey. Whereas this hybrid model rating curve was in agreement with the direct measurements of discharge, the LiDAR model rating curve was equally in agreement with the medium and high flow measurements based on confidence intervals calculated from the direct measurements. The discrepancy between the LiDAR model rating curve and the low flow measurements was likely due to reduced roughness associated with unresolved submerged bed topography. Scanning during periods of low flow can help minimize this deficiency. These results suggest that combined ground surveys and LiDAR scans or multifrequency LiDAR scans that see 'below' the water surface (bathymetric LiDAR) could be useful in generating data needed to run such a fluid mechanics-based model. This opens a realm of possibility to remotely sense and monitor stream flows in channels in remote

  9. Georeferenced LiDAR 3D Vine Plantation Map Generation

    PubMed Central

    Llorens, Jordi; Gil, Emilio; Llop, Jordi; Queraltó, Meritxell

    2011-01-01

    The use of electronic devices for canopy characterization has recently been widely discussed. Among such devices, LiDAR sensors appear to be the most accurate and precise. Information obtained with LiDAR sensors during reading while driving a tractor along a crop row can be managed and transformed into canopy density maps by evaluating the frequency of LiDAR returns. This paper describes a proposed methodology to obtain a georeferenced canopy map by combining the information obtained with LiDAR with that generated using a GPS receiver installed on top of a tractor. Data regarding the velocity of LiDAR measurements and UTM coordinates of each measured point on the canopy were obtained by applying the proposed transformation process. The process allows overlap of the canopy density map generated with the image of the intended measured area using Google Earth®, providing accurate information about the canopy distribution and/or location of damage along the rows. This methodology was applied and tested on different vine varieties and crop stages in two important vine production areas in Spain. The results indicate that the georeferenced information obtained with LiDAR sensors appears to be an interesting tool with the potential to improve crop management processes. PMID:22163952

  10. Georeferenced LiDAR 3D vine plantation map generation.

    PubMed

    Llorens, Jordi; Gil, Emilio; Llop, Jordi; Queraltó, Meritxell

    2011-01-01

    The use of electronic devices for canopy characterization has recently been widely discussed. Among such devices, LiDAR sensors appear to be the most accurate and precise. Information obtained with LiDAR sensors during reading while driving a tractor along a crop row can be managed and transformed into canopy density maps by evaluating the frequency of LiDAR returns. This paper describes a proposed methodology to obtain a georeferenced canopy map by combining the information obtained with LiDAR with that generated using a GPS receiver installed on top of a tractor. Data regarding the velocity of LiDAR measurements and UTM coordinates of each measured point on the canopy were obtained by applying the proposed transformation process. The process allows overlap of the canopy density map generated with the image of the intended measured area using Google Earth(®), providing accurate information about the canopy distribution and/or location of damage along the rows. This methodology was applied and tested on different vine varieties and crop stages in two important vine production areas in Spain. The results indicate that the georeferenced information obtained with LiDAR sensors appears to be an interesting tool with the potential to improve crop management processes. PMID:22163952

  11. LiDAR Vegetation Investigation and Signature Analysis System (LVISA)

    NASA Astrophysics Data System (ADS)

    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

  12. Uas Topographic Mapping with Velodyne LiDAR Sensor

    NASA Astrophysics Data System (ADS)

    Jozkow, G.; Toth, C.; Grejner-Brzezinska, D.

    2016-06-01

    Unmanned Aerial System (UAS) technology is nowadays willingly used in small area topographic mapping due to low costs and good quality of derived products. Since cameras typically used with UAS have some limitations, e.g. cannot penetrate the vegetation, LiDAR sensors are increasingly getting attention in UAS mapping. Sensor developments reached the point when their costs and size suit the UAS platform, though, LiDAR UAS is still an emerging technology. One issue related to using LiDAR sensors on UAS is the limited performance of the navigation sensors used on UAS platforms. Therefore, various hardware and software solutions are investigated to increase the quality of UAS LiDAR point clouds. This work analyses several aspects of the UAS LiDAR point cloud generation performance based on UAS flights conducted with the Velodyne laser scanner and cameras. The attention was primarily paid to the trajectory reconstruction performance that is essential for accurate point cloud georeferencing. Since the navigation sensors, especially Inertial Measurement Units (IMUs), may not be of sufficient performance, the estimated camera poses could allow to increase the robustness of the estimated trajectory, and subsequently, the accuracy of the point cloud. The accuracy of the final UAS LiDAR point cloud was evaluated on the basis of the generated DSM, including comparison with point clouds obtained from dense image matching. The results showed the need for more investigation on MEMS IMU sensors used for UAS trajectory reconstruction. The accuracy of the UAS LiDAR point cloud, though lower than for point cloud obtained from images, may be still sufficient for certain mapping applications where the optical imagery is not useful.

  13. Wet Channel Network Extraction based on LiDAR Data

    NASA Astrophysics Data System (ADS)

    Hooshyar, M.; Kim, S.; Wang, D.; Medeiros, S. C.

    2015-12-01

    The temporal dynamics of stream network is vitally important for understanding hydrologic processes including groundwater interactions and hydrograph recessions. However, observations are limited on flowing channel heads, which are usually located in headwater catchments and under canopy. Near infrared LiDAR data provides an opportunity to map the flowing channel network owing to the fine spatial resolution, canopy penetration, and strong absorption of the light energy by the water surface. A systematic method is developed herein to map flowing channel networks based on the signal intensity of ground LiDAR return, which is lower on water surfaces than on dry surfaces. Based on the selected sample sites where the wetness conditions are known, the signal intensities of ground returns are extracted from the LiDAR point data. The frequency distributions of wet surface and dry surface returns are constructed. With the aid of LiDAR-based ground elevation, the signal intensity thresholds are identified for mapping flowing channels. The developed method is applied to Lake Tahoe area based on eight LiDAR snapshots during recession periods in five watersheds. A power-law relationship between streamflow and flowing channel length during the recession period is derived based on the result.

  14. Biomass Estimation for Individual Trees using Waveform LiDAR

    NASA Astrophysics Data System (ADS)

    Wang, K.; Kumar, P.; Dutta, D.

    2015-12-01

    Vegetation biomass information is important for many ecological models that include terrestrial vegetation in their simulations. Biomass has strong influences on carbon, water, and nutrient cycles. Traditionally biomass estimation requires intensive, and often destructive, field measurements. However, with advances in technology, airborne LiDAR has become a convenient tool for acquiring such information on a large scale. In this study, we use infrared full waveform LiDAR to estimate biomass information for individual trees in the Sangamon River basin in Illinois, USA. During this process, we also develop automated geolocation calibration algorithms for raw waveform LiDAR data. In the summer of 2014, discrete and waveform LiDAR data were collected over the Sangamon River basin. Field measurements commonly used in biomass equations such as diameter at breast height and total tree height were also taken for four sites across the basin. Using discrete LiDAR data, individual trees are delineated. For each tree, a voxelization methods is applied to all waveforms associated with the tree to result in a pseudo-waveform. By relating biomass extrapolated using field measurements from a training set of trees to waveform metrics for each corresponding tree, we are able to estimate biomass on an individual tree basis. The results can be especially useful as current models increase in resolution.

  15. Automated Probabilistic LiDAR Swath Registration

    NASA Astrophysics Data System (ADS)

    Jalobeanu, A.; Gonçalves, G. R.

    2014-12-01

    We recently developed a new point cloud registration algorithm. Compared to Iterated Closest Point (ICP) techniques, it is robust to noise and outliers, and easier to use, as it is less sensitive to initial conditions. It minimizes the entropy of the joint point cloud (including intensity attributes to help register areas with poor relief), uses a voxel space and B-Spline interpolation to accelerate computation. A natural application of registration is swath alignment in airborne light detection and ranging (LiDAR). Indeed, due to uncertainty in the inertial navigation system (INS), attitude angles are subject to time-dependent errors. Such errors can be understood as a sum of three terms: 1) a global term, or boresight error, which can be addressed using several existing techniques; 2) a low-frequency term, which is modeled as a constant attitude error for regions several hundred meters along-track; 3) a high-frequency term, responsible for corduroy artifacts (not addressed here). We propose to use the new registration algorithm to correct the low-frequency attitude variations. Relative geometric errors are significantly reduced, as pairs of swaths are registered onto each other local corrections. Absolute geometric errors are reduced during a second step, by applying all the corrections together to the entire dataset. We used a test area of 200 km2 in Portugal, with a density of 3-4 pts/m2. The point clouds were derived from waveform data, and include predictive range uncertainties estimated within a Bayesian framework. The data collection was supported by FCT and FEDER as part of the AutoProbaDTM research project (2009-2012). Modeling and reducing geometric error helps build consistent uncertainty maps. After correction, residual errors are taken into account in the final 3D error budget. For gridded elevation models a vertical uncertainty map is computed. Finally, it is possible to use the inter-swath registration parameters to estimate the distribution of

  16. Application of LiDAR's multiple attributes for wetland classification

    NASA Astrophysics Data System (ADS)

    Ding, Qiong; Ji, Shengyue; Chen, Wu

    2016-03-01

    Wetlands have received intensive interdisciplinary attention as a unique ecosystem and valuable resources. As a new technology, the airborne LiDAR system has been applied in wetland research these years. However, most of the studies used only one or two LiDAR observations to extract either terrain or vegetation in wetlands. This research aims at integrating LiDAR's multiple attributes (DSM, DTM, off-ground features, Slop map, multiple pulse returns, and normalized intensity) to improve mapping and classification of wetlands based on a multi-level object-oriented classification method. By using this method, we are able to classify the Yellow River Delta wetland into eight classes with overall classification accuracy of 92.5%

  17. Integrating LiDAR Data into Earth Science Education

    NASA Astrophysics Data System (ADS)

    Robinson, S. E.; Arrowsmith, R.; de Groot, R. M.; Crosby, C. J.; Whitesides, A. S.; Colunga, J.

    2010-12-01

    The use of high-resolution topography derived from Light Detection and Ranging (LiDAR) in the study of active tectonics is widespread and has become an indispensable tool to better understand earthquake hazards. For this reason and the spectacular representation of the phenomena the data provide, it is appropriate to integrate these data into the Earth science education curriculum. A collaboration between Arizona State University, the OpenTopography Facility, and the Southern California Earthquake Center are developing, three earth science education products to inform students and other audiences about LiDAR and its application to active tectonics research. First, a 10-minute introductory video titled LiDAR: Illuminating Earthquakes was produced and is freely available online through the OpenTopography portal and SCEC. The second product is an update and enhancement of the Wallace Creek Interpretive Trail website (www.scec.org/wallacecreek). LiDAR topography data products have been added along with the development of a virtual tour of the offset channels at Wallace Creek using the B4 LiDAR data within the Google Earth environment. The virtual tour to Wallace Creek is designed as a lab activity for introductory undergraduate geology courses to increase understanding of earthquake hazards through exploration of the dramatic offset created by the San Andreas Fault (SAF) at Wallace Creek and Global Positioning System-derived displacements spanning the SAF at Wallace Creek . This activity is currently being tested in courses at Arizona State University. The goal of the assessment is to measure student understanding of plate tectonics and earthquakes after completing the activity. Including high-resolution topography LiDAR data into the earth science education curriculum promotes understanding of plate tectonics, faults, and other topics related to earthquake hazards.

  18. Modeling loblolly pine dominant height using airborne LiDAR

    NASA Astrophysics Data System (ADS)

    Maceyka, Andy

    The dominant height of 73 georeferenced field sample plots were modeled from various canopy height metrics derived by means of a small-footprint laser scanning technology, known as light detection and ranging (or just LiDAR), over young and mature forest stands using regression analysis. LiDAR plot metrics were regressed against field measured dominant height using Best Subsets Regression to reduce the number of models. From those models, regression assumptions were evaluated to determine which model was actually the best. The best model included the 1st and 90th height percentiles as predictors and explained 95% of the variance in average dominant height.

  19. 47 CFR 25.401 - Satellite DARS applications subject to competitive bidding.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 47 Telecommunication 2 2014-10-01 2014-10-01 false Satellite DARS applications subject to...) COMMON CARRIER SERVICES SATELLITE COMMUNICATIONS Competitive Bidding Procedures for DARS § 25.401 Satellite DARS applications subject to competitive bidding. Mutually exclusive initial applications for...

  20. 47 CFR 25.401 - Satellite DARS applications subject to competitive bidding.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 47 Telecommunication 2 2013-10-01 2013-10-01 false Satellite DARS applications subject to...) COMMON CARRIER SERVICES SATELLITE COMMUNICATIONS Competitive Bidding Procedures for DARS § 25.401 Satellite DARS applications subject to competitive bidding. Mutually exclusive initial applications for...

  1. 47 CFR 25.401 - Satellite DARS applications subject to competitive bidding.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 47 Telecommunication 2 2011-10-01 2011-10-01 false Satellite DARS applications subject to...) COMMON CARRIER SERVICES SATELLITE COMMUNICATIONS Competitive Bidding Procedures for DARS § 25.401 Satellite DARS applications subject to competitive bidding. Mutually exclusive initial applications for...

  2. 47 CFR 25.401 - Satellite DARS applications subject to competitive bidding.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 47 Telecommunication 2 2012-10-01 2012-10-01 false Satellite DARS applications subject to...) COMMON CARRIER SERVICES SATELLITE COMMUNICATIONS Competitive Bidding Procedures for DARS § 25.401 Satellite DARS applications subject to competitive bidding. Mutually exclusive initial applications for...

  3. 47 CFR 25.401 - Satellite DARS applications subject to competitive bidding.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 47 Telecommunication 2 2010-10-01 2010-10-01 false Satellite DARS applications subject to...) COMMON CARRIER SERVICES SATELLITE COMMUNICATIONS Competitive Bidding Procedures for DARS § 25.401 Satellite DARS applications subject to competitive bidding. Mutually exclusive initial applications for...

  4. Modeling low-height vegetation with airborne LiDAR

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Low-height vegetation, common in semiarid regions, is difficult to characterize with LiDAR (Light Detection and Ranging) due to similarities, in time and space, of the point returns of vegetation and ground. Other complications may occur due to the low-height vegetation structural characteristics a...

  5. DArT marker development and applications in oat

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Progress of genomic research in oat has been limited by a lack of common markers and consensus maps that would provide integration platforms for structural genomic analysis. Diversity Array Technology (DArT) is a strategy that provides a high density of molecular markers that can be tested in par...

  6. High-intensity cyclotron for the IsoDAR experiment

    NASA Astrophysics Data System (ADS)

    Campo, D.; IsoDAR Collaboration

    2015-03-01

    The IsoDAR experiment is the MIT proposal to investigate about several neutrino properties, in order to explain some anomalies experimentally observed. It requires 10mA of proton beam at the energy of 60MeV to produce a high-intensity electron antineutrino flux from the production and the decay of 8Li: it is an ambitious goal for the accelerator design, due also to the fact that the machine has to be placed near a neutrino detector, like KAMLAND or WATCHMAN, located in underground sites. A compact cyclotron able to accelerate H2+ molecule beam up to energy of 60MeV/amu is under study. The critical issues of this machine concern the beam injection due to the effects of space charge, the efficiency of the beam extraction and the technical solutions needed to the machine assembly. Here, the innovative solutions and the preliminary results achieved by the IsoDAR team are discussed.

  7. Volume component analysis for classification of LiDAR data

    NASA Astrophysics Data System (ADS)

    Varney, Nina M.; Asari, Vijayan K.

    2015-03-01

    One of the most difficult challenges of working with LiDAR data is the large amount of data points that are produced. Analysing these large data sets is an extremely time consuming process. For this reason, automatic perception of LiDAR scenes is a growing area of research. Currently, most LiDAR feature extraction relies on geometrical features specific to the point cloud of interest. These geometrical features are scene-specific, and often rely on the scale and orientation of the object for classification. This paper proposes a robust method for reduced dimensionality feature extraction of 3D objects using a volume component analysis (VCA) approach.1 This VCA approach is based on principal component analysis (PCA). PCA is a method of reduced feature extraction that computes a covariance matrix from the original input vector. The eigenvectors corresponding to the largest eigenvalues of the covariance matrix are used to describe an image. Block-based PCA is an adapted method for feature extraction in facial images because PCA, when performed in local areas of the image, can extract more significant features than can be extracted when the entire image is considered. The image space is split into several of these blocks, and PCA is computed individually for each block. This VCA proposes that a LiDAR point cloud can be represented as a series of voxels whose values correspond to the point density within that relative location. From this voxelized space, block-based PCA is used to analyze sections of the space where the sections, when combined, will represent features of the entire 3-D object. These features are then used as the input to a support vector machine which is trained to identify four classes of objects, vegetation, vehicles, buildings and barriers with an overall accuracy of 93.8%

  8. Compact Adaptable Mobile LiDAR System Deployment

    NASA Astrophysics Data System (ADS)

    Glennie, C. L.; Brooks, B. A.; Ericksen, T. L.; Hudnut, K. W.; Foster, J. H.; Hauser, D.; Avery, J.

    2012-12-01

    Airborne LiDAR (LIght Detection And Ranging) systems have become a standard mechanism for acquiring dense high-precision topography, making it possible to perform large scale documentation (100's of km2) per day at spatial scales as fine as a few decimeters horizontally and a few centimeters vertically. However, current airborne and terrestrial LiDAR systems suffer from a number of drawbacks. They are expensive, bulky, require significant power supplies, and are often optimized for use in only one type of mobility platform. It would therefore be advantageous to design a lightweight, compact and relatively inexpensive multipurpose LiDAR and imagery system that could be used from a variety of mobility platforms - both terrestrial and airborne. The system should be quick and easy to deploy, and require a minimum amount of existing infrastructure for operational support. With these goals in mind, our research teams have developed a prototype field deployable compact dynamic laser scanning system that is configured for use on a variety of mobility platforms, including backpack wearable, as well as unmanned aerial vehicles (e.g. balloons & helicopters) and small off-road vehicles such as ATV's. The system is small, self-contained, relatively inexpensive, and easy to deploy. The first version of this multipurpose LiDAR system has been successfully tested in both backpack configuration and on a tethered flight attached to a helium balloon. We will present system design and development details, along with field experiences and a detailed accuracy analysis of the acquired point clouds which show that accuracy of 3-5 cm (1 sigma) vertical can be achieved in both backpack and balloon modalities.

  9. Rapid topographic and bathymetric reconnaissance using airborne LiDAR

    NASA Astrophysics Data System (ADS)

    Axelsson, Andreas

    2010-10-01

    Today airborne LiDAR (Light Detection And Ranging) systems has gained acceptance as a powerful tool to rapidly collect invaluable information to assess the impact from either natural disasters, such as hurricanes, earthquakes and flooding, or human inflicted disasters such as terrorist/enemy activities. Where satellite based imagery provides an excellent tool to remotely detect changes in the environment, the LiDAR systems, being active remote sensors, provide an unsurpassed method to quantify these changes. The strength of the active laser based systems is especially evident in areas covered by occluding vegetation or in the shallow coastal zone as the laser can penetrate the vegetation or water body to unveil what is below. The purpose of this paper is to address the task to survey complex areas with help of the state-of-the-art airborne LiDAR systems and also discuss scenarios where the method is used today and where it may be used tomorrow. Regardless if it is a post-hurricane survey or a preparation stage for a landing operation in unchartered waters, it is today possible to collect, process and present a dense 3D model of the area of interest within just a few hours from deployment. By utilizing the advancement in processing power and wireless network capabilities real-time presentation would be feasible.

  10. LiDAR observation of the flow structure in typhoons

    NASA Astrophysics Data System (ADS)

    Wu, Yu-Ting; Hsuan, Chung-Yao; Lin, Ta-Hui

    2015-04-01

    Taiwan is subject to 3.4 landfall typhoons each year in average, generally occurring in the third quarter of every year (July-September). Understanding of boundary-layer turbulence characteristics of a typhoon is needed to ensure the safety of both onshore and offshore wind turbines used for power generation. In this study, a floating LiDAR (Light Detection and Ranging) was deployed in a harbor to collect data of wind turbulence, atmospheric pressure, and temperature in three typhoon events (Matmo typhoon, Soulik typhoon, Trami typhoon). Data collected from the floating LiDAR and from meteorological stations located at Taipei, Taichung and Kaohsiung are adopted to analyse the wind turbulence characteristics in the three typhoon events. The measurement results show that the maximum 10-min average wind speed measured with the floating LiDAR is up to 24 m/s at a height of 200 m. Compared with other normal days, the turbulence intensity is lower in the three typhoon events where the wind speed has a rapid increase. Changes of wind direction take place clearly as the typhoons cross Taiwan from East to West. Within the crossing intervals, the vertical momentum flux is observed to have a significant pattern with both upward and downward propagating waves which are relevant to the flow structure of the typhoons.

  11. Rockfall hazard analysis using LiDAR and spatial modeling

    NASA Astrophysics Data System (ADS)

    Lan, Hengxing; Martin, C. Derek; Zhou, Chenghu; Lim, Chang Ho

    2010-05-01

    Rockfalls have been significant geohazards along the Canadian Class 1 Railways (CN Rail and CP Rail) since their construction in the late 1800s. These rockfalls cause damage to infrastructure, interruption of business, and environmental impacts, and their occurrence varies both spatially and temporally. The proactive management of these rockfall hazards requires enabling technologies. This paper discusses a hazard assessment strategy for rockfalls along a section of a Canadian railway using LiDAR and spatial modeling. LiDAR provides accurate topographical information of the source area of rockfalls and along their paths. Spatial modeling was conducted using Rockfall Analyst, a three dimensional extension to GIS, to determine the characteristics of the rockfalls in terms of travel distance, velocity and energy. Historical rockfall records were used to calibrate the physical characteristics of the rockfall processes. The results based on a high-resolution digital elevation model from a LiDAR dataset were compared with those based on a coarse digital elevation model. A comprehensive methodology for rockfall hazard assessment is proposed which takes into account the characteristics of source areas, the physical processes of rockfalls and the spatial attribution of their frequency and energy.

  12. Increasing the Efficiency of LiDAR Based Forest Inventories: A Novel Approach for Integrating Variable Radius Inventory Plots with LiDAR Data.

    NASA Astrophysics Data System (ADS)

    Falkowski, M. J.; Fekety, P.; Silva, C. A.; Hudak, A. T.

    2015-12-01

    LiDAR data are increasingly applied to support forest inventory and assessment across a variety of spatial scales. Typically this is achieved by integrating LiDAR data with forest inventory collected at fixed radius forest inventory plots. A well-designed forest inventory, one that covers the full range of structural and compositional variation across the forest of interest, is costly especially when collecting fixed radius plot data. Variable radius plots offer an alternative inventory protocol that is more efficient in terms of both time and money. However, integrating variable radius plot data with LiDAR data is problematic because the plots have unknown sizes that vary with variation in tree size. This leads to a spatial mismatch between LiDAR metrics (e.g., mean height, canopy cover, density, etc.) and plot data, which ultimately translates into errors in LiDAR derived forest inventory predictions. We propose and evaluate and novel approach for integrating variable radius plot data into a LiDAR based forest inventories in two different forest systems, one in the inland northwest and another in the northern lakes states of the USA. The novel approach calculates LiDAR metrics by weighting the point cloud proportional to return height, mimicking the way in which variable radius plot data weights tree measurements by tree size. This could increase inventory sampling efficiency, allowing for the collection of a greater number of inventory plots, and ultimately improve the performance of LiDAR based inventories.

  13. Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories

    NASA Astrophysics Data System (ADS)

    Tao, Shengli; Wu, Fangfang; Guo, Qinghua; Wang, Yongcai; Li, Wenkai; Xue, Baolin; Hu, Xueyang; Li, Peng; Tian, Di; Li, Chao; Yao, Hui; Li, Yumei; Xu, Guangcai; Fang, Jingyun

    2015-12-01

    The rapid development of light detection and ranging (LiDAR) techniques is advancing ecological and forest research. During the last decade, numerous single tree segmentation techniques have been developed using airborne LiDAR data. However, accurate crown segmentation using terrestrial or mobile LiDAR data, which is an essential prerequisite for extracting branch level forest characteristics, is still challenging mainly because of the difficulties posed by tree crown intersection and irregular crown shape. In the current work, we developed a comparative shortest-path algorithm (CSP) for segmenting tree crowns scanned using terrestrial (T)-LiDAR and mobile LiDAR. The algorithm consists of two steps, namely trunk detection and subsequent crown segmentation, with the latter inspired by the well-proved metabolic ecology theory and the ecological fact that vascular plants tend to minimize the transferring distance to the root. We tested the algorithm on mobile-LiDAR-scanned roadside trees and T-LiDAR-scanned broadleaved and coniferous forests in China. Point-level quantitative assessments of the segmentation results showed that for mobile-LiDAR-scanned roadside trees, all the points were classified to their corresponding trees correctly, and for T-LiDAR-scanned broadleaved and coniferous forests, kappa coefficients ranging from 0.83 to 0.93 were obtained. We believe that our algorithm will make a contribution to solving the problem of crown segmentation in T-LiDAR scanned-forests, and might be of interest to researchers in LiDAR data processing and to forest ecologists. In addition, our research highlights the advantages of using ecological theories as guidelines for processing LiDAR data.

  14. Synergy Between LiDAR and Image Data in Context of Building Extraction

    NASA Astrophysics Data System (ADS)

    Dal Poz, A. P.

    2014-11-01

    This paper compares the paradigms of LiDAR and aerophotogrammetry in the context of building extraction and briefly discusses a photogrammetric strategy for refining building roof polyhedrons previously extracted from LiDAR data. In general, empirical and theoretical studies have confirmed that LiDAR-based methodologies are more suitable in extracting planar roof faces and ridges of the roof, whereas the aerophotogrammetry are more suitable in extracting building roof outlines. In order to exemplify how to explore these properties, it is presented a photogrammetric method for refining 3D building roof contours extracted from airborne LiDAR data. Examples of application are provided for this refining approach.

  15. The Daily Activity Report (DAR) a Novel Measure of Functional Outcome for Serious Mental Illness.

    PubMed

    Velligan, Dawn I; Mintz, Jim; Sierra, Cynthia; Martin, Mona L; Fredrick, Megan; Maglinte, Gregory A; Corey-Lisle, Patricia K

    2016-05-01

    The assessment of real-world functional outcomes in clinical trials for medications targeting negative symptoms and cognitive impairment is extremely important. We tested the psychometric properties of the Daily Activity Report (DAR), a novel assessment of productive daily activity. We administered the DAR and additional assessments of functional outcome, functional capacity, cognition and symptomatology to 50 individuals with schizophrenia at 2 time points, 1 month apart and to 25 healthy controls. The DAR records a person's daily activity for 7 consecutive days based upon phone calls made 3 times a day. A total score and scores in 3 domains; instrumental activities (ie, independent living), social and work or school related activities are generated for the DAR. Inter-item consistency was high 0.89-0.94 for each domain and 0.88 overall. Test-retest reliability across 1 month for the total DAR score was 0.67,P< .0001. The total DAR score as well as scores for social activity and nondomestic work/school differed significantly between control and patient participants (P< .0001). DAR domain scores were associated with negative symptoms and functional outcomes, but the primary score related to these measures was the work/school dimension of the DAR. DAR scores were only weakly and nonsignificantly related to positive symptoms. This study provides preliminary support for the reliability and validity of the DAR using interviewer administration. The development of a patient reported version of the DAR using smart phone technology with automatic scoring is the next step. PMID:26712856

  16. The Daily Activity Report (DAR) a Novel Measure of Functional Outcome for Serious Mental Illness

    PubMed Central

    Velligan, Dawn I.; Mintz, Jim; Sierra, Cynthia; Martin, Mona L.; Fredrick, Megan; Maglinte, Gregory A.; Corey-Lisle, Patricia K.

    2016-01-01

    The assessment of real-world functional outcomes in clinical trials for medications targeting negative symptoms and cognitive impairment is extremely important. We tested the psychometric properties of the Daily Activity Report (DAR), a novel assessment of productive daily activity. We administered the DAR and additional assessments of functional outcome, functional capacity, cognition and symptomatology to 50 individuals with schizophrenia at 2 time points, 1 month apart and to 25 healthy controls. The DAR records a person’s daily activity for 7 consecutive days based upon phone calls made 3 times a day. A total score and scores in 3 domains; instrumental activities (ie, independent living), social and work or school related activities are generated for the DAR. Inter-item consistency was high 0.89–0.94 for each domain and 0.88 overall. Test–retest reliability across 1 month for the total DAR score was 0.67, P < .0001. The total DAR score as well as scores for social activity and nondomestic work/school differed significantly between control and patient participants (P < .0001). DAR domain scores were associated with negative symptoms and functional outcomes, but the primary score related to these measures was the work/school dimension of the DAR. DAR scores were only weakly and nonsignificantly related to positive symptoms. This study provides preliminary support for the reliability and validity of the DAR using interviewer administration. The development of a patient reported version of the DAR using smart phone technology with automatic scoring is the next step. PMID:26712856

  17. Identifying Colluvial Slopes by Airborne LiDAR Analysis

    NASA Astrophysics Data System (ADS)

    Kasai, M.; Marutani, T.; Yoshida, H.

    2015-12-01

    Colluvial slopes are one of major sources of landslides. Identifying the locations of the slopes will help reduce the risk of disasters, by avoiding building infrastructure and properties nearby, or if they are already there, by applying appropriate counter measures before it suddenly moves. In this study, airborne LiDAR data was analyzed to find their geomorphic characteristics to use for extracting their locations. The study site was set in the suburb of Sapporo City, Hokkaido in Japan. The area is underlain by Andesite and Tuff and prone to landslides. Slope angle and surface roughness were calculated from 5 m resolution DEM. These filters were chosen because colluvial materials deposit at around the angle of repose and accumulation of loose materials was considered to form a peculiar surface texture differentiable from other slope types. Field survey conducted together suggested that colluvial slopes could be identified by the filters with a probability of 80 percent. Repeat LiDAR monitoring of the site by an unmanned helicopter indicated that those slopes detected as colluviums appeared to be moving at a slow rate. In comparison with a similar study from the crushed zone in Japan, the range of slope angle indicative of colluviums agreed with the Sapporo site, while the texture was rougher due to larger debris composing the slopes.

  18. Performance testing of LiDAR exploitation software

    NASA Astrophysics Data System (ADS)

    Varela-González, M.; González-Jorge, H.; Riveiro, B.; Arias, P.

    2013-04-01

    Mobile LiDAR systems are being used widely in recent years for many applications in the field of geoscience. One of most important limitations of this technology is the large computational requirements involved in data processing. Several software solutions for data processing are available in the market, but users are often unknown about the methodologies to verify their performance accurately. In this work a methodology for LiDAR software performance testing is presented and six different suites are studied: QT Modeler, AutoCAD Civil 3D, Mars 7, Fledermaus, Carlson and TopoDOT (all of them in x64). Results depict as QTModeler, TopoDOT and AutoCAD Civil 3D allow the loading of large datasets, while Fledermaus, Mars7 and Carlson do not achieve these powerful performance. AutoCAD Civil 3D needs large loading time in comparison with the most powerful softwares such as QTModeler and TopoDOT. Carlson suite depicts the poorest results among all the softwares under study, where point clouds larger than 5 million points cannot be loaded and loading time is very large in comparison with the other suites even for the smaller datasets. AutoCAD Civil 3D, Carlson and TopoDOT show more threads than other softwares like QTModeler, Mars7 and Fledermaus.

  19. Remote sensing of Sonoran Desert vegetation structure and phenology with ground-based LiDAR

    USGS Publications Warehouse

    Sankey, Joel B.; Munson, Seth M.; Webb, Robert H.; Wallace, Cynthia S.A.; Duran, Cesar M.

    2015-01-01

    Long-term vegetation monitoring efforts have become increasingly important for understanding ecosystem response to global change. Many traditional methods for monitoring can be infrequent and limited in scope. Ground-based LiDAR is one remote sensing method that offers a clear advancement to monitor vegetation dynamics at high spatial and temporal resolution. We determined the effectiveness of LiDAR to detect intra-annual variability in vegetation structure at a long-term Sonoran Desert monitoring plot dominated by cacti, deciduous and evergreen shrubs. Monthly repeat LiDAR scans of perennial plant canopies over the course of one year had high precision. LiDAR measurements of canopy height and area were accurate with respect to total station survey measurements of individual plants. We found an increase in the number of LiDAR vegetation returns following the wet North American Monsoon season. This intra-annual variability in vegetation structure detected by LiDAR was attributable to a drought deciduous shrub Ambrosia deltoidea, whereas the evergreen shrub Larrea tridentata and cactus Opuntia engelmannii had low variability. Benefits of using LiDAR over traditional methods to census desert plants are more rapid, consistent, and cost-effective data acquisition in a high-resolution, 3-dimensional context. We conclude that repeat LiDAR measurements can be an effective method for documenting ecosystem response to desert climatology and drought over short time intervals and at detailed-local spatial scale.

  20. LiDAR, a great tool for archaeologists, but how do you interpret it?

    NASA Astrophysics Data System (ADS)

    Leisz, S.; Fisher, C.

    2013-05-01

    This paper focuses on the use of airborne LiDAR to identify archaeological features below forest canopies in Mesoamerica and the challenges faced in interpreting the data. To illustrate the issues involved in interpreting LiDAR point clouds and derived data sets for archaeological purposes, the case study of the use of airborne LiDAR at the archaeological site of Angamuco in West-Central Mexico is discussed. The case study details the reason LiDAR was collected, the challenges in interpreting it, methods and techniques that the authors are investigating to improve the interpretation of the LiDAR, and discoveries that have so far been made through the use of LiDAR. A key point discussed is the need to analyze the LiDAR point cloud in conjunction with products developed from the point cloud. Analyzing the various data sets jointly allows the user to better identify archaeological features of interest. New ways of utilizing hillshades of DEMs, such as creating 360 degree hillshades of the derived DEMs, are also presented. Last the authors discuss their experience in using object-based classification of the products derived from the LiDAR point cloud as an example of one possible technique for automating the delineation and classification of archaeological features.

  1. Dia de Dar Gracias. Modulo Nivel Primario. (Day to Give Thanks. Module Primary Level.)

    ERIC Educational Resources Information Center

    Espinoza, Delia; Lopez, Santiago, III

    Dia de Dar Gracias (Thanksgiving) is the subject of this primary level unit. The unit objectives are to: (1) know about El Dia de Dar Gracias as it is celebrated in the United States; (2) know how the Mayas celebrated it; (3) understand the context of the stories in the unit; (4) know about the main food used, the turkey; (5) distinguish other…

  2. 4D Terrestrial LiDAR Data Collection: Geomorphic and Hydraulic Applications (Invited)

    NASA Astrophysics Data System (ADS)

    Minear, J. T.; Wright, S. A.; Kinzel, P. J.; Draut, A. E.; Logan, J.

    2013-12-01

    Terrestrial LiDAR, also known as T-LiDAR, ground-based LiDAR, or Terrestrial Laser Scanning, can provide great insights into some types of geomorphic and hydraulic studies, particularly when collected repeatedly over time. Because T-LiDAR collects a large amount of data on a set grid, oftentimes processes are inadvertently captured that are not part of the initial research question but can be important factors in their own right. In addition, though T-LiDAR is most often used at relatively small sites for high-precision scanning, it also can be used for relatively rapid meso-scale site measurements, albeit typically with less precision. Using examples from the Elwha River dam removals, WA, a canal experiment in NE, and several small river restoration sites in CA, we highlight several important and innovative uses of T-LiDAR measurements, including quick temporal scale changes in water surface features and larger temporal- and spatial-scale changes in reservoir deltaic deposits and longitudinal profile features. Also discussed will be some considerations for improving T-LiDAR error estimation and a comparison to other data collection techniques, including aerial LiDAR, structure-from-motion photogrammetry, and UAV- and plane-captured photogrammetry.

  3. Wetland inundation mapping and change monitoring using landsat and airborne LiDAR data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper presents a new approach for mapping wetland inundation change using Landsat and LiDAR intensity data. In this approach, LiDAR data were used to derive highly accurate reference subpixel inundation percentage (SIP) maps at the 30-m resolution. The reference SIP maps were then used to est...

  4. Applicability of Aerial Green LiDAR to a Large River in the Western United States

    NASA Astrophysics Data System (ADS)

    Conner, J. T.; Welcker, C. W.; Cooper, C.; Faux, R.; Butler, M.; Nayegandhi, A.

    2013-12-01

    In October 2012, aerial green LiDAR data were collected in the Snake River (within Idaho and Oregon) to test this emerging technology in a large river with poor water clarity. Six study areas (total of 30 river miles spread out over 250 river miles) were chosen to represent a variety of depths, channel types, and surface conditions to test the accuracy, depth penetration, data density of aerial green LiDAR. These characteristics along with cost and speed of acquisition were compared to other bathymetric survey techniques including rod surveys (total station and RTK-GPS), single-beam sonar, and multibeam echosounder (MBES). The green LiDAR system typically measured returns from the riverbed through 1-2 meters of water, which was less than one Secchi depth. However, in areas with steep banks or aquatic macrophytes, LiDAR returns from the riverbed were less frequent or non-existent. In areas of good return density, depths measured from green LiDAR data corresponded well with previously collected data sets from traditional bathymetric survey techniques. In such areas, the green LiDAR point density was much higher than both rod and single beam sonar surveys, yet lower than MBES. The green LiDAR survey was also collected more efficiently than all other methods. In the Snake River, green LiDAR does not provide a method to map the entire riverbed as it only receives bottom returns in shallow water, typically at the channel margins. However, green LiDAR does provide survey data that is an excellent complement to MBES, which is more effective at surveying the deeper portions of the channel. In some cases, the green LiDAR was able to provide data in areas that the MBES could not, often due to issues with navigating the survey boat in shallow water. Even where both MBES and green LiDAR mapped the river bottom, green LiDAR often provides more accurate data through a better angle of incidence and less shadowing than the MBES survey. For one MBES survey in 2013, the green LiDAR

  5. Multipath Estimation in Urban Environments from Joint GNSS Receivers and LiDAR Sensors

    PubMed Central

    Ali, Khurram; Chen, Xin; Dovis, Fabio; De Castro, David; Fernández, Antonio J.

    2012-01-01

    In this paper, multipath error on Global Navigation Satellite System (GNSS) signals in urban environments is characterized with the help of Light Detection and Ranging (LiDAR) measurements. For this purpose, LiDAR equipment and Global Positioning System (GPS) receiver implementing a multipath estimating architecture were used to collect data in an urban environment. This paper demonstrates how GPS and LiDAR measurements can be jointly used to model the environment and obtain robust receivers. Multipath amplitude and delay are estimated by means of LiDAR feature extraction and multipath mitigation architecture. The results show the feasibility of integrating the information provided by LiDAR sensors and GNSS receivers for multipath mitigation. PMID:23202177

  6. Multipath estimation in urban environments from joint GNSS receivers and LiDAR sensors.

    PubMed

    Ali, Khurram; Chen, Xin; Dovis, Fabio; De Castro, David; Fernández, Antonio J

    2012-01-01

    In this paper, multipath error on Global Navigation Satellite System (GNSS) signals in urban environments is characterized with the help of Light Detection and Ranging (LiDAR) measurements. For this purpose, LiDAR equipment and Global Positioning System (GPS) receiver implementing a multipath estimating architecture were used to collect data in an urban environment. This paper demonstrates how GPS and LiDAR measurements can be jointly used to model the environment and obtain robust receivers. Multipath amplitude and delay are estimated by means of LiDAR feature extraction and multipath mitigation architecture. The results show the feasibility of integrating the information provided by LiDAR sensors and GNSS receivers for multipath mitigation. PMID:23202177

  7. Using a multiwavelength LiDAR for improved remote sensing of natural waters.

    PubMed

    Gray, Deric J; Anderson, John; Nelson, Jean; Edwards, Jarrod

    2015-11-01

    This paper describes research to characterize the benefits of a multiwavelength oceanographic LiDAR for various water types. Field measurements were conducted to establish endmembers representative of both typical and extremely challenging natural conditions. Laboratory tests were performed using a prototype multiwavelength LiDAR in water tanks with optical conditions simulating both sediment-laden and biologically rich water types. LiDAR models were used to simulate the LiDAR signal from both field and laboratory experiments. Our measurements and models show that using a laser wavelength of 470-490 nm in the open ocean leads to an improvement factor of 1.50-1.75 compared to a 532 nm system. In more turbid areas using a laser wavelength of 560-580 nm leads to an improvement factor of 1.25. We conclude by demonstrating how using multiple LiDAR wavelengths can help detect and characterize constituents in the water column. PMID:26560612

  8. Modelling Sensor and Target effects on LiDAR Waveforms

    NASA Astrophysics Data System (ADS)

    Rosette, J.; North, P. R.; Rubio, J.; Cook, B. D.; Suárez, J.

    2010-12-01

    The aim of this research is to explore the influence of sensor characteristics and interactions with vegetation and terrain properties on the estimation of vegetation parameters from LiDAR waveforms. This is carried out using waveform simulations produced by the FLIGHT radiative transfer model which is based on Monte Carlo simulation of photon transport (North, 1996; North et al., 2010). The opportunities for vegetation analysis that are offered by LiDAR modelling are also demonstrated by other authors e.g. Sun and Ranson, 2000; Ni-Meister et al., 2001. Simulations from the FLIGHT model were driven using reflectance and transmittance properties collected from the Howland Research Forest, Maine, USA in 2003 together with a tree list for a 200m x 150m area. This was generated using field measurements of location, species and diameter at breast height. Tree height and crown dimensions of individual trees were calculated using relationships established with a competition index determined for this site. Waveforms obtained by the Laser Vegetation Imaging Sensor (LVIS) were used as validation of simulations. This provided a base from which factors such as slope, laser incidence angle and pulse width could be varied. This has enabled the effect of instrument design and laser interactions with different surface characteristics to be tested. As such, waveform simulation is relevant for the development of future satellite LiDAR sensors, such as NASA’s forthcoming DESDynI mission (NASA, 2010), which aim to improve capabilities of vegetation parameter estimation. ACKNOWLEDGMENTS We would like to thank scientists at the Biospheric Sciences Branch of NASA Goddard Space Flight Center, in particular to Jon Ranson and Bryan Blair. This work forms part of research funded by the NASA DESDynI project and the UK Natural Environment Research Council (NE/F021437/1). REFERENCES NASA, 2010, DESDynI: Deformation, Ecosystem Structure and Dynamics of Ice. http

  9. Canopy wake measurements using multiple scanning wind LiDARs

    NASA Astrophysics Data System (ADS)

    Markfort, Corey D.; Carbajo Fuertes, Fernando; Valerio Iungo, Giacomo; Stefan, Heinz; Porté-Agel, Fernando

    2014-05-01

    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 35-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 35 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 vector near the surface, downwind of the canopy. The implications of canopy wakes for measurement and modeling of surface fluxes will be discussed.

  10. Canopy wake measurements using multiple scanning wind LiDARs

    NASA Astrophysics Data System (ADS)

    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.

  11. LiDAR Analysis of Hector Mine Fault Scarp Degradation

    NASA Astrophysics Data System (ADS)

    Zhang, X.; Hudnut, K. W.; Glennie, C. L.; Sousa, F.; Stock, J. M.; Akciz, S. O.

    2014-12-01

    The Mw 7.1 right-lateral strike-slip Hector Mine earthquake occurred on 10/16/1999 and generated an approximately 48 km long surface rupture. The Lavic Lake fault and the central section of the Bullion fault and several lesser faults ruptured, characterized by maximum strike slip of 5.25 ±0.85 m [Treiman, 2002]. As a very remote and un-populated area of the Mojave Desert, southern California, the study area is highly favorable for fault degradation studies with essentially no influence from vegetation or human activity. Airborne LiDAR (light detection and ranging) data and terrestrial laser scanning (TLS) are used to evaluate the form and rate of degradation of scarps along the Hector Mine fault rupture, California, USA. Airborne LiDAR data were acquired in 2000 and 2012 and these data were differenced using a newly developed algorithm for point cloud matching, which is improved over prior methods by accounting for scan geometry error sources. Using the bi-temporal data (scrutinizing profiles from 2000 & 2012), parameters for a fault scarp diffusion model are estimated and then a simulation result is generated to predict the evolved landform shape at the time of the 2014 TLS data set. Results are checked against TLS 2014 data collected at five key sites within the maximum slip field study area. In the past, scarp degradation has been mostly investigated using traditional survey methods (e.g., measuring elevations of points in a line perpendicular to the scarp) that require time-consuming field work and tend to introduce bias and variance due to data limitations. Airborne, mobile and terrestrial LiDAR data offer great potential to precisely document and rigorously determine morphologic degradation of fault scarps [Hilley et al., 2010]. In the present study, a unique set of data have been acquired at three points in time across several classic types of fault scarps and offset fault zone features. This allows progress in assessing the fitting of functions and

  12. Dynamic LiDAR-NDVI classification of fluvial landscape units

    NASA Astrophysics Data System (ADS)

    Ramírez-Núñez, Carolina; Parrot, Jean-François

    2015-04-01

    The lower basin of the Coatzacoalcos River is a wide floodplain in which, during the wet season, local and major flooding are distinguished. Both types of floods, intermittent and regional, are important in terms of resources; the regional flood sediments enrich the soils of the plains and intermittent floods allow obtaining aquatic resources for subsistence during the heatwave. In the floodplain different abandoned meanders and intermittent streams are quickly colonized by aquatic vegetation. However, from the 1990s, the Coatzacoalcos River floodplain has important topographic changes due to mining, road and bridges construction; erosion and sedimentation requires continuous parcel boundaries along with the increasing demand of channel reparation, embankments, levees and bridges associated to tributaries. NDVI data, LiDAR point cloud and various types of flood simulations taking into account the DTM are used to classify the dynamic landscape units. These units are associated to floods in relation with water resources, agriculture and livestock. In the study area, the first returns of the point cloud allow extracting vegetation strata. The last returns correspond to the bare earth surface, especially in this area with few human settlements. The surface that is not covered by trees or by aquatic vegetation, correspond to crops, pastures and bare soils. The classification is obtained by using the NDVI index coupled with vegetation strata and water bodies. The result shows that 47.96% of the area does not present active vegetation and it includes 31.53% of bare soils. Concerning the active vegetation, pastures, bushes and trees represent respectively 25.59%, 11.14% and 13.25%. The remaining 1.25% is distributed between water bodies with aquatic vegetation, trees and shrubs. Dynamic landscape units' classification represents a tool for monitoring water resources in a fluvial plain. This approach can be also applied to forest management, environmental services and

  13. Urban agriculture and Anopheles habitats in Dar es Salaam, Tanzania.

    PubMed

    Dongus, Stefan; Nyika, Dickson; Kannady, Khadija; Mtasiwa, Deo; Mshinda, Hassan; Gosoniu, Laura; Drescher, Axel W; Fillinger, Ulrike; Tanner, Marcel; Killeen, Gerry F; Castro, Marcia C

    2009-05-01

    A cross-sectional survey of agricultural areas, combined with routinely monitored mosquito larval information, was conducted in urban Dar es Salaam, Tanzania, to investigate how agricultural and geographical features may influence the presence of Anopheles larvae. Data were integrated into a geographical information systems framework, and predictors of the presence of Anopheles larvae in farming areas were assessed using multivariate logistic regression with independent random effects. It was found that more than 5% of the study area (total size 16.8 km2) was used for farming in backyard gardens and larger open spaces. The proportion of habitats containing Anopheles larvae was 1.7 times higher in agricultural areas compared to other areas (95% confidence interval = 1.56-1.92). Significant geographic predictors of the presence of Anopheles larvae in gardens included location in lowland areas, proximity to river, and relatively impermeable soils. Agriculture-related predictors comprised specific seedbed types, mid-sized gardens, irrigation by wells, as well as cultivation of sugar cane or leafy vegetables. Negative predictors included small garden size, irrigation by tap water, rainfed production and cultivation of leguminous crops or fruit trees. Although there was an increased chance of finding Anopheles larvae in agricultural sites, it was found that breeding sites originated by urban agriculture account for less than a fifth of all breeding sites of malaria vectors in Dar es Salaam. It is suggested that strategies comprising an integrated malaria control effort in malaria-endemic African cities include participatory involvement of farmers by planting shade trees near larval habitats. PMID:19440962

  14. S-DARS broadcast from inclined, elliptical orbits

    NASA Astrophysics Data System (ADS)

    Briskman, Robert D.; Prevaux, Robert J.

    2004-04-01

    The first Sirius spacecraft was launched on July 1, 2000. Exactly 5 months later, on December 1, the third spacecraft was launched, completing the three satellite S-DARS (Satellite Digital Audio Radio Service) constellation. The three satellites are deployed in inclined, elliptical, geosynchronous orbits, which allow seamless broadcast coverage to mobile users in the contiguous US. Terrestrial broadcast repeaters provide service in urban cores. The system is in operation, providing the first ever S-DARS service. The constellation design results in satellite ground tracks over North America with two satellites always above the equator. High elevation look angles from the mobile ground terminals to the satellites minimize performance degradation due to blockage, foliage attenuation and multi-path. The spacecraft were built by Space Systems/Loral using the 1300 bus modified for operation in high inclination orbits. Each spacecraft was launched using a dedicated Russian Proton booster. The satellite payload is a bent pipe repeater using 7.1 GHz for the uplink and 2.3 GHz for the broadcast transmission. The repeater high-power amplification stage consists of 32 Traveling Wave Tube Amplifiers phase combined to yield a total radio frequency output power of nearly 4 kW at saturated operation. The satellite antennas are mechanically steered to maintain the transmit beam centered on the Contiguous United States and the receive beam centered on the uplink earth station located in Vernon Valley, New Jersey. The satellite payload design and performance are described. The principal spacecraft bus systems are described with emphasis on improvements made for operation in the inclined, elliptical geosynchronous orbits.

  15. Advances in animal ecology from 3D ecosystem mapping with LiDAR

    NASA Astrophysics Data System (ADS)

    Davies, A.; Asner, G. P.

    2015-12-01

    The advent and recent advances of Light Detection and Ranging (LiDAR) have enabled accurate measurement of 3D ecosystem structure. Although the use of LiDAR data is widespread in vegetation science, it has only recently (< 14 years) been applied to animal ecology. Despite such recent application, LiDAR has enabled new insights in the field and revealed the fundamental importance of 3D ecosystem structure for animals. We reviewed the studies to date that have used LiDAR in animal ecology, synthesising the insights gained. Structural heterogeneity is most conducive to increased animal richness and abundance, and increased complexity of vertical vegetation structure is more positively influential than traditionally measured canopy cover, which produces mixed results. However, different taxonomic groups interact with a variety of 3D canopy traits and some groups with 3D topography. LiDAR technology can be applied to animal ecology studies in a wide variety of environments to answer an impressive array of questions. Drawing on case studies from vastly different groups, termites and lions, we further demonstrate the applicability of LiDAR and highlight new understanding, ranging from habitat preference to predator-prey interactions, that would not have been possible from studies restricted to field based methods. We conclude with discussion of how future studies will benefit by using LiDAR to consider 3D habitat effects in a wider variety of ecosystems and with more taxa to develop a better understanding of animal dynamics.

  16. Applications of High-Resolution LiDAR Data for the Christina River Basin CZO

    NASA Astrophysics Data System (ADS)

    Hicks, N. S.; Aufdenkampe, A. K.; Hicks, S. D.

    2011-12-01

    High-resolution LiDAR data allows for fine scale geomorphic assessment over relatively large spatial extents. Previously available DEMs with a resolution of ten meters or more did not provide adequate resolution for geomorphic characterization of small streams and watersheds or the identification of changes in stream morphology over time. High-resolution LiDAR data for a portion of the Christina River Basin Critical Zone Observatory (CRB-CZO) was obtained during both leaf-off and leaf-on time periods in 2010. Topographic data from these flights is being analyzed with the intent of geomorphic applications such as stream morphology, sediment transport studies, and the evaluation of alluvial deposits. These data and resultant products will also be used in hydrologic and biogeochemical modeling and in biologic and biogeochemical studies of these streams, which are long-term study sites. The LiDAR data also facilitate informed instrument placement and will be used for vegetation studies. The LiDAR data for the CRB-CZO has been used to create a variety of LiDAR based topographic data products including TINs and 0.5-m DEMs. LiDAR derived slope and elevation products were combined with LiDAR intensity images to identify stream channel boundaries and stream centerlines for third through first-order streams. High-resolution slope data also aided in floodplain characterization of these small streams. These high precision stream channel and floodplain characterizations would not have been otherwise possible without extensive field surveying. Future LiDAR flights will allow for the identification of changes in channel morphology over time in low order basins. These characterizations are of particular interest in comparisons between forested and meadow reaches, and in studying the effects of changes in land-use on channel morphology. High-resolution LiDAR data allow for the generation of surface characterizations of importance to a wide range of interdisciplinary researchers.

  17. Automatic registration of UAV-borne sequent images and LiDAR data

    NASA Astrophysics Data System (ADS)

    Yang, Bisheng; Chen, Chi

    2015-03-01

    Use of direct geo-referencing data leads to registration failure between sequent images and LiDAR data captured by mini-UAV platforms because of low-cost sensors. This paper therefore proposes a novel automatic registration method for sequent images and LiDAR data captured by mini-UAVs. First, the proposed method extracts building outlines from LiDAR data and images and estimates the exterior orientation parameters (EoPs) of the images with building objects in the LiDAR data coordinate framework based on corresponding corner points derived indirectly by using linear features. Second, the EoPs of the sequent images in the image coordinate framework are recovered using a structure from motion (SfM) technique, and the transformation matrices between the LiDAR coordinate and image coordinate frameworks are calculated using corresponding EoPs, resulting in a coarse registration between the images and the LiDAR data. Finally, 3D points are generated from sequent images by multi-view stereo (MVS) algorithms. Then the EoPs of the sequent images are further refined by registering the LiDAR data and the 3D points using an iterative closest-point (ICP) algorithm with the initial results from coarse registration, resulting in a fine registration between sequent images and LiDAR data. Experiments were performed to check the validity and effectiveness of the proposed method. The results show that the proposed method achieves high-precision robust co-registration of sequent images and LiDAR data captured by mini-UAVs.

  18. 4D Near Real-Time Environmental Monitoring Using Highly Temporal LiDAR

    NASA Astrophysics Data System (ADS)

    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

  19. Frontiers in Using LiDAR to Analyze Urban Landscape Heterogeneity

    NASA Astrophysics Data System (ADS)

    Singh, Kunwar Krishna Veer

    Light Detection and Ranging (LiDAR) technology has facilitated extraordinary advances in our ability to remotely sense precise details of both built and natural environments. The inherent complexity of urban landscapes and the massive data volumes produced by LiDAR require unique methodological considerations for big data remote sensing over large metropolitan regions. The heterogeneous landscapes of the rapidly urbanizing Charlotte Metropolitan Region of North Carolina provided an ideal testing ground for developing methods of analysis for urban ecosystems over large regional extents, including: (1) fusion of LiDAR digital surface models (DSMs) with Landsat TM imagery to balance spatial resolution, data volume, and mapping accuracy of urban land covers, (2) comparison of LiDAR-derived metrics to fine grain optical imagery -- and their integration -- for detecting forest understory plant invaders, and (3) data reduction techniques for computationally efficient estimation of aboveground woody biomass in urban forests. In Chapter 1, I examined tradeoffs between potential gains in mapping accuracy and computational costs by integrating DSMs (structural and intensity) extracted from LiDAR with TM imagery and evaluating the degree to which TM, LiDAR, and LiDAR-TM fusion data discriminated land covers. I used Maximum Likelihood and Classification Tree algorithms to classify TM data, LiDAR data, and LiDAR-TM fusions. I assessed the relative contributions of LiDAR DSMs to map classification accuracy and identified an optimal spatial resolution of LiDAR DSMs for large area assessments of urban land cover. In Chapter 2, I analyzed combinations of datasets developed from categorized LiDAR-derived variables (Overstory, Understory, Topography, and Overall Vegetation Characteristics) and IKONOS imagery ( Optical) to detect and map the understory plant invader, Ligustrum sinense, using Random Forest (RF) and logistic regression (LR) algorithms, and I assessed the relative

  20. Surface characteristics modeling and performance evaluation of urban building materials using LiDAR data.

    PubMed

    Li, Xiaolu; Liang, Yu

    2015-05-20

    Analysis of light detection and ranging (LiDAR) intensity data to extract surface features is of great interest in remote sensing research. One potential application of LiDAR intensity data is target classification. A new bidirectional reflectance distribution function (BRDF) model is derived for target characterization of rough and smooth surfaces. Based on the geometry of our coaxial full-waveform LiDAR system, the integration method is improved through coordinate transformation to establish the relationship between the BRDF model and intensity data of LiDAR. A series of experiments using typical urban building materials are implemented to validate the proposed BRDF model and integration method. The fitting results show that three parameters extracted from the proposed BRDF model can distinguish the urban building materials from perspectives of roughness, specular reflectance, and diffuse reflectance. A comprehensive analysis of these parameters will help characterize surface features in a physically rigorous manner. PMID:26192511

  1. The Krüppel-Like Factor Dar1 Determines Multipolar Neuron Morphology

    PubMed Central

    Wang, Xin; Zhang, Macy W.; Kim, Jung Hwan; Macara, Ann Marie; Sterne, Gabriella; Yang, Tao

    2015-01-01

    Neurons typically assume multipolar, bipolar, or unipolar morphologies. Little is known about the mechanisms underlying the development of these basic morphological types. Here, we show that the Krüppel-like transcription factor Dar1 determines the multipolar morphology of postmitotic neurons in Drosophila. Dar1 is specifically expressed in multipolar neurons and loss of dar1 gradually converts multipolar neurons into the bipolar or unipolar morphology without changing neuronal identity. Conversely, misexpression of Dar1 or its mammalian homolog in unipolar and bipolar neurons causes them to assume multipolar morphologies. Dar1 regulates the expression of several dynein genes and nuclear distribution protein C (nudC), which is an essential component of a specialized dynein complex that positions the nucleus in a cell. We further show that these genes are required for Dar1-induced multipolar neuron morphology. Dar1 likely functions as a terminal selector gene for the basic layout of neuron morphology by regulating both dendrite extension and the dendrite–nucleus coupling. SIGNIFICANCE STATEMENT The three basic morphological types of neurons—unipolar, bipolar, and multipolar—are important for information processing and wiring of neural circuits. Little progress has been made toward understanding the molecular and cellular programs that generate these types since their discovery over a century ago. It is generally assumed that basic morphological types of neurons are determined by the number of dendrites growing out from the cell body. Here, we show that this model alone is insufficient. We introduce the positioning of nucleus as a critical factor in this process and report that the transcription factor Dar1 determines multipolar neuron morphology in postmitotic neurons by regulating genes involved in nuclear positioning. PMID:26490864

  2. Detecting understory plant invasion in urban forests using LiDAR

    NASA Astrophysics Data System (ADS)

    Singh, Kunwar K.; Davis, Amy J.; Meentemeyer, Ross K.

    2015-06-01

    Light detection and ranging (LiDAR) data are increasingly used to measure structural characteristics of urban forests but are rarely used to detect the growing problem of exotic understory plant invaders. We explored the merits of using LiDAR-derived metrics alone and through integration with spectral data to detect the spatial distribution of the exotic understory plant Ligustrum sinense, a rapidly spreading invader in the urbanizing region of Charlotte, North Carolina, USA. We analyzed regional-scale L. sinense occurrence data collected over the course of three years with LiDAR-derived metrics of forest structure that were categorized into the following groups: overstory, understory, topography, and overall vegetation characteristics, and IKONOS spectral features - optical. Using random forest (RF) and logistic regression (LR) classifiers, we assessed the relative contributions of LiDAR and IKONOS derived variables to the detection of L. sinense. We compared the top performing models developed for a smaller, nested experimental extent using RF and LR classifiers, and used the best overall model to produce a predictive map of the spatial distribution of L. sinense across our country-wide study extent. RF classification of LiDAR-derived topography metrics produced the highest mapping accuracy estimates, outperforming IKONOS data by 17.5% and the integration of LiDAR and IKONOS data by 5.3%. The top performing model from the RF classifier produced the highest kappa of 64.8%, improving on the parsimonious LR model kappa by 31.1% with a moderate gain of 6.2% over the county extent model. Our results demonstrate the superiority of LiDAR-derived metrics over spectral data and fusion of LiDAR and spectral data for accurately mapping the spatial distribution of the forest understory invader L. sinense.

  3. Synergy of VSWIR and LiDAR for Ecosystem Structure, Biomass, and Canopy Diversity

    NASA Technical Reports Server (NTRS)

    Cook, Bruce D.; Asner, Gregory P.

    2010-01-01

    This slide presentation reviews the use of Visible ShortWave InfraRed (VSWIR) Imaging Spectrometer and LiDAR to study ecosystem structure, biomass and canopy diversity. It is shown that the biophysical data from LiDAR and biochemical information from hyperspectral remote sensing provides complementary data for: (1) describing spatial patterns of vegetation and biodiversity, (2) characterizing relationships between ecosystem form and function, and (3) detecting natural and human induced change that affects the biogeochemical cycles.

  4. Modeling marbled murrelet (Brachyramphus marmoratus) habitat using LiDAR-derived canopy data

    USGS Publications Warehouse

    Hagar, Joan C.; Eskelson, Bianca N.I.; Haggerty, Patricia K.; Nelson, S. Kim; Vesely, David G.

    2014-01-01

    LiDAR (Light Detection And Ranging) is an emerging remote-sensing tool that can provide fine-scale data describing vertical complexity of vegetation relevant to species that are responsive to forest structure. We used LiDAR data to estimate occupancy probability for the federally threatened marbled murrelet (Brachyramphus marmoratus) in the Oregon Coast Range of the United States. Our goal was to address the need identified in the Recovery Plan for a more accurate estimate of the availability of nesting habitat by developing occupancy maps based on refined measures of nest-strand structure. We used murrelet occupancy data collected by the Bureau of Land Management Coos Bay District, and canopy metrics calculated from discrete return airborne LiDAR data, to fit a logistic regression model predicting the probability of occupancy. Our final model for stand-level occupancy included distance to coast, and 5 LiDAR-derived variables describing canopy structure. With an area under the curve value (AUC) of 0.74, this model had acceptable discrimination and fair agreement (Cohen's κ = 0.24), especially considering that all sites in our sample were regarded by managers as potential habitat. The LiDAR model provided better discrimination between occupied and unoccupied sites than did a model using variables derived from Gradient Nearest Neighbor maps that were previously reported as important predictors of murrelet occupancy (AUC = 0.64, κ = 0.12). We also evaluated LiDAR metrics at 11 known murrelet nest sites. Two LiDAR-derived variables accurately discriminated nest sites from random sites (average AUC = 0.91). LiDAR provided a means of quantifying 3-dimensional canopy structure with variables that are ecologically relevant to murrelet nesting habitat, and have not been as accurately quantified by other mensuration methods.

  5. Suicide in the Dar es Salaam region, Tanzania, 2005.

    PubMed

    Mgaya, Edward; Kazaura, Method R; Outwater, Anne; Kinabo, Lina

    2008-04-01

    Suicide surveillance was launched at the Muhimbili National Hospital mortuary in Dar es Salaam Region, Tanzania from 1st January to 31st December, 2005 to determine its magnitude and characteristics. Following the WHO guidelines with minor modifications, information on sex, dates of birth and death, places of residence and death, occupation, reasons and means of suicide were collected. There were 65 (2.3 per 100,000 population) suicides recorded in 2005. The suicide rate for males was 3.4/100,000 and for females was 1.2/100,000 which maybe some of the lowest rates ever reported in the world. The mean age at suicide was 32.9 (SD=13.1) years. Males were about three times more likely to commit suicide as females. The main motive behind suicide was recorded for 26 (40%) victims as family-related and for 11 (17%) as health related. Although there was a wide range of ages at which people committed suicide, the average age seems to be very low. Since reasons for suicide are coated with family problems, strategies to improve awareness of psychological and mental health services and to provide alternative economic and social support networks are advocated. PMID:18313013

  6. A Cyberinfrastructure Platform for Distribution of GeoEarthScope LiDAR Topography Data

    NASA Astrophysics Data System (ADS)

    Crosby, C. J.; Nandigam, V.; Arrowsmith, J. R.; Balakrishnan, S.; Alex, N.; Baru, C.

    2008-12-01

    The recently completed GeoEarthScope airborne LiDAR (Light Detection And Ranging) topography acquisition will provide unprecedented data adjacent to active faults throughout the plate boundary region of western North America. Totaling more than 5000 square kilometers, these community-oriented data offer an high-resolution representation of fault zone topography and should be a revolutionary resource for researchers studying earthquake hazards, active faulting, landscape processes, and ground deformation. Since spring of 2007, the NSF-funded GeoEarthScope LiDAR project has acquired data for the San Andreas fault system in northern California, faults in southern California, the Yakima Fold and Thrust Belt in Washington, Yellowstone National Park, the Tetons, the Wasatch Front, and Alaska. These data will be made available via the OpenTopography Portal (www.opentopography.org), a domain-specific component of the GEON project, as they are processed and delivered by the National Center for Airborne Laser Mapping. The OpenTopography Portal (OpenToPo) provides access to a variety of GeoEarthScope LiDAR data products and uses several cyberinfrastructure components developed by the GEON project. These products range from simple Google Earth visualizations of LiDAR hillshades to standard digital elevation model (DEM) products as well as LiDAR point cloud data. The wide spectrum of LiDAR users have variable scientific applications, computing resources and technical experience and thus require a data distribution system that provides various levels of access to the data. Standard DEM products in OpenToPo are accessed via a Google Map and/or Google Earth-based interface that allow users to browse and download the data products. For users who wish to explore the full potential of the LiDAR data, we provide access to the raw LiDAR point data and a suite of DEM generation tools to enable users to create custom DEMs to best fit their science applications. Storage and management of

  7. Classification of LiDAR Data with Point Based Classification Methods

    NASA Astrophysics Data System (ADS)

    Yastikli, N.; Cetin, Z.

    2016-06-01

    LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatial-based features

  8. Effects of LiDAR point density and landscape context on estimates of urban forest biomass

    NASA Astrophysics Data System (ADS)

    Singh, Kunwar K.; Chen, Gang; McCarter, James B.; Meentemeyer, Ross K.

    2015-03-01

    Light Detection and Ranging (LiDAR) data is being increasingly used as an effective alternative to conventional optical remote sensing to accurately estimate aboveground forest biomass ranging from individual tree to stand levels. Recent advancements in LiDAR technology have resulted in higher point densities and improved data accuracies accompanied by challenges for procuring and processing voluminous LiDAR data for large-area assessments. Reducing point density lowers data acquisition costs and overcomes computational challenges for large-area forest assessments. However, how does lower point density impact the accuracy of biomass estimation in forests containing a great level of anthropogenic disturbance? We evaluate the effects of LiDAR point density on the biomass estimation of remnant forests in the rapidly urbanizing region of Charlotte, North Carolina, USA. We used multiple linear regression to establish a statistical relationship between field-measured biomass and predictor variables derived from LiDAR data with varying densities. We compared the estimation accuracies between a general Urban Forest type and three Forest Type models (evergreen, deciduous, and mixed) and quantified the degree to which landscape context influenced biomass estimation. The explained biomass variance of the Urban Forest model, using adjusted R2, was consistent across the reduced point densities, with the highest difference of 11.5% between the 100% and 1% point densities. The combined estimates of Forest Type biomass models outperformed the Urban Forest models at the representative point densities (100% and 40%). The Urban Forest biomass model with development density of 125 m radius produced the highest adjusted R2 (0.83 and 0.82 at 100% and 40% LiDAR point densities, respectively) and the lowest RMSE values, highlighting a distance impact of development on biomass estimation. Our evaluation suggests that reducing LiDAR point density is a viable solution to regional

  9. Independent evaluation of the SNODAS snow depth product using regional scale LiDAR-derived measurements

    NASA Astrophysics Data System (ADS)

    Hedrick, A.; Marshall, H.-P.; Winstral, A.; Elder, K.; Yueh, S.; Cline, D.

    2014-06-01

    Repeated Light Detection and Ranging (LiDAR) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km2 LiDAR-derived dataset of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically-based energy-and-mass-balance snow model with satellite, airborne and automated ground-based observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the coterminous United States. Independent validation data is scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation dataset with substantial geographic coverage. Within twelve distinctive 500 m × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 LiDAR acquisitions. This supplied a dataset for constraining the uncertainty of upscaled LiDAR estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled LiDAR snow depths were then compared to the SNODAS-estimates over the entire study area for the dates of the LiDAR flights. The remotely-sensed snow depths provided a more spatially continuous comparison dataset and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between LiDAR observations and SNODAS estimates were most drastic, suggesting natural processes specific to these regions as causal influences on model uncertainty.

  10. Estimating FPAR of maize canopy using airborne discrete-return LiDAR data.

    PubMed

    Luo, Shezhou; Wang, Cheng; Xi, Xiaohuan; Pan, Feifei

    2014-03-10

    The fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter for ecosystem modeling, crop growth monitoring and yield prediction. Ground-based FPAR measurements are time consuming and labor intensive. Remote sensing provides an alternative method to obtain repeated, rapid and inexpensive estimates of FPAR over large areas. LiDAR is an active remote sensing technology and can be used to extract accurate canopy structure parameters. A method to estimating FPAR of maize from airborne discrete-return LiDAR data was developed and tested in this study. The raw LiDAR point clouds were processed to separate ground returns from vegetation returns using a filter method over a maize field in the Heihe River Basin, northwest China. The fractional cover (fCover) of maize canopy was computed using the ratio of canopy return counts or intensity sums to the total of returns or intensities. FPAR estimation models were established based on linear regression analysis between the LiDAR-derived fCover and the field-measured FPAR (R(2) = 0.90, RMSE = 0.032, p < 0.001). The reliability of the constructed regression model was assessed using the leave-one-out cross-validation procedure and results show that the regression model is not overfitting the data and has a good generalization capability. Finally, 15 independent field-measured FPARs were used to evaluate accuracy of the LiDAR-predicted FPARs and results show that the LiDAR-predicted FPAR has a high accuracy (R(2) = 0.89, RMSE = 0.034). In summary, this study suggests that the airborne discrete-return LiDAR data could be adopted to accurately estimate FPAR of maize. PMID:24663850

  11. Estimation of effective plant area index for South Korean forests using LiDAR system.

    PubMed

    Kwak, Doo-Ahn; Lee, Woo-Kyun; Kafatos, Menas; Son, Yowhan; Cho, Hyun-Kook; Lee, Seung-Ho

    2010-07-01

    Light Detection and Ranging (LiDAR) systems can be used to estimate both vertical and horizontal forest structure. Woody components, the leaves of trees and the understory can be described with high precision, using geo-registered 3D-points. Based on this concept, the Effective Plant Area Indices (PAI(e)) for areas of Korean Pine (Pinus koraiensis), Japanese Larch (Larix leptolepis) and Oak (Quercus spp.) were estimated by calculating the ratio of intercepted and incident LIDAR laser rays for the canopies of the three forest types. Initially, the canopy gap fraction (G ( LiDAR )) was generated by extracting the LiDAR data reflected from the canopy surface, or inner canopy area, using k-means statistics. The LiDAR-derived PAI(e) was then estimated by using G ( LIDAR ) with the Beer-Lambert law. A comparison of the LiDAR-derived and field-derived PAI(e) revealed the coefficients of determination for Korean Pine, Japanese Larch and Oak to be 0.82, 0.64 and 0.59, respectively. These differences between field-based and LIDAR-based PAI(e) for the different forest types were attributed to the amount of leaves and branches in the forest stands. The absence of leaves, in the case of both Larch and Oak, meant that the LiDAR pulses were only reflected from branches. The probability that the LiDAR pulses are reflected from bare branches is low as compared to the reflection from branches with a high leaf density. This is because the size of the branch is smaller than the resolution across and along the 1 meter LIDAR laser track. Therefore, a better predictive accuracy would be expected for the model if the study would be repeated in late spring when the shoots and leaves of the deciduous trees begin to appear. PMID:20697878

  12. Improved estimates of forest vegetation structure and biomass with a LiDAR-optimized sampling design

    NASA Astrophysics Data System (ADS)

    Hawbaker, Todd J.; Keuler, Nicholas S.; Lesak, Adrian A.; Gobakken, Terje; Contrucci, Kirk; Radeloff, Volker C.

    2009-06-01

    LiDAR data are increasingly available from both airborne and spaceborne missions to map elevation and vegetation structure. Additionally, global coverage may soon become available with NASA's planned DESDynI sensor. However, substantial challenges remain to using the growing body of LiDAR data. First, the large volumes of data generated by LiDAR sensors require efficient processing methods. Second, efficient sampling methods are needed to collect the field data used to relate LiDAR data with vegetation structure. In this paper, we used low-density LiDAR data, summarized within pixels of a regular grid, to estimate forest structure and biomass across a 53,600 ha study area in northeastern Wisconsin. Additionally, we compared the predictive ability of models constructed from a random sample to a sample stratified using mean and standard deviation of LiDAR heights. Our models explained between 65 to 88% of the variability in DBH, basal area, tree height, and biomass. Prediction errors from models constructed using a random sample were up to 68% larger than those from the models built with a stratified sample. The stratified sample included a greater range of variability than the random sample. Thus, applying the random sample model to the entire population violated a tenet of regression analysis; namely, that models should not be used to extrapolate beyond the range of data from which they were constructed. Our results highlight that LiDAR data integrated with field data sampling designs can provide broad-scale assessments of vegetation structure and biomass, i.e., information crucial for carbon and biodiversity science.

  13. LiDAR Applications in Resource Geology and Benefits for Land Management

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

    The US Forest Service (US Department of Agriculture) manages a broad range of geologic resources and hazards on National Forests and Grass Lands throughout the United States. Resources include rock and earth materials, groundwater, caves and paleontological resources, minerals, energy resources, and unique geologic areas. Hazards include landslides, floods, earthquakes, volcanic eruptions, and naturally hazardous materials (e.g., asbestos, radon). Forest Service Geologists who address these issues are Resource Geologists. They have been exploring LiDAR as a revolutionary tool to efficiently manage all of these hazards and resources. However, most LiDAR applications for management have focused on timber and fuels management, rather than landforms. This study shows the applications and preliminary results of using LiDAR for managing geologic resources and hazards on public lands. Applications shown include calculating sediment budgets, mapping and monitoring landslides, mapping and characterizing borrow pits or mines, determining landslide potential, mapping faults, and characterizing groundwater dependent ecosystems. LiDAR can be used to model potential locations of groundwater dependent ecosystems with threatened or endangered plant species such as Howellia aquatilis. This difficult to locate species typically exists on the Mendocino National Forest within sag ponds on landslide benches. LiDAR metrics of known sites are used to model potential habitat. Thus LiDAR can link the disciplines of geology, hydrology, botany, archaeology and others for enhanced land management. As LiDAR acquisition costs decrease and it becomes more accessible, land management organizations will find a wealth of applications with potential far-reaching benefits for managing geologic resources and hazards.

  14. Geotechnical applications of LiDAR pertaining to geomechanical evaluation and hazard identification

    NASA Astrophysics Data System (ADS)

    Lato, Matthew J.

    Natural hazards related to ground movement that directly affect the safety of motorists and highway infrastructure include, but are not limited to, rockfalls, rockslides, debris flows, and landslides. This thesis specifically deals with the evaluation of rockfall hazards through the evaluation of LiDAR data. Light Detection And Ranging (LiDAR) is an imaging technology that can be used to delineate and evaluate geomechanically-controlled hazards. LiDAR has been adopted to conduct hazard evaluations pertaining to rockfall, rock-avalanches, debris flows, and landslides. Characteristics of LiDAR surveying, such as rapid data acquisition rates, mobile data collection, and high data densities, pose problems to traditional CAD or GIS-based mapping methods. New analyses methods, including tools specifically oriented to geomechanical analyses, are needed. The research completed in this thesis supports development of new methods, including improved survey techniques, innovative software workflows, and processing algorithms to aid in the detection and evaluation of geomechanically controlled rockfall hazards. The scientific research conducted between the years of 2006-2010, as presented in this thesis, are divided into five chapters, each of which has been published by or is under review by an international journal. The five research foci are: (i) geomechanical feature extraction and analysis using LiDAR data in active mining environments; (ii) engineered monitoring of rockfall hazards along transportation corridors: using mobile terrestrial LiDAR; (iii) optimization of LiDAR scanning and processing for automated structural evaluation of discontinuities in rockmasses; (iv) location orientation bias when using static LiDAR data for geomechanical analysis; and (v) evaluating roadside rockmasses for rockfall hazards from LiDAR data: optimizing data collection and processing protocols. The research conducted pertaining to this thesis has direct and significant implications with

  15. Spinning a laser web: predicting spider distributions using LiDAR.

    PubMed

    Vierling, K T; Bässler, C; Brandl, R; Vierling, L A; Weiss, I; Müller, J

    2011-03-01

    LiDAR remote sensing has been used to examine relationships between vertebrate diversity and environmental characteristics, but its application to invertebrates has been limited. Our objectives were to determine whether LiDAR-derived variables could be used to accurately describe single-species distributions and community characteristics of spiders in remote forested and mountainous terrain. We collected over 5300 spiders across multiple transects in the Bavarian National Park (Germany) using pitfall traps. We examined spider community characteristics (species richness, the Shannon index, the Simpson index, community composition, mean body size, and abundance) and single-species distribution and abundance with LiDAR variables and ground-based measurements. We used the R2 and partial R2 provided by variance partitioning to evaluate the predictive power of LiDAR-derived variables compared to ground measurements for each of the community characteristics. The total adjusted R2 for species richness, the Shannon index, community species composition, and body size had a range of 25-57%. LiDAR variables and ground measurements both contributed >80% to the total predictive power. For species composition, the explained variance was approximately 32%, which was significantly greater than expected by chance. The predictive power of LiDAR-derived variables was comparable or superior to that of the ground-based variables for examinations of single-species distributions, and it explained up to 55% of the variance. The predictability of species distributions was higher for species that had strong associations with shade in open-forest habitats, and this niche position has been well documented across the European continent for spider species. The similar statistical performance between LiDAR and ground-based measures at our field sites indicated that deriving spider community and species distribution information using LiDAR data can provide not only high predictive power at

  16. Evolutionary feature selection to estimate forest stand variables using LiDAR

    NASA Astrophysics Data System (ADS)

    Garcia-Gutierrez, Jorge; Gonzalez-Ferreiro, Eduardo; Riquelme-Santos, Jose C.; Miranda, David; Dieguez-Aranda, Ulises; Navarro-Cerrillo, Rafael M.

    2014-02-01

    Light detection and ranging (LiDAR) has become an important tool in forestry. LiDAR-derived models are mostly developed by means of multiple linear regression (MLR) after stepwise selection of predictors. An increasing interest in machine learning and evolutionary computation has recently arisen to improve regression use in LiDAR data processing. Although evolutionary machine learning has already proven to be suitable for regression, evolutionary computation may also be applied to improve parametric models such as MLR. This paper provides a hybrid approach based on joint use of MLR and a novel genetic algorithm for the estimation of the main forest stand variables. We show a comparison between our genetic approach and other common methods of selecting predictors. The results obtained from several LiDAR datasets with different pulse densities in two areas of the Iberian Peninsula indicate that genetic algorithms perform better than the other methods statistically. Preliminary studies suggest that a lack of parametric conditions in field data and possible misuse of parametric tests may be the main reasons for the better performance of the genetic algorithm. This research confirms the findings of previous studies that outline the importance of evolutionary computation in the context of LiDAR analisys of forest data, especially when the size of fieldwork datatasets is reduced.

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

    NASA Astrophysics Data System (ADS)

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

    2014-02-01

    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.

  18. Detection of fault structures with airborne LiDAR point-cloud data

    NASA Astrophysics Data System (ADS)

    Chen, Jie; Du, Lei

    2015-08-01

    The airborne LiDAR (Light Detection And Ranging) technology is a new type of aerial earth observation method which can be used to produce high-precision DEM (Digital Elevation Model) quickly and reflect ground surface information directly. Fault structure is one of the key forms of crustal movement, and its quantitative description is the key to the research of crustal movement. The airborne LiDAR point-cloud data is used to detect and extract fault structures automatically based on linear extension, elevation mutation and slope abnormal characteristics. Firstly, the LiDAR point-cloud data is processed to filter out buildings, vegetation and other non-surface information with the TIN (Triangulated Irregular Network) filtering method and Burman model calibration method. TIN and DEM are made from the processed data sequentially. Secondly, linear fault structures are extracted based on dual-threshold method. Finally, high-precision DOM (Digital Orthophoto Map) and other geological knowledge are used to check the accuracy of fault structure extraction. An experiment is carried out in Beiya Village of Yunnan Province, China. With LiDAR technology, results reveal that: the airborne LiDAR point-cloud data can be utilized to extract linear fault structures accurately and automatically, measure information such as height, width and slope of fault structures with high precision, and detect faults in areas with vegetation coverage effectively.

  19. [Analysis of an Air Pollution Process Using LiDAR in Nanjing, Spring of 2014].

    PubMed

    Bao, Qing; He, Jun-liang; Zha, Yong; Cheng, Feng; Li, Qian-nan

    2015-04-01

    Based on environmental monitoring data, meteorological data and the results of numerical simulation, a typical air pollution process in Nanjing, from 26th May to 1st June, 2014 was deeply analyzed combining aerosol extinction coefficient derived from LiDAR system. Experimental results showed that the entire pollution process was affected by both local pollution and exogenous inputs including dust and smoke. Meteorological factors played a significant role in the generation and elimination of pollutants. Low pressure and temperature inversion also hindered the diffusion of pollutants, while strong rainfall terminated the pollution process. During the pollution, the height of atmospheric boundary layer was lower than normal situation and changed little during the pollution period, which provided a poor diffusion condition for pollutants. LiDAR could accurately detect aerosol vertical structure which was able to capture the temporal and spatial variation of pollutant distributions. Therefore, LiDAR can be of great significance for the atmospheric pollution monitoring. PMID:26164889

  20. Automatic extraction of building boundaries using aerial LiDAR data

    NASA Astrophysics Data System (ADS)

    Wang, Ruisheng; Hu, Yong; Wu, Huayi; Wang, Jian

    2016-01-01

    Building extraction is one of the main research topics of the photogrammetry community. This paper presents automatic algorithms for building boundary extractions from aerial LiDAR data. First, segmenting height information generated from LiDAR data, the outer boundaries of aboveground objects are expressed as closed chains of oriented edge pixels. Then, building boundaries are distinguished from nonbuilding ones by evaluating their shapes. The candidate building boundaries are reconstructed as rectangles or regular polygons by applying new algorithms, following the hypothesis verification paradigm. These algorithms include constrained searching in Hough space, enhanced Hough transformation, and the sequential linking technique. The experimental results show that the proposed algorithms successfully extract building boundaries at rates of 97%, 85%, and 92% for three LiDAR datasets with varying scene complexities.

  1. Octree-based segmentation for terrestrial LiDAR point cloud data in industrial applications

    NASA Astrophysics Data System (ADS)

    Su, Yun-Ting; Bethel, James; Hu, Shuowen

    2016-03-01

    Automated and efficient algorithms to perform segmentation of terrestrial LiDAR data is critical for exploitation of 3D point clouds, where the ultimate goal is CAD modeling of the segmented data. In this work, a novel segmentation technique is proposed, starting with octree decomposition to recursively divide the scene into octants or voxels, followed by a novel split and merge framework that uses graph theory and a series of connectivity analyses to intelligently merge components into larger connected components. The connectivity analysis, based on a combination of proximity, orientation, and curvature connectivity criteria, is designed for the segmentation of pipes, vessels, and walls from terrestrial LiDAR data of piping systems at industrial sites, such as oil refineries, chemical plants, and steel mills. The proposed segmentation method is exercised on two terrestrial LiDAR datasets of a steel mill and a chemical plant, demonstrating its ability to correctly reassemble and segregate features of interest.

  2. Using 3D visual tools with LiDAR for environmental outreach

    NASA Astrophysics Data System (ADS)

    Glenn, N. F.; Mannel, S.; Ehinger, S.; Moore, C.

    2009-12-01

    The project objective is to develop visualizations using light detection and ranging (LiDAR) data and other data sources to increase community understanding of remote sensing data for earth science. These data are visualized using Google Earth and other visualization methods. Final products are delivered to K-12, state, and federal agencies to share with their students and community constituents. Once our partner agencies were identified, we utilized a survey method to better understand their technological abilities and use of visualization products. The final multimedia products include a visualization of LiDAR and well data for water quality mapping in a southeastern Idaho watershed; a tour of hydrologic points of interest in southeastern Idaho visited by thousands of people each year, and post-earthquake features near Borah Peak, Idaho. In addition to the customized multimedia materials, we developed tutorials to encourage our partners to utilize these tools with their own LiDAR and other scientific data.

  3. Estimating stem volume and biomass of Pinus koraiensis using LiDAR data.

    PubMed

    Kwak, Doo-Ahn; Lee, Woo-Kyun; Cho, Hyun-Kook; Lee, Seung-Ho; Son, Yowhan; Kafatos, Menas; Kim, So-Ra

    2010-07-01

    The objective of this study was to estimate the stem volume and biomass of individual trees using the crown geometric volume (CGV), which was extracted from small-footprint light detection and ranging (LiDAR) data. Attempts were made to analyze the stem volume and biomass of Korean Pine stands (Pinus koraiensis Sieb. et Zucc.) for three classes of tree density: low (240 N/ha), medium (370 N/ha), and high (1,340 N/ha). To delineate individual trees, extended maxima transformation and watershed segmentation of image processing methods were applied, as in one of our previous studies. As the next step, the crown base height (CBH) of individual trees has to be determined; information for this was found in the LiDAR point cloud data using k-means clustering. The LiDAR-derived CGV and stem volume can be estimated on the basis of the proportional relationship between the CGV and stem volume. As a result, low tree-density plots had the best performance for LiDAR-derived CBH, CGV, and stem volume (R (2) = 0.67, 0.57, and 0.68, respectively) and accuracy was lowest for high tree-density plots (R (2) = 0.48, 0.36, and 0.44, respectively). In the case of medium tree-density plots accuracy was R (2) = 0.51, 0.52, and 0.62, respectively. The LiDAR-derived stem biomass can be predicted from the stem volume using the wood basic density of coniferous trees (0.48 g/cm(3)), and the LiDAR-derived above-ground biomass can then be estimated from the stem volume using the biomass conversion and expansion factors (BCEF, 1.29) proposed by the Korea Forest Research Institute (KFRI). PMID:20182905

  4. LiDAR data and SAR imagery acquired by an unmanned helicopter for rapid landslide investigation

    NASA Astrophysics Data System (ADS)

    Kasai, M.; Tanaka, Y.; Yamazaki, T.

    2012-12-01

    When earthquakes or heavy rainfall hits a landslide prone area, initial actions require estimation of the size of damage to people and infrastructure. This includes identifying the number and size of newly collapsed or expanded landslides, and appraising subsequent risks from remobilization of landslides and debris materials. In inapproachable areas, the UAV (Unmanned Aerial Vehicles) is likely to be of greatest use. In addition, repeat monitoring of sites after the event is a way of utilizing UAVs, particularly in terms of cost and convenience. In this study, LiDAR (SkEyesBox MP-1) data and SAR (Nano SAR) imagery, acquired over 0.5 km2 landslide prone area, are presented to assess the practicability of using unmanned helicopters (in this case a 10 year old YAMAHA RMAX G1) in these situations. LiDAR data was taken in July 2012, when tree foliage covered the ground surface. However, imagery was of sufficient quality to identify and measure landslide features. Nevertheless, LiDAR data obtained by a manned helicopter in the same area in August 2008 was more detailed, reflecting the function of the LiDAR scanner. On the other hand, 2 m resolution Nano SAR imagery produced reasonable results to elucidate hillslope condition. A quick method for data processing without loss of image quality was also investigated. In conclusion, the LiDAR scanner and UAV employed here could be used to plan immediate remedial activity of the area, before LiDAR measurement with a manned helicopter can be organized. SAR imagery from UAV is also available for this initial activity, and can be further applied to long term monitoring.

  5. Reconstruction and analysis of a deciduous sapling using digital photographs or terrestrial-LiDAR technology

    PubMed Central

    Delagrange, Sylvain; Rochon, Pascal

    2011-01-01

    Background and Aims To meet the increasing need for rapid and non-destructive extraction of canopy traits, two methods were used and compared with regard to their accuracy in estimatating 2-D and 3-D parameters of a hybrid poplar sapling. Methods The first method consisted of the analysis of high definition photographs in Tree Analyser (TA) software (PIAF-INRA/Kasetsart University). TA allowed the extraction of individual traits using a space carving approach. The second method utilized 3-D point clouds acquired from terrestrial light detection and ranging (T-LiDAR) scans. T-LiDAR scans were performed on trees without leaves to reconstruct the lignified structure of the sapling. From this skeleton, foliage was added using simple modelling rules extrapolated from field measurements. Validation of the estimated dimension and the accuracy of reconstruction was then achieved by comparison with an empirical data set. Key Results TA was found to be slightly less precise than T-LiDAR for estimating tree height, canopy height and mean canopy diameter, but for 2-D traits both methods were, however, fully satisfactory. TA tended to over-estimate total leaf area (error up to 50 %), but better estimates were obtained by reducing the size of the voxels used for calculations. In contrast, T-LiDAR estimated total leaf area with an error of <6 %. Finally, both methods led to an over-estimation of canopy volume. With respect to this trait, T-LiDAR (14·5 % deviation) greatly surpassed the accuracy of TA (up to 50 % deviation), even if the voxels used were reduced in size. Conclusions Taking into account their magnitude of data acquisition and analysis and their accuracy in trait estimations, both methods showed contrasting potential future uses. Specifically, T-LiDAR is a particularly promising tool for investigating the development of large perennial plants, by itself or in association with plant modelling. PMID:21515607

  6. Detailed Hydrographic Feature Extraction from High-Resolution LiDAR Data

    SciTech Connect

    Danny L. Anderson

    2012-05-01

    Detailed hydrographic feature extraction from high-resolution light detection and ranging (LiDAR) data is investigated. Methods for quantitatively evaluating and comparing such extractions are presented, including the use of sinuosity and longitudinal root-mean-square-error (LRMSE). These metrics are then used to quantitatively compare stream networks in two studies. The first study examines the effect of raster cell size on watershed boundaries and stream networks delineated from LiDAR-derived digital elevation models (DEMs). The study confirmed that, with the greatly increased resolution of LiDAR data, smaller cell sizes generally yielded better stream network delineations, based on sinuosity and LRMSE. The second study demonstrates a new method of delineating a stream directly from LiDAR point clouds, without the intermediate step of deriving a DEM. Direct use of LiDAR point clouds could improve efficiency and accuracy of hydrographic feature extractions. The direct delineation method developed herein and termed “mDn”, is an extension of the D8 method that has been used for several decades with gridded raster data. The method divides the region around a starting point into sectors, using the LiDAR data points within each sector to determine an average slope, and selecting the sector with the greatest downward slope to determine the direction of flow. An mDn delineation was compared with a traditional grid-based delineation, using TauDEM, and other readily available, common stream data sets. Although, the TauDEM delineation yielded a sinuosity that more closely matches the reference, the mDn delineation yielded a sinuosity that was higher than either the TauDEM method or the existing published stream delineations. Furthermore, stream delineation using the mDn method yielded the smallest LRMSE.

  7. LiDAR remote sensing observations for forest assessment and recovery responses following disturbance

    NASA Astrophysics Data System (ADS)

    Rosette, J.; Suárez, J.; Fonweben, J.; North, P.

    2013-12-01

    LiDAR data covering 400 km2 in the Cowal and Trossacs Forest District, Scotland, U.K., were used to provide a low cost solution to update the database of public forests and to produce multi-scale cartographic products for supporting management decisions in the event of forest disturbance such as infestation or wind damage. All parameter estimates were directly obtained from the LiDAR data without the necessity of field calibration. This was achieved using a hybrid approach integrating current stand models for Sitka spruce (Picea sitchensis bong. Carr) and LiDAR analysis. More conventional field methods offer percentage sampling, permitting only a proportion of stands to be surveyed each year and aiming to represent stand-level conditions. The use of LiDAR is advantageous in allowing a complete observation-based assessment throughout the forest and greatly-improved spatial representation of important forest parameters. Time-series analysis was performed using LiDAR data collected in the past 10 years. This analysis allowed us to establish growth trajectories in the forest stands, automatically discriminating areas of growth, those whose growth had been affected by disease and the occurrence of windthrow gaps. The results were compared to the cartography produced by the Forest District after a severe wind storm that affected the area in 2012. This analysis showed the ability of LiDAR to create a more precise location and extent of catastrophic damage and windthrow gaps. In addition, once windthrow has occurred, progression of further damage in existing canopy gaps can be observed. This approach additionally allows the impact of disease on forest growth and subsequent recovery response to be monitored.

  8. Direct injection into the IsoDAR Cyclotron using a RFQ

    NASA Astrophysics Data System (ADS)

    Axani, Spencer; IsoDAR Collaboration

    2015-04-01

    Beginning in the 1970s, the use of Radio Frequency Quadrupoles (RFQs) has been pervasive in linear accelerators in order to accelerate, bunch, and separate ion species. Current research suggests this may be an ideal way to inject a low energy H2+ beam axially into a cyclotron. The IsoDAR (Isotope Decay At Rest) experiment aims to implement this injection system in order to achieve higher Low Energy Beam Transport (LEBT) efficiencies and ultimately construct a novel compact neutrino factory to test the hypothesis of sterile neutrinos. This talk will focus on the research and development needed to implement a RFQ into the IsoDAR experiment.

  9. Drug-to-antibody ratio (DAR) and drug load distribution by LC-ESI-MS.

    PubMed

    Basa, Louisette

    2013-01-01

    This chapter describes an LC-ESI-MS method for the DAR and drug load distribution analysis that is suitable for lysine-linked ADCs. The ADC sample is desalted using a reversed-phase LC column with an acetonitrile gradient prior to online MS analysis. The MS spectrum is processed (deconvoluted) and converted to a series of zero charge state masses that corresponds to the increasing number of drugs in the ADC. Integration of the mass peak area allows the calculation of the DAR and drug load distribution of ADCs. PMID:23913155

  10. KML-Based Access and Visualization of High Resolution LiDAR Topography

    NASA Astrophysics Data System (ADS)

    Crosby, C. J.; Blair, J. L.; Nandigam, V.; Memon, A.; Baru, C.; Arrowsmith, J. R.

    2008-12-01

    Over the past decade, there has been dramatic growth in the acquisition of LiDAR (Light Detection And Ranging) high-resolution topographic data for earth science studies. Capable of providing digital elevation models (DEMs) more than an order of magnitude higher resolution than those currently available, LiDAR data allow earth scientists to study the processes that contribute to landscape evolution at resolutions not previously possible yet essential for their appropriate representation. These datasets also have significant implications for earth science education and outreach because they provide an accurate representation of landforms and geologic hazards. Unfortunately, the massive volume of data produced by LiDAR mapping technology can be a barrier to their use. To make these data available to a larger user community, we have been exploring the use of Keyhole Markup Language (KML) and Google Earth to provide access to LiDAR data products and visualizations. LiDAR digital elevation models are typically delivered in a tiled format that lends itself well to a KML-based distribution system. For LiDAR datasets hosted in the GEON OpenTopography Portal (www.opentopography.org) we have developed KML files that show the extent of available LiDAR DEMs and provide direct access to the data products. Users interact with these KML files to explore the extent of the available data and are able to select DEMs that correspond to their area of interest. Selection of a tile loads a download that the user can then save locally for analysis in their software of choice. The GEON topography system also has tools available that allow users to generate custom DEMs from LiDAR point cloud data. This system is powerful because it enables users to access massive volumes of raw LiDAR data and to produce DEM products that are optimized to their science applications. We have developed a web service that converts the custom DEM models produced by the system to a hillshade that is delivered to

  11. Visualization of High-Resolution LiDAR Topography in Google Earth

    NASA Astrophysics Data System (ADS)

    Crosby, C. J.; Nandigam, V.; Arrowsmith, R.; Blair, J. L.

    2009-12-01

    The growing availability of high-resolution LiDAR (Light Detection And Ranging) topographic data has proven to be revolutionary for Earth science research. These data allow scientists to study the processes acting on the Earth’s surfaces at resolutions not previously possible yet essential for their appropriate representation. In addition to their utility for research, the data have also been recognized as powerful tools for communicating earth science concepts for education and outreach purposes. Unfortunately, the massive volume of data produced by LiDAR mapping technology can be a barrier to their use. To facilitate access to these powerful data for research and educational purposes, we have been exploring the use of Keyhole Markup Language (KML) and Google Earth to deliver LiDAR-derived visualizations. The OpenTopography Portal (http://www.opentopography.org/) is a National Science Foundation-funded facility designed to provide access to Earth science-oriented LiDAR data. OpenTopography hosts a growing collection of LiDAR data for a variety of geologic domains, including many of the active faults in the western United States. We have found that the wide spectrum of LiDAR users have variable scientific applications, computing resources, and technical experience and thus require a data distribution system that provides various levels of access to the data. For users seeking a synoptic view of the data, and for education and outreach purposes, delivering full-resolution images derived from LiDAR topography into the Google Earth virtual globe is powerful. The virtual globe environment provides a freely available and easily navigated viewer and enables quick integration of the LiDAR visualizations with imagery, geographic layers, and other relevant data available in KML format. Through region-dependant network linked KML, OpenTopography currently delivers over 20 GB of LiDAR-derived imagery to users via simple, easily downloaded KMZ files hosted at the Portal

  12. Validating LiDAR Derived Estimates of Canopy Height, Structure and Fractional Cover in Riparian Areas: A Comparison of Leaf-on and Leaf-off LiDAR Data

    NASA Astrophysics Data System (ADS)

    Wasser, L. A.; Chasmer, L. E.; Taylor, A.; Day, R.

    2010-12-01

    Characterization of riparian buffers is integral to understanding the landscape scale impacts of disturbance on wildlife and aquatic ecosystems. Riparian buffers may be characterized using in situ plot sampling or via high resolution remote sensing. Field measurements are time-consuming and may not cover a broad range of ecosystem types. Further, spectral remote sensing methods introduce a compromise between spatial resolution (grain) and area extent. Airborne LiDAR can be used to continuously map and characterize riparian vegetation structure and composition due to the three-dimensional reflectance of laser pulses within and below the canopy, understory and at the ground surface. The distance between reflections (or ‘returns’) allows for detection of narrow buffer corridors at the landscape scale. There is a need to compare leaf-off and leaf-on surveyed LiDAR data with in situ measurements to assess accuracy in landscape scale analysis. These comparisons are particularly important considering increased availability of leaf-off surveyed LiDAR datasets. And given this increased availability, differences between leaf-on and leaf-off derived LiDAR metrics are largely unknown for riparian vegetation of varying composition and structure. This study compares the effectiveness of leaf-on and leaf-off LiDAR in characterizing riparian buffers of varying structure and composition as compared to field measurements. Field measurements were used to validate LiDAR derived metrics. Vegetation height, canopy cover, density and overstory and understory species composition were recorded in 80 random plots of varying vegetation type, density and structure within a Pennsylvania watershed (-77.841, 40.818). Plot data were compared with LiDAR data collected during leaf on and leaf off conditions to determine 1) accuracy of LiDAR derived metrics compared to field measures and 2) differences between leaf-on and leaf-off LiDAR metrics. Results illustrate that differences exist between

  13. Genetics and Human Agency: Comment on Dar-Nimrod and Heine (2011)

    ERIC Educational Resources Information Center

    Turkheimer, Eric

    2011-01-01

    Dar-Nimrod and Heine (2011) decried genetic essentialism without denying the importance of genetics in the genesis of human behavior, and although I agree on both counts, a deeper issue remains unaddressed: how should we adjust our cognitions about our own behavior in light of genetic influence, or is it perhaps not necessary to take genetics into…

  14. Integrating ICT into Teaching and Learning at the University of Dar es Salaam

    ERIC Educational Resources Information Center

    Mtebe, Joel S.; Dachi, Hilary; Raphael, Christina

    2011-01-01

    Since 1985, Tanzania has been undergoing significant political and economic changes from a centralized to a more market-oriented and globally connected economy. The University of Dar es Salaam (UDSM) has responded to these changes by reviewing its legal status, vision, and functions, particularly those related to research, teaching, and public…

  15. High-throughput genotyping of hop (Humulus lupulus L.) utilising diversity arrays technology (DArT).

    PubMed

    Howard, E L; Whittock, S P; Jakše, J; Carling, J; Matthews, P D; Probasco, G; Henning, J A; Darby, P; Cerenak, A; Javornik, B; Kilian, A; Koutoulis, A

    2011-05-01

    Implementation of molecular methods in hop (Humulus lupulus L.) breeding is dependent on the availability of sizeable numbers of polymorphic markers and a comprehensive understanding of genetic variation. However, use of molecular marker technology is limited due to expense, time inefficiency, laborious methodology and dependence on DNA sequence information. Diversity arrays technology (DArT) is a high-throughput cost-effective method for the discovery of large numbers of quality polymorphic markers without reliance on DNA sequence information. This study is the first to utilise DArT for hop genotyping, identifying 730 polymorphic markers from 92 hop accessions. The marker quality was high and similar to the quality of DArT markers previously generated for other species; although percentage polymorphism and polymorphism information content (PIC) were lower than in previous studies deploying other marker systems in hop. Genetic relationships in hop illustrated by DArT in this study coincide with knowledge generated using alternate methods. Several statistical analyses separated the hop accessions into genetically differentiated North American and European groupings, with hybrids between the two groups clearly distinguishable. Levels of genetic diversity were similar in the North American and European groups, but higher in the hybrid group. The markers produced from this time and cost-efficient genotyping tool will be a valuable resource for numerous applications in hop breeding and genetics studies, such as mapping, marker-assisted selection, genetic identity testing, guidance in the maintenance of genetic diversity and the directed breeding of superior cultivars. PMID:21243330

  16. Clinical, Virologic, and Epidemiologic Characteristics of Dengue Outbreak, Dar es Salaam, Tanzania, 2014

    PubMed Central

    Mboera, Leonard E.G.; De Nardo, Pasquale; Oriyo, Ndekya M.; Meschi, Silvia; Rumisha, Susan F.; Colavita, Francesca; Mhina, Athanas; Carletti, Fabrizio; Mwakapeje, Elibariki; Capobianchi, Maria Rosaria; Castilletti, Concetta; Di Caro, Antonino; Nicastri, Emanuele; Malecela, Mwelecele N.; Ippolito, Giuseppe

    2016-01-01

    We investigated a dengue outbreak in Dar es Salaam, Tanzania, in 2014, that was caused by dengue virus (DENV) serotype 2. DENV infection was present in 101 (20.9%) of 483 patients. Patient age and location of residence were associated with infection. Seven (4.0%) of 176 patients were co-infected with malaria and DENV. PMID:27088845

  17. Geospatial revolution and remote sensing LiDAR in Mesoamerican archaeology.

    PubMed

    Chase, Arlen F; Chase, Diane Z; Fisher, Christopher T; Leisz, Stephen J; Weishampel, John F

    2012-08-01

    The application of light detection and ranging (LiDAR), a laser-based remote-sensing technology that is capable of penetrating overlying vegetation and forest canopies, is generating a fundamental shift in Mesoamerican archaeology and has the potential to transform research in forested areas world-wide. Much as radiocarbon dating that half a century ago moved archaeology forward by grounding archaeological remains in time, LiDAR is proving to be a catalyst for an improved spatial understanding of the past. With LiDAR, ancient societies can be contextualized within a fully defined landscape. Interpretations about the scale and organization of densely forested sites no longer are constrained by sample size, as they were when mapping required laborious on-ground survey. The ability to articulate ancient landscapes fully permits a better understanding of the complexity of ancient Mesoamerican urbanism and also aids in modern conservation efforts. The importance of this geospatial innovation is demonstrated with newly acquired LiDAR data from the archaeological sites of Caracol, Cayo, Belize and Angamuco, Michoacán, Mexico. These data illustrate the potential of technology to act as a catalytic enabler of rapid transformational change in archaeological research and interpretation and also underscore the value of on-the-ground archaeological investigation in validating and contextualizing results. PMID:22802623

  18. Pit Latrine Emptying Behavior and Demand for Sanitation Services in Dar Es Salaam, Tanzania

    PubMed Central

    Jenkins, Marion W.; Cumming, Oliver; Cairncross, Sandy

    2015-01-01

    Pit latrines are the main form of sanitation in unplanned areas in many rapidly growing developing cities. Understanding demand for pit latrine fecal sludge management (FSM) services in these communities is important for designing demand-responsive sanitation services and policies to improve public health. We examine latrine emptying knowledge, attitudes, behavior, trends and rates of safe/unsafe emptying, and measure demand for a new hygienic latrine emptying service in unplanned communities in Dar Es Salaam (Dar), Tanzania, using data from a cross-sectional survey at 662 residential properties in 35 unplanned sub-wards across Dar, where 97% had pit latrines. A picture emerges of expensive and poor FSM service options for latrine owners, resulting in widespread fecal sludge exposure that is likely to increase unless addressed. Households delay emptying as long as possible, use full pits beyond what is safe, face high costs even for unhygienic emptying, and resort to unsafe practices like ‘flooding out’. We measured strong interest in and willingness to pay (WTP) for the new pit emptying service at 96% of residences; 57% were WTP ≥U.S. $17 to remove ≥200 L of sludge. Emerging policy recommendations for safe FSM in unplanned urban communities in Dar and elsewhere are discussed. PMID:25734790

  19. Engineering monitoring of rockfall hazards along transportation corridors: using mobile terrestrial LiDAR

    NASA Astrophysics Data System (ADS)

    Lato, M.; Hutchinson, J.; Diederichs, M.; Ball, D.; Harrap, R.

    2009-06-01

    Geotechnical hazards along linear transportation corridors are challenging to identify and often require constant monitoring. Inspecting corridors using traditional, manual methods requires the engineer to be unnecessarily exposed to the hazard. It also requires closure of the corridor to ensure safety of the worker from passing vehicles. This paper identifies the use of mobile terrestrial LiDAR data as a compliment to traditional field methods. Mobile terrestrial LiDAR is an emerging remote data collection technique capable of generating accurate fully three-dimensional virtual models while driving at speeds up to 100 km/h. Data is collected from a truck that causes no delays to active traffic nor does it impede corridor use. These resultant georeferenced data can be used for geomechanical structural feature identification and kinematic analysis, rockfall path identification and differential monitoring of rock movement or failure over time. Comparisons between mobile terrestrial and static LiDAR data collection and analysis are presented. As well, detailed discussions on workflow procedures for possible implementation are discussed. Future use of mobile terrestrial LiDAR data for corridor analysis will focus on repeated surveys and developing dynamic four-dimensional models, higher resolution data collection. As well, computationally advanced, spatially accurate, geomechanically controlled three-dimensional rockfall simulations should be investigated.

  20. Hyperspectral and LiDAR remote sensing of fire fuels in Hawaii Volcanoes National Park.

    PubMed

    Varga, Timothy A; Asner, Gregory P

    2008-04-01

    Alien invasive grasses threaten to transform Hawaiian ecosystems through the alteration of ecosystem dynamics, especially the creation or intensification of a fire cycle. Across sub-montane ecosystems of Hawaii Volcanoes National Park on Hawaii Island, we quantified fine fuels and fire spread potential of invasive grasses using a combination of airborne hyperspectral and light detection and ranging (LiDAR) measurements. Across a gradient from forest to savanna to shrubland, automated mixture analysis of hyperspectral data provided spatially explicit fractional cover estimates of photosynthetic vegetation, non-photosynthetic vegetation, and bare substrate and shade. Small-footprint LiDAR provided measurements of vegetation height along this gradient of ecosystems. Through the fusion of hyperspectral and LiDAR data, a new fire fuel index (FFI) was developed to model the three-dimensional volume of grass fuels. Regionally, savanna ecosystems had the highest volumes of fire fuels, averaging 20% across the ecosystem and frequently filling all of the three-dimensional space represented by each image pixel. The forest and shrubland ecosystems had lower FFI values, averaging 4.4% and 8.4%, respectively. The results indicate that the fusion of hyperspectral and LiDAR remote sensing can provide unique information on the three-dimensional properties of ecosystems, their flammability, and the potential for fire spread. PMID:18488621

  1. Clinical, Virologic, and Epidemiologic Characteristics of Dengue Outbreak, Dar es Salaam, Tanzania, 2014.

    PubMed

    Vairo, Francesco; Mboera, Leonard E G; De Nardo, Pasquale; Oriyo, Ndekya M; Meschi, Silvia; Rumisha, Susan F; Colavita, Francesca; Mhina, Athanas; Carletti, Fabrizio; Mwakapeje, Elibariki; Capobianchi, Maria Rosaria; Castilletti, Concetta; Di Caro, Antonino; Nicastri, Emanuele; Malecela, Mwelecele N; Ippolito, Giuseppe

    2016-05-01

    We investigated a dengue outbreak in Dar es Salaam, Tanzania, in 2014, that was caused by dengue virus (DENV) serotype 2. DENV infection was present in 101 (20.9%) of 483 patients. Patient age and location of residence were associated with infection. Seven (4.0%) of 176 patients were co-infected with malaria and DENV. PMID:27088845

  2. Registration of optical imagery and LiDAR data using an inherent geometrical constraint.

    PubMed

    Zhang, Wuming; Zhao, Jing; Chen, Mei; Chen, Yiming; Yan, Kai; Li, Linyuan; Qi, Jianbo; Wang, Xiaoyan; Luo, Jinghui; Chu, Qing

    2015-03-23

    A novel method for registering imagery with Light Detection And Ranging (LiDAR) data is proposed. It is based on the phenomenon that the back-projection of LiDAR point cloud of an object should be located within the object boundary in the image. Using this inherent geometrical constraint, the registration parameters computation of both data sets only requires LiDAR point clouds of several objects and their corresponding boundaries in the image. The proposed registration method comprises of four steps: point clouds extraction, boundary extraction, back-projection computation and registration parameters computation. There are not any limitations on the geometrical and spectral properties of the object. So it is suitable not only for structured scenes with man-made objects but also for natural scenes. Moreover, the proposed method based on the inherent geometrical constraint can register two data sets derived from different parts of an object. It can be used to co-register TLS (Terrestrial Laser Scanning) LiDAR point cloud and UAV (Unmanned aerial vehicle) image, which are obtaining more attention in the forest survey application. Using initial registration parameters comparable to POS (position and orientation system) accuracy, the performed experiments validated the feasibility of the proposed registration method. PMID:25837107

  3. Using regional-scale LiDAR surveys to validate operational snow models

    NASA Astrophysics Data System (ADS)

    Hedrick, A. R.; Marshall, H. P.; Winstral, A. H.; Elder, K.; Yueh, S. H.; Cline, D. W.

    2014-12-01

    As survey costs continue to plummet and storage capabilities soar, large-scale multitemporal airborne Light Detection and Ranging (LiDAR) surveys for high-resolution snow depth measurements are becoming commonplace in mountain research watersheds. Though there are disadvantages to the technique (e.g. poor temporal representation and high uncertainty in steep terrain and dense vegetation), the wealth of information with regard to previously unknown spatial snow depth distributions can be an valuable tool for assessing spatially distributed operational snow models. As a portion of NASA's second Cold Lands Processes Experiment (CLPX-2), two 750-km2 LiDAR surveys were conducted over Northern Colorado in December and February of the 2006/2007 winter season. The resulting 5-m gridded changes in snow depth overlay 980 individual pixels of the SNOw Data Assimilation System (SNODAS) spatial framework. As an important operational snow model developed by NOAA's National Operational Hydrologic Remote Sensing Center (NOHRSC), SNODAS generally lacks independent validation datasets due to the data assimilation step critical for adjusting the energy balance and downscaled Numerical Weather Prediction (NWP) model components. The influence of sub-grid variability on SNODAS performance is assessed using the independent high resolution CLPX-2 LiDAR changes in snow depth. This method provides a foundation for further studies to quantitatively address the affect of small-scale physiographic variables on various large-scale operational snow models by making use of forthcoming large-scale LiDAR datasets.

  4. Child Labour in Urban Agriculture: The Case of Dar es Salaam, Tanzania.

    ERIC Educational Resources Information Center

    Mlozi, Malongo R. S.

    1995-01-01

    Urban agriculture in Dar es Salaam was found to use child labor of both children with parents of higher and lower socioeconomic status (SES). Discusses policy implications and calls for the education of parents of lower SES not to expect an economic contribution from their children's labor, and the education of children about their rights. (LZ)

  5. a Data Driven Method for Building Reconstruction from LiDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Sajadian, M.; Arefi, H.

    2014-10-01

    Airborne laser scanning, commonly referred to as LiDAR, is a superior technology for three-dimensional data acquisition from Earth's surface with high speed and density. Building reconstruction is one of the main applications of LiDAR system which is considered in this study. For a 3D reconstruction of the buildings, the buildings points should be first separated from the other points such as; ground and vegetation. In this paper, a multi-agent strategy has been proposed for simultaneous extraction and segmentation of buildings from LiDAR point clouds. Height values, number of returned pulse, length of triangles, direction of normal vectors, and area are five criteria which have been utilized in this step. Next, the building edge points are detected using a new method named "Grid Erosion". A RANSAC based technique has been employed for edge line extraction. Regularization constraints are performed to achieve the final lines. Finally, by modelling of the roofs and walls, 3D building model is reconstructed. The results indicate that the proposed method could successfully extract the building from LiDAR data and generate the building models automatically. A qualitative and quantitative assessment of the proposed method is then provided.

  6. A comparison of two open source LiDAR surface classification algorithms

    Technology Transfer Automated Retrieval System (TEKTRAN)

    With the progression of LiDAR (Light Detection and Ranging) towards a mainstream resource management tool, it has become necessary to understand how best to process and analyze the data. While most ground surface identification algorithms remain proprietary and have high purchase costs; a few are op...

  7. Mapping of post-event earthquake induced landslides in Sg. Mesilou using LiDAR

    NASA Astrophysics Data System (ADS)

    Hanan Mat Yusoff, Habibah; Azahari Razak, Khamarrul; Yuen, Florence; Harun, Afifi; Talib, Jasmi; Mohamad, Zakaria; Ramli, Zamri; Abd Razab, Razain

    2016-06-01

    Earthquake is a common natural disaster in active tectonic regions. The disaster can induce cascading disasters such as debris flow, mudflow and reactivated old landslides. M 6.0 Ranau earthquake dated on June 05, 2015 coupling with intense and prolonged rainfall caused several mass movements such as debris flow, deep-seated and shallow landslides in Mesilou, Sabah. This study aims at providing a better insight into the use of advanced LiDAR mapping technology for recognizing landslide induced by earthquakes particularly in a vegetated terrain, assessing post event hazard and analyzing its distribution for hazard zonation. We developed the landslide inventory using LiDAR-derived visual analysis method and validated in the field. A landslide inventory map improved with the support of LiDAR derivative data. Finally, landslide inventory was analysed by emphasizing its distribution and density in such a way that it provides clues of risky zone as a result of debris flow. We recommend that mitigation action and risk reduction should be taken place at a transport zone of the channel compared to other zones. This study indicates that modern airborne LiDAR can be a good complementary tool for improving landslide inventory in a complex environment, and an effective tool for rapid regional hazard and risk assessment in the tropics.

  8. Spatially-aware Processing of Large Raw LiDAR Data Sets

    NASA Astrophysics Data System (ADS)

    Strane, M. D.; Oskin, M.

    2004-12-01

    An ultimate goal of LiDAR (LIght Detection And Ranging) data acquisition is to produce a regularly sampled accurate topographic view of the surface of the Earth. Last-return and inverse-distance weighted sampling of raw LiDAR data do not take into account the non-random distribution of raw data points. While elevation data produced by these methods is of high accuracy, gradients are not well-resolved and aliasing artifacts are produced, especially on low gradient surfaces. Because of the volume of data involved, resampling schemes that take into account the spatial distribution of raw data have been cumbersome to implement. We have developed a resampling method that uses the free open-source PostgresSQL database to store the raw LiDAR data indexed spatially and as its original time series. This database permits rapid access to raw data points via spatial queries. A robust and expedient algorithm has been implemented to produce regularly gridded resampled data with a least squares plane fit regression. This algorithm reduces aliasing artifacts on low gradient surfaces. The algorithm is also a proof-of-concept to show that complex spatially-aware processing of large LiDAR data sets is feasible on a reasonable time scale, and will be the basis for further improvements such as vegetation removal.

  9. Pit latrine emptying behavior and demand for sanitation services in Dar Es Salaam, Tanzania.

    PubMed

    Jenkins, Marion W; Cumming, Oliver; Cairncross, Sandy

    2015-03-01

    Pit latrines are the main form of sanitation in unplanned areas in many rapidly growing developing cities. Understanding demand for pit latrine fecal sludge management (FSM) services in these communities is important for designing demand-responsive sanitation services and policies to improve public health. We examine latrine emptying knowledge, attitudes, behavior, trends and rates of safe/unsafe emptying, and measure demand for a new hygienic latrine emptying service in unplanned communities in Dar Es Salaam (Dar), Tanzania, using data from a cross-sectional survey at 662 residential properties in 35 unplanned sub-wards across Dar, where 97% had pit latrines. A picture emerges of expensive and poor FSM service options for latrine owners, resulting in widespread fecal sludge exposure that is likely to increase unless addressed. Households delay emptying as long as possible, use full pits beyond what is safe, face high costs even for unhygienic emptying, and resort to unsafe practices like 'flooding out'. We measured strong interest in and willingness to pay (WTP) for the new pit emptying service at 96% of residences; 57% were WTP≥U.S. $17 to remove ≥200 L of sludge. Emerging policy recommendations for safe FSM in unplanned urban communities in Dar and elsewhere are discussed. PMID:25734790

  10. Spatial Patterns of Trees from Airborne LiDAR Using a Simple Tree Segmentation Algorithm

    NASA Astrophysics Data System (ADS)

    Jeronimo, S.; Kane, V. R.; McGaughey, R. J.; Franklin, J. F.

    2015-12-01

    Objectives for management of forest ecosystems on public land incorporate a focus on maintenance and restoration of ecological functions through silvicultural manipulation of forest structure. The spatial pattern of residual trees - the horizontal element of structure - is a key component of ecological restoration prescriptions. We tested the ability of a simple LiDAR individual tree segmentation method - the watershed transform - to generate spatial pattern metrics similar to those obtained by the traditional method - ground-based stem mapping - on forested plots representing the structural diversity of a large wilderness area (Yosemite NP) and a large managed area (Sierra NF) in the Sierra Nevada, Calif. Most understory and intermediate-canopy trees were not detected by the LiDAR segmentation; however, LiDAR- and field-based assessments of spatial pattern in terms of tree clump size distributions largely agreed. This suggests that (1) even when individual tree segmentation is not effective for tree density estimates, it can provide a good measurement of tree spatial pattern, and (2) a simple segmentation method is adequate to measure spatial pattern of large areas with a diversity of structural characteristics. These results lay the groundwork for a LiDAR tool to assess clumping patterns across forest landscapes in support of restoration silviculture. This tool could describe spatial patterns of functionally intact reference ecosystems, measure departure from reference targets in treatment areas, and, with successive acquisitions, monitor treatment efficacy.

  11. Airborne hyperspectral and LiDAR data integration for weed detection

    NASA Astrophysics Data System (ADS)

    Tamás, János; Lehoczky, Éva; Fehér, János; Fórián, Tünde; Nagy, Attila; Bozsik, Éva; Gálya, Bernadett; Riczu, Péter

    2014-05-01

    Agriculture uses 70% of global available fresh water. However, ca. 50-70% of water used by cultivated plants, the rest of water transpirated by the weeds. Thus, to define the distribution of weeds is very important in precision agriculture and horticulture as well. To survey weeds on larger fields by traditional methods is often time consuming. Remote sensing instruments are useful to detect weeds in larger area. In our investigation a 3D airborne laser scanner (RIEGL LMS-Q680i) was used in agricultural field near Sopron to scouting weeds. Beside the airborne LiDAR, hyperspectral imaging system (AISA DUAL) and air photos helped to investigate weed coverage. The LiDAR survey was carried out at early April, 2012, before sprouting of cultivated plants. Thus, there could be detected emerging of weeds and direction of cultivation. However airborne LiDAR system was ideal to detect weeds, identification of weeds at species level was infeasible. Higher point density LiDAR - Terrestrial laser scanning - systems are appropriate to distinguish weed species. Based on the results, laser scanner is an effective tool to scouting of weeds. Appropriate weed detection and mapping systems could contribute to elaborate water and herbicide saving management technique. This publication was supported by the OTKA project K 105789.

  12. An Analysis of Student Reading as Measured on the Diagnostic Assessment of Reading (DAR)

    ERIC Educational Resources Information Center

    Baca, Jo-Ann M.; Shepperson, Barbara A.

    2006-01-01

    As part of the reporting of Delaware's State Improvement Grant (DelaSIG), the Delaware Education Research and Development Center (R&D Center) completed a study on the Diagnostic Assessment of Reading (DAR) scores of students whose teachers attended a professional development program designed to help focus teacher instruction of struggling readers…

  13. The Effect of Lava Texture on LiDAR Attributes and Full Waveform

    NASA Astrophysics Data System (ADS)

    Anderson, S. W.; Finnegan, D. C.; LeWinter, A.

    2013-12-01

    The distribution of glassy, vesicular, and crystalline textures on lava flow and dome surfaces provides insights regarding the physical and chemical processes occurring during emplacement. For silicic flows, these textures may reflect variations in the volatile content of lava upon eruption. To assess the efficacy of texture detection with our terrestrial full waveform LiDAR system capable of measuring ~125,000 topographic points/second, we analyzed attribute and full waveform data from a variety of lava textures displayed on recent rhyolitic obsidian flows of the Inyo Dome chain (California) and pahoehoe and aa flows at Kilauea volcano (Hawaii). We find that attributes such as intensity, amplitude and deviation of the returned 1550nm laser pulse fall into discrete ranges associated with glassy, pumiceous and crystalline textures on both the rhyolitic and basaltic surfaces. This enables detection of vesicularity at ranges in excess of 500 m, making LiDAR a useful tool for remotely determining lava texture. Scan times using our Riegl VZ1000 and VZ400 systems require only minutes, allowing for repeated scans over a short time period, and processing times are <1 hour. We have also analyzed the full digitized waveforms of LiDAR pulses returned from these surfaces, and find that they also have unique signatures related to texture. We therefore suggest that LiDAR can provide reliable information on lava texture during eruption, aiding in the interpretation of eruption hazards from increasing volatile contents.

  14. Geospatial revolution and remote sensing LiDAR in Mesoamerican archaeology

    PubMed Central

    Chase, Arlen F.; Fisher, Christopher T.; Leisz, Stephen J.; Weishampel, John F.

    2012-01-01

    The application of light detection and ranging (LiDAR), a laser-based remote-sensing technology that is capable of penetrating overlying vegetation and forest canopies, is generating a fundamental shift in Mesoamerican archaeology and has the potential to transform research in forested areas world-wide. Much as radiocarbon dating that half a century ago moved archaeology forward by grounding archaeological remains in time, LiDAR is proving to be a catalyst for an improved spatial understanding of the past. With LiDAR, ancient societies can be contextualized within a fully defined landscape. Interpretations about the scale and organization of densely forested sites no longer are constrained by sample size, as they were when mapping required laborious on-ground survey. The ability to articulate ancient landscapes fully permits a better understanding of the complexity of ancient Mesoamerican urbanism and also aids in modern conservation efforts. The importance of this geospatial innovation is demonstrated with newly acquired LiDAR data from the archaeological sites of Caracol, Cayo, Belize and Angamuco, Michoacán, Mexico. These data illustrate the potential of technology to act as a catalytic enabler of rapid transformational change in archaeological research and interpretation and also underscore the value of on-the-ground archaeological investigation in validating and contextualizing results. PMID:22802623

  15. Students' Experiences and Challenges of Blended Learning at the University of Dar Es Salaam, Tanzania

    ERIC Educational Resources Information Center

    Mtebe, Joel S.; Raphael, Christina

    2013-01-01

    Recent developments in Information and Communication Technologies (ICTs), especially eLearning, have heightened the need for University of Dar es Salaam (UDSM) to supplement on-campus face-to-face delivery as well as meeting increased students' enrolments through blended distance learning. Since 2008, the University has been offering three…

  16. High-throughput genotyping of hop (Humulus lupulus L.) utilising diversity arrays technology (DArT)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Implementation of molecular methods in hop breeding is dependent on the availability of sizeable numbers of polymorphic markers and a comprehensive understanding of genetic variation. Diversity Arrays Technology (DArT) is a high-throughput cost-effective method for the discovery of large numbers of...

  17. Genetic Essentialism, Neuroessentialism, and Stigma: Commentary on Dar-Nimrod and Heine (2011)

    ERIC Educational Resources Information Center

    Haslam, Nick

    2011-01-01

    Dar-Nimrod and Heine (2011) presented a masterfully broad review of the implications of genetic essentialism for understandings of human diversity. This commentary clarifies the reasons that essentialist thinking has problematic social consequences and links genetic forms of essentialism to those invoking neural essences. The mounting evidence…

  18. Diversity Arrays Technology (DArT) platform for genotyping and mapping in carrot (Daucus carota L.)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Carrot is one of the most important root vegetable crops grown worldwide on more than one million hectares. Its progenitor, wild Daucus carota, is a weed commonly occurring across continents in the temperate climatic zone. Diversity Array Technology (DArT) is a microarray-based molecular marker syst...

  19. Errors in LiDAR-derived shrub height and crown area on sloped terrain

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This study developed and tested four methodologies for determining shrub height measurements with LiDAR data in a semiarid shrub-steppe in southwestern Idaho, USA. Unique to this study was the focus of sagebrush height measurements on sloped terrain. The study also developed one of the first metho...

  20. Biomass estimation of Douglas fir stands using airborne LiDAR data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Biomass is an important parameter not only for carbon cycle modeling, but also for supporting land management operations (e.g. land use policy, forest fire management). Various remote sensing data have been utilized for biomass estimation, especially in forested areas. LiDAR (Light Detection And Ran...

  1. Using Satellite and Airborne LiDAR to Model Woodpecker Habitat Occupancy at the Landscape Scale

    PubMed Central

    Vierling, Lee A.; Vierling, Kerri T.; Adam, Patrick; Hudak, Andrew T.

    2013-01-01

    Incorporating vertical vegetation structure into models of animal distributions can improve understanding of the patterns and processes governing habitat selection. LiDAR can provide such structural information, but these data are typically collected via aircraft and thus are limited in spatial extent. Our objective was to explore the utility of satellite-based LiDAR data from the Geoscience Laser Altimeter System (GLAS) relative to airborne-based LiDAR to model the north Idaho breeding distribution of a forest-dependent ecosystem engineer, the Red-naped sapsucker (Sphyrapicus nuchalis). GLAS data occurred within ca. 64 m diameter ellipses spaced a minimum of 172 m apart, and all occupancy analyses were confined to this grain scale. Using a hierarchical approach, we modeled Red-naped sapsucker occupancy as a function of LiDAR metrics derived from both platforms. Occupancy models based on satellite data were weak, possibly because the data within the GLAS ellipse did not fully represent habitat characteristics important for this species. The most important structural variables influencing Red-naped Sapsucker breeding site selection based on airborne LiDAR data included foliage height diversity, the distance between major strata in the canopy vertical profile, and the vegetation density near the ground. These characteristics are consistent with the diversity of foraging activities exhibited by this species. To our knowledge, this study represents the first to examine the utility of satellite-based LiDAR to model animal distributions. The large area of each GLAS ellipse and the non-contiguous nature of GLAS data may pose significant challenges for wildlife distribution modeling; nevertheless these data can provide useful information on ecosystem vertical structure, particularly in areas of gentle terrain. Additional work is thus warranted to utilize LiDAR datasets collected from both airborne and past and future satellite platforms (e.g. GLAS, and the planned IceSAT2

  2. Using satellite and airborne LiDAR to model woodpecker habitat occupancy at the landscape scale.

    PubMed

    Vierling, Lee A; Vierling, Kerri T; Adam, Patrick; Hudak, Andrew T

    2013-01-01

    Incorporating vertical vegetation structure into models of animal distributions can improve understanding of the patterns and processes governing habitat selection. LiDAR can provide such structural information, but these data are typically collected via aircraft and thus are limited in spatial extent. Our objective was to explore the utility of satellite-based LiDAR data from the Geoscience Laser Altimeter System (GLAS) relative to airborne-based LiDAR to model the north Idaho breeding distribution of a forest-dependent ecosystem engineer, the Red-naped sapsucker (Sphyrapicus nuchalis). GLAS data occurred within ca. 64 m diameter ellipses spaced a minimum of 172 m apart, and all occupancy analyses were confined to this grain scale. Using a hierarchical approach, we modeled Red-naped sapsucker occupancy as a function of LiDAR metrics derived from both platforms. Occupancy models based on satellite data were weak, possibly because the data within the GLAS ellipse did not fully represent habitat characteristics important for this species. The most important structural variables influencing Red-naped Sapsucker breeding site selection based on airborne LiDAR data included foliage height diversity, the distance between major strata in the canopy vertical profile, and the vegetation density near the ground. These characteristics are consistent with the diversity of foraging activities exhibited by this species. To our knowledge, this study represents the first to examine the utility of satellite-based LiDAR to model animal distributions. The large area of each GLAS ellipse and the non-contiguous nature of GLAS data may pose significant challenges for wildlife distribution modeling; nevertheless these data can provide useful information on ecosystem vertical structure, particularly in areas of gentle terrain. Additional work is thus warranted to utilize LiDAR datasets collected from both airborne and past and future satellite platforms (e.g. GLAS, and the planned IceSAT2

  3. Effects of atmospheric stability on the evolution of wind turbine wakes: Volumetric LiDAR scans

    NASA Astrophysics Data System (ADS)

    Valerio Iungo, Giacomo; Porté-Agel, Fernando

    2014-05-01

    Aerodynamic optimization of wind farm layout is a fundamental task to reduce wake effects on downstream wind turbines, thus to maximize wind power harvesting. However, downstream evolution and recovery of wind turbine wakes are strongly affected by the characteristics of the incoming atmospheric boundary layer (ABL) flow, like the vertical profiles of the mean wind velocity and the turbulence intensity, which are in turn affected by the ABL stability regime. Therefore, the characterization of the variability of wind turbine wakes under different ABL stability regimes becomes fundamental to better predict wind power harvesting and improve wind farm efficiency. To this aim, wind velocity measurements of the wake produced by a 2 MW Enercon E-70 wind turbine were performed with three scanning Doppler wind Light Detection and Ranging (LiDAR) instruments. One LiDAR was typically devoted to the characterization of the incoming wind, in particular wind velocity, shear and turbulence intensity at the height of the rotor disc. The other two LiDARs performed scans in order to characterize the wake velocity field produced by the tested wind turbine. The main challenge in performing field measurements of wind turbine wakes is represented by the varying wind conditions, and by the consequent adjustments of the turbine yaw angle needed to maximize power production. Consequently, taking into account possible variations of the relative position between LiDAR measurement volume and wake location, different LiDAR measurement procedures were carried out in order to perform 2-D and 3-D characterizations of the mean wake velocity field. However, larger measurement volumes and higher spatial resolution require longer sampling periods; thus, to investigate wake turbulence tests were also performed by staring the LiDAR laser beam over fixed directions and with the maximum sampling frequency. Furthermore, volumetric scans of the wind turbine wake were performed under different wind

  4. Field and LiDAR observations of the Hector Mine California 1999 surface rupture

    NASA Astrophysics Data System (ADS)

    Sousa, F.; Akciz, S. O.; Harvey, J. C.; Hudnut, K. W.; Lynch, D. K.; Scharer, K. M.; Stock, J. M.; Witkosky, R.; Kendrick, K. J.; Wespestad, C.

    2014-12-01

    We report new field- and computer-based investigations of the surface rupture of the October 16, 1999 Hector Mine Earthquake. Since May 2012, in cooperation with the United States Marine Corps Air Ground Combat Center (MCAGCC) at Twentynine Palms, CA, our team has been allowed ground and aerial access to the entire surface rupture. We have focused our new field-based research and imagery analysis along the ~10 kilometer-long maximum slip zone (MSZ) which roughly corresponds to the zone of >4 meter dextral horizontal offset. New data include: 1) a 1 km wide aerial LiDAR survey along the entire surface rupture (@ 10 shots/m2, May 2012, www.opentopography.org); 2) terrestrial LiDAR surveys at 5 sites within the MSZ (@ >1000 shots/m2, April 2014); 3) low altitude aerial photography and ground based photography of the entire MSZ; 4) a ground-truthed database of 87 out of the 94 imagery-based offset measurements made within the MSZ; and 5) a database of 50 new field-based offset measurements made within the MSZ by our team on the ground, 31 of which have also been made on the computer (Ladicaoz) with both the 2000 LiDAR data (@ 0.5 m DEM resolution; Chen et al, in review) and 2012 LiDAR data (@ 35 cm DEM resolution; our team). New results to date include 1) significant variability (> 2 m) in horizontal offsets measured along short distances of the surface rupture (~100 m) within segments of the surface rupture that are localized to a single fault strand; 2) strong dependence of decadal scale fault scarp preservation on local lithology (bedrock vs. alluvial fan vs. fine sediment) and geomorphology (uphill vs. downhill facing scarp); 3) newly observed offset features which were never measured during the post-event field response; 4) newly observed offset features too small to be resolved in airborne LiDAR data (< 1 m); 5) nearly 25% of LiDAR imagery-based measurements that were later ground-truthed were judged by our team to warrant removal from the database due to

  5. Application of LiDAR to hydrologic flux estimation in Australian eucalypt forests (Invited)

    NASA Astrophysics Data System (ADS)

    Lane, P. N.; Mitchell, P. J.; Jaskierniak, D.; Hawthorne, S. N.; Griebel, A.

    2013-12-01

    The potential of LiDAR in ecohydrology is significant as characterising catchment vegetation is crucial to accurate estimation of evapotranspiration (ET). While this may be done at large scales for model parameterisation, stand-scale applications are equally appropriate where traditional methods of measurement of LAI or sapwood areas are time consuming and reliant on assumptions of representative sampling. This is particularly challenging in mountain forests where aspect, soil properties and energy budgets can vary significantly, reflected in the vegetation or where there are changes in the spatial distribution of structural attributes following disturbance. Recent research has investigated the spatial distribution of ET in a eucalypt forest in SE Australia using plot-scale sapflow, interception and forest floor ET measurements. LiDAR was used scale up these measurements. LiDAR (0.16 m scanner footprint) canopy indices were correlated via stepwise regression with 4 water use scalars: basal area (BA), sapwood area (SA), leaf area index (LAI) and canopy coverage (C), with Hmed, Hmean, H80, H95 the best predictors. Combining these indices with empirical relationships between SA and BA, and SA and transpiration (T), and inventory plot 'ground truthing' transpiration was estimated across the 1.3 km2 catchment. Interception was scaled via the Gash model with LiDAR derived inputs. The up-scaling showed a significant variability in the spatial distribution of ET, related to the distribution of SA. The use of LiDAR meant scaling could be achieved at an appropriate spatial scale (20 x 20 m) to the measurements. The second example is the use of airborne LiDAR in developing growth forest models for hydrologic modeling. LiDAR indices were used to stratify multilayered forests using mixed-effect models with a wide range of theoretical distribution functions. When combined with historical plot-scale inventory data we show demonstrated improved growth modeling over traditional

  6. Distinguishing grass from ground using LiDAR: Techniques and applications

    NASA Astrophysics Data System (ADS)

    Pelletier, J. D.; Swetnam, T.; Papuga, S. A.; Nelson, K.; Brooks, P. D.; Harpold, A. A.; Chorover, J.

    2011-12-01

    Standard protocols exist for extracting bare-earth Digital Elevation Models (DEMs) from LiDAR point clouds that include trees and other large woody vegetation. Grasses and other herbaceous plants can also obscure the ground surface, yet methods for optimally distinguishing grass from ground to generate accurate LiDAR-based raster products for geomorphic and ecological applications are still under development. Developing such methods is important because LiDAR-based difference products (e.g. snow thickness) require accurate representations of the ground surface and because raster data for grass height and density have important applications in ecology. In this study, we developed and tested methods for constructing optimal bare-earth and grass height raster layers from LiDAR point clouds and compared the results to high-quality field-based measurements of grass height, density, and species type for nearly 1000 precisely geo-referenced locations collected during the acquisition of a >200 km^2 airborne LiDAR flight of the Valles Caldera National Preserve (New Mexico). In cases of partially bare ground (where the skewness of return heights above a plane fit to the lowest first returns is sufficiently large), a planar fit to the lowest first returns provides a good method of producing an accurate bare-earth DEM and the statistics of the first returns above that planar fit provide good estimates of the mean and variance of grass height. In areas of relatively thick grass cover, however, a fit to the lowest first returns yields a bare-earth DEM that may be a meter or more above the actual ground surface. Here we propose a method to solve this problem using field-measured correlations among the mean, variance, and skewness of grass heights. In this method, the variance and skewness of the differences between LiDAR first returns and a 10m^2 planar fit to the lowest first returns is used, together with field-based correlations of grass height statistics, to estimate the mean

  7. Combined use of LiDAR data and multispectral earth observation imagery for wetland habitat mapping

    NASA Astrophysics Data System (ADS)

    Rapinel, Sébastien; Hubert-Moy, Laurence; Clément, Bernard

    2015-05-01

    Although wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, the inventory and characterization of wetland habitats are most often limited to small areas. This explains why the understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few hectares. While LiDAR data and multispectral Earth Observation (EO) images are often used separately to map wetland habitats, their combined use is currently being assessed for different habitat types. The aim of this study is to evaluate the combination of multispectral and multiseasonal imagery and LiDAR data to precisely map the distribution of wetland habitats. The image classification was performed combining an object-based approach and decision-tree modeling. Four multispectral images with high (SPOT-5) and very high spatial resolution (Quickbird, KOMPSAT-2, aerial photographs) were classified separately. Another classification was then applied integrating summer and winter multispectral image data and three layers derived from LiDAR data: vegetation height, microtopography and intensity return. The comparison of classification results shows that some habitats are better identified on the winter image and others on the summer image (overall accuracies = 58.5 and 57.6%). They also point out that classification accuracy is highly improved (overall accuracy = 86.5%) when combining LiDAR data and multispectral images. Moreover, this study highlights the advantage of integrating vegetation height, microtopography and intensity parameters in the classification process. This article demonstrates that information provided by the synergetic use of multispectral images and LiDAR data can help in wetland functional assessment

  8. A universal airborne LiDAR approach for tropical forest carbon mapping.

    PubMed

    Asner, Gregory P; Mascaro, Joseph; Muller-Landau, Helene C; Vieilledent, Ghislain; Vaudry, Romuald; Rasamoelina, Maminiaina; Hall, Jefferson S; van Breugel, Michiel

    2012-04-01

    Airborne light detection and ranging (LiDAR) is fast turning the corner from demonstration technology to a key tool for assessing carbon stocks in tropical forests. With its ability to penetrate tropical forest canopies and detect three-dimensional forest structure, LiDAR may prove to be a major component of international strategies to measure and account for carbon emissions from and uptake by tropical forests. To date, however, basic ecological information such as height-diameter allometry and stand-level wood density have not been mechanistically incorporated into methods for mapping forest carbon at regional and global scales. A better incorporation of these structural patterns in forests may reduce the considerable time needed to calibrate airborne data with ground-based forest inventory plots, which presently necessitate exhaustive measurements of tree diameters and heights, as well as tree identifications for wood density estimation. Here, we develop a new approach that can facilitate rapid LiDAR calibration with minimal field data. Throughout four tropical regions (Panama, Peru, Madagascar, and Hawaii), we were able to predict aboveground carbon density estimated in field inventory plots using a single universal LiDAR model (r ( 2 ) = 0.80, RMSE = 27.6 Mg C ha(-1)). This model is comparable in predictive power to locally calibrated models, but relies on limited inputs of basal area and wood density information for a given region, rather than on traditional plot inventories. With this approach, we propose to radically decrease the time required to calibrate airborne LiDAR data and thus increase the output of high-resolution carbon maps, supporting tropical forest conservation and climate mitigation policy. PMID:22033763

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

    NASA Astrophysics Data System (ADS)

    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.

  10. LiDAR Segmentation using Suitable Seed Points for 3D Building Extraction

    NASA Astrophysics Data System (ADS)

    Abdullah, S. M.; Awrangjeb, M.; Lu, G.

    2014-08-01

    Effective building detection and roof reconstruction has an influential demand over the remote sensing research community. In this paper, we present a new automatic LiDAR point cloud segmentation method using suitable seed points for building detection and roof plane extraction. Firstly, the LiDAR point cloud is separated into "ground" and "non-ground" points based on the analysis of DEM with a height threshold. Each of the non-ground point is marked as coplanar or non-coplanar based on a coplanarity analysis. Commencing from the maximum LiDAR point height towards the minimum, all the LiDAR points on each height level are extracted and separated into several groups based on 2D distance. From each group, lines are extracted and a coplanar point which is the nearest to the midpoint of each line is considered as a seed point. This seed point and its neighbouring points are utilised to generate the plane equation. The plane is grown in a region growing fashion until no new points can be added. A robust rule-based tree removal method is applied subsequently to remove planar segments on trees. Four different rules are applied in this method. Finally, the boundary of each object is extracted from the segmented LiDAR point cloud. The method is evaluated with six different data sets consisting hilly and densely vegetated areas. The experimental results indicate that the proposed method offers a high building detection and roof plane extraction rates while compared to a recently proposed method.

  11. Building Damage Assessment after Earthquake Using Post-Event LiDAR Data

    NASA Astrophysics Data System (ADS)

    Rastiveis, H.; Eslamizade, F.; Hosseini-Zirdoo, E.

    2015-12-01

    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.

  12. Mapping the Risk of Forest Wind Damage Using Airborne Scanning LiDAR

    NASA Astrophysics Data System (ADS)

    Saarinen, N.; Vastaranta, M.; Honkavaara, E.; Wulder, M. A.; White, J. C.; Litkey, P.; Holopainen, M.; Hyyppä, J.

    2015-03-01

    Wind damage is known for causing threats to sustainable forest management and yield value in boreal forests. Information about wind damage risk can aid forest managers in understanding and possibly mitigating damage impacts. The objective of this research was to better understand and quantify drivers of wind damage, and to map the probability of wind damage. To accomplish this, we used open-access airborne scanning light detection and ranging (LiDAR) data. The probability of wind-induced forest damage (PDAM) in southern Finland (61°N, 23°E) was modelled for a 173 km2 study area of mainly managed boreal forests (dominated by Norway spruce and Scots pine) and agricultural fields. Wind damage occurred in the study area in December 2011. LiDAR data were acquired prior to the damage in 2008. High spatial resolution aerial imagery, acquired after the damage event (January, 2012) provided a source of model calibration via expert interpretation. A systematic grid (16 m x 16 m) was established and 430 sample grid cells were identified systematically and classified as damaged or undamaged based on visual interpretation using the aerial images. Potential drivers associated with PDAM were examined using a multivariate logistic regression model. Risk model predictors were extracted from the LiDAR-derived surface models. Geographic information systems (GIS) supported spatial mapping and identification of areas of high PDAM across the study area. The risk model based on LiDAR data provided good agreement with detected risk areas (73 % with kappa-value 0,47). The strongest predictors in the risk model were mean canopy height and mean elevation. Our results indicate that open-access LiDAR data sets can be used to map the probability of wind damage risk without field data, providing valuable information for forest management planning.

  13. Applying a weighted random forests method to extract karst sinkholes from LiDAR data

    NASA Astrophysics Data System (ADS)

    Zhu, Junfeng; Pierskalla, William P.

    2016-02-01

    Detailed mapping of sinkholes provides critical information for mitigating sinkhole hazards and understanding groundwater and surface water interactions in karst terrains. LiDAR (Light Detection and Ranging) measures the earth's surface in high-resolution and high-density and has shown great potentials to drastically improve locating and delineating sinkholes. However, processing LiDAR data to extract sinkholes requires separating sinkholes from other depressions, which can be laborious because of the sheer number of the depressions commonly generated from LiDAR data. In this study, we applied the random forests, a machine learning method, to automatically separate sinkholes from other depressions in a karst region in central Kentucky. The sinkhole-extraction random forest was grown on a training dataset built from an area where LiDAR-derived depressions were manually classified through a visual inspection and field verification process. Based on the geometry of depressions, as well as natural and human factors related to sinkholes, 11 parameters were selected as predictive variables to form the dataset. Because the training dataset was imbalanced with the majority of depressions being non-sinkholes, a weighted random forests method was used to improve the accuracy of predicting sinkholes. The weighted random forest achieved an average accuracy of 89.95% for the training dataset, demonstrating that the random forest can be an effective sinkhole classifier. Testing of the random forest in another area, however, resulted in moderate success with an average accuracy rate of 73.96%. This study suggests that an automatic sinkhole extraction procedure like the random forest classifier can significantly reduce time and labor costs and makes its more tractable to map sinkholes using LiDAR data for large areas. However, the random forests method cannot totally replace manual procedures, such as visual inspection and field verification.

  14. Water turbidity estimation from airborne hyperspectral imagery and full waveform bathymetric LiDAR

    NASA Astrophysics Data System (ADS)

    Pan, Z.; Glennie, C. L.; Fernandez-Diaz, J. C.

    2015-12-01

    The spatial and temporal variations in water turbidity are of great interest for the study of fluvial and coastal environments; and for predicting the performance of remote sensing systems that are used to map these. Conventional water turbidity estimates from remote sensing observations have normally been derived using near infrared reflectance. We have investigated the potential of determining water turbidity from additional remote sensing sources, namely airborne hyperspectral imagery and single wavelength bathymetric LiDAR (Light Detection and Ranging). The confluence area of the Blue and Colorado River, CO was utilized as a study area to investigate the capabilities of both airborne bathymetric LiDAR and hyperspectral imagery for water turbidity estimation. Discrete and full waveform bathymetric data were collected using Optech's Gemini (1064 nm) and Aquarius (532 nm) LiDAR sensors. Hyperspectral imagery (1.2 m pixel resolution and 72 spectral bands) was acquired using an ITRES CASI-1500 imaging system. As an independent reference, measurements of turbidity were collected concurrent with the airborne remote sensing acquisitions, using a WET Labs EcoTriplet deployed from a kayak and turbidity was then derived from the measured backscatter. The bathymetric full waveform dataset contains a discretized sample of the full backscatter of water column and benthic layer. Therefore, the full waveform records encapsulate the water column characteristics of turbidity. A nonparametric support vector regression method is utilized to estimate water turbidity from both hyperspectral imagery and voxelized full waveform LiDAR returns, both individually and as a fused dataset. Results of all the evaluations will be presented, showing an initial turbidity prediction accuracy of approximately 1.0 NTU. We will also discuss our future strategy for enhanced fusion of the full waveform LiDAR and hyperspectral imagery for improved turbidity estimation.

  15. Spatial accounting for errors in LiDAR-derived products: Snow volume and snow water equivalent estimation

    NASA Astrophysics Data System (ADS)

    Tinkham, W. T.; Hoffman, C. M.; Falkowski, M. J.; Smith, A. M.; Link, T. E.; Marshall, H.

    2011-12-01

    Light Detection and Ranging (LiDAR) has become one of the most effective and reliable means of characterizing surface topography and vegetation structure. Most LiDAR-derived estimates such as vegetation height, snow depth, and floodplain boundaries rely on the accurate creation of digital terrain models (DTM). As a result of the importance of an accurate DTM in using LiDAR data to estimate snow depth, it is necessary to understand the variables that influence the DTM accuracy in order to assess snow depth error. A series of 4 x 4 m plots that were surveyed at 0.5 m spacing in a semi-arid catchment were used for training the Random Forests algorithm along with a series of 35 variables in order to spatially predict vertical error within a LiDAR derived DTM. The final model was utilized to predict the combined error resulting from snow volume and snow water equivalent estimates derived from a snow-free LiDAR DTM and a snow-on LiDAR acquisition of the same site. The methodology allows for a statistical quantification of the spatially-distributed error patterns that are incorporated into the estimation of snow volume and snow water equivalents from LiDAR.

  16. Development and validation of the Dimensional Anhedonia Rating Scale (DARS) in a community sample and individuals with major depression.

    PubMed

    Rizvi, Sakina J; Quilty, Lena C; Sproule, Beth A; Cyriac, Anna; Michael Bagby, R; Kennedy, Sidney H

    2015-09-30

    Anhedonia, a core symptom of Major Depressive Disorder (MDD), is predictive of antidepressant non-response. In contrast to the definition of anhedonia as a "loss of pleasure", neuropsychological studies provide evidence for multiple facets of hedonic function. The aim of the current study was to develop and validate the Dimensional Anhedonia Rating Scale (DARS), a dynamic scale that measures desire, motivation, effort and consummatory pleasure across hedonic domains. Following item selection procedures and reliability testing using data from community participants (N=229) (Study 1), the 17-item scale was validated in an online study with community participants (N=150) (Study 2). The DARS was also validated in unipolar or bipolar depressed patients (n=52) and controls (n=50) (Study 3). Principal components analysis of the 17-item DARS revealed a 4-component structure mapping onto the domains of anhedonia: hobbies, food/drink, social activities, and sensory experience. Reliability of the DARS subscales was high across studies (Cronbach's α=0.75-0.92). The DARS also demonstrated good convergent and divergent validity. Hierarchical regression analysis revealed the DARS showed additional utility over the Snaith-Hamilton Pleasure Scale (SHAPS) in predicting reward function and distinguishing MDD subgroups. These studies provide support for the reliability and validity of the DARS. PMID:26250147

  17. Change detection of riverbed movements using river cross-sections and LiDAR data

    NASA Astrophysics Data System (ADS)

    Vetter, Michael; Höfle, Bernhard; Mandlburger, Gottfried; Rutzinger, Martin

    2010-05-01

    Today, Airborne LiDAR derived digital terrain models (DTMs) are used for several aspects in different scientific disciplines, such as hydrology, geomorphology or archaeology. In the field of river geomorphology, LiDAR data sets can provide information on the riverine vegetation, the level and boundary of the water body, the elevation of the riparian foreland and their roughness. The LiDAR systems in use for topographic data acquisition mainly operate with wavelengths of at least 1064nm and, thus, are not able to penetrate water. LiDAR sensors with two wavelengths are available (bathymetric LiDAR), but they can only provide elevation information of riverbeds or lakes, if the water is clear and the minimum water depth exceeds 1.5m. In small and shallow rivers it is impossible to collect information of the riverbed, regardless of the used LiDAR sensor. In this article, we present a method to derive a high-resolution DTM of the riverbed and to combine it with the LiDAR DTM resulting in a watercourse DTM (DTM-W) as a basis for calculating the changes in the riverbed during several years. To obtain such a DTM-W we use river cross-sections acquired by terrestrial survey or echo-sounding. First, a differentiation between water and land has to be done. A highly accurate water surface can be derived by using a water surface delineation algorithm, which incorporates the amplitude information of the LiDAR point cloud and additional geometrical features (e.g. local surface roughness). The second step is to calculate a thalweg line, which is the lowest flow path in the riverbed. This is achieved by extracting the lowest point of each river cross section and by fitting a B-spline curve through those points. In the next step, the centerline of the river is calculated by applying a shrinking algorithm of the water boundary polygon. By averaging the thalweg line and the centerline, a main flow path line can be computed. Subsequently, a dense array of 2D-profiles perpendicular to the

  18. Multi-component wind measurements of wind turbine wakes performed with three LiDARs

    NASA Astrophysics Data System (ADS)

    Iungo, G. V.; Wu, Y.-T.; Porté-Agel, F.

    2012-04-01

    Field measurements of the wake flow produced from the interaction between atmospheric boundary layer and a wind turbine are performed with three wind LiDARs. The tested wind turbine is a 2 MW Enercon E-70 located in Collonges, Switzerland. First, accuracy of mean values and frequency resolution of the wind measurements are surveyed as a function of the number of laser rays emitted for each measurement. Indeed, measurements performed with one single ray allow maximizing sampling frequency, thus characterizing wake turbulence. On the other hand, if the number of emitted rays is increased accuracy of mean wind is increased due to the longer sampling period. Subsequently, two-dimensional measurements with a single LiDAR are carried out over vertical sections of the wind turbine wake and mean wake flow is obtained by averaging 2D measurements consecutively performed. The high spatial resolution of the used LiDAR allows characterizing in details velocity defect present in the central part of the wake and its downstream recovery. Single LiDAR measurements are also performed by staring the laser beam at fixed directions for a sampling period of about ten minutes and maximizing the sampling frequency in order to characterize wake turbulence. From these tests wind fluctuation peaks are detected in the wind turbine wake at blade top-tip height for different downstream locations. The magnitude of these turbulence peaks is generally reduced by moving downstream. This increased turbulence level at blade top-tip height observed for a real wind turbine has been already detected from previous wind tunnel tests and Large Eddy simulations, thus confirming the presence of a source of dangerous fatigue loads for following wind turbines within a wind farm. Furthermore, the proper characterization of wind fluctuations through LiDAR measurements is proved by the detection of the inertial subrange from spectral analysis of these velocity signals. Finally, simultaneous measurements with two

  19. Capabilities of the bathymetric Hawk Eye LiDAR for coastal habitat mapping: A case study within a Basque estuary

    NASA Astrophysics Data System (ADS)

    Chust, Guillem; Grande, Maitane; Galparsoro, Ibon; Uriarte, Adolfo; Borja, Ángel

    2010-10-01

    The bathymetric LiDAR system is an airborne laser that detects sea bottom at high vertical and horizontal resolutions in shallow coastal waters. This study assesses the capabilities of the airborne bathymetric LiDAR sensor (Hawk Eye system) for coastal habitat mapping in the Oka estuary (within the Biosphere Reserve of Urdaibai, SE Bay of Biscay, northern Spain), where water conditions are moderately turbid. Three specific objectives were addressed: 1) to assess the data quality of the Hawk Eye LiDAR, both for terrestrial and subtidal zones, in terms of height measurement density, coverage, and vertical accuracy; 2) to compare bathymetric LiDAR with a ship-borne multibeam echosounder (MBES) for different bottom types and depth ranges; and 3) to test the discrimination potential of LiDAR height and reflectance information, together with multi-spectral imagery (three visible and near infrared bands), for the classification of 22 salt marsh and rocky shore habitats, covering supralittoral, intertidal and subtidal zones. The bathymetric LiDAR Hawk Eye data enabled the generation of a digital elevation model (DEM) of the Oka estuary, at 2 m of horizontal spatial resolution in the terrestrial zone (with a vertical accuracy of 0.15 m) and at 4 m within the subtidal, extending a water depth of 21 m. Data gaps occurred in 14.4% of the area surveyed with the LiDAR (13.69 km 2). Comparison of the LiDAR system and the MBES showed no significant mean difference in depth. However, the Root Mean Square error of the former was high (0.84 m), especially concentrated upon rocky (0.55-1.77 m) rather than in sediment bottoms (0.38-0.62 m). The potential of LiDAR topographic variables and reflectance alone for discriminating 15 intertidal and submerged habitats was low (with overall classification accuracy between 52.4 and 65.4%). In particular, reflectance retrieved for this case study has been found to be not particularly useful for classification purposes. The combination of the LiDAR

  20. Quantifying spatial distribution of snow depth errors from LiDAR using Random Forests

    NASA Astrophysics Data System (ADS)

    Tinkham, W.; Smith, A. M.; Marshall, H.; Link, T. E.; Falkowski, M. J.; Winstral, A. H.

    2013-12-01

    There is increasing need to characterize the distribution of snow in complex terrain using remote sensing approaches, especially in isolated mountainous regions that are often water-limited, the principal source of terrestrial freshwater, and sensitive to climatic shifts and variations. We apply intensive topographic surveys, multi-temporal LiDAR, and Random Forest modeling to quantify snow volume and characterize associated errors across seven land cover types in a semi-arid mountainous catchment at a 1 and 4 m spatial resolution. The LiDAR-based estimates of both snow-off surface topology and snow depths were validated against ground-based measurements across the catchment. Comparison of LiDAR-derived snow depths to manual snow depth surveys revealed that LiDAR based estimates were more accurate in areas of low lying vegetation such as shrubs (RMSE = 0.14 m) as compared to areas consisting of tree cover (RMSE = 0.20-0.35 m). The highest errors were found along the edge of conifer forests (RMSE = 0.35 m), however a second conifer transect outside the catchment had much lower errors (RMSE = 0.21 m). This difference is attributed to the wind exposure of the first site that led to highly variable snow depths at short spatial distances. The Random Forest modeled errors deviated from the field measured errors with a RMSE of 0.09-0.34 m across the different cover types. Results show that snow drifts, which are important for maintaining spring and summer stream flows and establishing and sustaining water-limited plant species, contained 30 × 5-6% of the snow volume while only occupying 10% of the catchment area similar to findings by prior physically-based modeling approaches. This study demonstrates the potential utility of combining multi-temporal LiDAR with Random Forest modeling to quantify the distribution of snow depth with a reasonable degree of accuracy. Future work could explore the utility of Terrestrial LiDAR Scanners to produce validation of snow-on surface

  1. Investigating the spatial distribution of water levels in the Mackenzie Delta using airborne LiDAR

    USGS Publications Warehouse

    Hopkinson, C.; Crasto, N.; Marsh, P.; Forbes, D.; Lesack, L.

    2011-01-01

    Airborne light detection and ranging (LiDAR) data were used to map water level (WL) and hydraulic gradients (??H/??x) in the Mackenzie Delta. The LiDAR WL data were validated against eight independent hydrometric gauge measurements and demonstrated mean offsets from - 0??22 to + 0??04 m (??< 0??11). LiDAR-based WL gradients could be estimated with confidence over channel lengths exceeding 5-10 km where the WL change exceeded local noise levels in the LiDAR data. For the entire Delta, the LiDAR sample coverage indicated a rate of change in longitudinal gradient (??2H/??x) of 5??5 ?? 10-10 m m-2; therefore offering a potential means to estimate average flood stage hydraulic gradient for areas of the Delta not sampled or monitored. In the Outer Delta, within-channel and terrain gradient measurements all returned a consistent estimate of - 1 ?? 10-5 m m-1, suggesting that this is a typical hydraulic gradient for the downstream end of the Delta. For short reaches (<10 km) of the Peel and Middle Channels in the middle of the Delta, significant and consistent hydraulic gradient estimates of - 5 ?? 10-5 m m-1 were observed. Evidence that hydraulic gradients can vary over short distances, however, was observed in the Peel Channel immediately upstream of Aklavik. A positive elevation anomaly (bulge) of > 0??1 m was observed at a channel constriction entering a meander bend, suggesting a localized modification of the channel hydraulics. Furthermore, water levels in the anabranch channels of the Peel River were almost 1 m higher than in Middle Channel of the Mackenzie River. This suggests: (i) the channels are elevated and have shallower bank heights in this part of the delta, leading to increased cross-delta and along-channel hydraulic gradients; and/or (ii) a proportion of the Peel River flow is lost to Middle Channel due to drainage across the delta through anastamosing channels. This study has demonstrated that airborne LiDAR data contain valuable information describing

  2. Importance of High-Resolution LiDAR Data in Modeling Runoff Levels Over Impervious Surfaces

    NASA Astrophysics Data System (ADS)

    Melosh, C.; Rao, M.

    2013-12-01

    Directly connected impervious areas collect and deliver unfiltered runoff to modified and impacted waterways. Modeling water flow over the landscape is an effective method of observing drainage patterns and predicting pollutant and sediment loadings. Improved models applying high-resolution elevation data can identify key areas with high pollutant output. This is a crucial issue in the Lake Tahoe Basin where lakeshore urban development has increased and lake clarity has been declining for years. This study aims to evaluate an integrated LiDAR and GIS-based modeling approach that uses a fine-scaled ground surface and impervious surface connectivity to predict the pollutant load in the Lake Tahoe Basin This study produced a fine-scaled surface model of nine subset catchments in the South Tahoe basin, including areas of low (below 20%), medium (30% to 50%) and high (above 50%) impervious surface cover. Our method integrated LiDAR, multispectral imagery, and GIS data to develop accurate terrain models, hydrologic routing, and directly connected impervious area layers for the Lake Tahoe basin. The high-density ground and object elevation data collected using Light Detection and Ranging (LiDAR) creates an accurate picture of water flow over the land, and obstacles to the flow such as buildings. High-resolution LiDAR data was obtained from the Round 10 Lake Tahoe Southern Nevada Public Land Management capital program from the year 2010. This data was processed to create a digital elevation model of the ground surface. Land use classification used object height information from the LiDAR cloud, NAIP 4-band images with 1-meter resolution and a normalized difference vegetation index image derived from the NAIP imagery. The US Army Core of Engineers hydrologic modeling system (HEC-HMS) will be used to model runoff. Based on long-term simulations the effect of directly connected impervious area on rainfall-runoff characteristics for the South Lake Tahoe catchments will be

  3. Potential of Airborne LiDAR in Geomorphology - A Technological Perspective

    NASA Astrophysics Data System (ADS)

    Höfle, B.; Mandlburger, G.; Pfeifer, N.; Rutzinger, M.; Bell, R.

    2009-04-01

    Airborne LiDAR, also referred to as Airborne Laser Scanning, is widely used for high-resolution topographic data acquisition, offering a planimetric (<50cm) and vertical accuracy (<20cm) suited for many applications (e.g. in natural hazard management, forestry). Due to the direct determination of elevation and the penetration capabilities of the laser beam through gaps in vegetation, the LiDAR technology exceeds other methods such as stereo-photogrammetry or interferometric SAR particularly in vegetated areas. This contribution gives a review of recent developments of LiDAR systems but also advances in data processing, resulting in a higher data density and quality for geomorphological applications. Besides the elevation information most systems additionally record the strength of the received backscatter or even the full temporal distribution of the received energy (i.e. full-waveform). This radiometric information is a valuable parameter for further classification of the scanned areas, in particular for objects being not distinguishable by their geometry. In geomorphology airborne LiDAR data can either be used directly in the form of digital elevation data (e.g. digital terrain and surface model, original point cloud) and therein detected surface discontinuities (e.g. breaklines, lineaments) and forms (e.g. fans, rock glaciers), or indirectly by classification of surface features (e.g. vegetation and water) relevant for geomorphological processes. Furthermore, these datasets can be used for visual interpretation and mapping by experts or for automatic derivation of land-surface parameters by means of geomorphometry. With the availability of multitemporal datasets the investigation and quantification of dynamic processes becomes possible. Recent studies show the advantages by using full-waveform LiDAR system, which enable an improved echo detection and radiometric calibration of the received backscatter. The availability of additional echo attributes (e

  4. Evaluation of the contribution of LiDAR data and postclassification procedures to object-based classification accuracy

    NASA Astrophysics Data System (ADS)

    Styers, Diane M.; Moskal, L. Monika; Richardson, Jeffrey J.; Halabisky, Meghan A.

    2014-01-01

    Object-based image analysis (OBIA) is becoming an increasingly common method for producing land use/land cover (LULC) classifications in urban areas. In order to produce the most accurate LULC map, LiDAR data and postclassification procedures are often employed, but their relative contributions to accuracy are unclear. We examined the contribution of LiDAR data and postclassification procedures to increase classification accuracies over using imagery alone and assessed sources of error along an ecologically complex urban-to-rural gradient in Olympia, Washington. Overall classification accuracy and user's and producer's accuracies for individual classes were evaluated. The addition of LiDAR data to the OBIA classification resulted in an 8.34% increase in overall accuracy, while manual postclassification to the imagery+LiDAR classification improved accuracy only an additional 1%. Sources of error in this classification were largely due to edge effects, from which multiple different types of errors result.

  5. Cosmogenic Records in 18 Ordinary Chondrites from the Dar Al Gani Region, Libya. 1; Noble Gases

    NASA Technical Reports Server (NTRS)

    Schultz, L.; Franke, L.; Welten, K. C.; Nishiizumi, K.; Jull, A. J. T.

    2003-01-01

    In the last decade thousands of meteorites have been recovered from hot deserts in the Sahara and Oman. One of the main meteorite concentration surfaces in the Sahara is the Dar al Gani plateau in Libya, which covers a total area of 8000 km2. More than 1000 meteorites have been reported from this area. The geological setting, meteorite pairings and the meteorite density of the Dar al Gani (DaG) field are described in more detail in [1]. In this work we report concentrations of the noble gas isotopes of He, Ne, Ar as well as 84Kr and 132Xe in 18 DaG meteorites. In a separate paper we will report the cosmogenic radionuclides [2]. We discuss the thermal history and cosmic-ray exposure (CRE) history of these meteorites, and evaluate the effects of the hot desert environment on the noble gas record.

  6. Advances in animal ecology from 3D-LiDAR ecosystem mapping.

    PubMed

    Davies, Andrew B; Asner, Gregory P

    2014-12-01

    The advent and recent advances of Light Detection and Ranging (LiDAR) have enabled accurate measurement of 3D ecosystem structure. Here, we review insights gained through the application of LiDAR to animal ecology studies, revealing the fundamental importance of structure for animals. Structural heterogeneity is most conducive to increased animal richness and abundance, and increased complexity of vertical vegetation structure is more positively influential compared with traditionally measured canopy cover, which produces mixed results. However, different taxonomic groups interact with a variety of 3D canopy traits and some groups with 3D topography. To develop a better understanding of animal dynamics, future studies will benefit from considering 3D habitat effects in a wider variety of ecosystems and with more taxa. PMID:25457158

  7. Characterization of the OPAL obscurant penetrating LiDAR in various degraded visual environments

    NASA Astrophysics Data System (ADS)

    Trickey, Evan; Church, Philip; Cao, Xiaoying

    2013-05-01

    The OPAL obscurant penetrating LiDAR was developed by Neptec and characterized in various degraded visual environments (DVE) over the past five years. Quantitative evaluations of obscurant penetration were performed using the Defence RD Canada - Valcartier (DRDC Valcartier) instrumented aerosol chamber for obscurants such as dust and fog. Experiments were done with the sensor both at a standoff distance and totally engulfed in the obscurants. Field trials were also done to characterize the sensor in snow conditions and in smoke. Finally, the OPAL was also mounted on a Bell 412 helicopter to characterize its dust penetration capabilities, in environment such as Yuma Proving Ground. The paper provides a summary of the results of the OPAL evaluations demonstrating it to be a true "see through" obscurant penetrating LiDAR and explores commercial applications of the technology.

  8. Landslide investigation using LiDAR data acquired by an unmanned helicopter

    NASA Astrophysics Data System (ADS)

    Kasai, M.; Tanaka, Y.; Marutani, T.; Saito, Y.

    2013-12-01

    In this study, LiDAR data acquired over 0.5 km2 landslide prone area by an unmanned helicopter is presented. The data was taken in summer 2012 and 2013, when tree foliage covered the ground surface. Imagery was of sufficient quality to identify and measure landslide features. These data together with LiDAR data obtained by a manned helicopter in the same area in August 2008 were examined to find active slopes on landslides during the period from 2008 to 2013. Morphological characteristics of these slopes were also analyzed to utilize the notion to discover active but hiding landslides in the region. In inapproachable areas, the UAV (Unmanned Aerial Vehicles) is likely to be of greatest use. In addition, this study showed that repeat monitoring of sites is a way of utilizing UAVs, particularly in terms of cost and convenience.

  9. Quantifying post-wildfire erosion patterns using terrestrial LiDAR

    NASA Astrophysics Data System (ADS)

    Rengers, F.; Tucker, G. E.; Moody, J. A.

    2012-12-01

    Wildfires are becoming increasingly frequent in the western United States. In burned landscapes, geomorphic change can take place rapidly during rainstorms following a wildfire. Rainfall over a burned area tends to mobilize more sediment than in unburned basins because the wildfire changes soil properties, creating more overland flow. A dearth of ground debris allows for deeper and faster flow that can entrain sediment. We apply terrestrial LiDAR to post-wildfire geomorphic change analysis to determine the pattern and magnitude of erosion following rain storms. By differencing digital elevation models created from terrestrial LiDAR surveys, we can measure post-wildfire geomorphic change. Topographic analysis with LiDAR allows us to monitor landscape recovery and evolution following a wildfire. Traditional methods of post-wildfire erosion analysis have focused on measurements such as erosion pins and silt fences. These capture erosion or deposition at a point or cumulative deposition of the sediment from some unknown contributing area upstream of the silt fence. This requires researchers to integrate measurements over a large area to determine basin-wide erosion. By contrast, successive terrestrial LiDAR surveys allow us to map changes in topography over an entire basin or hillslope to determine the spatial distribution of erosion within a basin or on a hillslope and to correlate the erosion with the hydrologic processes between surveys. Our study site is a high-severity burn hillslope, burned by the 2010 Fourmile Canyon fire about 15 km west of Boulder, CO. The wildfire was contained on 16 September 2010 and the first LiDAR survey was on 7 October 2010 prior to any significant rain storms. Following this baseline survey, we have used terrestrial LiDAR to capture the landscape state before and after unique hydrologic events such as: low-intensity rain storms, winter snowmelt, and summer convective thunderstorms. Comparing the landscape topography before and after

  10. An automatic and overlap based method for LiDAR intensity correction

    NASA Astrophysics Data System (ADS)

    Ding, Qiong

    2016-03-01

    LiDAR provides intensity data that reflect the material characteristics of objects. However, intensity values need to be corrected before they can be reliably used for applications because of the error during data acquisition. This study proposed an automatic and overlap based method for intensity correction. Firstly, a radar equation based method was employed for removal of main errors. Then, nearest neighbor algorithm was used to find out homologous points of overlap regions and assumption was made that homologous points should have same intensity. Finally, an improved model was utilized to eliminate overlap discrepancies. This method can be considered as a potential aid to enhance the accuracy of LiDAR intensity data and improve the automation of data process.

  11. Characterization of Forest Ecosystems by combined Radiative Transfer Modeling for Imaging Spectrometer and LiDAR

    NASA Astrophysics Data System (ADS)

    Koetz, B.; Sun, G.; Morsdorf, F.; Rubio, J.; Kimes, D.; Ranson, J.

    2009-04-01

    This research was motivated by the increased information dimensionality provided by current Earth Observation systems measuring the complex and dynamic medium of the vegetated surface of the Earth. Advanced and reliable algorithms that fully exploit this enhanced Earth Observation information are needed to deliver consistent data sets of the Earth vegetation condition describing its spatial distribution and change over time. Spectral observation provided by imaging spectrometers and the waveform from large-footprint LiDAR are now available from space for forest ecosystem studies. The imaging spectrometer data contains information about the biochemical composition of the canopy foliage, and is widely used to estimate biophysical canopy parameters such as LAI and fractional cover. LiDAR responds to the vertical distribution of scatters and permits inferences about the plant structures required to supply water and mechanical support to those surfaces. Various canopy height indices derived from LiDAR waveform have been successfully used to infer forest above-ground biomass and the characterization of canopy structure. The structure parameters derived from LiDAR data can improve the accuracy and robustness of canopy parameter retrieval from imaging spectrometer by reducing uncertainties related to the canopy structure. The specific information content, inherent to the observations of imaging spectrometry and LIDAR, assesses thus different but complementary characteristics of the complex vegetation canopy. The combination of these two information dimensions offers a unique and reliable canopy characterization including information relevant to different aspects of the biochemical and biophysical properties and thus understanding of processes within forest ecosystems. A comprehensive canopy characterization of a forest ecosystem is derived from the combined remote sensing signal of imaging spectrometry and large footprint LIDAR. The inversion of two linked physically based

  12. Remote sensing based detection of forested wetlands: An evaluation of LiDAR, aerial imagery, and their data fusion

    NASA Astrophysics Data System (ADS)

    Suiter, Ashley Elizabeth

    Multi-spectral imagery provides a robust and low-cost dataset for assessing wetland extent and quality over broad regions and is frequently used for wetland inventories. However in forested wetlands, hydrology is obscured by tree canopy making it difficult to detect with multi-spectral imagery alone. Because of this, classification of forested wetlands often includes greater errors than that of other wetlands types. Elevation and terrain derivatives have been shown to be useful for modelling wetland hydrology. But, few studies have addressed the use of LiDAR intensity data detecting hydrology in forested wetlands. Due the tendency of LiDAR signal to be attenuated by water, this research proposed the fusion of LiDAR intensity data with LiDAR elevation, terrain data, and aerial imagery, for the detection of forested wetland hydrology. We examined the utility of LiDAR intensity data and determined whether the fusion of Lidar derived data with multispectral imagery increased the accuracy of forested wetland classification compared with a classification performed with only multi-spectral image. Four classifications were performed: Classification A -- All Imagery, Classification B -- All LiDAR, Classification C -- LiDAR without Intensity, and Classification D -- Fusion of All Data. These classifications were performed using random forest and each resulted in a 3-foot resolution thematic raster of forested upland and forested wetland locations in Vermilion County, Illinois. The accuracies of these classifications were compared using Kappa Coefficient of Agreement. Importance statistics produced within the random forest classifier were evaluated in order to understand the contribution of individual datasets. Classification D, which used the fusion of LiDAR and multi-spectral imagery as input variables, had moderate to strong agreement between reference data and classification results. It was found that Classification A performed using all the LiDAR data and its derivatives

  13. Single Pass LiDAR-derived Estimate of Site Productivity in Western Oregon

    NASA Astrophysics Data System (ADS)

    McAdam, E.; Hilker, T.; Waring, R. H.; Sousa, C. H. R. D.; Moura, Y. M.

    2014-12-01

    Accurate estimates of forest growth at different ages are essential to evaluate the effect of a changing climate and to adjust management practices accordingly. Most current approaches are spatially discrete and therefore unable to predict forest growth accurately across landscapes. While airborne LiDAR has been widely used in forestry, it can only estimate growth rates with repeated passes. In contrast, Landsat imagery records disturbances (at 30 m resolution) but is unable to measure changes in growth rates. Historical archives of Landsat imagery provided us a way of knowing when and where even-aged stands of Pseudotsuga menziesii (Douglas-fir) were cut and replanted. Since early growth rates are nearly linear with age, the height of dominant trees recorded in one pass by LiDAR yields a direct measure of growth and likely changes as stands age under recent climatic conditions. Process-based growth models are available to assess possible shifts in the growth rates of stands under a changing climate; the accuracy of such model predictions can be evaluated with additional LiDAR coverage. In this study we use the Physiological Principles Predicting Growth Model (3-PG) to estimate site index at the landscape level to predict site productivity based on the year of stand establishment obtained from Landsat, and one-pass airborne LiDAR measurement of forest height. We are monitoring forest plantations of known ages and with data on their current age we will calculate site index for 60 separate sites across western Oregon. The results of this study will allow us to create updated site index maps for the state of Oregon under varying climate scenarios.

  14. Quantitative study of tectonic geomorphology along Haiyuan fault based on airborne LiDAR

    USGS Publications Warehouse

    Chen, Tao; Zhang, Pei Zhen; Liu, Jing; Li, Chuan You; Ren, Zhi Kun; Hudnut, Kenneth W.

    2014-01-01

    High-precision and high-resolution topography are the fundamental data for active fault research. Light detection and ranging (LiDAR) presents a new approach to build detailed digital elevation models effectively. We take the Haiyuan fault in Gansu Province as an example of how LiDAR data may be used to improve the study of active faults and the risk assessment of related hazards. In the eastern segment of the Haiyuan fault, the Shaomayin site has been comprehensively investigated in previous research because of its exemplary tectonic topographic features. Based on unprecedented LiDAR data, the horizontal and vertical coseismic offsets at the Shaomayin site are described. The measured horizontal value is about 8.6 m, and the vertical value is about 0.8 m. Using prior dating ages sampled from the same location, we estimate the horizontal slip rate as 4.0 ± 1.0 mm/a with high confidence and define that the lower bound of the vertical slip rate is 0.4 ± 0.1 mm/a since the Holocene. LiDAR data can repeat the measurements of field work on quantifying offsets of tectonic landform features quite well. The offset landforms are visualized on an office computer workstation easily, and specialized software may be used to obtain displacement quantitatively. By combining precious chronological results, the fundamental link between fault activity and large earthquakes is better recognized, as well as the potential risk for future earthquake hazards.

  15. Mapping Vegetation Canopy Structure and Distribution for Great Smoky Mountains National Park Using LiDAR

    NASA Astrophysics Data System (ADS)

    Wu, Z.; Weiner, J.; Kumar, J.; Norman, S. P.; Hargrove, W. W.; Collier, N.; Hoffman, F. M.

    2015-12-01

    A major challenge in forest management is the inaccessibility of large swaths of land, which makes accurate monitoring of forest change difficult. Remote sensing methods can help address this issue by allowing investigators to monitor remote or inaccessible regions using aerial or satellite-based platforms. However, most remote sensing methods do not provide a full three-dimensional (3D) description of the area. Rather, they return only a single elevation point or landcover description. Multiple-return LiDAR (Light Detection and Ranging) gathers data in a 3D point cloud, which allows forest managers to more accurately characterize and monitor changes in canopy structure and vegetation-type distribution. Our project used high-resolution aerial multiple-return LiDAR data to determine vegetation canopy structures and their spatial distribution in Great Smoky Mountains National Park. To ensure sufficient data density and to match LANDSAT resolution, we gridded the data into 30m x 30m cells. The LiDAR data points within each cell were then used to generate the vertical canopy structure for that cell. After vertical profiles had been created, we used a k-means cluster analysis algorithm to classify the landscape based on the canopy structure. The spatial distribution of distinct and unique canopy structures was mapped across the park and compared to a vegetation-type map to determine the correlation of canopy structure to vegetation types. Preliminary analysis conducted at a number of phenology sites maintained by the Great Smoky Mountains Institute at Tremont shows strong correspondence between canopy structure and vegetation type. However, more validation is needed in other regions of the park to establish this method as a reliable tool. LiDAR data has a unique ability to map full 3D structures of vegetation and the methods developed in this project offer an extensible tool for forest mapping and monitoring.

  16. Strategies for minimizing sample size for use in airborne LiDAR-based forest inventory

    USGS Publications Warehouse

    Junttila, Virpi; Finley, Andrew O.; Bradford, John B.; Kauranne, Tuomo

    2013-01-01

    Recently airborne Light Detection And Ranging (LiDAR) has emerged as a highly accurate remote sensing modality to be used in operational scale forest inventories. Inventories conducted with the help of LiDAR are most often model-based, i.e. they use variables derived from LiDAR point clouds as the predictive variables that are to be calibrated using field plots. The measurement of the necessary field plots is a time-consuming and statistically sensitive process. Because of this, current practice often presumes hundreds of plots to be collected. But since these plots are only used to calibrate regression models, it should be possible to minimize the number of plots needed by carefully selecting the plots to be measured. In the current study, we compare several systematic and random methods for calibration plot selection, with the specific aim that they be used in LiDAR based regression models for forest parameters, especially above-ground biomass. The primary criteria compared are based on both spatial representativity as well as on their coverage of the variability of the forest features measured. In the former case, it is important also to take into account spatial auto-correlation between the plots. The results indicate that choosing the plots in a way that ensures ample coverage of both spatial and feature space variability improves the performance of the corresponding models, and that adequate coverage of the variability in the feature space is the most important condition that should be met by the set of plots collected.

  17. Mapping and Monitoring Delmarva Fox Squirrel Habitat Using an Airborne LiDAR Profiler

    NASA Technical Reports Server (NTRS)

    Nelson, Ross; Ratnaswamy, Mary; Keller, Cherry

    2004-01-01

    Twenty five hundred thirty nine kilometers of airborne laser profiling and videography data were acquired over the state of Delaware during the summer of 2000. The laser ranging measurements and video from approximately one-half of that data set (1304 km) were analyzed to identify and locate forested sites that might potentially support populations of Delmarva fox squirrel (DFS, Sciurus niger cinereus). The DFS is an endangered species previously endemic to tall, dense, mature forests with open understories on the Eastern Shore of the Chesapeake Bay. The airborne LiDAR employed in this study can measure forest canopy height and canopy closure, but cannot measure or infer understory canopy conditions. Hence the LiDAR must be viewed as a tool to map potential, not actual, habitat. Fifty-three potentially suitable DFS sites were identified in the 1304 km of flight transect data. Each of the 53 sites met the following criteria according to the LiDAR and video record: (1 ) at least 120m of contiguous forest; (2) an average canopy height greater than 20m; (3) an average canopy closure of >80%; and (4) no roofs, impervious surface (e.g., asphalt, concrete), and/or open water anywhere along the 120m length of the laser segment. Thirty-two of the 53 sites were visited on the ground and measurements taken for a DFS habitat suitability model. Seventy eight percent of the sites (25 of 32) were judged by the model to be suited to supporting a DFS population. Twenty-eight of the 32 sites visited in the field were in forest cover types (hardwood, mixed wood, conifer, wetlands) according to a land cover GIS map. Of these, 23 (82%) were suited to support DFS. The remaining 4 sites were located in nonforest cover types - agricultural or residential areas. Two of the four, or 50% were suited to the DFS. All of the LiDAR flight data, 2539 km, were analyzed to

  18. Flying Under the LiDAR: Relating Forest Structure to Bat Community Diversity

    NASA Astrophysics Data System (ADS)

    Swanson, A. C.; Weishampel, J. F.

    2015-12-01

    Bats are important to many ecological processes such as pollination, insect (and by proxy, disease) control, and seed dispersal and can be used to monitor ecosystem health. However, they are facing unprecedented extinction risks from habitat degradation as well as pressures from pathogens (e.g., white-nose syndrome) and wind turbines. LiDAR allows ecologists to measure structural variables of forested landscapes with increased precision and accuracy at broader spatial scales than previously possible. This study used airborne LiDAR to classify forest habitat/canopy structure at the Ordway-Swisher Biological Station (OSBS) in north central Florida. LiDAR data were acquired by the NEON airborne observation platform in summer 2014. OSBS consists of open-canopy pine savannas, closed-canopy hardwood hammocks, and seasonally wet prairies. Multiple forest structural parameters (e.g., mean, maximum, and standard deviation of height returns) were derived from LiDAR point clouds using the USDA software program FUSION. K-means clustering was used to segregate each 5x5 m raster across the ~3765 ha OSBS area into six different clusters based on the derived canopy metrics. Cluster averages for maximum, mean, and standard deviation of return heights ranged from 0 to 19.4 m, 0 to 15.3 m, and 0 to 3.0 m, respectively. To determine the relationships among these landscape-canopy features and bat species diversity and abundances, AnaBat II bat detectors were deployed from May to September in 2015 stratified by these distinct clusters. Bat calls were recorded from sunset to sunrise during each sampling period. Species were identified using AnalookW. A statistical regression model selection approach was performed in order to evaluate how forest attributes such as understory clutter, open regions, open and closed canopy, etc. influence bat communities. This knowledge provides a deeper understanding of habitat-species interactions to better manage survival of these species.

  19. Topographic and Thermal Investigations of Active Pahoehoe Lava Flows Using Coupled LiDAR/FLIR Datasets

    NASA Astrophysics Data System (ADS)

    Crown, D. A.; Anderson, S. W.; Finnegan, D. C.; LeWinter, A. L.; Ramsey, M.

    2012-12-01

    Pahoehoe lava flows consist of multiple overlapping and interfingering lobes and exhibit morphologically diverse surfaces characterized by channels, smooth-surfaced sheets, and numerous, small networks of interconnected pahoehoe toes. In order to analyze the different pahoehoe emplacement regimes, we have acquired simultaneous high-resolution topographic and thermal measurements of advancing and inflating flow lobes at high temporal frequency. These datasets allow the creation of flow lobe maps at regular intervals during flow emplacement that document morphologic, thermal, and morphometric characteristics of individual pahoehoe elements (e.g., pahoehoe toes) as well as compound pahoehoe features (e.g., toe networks, channels with lateral levees). These datasets reveal patterns in flow behavior and provide quantitative documentation of flow emplacement processes. Field investigations were conducted in February and March, 2012 on tube-fed pahoehoe flows in the Puu Oo flow field, Kilauea Volcano, Hawaii. We utilized a ground-based, full-waveform scanning LiDAR and FLIR SC645 thermal infrared camera, supplemented by high-definition video and time-lapse photography. The LiDAR scanner is capable of acquiring rapid, successive scans with reproducible 5 mm resolution data at a rate of 300 kHz. The FLIR camera acquires calibrated thermal images in the 7.5 - 13 mm range; the object temperature range is -20°C to +2000°C, with a thermal sensitivity of <0.05°C at 30°C. An RTK GPS was used to acquire precise locations of scan positions and to georeference LiDAR point cloud data to real-world coordinates. The combined LiDAR/FLIR system provides rapid acquisition of high-resolution spatial and high-precision thermal datasets for advancing pahoehoe flows.

  20. NASA Goddards LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager

    NASA Technical Reports Server (NTRS)

    Cook, Bruce D.; Corp, Lawrence A.; Nelson, Ross F.; Middleton, Elizabeth M.; Morton, Douglas C.; McCorkel, Joel T.; Masek, Jeffrey G.; Ranson, Kenneth J.; Ly, Vuong; Montesano, Paul M.

    2013-01-01

    The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system. Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager is a unique system that permits simultaneous measurements of vegetation structure, foliar spectra and surface temperatures at very high spatial resolution (approximately 1 m) on a wide range of airborne platforms. The complementary nature of LiDAR, optical and thermal data provide an analytical framework for the development of new algorithms to map plant species composition, plant functional types, biodiversity, biomass and carbon stocks, and plant growth. In addition, G-LiHT data enhance our ability to validate data from existing satellite missions and support NASA Earth Science research. G-LiHT's data processing and distribution system is designed to give scientists open access to both low- and high-level data products (http://gliht.gsfc.nasa.gov), which will stimulate the community development of synergistic data fusion algorithms. G-LiHT has been used to collect more than 6,500 km2 of data for NASA-sponsored studies across a broad range of ecoregions in the USA and Mexico. In this paper, we document G-LiHT design considerations, physical specifications, instrument performance and calibration and acquisition parameters. In addition, we describe the data processing system and higher-level data products that are freely distributed under NASA's Data and Information policy.

  1. Reduction of training costs using active classification in fused hyperspectral and LiDAR data

    NASA Astrophysics Data System (ADS)

    Wuttke, Sebastian; Schilling, Hendrik; Middelmann, Wolfgang

    2012-11-01

    This paper presents a novel approach for the reduction of training costs in classification with co-registered hyperspectral (HS) and Light Detection and Ranging (LiDAR) data using an active classification framework. Fully automatic classification can be achieved by unsupervised learning, which is not suited for adjustment to specific classes. On the other hand, supervised classification with predefined classes needs a lot of training examples, which need to be labeled with the ground truth, usually at a significant cost. The concept of active classification alleviates these problems by the use of a selection strategy: only selected samples are ground truth labeled and used as training data. One common selection strategy is to incorporate in a first step the current state of the classification algorithm and choose only the examples for which the expected information gain is maximized. In the second step a conventional classification algorithm is trained using this data. By alternating between these two steps the algorithm reaches high classification accuracy results with less training samples and therefore lower training costs. The approach presented in this paper involves the user in the active selection strategy and the k-NN algorithm is chosen for classification. The results further benefit from fusing the heterogeneous information of HS and LiDAR data within the classification algorithm. For this purpose, several HS features, such as vegetation indices, and LiDAR features, such as relative height and roughness, are extracted. This increases the separability between different classes and reduces the dimensionality of the HS data. The practicability and performance of this framework is shown for the detection and separation of different kinds of vegetation, e.g. trees and grass in an urban area of Berlin. The HS data was obtained by the SPECIM AISA Eagle 2 sensor, LiDAR data by Riegl LMS Q560.

  2. Evaluation of Landslide Mapping Techniques and LiDAR-based Conditioning Factors

    NASA Astrophysics Data System (ADS)

    Mahalingam, R.; Olsen, M. J.

    2014-12-01

    Landslides are a major geohazard, which result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping can help communities to plan and prepare for these damaging events. Mapping landslide susceptible locations using GIS and remote sensing techniques is gaining popularity in the past three decades. These efforts use a wide variety of procedures and consider a wide range of factors. Unfortunately, each study is often completed differently and independently of others. Further, the quality of the datasets used varies in terms of source, data collection, and generation, which can propagate errors or inconsistencies into the resulting output maps. Light detection and ranging (LiDAR) has proved to have higher accuracy in representing the continuous topographic surface, which can help minimize this uncertainty. The primary objectives of this paper are to investigate the applicability and performance of terrain factors in landslide hazard mapping, determine if LiDAR-derived datasets (slope, slope roughness, terrain roughness, stream power index and compound topographic index) can be used for predictive mapping without data representing other common landslide conditioning factors, and evaluate the differences in landslide susceptibility mapping using widely-used statistical approaches. The aforementioned factors were used to produce landslide susceptibility maps for a 140 km2 study area in northwest Oregon using six representative techniques: frequency ratio, weights of evidence, logistic regression, discriminant analysis, artificial neural network, and support vector machine. Most notably, the research showed an advantage in selecting fewer critical conditioning factors. The most reliable factors all could be derived from a single LiDAR DEM, reducing the need for laborious and costly data gathering. Most of the six techniques showed similar statistical results; however, ANN showed less accuracy for predictive mapping. Keywords : LiDAR

  3. Estimating Volume, Biomass, and Carbon in Hedmark County, Norway Using a Profiling LiDAR

    NASA Technical Reports Server (NTRS)

    Nelson, Ross; Naesset, Erik; Gobakken, T.; Gregoire, T.; Stahl, G.

    2009-01-01

    A profiling airborne LiDAR is used to estimate the forest resources of Hedmark County, Norway, a 27390 square kilometer area in southeastern Norway on the Swedish border. One hundred five profiling flight lines totaling 9166 km were flown over the entire county; east-west. The lines, spaced 3 km apart north-south, duplicate the systematic pattern of the Norwegian Forest Inventory (NFI) ground plot arrangement, enabling the profiler to transit 1290 circular, 250 square meter fixed-area NFI ground plots while collecting the systematic LiDAR sample. Seven hundred sixty-three plots of the 1290 plots were overflown within 17.8 m of plot center. Laser measurements of canopy height and crown density are extracted along fixed-length, 17.8 m segments closest to the center of the ground plot and related to basal area, timber volume and above- and belowground dry biomass. Linear, nonstratified equations that estimate ground-measured total aboveground dry biomass report an R(sup 2) = 0.63, with an regression RMSE = 35.2 t/ha. Nonstratified model results for the other biomass components, volume, and basal area are similar, with R(sup 2) values for all models ranging from 0.58 (belowground biomass, RMSE = 8.6 t/ha) to 0.63. Consistently, the most useful single profiling LiDAR variable is quadratic mean canopy height, h (sup bar)(sub qa). Two-variable models typically include h (sup bar)(sub qa) or mean canopy height, h(sup bar)(sub a), with a canopy density or a canopy height standard deviation measure. Stratification by productivity class did not improve the nonstratified models, nor did stratification by pine/spruce/hardwood. County-wide profiling LiDAR estimates are reported, by land cover type, and compared to NFI estimates.

  4. Characterizing active volcanic processes at Kilauea volcano using LiDAR scanning

    NASA Astrophysics Data System (ADS)

    LeWinter, A. L.; Finnegan, D. C.; Patrick, M. R.; Anderson, S. W.; Orr, T. R.

    2012-12-01

    Active craters and lava lakes evolve in response to a variety of volcanic processes. Quantifying those changes can be difficult or even impossible, for safety reasons, due to the technical limitations of sensors that require a minimum standoff distance. In recent years, advancements in ground-based Light Detection and Ranging (LiDAR) scanners and accessibility to these systems have enhanced our ability to capture data in a diversity of volcanic settings at the highest spatial and temporal resolutions yet seen. Moreover, advancements in full-waveform digitization have significantly improved the ability to acquire data in environments where ash, steam, and sulfur dioxide emissions have historically hampered efforts. Kilauea's ongoing summit eruption, which began in March 2008, has been characterized in part by the evolution of its vent into a 160-meter diameter collapse crater holding an active lava lake. This process has been documented in detail by field and webcam observations, but has not been accurately quantified. Our research focuses on acquiring repeat, high-resolution full-waveform LiDAR data throughout 2012 to monitor changes in the geometry of Kilauea's active lava lake and the crater to which it is confined. We collected LiDAR data in February and July 2012, with plans for an additional survey in October 2012. Our results show changes in the shape of the vent walls and the shape and level of the confined lava lake. Specifically, the LiDAR data has revealed 1) changes in the lava lake level, corresponding to tiltmeter observations of pressure fluctuations in the summit magma reservoir, 2) enlargement of the vent cavity, due to frequent rock falls, and 3) modifications to the lake size and surrounding lava ledges due to competing processes of accretion and collapse. The rapid acquisition of repeat, high-resolution topographic data enables researchers to more accurately characterize shape and volume changes involved in a range of eruptive systems, while

  5. Road centerline extraction from airborne LiDAR point cloud based on hierarchical fusion and optimization

    NASA Astrophysics Data System (ADS)

    Hui, Zhenyang; Hu, Youjian; Jin, Shuanggen; Yevenyo, Yao Ziggah

    2016-08-01

    Road information acquisition is an important part of city informatization construction. Airborne LiDAR provides a new means of acquiring road information. However, the existing road extraction methods using LiDAR point clouds always decide the road intensity threshold based on experience, which cannot obtain the optimal threshold to extract a road point cloud. Moreover, these existing methods are deficient in removing the interference of narrow roads and several attached areas (e.g., parking lot and bare ground) to main roads extraction, thereby imparting low completeness and correctness to the city road network extraction result. Aiming at resolving the key technical issues of road extraction from airborne LiDAR point clouds, this paper proposes a novel method to extract road centerlines from airborne LiDAR point clouds. The proposed approach is mainly composed of three key algorithms, namely, Skewness balancing, Rotating neighborhood, and Hierarchical fusion and optimization (SRH). The skewness balancing algorithm used for the filtering was adopted as a new method for obtaining an optimal intensity threshold such that the "pure" road point cloud can be obtained. The rotating neighborhood algorithm on the other hand was developed to remove narrow roads (corridors leading to parking lots or sidewalks), which are not the main roads to be extracted. The proposed hierarchical fusion and optimization algorithm caused the road centerlines to be unaffected by certain attached areas and ensured the road integrity as much as possible. The proposed method was tested using the Vaihingen dataset. The results demonstrated that the proposed method can effectively extract road centerlines in a complex urban environment with 91.4% correctness and 80.4% completeness.

  6. 3D campus modeling using LiDAR point cloud data

    NASA Astrophysics Data System (ADS)

    Kawata, Yoshiyuki; Yoshii, Satoshi; Funatsu, Yukihiro; Takemata, Kazuya

    2012-10-01

    The importance of having a 3D urban city model is recognized in many applications, such as management offices of risk and disaster, the offices for city planning and developing and others. As an example of urban model, we reconstructed 3D KIT campus manually in this study, by utilizing airborne LiDAR point cloud data. The automatic extraction of building shapes was left in future work.

  7. Urban 3D GIS From LiDAR and digital aerial images

    NASA Astrophysics Data System (ADS)

    Zhou, Guoqing; Song, C.; Simmers, J.; Cheng, P.

    2004-05-01

    This paper presents a method, which integrates image knowledge and Light Detection And Ranging (LiDAR) point cloud data for urban digital terrain model (DTM) and digital building model (DBM) generation. The DBM is an Object-Oriented data structure, in which each building is considered as a building object, i.e., an entity of the building class. The attributes of each building include roof types, polygons of the roof surfaces, height, parameters describing the roof surfaces, and the LiDAR point array within the roof surfaces. Each polygon represents a roof surface of building. This type of data structure is flexible for adding other building attributes in future, such as texture information and wall information. Using image knowledge extracted, we developed a new method of interpolating LiDAR raw data into grid digital surface model (DSM) with considering the steep discontinuities of buildings. In this interpolation method, the LiDAR data points, which are located in the polygon of roof surfaces, first are determined, and then interpolation via planar equation is employed for grid DSM generation. The basic steps of our research are: (1) edge detection by digital image processing algorithms; (2) complete extraction of the building roof edges by digital image processing and human-computer interactive operation; (3) establishment of DBM; (4) generation of DTM by removing surface objects. Finally, we implement the above functions by MS VC++ programming. The outcome of urban 3D DSM, DTM and DBM is exported into urban database for urban 3D GIS.

  8. Abu Dhabi Basemap Update Using the LiDAR Mobile Mapping Technology

    NASA Astrophysics Data System (ADS)

    Alshaiba, Omar; Amparo Núñez-Andrés, M.; Lantada, Nieves

    2016-04-01

    Mobile LiDAR system provides a new technology which can be used to update geospatial information by direct and rapid data collection. This technology is faster than the traditional survey ways and has lower cost. Abu Dhabi Municipal System aims to update its geospatial system frequently as the government entities have invested heavily in GIS technology and geospatial data to meet the repaid growth in the infrastructure and construction projects in recent years. The Emirate of Abu Dhabi has witnessed a huge growth in infrastructure and construction projects in recent years. Therefore, it is necessary to develop and update its basemap system frequently to meet their own organizational needs. Currently, the traditional ways are used to update basemap system such as human surveyors, GPS receivers and controller (GPS assigned computer). Then the surveyed data are downloaded, edited and reviewed manually before it is merged to the basemap system. Traditional surveying ways may not be applicable in some conditions such as; bad weather, difficult topographic area and boundary area. This paper presents a proposed methodology which uses the Mobile LiDAR system to update basemap in Abu Dhabi by using daily transactions services. It aims to use and integrate the mobile LiDAR technology into the municipality's daily workflow such that it becomes the new standard cost efficiency operating procedure for updating the base-map in Abu Dhabi Municipal System. On another note, the paper will demonstrate the results of the innovated workflow for the base-map update using the mobile LiDAR point cloud and few processing algorithms.

  9. Skeleton-based botanic tree diameter estimation from dense LiDAR data

    NASA Astrophysics Data System (ADS)

    Bucksch, Alexander; Lindenbergh, Roderik; Menenti, Massimo; Rahman, Muhammad Z.

    2009-08-01

    New airborne LiDAR (Light Detection and Ranging) measurement systems, like the FLI-MAP 400 System, make it possible to obtain high density data containing far more information about single objects, like trees, than traditional airborne laser systems. Therefore, it becomes feasible to analyze geometric properties of trees on the individual object level. In this paper a new 3-step strategy is presented to calculate the stem diameter of individual natural trees at 1.3m height, the so-called breast height diameter, which is an important parameter for forest inventory and flooding simulations. Currently, breast height diameter estimates are not obtained from direct measurements, but are derived using species dependent allometric constraints. Our strategy involves three independent steps: 1. Delineation of the individual trees as represented by the LiDAR data, 2. Skeletonization of the single trees, and 3. Determination of the breast height diameter computing the distance of a suited subset of LiDAR points to the local skeleton. The use of a recently developed skeletonization algorithm based on graph-reduction is the key to the breast height measurement. A set of four relevant test cases is presented and validated against hand measurements. It is shown that the new 3-step approach automatically derives breast height diameters deviating only 10% from hand measurements in four test cases. The potential of the introduced method in practice is demonstrated on the fully automatic analysis of a LiDAR data set representing a patch of forest consisting of 49 individual trees.

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

    NASA Astrophysics Data System (ADS)

    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 (<10 cm) and over large extents (>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.

  11. Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters.

    PubMed

    Luo, Shezhou; Chen, Jing M; Wang, Cheng; Xi, Xiaohuan; Zeng, Hongcheng; Peng, Dailiang; Li, Dong

    2016-05-30

    Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R2 = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m2), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data. PMID:27410085

  12. Assessment of human thermal perception in the hot-humid climate of Dar es Salaam, Tanzania

    NASA Astrophysics Data System (ADS)

    Ndetto, Emmanuel L.; Matzarakis, Andreas

    2016-06-01

    Dar es Salaam, Tanzania, is a typical African city along the Indian Ocean coast, and therefore an important urban area to examine human thermal perception in the hot-humid tropical climate. Earlier research on human bioclimate at Dar es Salaam indicated that heat stress prevails during the hot season from October to March, peaking between December and February, particularly the early afternoons. In order to assess the human thermal perception and adaptation, two popular places, one at an urban park and another at a beach environment, were selected and questionnaire surveys were conducted in August-September 2013 and January 2014, concurrently with local micro-meteorological measurements at survey locations. The thermal conditions were quantified in terms of the thermal index of the physiologically equivalent temperature (PET) using the micro-scale climate model RayMan. The thermal comfort range of human thermal comfort and the local thermal adaptive capacity were determined in respect to the thermal index by binning thermal sensation votes. The thermal comfort range was found to be well above that in temperate climates at about 23-31 °C of PET. The study could significantly contribute to urban planning in Dar es Salaam and other coastal cities in the tropics.

  13. Fusion of waveform LiDAR data and hyperspectral imagery for land cover classification

    NASA Astrophysics Data System (ADS)

    Wang, Hongzhou; Glennie, Craig

    2015-10-01

    Current research into the fusion of hyperspectral imagery (HI) and full waveform LiDAR (Light Detection And Ranging) has relied on first processing the full waveform LiDAR (FWL) data to a set of discrete returns before merging because the data structure and sampling interval of HI and FWL are distinctly different. However, additional information about target properties can potentially be recovered if the waveform shape is preserved in the fusion process. This paper proposes a "voxelization" method to register FWL data to HI by dividing the waveform data into voxels, and then synthesizing all waveforms which intersect a voxel column into one three-dimensional superposition waveform: the synthesized waveform (SWF). A vertical energy distribution coefficients (VEDC) feature is proposed for extracting features from SWF, and then the SWF and HI are fused to form a complete feature space for classification. A pairwise classifier was adapted and completed using both Maximum Likelihood and Support Vector Machine classifiers for the combined SWF/HI features. Results show that this method of generating SWF from FWL data can effectively preserve information from the original waveforms, and the fusion of SWF and HI enhanced land cover classification compared to both using either data set alone or the merging of HI with a discrete LiDAR return point cloud.

  14. Analyzing Hydro-Geomorphic Responses in Post-Fire Stream Channels with Terrestrial LiDAR

    NASA Astrophysics Data System (ADS)

    Nourbakhshbeidokhti, S.; Kinoshita, A. M.; Chin, A.

    2015-12-01

    Wildfires have potential to significantly alter soil properties and vegetation within watersheds. These alterations often contribute to accelerated erosion, runoff, and sediment transport in stream channels and hillslopes. This research applies repeated Terrestrial Laser Scanning (TLS) Light Detection and Ranging (LiDAR) to stream reaches within the Pike National Forest in Colorado following the 2012 Waldo Canyon Fire. These scans allow investigation of the relationship between sediment delivery and environmental characteristics such as precipitation, soil burn severity, and vegetation. Post-fire LiDAR images provide high resolution information of stream channel changes in eight reaches for three years (2012-2014). All images are processed with RiSCAN PRO to remove vegetation and triangulated and smoothed to create a Digital Elevation Model (DEM) with 0.1 m resolution. Study reaches with two or more successive DEM images are compared using a differencing method to estimate the volume of sediment erosion and deposition. Preliminary analysis of four channel reaches within Williams Canyon and Camp Creek yielded erosion estimates between 0.035 and 0.618 m3 per unit area. Deposition was estimated as 0.365 to 1.67 m3 per unit area. Reaches that experienced higher soil burn severity or larger rainfall events produced the greatest geomorphic changes. Results from LiDAR analyses can be incorporated into post-fire hydrologic models to improve estimates of runoff and sediment yield. These models will, in turn, provide guidance for water resources management and downstream hazards mitigation.

  15. Geodetic imaging with airborne LiDAR: the Earth's surface revealed.

    PubMed

    Glennie, C L; Carter, W E; Shrestha, R L; Dietrich, W E

    2013-08-01

    The past decade has seen an explosive increase in the number of peer reviewed papers reporting new scientific findings in geomorphology (including fans, channels, floodplains and landscape evolution), geologic mapping, tectonics and faulting, coastal processes, lava flows, hydrology (especially snow and runoff routing), glaciers and geo-archaeology. A common genesis of such findings is often newly available decimeter resolution 'bare Earth' geodetic images, derived from airborne laser swath mapping, a.k.a. airborne LiDAR, observations. In this paper we trace nearly a half century of advances in geodetic science made possible by space age technology, such as the invention of short-pulse-length high-pulse-rate lasers, solid state inertial measurement units, chip-based high speed electronics and the GPS satellite navigation system, that today make it possible to map hundreds of square kilometers of terrain in hours, even in areas covered with dense vegetation or shallow water. To illustrate the impact of the LiDAR observations we present examples of geodetic images that are not only stunning to the eye, but help researchers to develop quantitative models explaining how terrain evolved to its present form, and how it will likely change with time. Airborne LiDAR technology continues to develop quickly, promising ever more scientific discoveries in the years ahead. PMID:23828665

  16. 3D graph segmentation for target detection in FOPEN LiDAR data

    NASA Astrophysics Data System (ADS)

    Shorter, Nicholas; Locke, Judson; Smith, O'Neil; Keating, Emma; Smith, Philip

    2013-05-01

    A novel use of Felzenszwalb's graph based efficient image segmentation algorithm* is proposed for segmenting 3D volumetric foliage penetrating (FOPEN) Light Detection and Ranging (LiDAR) data for automated target detection. The authors propose using an approximate nearest neighbors algorithm to establish neighbors of points in 3D and thus form the graph for segmentation. Following graph formation, the angular difference in the points' estimated normal vectors is proposed for the graph edge weights. Then the LiDAR data is segmented, in 3D, and metrics are calculated from the segments to determine their geometrical characteristics and thus likelihood of being a target. Finally, the bare earth within the scene is automatically identified to avoid confusion of flat bare earth with flat targets. The segmentation, the calculated metrics, and the bare earth all culminate in a target detection system deployed for FOPEN LiDAR. General purpose graphics processing units (GPGPUs) are leveraged to reduce processing times for the approximate nearest neighbors and point normal estimation algorithms such that the application can be run in near real time. Results are presented on several data sets.

  17. Motion field estimation for a dynamic scene using a 3D LiDAR.

    PubMed

    Li, Qingquan; Zhang, Liang; Mao, Qingzhou; Zou, Qin; Zhang, Pin; Feng, Shaojun; Ochieng, Washington

    2014-01-01

    This paper proposes a novel motion field estimation method based on a 3D light detection and ranging (LiDAR) sensor for motion sensing for intelligent driverless vehicles and active collision avoidance systems. Unlike multiple target tracking methods, which estimate the motion state of detected targets, such as cars and pedestrians, motion field estimation regards the whole scene as a motion field in which each little element has its own motion state. Compared to multiple target tracking, segmentation errors and data association errors have much less significance in motion field estimation, making it more accurate and robust. This paper presents an intact 3D LiDAR-based motion field estimation method, including pre-processing, a theoretical framework for the motion field estimation problem and practical solutions. The 3D LiDAR measurements are first projected to small-scale polar grids, and then, after data association and Kalman filtering, the motion state of every moving grid is estimated. To reduce computing time, a fast data association algorithm is proposed. Furthermore, considering the spatial correlation of motion among neighboring grids, a novel spatial-smoothing algorithm is also presented to optimize the motion field. The experimental results using several data sets captured in different cities indicate that the proposed motion field estimation is able to run in real-time and performs robustly and effectively. PMID:25207868

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

    NASA Astrophysics Data System (ADS)

    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.

  19. LiDAR scan and smart piezo layer combined damage detection

    NASA Astrophysics Data System (ADS)

    Chen, Shenen; Chung, Howard; Park, Youngjin

    2013-04-01

    The motivation of this study is to determine a technique to completely describe the damage state of large deformed structures commonly found during forensic investigations. The combination of Laser Detecting and Ranging (LiDAR) and Piezoelectric (PZT) Sensing Technologies for damage quantification is suggested to generate the full-field description of large deformation of a plate. The test subject is a 16 inch by 16 inch aluminum plate subjected to different damage scenarios. LiDAR is a static scanning laser that provides a 3-dimensional picture of the object. Smart Layer is a commercial PZT actuator/sensor network system that generates stress waves for internal damage evaluation. Both techniques were applied to the test plate after damages are introduced. In order to effectively analyze the results, the images for each test were superimposed. Frequencies that depicted the best interpretation of damage in the direct path images were superimposed with the 3-dimensional LiDAR images. Four damage scenarios were imposed on an aluminum plate including saw cuts at different depths using an electric saw. The final damage is a severe bending of the plate. The bending of the specimen produced an image that located the most severe damage directly under the left hand portion and directly above the right hand portion of the bend.

  20. Supporting Indonesia's National Forest Monitoring System with LiDAR Observations

    NASA Astrophysics Data System (ADS)

    Hagen, S. C.

    2015-12-01

    Scientists at Applied GeoSolutions, Jet Propulsion Laboratory, Winrock International, and the University of New Hampshire are working with the government of Indonesia to enhance the National Forest Monitoring System in Kalimantan, Indonesia. The establishment of a reliable, transparent, and comprehensive NFMS has been limited by a dearth of relevant data that are accurate, low-cost, and spatially resolved at subnational scales. In this NASA funded project, we are developing, evaluating, and validating several critical components of a NFMS in Kalimantan, Indonesia, focusing on the use of LiDAR and radar imagery for improved carbon stock and forest degradation information. Applied GeoSolutions and the University of New Hampshire have developed an Open Source Software package to process large amounts LiDAR data quickly, easily, and accurately. The Open Source project is called lidar2dems and includes the classification of raw LAS point clouds and the creation of Digital Terrain Models (DTMs), Digital Surface Models (DSMs), and Canopy Height Models (CHMs). Preliminary estimates of forest structure and forest damage from logging from these data sets support the idea that comprehensive, well documented, freely available software for processing LiDAR data can enable countries such as Indonesia to cost effectively monitor their forests with high precision.

  1. Mutations in DARS Cause Hypomyelination with Brain Stem and Spinal Cord Involvement and Leg Spasticity

    PubMed Central

    Taft, Ryan J.; Vanderver, Adeline; Leventer, Richard J.; Damiani, Stephen A.; Simons, Cas; Grimmond, Sean M.; Miller, David; Schmidt, Johanna; Lockhart, Paul J.; Pope, Kate; Ru, Kelin; Crawford, Joanna; Rosser, Tena; de Coo, Irenaeus F.M.; Juneja, Monica; Verma, Ishwar C.; Prabhakar, Prab; Blaser, Susan; Raiman, Julian; Pouwels, Petra J.W.; Bevova, Marianna R.; Abbink, Truus E.M.; van der Knaap, Marjo S.; Wolf, Nicole I.

    2013-01-01

    Inherited white-matter disorders are a broad class of diseases for which treatment and classification are both challenging. Indeed, nearly half of the children presenting with a leukoencephalopathy remain without a specific diagnosis. Here, we report on the application of high-throughput genome and exome sequencing to a cohort of ten individuals with a leukoencephalopathy of unknown etiology and clinically characterized by hypomyelination with brain stem and spinal cord involvement and leg spasticity (HBSL), as well as the identification of compound-heterozygous and homozygous mutations in cytoplasmic aspartyl-tRNA synthetase (DARS). These mutations cause nonsynonymous changes to seven highly conserved amino acids, five of which are unchanged between yeast and man, in the DARS C-terminal lobe adjacent to, or within, the active-site pocket. Intriguingly, HBSL bears a striking resemblance to leukoencephalopathy with brain stem and spinal cord involvement and elevated lactate (LBSL), which is caused by mutations in the mitochondria-specific DARS2, suggesting that these two diseases might share a common underlying molecular pathology. These findings add to the growing body of evidence that mutations in tRNA synthetases can cause a broad range of neurologic disorders. PMID:23643384

  2. Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR

    PubMed Central

    Li, Qingquan; Zhang, Liang; Mao, Qingzhou; Zou, Qin; Zhang, Pin; Feng, Shaojun; Ochieng, Washington

    2014-01-01

    This paper proposes a novel motion field estimation method based on a 3D light detection and ranging (LiDAR) sensor for motion sensing for intelligent driverless vehicles and active collision avoidance systems. Unlike multiple target tracking methods, which estimate the motion state of detected targets, such as cars and pedestrians, motion field estimation regards the whole scene as a motion field in which each little element has its own motion state. Compared to multiple target tracking, segmentation errors and data association errors have much less significance in motion field estimation, making it more accurate and robust. This paper presents an intact 3D LiDAR-based motion field estimation method, including pre-processing, a theoretical framework for the motion field estimation problem and practical solutions. The 3D LiDAR measurements are first projected to small-scale polar grids, and then, after data association and Kalman filtering, the motion state of every moving grid is estimated. To reduce computing time, a fast data association algorithm is proposed. Furthermore, considering the spatial correlation of motion among neighboring grids, a novel spatial-smoothing algorithm is also presented to optimize the motion field. The experimental results using several data sets captured in different cities indicate that the proposed motion field estimation is able to run in real-time and performs robustly and effectively. PMID:25207868

  3. Estimating Basin Snow Volume Using Aerial LiDAR and Binary Regression Trees (Invited)

    NASA Astrophysics Data System (ADS)

    Shallcross, A. T.; McNamara, J. P.; Flores, A. N.; Marshall, H.; Marks, D. G.; Glenn, N. F.

    2010-12-01

    Snow cover derived from airborne LiDAR (Light Detection And Ranging) is combined with binary regression trees to improve the prediction of total basin snow volume for the Dry Creek Experimental Watershed (DCEW), ID. These methods are used to identify site-specific topographic controls on the spatial distribution of snow so that future point measurements of snow depth can be distributed through space efficiently. LiDAR is used to map snow cover by differencing the digital elevation models (DEMs) obtained from a snow-covered overflight and a snow-free overflight. Topographic parameters known to control snow distribution are calculated from the snow free LiDAR dataset. Here, mean vegetation height, slope, aspect, solar radiation, and elevation are used to predict snow depth via a binary regression tree using ten-fold cross-validation. The branches leading to the terminal nodes of the regression tree are used to segment the watershed into homogeneous snow distribution units. Preliminary results indicate that 23 statistically significant discrete units exist. Thus, during future field campaigns, point measurements of snow depth can be gathered and distributed throughout these units. Mean measured SWE/depth of each unit can be summed to determine the total basin snow volume. This method should decrease field time and improve the accuracy of basin snow volume estimates for watershed analyses.

  4. Geodetic imaging with airborne LiDAR: the Earth's surface revealed

    NASA Astrophysics Data System (ADS)

    Glennie, C. L.; Carter, W. E.; Shrestha, R. L.; Dietrich, W. E.

    2013-08-01

    The past decade has seen an explosive increase in the number of peer reviewed papers reporting new scientific findings in geomorphology (including fans, channels, floodplains and landscape evolution), geologic mapping, tectonics and faulting, coastal processes, lava flows, hydrology (especially snow and runoff routing), glaciers and geo-archaeology. A common genesis of such findings is often newly available decimeter resolution ‘bare Earth’ geodetic images, derived from airborne laser swath mapping, a.k.a. airborne LiDAR, observations. In this paper we trace nearly a half century of advances in geodetic science made possible by space age technology, such as the invention of short-pulse-length high-pulse-rate lasers, solid state inertial measurement units, chip-based high speed electronics and the GPS satellite navigation system, that today make it possible to map hundreds of square kilometers of terrain in hours, even in areas covered with dense vegetation or shallow water. To illustrate the impact of the LiDAR observations we present examples of geodetic images that are not only stunning to the eye, but help researchers to develop quantitative models explaining how terrain evolved to its present form, and how it will likely change with time. Airborne LiDAR technology continues to develop quickly, promising ever more scientific discoveries in the years ahead.

  5. Automatic extraction of highway light poles and towers from mobile LiDAR data

    NASA Astrophysics Data System (ADS)

    Yan, Wai Yeung; Morsy, Salem; Shaker, Ahmed; Tulloch, Mark

    2016-03-01

    Mobile LiDAR has been recently demonstrated as a viable technique for pole-like object detection and classification. Despite that a desirable accuracy (around 80%) has been reported in the existing studies, majority of them were presented in the street level with relatively flat ground and very few of them addressed how to extract the entire pole structure from the ground or curb surface. Therefore, this paper attempts to fill the research gap by presenting a workflow for automatic extraction of light poles and towers from mobile LiDAR data point cloud, with a particular focus on municipal highway. The data processing workflow includes (1) an automatic ground filtering mechanism to separate aboveground and ground features, (2) an unsupervised clustering algorithm to cluster the aboveground data point cloud, (3) a set of decision rules to identify and classify potential light poles and towers, and (4) a least-squares circle fitting algorithm to fit the circular pole structure so as to remove the ground points. The workflow was tested with a set of mobile LiDAR data collected for a section of highway 401 located in Toronto, Ontario, Canada. The results showed that the proposed method can achieve an over 91% of detection rate for five types of light poles and towers along the study area.

  6. DARS-associated leukoencephalopathy can mimic a steroid-responsive neuroinflammatory disorder

    PubMed Central

    Toro, Camilo; Kister, Ilya; Latif, Kartikasalwah Abd; Leventer, Richard; Pizzino, Amy; Simons, Cas; Abbink, Truus E.M.; Taft, Ryan J.; van der Knaap, Marjo S.; Vanderver, Adeline

    2015-01-01

    Objective: To describe the expanding clinical spectrum of a recently described hereditary leukoencephalopathy, hypomyelination with brainstem and spinal cord involvement and leg spasticity, which is caused by mutations in the aspartyl tRNA-synthetase encoding gene DARS, including patients with an adolescent onset. Methods: Three patients with mutations in DARS were identified by combining MRI pattern recognition and genetic analysis. Results: One patient had the typical infantile presentation, but 2 patients with onset in late adolescence had a disease mimicking an acquired inflammatory CNS disorder. Adolescent-onset patients presented with subacute spastic paraplegia and had positive response to steroids. They had only minor focal supratentorial white matter abnormalities, but identical spinal cord changes involving dorsal columns and corticospinal tracts. Clinical presentation included subacute spastic paraplegia with partial improvement on steroids. Conclusions: Focal T2 hyperintense white matter changes on brain MRI in combination with spinal cord signal abnormalities usually suggest acquired inflammatory conditions such as multiple sclerosis, especially in the context of relapsing course and a positive response to steroid treatment. Adolescents with mutations in DARS can present with a comparable clinical picture, broadening the clinical spectrum of hypomyelination with brainstem and spinal cord involvement and leg spasticity. PMID:25527264

  7. Downstream hydraulic geometry relationships: Gathering reference reach-scale width values from LiDAR

    NASA Astrophysics Data System (ADS)

    Sofia, G.; Tarolli, P.; Cazorzi, F.; Dalla Fontana, G.

    2015-12-01

    This paper examines the ability of LiDAR topography to provide reach-scale width values for the analysis of downstream hydraulic geometry relationships along some streams in the Dolomites (northern Italy). Multiple reach-scale dimensions can provide representative geometries and statistics characterising the longitudinal variability in the channel, improving the understanding of geomorphic processes across networks. Starting from the minimum curvature derived from a LiDAR DTM, the proposed algorithm uses a statistical approach for the identification of the scale of analysis, and for the automatic characterisation of reach-scale bankfull widths. The downstream adjustment in channel morphology is then related to flow parameters (drainage area and stream power). With the correct planning of a LiDAR survey, uncertainties in the procedure are principally due to the resolution of the DTM. The outputs are in general comparable in quality to field survey measurements, and the procedure allows the quick comparison among different watersheds. The proposed automatic approach could improve knowledge about river systems with highly variable widths, and about systems in areas covered by vegetation or inaccessible to field surveys. With proven effectiveness, this research could offer an interesting starting point for the analysis of differences between watersheds, and to improve knowledge about downstream channel adjustment in relation, for example, to scale and landscape forcing (e.g. sediment transport, tectonics, lithology, climate, geomorphology, and anthropic pressure).

  8. [Estimating individual tree aboveground biomass of the mid-subtropical forest using airborne LiDAR technology].

    PubMed

    Liu, Feng; Tan, Chang; Lei, Pi-Feng

    2014-11-01

    Taking Wugang forest farm in Xuefeng Mountain as the research object, using the airborne light detection and ranging (LiDAR) data under leaf-on condition and field data of concomitant plots, this paper assessed the ability of using LiDAR technology to estimate aboveground biomass of the mid-subtropical forest. A semi-automated individual tree LiDAR cloud point segmentation was obtained by using condition random fields and optimization methods. Spatial structure, waveform characteristics and topography were calculated as LiDAR metrics from the segmented objects. Then statistical models between aboveground biomass from field data and these LiDAR metrics were built. The individual tree recognition rates were 93%, 86% and 60% for coniferous, broadleaf and mixed forests, respectively. The adjusted coefficients of determination (R(2)adj) and the root mean squared errors (RMSE) for the three types of forest were 0.83, 0.81 and 0.74, and 28.22, 29.79 and 32.31 t · hm(-2), respectively. The estimation capability of model based on canopy geometric volume, tree percentile height, slope and waveform characteristics was much better than that of traditional regression model based on tree height. Therefore, LiDAR metrics from individual tree could facilitate better performance in biomass estimation. PMID:25898621

  9. Timely binding of IHF and Fis to DARS2 regulates ATP–DnaA production and replication initiation

    PubMed Central

    Kasho, Kazutoshi; Fujimitsu, Kazuyuki; Matoba, Toshihiro; Oshima, Taku; Katayama, Tsutomu

    2014-01-01

    In Escherichia coli, the ATP-bound form of DnaA (ATP–DnaA) promotes replication initiation. During replication, the bound ATP is hydrolyzed to ADP to yield the ADP-bound form (ADP–DnaA), which is inactive for initiation. The chromosomal site DARS2 facilitates the regeneration of ATP–DnaA by catalyzing nucleotide exchange between free ATP and ADP bound to DnaA. However, the regulatory mechanisms governing this exchange reaction are unclear. Here, using in vitro reconstituted experiments, we show that two nucleoid-associated proteins, IHF and Fis, bind site-specifically to DARS2 to activate coordinately the exchange reaction. The regenerated ATP–DnaA was fully active in replication initiation and underwent DnaA–ATP hydrolysis. ADP–DnaA formed heteromultimeric complexes with IHF and Fis on DARS2, and underwent nucleotide dissociation more efficiently than ATP–DnaA. Consistently, mutant analyses demonstrated that specific binding of IHF and Fis to DARS2 stimulates the formation of ATP–DnaA production, thereby promoting timely initiation. Moreover, we show that IHF–DARS2 binding is temporally regulated during the cell cycle, whereas Fis only binds to DARS2 in exponentially growing cells. These results elucidate the regulation of ATP–DnaA and replication initiation in coordination with the cell cycle and growth phase. PMID:25378325

  10. Intensity normalization and automatic gain control correction of airborne LiDAR data for classifying a rangeland ecosystem

    NASA Astrophysics Data System (ADS)

    Shrestha, R.; Glenn, N. F.; Spaete, L.; Mitchell, J.

    2011-12-01

    Airborne LiDAR not only records elevation but also the intensity, or the amplitude, of the returning light beam. LiDAR intensity information can be useful for many applications, including landcover classification. Intensity is directly associated with the reflectance of the target surface and can be influenced by factors such as flying altitude and sensor settings. LiDAR intensity data must be calibrated before use and this is especially important for multi-temporal studies where differing flight conditions can cause more variations. Some sensors such as the Leica ALS50 Phase II also records automatic gain control (AGC), which controls the gain of the LiDAR signal, allowing information from low-reflectance surfaces. We demonstrate a post-processing method for calibrating intensity using airborne LiDAR data collected over a sage-steppe ecosystem in southeastern Idaho, USA. Range normalization with respect to the sensor-to-object distance is performed by using smoothed best estimated trajectory information collected at an interval of every second. Optimal parameters for calibrating AGC data are determined by collecting spectral reference data at the time of overflights, in test areas with homogenous backscatter properties. Intensity calibration results are compared with vendor corrected intensity data, and used to perform landcover classification using the Random Forests method. We also test this intensity calibration approach using a separate multi-temporal LiDAR data set collected by the same sensor.

  11. Classification and Characterization of Neotropical Rainforest Vegetation from Hyperspectral and LiDAR Data

    NASA Astrophysics Data System (ADS)

    Crawford, M. M.; Prasad, S.; Jung, J.; Yang, H.; Zhang, Y.

    2013-12-01

    Mapping species and forest vertical structure at regional, continental, and global scale is of increasing importance for climate science and decision support systems. Remote sensing technologies have been widely utilized to achieve this goal since they help overcome limitations of the direct and indirect measurement approaches. While the use of multi-sensor data for characterizing forest structure has gained significant attention in recent years, research on the integration of full waveform LiDAR and hyperspectral data for a) classification and b) characterization of vegetation structure has been limited. Given sufficient labeled ground reference samples, supervised learning methods have evolved to effectively classify data in a high dimensional feature space. However, it is expensive and time-consuming to obtain labeled data, although the very high dimensionality of feature spaces from hyperspectral and LiDAR inputs make it difficult to design reliable classifiers with a limited quantity of labeled data. Therefore, it is important to concentrate on developing training data sets which are the most 'informative' and 'useful' for the classification task. Active learning (AL) was developed in the machine learning community, and has been demonstrated to be useful for classification of remote sensing data. In the active learning framework, classifiers are initially trained on a very limited pool of training samples, but additional informative and representative samples are identified from the abundant unlabeled data, labeled, and then inducted into this pool, thereby growing the training dataset in a systematic way. The goal is to choose data points such that a more accurate classification boundary is learned. We propose a novel Multi-kernel Active Learning (MKL-AL) approach that incorporates features from multiple sensors with an automatically optimized kernel composite ¬function, and kernel parameters are selected intelligently during the AL learning process. The

  12. Mobile LiDAR Measurement for Aerosol Investigation in South-Central Hebei, China

    NASA Astrophysics Data System (ADS)

    qin, kai; Wu, Lixin; Zheng, Yunhui; Wong Man, Sing; Wang, Runfeng; Hu, Mingyu; Lang, Hongmei; Wang, Luyao; Bai, Yang; Rao, Lanlan

    2016-04-01

    With the rapid industrialization and urbanization in China during the last decades, the increasing anthropogenic pollutant emissions have significantly caused serious air pollution problems which are adversely influencing public health. Hebei is one of the most air polluted provinces in China. In January 2013, an extremely severe and persistent haze episode with record-breaking PM2.5 outbreak affecting hundreds of millions of people occurred over eastern and northern China. During that haze episode, 7 of the top 10 most polluted cities in China were located in the Hebei Province according to the report of China's Ministry of Environmental Protection. To investigate and the spatial difference and to characterize the vertical distribution of aerosol in different regions of south-central Hebei, mobile measurements were carried out using a mini micro pulse LiDAR system (model: MiniMPL) in March 2014. The mobile LiDAR kit consisting of a MiniMPL, a vibration reduction mount, a power inverter, a Windows surface tablet and a GPS receiver were mounted in a car watching though the sunroof opening. For comparison, a fixed measurement using a traditional micro pulse LiDAR system (model: MPL-4B) was conducted simultaneously in Shijiazhuang, the capital of Hebei Province. The equipped car was driven from downtown Shijiazhuang by way of suburban and rural area to downtown Cangzhou, Handan, and Baoding respectively at almost stable speed around 100Km per hour along different routes which counted in total more than 1000Km. The results can be summarized as: 1) the spatial distribution of total aerosol optical depth along the measurement routes in south-central Hebei was controlled by local terrain and population in general, with high values in downtown and suburban in the plain areas, and low values in rural areas along Taihang mountain to the west and Yan mountain to the north; 2) obviously high AODs were obtained at roads crossing points, inside densely populated area and nearby

  13. Multiple-LiDAR measurements of wind turbine wakes: effect of the atmospheric stability

    NASA Astrophysics Data System (ADS)

    Valerio Iungo, Giacomo; Porté-Agel, Fernando

    2013-04-01

    Aerodynamic design and optimization of a wind farm layout are mainly based on the evaluation of wind turbine wake recovery by moving downstream, and on the characterization of wake interactions within a wind farm. Indeed, the power production of downstream wind turbine rows is strictly affected by the cumulative wake produced by the turbines deployed upstream. Wind turbine wakes are dependent on their aerodynamic features, and being immersed in the atmospheric boundary layer (ABL), they are also affected by surface heterogeneity, e.g. site topography and surface coverage, and atmospheric stability. The ABL stability is typically classified as neutral, convective or stable. In a neutral ABL the mechanical turbulent production is the dominating phenomenon. Conversely, for a convective ABL the turbulent kinetic energy and vertical transport phenomena are enhanced by positive buoyancy. Finally, for a stable ABL, a lower turbulence level is typically observed with an increased wind shear. For the present campaign convective ABL was typically observed during day-time, and neutral ABL for early morning and sunset periods. The aim of the present work is the evaluation of the influence of the ABL stability on downstream evolution of wind turbine wakes, which is mainly controlled by different ABL turbulence characteristics. Field measurements of the wake produced from a 2 MW Enercon E-70 wind turbine were performed with three scanning Doppler wind LiDARs. The wind and atmospheric conditions were characterized through a sonic anemometer deployed in proximity of the wind turbine. One LiDAR was placed at a distance about 12 rotor diameters upstream of the turbine in order to characterize the incoming wind. Two additional LiDARs were typically used to perform wake measurements. Tests were performed over the wake vertical symmetry plane in order to characterize wake recovery. Measurements were also carried out over conical surfaces in order to investigate the wind turbine wake

  14. LESTO: an Open Source GIS-based toolbox for LiDAR analysis

    NASA Astrophysics Data System (ADS)

    Franceschi, Silvia; Antonello, Andrea; Tonon, Giustino

    2015-04-01

    During the last five years different research institutes and private companies stared to implement new algorithms to analyze and extract features from LiDAR data but only a few of them also created a public available software. In the field of forestry there are different examples of software that can be used to extract the vegetation parameters from LiDAR data, unfortunately most of them are closed source (even if free), which means that the source code is not shared with the public for anyone to look at or make changes to. In 2014 we started the development of the library LESTO (LiDAR Empowered Sciences Toolbox Opensource): a set of modules for the analysis of LiDAR point cloud with an Open Source approach with the aim of improving the performance of the extraction of the volume of biomass and other vegetation parameters on large areas for mixed forest structures. LESTO contains a set of modules for data handling and analysis implemented within the JGrassTools spatial processing library. The main subsections are dedicated to 1) preprocessing of LiDAR raw data mainly in LAS format (utilities and filtering); 2) creation of raster derived products; 3) flight-lines identification and normalization of the intensity values; 4) tools for extraction of vegetation and buildings. The core of the LESTO library is the extraction of the vegetation parameters. We decided to follow the single tree based approach starting with the implementation of some of the most used algorithms in literature. These have been tweaked and applied on LiDAR derived raster datasets (DTM, DSM) as well as point clouds of raw data. The methods range between the simple extraction of tops and crowns from local maxima, the region growing method, the watershed method and individual tree segmentation on point clouds. The validation procedure consists in finding the matching between field and LiDAR-derived measurements at individual tree and plot level. An automatic validation procedure has been developed

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

    NASA Astrophysics Data System (ADS)

    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

  16. A Geoinformatics Approach to LiDAR / ALSM Data Distribution, Interpolation, and Analysis

    NASA Astrophysics Data System (ADS)

    Crosby, C. J.; Conner, J.; Frank, E.; Arrowsmith, J. R.; Memon, A.; Nandigam, V.; Wurman, G.; Baru, C.

    2005-12-01

    Distribution, interpolation and analysis of large LiDAR (Light Distance And Ranging, also known as ALSM (Airborne Laser Swath Mapping)) datasets pushes the computational limits of typical data distribution and processing systems. The high point-density of LiDAR datasets makes grid interpolation difficult for geoscience users who lack the computing and software resources necessary to handle these massive data volumes. We are using a geoinformatics approach to the distribution, interpolation and analysis of LiDAR data that capitalizes on cyberinfrastructure being developed as part of the GEON project (http://www.geongrid.org). Our approach utilizes a comprehensive workflow-based solution that begins with user-defined selection of a subset of raw data and ends with download and visualization of interpolated surfaces and derived products. The workflow environment allows us to modularize and generalize the procedure. It provides the freedom to easily plug-in new processes, to utilize existing sub workflows within an analysis, and easily extend or modify the analysis using drag-and-drop functionality through the Kepler workflow management system. In this GEON-based workflow, the billions of points within a LiDAR dataset point cloud are hosted in an IBM DB2 spatial database running on the DataStar terascale computer at San Diego Supercomputer Center; a machine designed specifically for data intensive computations. Data selection is performed via an ArcIMS-based interface that allows users to execute spatial and attribute subset queries on the larger dataset. The subset of data is then passed to a GRASS Open Source GIS-based web service, "lservice", that handles interpolation to grid and analysis of the data. Lservice was developed entirely within the open source domain and offers spline and inverse distance weighted (IDW) interpolation to grid with user-defined resolution and parameters. We also compute geomorphic metrics such as slope, curvature, and aspect. Users may

  17. Seagrass Identification Using High-Resolution 532nm Bathymetric LiDAR and Hyperspectral Imagery

    NASA Astrophysics Data System (ADS)

    Pan, Z.; Prasad, S.; Starek, M. J.; Fernandez Diaz, J. C.; Glennie, C. L.; Carter, W. E.; Shrestha, R. L.; Singhania, A.; Gibeaut, J. C.

    2013-12-01

    Seagrass provides vital habitat for marine fisheries and is a key indicator species of coastal ecosystem vitality. Monitoring seagrass is therefore an important environmental initiative, but measuring details of seagrass distribution over large areas via remote sensing has proved challenging. Developments in airborne bathymetric light detection and ranging (LiDAR) provide great potential in this regard. Traditional bathymetric LiDAR systems have been limited in their ability to map within the shallow water zone (< 1 m) where seagrass is typically present due to limitations in receiver response and laser pulse length. Emergent short-pulse width bathymetric LiDAR sensors and waveform processing algorithms enable depth measurements in shallow water environments previously inaccessible. This 3D information of the benthic layer can be applied to detect seagrass and characterize its distribution. Researchers with the National Center for Airborne Laser Mapping (NCALM) at the University of Houston (UH) and the Coastal and Marine Geospatial Sciences Lab (CMGL) of the Harte Research Institute at Texas A&M University-Corpus Christi conducted a coordinated airborne and boat-based survey of the Redfish Bay State Scientific Area as part of a collaborative study to investigate the capabilities of bathymetric LiDAR and hyperspectral imaging for seagrass mapping. Redfish Bay, located along the middle Texas coast of the Gulf of Mexico, is a state scientific area designated for the purpose of protecting and studying native seagrasses. Redfish Bay is part of the broader Coastal Bend Bays estuary system recognized by the US Environmental Protection Agency (EPA) as a national estuary of significance. For this survey, UH acquired high-resolution discrete-return and full-waveform bathymetric data using their Optech Aquarius 532 nm green LiDAR. In a separate flight, UH collected 2 sets of hyperspectral imaging data (1.2-m pixel resolution and 72 bands, and 0.6m pixel resolution and 36

  18. Quantifying soil carbon loss and uncertainty from a peatland wildfire using multi-temporal LiDAR

    USGS Publications Warehouse

    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.

  19. An Evaluation of Vessel Based LiDAR Surveying as a Tool for Monitoring Short Term Change in Coastal Wetlands

    NASA Astrophysics Data System (ADS)

    Mueller, C.

    2010-12-01

    Coastal wetlands are rapidly changing due to the impacts of climate change, sea-level rise, and coastal development. In light of these rapid changes, accurate and timely information on the morphology and dynamics of coastal wetlands is essential to their proper management. Currently many management agencies use aerial LiDAR surveys to detect geomorphic change over large areas, allowing rapid assessment of rates of erosion and accretion. Aerial based surveys however typically can only detect vertical changes as small as 10 cm and achieve horizontal resolutions of 1 meter or more. As an alternative, vessel based LiDAR, a topgraphic LiDAR system mounted on a moving platform, allows for geomorphic change detection at much higher resolutions (< 1 cm horizontal and vertical for LiDAR), making it possible to monitor dynamic systems over a much shorter time period and at much finer scales. The efficacy of vessel based LIDAR surveying to detect short term changes was tested in Elkhorn Slough in Monterey Bay, California using vessel based LiDAR surveys completed in 2009 and 2010. These vessel-based LiDAR data were merged with multibeam sonar surveys which were collected at the same time, to create complete digital elevation models of Elkhorn Slough. These data will be compared with 1998 and 2004 aerial LiDAR surveys in a geographic information system for data quality, resolution, and efficacy as methods for erosion detection with results ready for presentation at the 2010 American Geophysical Union conference held in San Francisco, CA.

  20. Analysis of elevation changes detected from multi-temporal LiDAR surveys in forested landslide terrain in western Oregon

    USGS Publications Warehouse

    Burns, W.J.; Coe, J.A.; Kaya, B.S.; Ma, L.

    2010-01-01

    We examined elevation changes detected from two successive sets of Light Detection and Ranging (LiDAR) data in the northern Coast Range of Oregon. The first set of LiDAR data was acquired during leafon conditions and the second set during leaf-off conditions. We were able to successfully identify and map active landslides using a differential digital elevation model (DEM) created from the two LiDAR data sets, but this required the use of thresholds (0.50 and 0.75 m) to remove noise from the differential elevation data, visual pattern recognition of landslideinduced elevation changes, and supplemental QuickBird satellite imagery. After mapping, we field-verified 88 percent of the landslides that we had mapped with high confidence, but we could not detect active landslides with elevation changes of less than 0.50 m. Volumetric calculations showed that a total of about 18,100 m3 of material was missing from landslide areas, probably as a result of systematic negative elevation errors in the differential DEM and as a result of removal of material by erosion and transport. We also examined the accuracies of 285 leaf-off LiDAR elevations at four landslide sites using Global Positioning System and total station surveys. A comparison of LiDAR and survey data indicated an overall root mean square error of 0.50 m, a maximum error of 2.21 m, and a systematic error of 0.09 m. LiDAR ground-point densities were lowest in areas with young conifer forests and deciduous vegetation, which resulted in extensive interpolations of elevations in the leaf-on, bare-earth DEM. For optimal use of multi-temporal LiDAR data in forested areas, we recommend that all data sets be flown during leaf-off seasons.

  1. Improving LiDAR Data Post-Processing Techniques for Archaeological Site Management and Analysis: A Case Study from Canaveral National Seashore Park

    NASA Astrophysics Data System (ADS)

    Griesbach, Christopher

    Methods used to process raw Light Detection and Ranging (LiDAR) data can sometimes obscure the digital signatures indicative of an archaeological site. This thesis explains the negative effects that certain LiDAR data processing procedures can have on the preservation of an archaeological site. This thesis also presents methods for effectively integrating LiDAR with other forms of mapping data in a Geographic Information Systems (GIS) environment in order to improve LiDAR archaeological signatures by examining several pre-Columbian Native American shell middens located in Canaveral National Seashore Park (CANA).

  2. Similarity and Complementarity of Airborne and Terrestrial LiDAR Data in High Mountain Regions

    NASA Astrophysics Data System (ADS)

    Kamp, Nicole; Glira, Philipp; Pfeifer, Norbert

    2013-04-01

    Glacier melt and a consequential increased sediment transport (erosion, transportation and accumulation) in high mountain regions are causing a frequent occurrence of geomorphic processes such as landslides and other natural hazards. These effects are investigated at the Gepatschferner (Kaunertal, Oetztal Alps, Tyrol), the second largest glacier in Austria, in the PROSA project (Catholic University Eichstätt - Ingolstadt, Vienna University of Technology, Friedrich Alexander University Erlangen-Nürnberg, Martin-Luther-University Halle-Wittenberg, University of Innsbruck, Munich University of Technology). To monitor these geomorphic processes, data with a very high spatial and very high temporally accuracy and resolution are needed. For this purpose multi-temporal terrestrial and aerial laser scanning data are acquired, processed and analysed. Airborne LiDAR data are collected with a density of 10 points/m² over the whole study area of the glacier and its foreland. Terrestrial LiDAR data are gathered to complement and improve the airborne LiDAR data. The different viewing geometry results in differences between airborne and terrestrial data. Very steep slopes and rock faces (around 90°, depending on the viewing direction) are not visible from the airborne view point. On the other hand, terrestrial viewpoints exhibit shadows for areas above the scanner position and in viewing direction behind vertical or steep faces. In addition, the density of terrestrial data is varying strongly, but has for most of the covered area a much higher level of detail than the airborne dataset. A small temporal baseline is also inevitable and may cause differences between acquisition of airborne and terrestrial data. The goal of this research work is to develop a method for merging airborne and terrestrial LiDAR data. One prerequisite for merging is the identification of areas which are measurements of the same physical surface in either data set. This allows a transformation of the

  3. Quantifying riparian zone structure from airborne LiDAR: Vegetation filtering, anisotropic interpolation, and uncertainty propagation

    NASA Astrophysics Data System (ADS)

    Hutton, Christopher; Brazier, Richard

    2012-06-01

    SummaryAdvances in remote sensing technology, notably in airborne Light Detection And Ranging (LiDAR), have facilitated the acquisition of high-resolution topographic and vegetation datasets over increasingly large areas. Whilst such datasets may provide quantitative information on surface morphology and vegetation structure in riparian zones, existing approaches for processing raw LiDAR data perform poorly in riparian channel environments. A new algorithm for separating vegetation from topography in raw LiDAR data, and the performance of the Elliptical Inverse Distance Weighting (EIDW) procedure for interpolating the remaining ground points, are evaluated using data derived from a semi-arid ephemeral river. The filtering procedure, which first applies a threshold (either slope or elevation) to classify vegetation high-points, and second a regional growing algorithm from these high-points, avoids the classification of high channel banks as vegetation, preserving existing channel morphology for subsequent interpolation (2.90-9.21% calibration error; 4.53-7.44% error in evaluation for slope threshold). EIDW, which accounts for surface anisotropy by converting the remaining elevation points to streamwise co-ordinates, can outperform isoptropic interpolation (IDW) on channel banks, however, performs less well in isotropic conditions, and when local anisotropy is different to that of the main channel. A key finding of this research is that filtering parameter uncertainty affects the performance of the interpolation procedure; resultant errors may propagate into the Digital Elevation Model (DEM) and subsequently derived products, such as Canopy Height Models (CHMs). Consequently, it is important that this uncertainty is assessed. Understanding and developing methods to deal with such errors is important to inform users of the true quality of laser scanning products, such that they can be used effectively in hydrological applications.

  4. Horizontal geometrical reaction time model for two-beam nacelle LiDARs

    NASA Astrophysics Data System (ADS)

    Beuth, Thorsten; Fox, Maik; Stork, Wilhelm

    2015-06-01

    Wind energy is one of the leading sustainable energies. To attract further private and state investment in this technology, a broad scaled drop of the cost of energy has to be enforced. There is a trend towards using Laser Doppler Velocimetry LiDAR systems for enhancing power output and minimizing downtimes, fatigue and extreme forces. Since most used LiDARs are horizontally setup on a nacelle and work with two beams, it is important to understand the geometrical configuration which is crucial to estimate reaction times for the actuators to compensate wind gusts. In the beginning of this article, the basic operating modes of wind turbines are explained and the literature on wind behavior is analyzed to derive specific wind speed and wind angle conditions in relation to the yaw angle of the hub. A short introduction to the requirements for the reconstruction of the wind vector length and wind angle leads to the problem of wind shear detection of angled but horizontal homogeneous wind fronts due to the spatial separation of the measuring points. A distance is defined in which the wind shear of such homogeneous wind fronts is not present which is used as a base to estimate further distance calculations. The reaction time of the controller and the actuators are having a negative effect on the effective overall reaction time for wind regulation as well. In the end, exemplary calculations estimate benefits and disadvantages of system parameters for wind gust regulating LiDARs for a wind turbine of typical size. An outlook shows possible future improvements concerning the vertical wind behavior.

  5. LiDAR Individual Tree Detection for Assessing Structurally Diverse Forest Landscapes

    NASA Astrophysics Data System (ADS)

    Jeronimo, Sean

    Contemporary forest management on public land incorporates a focus on restoration and maintenance of ecological functions through silvicultural manipulation of forest structure on a landscape scale. Incorporating reference conditions into restoration treatment planning and monitoring can improve treatment efficacy, but the typical ground-based methods of quantifying reference condition data---and comparing it to pre- and post-treatment stands---are expensive, time-consuming, and limited in scale. Airborne LiDAR may be part of the solution to this problem, since LiDAR acquisitions have both broad coverage and high resolution. I evaluated the ability of LiDAR Individual Tree Detection (ITD) to describe forest structure across a structurally variable landscape in support of large-scale forest restoration. I installed nineteen 0.25 ha stem map plots across a range of structural conditions in potential reference areas (Yosemite National Park) and potential restoration treatment areas (Sierra National Forest) in the Sierra Nevada of California. I used the plots to evaluate a common ITD algorithm, the watershed transform, compare it to past uses of ITD, and determine which aspects of forest structure contributed to errors in ITD. I found that ITD across this structurally diverse landscape was generally less accurate than across the smaller and less diverse areas over which it has previously been studied. However, the pattern of tree recognition is consistent: regardless of forest structure, canopy dominants are almost always detected and relatively shorter trees are almost never detected. Correspondingly, metrics dominated by large trees, such as biomass, basal area, and spatial heterogeneity, can be measured using ITD, while metrics dominated by smaller trees, such as stand density, cannot. Bearing these limitations in mind, ITD can be a powerful tool for describing forest structure across heterogeneous landscape restoration project areas.

  6. Numerical simulation of groundwater flow in Dar es Salaam Coastal Plain (Tanzania)

    NASA Astrophysics Data System (ADS)

    Luciani, Giulia; Sappa, Giuseppe; Cella, Antonella

    2016-04-01

    They are presented the results of a groundwater modeling study on the Coastal Aquifer of Dar es Salaam (Tanzania). Dar es Salaam is one of the fastest-growing coastal cities in Sub-Saharan Africa, with with more than 4 million of inhabitants and a population growth rate of about 8 per cent per year. The city faces periodic water shortages, due to the lack of an adequate water supply network. These two factors have determined, in the last ten years, an increasing demand of groundwater exploitation, carried on by quite a number of private wells, which have been drilled to satisfy human demand. A steady-state three dimensional groundwater model has been set up by the MODFLOW code, and calibrated with the UCODE code for inverse modeling. The aim of the model was to carry out a characterization of groundwater flow system in the Dar es Salaam Coastal Plain. The inputs applied to the model included net recharge rate, calculated from time series of precipitation data (1961-2012), estimations of average groundwater extraction, and estimations of groundwater recharge, coming from zones, outside the area under study. Parametrization of the hydraulic conductivities was realized referring to the main geological features of the study area, based on available literature data and information. Boundary conditions were assigned based on hydrogeological boundaries. The conceptual model was defined in subsequent steps, which added some hydrogeological features and excluded other ones. Calibration was performed with UCODE 2014, using 76 measures of hydraulic head, taken in 2012 referred to the same season. Data were weighted on the basis of the expected errors. Sensitivity analysis of data was performed during calibration, and permitted to identify which parameters were possible to be estimated, and which data could support parameters estimation. Calibration was evaluated based on statistical index, maps of error distribution and test of independence of residuals. Further model

  7. Structural effects of liana presence in secondary tropical dry forests using ground LiDAR

    NASA Astrophysics Data System (ADS)

    Sánchez-Azofeifa, A.; Portillo-Quintero, C.; Durán, S. M.

    2015-10-01

    Lianas, woody vines, are a key component of tropical forest because they may reduce carbon storage potential. Lianas are increasing in density and biomass in tropical forests, but it is unknown what the potential consequences of these increases are for forest dynamics. Lianas may proliferate in disturbed areas, such as regenerating forests, but little is known about the role of lianas in secondary succession. In this study, we evaluated the potential of the ground LiDAR to detect differences in the vertical structure of stands of different ages with and without lianas in tropical dry forests. Specifically, we used a terrestrial laser scanner called VEGNET to assess whether liana presence influences the vertical signature of stands of different ages, and whether successional trajectories as detected by the VEGNET could be altered by liana presence. We deployed the VEGNET ground LiDAR system in 15 secondary forests of different ages early (21 years old since land abandonment), intermediate (32-35 years old) and late stages (> 80 years old) with and without lianas. We compared laser-derived vegetation components such as Plant Area Index (PAI), plant area volume density (PAVD), and the radius of gyration (RG) across forest stands between liana and no-liana treatments. In general forest stands without lianas show a clearer distinction of vertical strata and the vertical height of accumulated PAVD. A significant increase of PAI was found from intermediate to late stages in stands without lianas, but in stands where lianas were present there was not a significant trend. This suggests that lianas may be influencing successional trajectories in secondary forests, and these effects can be captured by terrestrial laser scanners such as the VEGNET. This research contributes to estimate the potential effects of lianas in secondary dry forests and highlight the role of ground LiDAR to monitor structural changes in tropical forests due to liana presence.

  8. High-Resolution LiDAR Topography of the Plate-Boundary Faults in Northern California

    NASA Astrophysics Data System (ADS)

    Prentice, C. S.; Phillips, D. A.; Furlong, K. P.; Brown, A.; Crosby, C. J.; Bevis, M.; Shrestha, R.; Sartori, M.; Brocher, T. M.; Brown, J.

    2007-12-01

    GeoEarthScope acquired more than 1500 square km of airborne LiDAR data in northern California, providing high-resolution topographic data of most of the major strike-slip faults in the region. The coverage includes the San Andreas Fault from its northern end near Shelter Cove to near Parkfield, as well as the Rodgers Creek, Maacama, Calaveras, Green Valley, Paicines, and San Gregorio Faults. The Hayward fault was added with funding provided by the US Geological Survey, the City of Berkeley, and the San Francisco Public Utilities Commission. Data coverage is typically one kilometer in width, centered on the fault. In areas of particular fault complexity the swath width was increased to two kilometers, and in selected areas swath width is as wide as five kilometers. A five-km-wide swath was flown perpendicular to the plate boundary immediately south of Cape Mendocino to capture previously unidentified faults and to understand off-fault deformation associated with the transition zone between the transform margin and the Cascadia subduction zone. The data were collected in conjunction with an intensive GPS campaign designed to improve absolute data accuracy and provide quality control. Data processing to classify the LiDAR point data by return type allows users to filter out vegetation and produce high-resolution DEMs of the ground surface beneath forested regions, revealing geomorphic features along and adjacent to the faults. These data will allow more accurate mapping of fault traces in regions where the vegetation canopy has hampered this effort in the past. In addition, the data provide the opportunity to locate potential sites for detailed paleoseismic studies aimed at providing slip rates and event chronologies. The GeoEarthScope LiDAR data will be made available via an interactive data distribution and processing workflow currently under development.

  9. LiDAR-based volume assessment of the origin of the Wadena drumlin field, Minnesota, USA

    NASA Astrophysics Data System (ADS)

    Sookhan, Shane; Eyles, Nick; Putkinen, Niko

    2016-06-01

    The Wadena drumlin field (WDF; ~ 7500 km2) in west-central Minnesota, USA, is bordered along its outer extremity by the till-cored Alexandria moraine marking the furthest extent of the southwesterly-flowing Wadena ice lobe at c. 15,000 kyr BP. Newly available high-resolution Light Detection and Ranging (LiDAR) data reveal new information regarding the number, morphology and extent of streamlined bedforms in the WDF. In addition, a newly-developed quantitative methodology based on relief curvature analysis of LiDAR elevation-based raster data is used to evaluate sediment volumes represented by the WDF and its bounding end moraine. These data are used to evaluate models for the origin of drumlins. High-resolution LiDAR-based mapping doubles the streamlined footprint of the Wadena Lobe to ~ 16,500 km2 increases the number of bedforms from ~ 2000 to ~ 6000, and most significantly, reclassifies large numbers of bedforms mapped previously as 'drumlins' as 'mega-scale glacial lineations' (MSGLs), indicating that the Wadena ice lobe experienced fast ice flow. The total volume of sediment in the Alexandria moraine is ~ 71-110 km3, that in the drumlins and MSGLs is ~ 2.83 km3, and the volume of swales between these bedforms is ~ 74.51 km3. The moraine volume is equivalent to a till layer 6.8 m thick across the entire bed of the Wadena lobe, suggesting drumlinization and moraine formation were accompanied by widespread lowering of the bed. This supports the hypothesis that drumlins and MSGLs are residual erosional features carved from a pre-existing till; swales represent 'missing sediment' that was eroded subglacially and advected downglacier to build the Alexandria Moraine during fast ice flow. Alternatively, the relatively small volume of sediment represented by subglacial bedforms indicates they could have formed rapidly by depositional processes.

  10. The offshore wind resources assessment application of floating LiDAR in the Taiwan Strait

    NASA Astrophysics Data System (ADS)

    Hsuan, Chung-Yao; Wu, Yu-Ting; Lin, Ta-Hui

    2015-04-01

    Wind and wave measurements of a Floating LiDAR (Light Detection And Ranging) Device (FLD) are performed on the site of Fuhai Offshore Wind Farm in the Taiwan Strait. The location of the deployment is situated 10 kilometers off-coast of Changhua County, and the anchored water depth is 25 meters. It is the very first time in Asia Pacific Region to use such device for tasks of offshore wind and wave measurement. Six range gate heights were set at 55m, 71m, 90m, 110m, 150m and 200m from the FLD sensor lens. Wind speeds and wind directions were measured by a remote sensing technology. Wave heights and periods were also measured by the buoy wave sensor. A validation campaign of NCKU WindSentinel has performed by a portable LiDAR (WINDCUBE v2) at Hsing-Da Harbor in the south of Taiwan from October 16th to 26th, 2013. The results showed good agreements with 10 minute averaged data of the wind speed and wind direction measured by the two LiDARs. NCKU WindSentinel data are planning comparisons with Fuhai's offshore fixed mast data when the meteorological mast is completed. The goal is to convince the wind energy community that FLD are a reliable and cost effective way of obtaining data for resource assessment. Until this moment, The FLD are observing and measuring the offshore wind farm's meteorological and oceanographic data. In September of 2014, a mild typhoon (Fung-Wong) passed through from east of Taiwan. NCKU WindSentinel continuously measured during typhoon period in the sea. The present preliminary measurements campaign presented the convenient and more cost effective option of the FLD, which may be a key tool for assessment of offshore wind resources in the near-future offshore wind farm developments.

  11. Quantification of uncertainty in aboveground biomass estimates derived from small-footprint LiDAR data

    NASA Astrophysics Data System (ADS)

    Xu, Q.; Greenberg, J. A.; Li, B.; Ramirez, C.; Balamuta, J. J.; Evans, K.; Man, A.; Xu, Z.

    2015-12-01

    A promising approach to determining aboveground biomass (AGB) in forests comes through the use of individual tree crown delineation (ITCD) techniques applied to small-footprint LiDAR data. These techniques, when combined with allometric equations, can produce per-tree estimates of AGB. At this scale, AGB estimates can be quantified in a manner similar to how ground-based forest inventories are produced. However, these approaches have significant uncertainties that are rarely described in full. Allometric equations are often based on species-specific diameter-at-breast height (DBH) relationships, but neither DBH nor species can be reliably determined using remote sensing analysis. Furthermore, many approaches to ITCD only delineate trees appearing in the upper canopy so subcanopy trees are often missing from the inventories. In this research, we performed a propagation-of-error analysis to determine the spatially varying uncertainties in AGB estimates at the individual plant and stand level for a large collection of LiDAR acquisitions covering a large portion of California. Furthermore, we determined the relative contribution of various aspects of the analysis towards the uncertainty, including errors in the ITCD results, the allometric equations, the taxonomic designation, and the local biophysical environment. Watershed segmentation was used to obtain the preliminary crown segments. Lidar points within the preliminary segments were extracted to form profiling data of the segments, and then mode detection algorithms were applied to identify the tree number and tree heights within each segment. As part of this analysis, we derived novel "remote sensing aware" allometric equations and their uncertainties based on three-dimensional morphological metrics that can be accurately derived from LiDAR data.

  12. Investigation on the contribution of LiDAR data in 3D cadastre

    NASA Astrophysics Data System (ADS)

    Giannaka, Olga; Dimopoulou, Efi; Georgopoulos, Andreas

    2014-08-01

    The existing 2D cadastral systems worldwide cannot provide a proper registration and representation of the land ownership rights, restrictions and responsibilities in a 3D context, which appear in our complex urban environment. Ιn such instances, it may be necessary to consider the development of a 3D Cadastre in which proprietary rights acquire appropriate three-dimensional space both above and below conventional ground level. Such a system should contain the topology and the coordinates of the buildings' outlines and infrastructure. The augmented model can be formed as a full 3D Cadastre, a hybrid Cadastre or a 2D Cadastre with 3D tags. Each country has to contemplate which alternative is appropriate, depending on the specific situation, the legal framework and the available technical means. In order to generate a 3D model for cadastral purposes, a system is required which should be able to exploit and represent 3D data such as LiDAR, a remote sensing technology which acquires three-dimensional point clouds that describe the earth's surface and the objects on it. LiDAR gives a direct representation of objects on the ground surface and measures their coordinates by analyzing the reflecting light. Moreover, it provides very accurate position and height information, although direct information about the objects' geometrical shape is not conveyed. In this study, an experimental implementation of 3D Cadastre using LiDAR data is developed, in order to investigate if this information can satisfy the specifications that are set for the purposes of the Hellenic Cadastre. GIS tools have been used for analyzing DSM and true orthophotos of the study area. The results of this study are presented and evaluated in terms of usability and efficiency.

  13. Residents’ perceptions of institutional performance in water supply in Dar es Salaam

    NASA Astrophysics Data System (ADS)

    Mwakalila, Shadrack

    This paper addresses the performance of institutions in water supply systems for improving social and economic benefits of people living in Dar es Salaam city. The methods employed in field data and information collection included interviews, questionnaire, focus group discussions and participatory observation. Kinondoni and Ilala Districts were used as case study. The study revealed that, the main water sources in the study areas are boreholes, shallow wells, rain water and water vendors. Other minor sources are piped water and natural water sources, such as rivers and streams. The supply of piped water by Dar es Salaam Water Sewerage and Sanitation Company (DAWASA/DAWASCO) meets only 45% of the total water demands. Individuals own and sell water from boreholes, shallow wells, piped water connected to their individual houses and natural wells located in their individual plots. The price of one 20 l bucket of water from a water vendor depends on the availability of water and the distance walked from the water source to the customer. Majority of the respondents (77.5%) indicated that individual water delivery systems provide sufficient water as compared to five years ago in the study areas. Few of the respondents (6.3%) said individual water delivery systems have no capacity to provide sufficient water while 16.3% indicate that individual water delivery systems provide moderate water supply but are important in supplementing other water providers in the study areas. The study reveals that a majority of the local population are satisfied with the capacity of individual water delivery systems in providing water for household uses. This paper recommends some improvements to be done to water supply systems in the Dar es Salaam city.

  14. The use of LiDAR-derived high-resolution DSM and intensity data to support modelling of urban flooding

    NASA Astrophysics Data System (ADS)

    Aktaruzzaman, Md.; Schmitt, Theo G.

    2011-11-01

    This paper addresses the issue of a detailed representation of an urban catchment in terms of hydraulic and hydrologic attributes. Modelling of urban flooding requires a detailed knowledge of urban surface characteristics. The advancement in spatial data acquisition technology such as airborne LiDAR (Light Detection and Ranging) has greatly facilitated the collection of high-resolution topographic information. While the use of the LiDAR-derived Digital Surface Model (DSM) has gained popularity over the last few years as input data for a flood simulation model, the use of LiDAR intensity data has remained largely unexplored in this regard. LiDAR intensity data are acquired along with elevation data during the data collection mission by an aircraft. The practice of using of just aerial images with RGB (Red, Green and Blue) wavebands is often incapable of identifying types of surface under the shadow. On the other hand, LiDAR intensity data can provide surface information independent of sunlight conditions. The focus of this study is the use of intensity data in combination with aerial images to accurately map pervious and impervious urban areas. This study presents an Object-Based Image Analysis (OBIA) framework for detecting urban land cover types, mainly pervious and impervious surfaces in order to improve the rainfall-runoff modelling. Finally, this study shows the application of highresolution DSM and land cover maps to flood simulation software in order to visualize the depth and extent of urban flooding phenomena.

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

    NASA Astrophysics Data System (ADS)

    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.

  16. Synergistic application of geometric and radiometric features of LiDAR data for urban land cover mapping.

    PubMed

    Qin, Yuchu; Li, Shihua; Vu, Tuong-Thuy; Niu, Zheng; Ban, Yifang

    2015-06-01

    Urban land cover map is essential for urban planning, environmental studies and management. This paper aims to demonstrate the potential of geometric and radiometric features derived from LiDAR waveform and point cloud data in urban land cover mapping with both parametric and non-parametric classification algorithms. Small footprint LiDAR waveform data acquired by RIEGL LMS-Q560 in Zhangye city, China is used in this study. A LiDAR processing chain is applied to perform waveform decomposition, range determination and radiometric characterization. With the synergic utilization of geometric and radiometric features derived from LiDAR data, urban land cover classification is then conducted using the Maximum Likelihood Classification (MLC), Support Vector Machines (SVM) and random forest algorithms. The results suggest that the random forest classifier achieved the most accurate result with overall classification accuracy of 91.82% and the kappa coefficient of 0.88. The overall accuracies of MLC and SVM are 84.02, and 88.48, respectively. The study suggest that the synergic utilization of geometric and radiometric features derived from LiDAR data can be efficiently used for urban land cover mapping, the non-parametric random forest classifier is a promising approach for the various features with different physical meanings. PMID:26072748

  17. INS/GPS/LiDAR Integrated Navigation System for Urban and Indoor Environments Using Hybrid Scan Matching Algorithm

    PubMed Central

    Gao, Yanbin; Liu, Shifei; Atia, Mohamed M.; Noureldin, Aboelmagd

    2015-01-01

    This paper takes advantage of the complementary characteristics of Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) to provide periodic corrections to Inertial Navigation System (INS) alternatively in different environmental conditions. In open sky, where GPS signals are available and LiDAR measurements are sparse, GPS is integrated with INS. Meanwhile, in confined outdoor environments and indoors, where GPS is unreliable or unavailable and LiDAR measurements are rich, LiDAR replaces GPS to integrate with INS. This paper also proposes an innovative hybrid scan matching algorithm that combines the feature-based scan matching method and Iterative Closest Point (ICP) based scan matching method. The algorithm can work and transit between two modes depending on the number of matched line features over two scans, thus achieving efficiency and robustness concurrently. Two integration schemes of INS and LiDAR with hybrid scan matching algorithm are implemented and compared. Real experiments are performed on an Unmanned Ground Vehicle (UGV) for both outdoor and indoor environments. Experimental results show that the multi-sensor integrated system can remain sub-meter navigation accuracy during the whole trajectory. PMID:26389906

  18. Development of the Philippine Hydrologic Dataset (PHD) from LiDAR and other remotely-sensed data

    NASA Astrophysics Data System (ADS)

    Perez, A. M. C.; Gaspa, M. C.; Aloc, D. S.; Mahor, M. A. P.; Gonzalez, K. A. C.; Borlongan, N. J. B.; De La Cruz, R. M.; Olfindo, N. T.; Blanco, A. C.

    2015-10-01

    Water resource monitoring and management has been an important concern in the Philippines, considering that the country is archipelagic in nature and is exposed to a lot of disasters imposed by the global effects of climate change. The design and implementation of an effective management scheme relies heavily on accurate, complete, and updated water resource inventories, usually in the form of digital maps and geodatabases. With the aim of developing a detailed and comprehensive database of all water resources in the Philippines, the 3-year project "Development of the Philippine Hydrologic Dataset (PHD) for Watersheds from LiDAR Surveys" under the Phil-LiDAR 2 Program (National Resource Inventory), has been initiated by the University of the Philippines Diliman (UPD) and the Department of Science and Technology (DOST). Various workflows has already been developed to extract inland hydrologic features in the Philippines using accurate Light Detection and Ranging (LiDAR) Digital Terrain Models (DTMs) and LiDAR point cloud data obtained through other government-funded programs such as Disaster Risk and Exposure Assessment for Mitigation (DREAM) and Phil-LiDAR 1, supplemented with other remotely-sensed imageries and ancillary information from Local Government Units (LGUs) and National Government Agencies (NGAs). The methodologies implemented are mainly combinations of object-based image analysis, pixel-based image analysis, modeling, and field surveys. This paper presents the PHD project, the methodologies developed, and some sample outputs produced.

  19. INS/GPS/LiDAR Integrated Navigation System for Urban and Indoor Environments Using Hybrid Scan Matching Algorithm.

    PubMed

    Gao, Yanbin; Liu, Shifei; Atia, Mohamed M; Noureldin, Aboelmagd

    2015-01-01

    This paper takes advantage of the complementary characteristics of Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) to provide periodic corrections to Inertial Navigation System (INS) alternatively in different environmental conditions. In open sky, where GPS signals are available and LiDAR measurements are sparse, GPS is integrated with INS. Meanwhile, in confined outdoor environments and indoors, where GPS is unreliable or unavailable and LiDAR measurements are rich, LiDAR replaces GPS to integrate with INS. This paper also proposes an innovative hybrid scan matching algorithm that combines the feature-based scan matching method and Iterative Closest Point (ICP) based scan matching method. The algorithm can work and transit between two modes depending on the number of matched line features over two scans, thus achieving efficiency and robustness concurrently. Two integration schemes of INS and LiDAR with hybrid scan matching algorithm are implemented and compared. Real experiments are performed on an Unmanned Ground Vehicle (UGV) for both outdoor and indoor environments. Experimental results show that the multi-sensor integrated system can remain sub-meter navigation accuracy during the whole trajectory. PMID:26389906

  20. The use of 1572 nm Mie LiDAR for observation of the optical properties of aerosols over Wuhan, China

    NASA Astrophysics Data System (ADS)

    Gong, Wei; Ma, Xin; Dong, Yanni; Lin, Hong; Li, Jun

    2014-03-01

    CO2 is a major component of greenhouse gases. When CO2 concentration is measured by satellites, calibration of the lower atmosphere becomes an essential procedure. Since the 1572 nm infrared region is widely used in remote sensing of CO2, we constructed a Mie LiDAR system, designed to work at 1572 nm, for measuring the optical properties of aerosols in the lower troposphere. Based on the particle size distribution measured by the heliograph, the LiDAR ratio is independently determined for Wuhan, China. The LiDAR echo signal is then processed by the Fernald method to calculate the extinction coefficient on both clear and cloudy days. The maximum detection height is restricted by the low laser energy and quantum efficiency of the Photomultiplier Tube (PMT) used. Moreover, a simplified method for detecting the position of clouds is presented and this method is verified using a variety of passive radiation instruments that offer partial support for calibrating and verifying LiDAR data. The observed results indicate that this LiDAR system could be a reliable source of data support for the spaceborne remote sensing of CO2.

  1. Calculating LiDAR Point Cloud Uncertainty and Propagating Uncertainty to Snow-Water Equivalent Data Products

    NASA Astrophysics Data System (ADS)

    Gadomski, P. J.; Deems, J. S.; Glennie, C. L.; Hartzell, P. J.; Butler, H.; Finnegan, D. C.

    2015-12-01

    The use of high-resolution topographic data in the form of three-dimensional point clouds obtained from laser scanning systems (LiDAR) is becoming common across scientific disciplines.However little consideration has typically been given to the accuracy and the precision of LiDAR-derived measurements at the individual point scale.Numerous disparate sources contribute to the aggregate precision of each point measurement, including uncertainties in the range measurement, measurement of the attitude and position of the LiDAR collection platform, uncertainties associated with the interaction between the laser pulse and the target surface, and more.We have implemented open-source software tools to calculate per-point stochastic measurement errors for a point cloud using the general LiDAR georeferencing equation.We demonstrate the use of these propagated uncertainties by applying our methods to data collected by the Airborne Snow Observatory ALS, a NASA JPL project using a combination of airborne hyperspectral and LiDAR data to estimate snow-water equivalent distributions over full river basins.We present basin-scale snow depth maps with associated uncertainties, and demonstrate the propagation of those uncertainties to snow volume and snow-water equivalent calculations.

  2. Temporal Analysis and Automatic Calibration of the Velodyne HDL-32E LiDAR System

    NASA Astrophysics Data System (ADS)

    Chan, T. O.; Lichti, D. D.; Belton, D.

    2013-10-01

    At the end of the first quarter of 2012, more than 600 Velodyne LiDAR systems had been sold worldwide for various robotic and high-accuracy survey applications. The ultra-compact Velodyne HDL-32E LiDAR has become a predominant sensor for many applications that require lower sensor size/weight and cost. For high accuracy applications, cost-effective calibration methods with minimal manual intervention are always desired by users. However, the calibrations are complicated by the Velodyne LiDAR's narrow vertical field of view and the very highly time-variant nature of its measurements. In the paper, the temporal stability of the HDL-32E is first analysed as the motivation for developing a new, automated calibration method. This is followed by a detailed description of the calibration method that is driven by a novel segmentation method for extracting vertical cylindrical features from the Velodyne point clouds. The proposed segmentation method utilizes the Velodyne point cloud's slice-like nature and first decomposes the point clouds into 2D layers. Then the layers are treated as 2D images and are processed with the Generalized Hough Transform which extracts the points distributed in circular patterns from the point cloud layers. Subsequently, the vertical cylindrical features can be readily extracted from the whole point clouds based on the previously extracted points. The points are passed to the calibration that estimates the cylinder parameters and the LiDAR's additional parameters simultaneously by constraining the segmented points to fit to the cylindrical geometric model in such a way the weighted sum of the adjustment residuals are minimized. The proposed calibration is highly automatic and this allows end users to obtain the time-variant additional parameters instantly and frequently whenever there are vertical cylindrical features presenting in scenes. The methods were verified with two different real datasets, and the results suggest that up to 78

  3. Scan Line Based Road Marking Extraction from Mobile LiDAR Point Clouds.

    PubMed

    Yan, Li; Liu, Hua; Tan, Junxiang; Li, Zan; Xie, Hong; Chen, Changjun

    2016-01-01

    Mobile Mapping Technology (MMT) is one of the most important 3D spatial data acquisition technologies. The state-of-the-art mobile mapping systems, equipped with laser scanners and named Mobile LiDAR Scanning (MLS) systems, have been widely used in a variety of areas, especially in road mapping and road inventory. With the commercialization of Advanced Driving Assistance Systems (ADASs) and self-driving technology, there will be a great demand for lane-level detailed 3D maps, and MLS is the most promising technology to generate such lane-level detailed 3D maps. Road markings and road edges are necessary information in creating such lane-level detailed 3D maps. This paper proposes a scan line based method to extract road markings from mobile LiDAR point clouds in three steps: (1) preprocessing; (2) road points extraction; (3) road markings extraction and refinement. In preprocessing step, the isolated LiDAR points in the air are removed from the LiDAR point clouds and the point clouds are organized into scan lines. In the road points extraction step, seed road points are first extracted by Height Difference (HD) between trajectory data and road surface, then full road points are extracted from the point clouds by moving least squares line fitting. In the road markings extraction and refinement step, the intensity values of road points in a scan line are first smoothed by a dynamic window median filter to suppress intensity noises, then road markings are extracted by Edge Detection and Edge Constraint (EDEC) method, and the Fake Road Marking Points (FRMPs) are eliminated from the detected road markings by segment and dimensionality feature-based refinement. The performance of the proposed method is evaluated by three data samples and the experiment results indicate that road points are well extracted from MLS data and road markings are well extracted from road points by the applied method. A quantitative study shows that the proposed method achieves an average

  4. Landslide displacement vectors derived from multi-temporal topographic LiDAR data

    NASA Astrophysics Data System (ADS)

    Fey, Christine; Rutzinger, Martin; Bremer, Magnus; Prager, Christoph; Zangerl, Christian

    2014-05-01

    Information about slope geometry and kinematics of landslides is essential for hazard assessment, monitoring and planning of protection and mitigation measures. Especially for remote and inaccessible slopes, subsurface data (e.g. boreholes, tunnels, investigation adits) are often not available and thus the deformation characteristics must be derived from surface displacement data. In recent years, multi-temporal topographic LiDAR (Light Detection and Ranging) data became an increasingly improved tool for detecting topographic surface deformations. In this context, LiDAR-based change detection is commonly applied for quantifying surface elevation changes. Advanced change detection methods derive displacement vectors with direction and velocities of slope movements. To extract displacement vectors from LiDAR raster data (i) an approach based on feature tracking by image correlation and (ii) an approach based on feature tracking by vectors breaklines are investigated. The image correlation method is based on the IMCORR software (National Snow and Ice Data Center, University of Colorado, Boulder), implemented in a SAGA GIS module. The image correlation algorithm is based on a normalized cross-covariance method. The algorithm searches tie points in two feature rasters derived from a digital surface model acquired at different time stamps. The method assesses automatically the displacement rates and directions of distinct terrain features e.g. displaced mountain ridges or striking boulders. In contrast the vector-based breakline methods require manual selection of tie points. The breaklines are the product of vectorized curvature raster images and extracting the "upper terrain edges" (topographic ridges) and "lower terrain edges" (topographic depressions). Both methods were tested on simulated terrain with determined displacement rates in order to quantify i) the accuracy ii) the minimum detectable movement rates iii) the influence of terrain characteristics iv) the

  5. Financial sustainability in municipal solid waste management--costs and revenues in Bahir Dar, Ethiopia.

    PubMed

    Lohri, Christian Riuji; Camenzind, Ephraim Joseph; Zurbrügg, Christian

    2014-02-01

    Providing good solid waste management (SWM) services while also ensuring financial sustainability of the system continues to be a major challenge in cities of developing countries. Bahir Dar in northwestern Ethiopia outsourced municipal waste services to a private waste company in 2008. While this institutional change has led to substantial improvement in the cleanliness of the city, its financial sustainability remains unclear. Is the private company able to generate sufficient revenues from their activities to offset the costs and generate some profit? This paper presents a cost-revenue analysis, based on data from July 2009 to June 2011. The analysis reveals that overall costs in Bahir Dar's SWM system increased significantly during this period, mainly due to rising costs related to waste transportation. On the other hand, there is only one major revenue stream in place: the waste collection fee from households, commercial enterprises and institutions. As the efficiency of fee collection from households is only around 50%, the total amount of revenues are not sufficient to cover the running costs. This results in a substantial yearly deficit. The results of the research therefore show that a more detailed cost structure and cost-revenue analysis of this waste management service is important with appropriate measures, either by the privates sector itself or with the support of the local authorities, in order to enhance cost efficiency and balance the cost-revenues towards cost recovery. Delays in mitigating the evident financial deficit could else endanger the public-private partnership (PPP) and lead to failure of this setup in the medium to long term, thus also endangering the now existing improved and currently reliable service. We present four options on how financial sustainability of the SWM system in Bahir Dar might be enhanced: (i) improved fee collection efficiency by linking the fees of solid waste collection to water supply; (ii) increasing the value

  6. Wallace Creek Virtual Field Trip: Teaching Geoscience Concepts with LiDAR

    NASA Astrophysics Data System (ADS)

    Robinson, S. E.; Arrowsmith, R.; Crosby, C. J.

    2009-12-01

    Recently available data such as LiDAR (Light Detection and Ranging) high-resolution topography can assist students to better visualize and understand geosciences concepts. It is important to bring these data into geosciences curricula as teaching aids while ensuring that the visualization tools, virtual environments, etc. do not serve as barriers to student learning. As a Southern California Earthquake Center ACCESS-G intern, I am creating a “virtual field trip” to Wallace Creek along the San Andreas Fault (SAF) using Google Earth as a platform and the B4 project LiDAR data. Wallace Creek is an excellent site for understanding the centennial-to-millennial record of SAF slip because of its dramatic stream offsets. Using the LiDAR data instead of, or alongside, traditional visualizations and teaching methods enhances a student’s ability to understand plate tectonics, the earthquake cycle, strike-slip faults, and geomorphology. Viewing a high-resolution representation of the topography in Google Earth allows students to analyze the landscape and answer questions about the behavior of the San Andreas Fault. The activity guides students along the fault allowing them to measure channel offsets using the Google Earth measuring tool. Knowing the ages of channels, they calculate slip rate. They look for the smallest channel offsets around Wallace Creek in order to determine the slip per event. At both a “LiDAR and Education” workshop and the Cyberinfrastructure Summer Institute for Geoscientists (CSIG), I presented the Wallace Creek activity to high school and college earth science teachers. The teachers were positive in their responses and had numerous important suggestions including the need for a teacher’s manual for instruction and scientific background, and that the student goals and science topics should be specific and well-articulated for the sake of both the teacher and the student. The teachers also noted that the technology in classrooms varies

  7. Unsupervised building detection from irregularly spaced LiDAR and aerial imagery

    NASA Astrophysics Data System (ADS)

    Shorter, Nicholas Sven

    As more data sources containing 3-D information are becoming available, an increased interest in 3-D imaging has emerged. Among these is the 3-D reconstruction of buildings and other man-made structures. A necessary preprocessing step is the detection and isolation of individual buildings that subsequently can be reconstructed in 3-D using various methodologies. Applications for both building detection and reconstruction have commercial use for urban planning, network planning for mobile communication (cell phone tower placement), spatial analysis of air pollution and noise nuisances, microclimate investigations, geographical information systems, security services and change detection from areas affected by natural disasters. Building detection and reconstruction are also used in the military for automatic target recognition and in entertainment for virtual tourism. Previously proposed building detection and reconstruction algorithms solely utilized aerial imagery. With the advent of Light Detection and Ranging (LiDAR) systems providing elevation data, current algorithms explore using captured LiDAR data as an additional feasible source of information. Additional sources of information can lead to automating techniques (alleviating their need for manual user intervention) as well as increasing their capabilities and accuracy. Several building detection approaches surveyed in the open literature have fundamental weaknesses that hinder their use; such as requiring multiple data sets from different sensors, mandating certain operations to be carried out manually, and limited functionality to only being able to detect certain types of buildings. In this work, a building detection system is proposed and implemented which strives to overcome the limitations seen in existing techniques. The developed framework is flexible in that it can perform building detection from just LiDAR data (first or last return), or just nadir, color aerial imagery. If data from both LiDAR and

  8. Scan Line Based Road Marking Extraction from Mobile LiDAR Point Clouds†

    PubMed Central

    Yan, Li; Liu, Hua; Tan, Junxiang; Li, Zan; Xie, Hong; Chen, Changjun

    2016-01-01

    Mobile Mapping Technology (MMT) is one of the most important 3D spatial data acquisition technologies. The state-of-the-art mobile mapping systems, equipped with laser scanners and named Mobile LiDAR Scanning (MLS) systems, have been widely used in a variety of areas, especially in road mapping and road inventory. With the commercialization of Advanced Driving Assistance Systems (ADASs) and self-driving technology, there will be a great demand for lane-level detailed 3D maps, and MLS is the most promising technology to generate such lane-level detailed 3D maps. Road markings and road edges are necessary information in creating such lane-level detailed 3D maps. This paper proposes a scan line based method to extract road markings from mobile LiDAR point clouds in three steps: (1) preprocessing; (2) road points extraction; (3) road markings extraction and refinement. In preprocessing step, the isolated LiDAR points in the air are removed from the LiDAR point clouds and the point clouds are organized into scan lines. In the road points extraction step, seed road points are first extracted by Height Difference (HD) between trajectory data and road surface, then full road points are extracted from the point clouds by moving least squares line fitting. In the road markings extraction and refinement step, the intensity values of road points in a scan line are first smoothed by a dynamic window median filter to suppress intensity noises, then road markings are extracted by Edge Detection and Edge Constraint (EDEC) method, and the Fake Road Marking Points (FRMPs) are eliminated from the detected road markings by segment and dimensionality feature-based refinement. The performance of the proposed method is evaluated by three data samples and the experiment results indicate that road points are well extracted from MLS data and road markings are well extracted from road points by the applied method. A quantitative study shows that the proposed method achieves an average

  9. Processing and evaluation of riverine waveforms acquired by an experimental bathymetric LiDAR

    NASA Astrophysics Data System (ADS)

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

    2010-12-01

    Accurate mapping of fluvial environments with airborne bathymetric LiDAR is challenged not only by environmental characteristics but also the development and application of software routines to post-process the recorded laser waveforms. During a bathymetric LiDAR survey, the transmission of the green-wavelength laser pulses through the water column is influenced by a number of factors including turbidity, the presence of organic material, and the reflectivity of the streambed. For backscattered laser pulses returned from the river bottom and digitized by the LiDAR detector, post-processing software is needed to interpret and identify distinct inflections in the reflected waveform. Relevant features of this energy signal include the air-water interface, volume reflection from the water column itself, and, ideally, a strong return from the bottom. We discuss our efforts to acquire, analyze, and interpret riverine surveys using the USGS Experimental Advanced Airborne Research LiDAR (EAARL) in a variety of fluvial environments. Initial processing of data collected in the Trinity River, California, using the EAARL Airborne Lidar Processing Software (ALPS) highlighted the difficulty of retrieving a distinct bottom signal in deep pools. Examination of laser waveforms from these pools indicated that weak bottom reflections were often neglected by a trailing edge algorithm used by ALPS to process shallow riverine waveforms. For the Trinity waveforms, this algorithm had a tendency to identify earlier inflections as the bottom, resulting in a shallow bias. Similarly, an EAARL survey along the upper Colorado River, Colorado, also revealed the inadequacy of the trailing edge algorithm for detecting weak bottom reflections. We developed an alternative waveform processing routine by exporting digitized laser waveforms from ALPS, computing the local extrema, and fitting Gaussian curves to the convolved backscatter. Our field data indicate that these techniques improved the

  10. Using High-Resolution Airborne LiDAR-Data for Landslide Mapping in the Eastern Alps

    NASA Astrophysics Data System (ADS)

    Kamp, N.

    2012-04-01

    Due to the increasing frequency of natural disasters like floods and landslides, the active remote sensing technique LiDAR (Light Detection and Ranging), has become a topic of great interest to the Federal State Government of Styria, Federal Republic of Austria. In a perennial project from 2008 to 2012 high-resolution 3D Airborne LiDAR Data of the Province of Styria, an area about 16.000km2 in south-eastern Austria were collected. These data were processed to create Digital Terrain Models (DTM) and Digital Surface Models (DSM) at 1m resolution with a vertical accuracy of 15 [cm] and a positional accuracy of 40 [cm]. High resolution DTMs can be used in different geo-related applications like geomorphological mapping or natural hazard mapping. DTMs show because of its high accuracy various natural and anthropogenic terrain features such as erosion scarps, alluvial fans, landslides, old creeks, topographic edges and karstforms, as well as walking paths and roads and in addition to that LiDAR data allows the detection and outlining of these different geomorphological and anthropogenic features with the help of ArcGIS 10 geoprocessing and analysing techniques, mathematical, statistical and image processing methods and the open source scripting language Python. As a result complex workflows and new geoprocessing tools can be implemented in an ArcGIS 10 workspace and are provided as easy to use toolbox contents. The landslide phenomena take in centre stage of the research work of the author. Thereby the main focus is targeted on sliding movements out of soils and bedrock. Factors like gravity take effect on slope stability directly and cause complex mass movements with a downslope directed, gliding movement of bed- and/or loose-rock as well as soil material. In this paper the author presents the result of her master thesis, an automatic ArcGIS 10 landslide mapping tool using high-resolution LiDAR data in the rock masses of the Eastern Alps (Province of Styria, Austria

  11. Using a new GUI tool to leverage LiDAR data to aid in hyperspectral image material detection in the radiance domain on RIT SHARE LiDAR/HSI data

    NASA Astrophysics Data System (ADS)

    Ientilucci, Emmett J.

    2013-09-01

    This paper looks at a data set, called the SHARE 2010 collect, that has been designed to analyze the various impacts of illumination change on materials. Similar fabric materials were placed on different backgrounds where spectral signatures were analyzed to determined impacts of background adjacency. Hyperspectral, multispectral, and LiDAR modalities were used to image the panels in the above mentioned scenarios. Applications such as material detection with results are used to assess difficulties with finding such panels. The incorporation of point LiDAR data sets and physical models will aid in approximating the correct per-pixel signature to be used in the above mentioned detection scheme. This technique can help mitigate issues related to varying illumination across a scene. All of the processing (i.e., LiDAR, MODTRAN, HSI and detection) is performed in a new GUI tool which runs in the ENVI software.

  12. The use of local indicators of spatial association to improve LiDAR-derived predictions of potential amphibian breeding ponds

    USGS Publications Warehouse

    Julian, J.T.; Young, J.A.; Jones, J.W.; Snyder, C.D.; Wright, C.W.

    2009-01-01

    We examined whether spatially explicit information improved models that use LiDAR return signal intensity to discriminate in-pond habitat from terrestrial habitat at 24 amphibian breeding ponds. The addition of Local Indicators of Spatial Association (LISA) to LiDAR return intensity data significantly improved predictive models at all ponds, reduced residual error by as much as 74%, and appeared to improve models by reducing classification errors associated with types of in-pond vegetation. We conclude that LISA statistics can help maximize the information content that can be extracted from time resolved LiDAR return data in models that predict the occurrence of small, seasonal ponds. ?? Springer-Verlag 2008.

  13. Tree Crown Delineation using Watershed Techniques and Forest Metrics from NEON LiDAR Data

    NASA Astrophysics Data System (ADS)

    Luong, K. Y.

    2014-12-01

    LiDAR is a powerful remote sensing tool allowing for forest metrics to be taken on varying scales, which ultimately provide important forestry variables used to calculate factors such as total biomass or leaf area index. These variables are most useful when calculated for individual trees throughout a stand, but in very dense forests, identifying single trees becomes more difficult by traditional means. Full forests can be quantified uniquely for the best understanding of ecological contributions as opposed to purely in situ tree inventories which are time consuming and extremely localized. Canopy height models (CHM) can be used to understand the forest as a whole. By inverting the CHM, the tree data becomes sinks in the ground, mimicking ponds; by applying watershed-related spatial analyst tools in ArcGIS and GrassGIS, the trees are delineated by makeshift "flooding." Within this algorithm, the crown peaks are also extracted as an intermediate step to delineation, but this is a reliable means to obtain an accurate number of trees, as well as their individual heights with high reliability (R2 = 0.87). Delineated tree polygons can be directly overlaid onto different rasters to get many forest variables. In tightly clustered and very sparse stands, this method of delineation has a high level of accuracy. Following the workflow studies conducted on NEON LiDAR data on the Soaproot Saddle site, a ground-truth comparison was made with the Teakettle Experimental Forest site due to the availability of tree inventory data.

  14. The use of social media among adolescents in Dar es Salaam and Mtwara, Tanzania.

    PubMed

    Pfeiffer, Constanze; Kleeb, Matthis; Mbelwa, Alice; Ahorlu, Collins

    2014-05-01

    Social media form part of the rapid worldwide digital development that is re-shaping the life of many young people. While the use of social media by youths is increasingly researched in the North, studies about youth in the South are missing. It therefore remains unclear how social media can be included in interventions that aim at informing young people in many countries of the global South about sexual and reproductive health. This paper presents findings of a mixed-methods study of young people's user behaviour on the internet and specifically of social media as a platform for sexual health promotion in Tanzania. The study used questionnaires with 60 adolescents and in-depth interviews with eight students aged 15 to 19 years in Dar es Salaam, and in Mtwara, Southern Tanzania. Findings show that youth in Dar es Salaam and Mtwara access the internet mainly through mobile phones. Facebook is by far the most popular internet site. Adolescents highlighted their interest in reproductive and sexual health messages and updates being delivered through humorous posts, links and clips, as well as by youth role models like music stars and actors that are entertaining and reflect up-to-date trends of modern youth culture. PMID:24908469

  15. Wet channel network extraction by integrating LiDAR intensity and elevation data

    NASA Astrophysics Data System (ADS)

    Hooshyar, Milad; Kim, Seoyoung; Wang, Dingbao; Medeiros, Stephen C.

    2015-12-01

    The temporal dynamics of stream networks are vitally important for understanding hydrologic processes including surface water and groundwater interaction and hydrograph recession. However, observations of wet channel networks are limited, especially in headwater catchments. Near-infrared LiDAR data provide an opportunity to map wet channel networks owing to the fine spatial resolution and strong absorption of light energy by water surfaces. A systematic method is developed to map wet channel networks by integrating elevation and signal intensity of ground returns. The signal intensity thresholds for identifying wet pixels are extracted from frequency distributions of intensity return within the convergent topography extent using a Gaussian mixture model. Moreover, the concept of edge in digital image processing, defined based on the intensity gradient, is utilized to enhance detection of small wet channels. The developed method is applied to the Lake Tahoe area based on eight LiDAR snapshots during recession periods in five watersheds. A power law relationship between streamflow and wetted channel length during recession periods is derived, and the scaling exponent (L∝Q0.44) is within the range of reported values from fieldwork in other regions.

  16. Serological evidence of Lyme borreliosis in Africa: results from studies in Dar es Salaam, Tanzania.

    PubMed

    Mhalu, F S; Matre, R

    1996-09-01

    Investigations were performed on sera from blood donors, pregnant women, patients with polyarthritis and from patients with clinical suspicion of syphilis in Dar es Salaam using Borrelia burgdorferi (Bb) flagellar antigen in a second generation ELISA test from DAKO A/S, Denmark, for specific IgM or IgG antibodies. An IgM and or IgG seropositivity rate of 30/100 (30%), 19/50 (7.2%), 10/20 (50%) and 11/20 (55%) was found in sera from the respective groups. These results compare with a Bb seroprevalence rate of 4/100 (4%), 1/52 (2%) and 363/5024 (7.2%) in blood donors, in pregnant women and in patients investigated serologically for Lyme borreliosis (Lb) respectively in Bergen, Norway, where cases of Lb are detected regularly. The high prevalence of antibodies to Bb flagellar antigen in Dar es Salaam, Tanzania where clinical conditions including erythema migrans, arthritis, mycocarditis and CNS diseases as well as tickbites are found call for further clinical, entomological and laboratory investigations. PMID:8991238

  17. Identifying Ancient Settlement Patterns through LiDAR in the Mosquitia Region of Honduras.

    PubMed

    Fisher, Christopher T; Fernández-Diaz, Juan Carlos; Cohen, Anna S; Neil Cruz, Oscar; Gonzáles, Alicia M; Leisz, Stephen J; Pezzutti, Florencia; Shrestha, Ramesh; Carter, William

    2016-01-01

    The Mosquitia ecosystem of Honduras occupies the fulcrum between the American continents and as such constitutes a critical region for understanding past patterns of socio-political development and interaction. Heavy vegetation, rugged topography, and remoteness have limited scientific investigation. This paper presents prehistoric patterns of settlement and landuse for a critical valley within the Mosquitia derived from airborne LiDAR scanning and field investigation. We show that (i) though today the valley is a wilderness it was densely inhabited in the past; (ii) that this population was organized into a three-tiered system composed of 19 settlements dominated by a city; and, (iii) that this occupation was embedded within a human engineered landscape. We also add to a growing body of literature that demonstrates the utility of LiDAR as means for rapid cultural assessments in undocumented regions for analysis and conservation. Our ultimate hope is for our work to promote protections to safeguard the unique and critically endangered Mosquitia ecosystem and other similar areas in need of preservation. PMID:27560962

  18. GeoEarthScope Airborne LiDAR and Satellite InSAR Imagery

    NASA Astrophysics Data System (ADS)

    Phillips, D. A.; Jackson, M. E.; Meertens, C.

    2008-12-01

    UNAVCO has successfully acquired a significant volume of aerial and satellite geodetic imagery as part of GeoEarthScope, a component of the EarthScope Facility project funded by the National Science Foundation. All GeoEarthScope acquisition activities are now complete. Airborne LiDAR data acquisitions took place in 2007 and 2008 and cover a total area of more than 5000 square kilometers. The primary LiDAR survey regions cover features in Northern California, Southern/Eastern California, the Pacific Northwest, the Intermountain Seismic Belt (including the Wasatch and Teton faults and Yellowstone), and Alaska. We have ordered and archived more than 28,000 scenes (more than 81,000 frames) of synthetic aperture radar (SAR) data suitable for interferometric analyses covering most of the western U.S. and parts of Alaska and Hawaii from several satellite platforms, including ERS-1/2, ENVISAT and RADARSAT. In addition to ordering data from existing archives, we also tasked the ESA ENVISAT satellite to acquire new SAR data in 2007 and 2008. GeoEarthScope activities were led by UNAVCO, guided by the community and conducted in partnership with the USGS and NASA. Processed imagery products, in addition to formats intended for use in standard research software, can also be viewed using general purpose tools such as Google Earth. We present a summary of these vast geodetic imagery datasets, totaling tens of terabytes, which are freely available to the community.

  19. Assessing riparian shade for the Lemhi River, Idaho using LiDAR: A point cloud analysis

    NASA Astrophysics Data System (ADS)

    Spaete, L.; Glenn, N. F.; Shrestha, R.; Shumar, M. L.; Mitchell, J.

    2012-12-01

    Riparian vegetation plays a crucial role in shading streams by reducing the amount of incoming solar insolation that would otherwise reach the water surface, negatively affecting water temperature and photosynthetic organisms within the water column. Unlike incoming solar insolation, riparian shade can be manipulated by adding or removing riparian vegetation, making it attractive for restoration as well as thermal credit trading programs. Before riparian shade can be evaluated in such trading programs, the existing riparian vegetation needs to be quantified. Several studies have investigated the utility of LiDAR derived canopy height models for estimating riparian shade, however, few to no studies have used point cloud data as a direct model input in order to improve the riparian shade estimates. Using point cloud data increases spatial resolution and the ability to extract vegetation shape information without losses due to interpolation/rasterization. In this study, we assessed the ability of LiDAR point cloud data to estimate riparian shade for 32 km of the Lemhi River in north central Idaho. Riparian shade quantification of the point cloud and canopy height models are compared to shade values calculated using established models in practice.

  20. Building Change Detection by Combining LiDAR Data and Ortho Image

    NASA Astrophysics Data System (ADS)

    Peng, Daifeng; Zhang, Yongjun

    2016-06-01

    The elevation information is not considered in the traditional building change detection methods. This paper presents an algorithm of combining LiDAR data and ortho image for 3D building change detection. The advantages of the proposed approach lie in the fusion of the height and spectral information by thematic segmentation. Furthermore, the proposed method also combines the advantages of pixel-level and object-level change detection by image differencing and object analysis. Firstly, two periods of LiDAR data are filtered and interpolated to generate their corresponding DSMs. Secondly, a binary image of the changed areas is generated by means of differencing and filtering the two DSMs, and then thematic layer is generated and projected onto the DSMs and DOMs. Thirdly, geometric and spectral features of the changed area are calculated, which is followed by decision tree classification for the purpose of extracting the changed building areas. Finally, the statistics of the elevation and area change information as well as the change type of the changed buildings are done for building change analysis. Experimental results show that the completeness and correctness of building change detection are close to 81.8% and 85.7% respectively when the building area is larger than 80 m2, which are increased about 10% when compared with using ortho image alone.

  1. A comprehensive framework of building model reconstruction from airborne LiDAR data

    NASA Astrophysics Data System (ADS)

    Xiao, Y.; Wang, C.; Xi, X. H.; Zhang, W. M.

    2014-03-01

    This paper presents a comprehensive framework of reconstructing 3D building models from airborne LiDAR data, which involves building extraction, roof segmentation and model generation. Firstly, building points are extracted from LiDAR point clouds by removing walls, trees, ground and noises. Walls and trees are identified by the normal and multi-return features respectively and then ground and noise are detected by the region growing algorithm which aims at extracting smooth surfaces. Then the connected component analysis is performed to extract building points. Secondly, once the building points are acquired, building roofs are separated by the region growing algorithm which employs the normal vector and curvature of points to detect planar clusters. Finally, by combining regular building outlines obtained from building points and roof intersections acquired from the roof segmentation results, 3D building models with high accuracy are derived. Experimental results demonstrate that the proposed method is able to correctly obtain building points and reconstruct 3D building models with high accuracy.

  2. LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments

    PubMed Central

    Tang, Jian; Chen, Yuwei; Niu, Xiaoji; Wang, Li; Chen, Liang; Liu, Jingbin; Shi, Chuang; Hyyppä, Juha

    2015-01-01

    A new scan that matches an aided Inertial Navigation System (INS) with a low-cost LiDAR is proposed as an alternative to GNSS-based navigation systems in GNSS-degraded or -denied environments such as indoor areas, dense forests, or urban canyons. In these areas, INS-based Dead Reckoning (DR) and Simultaneous Localization and Mapping (SLAM) technologies are normally used to estimate positions as separate tools. However, there are critical implementation problems with each standalone system. The drift errors of velocity, position, and heading angles in an INS will accumulate over time, and on-line calibration is a must for sustaining positioning accuracy. SLAM performance is poor in featureless environments where the matching errors can significantly increase. Each standalone positioning method cannot offer a sustainable navigation solution with acceptable accuracy. This paper integrates two complementary technologies—INS and LiDAR SLAM—into one navigation frame with a loosely coupled Extended Kalman Filter (EKF) to use the advantages and overcome the drawbacks of each system to establish a stable long-term navigation process. Static and dynamic field tests were carried out with a self-developed Unmanned Ground Vehicle (UGV) platform—NAVIS. The results prove that the proposed approach can provide positioning accuracy at the centimetre level for long-term operations, even in a featureless indoor environment. PMID:26184206

  3. Point Spread Function (PSF) noise filter strategy for geiger mode LiDAR

    NASA Astrophysics Data System (ADS)

    Smith, O'Neil; Stark, Robert; Smith, Philip; St. Romain, Randall; Blask, Steven

    2013-05-01

    LiDAR is an efficient optical remote sensing technology that has application in geography, forestry, and defense. The effectiveness is often limited by signal-to-noise ratio (SNR). Geiger mode avalanche photodiode (APD) detectors are able to operate above critical voltage, and a single photoelectron can initiate the current surge, making the device very sensitive. These advantages come at the expense of requiring computationally intensive noise filtering techniques. Noise is a problem which affects the imaging system and reduces the capability. Common noise-reduction algorithms have drawbacks such as over aggressive filtering, or decimating in order to improve quality and performance. In recent years, there has been growing interest on GPUs (Graphics Processing Units) for their ability to perform powerful massive parallel processing. In this paper, we leverage this capability to reduce the processing latency. The Point Spread Function (PSF) filter algorithm is a local spatial measure that has been GPGPU accelerated. The idea is to use a kernel density estimation technique for point clustering. We associate a local likelihood measure with every point of the input data capturing the probability that a 3D point is true target-return photons or noise (background photons, dark-current). This process suppresses noise and allows for detection of outliers. We apply this approach to the LiDAR noise filtering problem for which we have recognized a speed-up factor of 30-50 times compared to traditional sequential CPU implementation.

  4. Indirect Correspondence-Based Robust Extrinsic Calibration of LiDAR and Camera.

    PubMed

    Sim, Sungdae; Sock, Juil; Kwak, Kiho

    2016-01-01

    LiDAR and cameras have been broadly utilized in computer vision and autonomous vehicle applications. However, in order to convert data between the local coordinate systems, we must estimate the rigid body transformation between the sensors. In this paper, we propose a robust extrinsic calibration algorithm that can be implemented easily and has small calibration error. The extrinsic calibration parameters are estimated by minimizing the distance between corresponding features projected onto the image plane. The features are edge and centerline features on a v-shaped calibration target. The proposed algorithm contributes two ways to improve the calibration accuracy. First, we use different weights to distance between a point and a line feature according to the correspondence accuracy of the features. Second, we apply a penalizing function to exclude the influence of outliers in the calibration datasets. Additionally, based on our robust calibration approach for a single LiDAR-camera pair, we introduce a joint calibration that estimates the extrinsic parameters of multiple sensors at once by minimizing one objective function with loop closing constraints. We conduct several experiments to evaluate the performance of our extrinsic calibration algorithm. The experimental results show that our calibration method has better performance than the other approaches. PMID:27338416

  5. LiDAR-Assisted identification of an active fault near Truckee, California

    USGS Publications Warehouse

    Hunter, L.E.; Howle, J.F.; Rose, R.S.; Bawden, G.W.

    2011-01-01

    We use high-resolution (1.5-2.4 points/m2) bare-earth airborne Light Detection and Ranging (LiDAR) imagery to identify, map, constrain, and visualize fault-related geomorphology in densely vegetated terrain surrounding Martis Creek Dam near Truckee, California. Bare-earth LiDAR imagery reveals a previously unrecognized and apparently youthful right-lateral strike-slip fault that exhibits laterally continuous tectonic geomorphic features over a 35-km-long zone. If these interpretations are correct, the fault, herein named the Polaris fault, may represent a significant seismic hazard to the greater Truckee-Lake Tahoe and Reno-Carson City regions. Three-dimensional modeling of an offset late Quaternary terrace riser indicates a minimum tectonic slip rate of 0.4 ?? 0.1 mm/yr.Mapped fault patterns are fairly typical of regional patterns elsewhere in the northern Walker Lane and are in strong coherence with moderate magnitude historical seismicity of the immediate area, as well as the current regional stress regime. Based on a range of surface-rupture lengths and depths to the base of the seismogenic zone, we estimate a maximum earthquake magnitude (M) for the Polaris fault to be between 6.4 and 6.9.

  6. Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas

    NASA Astrophysics Data System (ADS)

    Zhao, Xiaoqian; Guo, Qinghua; Su, Yanjun; Xue, Baolin

    2016-07-01

    Filtering of light detection and ranging (LiDAR) data into the ground and non-ground points is a fundamental step in processing raw airborne LiDAR data. This paper proposes an improved progressive triangulated irregular network (TIN) densification (IPTD) filtering algorithm that can cope with a variety of forested landscapes, particularly both topographically and environmentally complex regions. The IPTD filtering algorithm consists of three steps: (1) acquiring potential ground seed points using the morphological method; (2) obtaining accurate ground seed points; and (3) building a TIN-based model and iteratively densifying TIN. The IPTD filtering algorithm was tested in 15 forested sites with various terrains (i.e., elevation and slope) and vegetation conditions (i.e., canopy cover and tree height), and was compared with seven other commonly used filtering algorithms (including morphology-based, slope-based, and interpolation-based filtering algorithms). Results show that the IPTD achieves the highest filtering accuracy for nine of the 15 sites. In general, it outperforms the other filtering algorithms, yielding the lowest average total error of 3.15% and the highest average kappa coefficient of 89.53%.

  7. An Airborne Scanning LiDAR System for Ocean and Coastal Applications

    NASA Astrophysics Data System (ADS)

    Reineman, B. D.; Lenain, L.; Castel, D.; Melville, W. K.

    2008-12-01

    We have developed an airborne scanning LiDAR (Light Detection And Ranging) system and demonstrated its functionality for terrestrial and oceanographic measurements. Differential GPS (DGPS) and an Inertial Navigation System (INS) are synchronized with the LiDAR, providing end result vertical rms errors of approximately 6~cm. Flying 170~m above the surface, we achieve a point density of ~ 0.7 m-2 and a swath width of 90 to 120~m over ocean and 200~m over land. Georeferencing algorithms were developed in-house and earth-referenced data are available several hours after acquisition. Surveys from the system are compared with ground DGPS surveys and existing airborne surveys of fixed targets. Twelve research flights in a Piper Twin Comanche from August 2007 to July 2008 have provided topography of the Southern California coastline and sea surface wave fields in the nearshore ocean environment. Two of the flights also documented the results of the October 2007 landslide on Mt.~Soledad in La Jolla, California. Eight research flights aboard a Cessna Caravan surveyed the topography, lagoon, reef, and surrounding seas of Lady Elliot Island (LEI) in Australia's Great Barrier Reef in April 2008. We describe applications for the system, including coastal topographic surveys, wave measurements, reef research, and ship wake studies.

  8. Characterizing Croatian Wheat Germplasm Diversity and Structure in a European Context by DArT Markers

    PubMed Central

    Novoselović, Dario; Bentley, Alison R.; Šimek, Ruđer; Dvojković, Krešimir; Sorrells, Mark E.; Gosman, Nicolas; Horsnell, Richard; Drezner, Georg; Šatović, Zlatko

    2016-01-01

    Narrowing the genetic base available for future genetic progress is a major concern to plant breeders. In order to avoid this, strategies to characterize and protect genetic diversity in regional breeding pools are required. In this study, 89 winter wheat cultivars released in Croatia between 1936 and 2006 were genotyped using 1,229 DArT (diversity array technology) markers to assess the diversity and population structure. In order to place Croatian breeding pool (CBP) in a European context, Croatian wheat cultivars were compared to 523 European cultivars from seven countries using a total of 166 common DArT markers. The results show higher genetic diversity in the wheat breeding pool from Central Europe (CE) as compared to that from Northern and Western European (NWE) countries. The most of the genetic diversity was attributable to the differences among cultivars within countries. When the geographical criterion (CE vs. NWE) was applied, highly significant difference between regions was obtained that accounted for 16.19% of the total variance, revealing that the CBP represents genetic variation not currently captured in elite European wheat. The current study emphasizes the important contribution made by plant breeders to maintaining wheat genetic diversity and suggests that regional breeding is essential to the maintenance of this diversity. The usefulness of open-access wheat datasets is also highlighted. PMID:26941756

  9. Identifying Ancient Settlement Patterns through LiDAR in the Mosquitia Region of Honduras

    PubMed Central

    Fernández-Diaz, Juan Carlos; Cohen, Anna S.; Neil Cruz, Oscar; Gonzáles, Alicia M.; Leisz, Stephen J.; Pezzutti, Florencia; Shrestha, Ramesh; Carter, William

    2016-01-01

    The Mosquitia ecosystem of Honduras occupies the fulcrum between the American continents and as such constitutes a critical region for understanding past patterns of socio-political development and interaction. Heavy vegetation, rugged topography, and remoteness have limited scientific investigation. This paper presents prehistoric patterns of settlement and landuse for a critical valley within the Mosquitia derived from airborne LiDAR scanning and field investigation. We show that (i) though today the valley is a wilderness it was densely inhabited in the past; (ii) that this population was organized into a three-tiered system composed of 19 settlements dominated by a city; and, (iii) that this occupation was embedded within a human engineered landscape. We also add to a growing body of literature that demonstrates the utility of LiDAR as means for rapid cultural assessments in undocumented regions for analysis and conservation. Our ultimate hope is for our work to promote protections to safeguard the unique and critically endangered Mosquitia ecosystem and other similar areas in need of preservation. PMID:27560962

  10. A comparison of waveform processing algorithms for single-wavelength LiDAR bathymetry

    NASA Astrophysics Data System (ADS)

    Wang, Chisheng; Li, Qingquan; Liu, Yanxiong; Wu, Guofeng; Liu, Peng; Ding, Xiaoli

    2015-03-01

    Due to the low-cost and lightweight units, single-wavelength LiDAR bathymetric systems are an ideal option for shallow-water (<12 m) bathymetry. However, one disadvantage of such systems is the lack of near-infrared and Raman channels, which results in difficulties in extracting the water surface. Therefore, the choice of a suitable waveform processing method is extremely important to guarantee the accuracy of the bathymetric retrieval. In this paper, we test six algorithms for single-wavelength bathymetric waveform processing, i.e. peak detection (PD), the average square difference function (ASDF), Gaussian decomposition (GD), quadrilateral fitting (QF), Richardson-Lucy deconvolution (RLD), and Wiener filter deconvolution (WD). To date, most of these algorithms have previously only been applied in topographic LiDAR waveforms captured over land. A simulated dataset and an Optech Aquarius dataset were used to assess the algorithms, with the focus being on their capability of extracting the depth and the bottom response. The influences of a number of water and equipment parameters were also investigated by the use of a Monte Carlo method. The results showed that the RLD method had a superior performance in terms of a high detection rate and low errors in the retrieved depth and magnitude. The attenuation coefficient, noise level, water depth, and bottom reflectance had significant influences on the measurement error of the retrieved depth, while the effects of scan angle and water surface roughness were not so obvious.