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Sample records for dar sharayet-e adi-e

  1. LiDAR: Providing structure

    USGS Publications Warehouse

    Vierling, Lee A.; Martinuzzi, Sebastián; Asner, Gregory P.; Stoker, Jason M.; Johnson, Brian R.

    2011-01-01

    Since the days of MacArthur, three-dimensional (3-D) structural information on the environment has fundamentally transformed scientific understanding of ecological phenomena (MacArthur and MacArthur 1961). Early data on ecosystem structure were painstakingly laborious to collect. However, as reviewed and reported in recent volumes of Frontiers(eg Vierling et al. 2008; Asner et al.2011), advances in light detection and ranging (LiDAR) remote-sensing technology provide quantitative and repeatable measurements of 3-D ecosystem structure that enable novel ecological insights at scales ranging from the plot, to the landscape, to the globe. Indeed, annual publication of studies using LiDAR to interpret ecological phenomena increased 17-fold during the past decade, with over 180 new studies appearing in 2010 (ISI Web of Science search conducted on 23 Mar 2011: [{lidar AND ecol*} OR {lidar AND fores*} OR {lidar AND plant*}]).

  2. LiDAR as an Exploration Tool

    DOE Data Explorer

    Boschmann, D.; Diles, J.; Clarno, J.; Meigs, A.; Walsh, P.

    2011-01-01

    Using LiDAR to identify structural and volcanic evolution of a Miocene-Pleistocene age bimodal volcanic complex and implications for geothermal potential. The file includes an updated geologic map, methods, and preliminary results.

  3. LiDAR utility for natural resource managers

    Treesearch

    Andrew Thomas Hudak; Jeffrey Scott Evans; Alistair Mattthew Stuart. Smith

    2009-01-01

    Applications of LiDAR remote sensing are exploding, while moving from the research to the operational realm. Increasingly, natural resource managers are recognizing the tremendous utility of LiDAR-derived information to make improved decisions. This review provides a cross-section of studies, many recent, that demonstrate the relevance of LiDAR across a suite of...

  4. Characterizing Lava Flows With LiDAR

    NASA Astrophysics Data System (ADS)

    Deligne, N. I.; Cashman, K. V.; Deardorff, N.; Dietterich, H. R.; House, P. K.; Soule, S.

    2009-12-01

    Digital elevation models (DEMs) have been used in volcanology in predictive modeling of lava flow paths, both for assessment of potential hazards and specific predictions of lava flow paths. Topographic analysis of a lava flow is potentially useful for mapping and quantifying flow surface morphologies, which in turn can be used to determine flow emplacement conditions, such as effusion rate, steadiness of flow, and interactions with pre-existing topography and surface water. However, this has been limited in application because of the coarse resolution of most DEMs. In recent years, use of Light Detection and Ranging (LiDAR) airborne laser altimetry, capable of producing high resolution (≤ 1 meter) DEMs, has become increasingly common in the geomorphic and mapping community. However, volcanologists have made little use of airborne LiDAR. Here we compare information obtained using field observations and standard (10 meter) DEMs against LiDAR high resolution DEMs to assess the usefulness, capabilities, and limitations of LiDAR as applicable to lava flows. We compare morphologic characteristics of five lava flows of different compositions, tectonic settings, flow extents, slopes, and eruption duration: (1) 1984 Mauna Loa lava flow, Hawaii; (2) December 1974 Kilauea lava flow, Hawaii; (3) c. 1600 ybp Collier Cone lava flow, central Oregon Cascades; (4) Holocene lava flows from the Sand Mountain volcanic chain, central Oregon Cascades; and (5) Pleistocene lava flows along the Owyhee River, eastern Oregon basin and range. These lava flows range in composition from basalt to andesite, and have eruption durations ranging from 6 hours (observed) to years (inferred). We measure channel width, levee and flow front heights, compression ridge amplitude, wavelength and tumuli dimensions, and surface roughness. For all but the smallest scale features, LiDAR is easily used to quantify these features, which often is impossible or technically challenging to do in the field, while

  5. DoD Architecture Registry System (DARS)

    DTIC Science & Technology

    2012-04-30

    Communications-Electronics Research, Development and Engineering Center (CERDEC), S ft E i i Di t t (SED) UNCLASSIFIED 5 o ware ng neer ng rec ora e...Army 1245 (1183, 5 %)  Air Force 960 (889, 8%)  OSD 358 (346, 3%) M i 223 ( %) ar nes 209, 7  Combatant Command 156 (155, 1%)  Joint Staff...Program • Deployed DARS Release 5 6 in April 29 2011 This releaseCompleted Support . . included: • Migrate Community Management and

  6. Recent development of hyperspectral LiDAR using supercontinuum laser

    NASA Astrophysics Data System (ADS)

    Wang, Zhen; Li, Chuanrong; Zhou, Mei; Zhang, Huijing; He, Wenjing; Li, Wei; Qiu, Yuanyuan

    2016-10-01

    Hyperspectral Light Detection And Ranging (Hyperspectral LiDAR), a recently developed technique, combines the advantages of the LiDAR and hyperspectral imaging and has been attractive for many applications. Supercontinuum laser (SC laser), a rapidly developing technique offers hyperspectral LiDAR a suitable broadband laser source and makes hyperspectral Lidar become an installation from a theory. In this paper, the recent research and progressing of the hyperspectral LiDAR are reviewed. The hyperspectral LiDAR has been researched in theory, prototype system, instrument, and application experiment. However, the pulse energy of the SC laser is low so that the range of the hyperspectral LiDAR is limited. Moreover, considering the characteristics of sensors and A/D converter, in order to obtain the full waveform of the echo, the repetition rate and the pulse width of the SC laser needs to be limited. Recently, improving the detection ability of hyperspectral LiDAR, especially improving the detection range, is a main research area. A higher energy pulse SC laser, a more sensitive sensor, or some algorithms are applied in hyperspectral LiDAR to improve the detection distance from 12 m to 1.5 km. At present, a lot of research has been focused on this novel technology which would be applied in more applications.

  7. LiDAR - An emerging tool for geological applications

    USGS Publications Warehouse

    Stoker, Jason M.

    2012-01-01

    Over the past five to ten years the use and applicability of light detection and ranging (LiDAR) technology has increased dramatically. As a result, more and more LiDAR data now are being collected across the country for a wide range of applications, and LiDAR currently is the technology of choice for high resolution terrain model creation, 3-D city and infrastructure modeling, forestry, and a wide range of scientific applications. LiDAR is a key technology for geological applications both within and outside the U.S. Geological Survey, and efforts are underway to try to collect high resolution LiDAR data for the entire United States (https://pubs.usgs.gov/fs/2012/3089/pdf/fs2012-3089.pdf).

  8. 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.

  9. 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.

  10. Automatic registration method for mobile LiDAR data

    NASA Astrophysics Data System (ADS)

    Wang, Ruisheng; Ferrie, Frank P.

    2015-01-01

    We present an automatic mutual information (MI) registration method for mobile LiDAR and panoramas collected from a driving vehicle. The suitability of MI for registration of aerial LiDAR and aerial oblique images has been demonstrated under an assumption that minimization of joint entropy (JE) is a sufficient approximation of maximization of MI. We show that this assumption is invalid for the ground-level data. The entropy of a LiDAR image cannot be regarded as approximately constant for small perturbations. Instead of minimizing the JE, we directly maximize MI to estimate corrections of camera poses. Our method automatically registers mobile LiDAR with spherical panoramas over an approximate 4-km drive, and is the first example we are aware of that tests MI registration in a large-scale context.

  11. Excreta disposal in Dar-es-Salaam.

    PubMed

    Chaggu, Esnati; Mashauri, Damas; van Buuren, Joost; Sanders, Wendy; Lettinga, Gatze

    2002-11-01

    The sociocultural and socioeconomic situation of sanitation in Dar-es-Salaam (Dsm), Tanzania, was studied with explicit emphasis on pit-latrines. Without considering the sociocultural conditions, the so-called best solution might not be the right one. Therefore, in order to achieve the intended goal, a literature review, a questionnaire survey, and personal visits to the chosen study areas were done. In total, 207 household questionnaires were filled in 16 areas of the city. Interviewers did house-to-house visits and questionnaires were filled out on the spot. Results indicated that the city population is about 3.8 million at present, with over 80% of the dwellers using pit-latrines; some 3% use septic tanks with soakage pits, about 6% are connected to the sewerage system, and 1% have no excreta disposal facility. Difficulties faced include dismal budget allocations, fragmentation of sanitation activities among subsectors, lack of or poor sanitation record keeping, unsatisfactory machinery for septic tank and pit-latrine emptying, lack of a clear policy on pit-latrine handling and, in competition for resources, low priority is accorded to an excreta disposal system among the people. City residents will continue to use the pit-latrines for a long time to come. Reusing the fecal sludge is not known by most city dwellers and is influenced by sociocultural habits. To prevent groundwater pollution and to recover useful products in human excreta and urine, ecological sanitation toilets and anaerobic digesters offer a good option.

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. LiDAR point classification based on sparse representation

    NASA Astrophysics Data System (ADS)

    Li, Nan; Pfeifer, Norbert; Liu, Chun

    2017-04-01

    In order to combine the initial spatial structure and features of LiDAR data for accurate classification. The LiDAR data is represented as a 4-order tensor. Sparse representation for classification(SRC) method is used for LiDAR tensor classification. It turns out SRC need only a few of training samples from each class, meanwhile can achieve good classification result. Multiple features are extracted from raw LiDAR points to generate a high-dimensional vector at each point. Then the LiDAR tensor is built by the spatial distribution and feature vectors of the point neighborhood. The entries of LiDAR tensor are accessed via four indexes. Each index is called mode: three spatial modes in direction X ,Y ,Z and one feature mode. Sparse representation for classification(SRC) method is proposed in this paper. The sparsity algorithm is to find the best represent the test sample by sparse linear combination of training samples from a dictionary. To explore the sparsity of LiDAR tensor, the tucker decomposition is used. It decomposes a tensor into a core tensor multiplied by a matrix along each mode. Those matrices could be considered as the principal components in each mode. The entries of core tensor show the level of interaction between the different components. Therefore, the LiDAR tensor can be approximately represented by a sparse tensor multiplied by a matrix selected from a dictionary along each mode. The matrices decomposed from training samples are arranged as initial elements in the dictionary. By dictionary learning, a reconstructive and discriminative structure dictionary along each mode is built. The overall structure dictionary composes of class-specified sub-dictionaries. Then the sparse core tensor is calculated by tensor OMP(Orthogonal Matching Pursuit) method based on dictionaries along each mode. It is expected that original tensor should be well recovered by sub-dictionary associated with relevant class, while entries in the sparse tensor associated with

  19. Waveform LiDAR across forest biomass gradients

    NASA Astrophysics Data System (ADS)

    Montesano, P. M.; Nelson, R. F.; Dubayah, R.; Sun, G.; Ranson, J.

    2011-12-01

    Detailed information on the quantity and distribution of aboveground biomass (AGB) is needed to understand how it varies across space and changes over time. Waveform LiDAR data is routinely used to derive the heights of scattering elements in each illuminated footprint, and the vertical structure of vegetation is related to AGB. Changes in LiDAR waveforms across vegetation structure gradients can demonstrate instrument sensitivity to land cover transitions. A close examination of LiDAR waveforms in footprints across a forest gradient can provide new insight into the relationship of vegetation structure and forest AGB. In this study we use field measurements of individual trees within Laser Vegetation Imaging Sensor (LVIS) footprints along transects crossing forest to non-forest gradients to examine changes in LVIS waveform characteristics at sites with low (< 50Mg/ha) AGB. We relate field AGB measurements to original and adjusted LVIS waveforms to detect the forest AGB interval along a forest - non-forest transition in which the LVIS waveform lose the ability to discern differences in AGB. Our results help identify the lower end the forest biomass range that a ~20m footprint waveform LiDAR can detect, which can help infer accumulation of biomass after disturbances and during forest expansion, and which can guide the use of LiDAR within a multi-sensor fusion biomass mapping approach.

  20. 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.

  1. Using LiDAR technology in forestry activities.

    PubMed

    Akay, Abdullah Emin; Oğuz, Hakan; Karas, Ismail Rakip; Aruga, Kazuhiro

    2009-04-01

    Managing natural resources in wide-scale areas can be highly time and resource consuming task which requires significant amount of data collection in the field and reduction of the data in the office to provide the necessary information. High performance LiDAR remote sensing technology has recently become an effective tool for use in applications of natural resources. In the field of forestry, the LiDAR measurements of the forested areas can provide high-quality data on three-dimensional characterizations of forest structures. Besides, LiDAR data can be used to provide very high quality and accurate Digital Elevation Model (DEM) for the forested areas. This study presents the progress and opportunities of using LiDAR remote sensing technology in various forestry applications. The results indicate that LiDAR based forest structure data and high-resolution DEMs can be used in wide-scale forestry activities such as stand characterizations, forest inventory and management, fire behaviour modeling, and forest operations.

  2. 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

  3. 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%

  4. Raster Vs. Point Cloud LiDAR Data Classification

    NASA Astrophysics Data System (ADS)

    El-Ashmawy, N.; Shaker, A.

    2014-09-01

    Airborne Laser Scanning systems with light detection and ranging (LiDAR) technology is one of the fast and accurate 3D point data acquisition techniques. Generating accurate digital terrain and/or surface models (DTM/DSM) is the main application of collecting LiDAR range data. Recently, LiDAR range and intensity data have been used for land cover classification applications. Data range and Intensity, (strength of the backscattered signals measured by the LiDAR systems), are affected by the flying height, the ground elevation, scanning angle and the physical characteristics of the objects surface. These effects may lead to uneven distribution of point cloud or some gaps that may affect the classification process. Researchers have investigated the conversion of LiDAR range point data to raster image for terrain modelling. Interpolation techniques have been used to achieve the best representation of surfaces, and to fill the gaps between the LiDAR footprints. Interpolation methods are also investigated to generate LiDAR range and intensity image data for land cover classification applications. In this paper, different approach has been followed to classifying the LiDAR data (range and intensity) for land cover mapping. The methodology relies on the classification of the point cloud data based on their range and intensity and then converted the classified points into raster image. The gaps in the data are filled based on the classes of the nearest neighbour. Land cover maps are produced using two approaches using: (a) the conventional raster image data based on point interpolation; and (b) the proposed point data classification. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to compare the results of the two approaches. Five different land cover classes can be distinguished in that area: buildings, roads and parking areas, trees, low vegetation (grass), and bare soil. The results show that an improvement of around 10 % in the

  5. Spectral LiDAR analysis for terrain classification

    NASA Astrophysics Data System (ADS)

    McIver, Charles A.; Metcalf, Jeremy P.; Olsen, Richard C.

    2017-05-01

    Data from the Optech Titan airborne laser scanner were collected over Monterey, CA, in three wavelengths (532 nm, 1064 nm, and 1550 nm), in May 2016, by the National Center for Airborne LiDAR Mapping (NCALM). Analysis techniques have been developed using spectral technology largely derived from the analysis of spectral imagery. Data are analyzed as individual points, vs techniques that emphasize spatial binning. The primary tool which allows for this exploitation is the N-Dimensional Visualizer contained in the ENVI software package. The results allow for significant improvement in classification accuracy compared to results obtained from techniques derived from standard LiDAR analysis tools

  6. The Ubiquitin Receptors DA1, DAR1, and DAR2 Redundantly Regulate Endoreduplication by Modulating the Stability of TCP14/15 in Arabidopsis

    PubMed Central

    Peng, Yuancheng; Chen, Liangliang; Lu, Yaru; Wu, Yingbao; Dumenil, Jack; Zhu, Zhengge; Bevan, Michael W.; Li, Yunhai

    2015-01-01

    Organ growth involves the coordination of cell proliferation and cell growth with differentiation. Endoreduplication is correlated with the onset of cell differentiation and with cell and organ size, but little is known about the molecular mechanisms linking cell and organ growth with endoreduplication. We have previously demonstrated that the ubiquitin receptor DA1 influences organ growth by restricting cell proliferation. Here, we show that DA1 and its close family members DAR1 and DAR2 are redundantly required for endoreduplication during leaf development. DA1, DAR1, and DAR2 physically interact with the transcription factors TCP14 and TCP15, which repress endoreduplication by directly regulating the expression of cell-cycle genes. We also show that DA1, DAR1, and DAR2 modulate the stability of TCP14 and TCP15 proteins in Arabidopsis thaliana. Genetic analyses demonstrate that DA1, DAR1, and DAR2 function in a common pathway with TCP14/15 to regulate endoreduplication. Thus, our findings define an important genetic and molecular mechanism involving the ubiquitin receptors DA1, DAR1, and DAR2 and the transcription factors TCP14 and TCP15 that links endoreduplication with cell and organ growth. PMID:25757472

  7. The ubiquitin receptors DA1, DAR1, and DAR2 redundantly regulate endoreduplication by modulating the stability of TCP14/15 in Arabidopsis.

    PubMed

    Peng, Yuancheng; Chen, Liangliang; Lu, Yaru; Wu, Yingbao; Dumenil, Jack; Zhu, Zhengge; Bevan, Michael W; Li, Yunhai

    2015-03-01

    Organ growth involves the coordination of cell proliferation and cell growth with differentiation. Endoreduplication is correlated with the onset of cell differentiation and with cell and organ size, but little is known about the molecular mechanisms linking cell and organ growth with endoreduplication. We have previously demonstrated that the ubiquitin receptor DA1 influences organ growth by restricting cell proliferation. Here, we show that DA1 and its close family members DAR1 and DAR2 are redundantly required for endoreduplication during leaf development. DA1, DAR1, and DAR2 physically interact with the transcription factors TCP14 and TCP15, which repress endoreduplication by directly regulating the expression of cell-cycle genes. We also show that DA1, DAR1, and DAR2 modulate the stability of TCP14 and TCP15 proteins in Arabidopsis thaliana. Genetic analyses demonstrate that DA1, DAR1, and DAR2 function in a common pathway with TCP14/15 to regulate endoreduplication. Thus, our findings define an important genetic and molecular mechanism involving the ubiquitin receptors DA1, DAR1, and DAR2 and the transcription factors TCP14 and TCP15 that links endoreduplication with cell and organ growth. © 2015 American Society of Plant Biologists. All rights reserved.

  8. Quantifying Ladder Fuels: A New Approach Using LiDAR

    Treesearch

    Heather Kramer; Brandon Collins; Maggi Kelly; Scott Stephens

    2014-01-01

    We investigated the relationship between LiDAR and ladder fuels in the northern Sierra Nevada, California USA. Ladder fuels are often targeted in hazardous fuel reduction treatments due to their role in propagating fire from the forest floor to tree crowns. Despite their importance, ladder fuels are difficult to quantify. One common approach is to calculate canopy base...

  9. Coastal monitoring with LiDAR: challenges, problems, and pitfalls

    NASA Astrophysics Data System (ADS)

    Kidner, David B.; Thomas, Malcolm C.; Leigh, Charlotte; Oliver, J. Robert; Morgan, Christopher G.

    2004-10-01

    The National Assembly for Wales (NAW) is responsible for monitoring the effects of dredging for fine aggregate from sandbanks off the coast of South Wales. A key monitoring objective is the analysis of changes to the sandbank bathymetry and the adjacent coastline. This paper reviews the monitoring strategy, with a particular emphasis on the use of laserscanning with LiDAR over the last six years for large-scale topographic beach mapping and analysis. The focus is on the methodologies that were implemented in order to make the data compatible, consistent and usable within a geographical information system (GIS). The issues that are addressed include data handling strategies; automatic error/blunder detection of spurious data; identifying sources of errors; projection and datum transformations; LiDAR artefacts; quality control; choice of digital terrain model and spatial resolution; choice of interpolation algorithm; the calibration of LiDAR surveys to ensure consistency; and LiDAR accuracy compared with land surveys. Some of these issues have proved problematic, which if not correctly resolved, can produce significant application errors, thus reducing confidence in this technology. The paper concludes with some examples of the analyses undertaken to date.

  10. Modeling low-height vegetation with airborne LiDAR

    USDA-ARS?s Scientific Manuscript database

    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...

  11. Airborne LiDAR based Mapping of Alpine Permafrost Distribution

    NASA Astrophysics Data System (ADS)

    Sailer, Rudolf; Bollmann, Erik; Briese, Christian; Fischer, Andrea; Krainer, Karl; Pfeifer, Norbert; Rieg, Lorenzo; Stötter, Johann

    2010-05-01

    Recent global climate change findings show an acceleration of melting of glaciers, ice-sheets and ice caps. Extended remote sensing and in-situ measurements demonstrate that glaciers are losing mass at an increasing rate. In contrast to glaciers with large mass losses (about 5 m per year at the lower parts of the glacier tongue of Hintereisferner, Ötztal Alps, Tyrol, Austria), moderate to small changing rates (centimetres to decimetres per year) characterize the surface variations caused by permafrost degradation in high mountain areas. For a reliable mapping of the spatial permafrost distribution advanced remote sensing techniques with a high degree of vertical accuracy have to be applied. Recent studies have shown that airborne LiDAR survey in mountainous regions provide high-resolution spatial data with a vertical accuracy range of centimetre to decimetre. This prediction is based on a world wide unique dataset of 18 airborne LiDAR campaigns covering the Hintereisferner region . Furthermore, the according multi-temporal dataset offers the opportunity to identify surface changes (altitudinal changes) outside glaciated areas, which have not been observed until now. Excluding gravitational induced processes these altitudinal changes have to be assigned to alpine permafrost degradations, although detailed information from prominent permafrost features like rock glaciers are missing. Beyond the detection of the climate induced permafrost degradation, based on the multi-temporal LiDAR data set, the method (point based and avoiding point to raster conversions) will be applied to identify altitudinal changes and displacement rates of prominent rock glaciers in the Stubai and Ötztal Alps (Tyrol, Austria). In contrast to the multi-temporal approach, with at least one LiDAR terrain model per ablation period (June to September), the analysis of the rock glacier features is based on a data set of only two LiDAR campaigns, which were carried out with a time shift of four

  12. 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...

  13. 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...

  14. 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...

  15. 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...

  16. A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments

    Treesearch

    Jeffrey S. Evans; Andrew T. Hudak

    2007-01-01

    One prerequisite to the use of light detection and ranging (LiDAR) across disciplines is differentiating ground from nonground returns. The objective was to automatically and objectively classify points within unclassified LiDAR point clouds, with few model parameters and minimal postprocessing. Presented is an automated method for classifying LiDAR returns as ground...

  17. Temporal transferability of LiDAR-based imputation of forest structure attributes

    Treesearch

    Patrick A. Fekety; Michael J. Falkowski; Andrew T. Hudak

    2015-01-01

    Forest inventory and planning decisions are frequently informed by LiDAR data. Repeated LiDAR acquisitions offer an opportunity to update forest inventories and potentially improve forest inventory estimates through time. We leveraged repeated LiDAR and ground measures for a study area in northern Idaho, U.S.A., to predict (via imputation) - across both space and time-...

  18. Demystifying LiDAR technologies for temperate rainforest in the Pacific Northwest

    Treesearch

    Rhonda Mazza; Demetrios Gatziolis

    2013-01-01

    Light detection and ranging (LiDAR), also known as airborne laser scanning, is a rapidly emerging technology for remote sensing. Used to help map, monitor, and assess natural resources, LiDAR data were first embraced by forestry professionals in Scandinavia as a tool for conducting forest inventories in the mid to late 1990s. Thus early LiDAR theory and applications...

  19. 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...

  20. Relationship between LiDAR-derived forest canopy height and Landsat images

    Treesearch

    Cristina Pascual; Antonio Garcia-Abril; Warren B. Cohen; Susana. Martin-Fernandez

    2010-01-01

    The mean and standard deviation (SD) of light detection and ranging (LiDAR)-derived canopy height are related to forest structure. However, LiDAR data typically cover a limited area and have a high economic cost compared with satellite optical imagery. Optical images may be required to extrapolate LiDAR height measurements across a broad landscape. Different spectral...

  1. Three-dimensional canopy fuel loading predicted using upward and downward sensing LiDAR systems

    Treesearch

    Nicholas S. Skowronski; Kenneth L. Clark; Matthew Duveneck; John. Hom

    2011-01-01

    We calibrated upward sensing profiling and downward sensing scanning LiDAR systems to estimates of canopy fuel loading developed from field plots and allometric equations, and then used the LiDAR datasets to predict canopy bulk density (CBD) and crown fuel weight (CFW) in wildfire prone stands in the New Jersey Pinelands. LiDAR-derived height profiles were also...

  2. 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%

  3. 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.

  4. 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.

  5. Road Curb Extraction From Mobile LiDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Xu, Sheng; Wang, Ruisheng; Zheng, Han

    2017-02-01

    Automatic extraction of road curbs from uneven, unorganized, noisy and massive 3D point clouds is a challenging task. Existing methods often project 3D point clouds onto 2D planes to extract curbs. However, the projection causes loss of 3D information which degrades the performance of the detection. This paper presents a robust, accurate and efficient method to extract road curbs from 3D mobile LiDAR point clouds. Our method consists of two steps: 1) extracting the candidate points of curbs based on the proposed novel energy function and 2) refining the candidate points using the proposed least cost path model. We evaluated our method on a large-scale of residential area (16.7GB, 300 million points) and an urban area (1.07GB, 20 million points) mobile LiDAR point clouds. Results indicate that the proposed method is superior to the state-of-the-art methods in terms of robustness, accuracy and efficiency. The proposed curb extraction method achieved a completeness of 78.62% and a correctness of 83.29%. These experiments demonstrate that the proposed method is a promising solution to extract road curbs from mobile LiDAR point clouds.

  6. 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.

  7. Characterizing Canopy Structure Using Waveform LiDAR

    NASA Astrophysics Data System (ADS)

    Wang, K.; Kumar, P.

    2016-12-01

    The structure of light penetration through the canopy plays an important role in water, carbon, and energy fluxes between the biosphere and the atmosphere. Canopy clumping, a description of foliage distribution, is one of the major aspects of canopy structure that significantly influence light and vegetation interaction. Airborne full-waveform LiDAR data contains large amounts of vegetation structural information, and is a powerful tool for providing detailed foliage distribution information for large areas of vegetation. In this study, we present a method for describing physical canopy clumping structure for individual trees that can resolve fine scale variations in foliage distribution. We first utilize the K-means clustering algorithm to extract structure from the large amounts of vegetation data provided by full-waveform LiDAR. Then we find representative traits for data clusters and use them to classify the clusters into three groups. Based on these traits, we draw conclusions about physical representations of each group, and identify two groups to contain structurally significant clusters. This study demonstrates that large amounts of canopy structural information can be extracted from waveform LiDAR data. The fine resolution canopy clumping structure found by the method described in this work can be used as valuable input for ecological models.

  8. 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.

  9. Immunogenicity and Protective Efficacy of the DAR-901 Booster Vaccine in a Murine Model of Tuberculosis

    PubMed Central

    Lahey, Timothy; Laddy, Dominick; Hill, Krystal; Schaeffer, Jacqueline; Hogg, Alison; Keeble, James; Dagg, Belinda; Ho, Mei Mei; Arbeit, Robert D.; von Reyn, C. Fordham

    2016-01-01

    Background The development of a novel tuberculosis vaccine is a leading global health priority. SRL172, an inactivated, whole-cell mycobacterial vaccine, was safe, immunogenic and reduced the incidence of culture-confirmed tuberculosis in a phase III trial in HIV-infected and BCG immunized adults in Tanzania. Here we describe the immunogenicity and protective efficacy of DAR-901, a booster vaccine against tuberculosis manufactured from the same seed strain using a new scalable method. Methods We evaluated IFN-γ responses by ELISpot and antibody responses by enzyme linked immunosorbent assay in C57BL/6 and BALB/c mice after three doses of DAR-901. In an aerosol challenge model, we evaluated the protective efficacy of the DAR-901 booster in C57BL/6 mice primed with BCG and boosted with two doses of DAR-901 at 4 dosage levels in comparison with homologous BCG boost. Results DAR-901 vaccination elicited IFN-γ responses to mycobacterial antigen preparations derived from both DAR-901 and Mycobacterium tuberculosis. DAR-901 immunization enhanced antibody responses to DAR-901 but not Mycobacterium tuberculosis lysate or purified protein derivative. Among animals primed with BCG, boosting with DAR-901 at 1 mg provided greater protection against aerosol challenge than a homologous BCG boost (lungs P = 0.036, spleen P = 0.028). Conclusions DAR-901 induces cellular and humoral immunity and boosts protection from M. tuberculosis compared to a homologous BCG boost. PMID:27997597

  10. Detecting and connecting agricultural ditches using LiDAR data

    NASA Astrophysics Data System (ADS)

    Roelens, Jennifer; Dondeyne, Stefaan; Van Orshoven, Jos; Diels, Jan

    2017-04-01

    High-resolution hydrological data are essential for spatially-targeted water resource management decisions and future modelling efforts. For Flanders, small water courses like agricultural ditches and their connection to the river network are incomplete in the official digital atlas. High-resolution LiDAR data offer the prospect for automated detection of ditches, but there is no established method or software to do so nor to predict how these are connected to each other and the wider hydrographic network. An aerial LiDAR database encompassing at least 16 points per square meter linked with simultaneously collected digital RGB aerial images, is available for Flanders. The potential of detecting agricultural ditches and their connectivity based on point LiDAR data was investigated in a 1.9 km2 study area located in the alluvial valley of the river Demer. The area consists of agricultural parcels and woodland with a ditch network of approximately 17 km. The entire network of open ditches, and the location of culverts were mapped during a field survey to test the effectiveness of the proposed method. In the first step of the proposed method, the LiDAR point data were transformed into a raster DEM with a 1-m resolution to reduce the amount of data to be analyzed. This was done by interpolating the bare earth points using the nearest neighborhood method. In a next step, a morphological approach was used for detecting a preliminary network as traditional flow algorithms are not suitable for detecting small water courses in low-lying areas. This resulted in a preliminary classified raster image with ditch and non-ditch cells. After eliminating small details that are the result of background noise, the resulting classified raster image was vectorized to match the format of the digital watercourse network. As the vectorisation does not always adequately represent the shape of linear features, the results did not meet the high-quality cartographic needs. The spatial accuracy

  11. 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.

  12. Development of a regional LiDAR field plot strategy for Oregon and Washington

    Treesearch

    Arvind Bhuta; Leah. Rathbun

    2015-01-01

    The National Forest System (NFS) Pacific Northwest Region (R6) has been flying LiDAR on a per project basis. Additional field data was also collected in situ to many of these LiDAR projects to aid in the development of predictive models and estimate values which are unattainable through LiDAR data alone (e.g. species composition, tree volume, and downed woody material...

  13. Can we estimate precipitation rate during snowfall using a scanning terrestrial LiDAR?

    NASA Astrophysics Data System (ADS)

    LeWinter, A. L.; Bair, E. H.; Davis, R. E.; Finnegan, D. C.; Gutmann, E. D.; Dozier, J.

    2012-12-01

    Accurate snowfall measurements in windy areas have proven difficult. To examine a new approach, we have installed an automatic scanning terrestrial LiDAR at Mammoth Mountain, CA. With this LiDAR, we have demonstrated effective snow depth mapping over a small study area of several hundred m2. The LiDAR also produces dense point clouds by detecting falling and blowing hydrometeors during storms. Daily counts of airborne detections from the LiDAR show excellent agreement with automated and manual snow water equivalent measurements, suggesting that LiDAR observations have the potential to directly estimate precipitation rate. Thus, we suggest LiDAR scanners offer advantages over precipitation radars, which could lead to more accurate precipitation rate estimates. For instance, uncertainties in mass-diameter and mass-fall speed relationships used in precipitation radar, combined with low reflectivity of snow in the microwave spectrum, produce errors of up to 3X in snowfall rates measured by radar. Since snow has more backscatter in the near-infrared wavelengths used by LiDAR compared to the wavelengths used by radar, and the LiDAR detects individual hydrometeors, our approach has more potential for directly estimating precipitation rate. A key uncertainty is hydrometeor mass. At our study site, we have also installed a Multi Angle Snowflake Camera (MASC) to measure size, fallspeed, and mass of individual hydrometeors. By combining simultaneous MASC and LiDAR measurements, we can estimate precipitation density and rate.

  14. Data management based on geocoding index and adaptive visualization for airborne LiDAR

    NASA Astrophysics Data System (ADS)

    Zhi, Xiaodong

    2008-10-01

    With more surveying practice and deeper application, data post-process for airborne LiDAR system has been extracted lots of attention in data accuracy, post-process, fusion, modeling, automation and visualization. However, post-process and flexible visualization were found to be the bottle-neck which limits the LiDAR data usage for industrial applications. The cause of above bottle-neck problems is great capacity for LiDAR system. Thus in article a geocoding index based multivariate data management and adaptive visualization will be studied for based on the feature of airborne LiDAR's data to improve automatization of post-process and surveying efficiency.

  15. Weibull approximation of LiDAR waveforms for estimating the beam attenuation coefficient.

    PubMed

    Montes-Hugo, Martin A; Vuorenkoski, Anni K; Dalgleish, Fraser R; Ouyang, Bing

    2016-10-03

    Tank experiments were performed at different water turbidities to examine relationships between the beam attenuation coefficient (c) and Weibull shape parameters derived from LiDAR waveforms measured with the Fine Structure Underwater LiDAR (FSUIL). Optical inversions were made at 532 nm, within a c range of 0.045-1.52 m-1, and based on a LiDAR system having two field-of-view (15 and 75.7 mrad) and two linear polarizations. Consistently, the Weibull scale parameter or P2 showed the strongest covariation with c and was a more accurate proxy with respect to the LiDAR attenuation coefficient.

  16. LiDAR and Image Point Cloud Comparison

    DTIC Science & Technology

    2014-09-01

    2.5D 2.5-dimensional 3D 3-dimensional CC CloudCompare DEM digital elevation model DSM digital surface model ETS electronic total station GCP...The product website also indicates it can accept inputs of JPEG, PNG, and BMP. Quick Terrain Modeler (QTM) and CloudCompare (CC) are visualization...Topographic Map (Left, after “Digital Wisdom,” 2014), Nadir View (Bottom) D. COMPARISON WITH LIDAR Turning to the CloudCompare software, the LiDAR

  17. 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

  18. 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.

  19. 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.

  20. Applying the Moment Distance Framework to LiDAR Waveforms

    NASA Astrophysics Data System (ADS)

    Salas, E. L.; Aguilar-Amuchastegui, N.; Henebry, G. M.

    2010-12-01

    In the past decade or so, there have only been limited approaches formulated for the analysis of waveform LiDAR data. We illustrate how the Moment Distance (MD) framework can characterize the shape of the LiDAR waveforms using simple, computationally fast, geometric operations. We assess the relationship of the MD metrics to some key waveform landmarks - such as locations of peaks, power of returns, and pseudo-heights - using LVIS datasets acquired over a tropical forest in La Selva, Costa Rica in 1998 and 2005. We also apply the MD framework to 2003 LVIS data from Howland Forest, Maine. We also explore the effects of noise on the MD Index (MDI). Our results reveal that the MDI can capture important dynamics in canopy structure. Movement in the location of the peaks is detected by shifts in the MDI. Because this new approach responds to waveform shape, it is more sensitive to changes of location of peak returns than to the power of the return. Results also suggest a positive relationship between the MDI and the canopy pseudo-height.

  1. 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.

  2. Layer stacking: A novel algorithm for individual forest tree segmentation from LiDAR point clouds

    Treesearch

    Elias Ayrey; Shawn Fraver; John A. Kershaw; Laura S. Kenefic; Daniel Hayes; Aaron R. Weiskittel; Brian E. Roth

    2017-01-01

    As light detection and ranging (LiDAR) technology advances, it has become common for datasets to be acquired at a point density high enough to capture structural information from individual trees. To process these data, an automatic method of isolating individual trees from a LiDAR point cloud is required. Traditional methods for segmenting trees attempt to isolate...

  3. Utility of LiDAR for large area forest inventory applications

    Treesearch

    Nicholas S. Skowronski; Andrew J. Lister

    2012-01-01

    Multi-resource inventory data are used in conjunction with Light Detection and Ranging (LiDAR) data from the Pennsylvania Department of Natural Resource's PAMAP Program to assess the utility of extensive LiDAR acquisitions for large area forest assessments. Background, justification, and initial study designs are presented. The proposed study will involve three...

  4. Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys

    Treesearch

    Andrew T. Hudak; Eva K. Strand; Lee A. Vierling; John C. Byrne; Jan U. H. Eitel; Sebastian Martinuzzi; Michael J. Falkowski

    2012-01-01

    Sound forest policy and management decisions to mitigate rising atmospheric CO2 depend upon accurate methodologies to quantify forest carbon pools and fluxes over large tracts of land. LiDAR remote sensing is a rapidly evolving technology for quantifying aboveground biomass and thereby carbon pools; however, little work has evaluated the efficacy of repeat LiDAR...

  5. Nacelle LiDAR online wind field reconstruction applied to feedforward pitch control

    NASA Astrophysics Data System (ADS)

    GUILLEMIN, F.; DOMENICO, D. DI; NGUYEN, N.; SABIRON, G.; BOQUET, M.; GIRARD, N.; COUPIAC, O.

    2016-09-01

    This paper presents innovative filtering and reconstruction techniques of nacelle LiDAR data, and exploitation of obtained wind anticipation capabilities for wind turbine control strategy. The implemented algorithms are applied under industrial constraints, on a MAIA EOLIS wind turbine, equipped with a LEOSPHERE 5-beams pulsed LiDAR, during experimental campaigns of SMARTEOLE collaborative project.

  6. 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.

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

    Treesearch

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

    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...

  8. 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…

  9. Comparisons between field- and LiDAR-based measures of stand structrual complexity

    Treesearch

    Van R. Kane; Robert J. McGaughey; Jonathan D. Bakker; Rolf F. Gersonde; James A. Lutz; Jerry F. Franklin

    2010-01-01

    Forest structure, as measured by the physical arrangement of trees and their crowns, is a fundamental attribute of forest ecosystems that changes as forests progress through successional stages. We examined whether LiDAR data could be used to directly assess the successional stage of forests by determining the degree to which the LiDAR data would show the same relative...

  10. Status and prospects for LiDAR remote sensing of forested ecosystems

    Treesearch

    M. A. Wulder; N. C. Coops; A. T. Hudak; F. Morsdorf; R. Nelson; G. Newnham; M. Vastaranta

    2013-01-01

    The science associated with the use of airborne and satellite Light Detection and Ranging (LiDAR) to remotely sense forest structure has rapidly progressed over the past decade. LiDAR has evolved from being a poorly understood, potentially useful tool to an operational technology in a little over a decade, and these instruments have become a major success story in...

  11. Using LiDAR surveys to document floods: A case study of the 2008 Iowa flood

    NASA Astrophysics Data System (ADS)

    Chen, Bo; Krajewski, Witold F.; Goska, Radek; Young, Nathan

    2017-10-01

    Can we use Light Detection and Ranging (LiDAR), an emergent remote sensing technology with wide applications, to document floods with high accuracy? To explore the feasibility of this application, we propose a method to extract distributed inundation depths from a LiDAR survey conducted during flooding. This method consists of three steps: (1) collecting LiDAR data during flooding; (2) classifying the LiDAR observational points as flooded water surface points and non-flooded points, and generating a floodwater surface elevation model; and (3) subtracting the bare earth Digital Terrain Model (DTM) from the flood surface elevation model to obtain a flood depth map. We applied this method to the 2008 Iowa flood in the United States and evaluated the results using the high-water mark measurements, flood extent extracted from SPOT (Small Programmable Object Technology) imagery, and the near-simultaneously acquired aerial photography. The root mean squared error of the LiDAR-derived floodwater surface profile to high-water marks was 30 cm, the consistency between the two flooded areas derived from LiDAR and SPOT imagery was 72% (81% if suspicious isolated ponds in the SPOT-derived extent were removed), and LiDAR-derived flood extent had a horizontal resolution of ∼3 m. This work demonstrates that LiDAR technology has the potential to provide calibration and validation reference data with appreciable accuracy for improved flood inundation modeling.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  13. 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

  14. 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

    2012-10-30

    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.

  15. 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

  16. 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.

  17. 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

  18. A General-purpose Framework for Parallel Processing of Large-scale LiDAR Data

    NASA Astrophysics Data System (ADS)

    Li, Z.; Hodgson, M.; Li, W.

    2016-12-01

    Light detection and ranging (LiDAR) technologies have proven efficiency to quickly obtain very detailed Earth surface data for a large spatial extent. Such data is important for scientific discoveries such as Earth and ecological sciences and natural disasters and environmental applications. However, handling LiDAR data poses grand geoprocessing challenges due to data intensity and computational intensity. Previous studies received notable success on parallel processing of LiDAR data to these challenges. However, these studies either relied on high performance computers and specialized hardware (GPUs) or focused mostly on finding customized solutions for some specific algorithms. We developed a general-purpose scalable framework coupled with sophisticated data decomposition and parallelization strategy to efficiently handle big LiDAR data. Specifically, 1) a tile-based spatial index is proposed to manage big LiDAR data in the scalable and fault-tolerable Hadoop distributed file system, 2) two spatial decomposition techniques are developed to enable efficient parallelization of different types of LiDAR processing tasks, and 3) by coupling existing LiDAR processing tools with Hadoop, this framework is able to conduct a variety of LiDAR data processing tasks in parallel in a highly scalable distributed computing environment. The performance and scalability of the framework is evaluated with a series of experiments conducted on a real LiDAR dataset using a proof-of-concept prototype system. The results show that the proposed framework 1) is able to handle massive LiDAR data more efficiently than standalone tools; and 2) provides almost linear scalability in terms of either increased workload (data volume) or increased computing nodes with both spatial decomposition strategies. We believe that the proposed framework provides valuable references on developing a collaborative cyberinfrastructure for processing big earth science data in a highly scalable environment.

  19. Backscattering of individual LiDAR pulses from forest canopies explained by photogrammetrically derived vegetation structure

    NASA Astrophysics Data System (ADS)

    Korpela, Ilkka; Hovi, Aarne; Korhonen, Lauri

    2013-09-01

    In recent years, airborne LiDAR sensors have shown remarkable performance in the mapping of forest vegetation. This experimental study looks at LiDAR data at the scale of individual pulses to elucidate the sources behind interpulse variation in backscattering. Close-range photogrammetry was used for obtaining the canopy reference measurements at the ratio scale. The experiments illustrated different orientation techniques in the field, LiDAR acquisitions and photogrammetry in both leaf-on and leaf-off conditions, and two-waveform recording LiDAR sensors. The intrafootprint branch silhouettes in zenith-looking images, in which the camera, footprint, and LiDAR sensor were collinear, were extracted and contrasted with LiDAR backscattering. An enhanced planimetric match (refinement of strip matching) was achieved by shifting the pulses in a strip and searching for the maximal correlation between the silhouette and LiDAR intensity. The relative silhouette explained up to 80-90% of the interpulse variation. We tested whether accounting for the Gaussian spread of intrafootprint irradiance would improve the correlations, but the effect was blurred by small-scale geometric noise. Accounting for receiver gain variations in the Leica ALS60 sensor data strengthened the dependences. The size of the vegetation objects required for triggering a LiDAR observation was analyzed. We demonstrated the use of LiDAR pulses adjacent to canopy vegetation, which did not trigger a canopy echo, for canopy mapping. Pulses not triggering an echo constitute the complement to the actual canopy. We conclude that field photogrammetry is a useful tool for mapping forest canopies from below and that quantitative analysis is feasible even at the scale of single pulses for enhanced understanding of LiDAR observations from vegetation.

  20. Full waveform hyperspectral LiDAR for terrestrial laser scanning.

    PubMed

    Hakala, Teemu; Suomalainen, Juha; Kaasalainen, Sanna; Chen, Yuwei

    2012-03-26

    We present the design of a full waveform hyperspectral light detection and ranging (LiDAR) and the first demonstrations of its applications in remote sensing. The novel instrument produces a 3D point cloud with spectral backscattered reflectance data. This concept has a significant impact on remote sensing and other fields where target 3D detection and identification is crucial, such as civil engineering, cultural heritage, material processing, or geomorphological studies. As both the geometry and spectral information on the target are available from a single measurement, this technology will extend the scope of imaging spectroscopy into spectral 3D sensing. To demonstrate the potential of the instrument in the remote sensing of vegetation, 3D point clouds with backscattered reflectance and spectral indices are presented for a specimen of Norway spruce.

  1. 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.

  2. 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

  3. Development of forest growth inputs from LiDAR

    NASA Astrophysics Data System (ADS)

    Tinkham, W. T.; Falkowski, M. J.; Smith, A. M.; Hudak, A. T.; Crookston, N. L.

    2012-12-01

    Investigating the potential impacts of climate change on forest dynamics is a critical area of science, especially given the vast amount of ecosystems services forests provide. Critical understanding of these impacts is lacking, most visibly, at regional to local scales where on-the-ground management activities are implemented. To plan ahead and mitigate the impacts of climate change, land managers need of decision support tools that can be used to evaluate the future impacts of climate change on forest conditions, so that sustainable management practices that enhance ecosystem resilience can be defensibly developed, evaluated, and implemented. However, to be applicable to both regional forest planning and local operational forest management decisions, approaches must be capable of simulating forest dynamics across large spatial extents (required for regional planning) while maintaining a high-level of spatial detail (required for operational management). Numerous studies have demonstrated that LiDAR remote sensing is an effective tool for accurately measuring forest structure at landscape scales, providing information with a level of detail appropriate for operational forest management. This study attempts to develop a system to spatially parameterize and supply critical initial conditions for Climate-FVS and other forest growth models across major ecoregions (in terms of forest structure and composition) in the Pacific Northwest (PNW) of the US, via an integration of airborne LiDAR, spatial measures of productivity, and local climate. Integrating these inputs into forest growth models would be a big step towards planning and optimizing landscape level alterations to forest structure and productivity from climate change.

  4. 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.

  5. DArT Markers Effectively Target Gene Space in the Rye Genome

    PubMed Central

    Gawroński, Piotr; Pawełkowicz, Magdalena; Tofil, Katarzyna; Uszyński, Grzegorz; Sharifova, Saida; Ahluwalia, Shivaksh; Tyrka, Mirosław; Wędzony, Maria; Kilian, Andrzej; Bolibok-Brągoszewska, Hanna

    2016-01-01

    Large genome size and complexity hamper considerably the genomics research in relevant species. Rye (Secale cereale L.) has one of the largest genomes among cereal crops and repetitive sequences account for over 90% of its length. Diversity Arrays Technology is a high-throughput genotyping method, in which a preferential sampling of gene-rich regions is achieved through the use of methylation sensitive restriction enzymes. We obtained sequences of 6,177 rye DArT markers and following a redundancy analysis assembled them into 3,737 non-redundant sequences, which were then used in homology searches against five Pooideae sequence sets. In total 515 DArT sequences could be incorporated into publicly available rye genome zippers providing a starting point for the integration of DArT- and transcript-based genomics resources in rye. Using Blast2Go pipeline we attributed putative gene functions to 1101 (29.4%) of the non-redundant DArT marker sequences, including 132 sequences with putative disease resistance-related functions, which were found to be preferentially located in the 4RL and 6RL chromosomes. Comparative analysis based on the DArT sequences revealed obvious inconsistencies between two recently published high density consensus maps of rye. Furthermore we demonstrated that DArT marker sequences can be a source of SSR polymorphisms. Obtained data demonstrate that DArT markers effectively target gene space in the large, complex, and repetitive rye genome. Through the annotation of putative gene functions and the alignment of DArT sequences relative to reference genomes we obtained information, that will complement the results of the studies, where DArT genotyping was deployed, by simplifying the gene ontology and microcolinearity based identification of candidate genes. PMID:27833625

  6. 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.

  7. 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

  8. LiDAR-derived snowpack data sets from mixed conifer forests across the Western United States

    NASA Astrophysics Data System (ADS)

    Harpold, A. A.; Guo, Q.; Molotch, N.; Brooks, P. D.; Bales, R.; Fernandez-Diaz, J. C.; Musselman, K. N.; Swetnam, T. L.; Kirchner, P.; Meadows, M. W.; Flanagan, J.; Lucas, R.

    2014-03-01

    Airborne-based Light Detection and Ranging (LiDAR) offers the potential to measure snow depth and vegetation structure at high spatial resolution over large extents and thereby increase our ability to quantify snow water resources. Here we present airborne LiDAR data products at four Critical Zone Observatories (CZO) in the Western United States: Jemez River Basin, NM, Boulder Creek Watershed, CO, Kings River Experimental Watershed, CA, and Wolverton Basin, CA. We make publicly available snow depth data products (1 m2 resolution) derived from LiDAR with an estimated accuracy of <30 cm compared to limited in situ snow depth observations.

  9. 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

  10. Compression strategies for LiDAR waveform cube

    NASA Astrophysics Data System (ADS)

    Jóźków, Grzegorz; Toth, Charles; Quirk, Mihaela; Grejner-Brzezinska, Dorota

    2015-01-01

    Full-waveform LiDAR data (FWD) provide a wealth of information about the shape and materials of the surveyed areas. Unlike discrete data that retains only a few strong returns, FWD generally keeps the whole signal, at all times, regardless of the signal intensity. Hence, FWD will have an increasingly well-deserved role in mapping and beyond, in the much desired classification in the raw data format. Full-waveform systems currently perform only the recording of the waveform data at the acquisition stage; the return extraction is mostly deferred to post-processing. Although the full waveform preserves most of the details of the real data, it presents a serious practical challenge for a wide use: much larger datasets compared to those from the classical discrete return systems. Atop the need for more storage space, the acquisition speed of the FWD may also limit the pulse rate on most systems that cannot store data fast enough, and thus, reduces the perceived system performance. This work introduces a waveform cube model to compress waveforms in selected subsets of the cube, aimed at achieving decreased storage while maintaining the maximum pulse rate of FWD systems. In our experiments, the waveform cube is compressed using classical methods for 2D imagery that are further tested to assess the feasibility of the proposed solution. The spatial distribution of airborne waveform data is irregular; however, the manner of the FWD acquisition allows the organization of the waveforms in a regular 3D structure similar to familiar multi-component imagery, as those of hyper-spectral cubes or 3D volumetric tomography scans. This study presents the performance analysis of several lossy compression methods applied to the LiDAR waveform cube, including JPEG-1, JPEG-2000, and PCA-based techniques. Wide ranges of tests performed on real airborne datasets have demonstrated the benefits of the JPEG-2000 Standard where high compression rates incur fairly small data degradation. In

  11. 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.

  12. Automatic representation and reconstruction of DBM from LiDAR data using Recursive Minimum Bounding Rectangle

    NASA Astrophysics Data System (ADS)

    Kwak, Eunju; Habib, Ayman

    2014-07-01

    Three-dimensional building models are important for various applications, such as disaster management and urban planning. The development of laser scanning sensor technologies has resulted in many different approaches for efficient building model generation using LiDAR data. Despite this effort, generation of these models lacks economical and reliable techniques that fully exploit the advantage of LiDAR data. Therefore, this research aims to develop a framework for fully-automated building model generation by integrating data-driven and model-driven methods using LiDAR datasets. The building model generation starts by employing LiDAR data for building detection and approximate boundary determination. The generated building boundaries are then integrated into a model-based processing strategy because LiDAR derived planes show irregular boundaries due to the nature of LiDAR point acquisition. The focus of the research is generating models for the buildings with right-angled-corners, which can be described with a collection of rectangles under the assumption that the majority of the buildings in urban areas belong to this category. Therefore, by applying the Minimum Bounding Rectangle (MBR) algorithm recursively, the LiDAR boundaries are decomposed into sets of rectangles for further processing. At the same time, the quality of the MBRs is examined to verify that the buildings, from which the boundaries are generated, are buildings with right-angled-corners. The parameters that define the model primitives are adjusted through a model-based boundary fitting procedure using LiDAR boundaries. The level of details in the final Digital Building Model is based on the number of recursions during the MBR processing, which in turn are determined by the LiDAR point density. The model-based boundary fitting improves the quality of the generated boundaries and as seen in experimental results, the quality depends on the average LiDAR point spacing. This research thus develops an

  13. Homicide death in Dar es Salaam, Tanzania 2005.

    PubMed

    Outwater, Anne H; Campbell, Jacquelyn C; Mgaya, Edward; Abraham, Alison G; Kinabo, Linna; Kazaura, Method; Kub, Joan

    2008-12-01

    Violence disproportionately affects low- and middle-income countries. Deeper understanding is needed in areas where little research has occurred. The objectives of the study were to: (a) ascertain rate of homicide death; (b) describe the victims and circumstances surrounding their deaths in Dar es Salaam, Tanzania in 2005. This study was developed by adapting the WHO/CDC Injury Surveillance Guidelines (Holder et al., 2001). Data on 12 variables were collected on all homicide deaths. Descriptive statistics and hypothesis tests were done when appropriate. Age standardised, age-specific and cause-specific mortality rates are presented. The overall homicide rate was 12.57 (males and females respectively: 22.26 and 2.64). Homicide deaths were 93.4% male, mostly unemployed, with a mean age of 28.2 years. Most deaths occurred in urban areas. Mob violence was the cause of 57% of deaths. The risk of homicide death for males was greater than the world average, but for females it was less. Most homicides were committed by community members policing against thieves.

  14. Validation points generation for LiDAR-extracted hydrologic features

    NASA Astrophysics Data System (ADS)

    Felicen, M. M.; De La Cruz, R. M.; Olfindo, N. T.; Borlongan, N. J. B.; Ebreo, D. J. R.; Perez, A. M. C.

    2016-10-01

    This paper discusses a novel way of generating sampling points of hydrologic features, specifically streams, irrigation network and inland wetlands, that could provide a promising measure of accuracy using combinations of traditional statistical sampling methods. Traditional statistical sampling techniques such as simple random sampling, systematic sampling, stratified sampling and disproportionate random sampling were all designed to generate points in an area where all the cells are classified and subjected to actual field validation. However, these sampling techniques are not applicable when generating points along linear features. This paper presents the Weighted Disproportionate Stratified Systematic Random Sampling (WDSSRS), a tool that combines the systematic and disproportionate stratified random sampling methods in generating points for accuracy computation. This tool makes use of a map series boundary shapefile covering around 27 by 27 kilometers at a scale of 1:50000, and the LiDAR-extracted hydrologic features shapefiles (e.g. wetland polygons and linear features of stream and irrigation network). Using the map sheet shapefile, a 10 x 10 grid is generated, and grid cells with water and non-water features are tagged accordingly. Cells with water features are checked for the presence of intersecting linear features, and the intersections are given higher weights in the selection of validation points. The grid cells with non-intersecting linear features are then evaluated and the remaining points are generated randomly along these features. For grid cells with nonwater features, the sample points are generated randomly.

  15. Have I Been Here Before? A Method for Detecting Loop Closure With LiDAR

    DTIC Science & Technology

    2015-01-01

    JOHN G ROGERS III (1 HC, 1 PDF) JASON M GREGORY (1 HC, 1 PDF) STUART H YOUNG (1 PDF) 18 INTENTIONALLY LEFT BLANK. ...Have I Been Here Before? A Method for Detecting Loop Closure With LiDAR by John G Rogers III and Jason M Gregory ARL-TR-7165 January...TR-7165 January 2015 Have I Been Here Before? A Method for Detecting Loop Closure With LiDAR John G Rogers III and Jason M

  16. 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.

  17. Automated Aerial Refueling Position Estimation Using a Scanning LiDAR

    DTIC Science & Technology

    2012-03-22

    early analysis of algorithms that use LiDAR measurements [20]. Powell et al. [21] have shown LiDAR simulation with commercial ray tracing software is... tracing , where ray tracing is considered the true measurement. The speedup from using these two methods over the brute force methods is required to...as dark green arrows, are used to determine the green set of points. These points trace out the base of a cone created by all the slope lines

  18. Change Detection from differential airborne LiDAR using a weighted Anisotropic Iterative Closest Point Algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, X.; Kusari, A.; Glennie, C. L.; Oskin, M. E.; Hinojosa-Corona, A.; Borsa, A. A.; Arrowsmith, R.

    2013-12-01

    Differential LiDAR (Light Detection and Ranging) from repeated surveys has recently emerged as an effective tool to measure three-dimensional (3D) change for applications such as quantifying slip and spatially distributed warping associated with earthquake ruptures, and examining the spatial distribution of beach erosion after hurricane impact. Currently, the primary method for determining 3D change is through the use of the iterative closest point (ICP) algorithm and its variants. However, all current studies using ICP have assumed that all LiDAR points in the compared point clouds have uniform accuracy. This assumption is simplistic given that the error for each LiDAR point is variable, and dependent upon highly variable factors such as target range, angle of incidence, and aircraft trajectory accuracy. Therefore, to rigorously determine spatial change, it would be ideal to model the random error for every LiDAR observation in the differential point cloud, and use these error estimates as apriori weights in the ICP algorithm. To test this approach, we implemented a rigorous LiDAR observation error propagation method to generate estimated random error for each point in a LiDAR point cloud, and then determine 3D displacements between two point clouds using an anistropic weighted ICP algorithm. The algorithm was evaluated by qualitatively and quantitatively comparing post earthquake slip estimates from the 2010 El Mayor-Cucapah Earthquake between a uniform weight and anistropically weighted ICP algorithm, using pre-event LiDAR collected in 2006 by Instituto Nacional de Estadística y Geografía (INEGI), and post-event LiDAR collected by The National Center for Airborne Laser Mapping (NCALM).

  19. Aboveground Biomass Modeling from Field and LiDAR Data in Brazilian Amazon Tropical Rain Forest

    NASA Astrophysics Data System (ADS)

    Silva, C. A.; Hudak, A. T.; Vierling, L. A.; Keller, M. M.; Klauberg Silva, C. K.

    2015-12-01

    Tropical forests are an important component of global carbon stocks, but tropical forest responses to climate change are not sufficiently studied or understood. Among remote sensing technologies, airborne LiDAR (Light Detection and Ranging) may be best suited for quantifying tropical forest carbon stocks. Our objective was to estimate aboveground biomass (AGB) using airborne LiDAR and field plot data in Brazilian tropical rain forest. Forest attributes such as tree density, diameter at breast height, and heights were measured at a combination of square plots and linear transects (n=82) distributed across six different geographic zones in the Amazon. Using previously published allometric equations, tree AGB was computed and then summed to calculate total AGB at each sample plot. LiDAR-derived canopy structure metrics were also computed at each sample plot, and random forest regression modelling was applied to predict AGB from selected LiDAR metrics. The LiDAR-derived AGB model was assessed using the random forest explained variation, adjusted coefficient of determination (Adj. R²), root mean square error (RMSE, both absolute and relative) and BIAS (both absolute and relative). Our findings showed that the 99th percentile of height and height skewness were the best LiDAR metrics for AGB prediction. The AGB model using these two best predictors explained 59.59% of AGB variation, with an Adj. R² of 0.92, RMSE of 33.37 Mg/ha (20.28%), and bias of -0.69 (-0.42%). This study showed that LiDAR canopy structure metrics can be used to predict AGC stocks in Tropical Forest with acceptable precision and accuracy. Therefore, we conclude that there is good potential to monitor carbon sequestration in Brazilian Tropical Rain Forest using airborne LiDAR data, large field plots, and the random forest algorithm.

  20. 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.

  1. 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.

  2. Development of LiDAR aware allometrics for Abies grandis: A Case Study

    NASA Astrophysics Data System (ADS)

    Stone, G. A.; Tinkham, W. T.; Smith, A. M.; Hudak, A. T.; Falkowski, M. J.; Keefe, R.

    2012-12-01

    Forest managers rely increasingly on accurate allometric relationships to inform decisions regarding stand rotations, silvilcultural treatments, timber harvesting, and biometric modeling. At the same time, advances in remote sensing techniques like LiDAR (light detection and ranging) have brought about opportunities to advance how we assess forest growth, and thus are contributing to the need for more accurate allometries. Past studies have attempted to relate LiDAR data to both plot and individual tree measures of forest biomass. However, many of these studies have been limited by the accuracy of their coincident observations. In this study, 24 Abies grandis were measured, felled, and dissected for the explicit objective of developing LiDAR aware allometrics. The analysis predicts spatial variables of competition, growth potential (e.g, trees per acre, aspect, elevation, etc.) and common statistical distributional metrics (e.g., mean, mode, percentiles, variance, skewness, kurtosis, etc.) derived from LiDAR point cloud returns to coincident in situ measures of Abies grandis stem biomass. The resulting allometries exemplify a new approach for predicting structural attributes of interest (biomass, basal area, volume, etc.) directly from LiDAR point cloud data, precluding the measurement errors that are propogated by indirectly predicting these structure attributes of interest from LiDAR data using traditional plot-based measurements.

  3. On the impact of a refined stochastic model for airborne LiDAR measurements

    NASA Astrophysics Data System (ADS)

    Bolkas, Dimitrios; Fotopoulos, Georgia; Glennie, Craig

    2016-09-01

    Accurate topographic information is critical for a number of applications in science and engineering. In recent years, airborne light detection and ranging (LiDAR) has become a standard tool for acquiring high quality topographic information. The assessment of airborne LiDAR derived DEMs is typically based on (i) independent ground control points and (ii) forward error propagation utilizing the LiDAR geo-referencing equation. The latter approach is dependent on the stochastic model information of the LiDAR observation components. In this paper, the well-known statistical tool of variance component estimation (VCE) is implemented for a dataset in Houston, Texas, in order to refine the initial stochastic information. Simulations demonstrate the impact of stochastic-model refinement for two practical applications, namely coastal inundation mapping and surface displacement estimation. Results highlight scenarios where erroneous stochastic information is detrimental. Furthermore, the refined stochastic information provides insights on the effect of each LiDAR measurement in the airborne LiDAR error budget. The latter is important for targeting future advancements in order to improve point cloud accuracy.

  4. 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

  5. Quantifying Forest Carbon and Structure with Terrestrial LiDAR

    NASA Astrophysics Data System (ADS)

    Stovall, A. E.; Shugart, H. H., Jr.

    2014-12-01

    Current rising atmospheric CO2 concentrations are a major concern with significant global ramifications, however, of the carbon (C) fluxes that are known to occur on Earth, the terrestrial sink has the greatest amount of uncertainty. Improved monitoring of forest cover and change is required for reducing emissions from deforestation and forest degradation (REDD). We determine C storage from volume measurements with a high-precision Terrestrial Laser Scanner (TLS), substantially improving current standard ground validation techniques. This technology is utilized on several 30 m x 30 m plots in a Virginia temperate forest. Aboveground C is calculated on each of the study sites with commonly used allometric equations to offer a realistic comparison of field-based estimations to TLS-derived methods. The TLS and aerial LiDAR point cloud data are compared via the development of canopy height models at the plot scale. The novel method of point cloud voxelization is applied to our TLS data in order to produce detailed volumetric calculations in these complex forest ecosystems. Statistical output from the TLS data allows us to resolve and compare forest structure on scales from the individual plot to the entire forest landscape. The estimates produced from this research will be used to inform more widely available remote sensing datasets provided by NASA's Landsat satellites, significantly reducing the uncertainty of the terrestrial C cycle in temperate forests. Preliminary findings corroborate previous research, suggesting the potential for highly detailed monitoring of forest C storage as defined by the REDD initiative and analysis of complex ecosystem structure.

  6. Ionospheric irregularities over Bahir Dar, Ethiopia during selected geomagnetic storms

    NASA Astrophysics Data System (ADS)

    Kassa, Tsegaye; Damtie, Baylie

    2017-07-01

    We have analyzed the effect of geomagnetic storms on the occurrence of ionospheric irregularities by considering seven case studies in the period of 2013-2014 over Bahir Dar, Ethiopia (11° N , 38° E). We inferred the irregularity indices from GPS phase fluctuation by computing the median of 1-min rate of change of total electron content (fp) along the ray paths from all satellites observed. The Fp -index was calculated as an hourly average fp -index values along the ray paths from all satellites observed during each hour. Our results revealed that the irregularity level was inhibited during post sunset hours of the main phase of the storms we considered. On average, the irregularity index has dropped from 400 (0.4 TECU/min) during quiet time to 50 (0.05 TECU/min) on disturbed time with an amount of 350 (0.35 TECU/min). However, in some of the cases, immediately after the onset of the storm, we observed the enhancement of irregularities. We found that only the observations on 01 June 2013 and 19 February 2014 exhibited a correspondence of the time of occurrence of the minimum of the Dst-index with inhibition of irregularities noted by other researchers. Our observations of the enhancement of irregularities on 17 March 2013 and 19 February 2014 can partly be explained by the orientation of the IMF BZ . Other measurements such as neutral wind, electric field are required to explain the observations on 29 June 2013, 06 July 2013, 09 November 2013 and 27 February 2014.

  7. Homicide of children in Dar es Salaam, Tanzania, 2005

    PubMed Central

    Outwater, Anne; Mgaya, Edward; Campbell, Jacquelyn C.; Becker, Stan; Kinabo, Linna; Menick, Daniel Mbassa

    2014-01-01

    Background Although data are sparse, it has been estimated that the highest rates of homicide death amongst children are in Africa. Little information is available on ages 0 -< 15 years. No reliable quantitative surveillance analysis of neonaticide (killed at less than one week) has been done. Methods A Violent Death Survey following WHO/CDC Guidelines was completed in Dar es Salaam region, Tanzania (DSM) (population 2.845 million) in 2005. Qualitative and quantitative data were gathered and analyzed using mixed methods techniques. Results The overall age adjusted rate of discarded and killed children in DSM was 2.05. The rate of neonaticide was 27.7 per 100,000) while the rate of homicide incidence for children > one day was Discussion The overall estimated homicide rate for Africa of children under age 15 was 4.53 per 100,000, whereas. The estimated global rate is 1.7 per 100,000 closer to DSM‘s rate. The results in DSM show that broad age groupings such as ” <1 year” or “0–4 years” or “0 – <15 years” may mask a high incidence of neonaticide and an otherwise low incidence of murdered children. The print media provided good in-depth coverage for a few cases but it is not known if the reported cases are representative. Conclusion Eighty percent of homicides of children in DSM are neonaticides. Since it is believed that the forces behind neonaticide are fundamentally different than homicides of older children, it is suggested that data of future surveys be parsed to include neonates, until the phenomenon is more clearly understood and addressed. Further understanding of the mother and father of the deceased is needed. Continued surveillance data collection is important to expand the sample size. PMID:22066333

  8. 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.

  9. 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

  10. Elements of systemic sensitivity and propagated uncertainty in LiDAR-based forest attribute maps (Invited)

    NASA Astrophysics Data System (ADS)

    Hopkinson, C.; Chasmer, L.; Kljun, N.; van Gorsel, E.

    2013-12-01

    The application of airborne LiDAR to vegetation and forest attribute extraction and modeling is now common place. Direct estimates of tree-, plot- or stand-level height and canopy cover are frequently made as pre-cursors to more complex and indirect attribute derivations such as leaf area, biomass, basal area, fuel, even species. Frequently, the faith placed in LiDAR to produce these spatial variables appears so complete that raw data properties or the methods employed in the modeling of direct or indirect attributes are glossed over. The assumption being that if basic variables and derivatives can be easily predicted across a few studies, then it follows this will always be the case. Few studies address explicitly the range of sensitivity in direct and indirect forest attribute estimations: a) derived from LiDAR data of differing fundamental acquisition or point cloud properties; or b) produced using different data extraction, filtering or raster interpolation approaches. The paper will illustrate some of the critical acquisition and point cloud attributes (such as pulse power, flight line configuration, timing and point density) that strongly influence mapped and modeled forest attributes at a range of case study sites in North America and Australia. Further, the influence of multiple seemingly defensible canopy height model generation criteria will be compared to illustrate the high sensitivity in even the most basic of LiDAR-based forest attribute maps. We conclude that not all LiDAR are created equal and that both raw data properties and all data manipulation steps must be communicated when utilising such data. Finally, we believe that as with more standard products like LiDAR point cloud formats and digital terrain models (DTMs), an international committee is needed to provide guidance on airborne LiDAR vegetation products so that uncertainties can be mitigated when data are shared or compared across sites and through time.

  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.

  12. 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.

  13. 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

  14. Demonstration of LiDAR and Orthophotography for Wide Area Assessment at Pueblo Precision Bombing Range #2, Colorado

    DTIC Science & Technology

    2008-01-01

    for classification of LiDAR points into vegetation, ground, and “other,” creating bare earth and surface model digital terrain models ( DTM ...for rectification of the photography to the DTM . 2.1.6 Data Analysis Once processed, the LiDAR /orthophotography datasets are analyzed to...creation: Triangulation results were loaded into ortho-processing software, along with a LiDAR -derived DTM and aerial photography. Aerial photographs

  15. Optimizing variable radius plot size and LiDAR resolution to model standing volume in conifer forests

    Treesearch

    Ram Kumar Deo; Robert E. Froese; Michael J. Falkowski; Andrew T. Hudak

    2016-01-01

    The conventional approach to LiDAR-based forest inventory modeling depends on field sample data from fixed-radius plots (FRP). Because FRP sampling is cost intensive, combining variable-radius plot (VRP) sampling and LiDAR data has the potential to improve inventory efficiency. The overarching goal of this study was to evaluate the integration of LiDAR and VRP data....

  16. In situ Oceanographic LiDAR as a Tool for Retrieving and Characterizing Particle Distributions in the Ocean

    NASA Astrophysics Data System (ADS)

    Flouros, A.; Zimmerman, R. C.; Collister, B.; Hill, V. J.

    2016-02-01

    An in situ LiDAR system (iLiDAR) was deployed from a surface vessel on a cruise in the Chesapeake Bay in June 2015, and the profiles retrieved were compared with other water column optical properties measured in situ. An iLiDAR offers several advantages when compared to airborne or satellite based LiDAR. Examples include the cost effectiveness of use on a cruise, the ability to make other measurements simultaneously, increased spatial coverage, and a shorter time frame of data collection. The system attenuation values (Ksys) retrieved from the iLiDAR profiles were compared to a variety of optical properties measured on station. A linear regression modeling the relationship between diffuse attenuation (Kd) and the iLiDAR system attenuation yielded a near 1:1 relationship (m=0.9903, R2=0.8144, p<0.05). The iLiDAR can provide a reasonable estimate of diffuse attenuation within the water column, which can be used to estimate chlorophyll and primary production. The depolarization ratio of the backscattered iLiDAR signal was compared to the backscatter ratio in an attempt to better understand the distribution of particles throughout the water column. The results of this analysis were not conclusive, but the potential for the iLiDAR to detect changes in types of particles in the water column is described.

  17. 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

  18. 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

  19. Localized Segment Based Processing for Automatic Building Extraction from LiDAR Data

    NASA Astrophysics Data System (ADS)

    Parida, G.; Rajan, K. S.

    2017-05-01

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

  20. LiDAR, UAV or compass-clinometer? Accuracy, coverage and the effects on structural models

    NASA Astrophysics Data System (ADS)

    Cawood, Adam J.; Bond, Clare E.; Howell, John A.; Butler, Robert W. H.; Totake, Yukitsugu

    2017-05-01

    Light Detection and Ranging (LiDAR) and Structure from Motion (SfM) provide large amounts of digital data from which virtual outcrops can be created. The accuracy of these surface reconstructions is critical for quantitative structural analysis. Assessment of LiDAR and SfM methodologies suggest that SfM results are comparable to high data-density LiDAR on individual surfaces. The effect of chosen acquisition technique on the full outcrop and the efficacy on its virtual form for quantitative structural analysis and prediction beyond single bedding surfaces, however, is less certain. Here, we compare the accuracy of digital virtual outcrop analysis with traditional field data, for structural measurements and along-strike prediction of fold geometry from Stackpole syncline. In this case, the SfM virtual outcrop, derived from UAV imagery, yields better along-strike predictions and a more reliable geological model, in spite of lower accuracy surface reconstructions than LiDAR. This outcome is attributed to greater coverage by UAV and reliable reconstruction of a greater number of bedding planes than terrestrial LiDAR, which suffers from the effects of occlusion. Irrespective of the chosen acquisition technique, we find that workflows must incorporate careful survey planning, data processing and quality checking of derived data if virtual outcrops are to be used for robust structural analysis and along-strike prediction.

  1. Development of LiDAR measurements for the German offshore test site

    NASA Astrophysics Data System (ADS)

    Rettenmeier, A.; Kühn, M.; Wächter, M.; Rahm, S.; Mellinghoff, H.; Siegmeier, B.; Reeder, L.

    2008-05-01

    The paper introduces the content of the recently started joint research project 'Development of LiDAR measurements for the German Offshore Test Site' which has the objective to support other research projects at the German offshore test site 'alpha ventus'. The project has started before the erection of the offshore wind farm and one aim is to give recommendations concerning LiDAR technology useable for offshore measurement campaigns and data analysis. The work is organized in four work packages. The work package LiDAR technology deals with the specification, acquisition and calibration of a commercial LiDAR system for the measurement campaigns. Power curve measurements are dedicated to power curve assessment with ground-based LiDAR using standard statistical methods. Additionally, it deals with the development of new methods for the measurement of non-steady short-term power curves. Wind field research aims at the development of wake loading simulation methods of wind turbines and the exploration of loading control strategies and nacelle-based wind field measurement techniques. Finally, dissemination of results to the industry takes place in work package Technology transfer.

  2. Building Roof Boundary Extraction from LiDAR and Image Data Based on Markov Random Field

    NASA Astrophysics Data System (ADS)

    Dal Poz, A. P.; Fernandes, V. J. M.

    2017-05-01

    In this paper a method for automatic extraction of building roof boundaries is proposed, which combines LiDAR data and highresolution aerial images. The proposed method is based on three steps. In the first step aboveground objects are extracted from LiDAR data. Initially a filtering algorithm is used to process the original LiDAR data for getting ground and non-ground points. Then, a region-growing procedure and the convex hull algorithm are sequentially used to extract polylines that represent aboveground objects from the non-ground point cloud. The second step consists in extracting corresponding LiDAR-derived aboveground objects from a high-resolution aerial image. In order to avoid searching for the interest objects over the whole image, the LiDAR-derived aboveground objects' polylines are photogrammetrically projected onto the image space and rectangular bounding boxes (sub-images) that enclose projected polylines are generated. Each sub-image is processed for extracting the polyline that represents the interest aboveground object within the selected sub-image. Last step consists in identifying polylines that represent building roof boundaries. We use the Markov Random Field (MRF) model for modelling building roof characteristics and spatial configurations. Polylines that represent building roof boundaries are found by optimizing the resulting MRF energy function using the Genetic Algorithm. Experimental results are presented and discussed in this paper.

  3. Challenges in diagnosing paediatric malaria in Dar es Salaam, Tanzania

    PubMed Central

    2013-01-01

    Background Malaria is a major cause of paediatric morbidity and mortality. As no clinical features clearly differentiate malaria from other febrile illnesses, and malaria diagnosis is challenged by often lacking laboratory equipment and expertise, overdiagnosis and overtreatment is common. Methods Children admitted with fever at the general paediatric wards at Muhimbili National Hospital (MNH), Dar es Salaam, Tanzania from January to June 2009 were recruited consecutively and prospectively. Demographic and clinical features were registered. Routine thick blood smear microscopy at MNH was compared to results of subsequent thin blood smear microscopy, and rapid diagnostics tests (RDTs). Genus-specific PCR of Plasmodium mitochondrial DNA was performed on DNA extracted from whole blood and species-specific PCR was done on positive samples. Results Among 304 included children, 62.6% had received anti-malarials during the last four weeks prior to admission and 65.1% during the hospital stay. Routine thick blood smears, research blood smears, PCR and RDT detected malaria in 13.2%, 6.6%, 25.0% and 13.5%, respectively. Positive routine microscopy was confirmed in only 43% (17/40), 45% (18/40) and 53% (21/40), by research microscopy, RDTs and PCR, respectively. Eighteen percent (56/304) had positive PCR but negative research microscopy. Reported low parasitaemia on routine microscopy was associated with negative research blood slide and PCR. RDT-positive cases were associated with signs of severe malaria. Palmar pallor, low haemoglobin and low platelet count were significantly associated with positive PCR, research microscopy and RDT. Conclusions The true morbidity attributable to malaria in the study population remains uncertain due to the discrepancies in results among the diagnostic methods. The current routine microscopy appears to result in overdiagnosis of malaria and, consequently, overuse of anti-malarials. Conversely, children with a false positive malaria diagnosis

  4. The IsoDAR high intensity H2+ transport and injection tests

    NASA Astrophysics Data System (ADS)

    Alonso, J.; Axani, S.; Calabretta, L.; Campo, D.; Celona, L.; Conrad, J. M.; Day, A.; Castro, G.; Labrecque, F.; Winklehner, D.

    2015-10-01

    This technical report reviews the tests performed at the Best Cyclotron Systems, Inc. facility in regards to developing a cost effective ion source, beam line transport system, and acceleration system capable of high H2+ current output for the IsoDAR (Isotope Decay At Rest) experiment. We begin by outlining the requirements for the IsoDAR experiment then provide overviews of the Versatile Ion Source (VIS), Low Energy Beam Transport (LEBT) system, spiral inflector, and cyclotron. The experimental measurements are then discussed and the results are compared with a thorough set of simulation studies. Of particular importance we note that the VIS proved to be a reliable ion source capable of generating a large amount of H2+ current. The results suggest that with further upgrades, the VIS could potentially be a suitable candidate for IsoDAR. The conclusion outlines the key results from our tests and introduces the forthcoming work this technical report has motivated.

  5. [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.

  6. The role of terrestrial 3D LiDAR scan in bridge health monitoring

    NASA Astrophysics Data System (ADS)

    Liu, Wanqiu; Chen, Shen-En; Sajedi, Allen; Hauser, Edd

    2010-04-01

    This paper addresses the potential applications of terrestrial 3D LiDAR scanning technologies for bridge monitoring. High resolution ground-based optical-photonic images from LiDAR scans can provide detailed geometric information about a bridge. Applications of simple algorithms can retrieve damage information from the geometric point cloud data, which can be correlated to possible damage quantification including concrete mass loss due to vehicle collisions, large permanent steel deformations, and surface erosions. However, any proposed damage detection technologies should provide information that is relevant and useful to bridge managers for their decision making process. This paper summaries bridge issues that can be detected from the 3D LiDAR technologies, establishes the general approach in using 3D point clouds for damage evaluation and suggests possible bridge state ratings that can be used as supplements to existing bridge management systems (BMS).

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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).

  13. Modeling rating curves using remotely-sensed LiDAR data

    NASA Astrophysics Data System (ADS)

    Nathanson, M.; Lyon, S. W.; Kean, J. W.; Grabs, T. J.; Seibert, J.; Laudon, H.

    2010-12-01

    Discharge is important since it integrates water from across the landscape. In remote locations, however, it is often difficult to obtain accurate streamflow information because of the difficulty of obtaining the discharge measurements necessary to define stage-discharge relationships (rating curves). The aim of this study is to investigate the feasibility of defining rating curves indirectly using a fluid-mechanically based model constrained with topographic data from airborne LiDAR scanning. The study is carried out for a small 8-m wide channel in the boreal landscape of northern Sweden. Helicopter-mounted LiDAR data with an approximately 30-cm average point spacing was used to define the channel geometry above a low flow water surface along a 90-m long reach. The channel topography below the surface was estimated using the simple assumption of a flat bed. The roughness for the modeled reach was back-calculated from a single direct measurement of discharge. This topographic and roughness information was then used to calculate a rating curve using the method of Kean and Smith (JGR-Earth Surface, 2010). The rating curve from the LiDAR scan was compared with direct measurements of discharge, as well as with a calculated rating curve developed using more detailed topographic data from a ground survey. In general, there was good agreement between all three methods. The calculated rating curve based on the detailed ground survey was in the best agreement with the direct measurements. The LiDAR-based rating curve was in good agreement with the medium and high flow measurements, but deviated from the direct measurements at low flows. The discrepancy between the LiDAR-based rating curve and the low flow measurements is due to unresolved bed topography, which could not be detected by the scan because of the cover of water. This deficiency can be minimized by scanning during periods of extremely low flow. The results so far suggest that further studies using combined site

  14. Comparing LiDAR-Generated to ground- surveyed channel cross-sectional profiles in a forested mountain stream

    Treesearch

    Brian C. Dietterick; Russell White; Ryan. Hilburn

    2012-01-01

    Airborne Light Detection and Ranging (LiDAR) holds promise to provide an alternative to traditional ground-based survey methods for stream channel characterization and some change detection purposes, even under challenging landscape conditions. This study compared channel characteristics measured at 53 ground-surveyed and LiDAR-derived crosssectional profiles located...

  15. Computer-based synthetic data to assess the tree delineation algorithm from airborne LiDAR survey

    Treesearch

    Lei Wang; Andrew G. Birt; Charles W. Lafon; David M. Cairns; Robert N. Coulson; Maria D. Tchakerian; Weimin Xi; Sorin C. Popescu; James M. Guldin

    2013-01-01

    Small Footprint LiDAR (Light Detection And Ranging) has been proposed as an effective tool for measuring detailed biophysical characteristics of forests over broad spatial scales. However, by itself LiDAR yields only a sample of the true 3D structure of a forest. In order to extract useful forestry relevant information, this data must be interpreted using mathematical...

  16. Quantitative analysis of woodpecker habitat using high-resolution airborne LiDAR estimates of forest structure and composition

    Treesearch

    James E. Garabedian; Robert J. McGaughey; Stephen E. Reutebuch; Bernard R. Parresol; John C. Kilgo; Christopher E. Moorman; M. Nils. Peterson

    2014-01-01

    Light detection and ranging (LiDAR) technology has the potential to radically alter the way researchers and managers collect data on wildlife–habitat relationships. To date, the technology has fostered several novel approaches to characterizing avian habitat, but has been limited by the lack of detailed LiDAR-habitat attributes relevant to species across a continuum of...

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

    Treesearch

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

    2012-01-01

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

  18. Comparison of LiDAR-derived data and high resolution true color imagery for extracting urban forest cover

    Treesearch

    Aaron E. Maxwell; Adam C. Riley; Paul. Kinder

    2013-01-01

    Remote sensing has many applications in forestry. Light detection and ranging (LiDAR) and high resolution aerial photography have been investigated as means to extract forest data, such as biomass, timber volume, stand dynamics, and gap characteristics. LiDAR return intensity data are often overlooked as a source of input raster data for thematic map creation. We...

  19. Investigation of the Hector Mine Earthquake Surface Rupture with Airborne LiDAR data

    NASA Astrophysics Data System (ADS)

    Chen, T.; Zhang, D.; Akciz, S. O.; Hudnut, K. W.

    2011-12-01

    The 16 October 1999 Hector Mine earthquake (Mw7.1) generated significant surface rupture along the Lavic Lake Fault through almost 60 kilometers of sparsely vegetated, relatively barren desert terrain. It was the first large earthquake for which post-earthquake airborne LiDAR, collected to image the fault surface rupture, exists. Despite the lack of pre-earthquake high-resolution topographic data, we were able to make both horizontal and vertical displacement measurements, which complement published field investigation results that include ~254 data points (164 of which are within LiDAR coverage area). We made 255 new horizontal and 83 vertical displacement measurements using a 0.5 m DEM generated from the LiDAR dataset. The maximum horizontal offset value is 6.6 ± 1.1 m, and is located approximately ~700 m south of the maximum horizontal offset observed during the field work. The average horizontal offset value from LiDAR measurements is ~2.27 m, whereas the average calculated from field data is ~2.5 m. The maximum vertical displacement is ~1.2 m, and the average vertical offset value is less than 1 m. No consistent trends are apparent in the sense of the vertical component, except in the north of the mountainous section, which is predominated by east-side-down measurements. Compared to field data, LiDAR-based measurements (a) have larger measurement uncertainties, (b) have slightly higher values, (c) do not include many measurements of offsets <1 m due to the DEM resolution, and (d) are spatially denser. The field investigation produced measurements of higher quality in alluvial deposits (e.g. tire tracks, offset rock or pebble lineaments) which are not typically visible with 0.5 m resolution DEMs unless a piercing feature has a very large or clear offset. LiDAR measurements included more geomorphic features with larger measurement uncertainties, which may not have been measured in the field due to their proximity to higher quality measurements. However, along

  20. 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

  1. Differential regulation of dendritic and axonal development by the novel Krüppel-like factor Dar1

    PubMed Central

    Ye, Bing; Kim, Jung Hwan; Yang, Limin; McLachlan, Ian; Younger, Susan; Jan, Lily Yeh; Jan, Yuh Nung

    2011-01-01

    Dendrites and axons are two major neuronal compartments with differences that are critical for neuronal functions. To learn about the differential regulation of dendritic and axonal development, we conducted a genetic screen in Drosophila and isolated the dendritic arbor reduction 1 (dar1) mutants, which display defects in dendritic but not axonal growth. The dar1 gene encodes a novel transcription regulator in the Krüppel-like factor family. Neurons lacking dar1 function have severely reduced growth of microtubule- but not F-actin-based dendritic branches. In contrast, overexpression of Dar1 dramatically increased the growth of microtubule-based dendritic branches. Our results suggest that Dar1 promotes dendrite growth in part by suppressing the expression of the microtubule severing protein Spastin. Our study thus uncovers a novel transcriptional program for microtubule regulation that preferentially controls dendrite growth. PMID:21368042

  2. Line-Based Registration of Panoramic Images and LiDAR Point Clouds for Mobile Mapping

    PubMed Central

    Cui, Tingting; Ji, Shunping; Shan, Jie; Gong, Jianya; Liu, Kejian

    2016-01-01

    For multi-sensor integrated systems, such as the mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between an optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and a LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for the panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and the LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, with the latter being more reliable. PMID:28042855

  3. 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…

  4. 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…

  5. Linking rainforest ecophysiology and microclimate through fusion of airborne LiDAR and hyperspectral imagery

    Treesearch

    Eben N. Broadbent; Angélica M. Almeyda Zambrano; Gregory P. Asner; Christopher B. Field; Brad E. Rosenheim; Ty Kennedy-Bowdoin; David E. Knapp; David Burke; Christian Giardina; Susan Cordell

    2014-01-01

    We develop and validate a high-resolution three-dimensional model of light and air temperature for a tropical forest interior in Hawaii along an elevation gradient varying greatly in structure but maintaining a consistent species composition. Our microclimate models integrate high-resolution airborne waveform light detection and ranging data (LiDAR) and hyperspectral...

  6. 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)

  7. 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…

  8. Draft genome sequence of Bacillus thuringiensis strain DAR 81934, which exhibits molluscicidal activity.

    PubMed

    Wang, Aisuo; Pattemore, Julie; Ash, Gavin; Williams, Angela; Hane, James

    2013-03-21

    Bacillus thuringiensis has been widely used as a biopesticide for a long time. Its molluscicidal activity, however, is rarely realized. Here, we report the genome sequence of B. thuringiensis strain DAR 81934, a strain with molluscicidal activity against the pest snail Cernuella virgata.

  9. Use of LiDAR to Assist in Delineating Waters of the United States, Including Wetlands

    DTIC Science & Technology

    2014-03-01

    Bathymetry Technical Center of Expertise KML Keyhole Markup Language LAS Log ASCII Standard LCT Land Cover Type LiDAR Light Detection and Ranging MLS...field investigation. ERDC/CRREL TR-14-3 48 References Anderson, K., J. Bennie, E. Milton , P. Hughes, R. Lindsay, and R. Meade. 2010

  10. 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…

  11. Draft Genome Sequence of Bacillus thuringiensis Strain DAR 81934, Which Exhibits Molluscicidal Activity

    PubMed Central

    Wang, Aisuo; Pattemore, Julie; Williams, Angela; Hane, James

    2013-01-01

    Bacillus thuringiensis has been widely used as a biopesticide for a long time. Its molluscicidal activity, however, is rarely realized. Here, we report the genome sequence of B. thuringiensis strain DAR 81934, a strain with molluscicidal activity against the pest snail Cernuella virgata. PMID:23516227

  12. 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.

  13. How Children Living in Poor Areas of Dar Es Salaam, Tanzania Perceive Their Own Multiple Intelligences

    ERIC Educational Resources Information Center

    Dixon, Pauline; Humble, Steve; Chan, David W.

    2016-01-01

    This study was carried out with 1,857 poor children from 17 schools, living in low-income areas of Dar Es Salaam, Tanzania. All children took the "Student Multiple Intelligences Profile" (SMIP) questionnaire as part of a bigger project that gathered data around concepts and beliefs of talent. This paper sets out two aims, first to…

  14. 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

  15. Interventions That Increase Enrolment of Women in Higher Education: The University of Dar es Salaam, Tanzania

    ERIC Educational Resources Information Center

    Kilango, Nasero Charles; Qin, Yu Hai; Nyoni, Watende Pius; Senguo, Richard Allen

    2017-01-01

    Gender equality and equity has long been a focus area in Tanzanian government, encouraging the increased recruitment of female students in to higher education. This article investigates the effectiveness of affirmative action policy interventions that introduced and designed to increase female students' enrolment at the University of Dar es…

  16. Total canopy transmittance estimated from small-footprint, full-waveform airborne LiDAR

    NASA Astrophysics Data System (ADS)

    Milenković, Milutin; Wagner, Wolfgang; Quast, Raphael; Hollaus, Markus; Ressl, Camillo; Pfeifer, Norbert

    2017-06-01

    Canopy transmittance is a directional and wavelength-specific physical parameter that quantifies the amount of radiation attenuated when passing through a vegetation layer. The parameter has been estimated from LiDAR data in many different ways over the years. While early LiDAR methods treated each returned echo equally or weighted the echoes according to their return order, recent methods have focused more on the echo energy. In this study, we suggest a new method of estimating the total canopy transmittance considering only the energy of ground echoes. Therefore, this method does not require assumptions for the reflectance or absorption behavior of vegetation. As the oblique looking geometry of LiDAR is explicitly considered, canopy transmittance can be derived for individual laser beams and can be mapped spatially. The method was applied on a contemporary full-waveform LiDAR data set collected under leaf-off conditions and over a study site that contains two sub regions: one with a mixed (coniferous and deciduous) forest and another that is predominantly a deciduous forest in an alluvial plain. The resulting canopy transmittance map was analyzed for both sub regions and compared to aerial photos and the well-known fractional cover method. A visual comparison with aerial photos showed that even single trees and small canopy openings are visible in the canopy transmittance map. In comparison with the fractional cover method, the canopy transmittance map showed no saturation, i.e., there was better separability between patches with different vegetation structure.

  17. An Algorithm to Identify and Localize Suitable Dock Locations from 3-D LiDAR Scans

    DTIC Science & Technology

    2013-05-10

    3-D) LiDARs have proved themselves very useful on many autonomous ground vehicles, such as the Google Driverless Car Project, the DARPA, Defense...appear in a typical point cloud data set, relative to other clusters such as cars , trees, boulders, etc. In this algorithm, these values were

  18. 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.

  19. Comparison of LiDAR- and photointerpretation-based estimates of canopy cover

    Treesearch

    Demetrios Gatziolis

    2012-01-01

    An evaluation of the agreement between photointerpretation- and LiDARbased estimates of canopy cover was performed using 397 90 x 90 m reference areas in Oregon. It was determined that at low canopy cover levels LiDAR estimates tend to exceed those from photointerpretation and that this tendency reverses at high canopy cover levels. Characteristics of the airborne...

  20. Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data.

    PubMed

    Silva, Carlos A; Klauberg, Carine; Hudak, Andrew T; Vierling, Lee A; Fennema, Scott J; Corte, Ana Paula D

    2017-01-01

    Basal area (BA) is a good predictor of timber stand volume and forest growth. This study developed predictive models using field and airborne LiDAR (Light Detection and Ranging) data for estimation of basal area in Pinus taeda plantation in south Brazil. In the field, BA was collected from conventional forest inventory plots. Multiple linear regression models for predicting BA from LiDAR-derived metrics were developed and evaluated for predictive power and parsimony. The best model to predict BA from a family of six models was selected based on corrected Akaike Information Criterion (AICc) and assessed by the adjusted coefficient of determination (adj. R²) and root mean square error (RMSE). The best model revealed an adj. R²=0.93 and RMSE=7.74%. Leave one out cross-validation of the best regression model was also computed, and revealed an adj. R² and RMSE of 0.92 and 8.31%, respectively. This study showed that LiDAR-derived metrics can be used to predict BA in Pinus taeda plantations in south Brazil with high precision. We conclude that there is good potential to monitor growth in this type of plantations using airborne LiDAR. We hope that the promising results for BA modeling presented herein will stimulate to operate this technology in Brazil.

  1. Multispectral LiDAR Data for Land Cover Classification of Urban Areas.

    PubMed

    Morsy, Salem; Shaker, Ahmed; El-Rabbany, Ahmed

    2017-04-26

    Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy.

  2. Multispectral LiDAR Data for Land Cover Classification of Urban Areas

    PubMed Central

    Morsy, Salem; Shaker, Ahmed; El-Rabbany, Ahmed

    2017-01-01

    Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy. PMID:28445432

  3. How Children Living in Poor Areas of Dar Es Salaam, Tanzania Perceive Their Own Multiple Intelligences

    ERIC Educational Resources Information Center

    Dixon, Pauline; Humble, Steve; Chan, David W.

    2016-01-01

    This study was carried out with 1,857 poor children from 17 schools, living in low-income areas of Dar Es Salaam, Tanzania. All children took the "Student Multiple Intelligences Profile" (SMIP) questionnaire as part of a bigger project that gathered data around concepts and beliefs of talent. This paper sets out two aims, first to…

  4. Modeling forest biomass and growth: Coupling long-term inventory and LiDAR data

    Treesearch

    Chad Babcock; Andrew O. Finley; Bruce D. Cook; Aaron Weiskittel; Christopher W. Woodall

    2016-01-01

    Combining spatially-explicit long-term forest inventory and remotely sensed information from Light Detection and Ranging (LiDAR) datasets through statistical models can be a powerful tool for predicting and mapping above-ground biomass (AGB) at a range of geographic scales. We present and examine a novel modeling approach to improve prediction of AGB and estimate AGB...

  5. 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.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  7. 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.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  9. 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…

  10. Mapping snags and understory shrubs for LiDAR based assessment of wildlife habitat suitability

    Treesearch

    Sebastian Martinuzzi; Lee A. Vierling; William A. Gould; Michael J. Falkowski; Jeffrey S. Evans; Andrew T. Hudak; Kerri T. Vierling

    2009-01-01

    The lack of maps depicting forest three-dimensional structure, particularly as pertaining to snags and understory shrub species distribution, is a major limitation for managing wildlife habitat in forests. Developing new techniques to remotely map snags and understory shrubs is therefore an important need. To address this, we first evaluated the use of LiDAR data for...

  11. LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

    Treesearch

    Chad Babcock; Andrew O. Finley; John B. Bradford; Randy Kolka; Richard Birdsey; Michael G. Ryan

    2015-01-01

    Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both...

  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 …

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

    Treesearch

    Wade T. Tinkham; Hongyu Huang; Alistair M.S. Smith; Rupesh Shrestha; Michael J. Falkowski; Andrew T. Hudak; Timothy E. Link; Nancy F. Glenn; Danny G. Marks

    2011-01-01

    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 openly available, free to use, and are supported by published results....

  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. Into the third dimension: Benefits of incorporating LiDAR data in wildlife habitat models

    Treesearch

    Melissa J. Merrick; John L. Koprowski; Craig Wilcox

    2013-01-01

    LiDAR (Light detection and ranging) is a tool with potential for characterizing wildlife habitat by providing detailed, three-dimensional landscape information not available from other remote sensing applications. The ability to accurately map structural components such as canopy height, canopy cover, woody debris, tree density, and ground surface has potential to...

  16. 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.

  17. Prioritizing treatment of second-growth forests using LiDAR

    Treesearch

    Lathrop P. Leonard; Daryl Van Dyke

    2012-01-01

    We used multi-return light detecting and ranging (LiDAR) to develop a costeffective method for describing forest conditions and prioritizing stands for treatment in over 14,000 ha of second-growth forests (11 to 85 years old) in Del Norte Coast Redwoods State Park (DNCRSP). DNCRSP consists primarily of redwood and Douglas-fir dominated forests with scattered tanoak...

  18. Using LiDAR data to define stream flow rating curves

    NASA Astrophysics Data System (ADS)

    Nathanson, M.; Kean, J. W.; Laudon, H.; Seibert, J.; Grabs, T.; Lyon, S. W.

    2012-04-01

    In remote locations, it is difficult to obtain stream flow information because of the difficulty making sufficient discharge measurements. In this study we investigate the feasibility to constrain a fluid mechanics-based flow model for defining stream flow rating curves with remotely sensed topographic data from airborne LiDAR scanning. A near infrared (NIR) LiDAR scan was carried out for an 8-m wide channel in northern Sweden. The topographic information from this NIR LiDAR scan along the 90-m surveyed reach was used to define channel geometry above the water surface. To fill in the channel bed topography below the water surface we used a detailed ground survey to create a hybrid model for comparison to a simple assumption of a flat bottom channel. Based on the boundaries of confidence intervals calculated from the direct measurements, we show that for the channel considered the simple flat bottom assumption performs just as well as the hybrid model with regards to estimating direct discharge measurements. The mismatch between the two models was greatest at low flows and may be associated with unresolved submerged bed topography. This deficiency, while rather small, could potentially be remedied by scanning during periods of low flow, or use other techniques such as multi-frequency bathymetric LiDAR or passive optical remote sensing that offer alternative ways for generating the necessary topographic information.

  19. 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…

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

    USDA-ARS?s Scientific Manuscript database

    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...

  1. 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.

  2. Impact of a refined airborne LiDAR stochastic model for natural hazard applications

    NASA Astrophysics Data System (ADS)

    Glennie, C. L.; Bolkas, D.; Fotopoulos, G.

    2016-12-01

    Airborne Light Detection and Ranging (LiDAR) is often employed to derive multi-temporal Digital Elevation Models (DEMs), that are used to estimate vertical displacement resulting from natural hazards such as landslides, rockfalls and erosion. Vertical displacements are estimated by computing the difference between two DEMs separated by a specified time period and applying a threshold to remove the inherent noise. Thus, reliable information about the accuracy of DEMs is essential. The assessment of airborne LiDAR errors is typically based on (i) independent ground control points (ii) forward error propagation utilizing the LiDAR geo-referencing equation. The latter approach is dependent on the stochastic model information of the LiDAR measurements. Furthermore, it provides the user with point-by-point accuracy estimation. In this study, a refined stochastic model is obtained through variance component estimation (VCE) for a dataset in Houston, Texas. Results show that initial stochastic information was optimistic by 35% for both horizontal coordinates and ellipsoidal heights. To assess the impact of a refined stochastic model, surface displacement simulations are evaluated. The simulations include scenarios with topographic slopes that vary from 10º to 60º, and vertical displacement of ±1 to ±5 m. Results highlight the cases where a reliable stochastic model is important. A refined stochastic model can be used in practical applications for determining appropriate noise thresholds in vertical displacement, improve quantitative analysis, and enhance relevant decision-making.

  3. 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

  4. Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass

    Treesearch

    Timothy G. Gregoire; Erik Næsset; Ronald E. McRoberts; Göran Ståhl; Hans Andersen; Terje Gobakken; Liviu Ene; Ross Nelson

    2016-01-01

    For many decades remotely sensed data have been used as a source of auxiliary information when conducting regional or national surveys of forest resources. In the past decade, airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool for sample surveys aimed at improving estimation of aboveground forest biomass. This technology is now...

  5. 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.

  6. Interpersonal Conflicts and Styles of Managing Conflicts among Students at Bahir Dar University, Ethiopia

    ERIC Educational Resources Information Center

    Bazezew, Arega; Neka, Mulugeta

    2017-01-01

    Interpersonal conflict happens everywhere and at any time and is inherent in all societies. However, the methods of managing such conflict are quite different from one organisation to the other. The general objective of the study was to assess interpersonal conflicts and styles of managing conflicts among students at Bahir Dar University.…

  7. Examining conifer canopy structural complexity across forest ages and elevations with LiDAR data

    Treesearch

    Van R. Kane; Jonathan D. Bakker; Robert J. McGaughey; James A. Lutz; Rolf F. Gersonde; Jerry F. Franklin

    2010-01-01

    LiDAR measurements of canopy structure can be used to classify forest stands into structural stages to study spatial patterns of canopy structure, identify habitat, or plan management actions. A key assumption in this process is that differences in canopy structure based on forest age and elevation are consistent with predictions from models of stand development. Three...

  8. 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

  9. Analysis of airborne LiDAR surveys to quantify the characteristic morphologies of northern forested wetlands

    Treesearch

    Murray C. Richardson; Carl P. J. Mitchell; Brian A. Branfireun; Randall K. Kolka

    2010-01-01

    A new technique for quantifying the geomorphic form of northern forested wetlands from airborne LiDAR surveys is introduced, demonstrating the unprecedented ability to characterize the geomorphic form of northern forested wetlands using high-resolution digital topography. Two quantitative indices are presented, including the lagg width index (LWI) which objectively...

  10. Multitemporal Error Analysis of LiDAR Data for Geomorphological Feature Detection

    NASA Astrophysics Data System (ADS)

    Sailer, R.; Höfle, B.; Bollmann, E.; Vetter, M.; Stötter, J.; Pfeifer, N.; Rutzinger, M.; Geist, T.

    2009-04-01

    Since 2001 airborne LiDAR measurements have been carried out regularly at the Hintereisferner region (Ötztal, Tyrol, Austria). This results in a worldwide unique data set, which is primarily used for multitemporal glacial and periglacial analyses. Several methods and tools i) to delineate the glacier boundary, ii) to derive standard glaciological mass balance parameters (e.g. volume changes), iii) to excerpt crevasse zones or iv) to classify glacier surface features (e.g. snow, firn, glacier ice, debris covered glacier ice) have been developed as yet. Furthermore, the available multitemporal LiDAR data set offers the opportunity to identify surface changes occurring outside the glacier boundary, which have not been recognized until now. The respective areas are characterized by small variations of the surface topography from year to year. These changes of the surface topography are primarily caused by periglacial processes further initiating secondary gravitative mass movements. The present study aims at quantifying the error range of LiDAR measurements. The error analysis, which is based on (at least) 66 cross-combinations of the single LiDAR measurement campaigns, excluding areas which are obviously related to glacial surface changes, results in statistically derived error margins. Hence, surface changes which exceed these error margins have to be assigned to periglacial or gravitative process activities. The study further aims at identifying areas which are explicitly related to those periglacial and gravitative processes.

  11. 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.

  12. Dynamic displacement estimation by fusing LDV and LiDAR measurements via smoothing based Kalman filtering

    NASA Astrophysics Data System (ADS)

    Kim, Kiyoung; Sohn, Hoon

    2017-01-01

    This paper presents a smoothing based Kalman filter to estimate dynamic displacement in real-time by fusing the velocity measured from a laser Doppler vibrometer (LDV) and the displacement from a light detection and ranging (LiDAR). LiDAR can measure displacement based on the time-of-flight information or the phase-shift of the laser beam reflected off form a target surface, but it typically has a high noise level and a low sampling rate. On the other hand, LDV primarily measures out-of-plane velocity of a moving target, and displacement is estimated by numerical integration of the measured velocity. Here, the displacement estimated by LDV suffers from integration error although LDV can achieve a lower noise level and a much higher sampling rate than LiDAR. The proposed data fusion technique estimates high-precision and high-sampling rate displacement by taking advantage of both LiDAR and LDV measurements and overcomes their limitations by adopting a real-time smoothing based Kalman filter. To verify the performance of the proposed dynamic displacement estimation technique, a series of lab-scale tests are conducted under various loading conditions.

  13. 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.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  15. Line-Based Registration of Panoramic Images and LiDAR Point Clouds for Mobile Mapping.

    PubMed

    Cui, Tingting; Ji, Shunping; Shan, Jie; Gong, Jianya; Liu, Kejian

    2016-12-31

    For multi-sensor integrated systems, such as the mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between an optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and a LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for the panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and the LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, with the latter being more reliable.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  17. Shape Detection from Raw LiDAR Data with Subspace Modeling.

    PubMed

    Wang, Jun; Xu, Kevin Kai

    2016-08-31

    LiDAR scanning has become a prevalent technique for digitalizing large-scale outdoor scenes. However, the raw LiDAR data often contain imperfections, e.g., missing large regions, anisotropy of sampling density, and contamination of noise and outliers, which are the major obstacles that hinder its more ambitious and higher level applications in digital city modeling. Observing that 3D urban scenes can be locally described with several low dimensional subspaces, we propose to locally classify the neighborhoods of the scans to model the substructures of the scenes. The key enabler is the adaptive kernel-scale scoring, filtering and clustering of substructures, making it possible to recover the local structures at all points simultaneously, even in the presence of severe data imperfections. Integrating the local analyses leads to robust shape detection from raw LiDAR data. On this basis, we develop several urban scene applications and verify them on a number of LiDAR scans with various complexities and styles, which demonstrates the effectiveness and robustness of our methods.

  18. 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

    2012-08-07

    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.

  19. Automatic extraction of pavement markings on streets from point cloud data of mobile LiDAR

    NASA Astrophysics Data System (ADS)

    Gao, Yang; Zhong, Ruofei; Tang, Tao; Wang, Liuzhao; Liu, Xianlin

    2017-08-01

    Pavement markings provide an important foundation as they help to keep roads users safe. Accurate and comprehensive information about pavement markings assists the road regulators and is useful in developing driverless technology. Mobile light detection and ranging (LiDAR) systems offer new opportunities to collect and process accurate pavement markings’ information. Mobile LiDAR systems can directly obtain the three-dimensional (3D) coordinates of an object, thus defining spatial data and the intensity of (3D) objects in a fast and efficient way. The RGB attribute information of data points can be obtained based on the panoramic camera in the system. In this paper, we present a novel method process to automatically extract pavement markings using multiple attribute information of the laser scanning point cloud from the mobile LiDAR data. This method process utilizes a differential grayscale of RGB color, laser pulse reflection intensity, and the differential intensity to identify and extract pavement markings. We utilized point cloud density to remove the noise and used morphological operations to eliminate the errors. In the application, we tested our method process on different sections of roads in Beijing, China, and Buffalo, NY, USA. The results indicated that both correctness (p) and completeness (r) were higher than 90%. The method process of this research can be applied to extract pavement markings from huge point cloud data produced by mobile LiDAR.

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

    PubMed

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

    2015-02-27

    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.

  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. 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

  3. 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

  4. 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

  5. Response categories and anger measurement: do fewer categories result in poorer measurement?: development of the DAR5.

    PubMed

    Hawthorne, Graeme; Mouthaan, Joanne; Forbes, David; Novaco, Raymond W

    2006-02-01

    Anger is a key long-term outcome from trauma exposure, regardless of trauma type, and it is implicated as a moderator of response to treatment. It therefore seems important that anger is assessed in both epidemiological studies of trauma sequelae and in intervention evaluation research. This study explored the measurement properties of a recently investigated anger scale, the Dimensions of Anger Reactions (DAR) Scale. In our previous study, the DAR was found to be a measure of trait anger, but although brief, the nine response categories per item may have confused respondents, suggesting fewer response categories may work equally well. Additionally, our previous analysis suggested there were two redundant items within the DAR. Three samples of Australian veterans were used to investigate the psychometric properties associated with alterations to the response categories of the DAR; veterans who participated in the DAR validation study, those participating in group therapy programmes for post-traumatic stress disorder, and veterans participating in lifestyle programmes. Item response theory analysis was used to explore the internal properties of competing DAR models, and models were assessed against external criteria. The results showed that the number of item responses in the DAR exceeded channel capacity, and that response bias occurred in the second half of the instrument. We hypothesized that this was due to respondents not discriminating among the many response categories. Based on a modelling exercise in which we reduced the number of DAR items from 7 to 5 and the number of response categories from 9 to 5, validation tests showed that there was no loss of sensitivity, reliability or validity. To avoid confusion with the DAR, we have referred to the revised version of the DAR as the DAR5. We conclude that the DAR5, which abbreviates the original DAR to half its original length, has similar psychometric properties and is therefore to be preferred especially for

  6. 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.

  7. 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.

  8. 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.

  9. Estimating Above Ground Biomass using LiDAR in the Northcoast Redwood Forests

    NASA Astrophysics Data System (ADS)

    Rao, M.; Stewart, E.

    2010-12-01

    In recent years, LiDAR (Light Intensity Detection Amplification and Ranging) is increasingly being used in estimating biophysical parameters related to forested environments. The main goal of the project is to estimate long-term biomass accumulation and carbon sequestration potential of the redwoods ecosystem. The project objectives are aimed at providing an assessment of carbon pools within the redwood ecosystem. Specifically, we intend to develop a relational model based on LiDAR-based canopy estimates and extensive ground-based measurements available for the old-growth redwood forest located within the Prairie Creek Redwoods State Park, CA. Our preliminary analysis involved developing a geospatial database, including LiDAR data collected in 2007 for the study site, and analyzing the data using USFS Fusion software. The study area comprised of a 12-acres section of coastal redwood (Sequoia sempervirens) in the Prairie Creek Redwoods State Park, located in Orick, CA. A series of analytical steps were executed using the USFS FUSION software to produce some intermediate data such as bare earth model, canopy height model, canopy coverage model, and canopy maxima treelist. Canopy maxima tree tops were compared to ground layer to determine height of tree tops. A total of over 1000 trees were estimated, and then with thinning (to eliminate errors due to low vegetation > 3 meters tall), a total of 950 trees were delineated. Ground measurements were imported as a point based shapefile and then compared to the treetop heights created from LiDAR data to the actual ground referenced data. The results were promising as most estimated treetops were within 1-3 meters of the ground measurements and generally within 3-5m of the actual tree height. Finally, we are in the process of applying some allometric equations to estimate above ground biomass using some of the LiDAR-derived canopy metrics.

  10. 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.

  11. Characterizing Wildlife Habitat With LiDAR Data: Distribution Mapping Of Snags And Understory Shrubs

    NASA Astrophysics Data System (ADS)

    Martinuzzi, S.; Vierling, L.; Gould, W.; Falkowski, M.; Evans, J.; Hudak, A.; Vierling, K.

    2008-12-01

    Spatial data about the distribution of snags and understory shrubs is a major need for managing wildlife habitat in forests. We are evaluating the use of discrete return LiDAR data for predicting the distribution (presence/absence) of understory shrubs and different classes (i.e. diameters) of snags, in a managed, mixed-conifer forest in Northern Idaho, US. We are using a variety of ground and vegetation metrics derived from LiDAR data and the Random Forest algorithm to build our distribution models, and have obtained overall accuracies >80%. These preliminary results indicate that LiDAR data are valuable for predicting the distribution of understory shrubs and common snag diameter classes in the study area. In particular, LiDAR-derived metrics allow us to 1) quantify a variety of ecological factors (e.g. canopy structure, topography) that are known to influence the distribution and abundance of understory vegetation and snags in temperate, mountainous forests, and 2) quantify structural characteristics that are known to directly or indirectly indicate the presence of our classes of interest, such as the percent of vegetation returns in the lower strata of the canopy (for the shrubs), and the structural heterogeneity of the forest canopy (for the snags). Finally, and in order to further evaluate the use of LiDAR data for characterizing wildlife habitat, we integrate our maps of snags and shrubs distribution into models of habitat suitability, using four avian species (i.e. three woodpeckers and a flycatcher) as a case study.

  12. 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.

  13. 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

  14. Object-Based Land Use Classification using Airborne LiDAR

    NASA Astrophysics Data System (ADS)

    Antonarakis, A. S.; Richards, K. S.; Brasington, J.

    2007-12-01

    Better information on roughness of various types of vegetation is needed for use in resistance equations and eventually in flood modelling. These types include woody riparian species with different structural characteristics. Remote Sensing information such as 3D point cloud data from LiDAR can be used as a tool for extracting simple roughness information relevant for the condition of below canopy flow, as well as roughness relevant for more complex tree morphology that affects the flow when it enters the canopy levels. A strategy for extracting roughness parameters from remote sensing techniques is to use a data fusion object classification model. This means that multiple datasets such as LiDAR, digital aerial photography, ground data and satellite data can be combined to produce roughness parameters estimated for different vegetative patches, which can subsequently be mapped spatially using a classification methodology. Airborne LiDAR is used in this study in order to classify forest and ground types quickly and efficiently without the need for manipulating multispectral image files. LiDAR has the advantage of being able to create elevation surfaces that are in 3D, while also having information on LiDAR intensity values, thus it is a spatial and spectral segmentation tool. This classification method also uses point distribution frequency criteria to differentiate between land cover types. The classification of three meanders of the Garonne and Allier rivers in France has demonstrated overall classification accuracies of 95%. Five types of riparian forest were classified with accuracies between 66-98%. These forest types included planted and natural forest stands of different ages. Classifications of short vegetation and bare earth also produced high accuracies averaging above 90%.

  15. Effects of LiDAR Derived DEM Resolution on Hydrographic Feature Extraction

    NASA Astrophysics Data System (ADS)

    Yang, P.; Ames, D. P.; Glenn, N. F.; Anderson, D.

    2010-12-01

    This paper examines the effect of LiDAR-derived digital elevation model (DEM) resolution on digitally extracted stream networks with respect to known stream channel locations. Two study sites, Reynolds Creek Experimental Watershed (RCEW) and Dry Creek Experimental Watershed (DCEW), which represent terrain characteristics for lower and intermediate elevation mountainous watersheds in the Intermountain West, were selected as study areas for this research. DEMs reflecting bare earth ground were created from the LiDAR observations at a series of raster cell sizes (from 1 m to 60 m) using spatial interpolation techniques. The effect of DEM resolution on resulting hydrographic feature (specifically stream channel) derivation was studied. Stream length, watershed area, and sinuosity were explored at each of the raster cell sizes. Also, variation from known channel location as estimated by root mean square error (RMSE) between surveyed channel location and extracted channel was computed for each of the DEMs and extracted stream networks. As expected, the results indicate that the DEM based hydrographic extraction process provides more detailed hydrographic features at a finer resolution. RMSE between the known channel location and modeled locations generally increased with larger cell size DEM with a greater effect in the larger RCEW. Sensitivity analyses on sinuosity demonstrated that the resulting shape of streams obtained from LiDAR data matched best with the reference data at an intermediate cell size instead of highest resolution, which is at a range of cell size from 5 to 10 m likely due to original point spacing, terrain characteristics, and LiDAR noise influence. More importantly, the absolute sinuosity deviation displayed a smallest value at the cell size of 10 m in both experimental watersheds, which suggests that optimal cell size for LiDAR-derived DEMs used for hydrographic feature extraction is 10 m.

  16. Object-based habitat mapping using very high spatial resolution multispectral and hyperspectral imagery with LiDAR data

    NASA Astrophysics Data System (ADS)

    Onojeghuo, Alex Okiemute; Onojeghuo, Ajoke Ruth

    2017-07-01

    This study investigated the combined use of multispectral/hyperspectral imagery and LiDAR data for habitat mapping across parts of south Cumbria, North West England. The methodology adopted in this study integrated spectral information contained in pansharp QuickBird multispectral/AISA Eagle hyperspectral imagery and LiDAR-derived measures with object-based machine learning classifiers and ensemble analysis techniques. Using the LiDAR point cloud data, elevation models (such as the Digital Surface Model and Digital Terrain Model raster) and intensity features were extracted directly. The LiDAR-derived measures exploited in this study included Canopy Height Model, intensity and topographic information (i.e. mean, maximum and standard deviation). These three LiDAR measures were combined with spectral information contained in the pansharp QuickBird and Eagle MNF transformed imagery for image classification experiments. A fusion of pansharp QuickBird multispectral and Eagle MNF hyperspectral imagery with all LiDAR-derived measures generated the best classification accuracies, 89.8 and 92.6% respectively. These results were generated with the Support Vector Machine and Random Forest machine learning algorithms respectively. The ensemble analysis of all three learning machine classifiers for the pansharp QuickBird and Eagle MNF fused data outputs did not significantly increase the overall classification accuracy. Results of the study demonstrate the potential of combining either very high spatial resolution multispectral or hyperspectral imagery with LiDAR data for habitat mapping.

  17. LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter

    PubMed Central

    Liu, Wanli

    2017-01-01

    The time delay calibration between Light Detection and Ranging (LiDAR) and Inertial Measurement Units (IMUs) is an essential prerequisite for its applications. However, the correspondences between LiDAR and IMU measurements are usually unknown, and thus cannot be computed directly for the time delay calibration. In order to solve the problem of LiDAR-IMU time delay calibration, this paper presents a fusion method based on iterative closest point (ICP) and iterated sigma point Kalman filter (ISPKF), which combines the advantages of ICP and ISPKF. The ICP algorithm can precisely determine the unknown transformation between LiDAR-IMU; and the ISPKF algorithm can optimally estimate the time delay calibration parameters. First of all, the coordinate transformation from the LiDAR frame to the IMU frame is realized. Second, the measurement model and time delay error model of LiDAR and IMU are established. Third, the methodology of the ICP and ISPKF procedure is presented for LiDAR-IMU time delay calibration. Experimental results are presented that validate the proposed method and demonstrate the time delay error can be accurately calibrated. PMID:28282897

  18. LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter.

    PubMed

    Liu, Wanli

    2017-03-08

    The time delay calibration between Light Detection and Ranging (LiDAR) and Inertial Measurement Units (IMUs) is an essential prerequisite for its applications. However, the correspondences between LiDAR and IMU measurements are usually unknown, and thus cannot be computed directly for the time delay calibration. In order to solve the problem of LiDAR-IMU time delay calibration, this paper presents a fusion method based on iterative closest point (ICP) and iterated sigma point Kalman filter (ISPKF), which combines the advantages of ICP and ISPKF. The ICP algorithm can precisely determine the unknown transformation between LiDAR-IMU; and the ISPKF algorithm can optimally estimate the time delay calibration parameters. First of all, the coordinate transformation from the LiDAR frame to the IMU frame is realized. Second, the measurement model and time delay error model of LiDAR and IMU are established. Third, the methodology of the ICP and ISPKF procedure is presented for LiDAR-IMU time delay calibration. Experimental results are presented that validate the proposed method and demonstrate the time delay error can be accurately calibrated.

  19. 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.

  20. Rapid, high-resolution measurement of leaf area and leaf orientation using terrestrial LiDAR scanning data

    NASA Astrophysics Data System (ADS)

    Bailey, Brian N.; Mahaffee, Walter F.

    2017-06-01

    The rapid evolution of high performance computing technology has allowed for the development of extremely detailed models of the urban and natural environment. Although models can now represent sub-meter-scale variability in environmental geometry, model users are often unable to specify the geometry of real domains at this scale given available measurements. An emerging technology in this field has been the use of terrestrial LiDAR scanning data to rapidly measure the three-dimensional geometry of trees, such as the distribution of leaf area. However, current LiDAR methods suffer from the limitation that they require detailed knowledge of leaf orientation in order to translate projected leaf area into actual leaf area. Common methods for measuring leaf orientation are often tedious or inaccurate, which places constraints on the LiDAR measurement technique. This work presents a new method to simultaneously measure leaf orientation and leaf area within an arbitrarily defined volume using terrestrial LiDAR data. The novelty of the method lies in the direct measurement of the fraction of projected leaf area G from the LiDAR data which is required to relate projected leaf area to total leaf area, and in the new way in which radiation transfer theory is used to calculate leaf area from the LiDAR data. The method was validated by comparing LiDAR-measured leaf area to (1) ‘synthetic’ or computer-generated LiDAR data where the exact area was known, and (2) direct measurements of leaf area in the field using destructive sampling. Overall, agreement between the LiDAR and reference measurements was very good, showing a normalized root-mean-squared-error of about 15% for the synthetic tests, and 13% in the field.

  1. DArT markers: diversity analyses, genomes comparison, mapping and integration with SSR markers in Triticum monococcum

    PubMed Central

    Jing, Hai-Chun; Bayon, Carlos; Kanyuka, Kostya; Berry, Simon; Wenzl, Peter; Huttner, Eric; Kilian, Andrzej; E Hammond-Kosack, Kim

    2009-01-01

    Background Triticum monococcum (2n = 2x = 14) is an ancient diploid wheat with many useful traits and is used as a model for wheat gene discovery. DArT (Diversity Arrays Technology) employs a hybridisation-based approach to type thousands of genomic loci in parallel. DArT markers were developed for T. monococcum to assess genetic diversity, compare relationships with hexaploid genomes, and construct a genetic linkage map integrating DArT and microsatellite markers. Results A DArT array, consisting of 2304 hexaploid wheat, 1536 tetraploid wheat, 1536 T. monococcum as well as 1536 T. boeoticum representative genomic clones, was used to fingerprint 16 T. monococcum accessions of diverse geographical origins. In total, 846 polymorphic DArT markers were identified, of which 317 were of T. monococcum origin, 246 of hexaploid, 157 of tetraploid, and 126 of T. boeoticum genomes. The fingerprinting data indicated that the geographic origin of T. monococcum accessions was partially correlated with their genetic variation. DArT markers could also well distinguish the genetic differences amongst a panel of 23 hexaploid wheat and nine T. monococcum genomes. For the first time, 274 DArT markers were integrated with 82 simple sequence repeat (SSR) and two morphological trait loci in a genetic map spanning 1062.72 cM in T. monococcum. Six chromosomes were represented by single linkage groups, and chromosome 4Am was formed by three linkage groups. The DArT and SSR genetic loci tended to form independent clusters along the chromosomes. Segregation distortion was observed for one third of the DArT loci. The Ba (black awn) locus was refined to a 23.2 cM region between the DArT marker locus wPt-2584 and the microsatellite locus Xgwmd33 on 1Am; and the Hl (hairy leaf) locus to a 4.0 cM region between DArT loci 376589 and 469591 on 5Am. Conclusion DArT is a rapid and efficient approach to develop many new molecular markers for genetic studies in T. monococcum. The constructed genetic

  2. 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

  3. 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

  4. 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

  5. Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer

    NASA Astrophysics Data System (ADS)

    Sun, Jia; Shi, Shuo; Gong, Wei; Yang, Jian; Du, Lin; Song, Shalei; Chen, Biwu; Zhang, Zhenbing

    2017-01-01

    Fast and nondestructive assessment of leaf nitrogen concentration (LNC) is critical for crop growth diagnosis and nitrogen management guidance. In the last decade, multispectral LiDAR (MSL) systems have promoted developments in the earth and ecological sciences with the additional spectral information. With more wavelengths than MSL, the hyperspectral LiDAR (HSL) system provides greater possibilities for remote sensing crop physiological conditions. This study compared the performance of ASD FieldSpec Pro FR, MSL, and HSL for estimating rice (Oryza sativa) LNC. Spectral reflectance and biochemical composition were determined in rice leaves of different cultivars (Yongyou 4949 and Yangliangyou 6) throughout two growing seasons (2014–2015). Results demonstrated that HSL provided the best indicator for predicting rice LNC, yielding a coefficient of determination (R2) of 0.74 and a root mean square error of 2.80 mg/g with a support vector machine, similar to the performance of ASD (R2 = 0.73). Estimation of rice LNC could be significantly improved with the finer spectral resolution of HSL compared with MSL (R2 = 0.56).

  6. Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer.

    PubMed

    Sun, Jia; Shi, Shuo; Gong, Wei; Yang, Jian; Du, Lin; Song, Shalei; Chen, Biwu; Zhang, Zhenbing

    2017-01-16

    Fast and nondestructive assessment of leaf nitrogen concentration (LNC) is critical for crop growth diagnosis and nitrogen management guidance. In the last decade, multispectral LiDAR (MSL) systems have promoted developments in the earth and ecological sciences with the additional spectral information. With more wavelengths than MSL, the hyperspectral LiDAR (HSL) system provides greater possibilities for remote sensing crop physiological conditions. This study compared the performance of ASD FieldSpec Pro FR, MSL, and HSL for estimating rice (Oryza sativa) LNC. Spectral reflectance and biochemical composition were determined in rice leaves of different cultivars (Yongyou 4949 and Yangliangyou 6) throughout two growing seasons (2014-2015). Results demonstrated that HSL provided the best indicator for predicting rice LNC, yielding a coefficient of determination (R(2)) of 0.74 and a root mean square error of 2.80 mg/g with a support vector machine, similar to the performance of ASD (R(2) = 0.73). Estimation of rice LNC could be significantly improved with the finer spectral resolution of HSL compared with MSL (R(2) = 0.56).

  7. Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer

    PubMed Central

    Sun, Jia; Shi, Shuo; Gong, Wei; Yang, Jian; Du, Lin; Song, Shalei; Chen, Biwu; Zhang, Zhenbing

    2017-01-01

    Fast and nondestructive assessment of leaf nitrogen concentration (LNC) is critical for crop growth diagnosis and nitrogen management guidance. In the last decade, multispectral LiDAR (MSL) systems have promoted developments in the earth and ecological sciences with the additional spectral information. With more wavelengths than MSL, the hyperspectral LiDAR (HSL) system provides greater possibilities for remote sensing crop physiological conditions. This study compared the performance of ASD FieldSpec Pro FR, MSL, and HSL for estimating rice (Oryza sativa) LNC. Spectral reflectance and biochemical composition were determined in rice leaves of different cultivars (Yongyou 4949 and Yangliangyou 6) throughout two growing seasons (2014–2015). Results demonstrated that HSL provided the best indicator for predicting rice LNC, yielding a coefficient of determination (R2) of 0.74 and a root mean square error of 2.80 mg/g with a support vector machine, similar to the performance of ASD (R2 = 0.73). Estimation of rice LNC could be significantly improved with the finer spectral resolution of HSL compared with MSL (R2 = 0.56). PMID:28091610

  8. 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.

  9. 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.

  10. Automatic segmentation of road overpasses and detection of mortar efflorescence using mobile LiDAR data

    NASA Astrophysics Data System (ADS)

    González-Jorge, H.; Puente, I.; Riveiro, B.; Martínez-Sánchez, J.; Arias, P.

    2013-12-01

    This manuscript presents a novel method to automatize the efflorescence detection process in road overpasses using the geometric and radiometric informations from mobile LiDAR data. The study is performed over three main groups of algorithms. First, a data reduction algorithm based on the point cloud normalization, radial and vegetation filters is implemented. A second group of segmentation and classification algorithms uses the incidence angle derived by the LiDAR sensors to separate overpasses from pavement data. Finally, an algorithm to classify efflorescence considering its reflectivity lower than the surrounding granite is developed. The experimental results demonstrate the effectiveness of the method, using field data from the New Bridge of Ourense (Spain).

  11. 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.

  12. Combining LiDAR and IKONOS data for eco-hydrological classification of an ombrotrophic peatland.

    PubMed

    Anderson, K; Bennie, J J; Milton, E J; Hughes, P D M; Lindsay, R; Meade, R

    2010-01-01

    Remote sensing techniques have potential for peatland monitoring, but most previous work has focused on spectral approaches that often result in poor discrimination of cover types and neglect structural information. Peatlands contain structural "microtopes" (e.g., hummocks and hollows) which are linked to hydrology, biodiversity and carbon sequestration, and information on surface structure is thus a useful proxy for peatland condition. The objective of this work was to develop and test a new eco-hydrological mapping technique for ombrotrophic (rain-fed) peatlands using a combined spectral-structural remote sensing approach. The study site was Wedholme Flow, Cumbria, UK. Airborne light dectection and ranging (LiDAR) data were used with IKONOS data in a combined multispectral-structural approach for mapping peatland condition classes. LiDAR data were preprocessed so that spatial estimates of minimum and maximum land surface height, variance and semi-variance (from semi-variogram analysis) were extracted. These were assimilated alongside IKONOS data into a maximum likelihood classification procedure, and thematic outputs were compared. Ecological survey data were used to validate the results. Considerable improvements in thematic separation of peatland classes were achieved when spatially-distributed measurements of LiDAR variance or semi-variance were included. Specifically, the classification accuracy improved from 71.8% (IKONOS data only) to 88.0% when a LiDAR semi-variance product was used. Of note was the improved delineation of management classes (including Eriophorum bog, active raised bog and degraded raised bog). The application of a combined textural-optical approach can improve land cover mapping in areas where reliance on purely spectral discrimination approaches would otherwise result in considerable thematic uncertainty.

  13. 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

  14. LiDAR-based Prediction of Arthropod Abundance at the Southern Slopes of Mt. Kilimanjaro

    NASA Astrophysics Data System (ADS)

    Ziegler, Alice

    2017-04-01

    LiDAR (Light Detection And Ranging) is a remote sensing technology that offers high-resolution three-dimensional information about the covered area. These three-dimensional datasets were used in this work to derive structural parameters of the vegetation to predict the abundance of eight different arthropod assemblages with several models. For the model training of each arthropod assemblage, different versions (extent, filters) of the LiDAR datasets were provided and evaluated. Furthermore the importance of each of the LiDAR-derived structural parameters for each model was calculated. The best input dataset and structural parameters were used for the prediction of the abundance of arthropod assemblages. The analyses of the prediction results across seven different landuse types and the eight arthropod assemblages exposed, that for the arthropod assemblages, LiDAR-based predictions were in general best feasible for "Orthoptera" (average R2 (coefficient of determination) over all landuses: 0.14), even though the predictions for the other arthropod assemblages reached values of the same magnitude. It was also found that the landuse type "disturbed forest" showed the best results (average R2 over all assemblages: 0.20), whereas "home garden" was the least predictable (average R2 over all assemblages: 0.04). Differenciated by arthropod-landuse pairs, the results showed distinct differences and the R2 values diverged clearly. It was shown, that when model settings were optimized for only one arthropod taxa, values for R2 could reach values up to 0.55 ("Orthoptera" in "disturbed forest"). The analysis of the importance of each structural parameter for the prediction revealed that about one third of the 18 used parameters were always among the most important ones for the prediction of all assemblages. This strong ranking of parameters implied that focus for further research needs to be put on the selection of predictor variables.

  15. 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.

  16. 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.

  17. 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.

  18. Quantifying tropical dry forest type and succession: substantial improvement with LiDAR

    Treesearch

    Sebastian Martinuzzi; William A. Gould; Lee A. Vierling; Andrew T. Hudak; Ross F. Nelson; Jeffrey S. Evans

    2012-01-01

    Improved technologies are needed to advance our knowledge of the biophysical and human factors influencing tropical dry forests, one of the world’s most threatened ecosystems. We evaluated the use of light detection and ranging (LiDAR) data to address two major needs in remote sensing of tropical dry forests, i.e., classification of forest types and delineation of...

  19. 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.

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

    NASA Astrophysics Data System (ADS)

    Weiner, J.; Kumar, J.; Norman, S. P.

    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.

  1. 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.

  2. 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.

  3. Urban Classification Techniques Using the Fusion of LiDAR and Spectral Data

    DTIC Science & Technology

    2012-09-01

    outlines . The analysis utilized pan- sharpened multi-spectral imagery from IKONOS in conjunction with LiDAR. Their study area was a subset of an...Photogrammetry and Remote Sensing, 62, 43–63. Stein, D., Beaven, S ., Hoff, L., Winter, E., Schaum , A., & Stocker, A. (2002). Anomaly detection from...FUNDING NUMBERS 6. AUTHOR Justin E. Mesina 7. PERFORMING ORGANIZATION NAME( S ) AND ADDRESS(ES) Naval Postgraduate School Monterey, CA 93943–5000 8

  4. 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.

  5. 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.

  6. Multispectral Airborne Mapping LiDAR Observations of the McMurdo Dry Valleys

    NASA Astrophysics Data System (ADS)

    Fernandez Diaz, J. C.; Fountain, A. G.; Morin, P. J.; Singhania, A.; Hauser, D.; Obryk, M.; Shrestha, R. L.; Carter, W. E.; Sartori, M. P.

    2015-12-01

    Field observations have documented dramatic changes over the past decade in the McMurdo Dry Valleys of Antarctica: extreme river incisions, significant glacier loss, and the appearance of numerous thermokarst slumps. To date these observations have been sporadic and localized, and have not been able to capture change on a valley-wide scale. During the 2014-2015 Antarctic summer season, specifically between December 4th, 2014 and January 19th, 2015, we undertook a widescale airborne laser mapping campaign to collect a baseline digital elevation model for 3500 km2 area of the Dry Valleys and other areas of interest. The airborne LiDAR observations were acquired with a novel multi-spectral LiDAR sensor with active laser observations at three light wavelengths (532 nm, 1064 nm, and 1550 nm) simultaneously; which not only allowed the generation of a high resolution elevation model of the area, but also provides multispectral signatures for observed terrain features. In addition to the LiDAR data, high resolution (5-15 cm pixels) digital color images were collected. During the six week survey campaign of the Dry Valleys a total of 30 flights were performed, in which about 20 billion LiDAR returns and 21,000 60-Mpixels images were collected. The primary objective of this project is to perform a topographic change detection analysis by comparing the recently acquired dataset to a lower resolution dataset collected by NASA in the 2001-2002 season. This presentation will describe the processing and analysis of this significant mapping dataset and will provide some initial observations from the high resolution topography acquired.

  7. Step by step error assessment in braided river sediment budget using airborne LiDAR data

    NASA Astrophysics Data System (ADS)

    Lallias-Tacon, S.; Liébault, F.; Piégay, H.

    2014-06-01

    Sequential airborne LiDAR surveys were used to reconstruct the sediment budget of a 7-km-long braided river channel in southeastern France following a 14-year return period flood and to improve its accuracy step by step. Data processing involved (i) surface matching of the sequential point clouds, (ii) spatially distributed propagation of uncertainty based on surface conditions of the channel, and (iii) water depth subtraction from the digital elevation models based on water depths measured in the field. The respective influence of each processing step on sediment budget computation was systematically documented. This showed that surface matching and water depth subtraction both have a considerable effect on the net sediment budget. Although DEM of difference thresholding based on uncertainty analysis on absolute elevation values had a smaller effect on the sediment budget, this step is crucial for the production of a comprehensive map of channel deformations. A large independent data set of RTK-GPS checkpoints was used to control the quality of the LiDAR altimetry. The results showed that high density (7-9 points/m2) airborne LiDAR surveys can provide a very high level of detection of elevation changes on the exposed surfaces of the channel, with a 95% confidence interval level of detection between 19 and 30 cm. Change detection from LiDAR data revealed that 54% of the pre-flood active channel was reworked by the flood. The braided channel pattern was highly disturbed by the flood owing to the occurrence of several channel avulsions.

  8. 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

  9. 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.

  10. Fast Surface Reconstruction and Segmentation with Terrestrial LiDAR Range Data

    DTIC Science & Technology

    2009-05-18

    SUPPLEMENTARY NOTES 14. ABSTRACT Recent advances in range measurement devices have opened up new opportunities and challenges for fast 3D modeling of...which are composed of partially ordered terrestrial range data. Our algorithms can be applied to a large class of LiDAR data acquisition systems, where...obtained by two different terrestrial acquisition systems. The first dataset contains 94 million points obtained by a vehicle-borne acquisition system

  11. 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.

  12. LiDAR Point Cloud and Stereo Image Point Cloud Fusion

    DTIC Science & Technology

    2013-09-01

    Photogrammetry , Fusion, Accuracy 15. NUMBER OF PAGES 81 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT Unclassified 18. SECURITY CLASSIFICATION...similar to those of Photogrammetry . In Photogrammetry , the objective is to extract precision three-dimensional representations of the earth’s surface...knowledge problems. 4 The history of LiDAR and photogrammetry fusion shows that many individuals with diverse interests are studying this problem

  13. Measuring and mapping forest wildlife habitat characteristics using LiDAR remote sensing and multi-sensor function

    NASA Astrophysics Data System (ADS)

    Hyde, Peter

    Managing forests for multiple, often competing uses is challenging; managing Sierra National Forest's fire regime and California spotted owl habitat is difficult and compounded by lack of information about habitat quality. Consistent and accurate measurements of forest structure will reduce uncertainties regarding the amount of habitat reduction or alteration that spotted owls can tolerate. Current methods of measuring spotted owl habitat are mostly field-based and emphasize the important of canopy cover. However, this is more because of convenience than because canopy cover is a definitive predictor of owl presence or fecundity. Canopy cover is consistently and accurately measured in the field using a moosehorn densitometer; comparable measurements can be made using airphoto interpretation or from examining satellite imagery, but the results are not consistent. LiDAR remote sensing can produce consistent and accurate measurements of canopy cover, as well as other aspects of forest structure (such as canopy height and biomass) that are known or thought to be at least as predictive as canopy cover. Moreover, LiDAR can be used to produce maps of forest structure rather than the point samples available from field measurements. However, LiDAR data sets are expensive and not available everywhere. Combining LiDAR with other, remote sensing data sets with less expensive, wall-to-wall coverage will result in broader scale maps of forest structure than have heretofore been possible; these maps can then be used to analyze spotted owl habitat. My work consists of three parts: comparison of LiDAR estimates of forest structure with field measurements, statistical fusion of LiDAR and other remote sensing data sets to produce broad scale maps of forest structure, and analysis of California spotted owl presence and fecundity as a function of LiDAR-derived canopy structure. I found that LiDAR was able to replicate field measurements accurately. Additionally, I was able to

  14. 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.

  15. Estimation of Forest Carbon Stock using LiDAR Data in Intact Tropical Rain Forest

    NASA Astrophysics Data System (ADS)

    Kim, E.

    2014-12-01

    Forest biomass has been recognized as an important indicator of carbon sequestration. However, field measurement of individual tree biomass is time-consuming, requires intensive labor, and is rather expensive, especially for tropical rain forest. As an alternative of this issue, application of remote sensing techniques to forest carbon quantification is essential. In this study, the plot biomass was predicted using LiDAR data and field measurements. The study area is located in Kuala Belalong Field Studies Centre (KBFSC), Brunei Temburong. KBFSC covers 25 ha native tropical rain forest and is consisted of mixed Dipterocarp. This study set total 54 plots (20m X 20m) in forest dynamics plot. The scheme of this study was as follows: 1) Calculate the plot biomass using field measurements, 2) Estimate the plot biomass using LiDAR data and field measurement, 3) Verify accuracy. The derived plot average biomass of LiDAR and field measurement were 376.16 Mg ha-1 and 391.85 Mg ha-1, respectively. This study focused on estimating forest biomass and it was converted to carbon stocks using carbon fraction. The result of this study is expected to be applied in decision making climate change adaptation policies.

  16. A signal denoising method for full-waveform LiDAR data

    NASA Astrophysics Data System (ADS)

    Azadbakht, M.; Fraser, C. S.; Zhang, C.; Leach, J.

    2013-10-01

    The lack of noise reduction methods resistant to waveform distortion can hamper correct and accurate decomposition in the processing of full-waveform LiDAR data. This paper evaluates a time-domain method for smoothing and reducing the noise level in such data. The Savitzky-Golay (S-G) approach approximates and smooths data by taking advantage of fitting a polynomial of degree d, using local least-squares. As a consequence of the integration of this method with the Singular Value Decomposition (SVD) approach, and applying this filter on the singular vectors of the SVD, satisfactory denoising results can be obtained. The results of this SVD-based S-G approach have been evaluated using two different LiDAR datasets and also compared with those of other popular methods in terms of the degree of preservation of the moments of the signal and closeness to the noisy signal. The results indicate that the SVD-based S-G approach has superior performance in denoising full-waveform LiDAR data.

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

    PubMed

    Ndetto, Emmanuel L; Matzarakis, Andreas

    2017-01-01

    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.

  18. 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

    2014-09-09

    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.

  19. 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

  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. Tree Species Classification Using A Fusion of LiDAR and Hyperspectral Datasets

    NASA Astrophysics Data System (ADS)

    Xu, Z.

    2016-12-01

    The accurate mapping of tree species would be beneficial to the management of forests. Remote sensing data from multiple sources including airborne LiDAR and hyperspectral sensors are widely available and have been used for tree species classification, although with often limited results. Species mapping at the individual tree level is particularly challenging in temperate forests due to high intraspecific spectral variability, irregular canopy shapes, and multiple vegetation strata. By combining LiDAR and hyperspectral datasets, we performed an individual tree level classification of tree species found in Allerton Park in central Illinois. LiDAR analysis was used to perform individual tree crown extraction, and these crowns were fused with hyperspectral imagery to provide spectral information for each crown in the upper canopy. We used per-tree 2- and 3-D morphological features as well as the spectral information as predictors into a machine learning classifier to produce the per-tree species classification. Finally, we used field data registered to individual tree crowns to validate our results.

  2. 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.

  3. 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.

  4. 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).

  5. Phylogenetic relationships between four Salix L. species based on DArT markers.

    PubMed

    Przyborowski, Jerzy A; Sulima, Paweł; Kuszewska, Anna; Załuski, Dariusz; Kilian, Andrzej

    2013-12-11

    The objectives of this study were to evaluate the usefulness of DArT markers in genotypic identification of willow species and describe genetic relationships between four willow species: Salix viminalis, S. purpurea, S. alba and S. triandra. The experimental plant material comprised 53 willow genotypes of these four species, which are popularly grown in Poland. DArT markers seem to identify Salix species with a high degree of accuracy. As a result, the examined species were divided into four distinct groups which corresponded to the four analyzed species. In our study, we observed that S. triandra was very different genetically from the other species, including S. alba which is generally classified into the same subgenus of Salix. The above corroborates the findings of other authors who relied on molecular methods to reveal that the classification of S. triandra to the subgenus Salix was erroneous. The Principal Coordinate Analysis (PCoA) and the neighbor-joining dendrogram also confirmed the clear division of the studied willow genotypes into four clusters corresponding to individual species. This confirmed the usefulness of DArT markers in taxonomic analyses and identification of willow species.

  6. Range determination for generating point clouds from airborne small footprint LiDAR waveforms.

    PubMed

    Qin, Yuchu; Vu, Tuong Thuy; Ban, Yifang; Niu, Zheng

    2012-11-05

    This paper presents a range determination approach for generating point clouds from small footprint LiDAR waveforms. Waveform deformation over complex terrain area is simulated using convolution. Drift of the peak center position is analyzed to identify the first echo returned by the illuminated objects in the LiDAR footprint. An approximate start point of peak in the waveform is estimated and adopted as the indicator of range calculation; range correction method is proposed to correct pulse widening over complex terrain surface. The experiment was carried out on small footprint LiDAR waveform data acquired by RIEGL LMS-Q560. The results suggest that the proposed approach generates more points than standard commercial products; based on field measurements, a comparative analysis between the point clouds generated by the proposed approach and the commercial software GeocodeWF indicates that: 1). the proposed approach obtained more accurate tree heights; 2). smooth surface can be achieved with low standard deviation. In summary, the proposed approach provides a satisfactory solution for range determination in estimating 3D coordinate values of point clouds, especially for correcting range information of waveforms containing deformed peaks.

  7. A Global Corrected SRTM DEM Product Over Vegetated Areas Using LiDAR Data

    NASA Astrophysics Data System (ADS)

    Zhao, X.; Guo, Q.; Su, Y.; Hu, T.

    2016-12-01

    The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is one of the most complete and frequently used global-scale DEM products in various applications. However, previous studies have shown that the SRTM DEM is systematically higher than the actual land surface in vegetated mountain areas. The objective of this study is to propose a procedure to calibrate the SRTM DEM over global vegetated mountain areas. To address this, we firstly collected airborne LiDAR data over 200,000 km2 globally used as ground truth data to analyze the uncertainty of the SRTM DEM. The Geoscience Laser Altimeter System (GLAS)/ICESat (Ice, Cloud, and land Elevation Satellite) data were used as complementary data in areas lack of airborne LiDAR data. Secondly, we modelled the SRTM DEM error for each vegetation type using regression methods. Tree height, canopy cover, and terrain slope were used as dependent variables to model the SRTM DEM error. Finally, these regression models were used to estimate the SRTM DEM error in vegetated mountain areas without LiDAR data coverage, and therefore correct the SRTM DEM. Our results show that the new corrected SRTM DEM can significantly reduce the systematic bias of the SRTM DEM in vegetated mountain areas.

  8. Extracting cross sections and water levels of vegetated ditches from LiDAR point clouds

    NASA Astrophysics Data System (ADS)

    Roelens, Jennifer; Dondeyne, Stefaan; Van Orshoven, Jos; Diels, Jan

    2016-12-01

    The hydrologic response of a catchment is sensitive to the morphology of the drainage network. Dimensions of bigger channels are usually well known, however, geometrical data for man-made ditches is often missing as there are many and small. Aerial LiDAR data offers the possibility to extract these small geometrical features. Analysing the three-dimensional point clouds directly will maintain the highest degree of information. A longitudinal and cross-sectional buffer were used to extract the cross-sectional profile points from the LiDAR point cloud. The profile was represented by spline functions fitted through the minimum envelop of the extracted points. The cross-sectional ditch profiles were classified for the presence of water and vegetation based on the normalized difference water index and the spatial characteristics of the points along the profile. The normalized difference water index was created using the RGB and intensity data coupled to the LiDAR points. The mean vertical deviation of 0.14 m found between the extracted and reference cross sections could mainly be attributed to the occurrence of water and partly to vegetation on the banks. In contrast to the cross-sectional area, the extracted width was not influenced by the environment (coefficient of determination R2 = 0.87). Water and vegetation influenced the extracted ditch characteristics, but the proposed method is still robust and therefore facilitates input data acquisition and improves accuracy of spatially explicit hydrological models.

  9. Sociocultural factors that reduce risks of homicide in Dar es Salaam: a case control study.

    PubMed

    Kibusi, Stephen Matthew; Ohnishi, Mayumi; Outwater, Anne; Seino, Kaoruko; Kizuki, Masashi; Takano, Takehito

    2013-10-01

    This study was performed to examine the potential contributions of sociocultural activities to reduce risks of death by homicide. This study was designed as a case control study. Relatives of 90 adult homicide victims in Dar es Salaam Region, Tanzania, in 2005 were interviewed. As controls, 211 participants matched for sex and 5-year age group were randomly selected from the same region and interviewed regarding the same contents. Bivariate analysis revealed significant differences between victims and controls regarding educational status, occupation, family structure, frequent heavy drinking, hard drug use and religious attendance. Conditional logistic regression analysis indicated that the following factors were significantly related to not becoming victims of homicide: being in employment (unskilled labour: OR=0.04, skilled labour: OR=0.07, others: OR=0.04), higher educational status (OR=0.02), residence in Dar es Salaam after becoming an adult (compared with those who have resided in Dar es Salaam since birth: OR=3.95), living with another person (OR=0.07), not drinking alcohol frequently (OR=0.15) and frequent religious service attendance (OR=0.12). Frequent religious service attendance, living in the same place for a long time and living with another person were shown to be factors that contribute to preventing death by homicide, regardless of place of residence and neighbourhood environment. Existing non-structural community resources and social cohesive networks strengthen individual and community resilience against violence.

  10. Automated object detection and tracking with a flash LiDAR system

    NASA Astrophysics Data System (ADS)

    Hammer, Marcus; Hebel, Marcus; Arens, Michael

    2016-10-01

    The detection of objects, or persons, is a common task in the fields of environment surveillance, object observation or danger defense. There are several approaches for automated detection with conventional imaging sensors as well as with LiDAR sensors, but for the latter the real-time detection is hampered by the scanning character and therefore by the data distortion of most LiDAR systems. The paper presents a solution for real-time data acquisition of a flash LiDAR sensor with synchronous raw data analysis, point cloud calculation, object detection, calculation of the next best view and steering of the pan-tilt head of the sensor. As a result the attention is always focused on the object, independent of the behavior of the object. Even for highly volatile and rapid changes in the direction of motion the object is kept in the field of view. The experimental setup used in this paper is realized with an elementary person detection algorithm in medium distances (20 m to 60 m) to show the efficiency of the system for objects with a high angular speed. It is easy to replace the detection part by any other object detection algorithm and thus it is easy to track nearly any object, for example a car or a boat or an UAV in various distances.

  11. 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.

  12. Sociocultural factors that reduce risks of homicide in Dar es Salaam: a case control study

    PubMed Central

    Kibusi, Stephen Matthew; Ohnishi, Mayumi; Outwater, Anne; Seino, Kaoruko; Kizuki, Masashi; Takano, Takehito

    2013-01-01

    Objectives This study was performed to examine the potential contributions of sociocultural activities to reduce risks of death by homicide. Methods This study was designed as a case control study. Relatives of 90 adult homicide victims in Dar es Salaam Region, Tanzania, in 2005 were interviewed. As controls, 211 participants matched for sex and 5-year age group were randomly selected from the same region and interviewed regarding the same contents. Results Bivariate analysis revealed significant differences between victims and controls regarding educational status, occupation, family structure, frequent heavy drinking, hard drug use and religious attendance. Conditional logistic regression analysis indicated that the following factors were significantly related to not becoming victims of homicide: being in employment (unskilled labour: OR=0.04, skilled labour: OR=0.07, others: OR=0.04), higher educational status (OR=0.02), residence in Dar es Salaam after becoming an adult (compared with those who have resided in Dar es Salaam since birth: OR=3.95), living with another person (OR=0.07), not drinking alcohol frequently (OR=0.15) and frequent religious service attendance (OR=0.12). Conclusions Frequent religious service attendance, living in the same place for a long time and living with another person were shown to be factors that contribute to preventing death by homicide, regardless of place of residence and neighbourhood environment. Existing non-structural community resources and social cohesive networks strengthen individual and community resilience against violence. PMID:23322260

  13. 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.

  14. 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.

  15. 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

    2017-01-01

    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.

  16. The OptD-multi method in LiDAR processing

    NASA Astrophysics Data System (ADS)

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

    2017-07-01

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

  17. Spatiotemporal analysis of stream network structure based on snow-on and snow-off LiDAR

    NASA Astrophysics Data System (ADS)

    Yang, P.; Ames, D. P.; Shrestha, R.

    2011-12-01

    The spatio-temporal analysis of ephemeral and intermittent stream structure provides valuable information for streamflow or discharge estimation, which can be important for snow pack volume and vegetation biomass calculation. Airborne LiDAR data collected under snow-on and snow-off conditions from Dry Creek Experimental Watershed (DCEW), a semi-arid region in southern Idaho, USA, were used to create a vegetation and snow height model based on the intensity information from the LiDAR point cloud. Stream networks under two conditions were delineated based on LiDAR-generated DEMs from snow and vegetation filtered point clouds. Ephemeral streams and perennial streams were identified through a difference analysis between snow-on and snow-off LiDAR derivatives, together with remote sensing imagery integration. A spatiotemporal analysis of stream network structure was carried out using a newly developed open source GIS software, HydroLiDAR, designed for accessing both point cloud and interpolated raster of LiDAR data. Field surveys are being conducted for validating and assessment.

  18. [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.

  19. 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

  20. 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

  1. 3D turbulence measurements using three intersecting Doppler LiDAR beams: validation against sonic anemometry

    NASA Astrophysics Data System (ADS)

    Carbajo Fuertes, Fernando; Valerio Iungo, Giacomo; Porté-Agel, Fernando

    2013-04-01

    Nowadays communities of researchers and industry in the wind engineering and meteorology sectors demand extensive and accurate measurements of atmospheric boundary layer turbulence for a better understanding of its role in a wide range of onshore and offshore applications: wind resource evaluation, wind turbine wakes, meteorology forecast, pollution and urban climate studies, etc. Atmospheric turbulence has been traditionally investigated through sonic anemometers installed on meteorological masts. However, the setup and maintenance of instrumented masts is generally very costly and the available location for the measurements is limited by the fixed position and height of the facility. In order to overcome the above-mentioned shortcomings, a measurement technique is proposed, based on the reconstruction of the three-dimensional velocity vector from simultaneous measurements of three intersecting Doppler wind LiDARs. This measuring technique presents the main advantage of being able to measure the wind velocity at any point in space inside a very large volume, which can be set and optimized for each test. Furthermore, it is very flexible regarding its transportation, installation and operation in any type of terrain. On the other hand, LiDAR measurements are strongly affected by the aerosol concentration in the air, precipitation, and the spatial and temporal resolution is poorer than that of a sonic anemometer. All this makes the comparison between these two kinds of measurements a complex task. The accuracy of the technique has been assessed by this study against sonic anemometer measurements carried out at different heights on the KNMI's meteorological mast at Cabauw's experimental site for atmospheric research (CESAR) in the Netherlands. An early uncertainty analysis shows that one of the most important parameters to be taken into account is the relative angles between the intersecting laser beams, i.e., the position of each LiDAR on the terrain and their

  2. Surface Water Detection Using Fused Synthetic Aperture Radar, Airborne LiDAR and Optical Imagery

    NASA Astrophysics Data System (ADS)

    Braun, A.; Irwin, K.; Beaulne, D.; Fotopoulos, G.; Lougheed, S. C.

    2016-12-01

    Each remote sensing technique has its unique set of strengths and weaknesses, but by combining techniques the classification accuracy can be increased. The goal of this project is to underline the strengths and weaknesses of Synthetic Aperture Radar (SAR), LiDAR and optical imagery data and highlight the opportunities where integration of the three data types can increase the accuracy of identifying water in a principally natural landscape. The study area is located at the Queen's University Biological Station, Ontario, Canada. TerraSAR-X (TSX) data was acquired between April and July 2016, consisting of four single polarization (HH) staring spotlight mode backscatter intensity images. Grey-level thresholding is used to extract surface water bodies, before identifying and masking zones of radar shadow and layover by using LiDAR elevation models to estimate the canopy height and applying simple geometry algorithms. The airborne LiDAR survey was conducted in June 2014, resulting in a discrete return dataset with a density of 1 point/m2. Radiometric calibration to correct for range and incidence angle is applied, before classifying the points as water or land based on corrected intensity, elevation, roughness, and intensity density. Panchromatic and multispectral (4-band) imagery from Quickbird was collected in September 2005 at spatial resolutions of 0.6m and 2.5m respectively. Pixel-based classification is applied to identify and distinguish water bodies from land. A classification system which inputs SAR-, LiDAR- and optically-derived water presence models in raster formats is developed to exploit the strengths and weaknesses of each technique. The total percentage of water detected in the sample area for SAR backscatter, LiDAR intensity, and optical imagery was 27%, 19% and 18% respectively. The output matrix of the classification system indicates that in over 72% of the study area all three methods agree on the classification. Analysis was specifically targeted

  3. 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

  4. Decomposition of LiDAR waveforms by B-spline-based modeling

    NASA Astrophysics Data System (ADS)

    Shen, Xiang; Li, Qing-Quan; Wu, Guofeng; Zhu, Jiasong

    2017-06-01

    Waveform decomposition is a widely used technique for extracting echoes from full-waveform LiDAR data. Most previous studies recommended the Gaussian decomposition approach, which employs the Gaussian function in laser pulse modeling. As the Gaussian-shape assumption is not always satisfied for real LiDAR waveforms, some other probability distributions (e.g., the lognormal distribution, the generalized normal distribution, and the Burr distribution) have also been introduced by researchers to fit sharply-peaked and/or heavy-tailed pulses. However, these models cannot be universally used, because they are only suitable for processing the LiDAR waveforms in particular shapes. In this paper, we present a new waveform decomposition algorithm based on the B-spline modeling technique. LiDAR waveforms are not assumed to have a priori shapes but rather are modeled by B-splines, and the shape of a received waveform is treated as the mixture of finite transmitted pulses after translation and scaling transformation. The performance of the new model was tested using two full-waveform data sets acquired by a Riegl LMS-Q680i laser scanner and an Optech Aquarius laser bathymeter, comparing with three classical waveform decomposition approaches: the Gaussian, generalized normal, and lognormal distribution-based models. The experimental results show that the B-spline model performed the best in terms of waveform fitting accuracy, while the generalized normal model yielded the worst performance in the two test data sets. Riegl waveforms have nearly Gaussian pulse shapes and were well fitted by the Gaussian mixture model, while the B-spline-based modeling algorithm produced a slightly better result by further reducing 6.4% of fitting residuals, largely benefiting from alleviating the adverse impact of the ringing effect. The pulse shapes of Optech waveforms, on the other hand, are noticeably right-skewed. The Gaussian modeling results deviated significantly from original signals, and

  5. Glacier surface feature detection and classification from airborne LiDAR data

    NASA Astrophysics Data System (ADS)

    Höfle, B.; Sailer, R.; Vetter, M.; Rutzinger, M.; Pfeifer, N.

    2009-04-01

    In recent years airborne LiDAR evolved to the state-of-the-art technology for topographic data acquisition. Up to now mainly the derived elevation information has been used in glaciology (e.g. roughness determination, multitemporal elevation and volume changes). Few studies have already shown the potential of using LiDAR signal intensities for glacier surface differentiation, primarily based on visual interpretation of signal intensity images. This contribution brings together the spatial and radiometric information provided by airborne LiDAR, in order to make an automatic glacier surface feature detection and classification possible. The automation of the processing workflow and the standardization of the used input data become important particularly for multitemporal analysis where surface changes and feature tracking are of major interest. This study is carried out at the Hintereisferner, Ötztal Alps/Austria, where 16 airborne LiDAR acquisitions have taken place since 2001. We aim at detecting the main glacier surface classes as defined by crevasses, snow, firn, ice and debris covered ice areas. Prior to the glacier facies differentiation, an automated glacier delineation based on roughness constraints is performed. It is assumed that the glacier surface, except the crevasse zone, tends to a smoother surface than the adjacent slopes and represents one large connected spatial unit. The developed method combines raster and point cloud based processing steps in an object-based segmentation and classification procedure where elevation and calibrated signal intensity are used as complementary input. The calibration of the recorded signal intensity removes known effects originating from the atmosphere, topography and scan geometry (e.g. distance to target) and hence provides a value proportional to surface reflectance in the wavelength of the laser system. Since the Bidirectional Reflectance Distribution Function (BRDF) of the scanned surface is not known beforehand

  6. Calculation and Evaluation of the Mass Balance of Hintereisferner using Airborne LiDAR Data

    NASA Astrophysics Data System (ADS)

    Bollmann, Erik; Fischer, Andrea; Fritzmann, Patrick; Sailer, Rudolf; Stötter, Hans

    2010-05-01

    Since 2001 airborne LiDAR measurements have been carried out regularly at the Hintereisferner region (Ötztal, Tyrol, Austria). This results in a worldwide unique data set of 18 airborne LiDAR flight campaigns, which is primarily used for multitemporal glacial and periglacial surface analyses. The potential of this data set for the quantification of glacier surface elevation changes with high spatial and temporal resolution has already been shown in several studies. In this study we go beyond this stage and calculate the net mass balance of Hintereisferner by applying the geodetic method on regular raster digital elevation models (DEMs) with 1 m spatial resolution. The total geodetic net mass balance of the glacier is determined on an interannual time-scale as well as over the whole investigation period from 2001 - 2008. The accuracy of the geodetic net mass balance mainly depends on the accuracy of the input airborne LiDAR data and on density assumptions which have to be made to convert surface elevation changes to mass changes. To determine the accuracy of the LiDAR data and the derived DEMs, an accuracy assessment was computed comprising i) deviations between dGPS- and LiDAR-points, ii) errors resulting from point to raster conversion and iii) accuracy dependence of the DEMs on terrain slope angles. The calculated geodetic net mass balances of Hintereisferner are compared to results from the direct glaciological method. Mass balance calculations using the direct glaciological method already started in glaciological year 1952/53 and are continued up to the present day. Thus, a wide experience and well-founded knowledge on the application of the method at Hintereisferner was obtained and its accuracy is determined to be ± 100 mm water equivalent a-1 for the mean specific mass balance. Comparing the results of the geodetic method to direct measurements on the total net mass balance on an interannual time-scale, some stronger deviations between the two methods

  7. 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

  8. 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

  9. 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

  10. 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

  11. An application of vessel-based LiDAR to quantify coastal retreat in Southern Monterey Bay, CA

    NASA Astrophysics Data System (ADS)

    Quan, S.; Kvitek, R.; Smith, D. P.

    2009-12-01

    Coastal erosion has become a prominent issue in Monterey Bay, California. Areas at high risk include native coastal dunes, private and public beachfront properties, municipal sewage lines, and areas of the highway 1 corridor. Traditional airborne LiDAR has been an effective method in measuring coastal topography by providing high resolution and great coverage, but it remains costly. In 1997 and 1998, NASA, USGS, and NOAA collaborated to conduct pre- and post- El Niño airborne LiDAR surveys of the California coastline. Since then, there have been no further, publically available LiDAR surveys of the Monterey Bay shoreline. The goal of this project is to apply a vessel-based LiDAR system to measure coastal geomorphology, determine the efficiency of vessel-based topographic LiDAR for mapping coastal geomorphology, and quantify the spatial distribution of coastal retreat for Monterey Bay, California. The area of study was the Monterey Bay coastline from the Monterey Bay Commercial Wharf II to Marina State Beach at Reservation Rd. Sea cliff morphology data were measured on Dec 9th and 10th, 2008 through the use of a terrestrial LiDAR system mounted atop the CSUMB Seafloor Mapping Lab’s R/V VenTresca. These vessel based LiDAR data were compared with 1998 NOAA Airborne Topographic Mapper LiDAR data using mapping, modeling and spatial analysis tools in ArcGIS to quantify the spatial distribution of coastal retreat and calculate annualized rates of erosion for the Monterey Bay shoreline over the past decade. Preliminary results show a slight correlation between volumetric change and distance along the coast from Wharf II, in keeping with previous published results. On the other hand, average sand dune apron retreat rate is 0.92 m/yr with a significant relationship between sand dune apron retreat rate and distance along the coast. The utilization of vessel based LiDAR is an effective and cost efficient method to frequently measure sea cliff geomorphology with very high

  12. The Hintereisferner - eight years of experience in method development for glacier monitoring with airborne LiDAR

    NASA Astrophysics Data System (ADS)

    Vetter, M.; Höfle, B.; Pfeifer, N.; Rutzinger, M.; Sailer, R.; Stötter, J.; Geist, T.

    2009-04-01

    Topographic data acquisition with LiDAR technology, airborne or terrestrial, has become the state-of-the-art procedure for Earth surface surveying. For glacier monitoring different remote sensing technologies are used for many years. With the advent of airborne LiDAR a paradigm shift in glacier monitoring has taken place. Eight years ago pioneer work within glacier surface surveying and monitoring has been carried out at the Institute of Geography (Innsbruck) within the OMEGA (Development of Operational Monitoring System for European Glacial Areas) project by using airborne LiDAR. Since 2001, 16 single airborne LiDAR campaigns have been carried out by collecting data of Hintereisferner, Kesselwandferner and adjacent small glaciers as well as their surrounding areas (Ötztal Alps, Tyrol, Austria). We present the main results of this period of glacier monitoring based on LiDAR data. One major task was to set up a geo-database system to manage the huge amount of LiDAR data, offering the opportunity to compute various point features and different rasterized data sets using this LiDAR data management and analysis system. In this context some basic routines were developed (e.g. a tool for intensity calibration, for derive intensity and point density images, and for modeling the locations of laser shot dropouts). In addition, tools for the analysis of the glacier surface have been developed: (a) a glacier delineation tool, using intensity and roughness information, (b) tools to compute and visualize the volume and elevation changes using multitemporal data, (c) a tool to calculate the ice flow velocity at the glacier surface, (d) a classification tool to detect crevasses, snow, firn, ice and debris covered ice areas, using calibrated intensity data, roughness information and modeled laser shot dropouts. For the analysis of glacial geomorphologic processes (i) a routine for the delineation of moraine ridges and rock glaciers works on the basis of break lines, (ii) a

  13. 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.

  14. 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.

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

    NASA Technical Reports Server (NTRS)

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

    2016-01-01

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

  16. Demonstration of LiDAR and Orthophotography for Wide Area Assessment at Pueblo Precision Bombing Range #2 Colorado and Borrego Military Wash Area, California

    DTIC Science & Technology

    2008-12-01

    software for rectification of the photography to the DTM . 2.2.3 Data Analysis Once processed, the LiDAR /orthophotography data sets are analyzed to...ESTCP Cost and Performance Report ENVIRONMENTAL SECURITY TECHNOLOGY CERTIFICATION PROGRAM U.S. Department of Defense (MM-0535) Demonstration of LiDAR ...DEC 2008 2. REPORT TYPE 3. DATES COVERED 00-00-2008 to 00-00-2008 4. TITLE AND SUBTITLE Demonstration of LiDAR and Orthophotography for Wide

  17. 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

  18. 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).

  19. Regional forest biomass estimation using ICESat/GLAS spaceborne LiDAR

    NASA Astrophysics Data System (ADS)

    Hayashi, M.; Saigusa, N.; Habura, B.; Sawada, Y.; Yamagata, Y.; Hirano, T.; Ichii, K.

    2015-12-01

    Spaceborne LiDAR can observe vertical structure of forests and provide a means for accurate forest monitoring, therefore, it may meet the growing demand of forest resources monitoring on a large scale. This study aims to clarify the potential of ICESat/GLAS, which had been the only spaceborne LiDAR up to now, for forest resources monitoring on a regional scale. The study areas were three regions: Hokkaido Island in Japan (cool-temperate forest), Borneo Island (tropical forest) and Siberia (boreal forest). Firstly, we conducted field measurements at 106 points in Hokkaido and 37 points in Borneo to measure the average canopy height (Lorey's height) and the above-ground biomass (AGB) for each GLAS-footprint, then, we developed some models to estimate canopy height and AGB from the GLAS waveform parameters. Next, we applied the developed models to the GLAS data which were 14,000 points in Hokkaido, and 130,000 points in Borneo, to estimate canopy height and AGB on a regional scale. As a result, we clarified the forest condition concerning canopy height and AGB for each region, namely, the average value, the comparison between the average of each forest type, and the spatial distribution. Furthermore, we detected the AGB change over the years (forest degradation) and estimated the forest loss rate of 1.6% yr-1 in Borneo. Next, we applied the developed models in Hokkaido to the 1,600,000 points GLAS data observed in Siberia. As a result, we clarified that the average AGB in Siberia was a remarkable low value as compared with those in Hokkaido and Borneo, and that the AGB change over the years (forest degradation) was significant in the southern region of western Siberia. This study showed that spaceborne LiDAR had an ability of forest resources monitoring on a regional scale for various forests over the world.

  20. 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

  1. 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.

  2. Fusing Hyperspectral and LiDAR data from CAO-VSWIR for Increased Data Dimensionality

    NASA Astrophysics Data System (ADS)

    Knapp, D. E.; Asner, G. P.; Boardman, J. W.; Kennedy-Bowdoin, T.; Eastwood, M.; Anderson, C.; Martin, R. E.; Green, R. O.

    2012-12-01

    The use of multi-sensor platforms for scientific data collection requires precise co-location in order to gain maximum data dimensionality for Earth system research. The different types of collection mechanisms of the sensors (e.g., scanning and pushbroom) can make it difficult to precisely match data from multiple sensors, even when the sensors are flown on the same aircraft at the same time. To overcome these problems, the Carnegie Airborne Observatory (CAO) AToMS sensor suite uses a method that maximizes the match between the Light Detection and Ranging (LiDAR), Visible-to-Near Infrared (VNIR), and Visible-to-Shortwave Infrared (VSWIR) sensors. This is done by generating an intensity image from the LiDAR data that serves as a base on which the spectrometers (VNIR and VSWIR) are matched using ground control points (GCPs). To do so, we employ the use of automated tie point matching in the overlap regions of the spectrometers to improve the co-location between flightlines. The combination of the GCPs and tie points produce data that is used to build camera models for the VNIR and VSWIR spectrometers such that they will match the LiDAR data. The result produces a matched hyper-dimensional data set with great scientific information content. We compare the data dimensionality of two contrasting scenes - a built environment at Stanford University and a lowland tropical forest in Amazonia. Principal components analysis revealed 336 dimensions (degrees of freedom) in the Stanford case, and 218 dimensions in the Amazon. The Amazon case presents what could be the highest level of remotely sensed data dimensionality ever reported for a forested ecosystem. Simulated misalignment of data streams reduced the effective information content by up to 48%, highlighting the critical role of achieving high precision when undertaking multi-sensor fusion. The instrumentation and methods described here are a pathfinder for future airborne applications undertaken by the National

  3. UAV-LiDAR accuracy and comparison to Structure from Motion photogrammetry

    NASA Astrophysics Data System (ADS)

    Kucharczyk, M.; Hugenholtz, C.; Zou, X.; Nesbit, P. R.; Barchyn, T.

    2016-12-01

    We compare the spatial accuracy of a UAV-LiDAR system with Structure from Motion (SfM) photogrammetry. UAV-based LiDAR remote sensing potentially offers advantages over SfM photogrammetry in vegetated terrain, particularly with respect to canopy penetration and related measurements of ground surface elevation and vegetation height; however, little quantitative evidence has been presented to date. To address this, we performed a case study at a field site in Alberta, Canada with six different land cover types: short grass, tall grass, short shrubs, tall shrubs, deciduous trees, and coniferous trees. Both UAV datasets were acquired on the same day. The SfM dataset was derived from images acquired by a senseFly eBee fixed-wing UAV equipped with a 16.1 megapixel RGB camera. The UAV-LiDAR system is a proprietary design that consists of a single-rotor helicopter (2-m rotor diameter) equipped with a Riegl VUX-1UAV laser scanner, KVH 1750 inertial measurement unit, and dual NovAtel GNSS receivers. We measured vegetation height from at least 30 samples in each land cover type and acquired check point measurements to determine horizontal and vertical accuracy. Vegetation height was measured manually for grasses and shrubs with a level staff, and with a total station for trees. Coordinates of horizontal and vertical check points were surveyed with real-time kinematic (RTK) GNSS. We followed standard methods for computing horizontal and vertical accuracies based on the 2015 guidelines from the American Society of Photogrammetry and Remote Sensing. Results will be presented at the AGU Fall Meeting.

  4. Modeling of a sensitive time-of-flight flash LiDAR system

    NASA Astrophysics Data System (ADS)

    Fathipour, V.; Wheaton, S.; Johnson, W. E.; Mohseni, H.

    2016-09-01

    used for monitoring and profiling structures, range, velocity, vibration, and air turbulence. Remote sensing in the IR region has several advantages over the visible region, including higher transmitter energy while maintaining eye-safety requirements. Electron-injection detectors are a new class of detectors with high internal avalanche-free amplification together with an excess-noise-factor of unity. They have a cutoff wavelength of 1700 nm. Furthermore, they have an extremely low jitter. The detector operates in linear-mode and requires only bias voltage of a few volts. This together with the feedback stabilized gain mechanism, makes formation of large-format high pixel density electron-injection FPAs less challenging compared to other detector technologies such as avalanche photodetectors. These characteristics make electron-injection detectors an ideal choice for flash LiDAR application with mm scale resolution at longer ranges. Based on our experimentally measured device characteristics, a detailed theoretical LiDAR model was developed. In this model we compare the performance of the electron-injection detector with commercially available linear-mode InGaAs APD from (Hamamatsu G8931-20) as well as a p-i-n diode (Hamamatsu 11193 p-i-n). Flash LiDAR images obtained by our model, show the electron-injection detector array (of 100 x 100 element) achieves better resolution with higher signal-to-noise compared with both the InGaAs APD and the p-i-n array (of 100 x 100 element).

  5. 3D Scene Reconstruction Using Omnidirectional Vision and LiDAR: A Hybrid Approach

    PubMed Central

    Vlaminck, Michiel; Luong, Hiep; Goeman, Werner; Philips, Wilfried

    2016-01-01

    In this paper, we propose a novel approach to obtain accurate 3D reconstructions of large-scale environments by means of a mobile acquisition platform. The system incorporates a Velodyne LiDAR scanner, as well as a Point Grey Ladybug panoramic camera system. It was designed with genericity in mind, and hence, it does not make any assumption about the scene or about the sensor set-up. The main novelty of this work is that the proposed LiDAR mapping approach deals explicitly with the inhomogeneous density of point clouds produced by LiDAR scanners. To this end, we keep track of a global 3D map of the environment, which is continuously improved and refined by means of a surface reconstruction technique. Moreover, we perform surface analysis on consecutive generated point clouds in order to assure a perfect alignment with the global 3D map. In order to cope with drift, the system incorporates loop closure by determining the pose error and propagating it back in the pose graph. Our algorithm was exhaustively tested on data captured at a conference building, a university campus and an industrial site of a chemical company. Experiments demonstrate that it is capable of generating highly accurate 3D maps in very challenging environments. We can state that the average distance of corresponding point pairs between the ground truth and estimated point cloud approximates one centimeter for an area covering approximately 4000 m2. To prove the genericity of the system, it was tested on the well-known Kitti vision benchmark. The results show that our approach competes with state of the art methods without making any additional assumptions. PMID:27854315

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 3D Scene Reconstruction Using Omnidirectional Vision and LiDAR: A Hybrid Approach.

    PubMed

    Vlaminck, Michiel; Luong, Hiep; Goeman, Werner; Philips, Wilfried

    2016-11-16

    In this paper, we propose a novel approach to obtain accurate 3D reconstructions of large-scale environments by means of a mobile acquisition platform. The system incorporates a Velodyne LiDAR scanner, as well as a Point Grey Ladybug panoramic camera system. It was designed with genericity in mind, and hence, it does not make any assumption about the scene or about the sensor set-up. The main novelty of this work is that the proposed LiDAR mapping approach deals explicitly with the inhomogeneous density of point clouds produced by LiDAR scanners. To this end, we keep track of a global 3D map of the environment, which is continuously improved and refined by means of a surface reconstruction technique. Moreover, we perform surface analysis on consecutive generated point clouds in order to assure a perfect alignment with the global 3D map. In order to cope with drift, the system incorporates loop closure by determining the pose error and propagating it back in the pose graph. Our algorithm was exhaustively tested on data captured at a conference building, a university campus and an industrial site of a chemical company. Experiments demonstrate that it is capable of generating highly accurate 3D maps in very challenging environments. We can state that the average distance of corresponding point pairs between the ground truth and estimated point cloud approximates one centimeter for an area covering approximately 4000 m 2 . To prove the genericity of the system, it was tested on the well-known Kitti vision benchmark. The results show that our approach competes with state of the art methods without making any additional assumptions.

  11. A linearly approximated iterative Gaussian decomposition method for waveform LiDAR processing

    NASA Astrophysics Data System (ADS)

    Mountrakis, Giorgos; Li, Yuguang

    2017-07-01

    Full-waveform LiDAR (FWL) decomposition results often act as the basis for key LiDAR-derived products, for example canopy height, biomass and carbon pool estimation, leaf area index calculation and under canopy detection. To date, the prevailing method for FWL product creation is the Gaussian Decomposition (GD) based on a non-linear Levenberg-Marquardt (LM) optimization for Gaussian node parameter estimation. GD follows a ;greedy; approach that may leave weak nodes undetected, merge multiple nodes into one or separate a noisy single node into multiple ones. In this manuscript, we propose an alternative decomposition method called Linearly Approximated Iterative Gaussian Decomposition (LAIGD method). The novelty of the LAIGD method is that it follows a multi-step ;slow-and-steady; iterative structure, where new Gaussian nodes are quickly discovered and adjusted using a linear fitting technique before they are forwarded for a non-linear optimization. Two experiments were conducted, one using real full-waveform data from NASA's land, vegetation, and ice sensor (LVIS) and another using synthetic data containing different number of nodes and degrees of overlap to assess performance in variable signal complexity. LVIS data revealed considerable improvements in RMSE (44.8% lower), RSE (56.3% lower) and rRMSE (74.3% lower) values compared to the benchmark GD method. These results were further confirmed with the synthetic data. Furthermore, the proposed multi-step method reduces execution times in half, an important consideration as there are plans for global coverage with the upcoming Global Ecosystem Dynamics Investigation LiDAR sensor on the International Space Station.

  12. Scaling wood volume estimates from inventory plots to landscapes with airborne LiDAR in temperate deciduous forest.

    PubMed

    Levick, Shaun R; Hessenmöller, Dominik; Schulze, E-Detlef

    2016-12-01

    Monitoring and managing carbon stocks in forested ecosystems requires accurate and repeatable quantification of the spatial distribution of wood volume at landscape to regional scales. Grid-based forest inventory networks have provided valuable records of forest structure and dynamics at individual plot scales, but in isolation they may not represent the carbon dynamics of heterogeneous landscapes encompassing diverse land-management strategies and site conditions. Airborne LiDAR has greatly enhanced forest structural characterisation and, in conjunction with field-based inventories, it provides avenues for monitoring carbon over broader spatial scales. Here we aim to enhance the integration of airborne LiDAR surveying with field-based inventories by exploring the effect of inventory plot size and number on the relationship between field-estimated and LiDAR-predicted wood volume in deciduous broad-leafed forest in central Germany. Estimation of wood volume from airborne LiDAR was most robust (R(2) = 0.92, RMSE = 50.57 m(3) ha(-1) ~14.13 Mg C ha(-1)) when trained and tested with 1 ha experimental plot data (n = 50). Predictions based on a more extensive (n = 1100) plot network with considerably smaller (0.05 ha) plots were inferior (R(2) = 0.68, RMSE = 101.01 ~28.09 Mg C ha(-1)). Differences between the 1 and 0.05 ha volume models from LiDAR were negligible however at the scale of individual land-management units. Sample size permutation tests showed that increasing the number of inventory plots above 350 for the 0.05 ha plots returned no improvement in R(2) and RMSE variability of the LiDAR-predicted wood volume model. Our results from this study confirm the utility of LiDAR for estimating wood volume in deciduous broad-leafed forest, but highlight the challenges associated with field plot size and number in establishing robust relationships between airborne LiDAR and field derived wood volume. We are moving into a forest management era where

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

    PubMed

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

    2016-06-17

    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

  14. 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

  15. 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

  16. 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

  17. Topobathymetric LiDAR point cloud processing and landform classification in a tidal environment

    NASA Astrophysics Data System (ADS)

    Skovgaard Andersen, Mikkel; Al-Hamdani, Zyad; Steinbacher, Frank; Rolighed Larsen, Laurids; Brandbyge Ernstsen, Verner

    2017-04-01

    Historically it has been difficult to create high resolution Digital Elevation Models (DEMs) in land-water transition zones due to shallow water depth and often challenging environmental conditions. This gap of information has been reflected as a "white ribbon" with no data in the land-water transition zone. In recent years, the technology of airborne topobathymetric Light Detection and Ranging (LiDAR) has proven capable of filling out the gap by simultaneously capturing topographic and bathymetric elevation information, using only a single green laser. We collected green LiDAR point cloud data in the Knudedyb tidal inlet system in the Danish Wadden Sea in spring 2014. Creating a DEM from a point cloud requires the general processing steps of data filtering, water surface detection and refraction correction. However, there is no transparent and reproducible method for processing green LiDAR data into a DEM, specifically regarding the procedure of water surface detection and modelling. We developed a step-by-step procedure for creating a DEM from raw green LiDAR point cloud data, including a procedure for making a Digital Water Surface Model (DWSM) (see Andersen et al., 2017). Two different classification analyses were applied to the high resolution DEM: A geomorphometric and a morphological classification, respectively. The classification methods were originally developed for a small test area; but in this work, we have used the classification methods to classify the complete Knudedyb tidal inlet system. References Andersen MS, Gergely Á, Al-Hamdani Z, Steinbacher F, Larsen LR, Ernstsen VB (2017). Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment. Hydrol. Earth Syst. Sci., 21: 43-63, doi:10.5194/hess-21-43-2017. Acknowledgements This work was funded by the Danish Council for Independent Research | Natural Sciences through the project "Process-based understanding and

  18. 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

  19. 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

  20. Gold - A novel deconvolution algorithm with optimization for waveform LiDAR processing

    NASA Astrophysics Data System (ADS)

    Zhou, Tan; Popescu, Sorin C.; Krause, Keith; Sheridan, Ryan D.; Putman, Eric

    2017-07-01

    Waveform Light Detection and Ranging (LiDAR) data have advantages over discrete-return LiDAR data in accurately characterizing vegetation structure. However, we lack a comprehensive understanding of waveform data processing approaches under different topography and vegetation conditions. The objective of this paper is to highlight a novel deconvolution algorithm, the Gold algorithm, for processing waveform LiDAR data with optimal deconvolution parameters. Further, we present a comparative study of waveform processing methods to provide insight into selecting an approach for a given combination of vegetation and terrain characteristics. We employed two waveform processing methods: (1) direct decomposition, (2) deconvolution and decomposition. In method two, we utilized two deconvolution algorithms - the Richardson-Lucy (RL) algorithm and the Gold algorithm. The comprehensive and quantitative comparisons were conducted in terms of the number of detected echoes, position accuracy, the bias of the end products (such as digital terrain model (DTM) and canopy height model (CHM)) from the corresponding reference data, along with parameter uncertainty for these end products obtained from different methods. This study was conducted at three study sites that include diverse ecological regions, vegetation and elevation gradients. Results demonstrate that two deconvolution algorithms are sensitive to the pre-processing steps of input data. The deconvolution and decomposition method is more capable of detecting hidden echoes with a lower false echo detection rate, especially for the Gold algorithm. Compared to the reference data, all approaches generate satisfactory accuracy assessment results with small mean spatial difference (<1.22 m for DTMs, <0.77 m for CHMs) and root mean square error (RMSE) (<1.26 m for DTMs, <1.93 m for CHMs). More specifically, the Gold algorithm is superior to others with smaller root mean square error (RMSE) (<1.01 m), while the direct decomposition

  1. Interoperable Data Systems for Satellite, Airborne, and Terrestrial LiDAR Data

    NASA Astrophysics Data System (ADS)

    Meertens, C. M.; Baru, C.; Blair, B.; Crosby, C. J.; Haran, T. M.; Harding, D. J.; Hofton, M. A.; Khalsa, S. S.; McWhirter, J.

    2010-12-01

    LiDAR (Light Detection and Ranging) technology is being widely applied to scientific problems on global to local scales using a range of laser technologies mounted on satellite, low- and high-altitude airborne and terrestrial platforms. Modern laser ranging instruments are increasingly capable of providing full waveform data, multiple detectors, higher sample rates and longer ranges. Accompanying these improvements, however, are rapidly growing data volumes and ever more complex data formats and processing algorithms. This presents significant challenges for existing Earth science data systems serving these data and creates barriers to the efficient use of these data by a growing and diverse community of scientific and other users who are studying deformation of the solid Earth, the cryosphere, vegetation structure, and land form evolution. To address these challenges, a group of data centers is collaborating under a project funded by the NASA ROSES ACCESS Program to develop interoperable LiDAR data access systems to provide integrated access to data and derived products in common data formats via simple-to-navigate web interfaces. The web service-based systems created by this project, called NLAS, will enhance access to existing laser data sources hosted at the National Snow and Ice Data Center DAAC, Goddard Space Flight Center LVIS Data Center, UNAVCO, and the OpenTopography Facility at the San Diego Supercomputer Center (SDSC). Through the OpenTopography portal, NLAS systems will provide access to satellite laser altimetry data from ICESat and high altitude airborne laser scanning data from LVIS, as well as low altitude airborne LiDAR and terrestrial laser scanning data hosted at OpenTopography and UNAVCO. NLAS will develop new web service interfaces for NASA data archives at GSFC/LVIS and NSIDC in an effort to improve and streamline access to these data archives. The OpenTopography portal will act as a client to the NLAS services and will provide integrated

  2. 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

  3. 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

  4. 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.

  5. Decomposition of small-footprint full waveform LiDAR data based on generalized Gaussian model and grouping LM optimization

    NASA Astrophysics Data System (ADS)

    Ma, Hongchao; Zhou, Weiwei; Zhang, Liang; Wang, Suyuan

    2017-04-01

    Full waveform airborne Light Detection And Ranging(LiDAR) data contains abundant information which may overcome some deficiencies of discrete LiDAR point cloud data provided by conventional LiDAR systems. Processing full waveform data to extract more information than coordinate values alone is of great significance for potential applications. The Levenberg-Marquardt (LM) algorithm is a traditional method used to estimate parameters of a Gaussian model when Gaussian decomposition of full waveform LiDAR data is performed. This paper employs the generalized Gaussian mixture function to fit a waveform, and proposes using the grouping LM algorithm to optimize the parameters of the function. It is shown that the grouping LM algorithm overcomes the common drawbacks which arise from the conventional LM for parameter optimization, such as the final results being influenced by the initial parameters, possible algorithm interruption caused by non-numerical elements that occurred in the Jacobian matrix, etc. The precision of the point cloud generated by the grouping LM is evaluated by comparing it with those provided by the LiDAR system and those generated by the conventional LM. Results from both simulation and real data show that the proposed algorithm can generate a higher-quality point cloud, in terms of point density and precision, and can extract other information, such as echo location, pulse width, etc., more precisely as well.

  6. 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

  7. 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.

  8. 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

    2015-09-15

    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.

  9. 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.

  10. Identification of hydrothermal alterations using Dar-Zarouk parameters and concept of anisotropy for 2D resistivity data

    NASA Astrophysics Data System (ADS)

    Permatasari, A. O.; Supriyanto, Kuswanto, A.

    2017-07-01

    Measurement of geoelectric methods is commonly performed by using homogeneous and isotropy approaches. However, these approaches are not entirely the same due to the earth's real condition. Therefore, it needs to be measured with inhomogeneous and anisotropy approach. This approach uses the parameter of Dar-Zarouk. The parameter of Dar-Zarouk is used to calculate the values of the resistivity of media and the coefficient of anisotropy. This research is intended for identifying the hydrothermal alteration that is not uniform in the field. The inhomogeneous and anisotropy approach is very appropriate to be used and expected to give a clearer cross section of true resistivity in subsurface imaging. The results of the model using the parameter of Dar-Zarouk sharpen the anomaly, hence the existence of alteration could be more visible and easier identified.

  11. Arrangements for enhanced measurements of a large turbine near-wake using LiDAR from the nacelle

    NASA Astrophysics Data System (ADS)

    Trujillo, J. J.; Rettenmeier, A.; Schlipf, D.

    2008-05-01

    New LiDAR techniques are being tested and developed to support the development of large offshore wind turbines. Our interest in this paper is concentrated in wake measurements; therefore, a pulsed standard LiDAR is adapted for fullscale wind field measurements from the nacelle of a large wind turbine. We show the conceptual framework for planned adaptations to a Windcube® LiDAR for operation at the nacelle of a 5 MW wind turbine. The standard scanning mode is to be modified to properly obtain downstream and also upstream wind speeds. The wind field measurements are intended for verification of models for near-wake wind speed, wake meandering and new predictive control estrategies.

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

    PubMed Central

    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

  13. Landscape metrics of coastal dunefields from LiDAR and hyper-spectral remote sensing

    NASA Astrophysics Data System (ADS)

    Zhang, L.; Baas, A. C.

    2010-12-01

    This paper presents an upscaling study extracting landscape metrics of coastal dunefields, calculated from local topography and vegetation-type abundance, from high-resolution LiDAR and collocated hyper-spectral remote-sensing imagery, at coastal sites in Wales, UK. The hyper-spectral data (Eagle & Hawk instruments on NERC’s ARSF aircraft in 2009) are analysed in combination with spectrometer ground-truthing to determine relative within-pixel (down-scaled) abundance maps of different vegetation types, using a novel method that combines linear spectral mixture modelling with a maximum likelihood classification. The resulting landscape metrics are the same state variables that have been used for classifying simulated dunefield landscapes in the DECAL model and for tracking the evolution of the ecogeomorphology in a 3D state space. The landscape metrics of the dunefields can now be plotted in the same space on the same ordinates to establish a direct and quantitative comparison beween simulated and real-world landscapes. For the Kenfig Dunefield in Wales, LiDAR and hyperspectral analysis has also been accomplished on archived (1997) data to investigate the changes in metrics over a 12-year period.

  14. 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.

  15. Invasive Shrub Mapping in an Urban Environment from Hyperspectral and LiDAR-Derived Attributes

    PubMed Central

    Chance, Curtis M.; Coops, Nicholas C.; Plowright, Andrew A.; Tooke, Thoreau R.; Christen, Andreas; Aven, Neal

    2016-01-01

    Proactive management of invasive species in urban areas is critical to restricting their overall distribution. The objective of this work is to determine whether advanced remote sensing technologies can help to detect invasions effectively and efficiently in complex urban ecosystems such as parks. In Surrey, BC, Canada, Himalayan blackberry (Rubus armeniacus) and English ivy (Hedera helix) are two invasive shrub species that can negatively affect native ecosystems in cities and managed urban parks. Random forest (RF) models were created to detect these two species using a combination of hyperspectral imagery, and light detection and ranging (LiDAR) data. LiDAR-derived predictor variables included irradiance models, canopy structural characteristics, and orographic variables. RF detection accuracy ranged from 77.8 to 87.8% for Himalayan blackberry and 81.9 to 82.1% for English ivy, with open areas classified more accurately than areas under canopy cover. English ivy was predicted to occur across a greater area than Himalayan blackberry both within parks and across the entire city. Both Himalayan blackberry and English ivy were mostly located in clusters according to a Local Moran’s I analysis. The occurrence of both species decreased as the distance from roads increased. This study shows the feasibility of producing highly accurate detection maps of plant invasions in urban environments using a fusion of remotely sensed data, as well as the ability to use these products to guide management decisions. PMID:27818664

  16. Scintillation measurements at Bahir Dar during the high solar activity phase of solar cycle 24

    NASA Astrophysics Data System (ADS)

    Kriegel, Martin; Jakowski, Norbert; Berdermann, Jens; Sato, Hiroatsu; Wassaie Mersha, Mogese

    2017-01-01

    Small-scale ionospheric disturbances may cause severe radio scintillations of signals transmitted from global navigation satellite systems (GNSSs). Consequently, small-scale plasma irregularities may heavily degrade the performance of current GNSSs such as GPS, GLONASS or Galileo. This paper presents analysis results obtained primarily from two high-rate GNSS receiver stations designed and operated by the German Aerospace Center (DLR) in cooperation with Bahir Dar University (BDU) at 11.6° N, 37.4° E. Both receivers collect raw data sampled at up to 50 Hz, from which characteristic scintillation parameters such as the S4 index are deduced. This paper gives a first overview of the measurement set-up and the observed scintillation events over Bahir Dar in 2015. Both stations are located close to one another and aligned in an east-west, direction which allows us to estimate the zonal drift velocity and spatial dimension of equatorial ionospheric plasma irregularities. Therefore, the lag times of moving electron density irregularities and scintillation patterns are derived by applying cross-correlation analysis to high-rate measurements of the slant total electron content (sTEC) along radio links between a GPS satellite and both receivers and to the associated signal power, respectively. Finally, the drift velocity is derived from the estimated lag time, taking into account the geometric constellation of both receiving antennas and the observed GPS satellites.

  17. 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.

  18. Method of flight planning for airborne LiDAR using genetic algorithms

    NASA Astrophysics Data System (ADS)

    Dashora, Ajay; Lohani, Bharat; Deb, Kalyanmoy

    2014-01-01

    Conventional methods of flight planning for airborne LiDAR are heuristic in nature and use an iterative trial and error approach. A new system-based approach of flight planning is presented in this paper. The presented approach automatically derives flight planning parameters by minimizing the cost of data acquisition, which is represented by flight duration. The flight duration, which is the sum of the strip time and turning time, is minimized using genetic algorithms under the constraints of mapping requirements, hardware limitations, user-defined preferences, and various other requirements. The proposed approach is first validated for conventionally known test cases of regular shapes (rectangular and triangular). Thereafter, it is implemented for an arbitrarily shaped simulated test site with two commercially available airborne LiDAR sensors. Statistical results are presented for the above. Further, flight planning is performed for two real test sites. The demonstrated approach not only produces optimal results, but also avoids the assumptions of conventional methods. Furthermore, the approach requires the least amount of human intervention and, thus, eliminates the subjectivity that is imposed by individual flight planners for determining the flight planning parameters. Encouraged by these results, the authors suggest that the proposed approach can be further developed to include all possible components of flight planning in a future work.

  19. Investigating the performance of LiDAR-derived biomass information in hydromechanic slope stability modelling

    NASA Astrophysics Data System (ADS)

    Schmaltz, Elmar; Steger, Stefan; Bogaard, Thom; Van Beek, Rens; Glade, Thomas

    2017-04-01

    Hydromechanic slope stability models are often used to assess the landslide susceptibility of hillslopes. Some of these models are able to account for vegetation related effects when assessing slope stability. However, spatial information of required vegetation parameters (especially of woodland) that are defined by land cover type, tree species and stand density are mostly underrepresented compared to hydropedological and geomechanical parameters. The aim of this study is to assess how LiDAR-derived biomass information can help to distinguish distinct tree stand-immanent properties (e.g. stand density and diversity) and further improve the performance of hydromechanic slope stability models. We used spatial vegetation data produced from sophisticated algorithms that are able to separate single trees within a stand based on LiDAR point clouds and thus allow an extraordinary detailed determination of the aboveground biomass. Further, this information is used to estimate the species- and stand-related distribution of the subsurface biomass using an innovative approach to approximate root system architecture and development. The hydrological tree-soil interactions and their impact on the geotechnical stability of the soil mantle are then reproduced in the dynamic and spatially distributed slope stability model STARWARS/PROBSTAB. This study highlights first advances in the approximation of biomechanical reinforcement potential of tree root systems in tree stands. Based on our findings, we address the advantages and limitations of highly detailed biomass information in hydromechanic modelling and physically based slope failure prediction.

  20. Family perceptions of intellectual disability: Understanding and support in Dar es Salaam

    PubMed Central

    2012-01-01

    When attempting to understand the construct of intellectual disability in different contexts, speaking to family members in addition to the individual with the disability may provide new insight about understandings of and responses to intellectual disability in society and may help to identify the forms of support that are available or needed to ensure the quality of life of people with disabilities. This article outlines and discusses interviews that were conducted in Dar es Salaam, Tanzania, with family members of children and adults with intellectual disabilities. These interviews explore how families came to understand that their child had an intellectual disability; the availability of family support; and family hopes and dreams for the future, and were a part of a wider exploratory study that gathered insight from individuals with disabilities, families, and other providers of support to explore understandings and perceptions of disability in Dar es Salaam. Understanding family experiences will help researchers, policy makers, non-governmental organisations, and others to identify family strengths and family support needs which can ultimately improve family quality of life and the quality of life of the member with a disability. PMID:28729979

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

    PubMed

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

    2015-07-10

    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.

  2. 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.

  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. Body-Art Practices Among Undergraduate Medical University Students in Dar Es Salaam, Tanzania, 2014

    PubMed Central

    Chacha, Chacha Emmanuel; Kazaura, Method R.

    2015-01-01

    Background: Body-art practices are increasing among adolescents and young adults. Although substantial data are available in developed countries, little has been documented about body-art practices in developing countries. Objective: To determine the magnitude, types and reasons for practicing body-art practices among undergraduate medical University students in Dar es Salaam, Tanzania. Materials and Methods: A cross-sectional descriptive study was conducteed among undergraduate University students in Dar es Salaam involving 536 respondents from two Universities. We used a self-administered questionnaire to collect data. Analyses were based on summary measures and bivariate analyses. Results: While 7.5% of undergraduate students reported having tattoos, 20% reported having body puncturing or piercing. Body piercing is reported more among female university undergraduate students than their male counterparts. Reported main reasons for undergoing body-art include “a mark of beauty,” 24%, “just wanted one,” 18% and “a mark of femininity or masculinity,” 17%. The majority (98%) of students were aware that unsafe body-art practices may lead to contracting HIV and more than half (52%) reported awareness of the risk of Hepatitis B infection. Conclusions: Despite high awareness of the potential risks involved in unsafe body arts that include tattoo and piercing, these practices are increasing among adolescents and young adults. There is need to have educational and counseling efforts so as to minimize associated health risks. PMID:25814729

  5. 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

  6. Genetic diversity of carotenoid-rich bananas evaluated by Diversity Arrays Technology (DArT)

    PubMed Central

    2009-01-01

    The aim of this work was to evaluate the carotenoid content and genetic variability of banana accessions from the Musa germplasm collection held at Embrapa Cassava and Tropical Fruits, Brazil. Forty-two samples were analyzed, including 21 diploids, 19 triploids and two tetraploids. The carotenoid content was analyzed spectrophotometrically and genetic variability was estimated using 653 DArT markers. The average carotenoid content was 4.73 μg.g -1 , and ranged from 1.06 μg.g -1 for the triploid Nanica (Cavendish group) to 19.24 μg.g -1 for the triploid Saney. The diploids Modok Gier and NBA-14 and the triploid Saney had a carotenoid content that was, respectively, 7-fold, 6-fold and 9-fold greater than that of cultivars from the Cavendish group (2.19 μg.g -1). The mean similarity among the 42 accessions was 0.63 (range: 0.24 to 1.00). DArT analysis revealed extensive genetic variability in accessions from the Embrapa Musa germplasm bank. PMID:21637652

  7. 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%.

  8. New SHARE 2010 HSI-LiDAR dataset: re-calibration, detection assessment and delivery

    NASA Astrophysics Data System (ADS)

    Ientilucci, Emmett J.

    2016-09-01

    This paper revisits hyperspectral data collected from the SpecTIR hyperspectral airborne Rochester Experiment (SHARE) in 2010. It has been determined that there were calibration issues in the SWIR portion of the data. This calibration issue is discussed and has been rectified. Approaches for calibration to radiance and compensation to reflectance are discussed based on in-scene information and radiative transfer codes. In addition to the entire flight line, a much large target detection test and evaluation chip has been created which includes an abundance of potential false alarms. New truth masks are created along with results from target detection algorithms. Co-registered LiDAR data is also presented. Finally, all ground truth information (ground photos, metadata, MODTRAN tape5, ASD ground spectral measurements, target truth masks, etc.), in addition to the HSI flight lines and co-registered LiDAR data, has been organized, packaged and uploaded to the Center for Imaging Science / Digital Imaging and Remote Sensing Lab web server for public use.

  9. A scalable approach for tree segmentation within small-footprint airborne LiDAR data

    NASA Astrophysics Data System (ADS)

    Hamraz, Hamid; Contreras, Marco A.; Zhang, Jun

    2017-05-01

    This paper presents a distributed approach that scales up to segment tree crowns within a LiDAR point cloud representing an arbitrarily large forested area. The approach uses a single-processor tree segmentation algorithm as a building block in order to process the data delivered in the shape of tiles in parallel. The distributed processing is performed in a master-slave manner, in which the master maintains the global map of the tiles and coordinates the slaves that segment tree crowns within and across the boundaries of the tiles. A minimal bias was introduced to the number of detected trees because of trees lying across the tile boundaries, which was quantified and adjusted for. Theoretical and experimental analyses of the runtime of the approach revealed a near linear speedup. The estimated number of trees categorized by crown class and the associated error margins as well as the height distribution of the detected trees aligned well with field estimations, verifying that the distributed approach works correctly. The approach enables providing information of individual tree locations and point cloud segments for a forest-level area in a timely manner, which can be used to create detailed remotely sensed forest inventories. Although the approach was presented for tree segmentation within LiDAR point clouds, the idea can also be generalized to scale up processing other big spatial datasets.

  10. Genetic diversity of carotenoid-rich bananas evaluated by Diversity Arrays Technology (DArT).

    PubMed

    Amorim, Edson P; Vilarinhos, Alberto D; Cohen, Kelly O; Amorim, Vanusia B O; Dos Santos-Serejo, Janay A; Silva, Sebastião Oliveira E; Pestana, Kátia N; Dos Santos, Vânia J; Paes, Norma S; Monte, Damares C; Dos Reis, Ronaldo V

    2009-01-01

    The aim of this work was to evaluate the carotenoid content and genetic variability of banana accessions from the Musa germplasm collection held at Embrapa Cassava and Tropical Fruits, Brazil. Forty-two samples were analyzed, including 21 diploids, 19 triploids and two tetraploids. The carotenoid content was analyzed spectrophotometrically and genetic variability was estimated using 653 DArT markers. The average carotenoid content was 4.73 μg.g (-1) , and ranged from 1.06 μg.g (-1) for the triploid Nanica (Cavendish group) to 19.24 μg.g (-1) for the triploid Saney. The diploids Modok Gier and NBA-14 and the triploid Saney had a carotenoid content that was, respectively, 7-fold, 6-fold and 9-fold greater than that of cultivars from the Cavendish group (2.19 μg.g (-1)). The mean similarity among the 42 accessions was 0.63 (range: 0.24 to 1.00). DArT analysis revealed extensive genetic variability in accessions from the Embrapa Musa germplasm bank.

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

    PubMed

    Sim, Sungdae; Sock, Juil; Kwak, Kiho

    2016-06-22

    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.

  12. 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

  13. 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.

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

    PubMed

    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.

  15. Research on high resolution spectral method of hyperspectral LiDAR

    NASA Astrophysics Data System (ADS)

    Li, Feng; Jiang, Chenghao; Zhu, Jingguo; Li, Menglin; Meng, Zhe

    2016-10-01

    Hyperspectral LiDAR using supercontinuum laser as light source, applying spectroscopic technology gets backscattered reflectance of different wavelengths, and can acquire both the geometry and spectral information on the target. Due to the development of the photoelectric sensor, hyperspectral LiDAR has fewer spectral channels, which limits its application in physical properties detection. To solve this problem, this paper proposes a new method based on the micro mirror array. By blaze grating, the supercontinuum laser is grating into monochromatic light in space, first projected to the micro mirror array, by controlling the micro mirror array flip, specific spectrum and reflection to corresponding photoelectric sensor channels, improve the spectral resolution. The micro mirror array photoelectric sensor resolution is much higher than the number of channels, through this method, can greatly improve the spectral resolution. In this paper, based on the micro mirror array, the simulation design is carried out and the feasibility of the method is verified by experiments. The simulation and experimental results show that the spectral resolution can be improved greatly by controlling the turning of the micro mirror.

  16. Characterization of the OPAL LiDAR under controlled obscurant conditions

    NASA Astrophysics Data System (ADS)

    Cao, Xiaoying; Church, Philip; Matheson, Justin

    2016-05-01

    Neptec Technologies' OPAL-120 3D LiDAR is optimized for obscurant penetration. The OPAL-120 uses a scanning mechanism based on the Risley prism pair. The scan patterns are created by rotating two prisms under independent motor control. The geometry and material properties of the prisms define the conical field-of-view of the sensor, which can be built to between 60 to 120 degrees. The OPAL-120 was recently evaluated using a controlled obscurant chamber capable of generating clouds of obscurants over a depth of 22m. Obscurants used in this investigation include: Arizona road dust, water fog, and fog-oil. The obscurant cloud optical densities were monitored with a transmissometer. Optical depths values ranged from an upper value of 6 and progressively decreased to 0. Targets were positioned at the back of the obscurant chamber at a distance of 60m from the LiDAR. The targets are made of a foreground array of equally spaced painted wood stripes in front of a solid background. Reflectivity contrasts were achieved with foreground/background combinations of white/white, white/black and black/white. Data analysis will be presented on the effect of optical densities on range and cross-range resolution, and accuracy. The analysis includes the combinations of all obscurant types and target reflectivity contrasts.

  17. Road traffic sign detection and classification from mobile LiDAR point clouds

    NASA Astrophysics Data System (ADS)

    Weng, Shengxia; Li, Jonathan; Chen, Yiping; Wang, Cheng

    2016-03-01

    Traffic signs are important roadway assets that provide valuable information of the road for drivers to make safer and easier driving behaviors. Due to the development of mobile mapping systems that can efficiently acquire dense point clouds along the road, automated detection and recognition of road assets has been an important research issue. This paper deals with the detection and classification of traffic signs in outdoor environments using mobile light detection and ranging (Li- DAR) and inertial navigation technologies. The proposed method contains two main steps. It starts with an initial detection of traffic signs based on the intensity attributes of point clouds, as the traffic signs are always painted with highly reflective materials. Then, the classification of traffic signs is achieved based on the geometric shape and the pairwise 3D shape context. Some results and performance analyses are provided to show the effectiveness and limits of the proposed method. The experimental results demonstrate the feasibility and effectiveness of the proposed method in detecting and classifying traffic signs from mobile LiDAR point clouds.

  18. Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR

    NASA Astrophysics Data System (ADS)

    Chen, Yang; Zhu, Xuan; Yebra, Marta; Harris, Sarah; Tapper, Nigel

    2016-10-01

    Fuel structural characteristics affect fire behavior including fire intensity, spread rate, flame structure, and duration, therefore, quantifying forest fuel structure has significance in understanding fire behavior as well as providing information for fire management activities (e.g., planned burns, suppression, fuel hazard assessment, and fuel treatment). This paper presents a method of forest fuel strata classification with an integration between terrestrial light detection and ranging (LiDAR) data and geographic information system for automatically assessing forest fuel structural characteristics (e.g., fuel horizontal continuity and vertical arrangement). The accuracy of fuel description derived from terrestrial LiDAR scanning (TLS) data was assessed by field measured surface fuel depth and fuel percentage covers at distinct vertical layers. The comparison of TLS-derived depth and percentage cover at surface fuel layer with the field measurements produced root mean square error values of 1.1 cm and 5.4%, respectively. TLS-derived percentage cover explained 92% of the variation in percentage cover at all fuel layers of the entire dataset. The outcome indicated TLS-derived fuel characteristics are strongly consistent with field measured values. TLS can be used to efficiently and consistently classify forest vertical layers to provide more precise information for forest fuel hazard assessment and surface fuel load estimation in order to assist forest fuels management and fire-related operational activities. It can also be beneficial for mapping forest habitat, wildlife conservation, and ecosystem management.

  19. Basic analysis of climate and urban bioclimate of Dar es Salaam, Tanzania

    NASA Astrophysics Data System (ADS)

    Ndetto, Emmanuel L.; Matzarakis, Andreas

    2013-10-01

    Better understanding of urban microclimate and bioclimate of any city is imperative today when the world is constrained by both urbanisation and global climate change. Urbanisation generally triggers changes in land cover and hence influencing the urban local climate. Dar es Salaam city in Tanzania is one of the fast growing cities. Assessment of its urban climate and the human biometeorological conditions was done using the easily available synoptic meteorological data covering the period 2001-2011. In particular, the physiologically equivalent temperature (PET) was calculated using the RayMan software and results reveal that the afternoon period from December to February (DJF season) is relatively the most thermal stressful period to human beings in Dar es Salaam where PET values of above 35 °C were found. Additionally, the diurnal cycle of the individual meteorological elements that influence the PET index were analysed and found that air temperature of 30-35 °C dominate the afternoon period from 12:00 to 15:00 hours local standard time at about 60 % of occurrence. The current results, though considered as preliminary to the ongoing urban climate study in the city, provide an insight on how urban climate research is of significant importance in providing useful climatic information for ensuring quality of life and wellbeing of city dwellers.

  20. Automatic building extraction from LiDAR data fusion of point and grid-based features

    NASA Astrophysics Data System (ADS)

    Du, Shouji; Zhang, Yunsheng; Zou, Zhengrong; Xu, Shenghua; He, Xue; Chen, Siyang

    2017-08-01

    This paper proposes a method for extracting buildings from LiDAR point cloud data by combining point-based and grid-based features. To accurately discriminate buildings from vegetation, a point feature based on the variance of normal vectors is proposed. For a robust building extraction, a graph cuts algorithm is employed to combine the used features and consider the neighbor contexture information. As grid feature computing and a graph cuts algorithm are performed on a grid structure, a feature-retained DSM interpolation method is proposed in this paper. The proposed method is validated by the benchmark ISPRS Test Project on Urban Classification and 3D Building Reconstruction and compared to the state-art-of-the methods. The evaluation shows that the proposed method can obtain a promising result both at area-level and at object-level. The method is further applied to the entire ISPRS dataset and to a real dataset of the Wuhan City. The results show a completeness of 94.9% and a correctness of 92.2% at the per-area level for the former dataset and a completeness of 94.4% and a correctness of 95.8% for the latter one. The proposed method has a good potential for large-size LiDAR data.

  1. 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.

  2. 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.

  3. Optical turbulence profiling with SloDAR in the Canadian High Arctic

    NASA Astrophysics Data System (ADS)

    Maire, Jérôme; Mieda, Etsuko; Steinbring, Eric; Murowinski, Richard; Graham, James R.; Carlberg, Raymond; Wright, Shelley A.; Law, Nicholas M.; Sivanandam, Suresh

    2014-07-01

    The Earth's polar regions offer unique advantages for ground-based astronomical observations with its cold and dry climate, long periods of darkness, and the potential for exquisite image quality. We present preliminary results from a site-testing campaign during nighttime from October to November 2012 at the Polar Environment Atmospheric Research Laboratory (PEARL), on a 610-m high ridge near the Eureka weatherstation on Ellesmere Island, Canada. A Shack-Hartmann wavefront sensor was employed, using the Slope Detection and Ranging (SloDAR) method. This instrument (Mieda et al, this conference) was designed to measure the altitude, strength and variability of atmospheric turbulence, in particular for operation under Arctic conditions. First SloDAR optical turbulence profiles above PEARL show roughly half of the optical turbulence confined to the boundary layer, below about 1 km, with the majority of the remainder in one or two thin layers between 2 km and 5 km, or above. The median seeing during this campaign was measured to be 0.65 arcsec.

  4. Automatic detection of zebra crossings from mobile LiDAR data

    NASA Astrophysics Data System (ADS)

    Riveiro, B.; González-Jorge, H.; Martínez-Sánchez, J.; Díaz-Vilariño, L.; Arias, P.

    2015-07-01

    An algorithm for the automatic detection of zebra crossings from mobile LiDAR data is developed and tested to be applied for road management purposes. The algorithm consists of several subsequent processes starting with road segmentation by performing a curvature analysis for each laser cycle. Then, intensity images are created from the point cloud using rasterization techniques, in order to detect zebra crossing using the Standard Hough Transform and logical constrains. To optimize the results, image processing algorithms are applied to the intensity images from the point cloud. These algorithms include binarization to separate the painting area from the rest of the pavement, median filtering to avoid noisy points, and mathematical morphology to fill the gaps between the pixels in the border of white marks. Once the road marking is detected, its position is calculated. This information is valuable for inventorying purposes of road managers that use Geographic Information Systems. The performance of the algorithm has been evaluated over several mobile LiDAR strips accounting for a total of 30 zebra crossings. That test showed a completeness of 83%. Non-detected marks mainly come from painting deterioration of the zebra crossing or by occlusions in the point cloud produced by other vehicles on the road.

  5. 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.

  6. Irrigation network extraction methodology from LiDAR DTM using Whitebox and ArcGIS

    NASA Astrophysics Data System (ADS)

    Mahor, M. A. P.; De La Cruz, R. M.; Olfindo, N. T.; Perez, A. M. C.

    2016-10-01

    Irrigation networks are important in distributing water resources to areas where rainfall is not enough to sustain agriculture. They are also crucial when it comes to being able to redirect vast amounts of water to decrease the risks of flooding in flat areas, especially near sources of water. With the lack of studies about irrigation feature extraction, which range from wide canals to small ditches, this study aims to present a method of extracting these features from LiDAR-derived digital terrain models (DTMs) using Geographic Information Systems (GIS) tools such as ArcGIS and Whitebox Geospatial Analysis Tools (Whitebox GAT). High-resolution LiDAR DTMs with 1-meter horizontal and 0.25-meter vertical accuracies were processed to generate the gully depth map. This map was then reclassified, converted to vector, and filtered according to segment length, and sinuosity to be able to isolate these irrigation features. Initial results in the test area show that the extraction completeness is greater than 80% when compared with data obtained from the National Irrigation Administration (NIA).

  7. Selection of LiDAR geometric features with adaptive neighborhood size for urban land cover classification

    NASA Astrophysics Data System (ADS)

    Dong, Weihua; Lan, Jianhang; Liang, Shunlin; Yao, Wei; Zhan, Zhicheng

    2017-08-01

    LiDAR has been an effective technology for acquiring urban land cover data in recent decades. Previous studies indicate that geometric features have a strong impact on land cover classification. Here, we analyzed an urban LiDAR dataset to explore the optimal feature subset from 25 geometric features incorporating 25 scales under 6 definitions for urban land cover classification. We performed a feature selection strategy to remove irrelevant or redundant features based on the correlation coefficient between features and classification accuracy of each features. The neighborhood scales were divided into small (0.5-1.5 m), medium (1.5-6 m) and large (>6 m) scale. Combining features with lower correlation coefficient and better classification performance would improve classification accuracy. The feature depicting homogeneity or heterogeneity of points would be calculated at a small scale, and the features to smooth points at a medium scale and the features of height different at large scale. As to the neighborhood definition, cuboid and cylinder were recommended. This study can guide the selection of optimal geometric features with adaptive neighborhood scale for urban land cover classification.

  8. A Voxel-based Method for Forest Change Detection after Fire Using LiDAR Data

    NASA Astrophysics Data System (ADS)

    Xu, Z.

    2015-12-01

    A Voxel-based Method for Forest Change Detection after Fire Using LiDAR DataZewei Xu and Jonathan A. Greenberg Traditional methods of forest fire modeling focus on the patterns of burning in two-dimensions at relatively coarse resolutions. However, fires spread in complex, three-dimensional patterns related to the horizontal and vertical distributions of woody fuel as well as the prevailing climate conditions, and the micro-scale patterns of fuel distributions over scales of only meters can determine the path that fire can take through a complex landscape. One challenge in understanding the full three-dimensional (3D) path that a fire takes through a landscape is a lack of data at landscape scales of these burns. Remote sensing approaches, while operating at landscape scales, typically focus on two-dimensional analyses using standard image-based change detection techniques. In this research, we develop a 3D voxel-based change detection method applied to multitemporal LiDAR data collected before and after forest fires in California to quantify the full 3D pattern of vegetation loss. By changing the size of the voxel, forest loss at different spatial scales is revealed. The 3D tunnel of fuel loss created by the fire was used to examine ground-to-crown transitions, firebreaks, and other three-dimensional aspects of a forest fire.

  9. Hydrogeology and water chemistry of Infranz catchment springs, Bahir Dar Area, Lake Tana Basin, Ethiopia

    NASA Astrophysics Data System (ADS)

    Abera, F. N.; Vancamp, M.; Walraevens, K.

    2016-12-01

    ABSTRACT The major springs in the Infranz catchment are a significant source of water for Bahir city and nearby villages, while they help to sustain Infranz River and the downstream wetlands. The aim of the research was to understand the hydrogeological conditions of these high-discharge springs, and to explain the hydrochemical composition of spring waters. Water samples from rainwater and springs were collected and analyzed and compared for major cations and anions. The hydrochemical data analysis showed that all water samples of the springs have freshwater chemistry, Ca-HCO3 type, while deep groundwater shows more evolved types. This indicates limited water-rock interaction and short residence time for the spring waters. The rise of NO3- and PO43- may indicate future water quality degradation unless the anthropogenic activities upgradient and nearby are restricted. The uptake of 75% of spring water for water supply of Bahir Dar results in wetland degradation. Key words: Spring water, Infranz River, Bahir Dar, Ethiopia, hydrochemistry

  10. Invasive Shrub Mapping in an Urban Environment from Hyperspectral and LiDAR-Derived Attributes.

    PubMed

    Chance, Curtis M; Coops, Nicholas C; Plowright, Andrew A; Tooke, Thoreau R; Christen, Andreas; Aven, Neal

    2016-01-01

    Proactive management of invasive species in urban areas is critical to restricting their overall distribution. The objective of this work is to determine whether advanced remote sensing technologies can help to detect invasions effectively and efficiently in complex urban ecosystems such as parks. In Surrey, BC, Canada, Himalayan blackberry (Rubus armeniacus) and English ivy (Hedera helix) are two invasive shrub species that can negatively affect native ecosystems in cities and managed urban parks. Random forest (RF) models were created to detect these two species using a combination of hyperspectral imagery, and light detection and ranging (LiDAR) data. LiDAR-derived predictor variables included irradiance models, canopy structural characteristics, and orographic variables. RF detection accuracy ranged from 77.8 to 87.8% for Himalayan blackberry and 81.9 to 82.1% for English ivy, with open areas classified more accurately than areas under canopy cover. English ivy was predicted to occur across a greater area than Himalayan blackberry both within parks and across the entire city. Both Himalayan blackberry and English ivy were mostly located in clusters according to a Local Moran's I analysis. The occurrence of both species decreased as the distance from roads increased. This study shows the feasibility of producing highly accurate detection maps of plant invasions in urban environments using a fusion of remotely sensed data, as well as the ability to use these products to guide management decisions.

  11. 3-D earthquake surface displacements from differencing pre- and post-event LiDAR point clouds

    NASA Astrophysics Data System (ADS)

    Krishnan, A. K.; Nissen, E.; Arrowsmith, R.; Saripalli, S.

    2012-12-01

    The explosion in aerial LiDAR surveying along active faults across the western United States and elsewhere provides a high-resolution topographic baseline against which to compare repeat LiDAR datasets collected after future earthquakes. We present a new method for determining 3-D coseismic surface displacements and rotations by differencing pre- and post-earthquake LiDAR point clouds using an adaptation of the Iterative Closest Point (ICP) algorithm, a point set registration technique widely used in medical imaging, computer vision and graphics. There is no need for any gridding or smoothing of the LiDAR data and the method works well even with large mismatches in the density of the two point clouds. To explore the method's performance, we simulate pre- and post-event point clouds using real ("B4") LiDAR data on the southern San Andreas Fault perturbed with displacements of known magnitude. For input point clouds with ~2 points per square meter, we are able to reproduce displacements with a 50 m grid spacing and with horizontal and vertical accuracies of ~20 cm and ~4 cm. In the future, finer grids and improved precisions should be possible with higher shot densities and better survey geo-referencing. By capturing near-fault deformation in 3-D, LiDAR differencing with ICP will complement satellite-based techniques such as InSAR which map only certain components of the surface deformation and which often break down close to surface faulting or in areas of dense vegetation. It will be especially useful for mapping shallow fault slip and rupture zone deformation, helping inform paleoseismic studies and better constrain fault zone rheology. Because ICP can image rotations directly, the technique will also help resolve the detailed kinematics of distributed zones of faulting where block rotations may be common.

  12. Using LiDAR to Estimate Total Aboveground Biomass of Redwood Stands in the Jackson Demonstration State Forest, Mendocino, California

    NASA Astrophysics Data System (ADS)

    Rao, M.; Vuong, H.

    2013-12-01

    The overall objective of this study is to develop a method for estimating total aboveground biomass of redwood stands in Jackson Demonstration State Forest, Mendocino, California using airborne LiDAR data. LiDAR data owing to its vertical and horizontal accuracy are increasingly being used to characterize landscape features including ground surface elevation and canopy height. These LiDAR-derived metrics involving structural signatures at higher precision and accuracy can help better understand ecological processes at various spatial scales. Our study is focused on two major species of the forest: redwood (Sequoia semperirens [D.Don] Engl.) and Douglas-fir (Pseudotsuga mensiezii [Mirb.] Franco). Specifically, the objectives included linear regression models fitting tree diameter at breast height (dbh) to LiDAR derived height for each species. From 23 random points on the study area, field measurement (dbh and tree coordinate) were collected for more than 500 trees of Redwood and Douglas-fir over 0.2 ha- plots. The USFS-FUSION application software along with its LiDAR Data Viewer (LDV) were used to to extract Canopy Height Model (CHM) from which tree heights would be derived. Based on the LiDAR derived height and ground based dbh, a linear regression model was developed to predict dbh. The predicted dbh was used to estimate the biomass at the single tree level using Jenkin's formula (Jenkin et al 2003). The linear regression models were able to explain 65% of the variability associated with Redwood's dbh and 80% of that associated with Douglas-fir's dbh.

  13. Coastal and tidal landform detection from high resolution topobathymetric LiDAR data

    NASA Astrophysics Data System (ADS)

    Skovgaard Andersen, Mikkel; Al-Hamdani, Zyad; Steinbacher, Frank; Rolighed Larsen, Laurids; Brandbyge Ernstsen, Verner

    2016-04-01

    Coastal and tidal environments are valuable ecosystems, which, however, are under pressure in many areas around the world due to globalisation and/or climate change. Detailed mapping of these environments is required in order to manage the coastal zone in a sustainable way. However, historically these transition zones between land and water are difficult or even impossible to map and investigate in high spatial resolution due to the challenging environmental conditions. The new generation of airborne topobathymetric light detection and ranging (LiDAR) potentially enables full-coverage and high-resolution mapping of these land-water transition zones. We have carried out topobathymetric LiDAR surveys in the Knudedyb tidal inlet system, a coastal environment in the Danish Wadden Sea which is part of the Wadden Sea National Park and UNESCO World Heritage. Detailed digital elevation models (DEMs) with a grid cell size of 0.5 m x 0.5 m were generated from the LiDAR point cloud with a mean point density in the order of 20 points/m2. The DEM was analysed morphometrically using a modification of the tool Benthic Terrain Modeler (BTM) developed by Wright et al. (2005). Initially, stage (the elevation in relation to tidal range) was used to divide the area of investigation into the different tidal zones, i.e. subtidal, intertidal and supratidal. Subsequently, morphometric units were identified and characterised by a combination of statistical neighbourhood analysis with varying window sizes (using the Bathymetric Positioning Index (BPI) from the BTM, moving average and standard deviation), slope parameters and area/perimeter ratios. Finally, these morphometric units were classified into six different types of landforms based on their stage and morphometric characteristics, i.e. either subtidal channel, intertidal flat, intertidal creek, linear bar, swash bar or beach dune. We hereby demonstrate the potential of using airborne topobathymetric LiDAR for seamless mapping of land

  14. Impact of survey workflow on precision and accuracy of terrestrial LiDAR datasets

    NASA Astrophysics Data System (ADS)

    Gold, P. O.; Cowgill, E.; Kreylos, O.

    2009-12-01

    Ground-based LiDAR (Light Detection and Ranging) survey techniques are enabling remote visualization and quantitative analysis of geologic features at unprecedented levels of detail. For example, digital terrain models computed from LiDAR data have been used to measure displaced landforms along active faults and to quantify fault-surface roughness. But how accurately do terrestrial LiDAR data represent the true ground surface, and in particular, how internally consistent and precise are the mosaiced LiDAR datasets from which surface models are constructed? Addressing this question is essential for designing survey workflows that capture the necessary level of accuracy for a given project while minimizing survey time and equipment, which is essential for effective surveying of remote sites. To address this problem, we seek to define a metric that quantifies how scan registration error changes as a function of survey workflow. Specifically, we are using a Trimble GX3D laser scanner to conduct a series of experimental surveys to quantify how common variables in field workflows impact the precision of scan registration. Primary variables we are testing include 1) use of an independently measured network of control points to locate scanner and target positions, 2) the number of known-point locations used to place the scanner and point clouds in 3-D space, 3) the type of target used to measure distances between the scanner and the known points, and 4) setting up the scanner over a known point as opposed to resectioning of known points. Precision of the registered point cloud is quantified using Trimble Realworks software by automatic calculation of registration errors (errors between locations of the same known points in different scans). Accuracy of the registered cloud (i.e., its ground-truth) will be measured in subsequent experiments. To obtain an independent measure of scan-registration errors and to better visualize the effects of these errors on a registered point

  15. QTL mapping for fruit quality in Citrus using DArTseq markers.

    PubMed

    Curtolo, Maiara; Cristofani-Yaly, Mariângela; Gazaffi, Rodrigo; Takita, Marco Aurélio; Figueira, Antonio; Machado, Marcos Antonio

    2017-04-12

    Citrus breeding programs have many limitations associated with the species biology and physiology, requiring the incorporation of new biotechnological tools to provide new breeding possibilities. Diversity Arrays Technology (DArT) markers, combined with next-generation sequencing, have wide applicability in the construction of high-resolution genetic maps and in quantitative trait locus (QTL) mapping. This study aimed to construct an integrated genetic map using full-sib progeny derived from Murcott tangor and Pera sweet orange and DArTseq™ molecular markers and to perform QTL mapping of twelve fruit quality traits. A controlled Murcott x Pera crossing was conducted at the Citrus Germplasm Repository at the Sylvio Moreira Citrus Centre of the Agronomic Institute (IAC) located in Cordeirópolis, SP, in 1997. In 2012, 278 F1 individuals out of a family of 312 confirmed hybrid individuals were analyzed for fruit traits and genotyped using the DArTseq markers. Using OneMap software to obtain the integrated genetic map, we considered only the DArT loci that showed no segregation deviation. The likelihood ratio and the genomic information from the available Citrus sinensis L. Osbeck genome were used to determine the linkage groups (LGs). The resulting integrated map contained 661 markers in 13 LGs, with a genomic coverage of 2,774 cM and a mean density of 0.23 markers/cM. The groups were assigned to the nine Citrus haploid chromosomes; however, some of the chromosomes were represented by two LGs due the lack of information for a single integration, as in cases where markers segregated in a 3:1 fashion. A total of 19 QTLs were identified through composite interval mapping (CIM) of the 12 analyzed fruit characteristics: fruit diameter (cm), height (cm), height/diameter ratio, weight (g), rind thickness (cm), segments per fruit, total soluble solids (TSS, %), total titratable acidity (TTA, %), juice content (%), number of seeds, TSS/TTA ratio and number of fruits per

  16. Modeling Urban Growth Spatial Dynamics: Case studies of Addis Ababa and Dar es Salaam

    NASA Astrophysics Data System (ADS)

    Buchta, Katja; Abo El Wafa, Hany; Printz, Andreas; Pauleit, Stephan

    2013-04-01

    Rapid urbanization, and consequently, the dramatic spatial expansion of mostly informal urban areas increases the vulnerability of African cities to the effects of climate change such as sea level rise, more frequent flooding, droughts and heat waves. The EU FP 7 funded project CLUVA (Climate Change and Urban Vulnerability in Africa, www.cluva.eu) aims to develop strategies for minimizing the risks of natural hazards caused by climate change and to improve the coping capacity of African cities. Green infrastructure may play a particular role in climate change adaptation by providing ecosystem services for flood protection, stormwater retention, heat island moderation and provision of food and fuel wood. In this context, a major challenge is to gain a better understanding of the spatial and temporal dynamics of the cities and how these impact on green infrastructure and hence their vulnerability. Urban growth scenarios for two African cities, namely Addis Ababa, Ethiopia and Dar es Salaam, Tanzania, were developed based on a characterization of their urban morphology. A population growth driven - GIS based - disaggregation modeling approach was applied. Major impact factors influencing the urban dynamics were identified both from literature and interviews with local experts. Location based factors including proximity to road infrastructure and accessibility, and environmental factors including slope, surface and flood risk areas showed a particular impact on urban growth patterns. In Addis Ababa and Dar es Salaam, population density scenarios were modeled comparing two housing development strategies. Results showed that a densification scenario significantly decreases the loss of agricultural and green areas such as forests, bushland and sports grounds. In Dar es Salaam, the scenario of planned new settlements with a population density of max. 350 persons per hectare would lead until 2025 to a loss of agricultural land (-10.1%) and green areas (-6.6%). On the other

  17. Quantification of LiDAR measurement uncertainty through propagation of errors due to sensor sub-systems and terrain morphology

    NASA Astrophysics Data System (ADS)

    Goulden, T.; Hopkinson, C.

    2013-12-01

    The quantification of LiDAR sensor measurement uncertainty is important for evaluating the quality of derived DEM products, compiling risk assessment of management decisions based from LiDAR information, and enhancing LiDAR mission planning capabilities. Current quality assurance estimates of LiDAR measurement uncertainty are limited to post-survey empirical assessments or vendor estimates from commercial literature. Empirical evidence can provide valuable information for the performance of the sensor in validated areas; however, it cannot characterize the spatial distribution of measurement uncertainty throughout the extensive coverage of typical LiDAR surveys. Vendor advertised error estimates are often restricted to strict and optimal survey conditions, resulting in idealized values. Numerical modeling of individual pulse uncertainty provides an alternative method for estimating LiDAR measurement uncertainty. LiDAR measurement uncertainty is theoretically assumed to fall into three distinct categories, 1) sensor sub-system errors, 2) terrain influences, and 3) vegetative influences. This research details the procedures for numerical modeling of measurement uncertainty from the sensor sub-system (GPS, IMU, laser scanner, laser ranger) and terrain influences. Results show that errors tend to increase as the laser scan angle, altitude or laser beam incidence angle increase. An experimental survey over a flat and paved runway site, performed with an Optech ALTM 3100 sensor, showed an increase in modeled vertical errors of 5 cm, at a nadir scan orientation, to 8 cm at scan edges; for an aircraft altitude of 1200 m and half scan angle of 15°. In a survey with the same sensor, at a highly sloped glacial basin site absent of vegetation, modeled vertical errors reached over 2 m. Validation of error models within the glacial environment, over three separate flight lines, respectively showed 100%, 85%, and 75% of elevation residuals fell below error predictions. Future

  18. Measuring the effects of morphological changes to sea turtle nesting beaches over time with LiDAR data

    NASA Astrophysics Data System (ADS)

    Yamamoto, Kristina H.; Anderson, Sharolyn J.; Sutton, Paul C.

    2015-10-01

    Sea turtle nesting beaches in southeastern Florida were evaluated for changes from 1999 to 2005 using LiDAR datasets. Changes to beach volume were correlated with changes in several elevation-derived characteristics, such as elevation and slope. In addition, these changes to beach geomorphology were correlated to changes in nest success, illustrating that beach alterations may affect sea turtle nesting behavior. The ability to use LiDAR datasets to quickly and efficiently conduct beach comparisons for habitat use represents another benefit to this high spatial resolution data.

  19. Object-Based Integration of Photogrammetric and LiDAR Data for Automated Generation of Complex Polyhedral Building Models

    PubMed Central

    Kim, Changjae; Habib, Ayman

    2009-01-01

    This research is concerned with a methodology for automated generation of polyhedral building models for complex structures, whose rooftops are bounded by straight lines. The process starts by utilizing LiDAR data for building hypothesis generation and derivation of individual planar patches constituting building rooftops. Initial boundaries of these patches are then refined through the integration of LiDAR and photogrammetric data and hierarchical processing of the planar patches. Building models for complex structures are finally produced using the refined boundaries. The performance of the developed methodology is evaluated through qualitative and quantitative analysis of the generated building models from real data. PMID:22346722

  20. Quantification of L-band InSAR decorrelation in volcanic terrains using airborne LiDAR data

    NASA Astrophysics Data System (ADS)

    Sedze, M.; Heggy, E.; Jacquemoud, S.; Bretar, F.

    2011-12-01

    Repeat-pass InSAR LOS measurements of the Piton de La Fournaise (La Reunion Island, France) suffer from substantial phase decorrelation due to the occurrence of vegetation and ash deposits on the caldera and slopes of the edifice. To correct this deficiency, we combine normalized airborne LiDAR (Light Detection and Ranging) intensity data with spaceborne InSAR coherence images from ALOS PALSAR L-band acquired over the volcano in 2008 and 2009, following the 2007 major eruption. The fusion of the two data sets improves the calculation of coherence and the textural classification of different volcanic surfaces. For future missions considering both InSAR and/or LiDAR such as DESDynI (Deformation, Ecosystem Structure and Dynamics of Ice), such data fusion studies can provide a better analysis of the spatiotemporal variations in InSAR coherence in order to enhance the monitoring of pre-eruptive ground displacements. The airborne surveys conducted in 2008 and 2009, cover different types of vegetation and terrain roughness on the central and western parts of the volcano. The topographic data are first processed to generate a high-resolution digital terrain model (DTM) of the volcanic edifice with elevation accuracy better than 1 m. For our purposes, the phase variations caused by the surface relief can be eliminated using the LiDAR-derived DTM. Then normalized LiDAR intensities are correlated to the L-band polarimetric coherence for different zones of the volcano to assess the LiDAR-InSAR statistical behavior of different lava flows, pyroclastics, and vegetated surfaces. Results suggest that each volcanic terrain type is characterized by a unique LiDAR-InSAR histogram pattern. We identified four LiDAR-InSAR distinguished relations: (1) pahoehoe lava flow surfaces show an agglomerate histogram pattern which may be explained by low surface scattering and low wave penetration into the geological medium; (2) eroded a'a lava surfaces is characterized by high standard deviation

  1. Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data

    NASA Astrophysics Data System (ADS)

    Luo, Shezhou; Wang, Cheng; Xi, Xiaohuan; Pan, Feifei; Qian, Mingjie; Peng, Dailiang; Nie, Sheng; Qin, Haiming; Lin, Yi

    2017-06-01

    Wetland biomass is essential for monitoring the stability and productivity of wetland ecosystems. Conventional field methods to measure or estimate wetland biomass are accurate and reliable, but expensive, time consuming and labor intensive. This research explored the potential for estimating wetland reed biomass using a combination of airborne discrete-return Light Detection and Ranging (LiDAR) and hyperspectral data. To derive the optimal predictor variables of reed biomass, a range of LiDAR and hyperspectral metrics at different spatial scales were regressed against the field-observed biomasses. The results showed that the LiDAR-derived H_p99 (99th percentile of the LiDAR height) and hyperspectral-calculated modified soil-adjusted vegetation index (MSAVI) were the best metrics for estimating reed biomass using the single regression model. Although the LiDAR data yielded a higher estimation accuracy compared to the hyperspectral data, the combination of LiDAR and hyperspectral data produced a more accurate prediction model for reed biomass (R2 = 0.648, RMSE = 167.546 g/m2, RMSEr = 20.71%) than LiDAR data alone. Thus, combining LiDAR data with hyperspectral data has a great potential for improving the accuracy of aboveground biomass estimation.

  2. Diameter at breast height estimation in Mt. Makiling, Laguna, Philippines using metrics derived from airborne LiDAR data and Worldview-2 bands

    NASA Astrophysics Data System (ADS)

    Tandoc, Fe Andrea M.; Paringit, Enrico C.; Bantayan, Nathaniel C.; Argamosa, Reginald Jay L.; Faelga, Regine Anne G.; Ibañez, Carlyn Ann G.; Posilero, Mark Anthony V.; Zaragosa, Gio P.; Malabanan, Matthew V.

    2016-05-01

    Airborne LiDAR is fast becoming an innovation for forest inventory. It aids in obtaining forest characteristics in areas or cases where actual field inventory would be very tedious. This study aims to estimate diameter at breast height (DBH) using airborne LiDAR point-cloud parameters with Worldview-2 satellite images, and to validate these with actual measurements done in the field. The study site is a field plot with forest inventory at Mt. Makiling, Laguna, Philippines that was surveyed into 20m, 10m and 5m subplots or grids. The estimation of DBH was carried out by extracting the said parameters from the LiDAR point-cloud, and extracting different bands from the Worldview image and performing linear and log-linear regression of these values. The regressions were done in four different cases, namely: LiDAR parameters without intensity (case1), LiDAR parameters without intensity with Worldview bands (case 2), intensity of LiDAR points (case 3), and LiDAR parameters with intensity and Worldview bands (case 4). From these it was found that the best case for estimating DBH is with the use of LiDAR parameters with intensity and Worldview bands in a 10x10 grid, in Log-Linear regression with a root mean squared error of 1.96 cm and an adjusted R2 value of 0.65. This was further improved through stepwise regression, and adjusted R2 value was 0.71.

  3. Aggregating pixel-level basal area predictions derived from LiDAR data to industrial forest stands in North-Central Idaho

    Treesearch

    Andrew T. Hudak; Jeffrey S. Evans; Nicholas L. Crookston; Michael J. Falkowski; Brant K. Steigers; Rob Taylor; Halli Hemingway

    2008-01-01

    Stand exams are the principal means by which timber companies monitor and manage their forested lands. Airborne LiDAR surveys sample forest stands at much finer spatial resolution and broader spatial extent than is practical on the ground. In this paper, we developed models that leverage spatially intensive and extensive LiDAR data and a stratified random sample of...

  4. A comparison of accuracy and cost of LiDAR versus stand exam data for landscape management on the Malheur National Forest

    Treesearch

    Susan Hummel; A. T. Hudak; E. H. Uebler; M. J. Falkowski; K. A. Megown

    2011-01-01

    Foresters are increasingly interested in remote sensing data because they provide an overview of landscape conditions, which is impractical with field sample data alone. Light Detection and Ranging (LiDAR) provides exceptional spatial detail of forest structure, but difficulties in processing LiDAR data have limited their application beyond the research community....

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

    NASA Astrophysics Data System (ADS)

    Selvarajan, Sowmya

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

  6. A LiDAR intensity correction model for vertical geological mapping

    NASA Astrophysics Data System (ADS)

    Carrea, Dario; Humair, Florian; Matasci, Battista; Abellan, Antonio; Derron, Marc-Henri; Jaboyedoff, Michel

    2015-04-01

    Ground-based LiDAR has been traditionally used for surveying purposes via 3D point clouds. In addition to XYZ coordinates, an intensity attribute is also recorded by the LiDAR devices but this parameter is rarely used for geological applications. The intensity of the backscattered signal can be a significant source of information in different geological applications, such as geological remote mapping of vertical surfaces, mineral exploration, stratigraphy, engineering, etc. However, the Intensity value recorded by the LiDAR is a function of several external parameters, thus a correction of the raw intensity information is required prior to make use of this parameter. This study proposes an intensity correction model which takes into account of the range, the incidence angle and the surface roughness based on Oren-Nayar reflectance model (Oren and Nayar, 1994). The Oren-Nayar reflectance model is based on the idea that a surface is composed of micro-facets of various slope angles. The simplified version of this model requires only one parameter to characterize a surface, the standard deviation of the slope angle of the facets. Different discrete pulse laser scanners of Optech's ILRIS category were used to understand how the back-scattered intensity evolves in function of range and incidence angle. This was performed by carrying out different indoor and outdoor experiments, using the following targets: 1) mobile 2m2 board covered by black/white paper, 2) white plaster corridor walls and 3) finally on natural outcrops. First of all, we carried out a simple experiment by placing the mobile board at different distances ranging from 10 to 1000 meters. The analysis of the datasets revealed that the intensity of the backscattered signal decreases with the square of the range to the target, as was expected. However, both for the wall and the natural outcrops, the influence of the incidence angle appears to be more complex than the classical cosine law due to the roughness

  7. Sexual behaviour among youths at high risk for HIV-1 infection in Dar es Salaam, Tanzania

    PubMed Central

    Mwakagile, D; Mmari, E; Makwaya, C; Mbwana, J; Biberfeld, G; Mhalu, F; Sandstrom, E

    2001-01-01

    Objectives: To investigate sex specific sexual behaviour in youths visiting a youth clinic for sexual and reproductive health in Dar es Saalam. Methods: A questionnaire was administered to a random sample of youths between 10 and 24 years of age attending the youth health clinic in Dar es Saalam. The clinical investigation included testing for syphilis and HIV-1 antibodies Results: 1423 youths attended the clinic between September 1997 and August 1998. The study population comprised 213 (53.5%) males and 185 (46.5%) females. 97 (24.4%) were below 20 years. The mean age at coitarche was 16.5 and 17.0 years of age for males and females, respectively. The coitarche was involuntary in 15 females (8.6%). 49.5% males reported more than five lifetime partners compared with 14.1% for females (p<0.0001). Males reported recent partners to be 2.5 years younger, while females reported them to be 5.0 years older. No contraceptive use was reported by 29.7% of the males and 40.3% of females. 52.7% females had been pregnant and 26 (14.1%) reported induced abortions. Genital discharge was found in 69.5% and 73.9% and GUD in 36.6% and 27.1% of males and females respectively. 12 males (5.9%) and 43 females (24.6%) were found to be HIV-1 infected. 13.8% of the females with only one lifetime partner were HIV-1 infected compared with 40.9% with more than five partners (p = 0.028). Conclusions: Many youths in Dar es Salaam engage in sexual behaviours that put them at risk of unwanted pregnancies and STIs including HIV infection. Female youths were more likely to contract HIV infection than males. In African urban areas youth oriented clinics can have a pivotal role in HIV/STI prevention and control Key Words: youth; sexual behaviour; HIV PMID:11463924

  8. Uncertainty in LiDAR derived Canopy Height Models in three unique forest ecosystems

    NASA Astrophysics Data System (ADS)

    Goulden, T.; Leisso, N.; Scholl, V.; Hass, B.

    2016-12-01

    The National Ecological Observatory Network (NEON) is a continental-scale ecological observation platform designed to collect and disseminate data that contributes to understanding and forecasting the impacts of climate change, land use change, and invasive species on ecology. NEON will collect in-situ and airborne data over 81 sites across the US, including Alaska, Hawaii, and Puerto Rico. The Airborne Observation Platform (AOP) group within the NEON project operates a payload suite that includes a waveform / discrete LiDAR, imaging spectrometer (NIS) and high resolution RGB camera. One of the products derived from the discrete LiDAR is a canopy height model (CHM) raster developed at 1 m spatial resolution. Currently, it is hypothesized that differencing annually acquired CHM products allows identification of tree growth at in-situ distributed plots throughout the NEON sites. To test this hypothesis, the precision of the CHM product was determined through a specialized flight plan that independently repeated up to 20 observations of the same area with varying view geometries. The flight plan was acquired at three NEON sites, each with a unique forest types including 1) San Joaquin Experimental Range (SJER, open woodland dominated by oaks), 2) Soaproot Saddle (SOAP, mixed conifer deciduous forest), and 3) Oak Ridge National Laboratory (ORNL, oak hickory and pine forest). A CHM was developed for each flight line at each site and the overlap area was used to empirically estimate a site-specific precision of the CHM. The average cell-by-cell CHM precision at SJER, SOAP and ORNL was 1.34 m, 4.24 m and 0.72 m respectively. Given the average growth rate of the dominant species at each site and the average CHM uncertainty, the minimum time interval required between LiDAR acquisitions to confidently conclude growth had occurred at the plot scale was estimated to be between one and four years. The minimum interval time was shown to be primarily dependent on the CHM

  9. High-throughput genotyping of wheat-barley amphiploids utilising diversity array technology (DArT).

    PubMed

    Castillo, Almudena; Ramírez, María C; Martín, Azahara C; Kilian, Andrzej; Martín, Antonio; Atienza, Sergio G

    2013-06-03

    Hordeum chilense, a native South American diploid wild barley, is one of the species of the genus Hordeum with a high potential for cereal breeding purposes, given its high crossability with other members of the Triticeae tribe. Hexaploid tritordeum (×Tritordeum Ascherson et Graebner, 2n=6×=42, AABBH(ch)H(ch)) is the fertile amphiploid obtained after chromosome doubling of hybrids between Hordeum chilense and durum wheat. Approaches used in the improvement of this crop have included crosses with hexaploid wheat to promote D/H(ch) chromosome substitutions. While this approach has been successful as was the case with triticale, it has also complicated the genetic composition of the breeding materials. Until now tritordeum lines were analyzed based on molecular cytogenetic techniques and screening with a small set of DNA markers. However, the recent development of DArT markers in H. chilense offers new possibilities to screen large number of accessions more efficiently. Here, we have applied DArT markers to genotype composition in forty-six accessions of hexaploid tritordeum originating from different stages of tritordeum breeding program and to H. chilense-wheat chromosome addition lines to allow their physical mapping. Diversity analyses were conducted including dendrogram construction, principal component analysis and structure inference. Euploid and substituted tritordeums were clearly discriminated independently of the method used. However, dendrogram and Structure analyses allowed the clearest discrimination among substituted tritordeums. The physically mapped markers allowed identifying these groups as substituted tritordeums carrying the following disomic substitutions (DS): DS1D (1H(ch)), DS2D (2H(ch)), DS5D (5H(ch)), DS6D (6H(ch)) and the double substitution DS2D (2H(ch)), DS5D (5H(ch)). These results were validated using chromosome specific EST and SSR markers and GISH analysis. In conclusion, DArT markers have proved to be very useful to detect

  10. Building a LiDAR point cloud simulator: Testing algorithms for high resolution topographic change

    NASA Astrophysics Data System (ADS)

    Carrea, Dario; Abellán, Antonio; Derron, Marc-Henri; Jaboyedoff, Michel

    2014-05-01

    Terrestrial laser technique (TLS) is becoming a common tool in Geosciences, with clear applications ranging from the generation of a high resolution 3D models to the monitoring of unstable slopes and the quantification of morphological changes. Nevertheless, like every measurement techniques, TLS still has some limitations that are not clearly understood and affect the accuracy of the dataset (point cloud). A challenge in LiDAR research is to understand the influence of instrumental parameters on measurement errors during LiDAR acquisition. Indeed, different critical parameters interact with the scans quality at different ranges: the existence of shadow areas, the spatial resolution (point density), and the diameter of the laser beam, the incidence angle and the single point accuracy. The objective of this study is to test the main limitations of different algorithms usually applied on point cloud data treatment, from alignment to monitoring. To this end, we built in MATLAB(c) environment a LiDAR point cloud simulator able to recreate the multiple sources of errors related to instrumental settings that we normally observe in real datasets. In a first step we characterized the error from single laser pulse by modelling the influence of range and incidence angle on single point data accuracy. In a second step, we simulated the scanning part of the system in order to analyze the shifting and angular error effects. Other parameters have been added to the point cloud simulator, such as point spacing, acquisition window, etc., in order to create point clouds of simple and/or complex geometries. We tested the influence of point density and vitiating point of view on the Iterative Closest Point (ICP) alignment and also in some deformation tracking algorithm with same point cloud geometry, in order to determine alignment and deformation detection threshold. We also generated a series of high resolution point clouds in order to model small changes on different environments

  11. Large Footprint LiDAR Data Processing for Ground Detection and Biomass Estimation

    NASA Astrophysics Data System (ADS)

    Zhuang, Wei

    Ground detection in large footprint waveform Light Detection And Ranging (LiDAR) data is important in calculating and estimating downstream products, especially in forestry applications. For example, tree heights are calculated as the difference between the ground peak and first returned signal in a waveform. Forest attributes, such as aboveground biomass, are estimated based on the tree heights. This dissertation investigated new metrics and algorithms for estimating aboveground biomass and extracting ground peak location in large footprint waveform LiDAR data. In the first manuscript, an accurate and computationally efficient algorithm, named Filtering and Clustering Algorithm (FICA), was developed based on a set of multiscale second derivative filters for automatically detecting the ground peak in an waveform from Land, Vegetation and Ice Sensor. Compared to existing ground peak identification algorithms, FICA was tested in different land cover type plots and showed improved accuracy in ground detections of the vegetation plots and similar accuracy in developed area plots. Also, FICA adopted a peak identification strategy rather than following a curve-fitting process, and therefore, exhibited improved efficiency. In the second manuscript, an algorithm was developed specifically for shrub waveforms. The algorithm only partially fitted the shrub canopy reflection and detected the ground peak by investigating the residual signal, which was generated by deducting a Gaussian fitting function from the raw waveform. After the deduction, the overlapping ground peak was identified as the local maximum of the residual signal. In addition, an applicability model was built for determining waveforms where the proposed PCF algorithm should be applied. In the third manuscript, a new set of metrics was developed to increase accuracy in biomass estimation models. The metrics were based on the results of Gaussian decomposition. They incorporated both waveform intensity

  12. Human Impact Intertwined with Glacial Legacy: Hydro-Geomorphologic Exploration using LiDAR data

    NASA Astrophysics Data System (ADS)

    Yan, Q.; Kumar, P.

    2014-12-01

    Intensively managed landscapes (IMLs) in the Midwestern United States are heavily modified by agriculture, artificial drainage, deforestation, urbanization, and wetland destruction. These landscapes have been shaped by repeated glacial events over geologic time scales followed with rapid human modifications for agriculture and drainage that are overlaid on extremely low gradient stream networks. In this study, using LiDAR data from the Upper Sangamon River Basin in Illinois, we attempt to understand how the long-term glacial legacy has shaped the landscape and what is the impact from short-term human activities, such as channel straightening and periodic dredging. Glacial and human legacy impact landscape dynamics simultaneously. Therefore, we evaluate the present-day dynamics of landscapes by attempting to address several questions. First, we explore whether the watershed is in equilibrium conditions or away from it due to human activities. Second, we study how this relates to the degree of maturity in the river valley. Moreover, we map the spatial distributions of terraces and floodplains to understand depositional and erosional history. High-resolution LiDAR data is ideal for such a study as it reveals the impact of both glacial episodes and human activities. Methods used for extraction of useful information from LiDAR data include the TerEx tool, Stream Profiler, and Hec-GeoRas, among others. We analyze the terrace and floodplain geomorphic features, quantify stream sinuosity, and cross section geometries. An integral method is built based on stream power incision model to obtain sub-basin steady state condition. These features help to reveal local and global watershed properties. A bounded relationship between terraces/floodplains, sinuosity, cross section maturity, as well as sub-basin equilibrium condition is explored. In general, we find that the glacial legacy and present-day human activity have opposed each other in regards to the sub-basin equilibrium

  13. Long-term persistence of throughfall yield assessed by small footprint LiDAR data

    NASA Astrophysics Data System (ADS)

    Bischoff, Sebastian; Levia, Delphis F.; Nieschulze, Jens; Schulz, Florian; Michalzik, Beate

    2016-04-01

    Throughfall (TF) represents an important relocation mechanism for the spatial distribution of intercepted precipitation and hence associated nutrients in wooded ecosystems. To date, a broad range of studies showed that the spatial patterns of TF distribution exhibit a pronounced temporal stability. These studies, however, have examined TF temporal stability at the tree scale or they were computed from event-based data. Here, we seek to evaluate the utility of temporally aggregated TF data at one, three, and six year intervals to determine whether such long-term TF monitoring data could serve as the basis for TF temporal persistence measurements for both beech and spruce forests. In addition, we examine the temporal persistence of TF in relation to small footprint LiDAR data. In context of the German Science Foundation (DFG) founded "Biodiversity Exploratories" (www.biodiversity-exploratories.de) we studied water-bound nutrient fluxes on a set of three differently managed forest plots (spruce plantation, age class forest beech, unmanaged beech) in central Germany throughout the vegetation periods of 2010 - 2015. For long-term monitoring purposes, TF samples were collected in biweekly routine sampling intervals using X-shaped transects of 20 bulk samplers (axis length 32 m) per experimental plot. In this study, we aim to identify canopy structural parameters explaining the temporal patterns observed. We therefore used small footprint LiDAR (Light Detection And Ranging) data to calculate several canopy structural parameters on base of a gridded canopy model (grid cell resolution = 0.75 m). As LiDAR allows a three-dimensional description of the complex forest canopy structure it might help to extend our understanding of complex canopy processes influencing the spatial dispersal of precipitation water, and hence associated nutrient fluxes, in wooded ecosystems. Preliminary data analysis reveals that normalized TF values identify a number of TF collectors on each of the

  14. An historically consistent and broadly applicable MRV system based on LiDAR sampling and Landsat time-series

    Treesearch

    W. Cohen; H. Andersen; S. Healey; G. Moisen; T. Schroeder; C. Woodall; G. Domke; Z. Yang; S. Stehman; R. Kennedy; C. Woodcock; Z. Zhu; J. Vogelmann; D. Steinwand; C. Huang

    2014-01-01

    The authors are developing a REDD+ MRV system that tests different biomass estimation frameworks and components. Design-based inference from a costly fi eld plot network was compared to sampling with LiDAR strips and a smaller set of plots in combination with Landsat for disturbance monitoring. Biomass estimation uncertainties associated with these different data sets...

  15. Fine-spatial scale predictions of understory species using climate- and LiDAR-derived terrain and canopy metrics

    NASA Astrophysics Data System (ADS)

    Nijland, Wiebe; Nielsen, Scott E.; Coops, Nicholas C.; Wulder, Michael A.; Stenhouse, Gordon B.

    2014-01-01

    Food and habitat resources are critical components of wildlife management and conservation efforts. The grizzly bear (Ursus arctos) has diverse diets and habitat requirements particularly for understory plant species, which are impacted by human developments and forest management activities. We use light detection and ranging (LiDAR) data to predict the occurrence of 14 understory plant species relevant to bear forage and compare our predictions with more conventional climate- and land cover-based models. We use boosted regression trees to model each of the 14 understory species across 4435 km2 using occurrence (presence-absence) data from 1941 field plots. Three sets of models were fitted: climate only, climate and basic land and forest covers from Landsat 30-m imagery, and a climate- and LiDAR-derived model describing both the terrain and forest canopy. Resulting model accuracies varied widely among species. Overall, 8 of 14 species models were improved by including the LiDAR-derived variables. For climate-only models, mean annual precipitation and frost-free periods were the most important variables. With inclusion of LiDAR-derived attributes, depth-to-water table, terrain-intercepted annual radiation, and elevation were most often selected. This suggests that fine-scale terrain conditions affect the distribution of the studied species more than canopy conditions.

  16. Using LiDAR and remote microclimate loggers to downscale near-surface air temperatures for site-level studies

    Treesearch

    Andrew D. George; Frank R. Thompson; John. Faaborg

    2015-01-01

    A spatial mismatch exists between regional climate models and conditions experienced by individual organisms. We demonstrate an approach to downscaling air temperatures for site-level studies using airborne LiDAR data and remote microclimate loggers. In 2012-2013, we established a temperature logger network in the forested region of central Missouri, USA, and obtained...

  17. A model for predicting GPS-GDOP and its probability using LiDAR data and ultra rapid product

    NASA Astrophysics Data System (ADS)

    Lohani, Bharat; Kumar, Raman

    2008-12-01

    This paper presents a model to predict the GDOP value (Geometric Dilution of Precision) and the probability of its occurrence at a point in space and time using airborne LiDAR (Light Detection and Ranging) data and the ultra-rapid product (URP) available from the International GPS Service. LiDAR data help to classify the terrain around a GPS (Global Positioning System) receiver into categories such as ground, opaque objects, translucent objects and transparent regions as per their response to the transmission of GPS signal. Through field experiments it is established that URP can be used satisfactorily to determine GDOP. Further experiments have shown that the translucent objects (mainly trees here) lower the GDOP quality as they obstruct the GPS signal. LiDAR data density on trees is used as a measure of the translucency of trees to the GPS signal. Through GPS observations taken in field a relationship has been established between LiDAR data density on trees and the probability that a satellite which is behind the tree is visible at the GPS receiver. A model is presented which, for all possible combinations of visible satellites, computes the GDOP value along with the probability of occurrence of this GDOP. A few results are presented to show the performance of the model developed and its possible application in location based queries.

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

    SciTech Connect

    Kumar, Jitendra; HargroveJr., William Walter; Norman, Steven P; Hoffman, Forrest M; Newcomb, Doug

    2015-01-01

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

  19. Mapping aboveground carbon stocks using LiDAR data in Eucalyptus spp. plantations in the state of Sao Paulo, Brazil

    Treesearch

    Carlos Alberto Silva; Carine Klauberg; Samuel de Padua Chaves e Carvalho; Andrew T. Hudak; e Luiz Carlos Estraviz. Rodriguez

    2014-01-01

    Fast growing plantation forests provide a low-cost means to sequester carbon for greenhouse gas abatement. The aim of this study was to evaluate airborne LiDAR (Light Detection And Ranging) to predict aboveground carbon (AGC) stocks in Eucalyptus spp. plantations. Biometric parameters (tree height (Ht) and diameter at breast height (DBH)) were collected from...

  20. Imputation of individual longleaf pine (Pinus palustris Mill.) tree attributes from field and LiDAR data

    Treesearch

    Carlos A. Silva; Andrew T. Hudak; Lee A. Vierling; E. Louise Loudermilk; Joseph J. O' Brien; J. Kevin Hiers; Steve B. Jack; Carlos Gonzalez-Benecke; Heezin Lee; Michael J. Falkowski; Anahita Khosravipour

    2016-01-01

    Light Detection and Ranging (LiDAR) has demonstrated potential for forest inventory at the individual-tree level. The aim in this study was to predict individual-tree height (Ht; m), basal area (BA; m2), and stem volume (V; m3...

  1. Calculating the ecosystem service of water storage in isolated wetlands using LiDAR in north central Florida, USA (presentation)

    EPA Science Inventory

    This study used remotely-sensed Light Detection and Ranging (LiDAR) data to estimate potential water storage capacity of isolated wetlands in north central Florida. The data were used to calculate the water storage potential of >8500 polygons identified as isolated wetlands. We f...

  2. Assessing Surface Fuel Hazard in Coastal Conifer Forests through the Use of LiDAR Remote Sensing

    NASA Astrophysics Data System (ADS)

    Koulas, Christos

    The research problem that this thesis seeks to examine is a method of predicting conventional fire hazards using data drawn from specific regions, namely the Sooke and Goldstream watershed regions in coastal British Columbia. This thesis investigates whether LiDAR data can be used to describe conventional forest stand fire hazard classes. Three objectives guided this thesis: to discuss the variables associated with fire hazard, specifically the distribution and makeup of fuel; to examine the relationship between derived LiDAR biometrics and forest attributes related to hazard assessment factors defined by the Capitol Regional District (CRD); and to assess the viability of the LiDAR biometric decision tree in the CRD based on current frameworks for use. The research method uses quantitative datasets to assess the optimal generalization of these types of fire hazard data through discriminant analysis. Findings illustrate significant LiDAR-derived data limitations, and reflect the literature in that flawed field application of data modelling techniques has led to a disconnect between the ways in which fire hazard models have been intended to be used by scholars and the ways in which they are used by those tasked with prevention of forest fires. It can be concluded that a significant trade-off exists between computational requirements for wildfire simulation models and the algorithms commonly used by field teams to apply these models with remote sensing data, and that CRD forest management practices would need to change to incorporate a decision tree model in order to decrease risk.

  3. Intimate Partner Violence and the Association with HIV Risk Behaviors among Young Men in Dar Es Salaam, Tanzania

    ERIC Educational Resources Information Center

    Maman, Suzanne; Yamanis, Thespina; Kouyoumdjian, Fiona; Watt, Melissa; Mbwambo, Jessie

    2010-01-01

    There is growing evidence of the association between gender-based violence and HIV from the perspective and experiences of women. The purpose of this study is to examine these associations from the perspective of young men living in Dar es Salaam, Tanzania. A community-based sample of 951 men were interviewed, of whom 360 had sex in the past 6…

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

    PubMed Central

    Habib, Ayman F.; Kersting, Ana P.; Shaker, Ahmed; Yan, Wai-Yeung

    2011-01-01

    LiDAR (Light Detection And Ranging) systems are capable of providing 3D positional and spectral information (in the utilized spectrum range) of the mapped surface. Due to systematic errors in the system parameters and measurements, LiDAR systems require geometric calibration and radiometric correction of the intensity data in order to maximize the benefit from the collected positional and spectral information. This paper presents a practical approach for the geometric calibration of LiDAR systems and radiometric correction of collected intensity data while investigating their impact on the quality of the derived products. The proposed approach includes the use of a quasi-rigorous geometric calibration and the radar equation for the radiometric correction of intensity data. The proposed quasi-rigorous calibration procedure requires time-tagged point cloud and trajectory position data, which are available to most of the data users. The paper presents a methodology for evaluating the impact of the geometric calibration on the relative and absolute accuracy of the LiDAR point cloud. Furthermore, the impact of the geometric calibration and radiometric correction on land cover classification accuracy is investigated. The feasibility of the proposed methods and their impact on the derived products are demonstrated through experimental results using real data. PMID:22164121

  5. Development of height-volume relationships in second growth Abies grandis for use with aerial LiDAR

    Treesearch

    Wade T. Tinkham; Alistair M. S. Smith; David L. R. Affleck; Jarred D. Saralecos; Michael J. Falkowski; Chad M. Hoffman; Andrew T. Hudak; Michael A. Wulder

    2016-01-01

    Following typical forest inventory protocols, individual tree volume estimates are generally derived via diameter-at-breast-height (DBH)-based allometry. Although effective, measurement of DBH is time consuming and potentially a costly element in forest inventories. The capacity of airborne light detection and ranging (LiDAR) to provide individual tree-level...

  6. A comparison of forest height prediction from FIA field measurement and LiDAR data via spatial models

    Treesearch

    Yuzhen Li

    2009-01-01

    Previous studies have shown a high correspondence between tree height measurements acquired from airborne LiDAR and that those measured using conventional field techniques. Though these results are very promising, most of the studies were conducted over small experimental areas and tree height was measured carefully or using expensive instruments in the field, which is...

  7. Crop 3D-a LiDAR based platform for 3D high-throughput crop phenotyping.

    PubMed

    Guo, Qinghua; Wu, Fangfang; Pang, Shuxin; Zhao, Xiaoqian; Chen, Linhai; Liu, Jin; Xue, Baolin; Xu, Guangcai; Li, Le; Jing, Haichun; Chu, Chengcai

    2017-12-06

    With the growing population and the reducing arable land, breeding has been considered as an effective way to solve the food crisis. As an important part in breeding, high-throughput phenotyping can accelerate the breeding process effectively. Light detection and ranging (LiDAR) is an active remote sensing technology that is capable of acquiring three-dimensional (3D) data accurately, and has a great potential in crop phenotyping. Given that crop phenotyping based on LiDAR technology is not common in China, we developed a high-throughput crop phenotyping platform, named Crop 3D, which integrated LiDAR sensor, high-resolution camera, thermal camera and hyperspectral imager. Compared with traditional crop phenotyping techniques, Crop 3D can acquire multi-source phenotypic data in the whole crop growing period and extract plant height, plant width, leaf length, leaf width, leaf area, leaf inclination angle and other parameters for plant biology and genomics analysis. In this paper, we described the designs, functions and testing results of the Crop 3D platform, and briefly discussed the potential applications and future development of the platform in phenotyping. We concluded that platforms integrating LiDAR and traditional remote sensing techniques might be the future trend of crop high-throughput phenotyping.

  8. Automatic construction of aerial corridor for navigation of unmanned aircraft systems in class G airspace using LiDAR

    NASA Astrophysics Data System (ADS)

    Feng, Dengchao; Yuan, Xiaohui

    2016-05-01

    According to the airspace classification by the Federal Aviation Agency, Class G airspace is the airspace at 1,200 feet or less to the ground, which is beneath class E airspace and between classes B-D cylinders around towered airstrips. However, the lack of flight supervision mechanism in this airspace, unmanned aerial system (UAS) missions pose many safety issues. Collision avoidance and route planning for UASs in class G airspace is critical for broad deployment of UASs in commercial and security applications. Yet, unlike road network, there is no stationary marker in airspace to identify corridors that are available and safe for UASs to navigate. In this paper, we present an automatic LiDAR-based airspace corridor construction method for navigation in class G airspace and a method for route planning to minimize collision and intrusion. Our idea is to combine LiDAR to automatically identify ground objects that pose navigation restrictions such as airports and high-rises. Digital terrain model (DTM) is derived from LiDAR point cloud to provide an altitude-based class G airspace description. Following the FAA Aeronautical Information Manual, the ground objects that define the restricted airspaces are used together with digital surface model derived from LiDAR data to construct the aerial corridor for navigation of UASs. Preliminary results demonstrate competitive performance and the construction of aerial corridor can be automated with much great efficiency.

  9. Challenges to estimating tree height via LiDAR in closed-canopy forest: a parable from western Oregon

    Treesearch

    Demetrios Gatziolis; Jeremy S. Fried; Vicente S. Monleon

    2010-01-01

    We examine the accuracy of tree height estimates obtained via light detection and ranging (LiDAR) in a temperate rainforest characterized by complex terrain, steep slopes, and high canopy cover. The evaluation was based on precise top and base locations for > 1,000 trees in 45 plots distributed across three forest types, a dense network of ground elevation...

  10. Calculating the ecosystem service of water storage in isolated wetlands using LiDAR in north central Florida, USA (presentation)

    EPA Science Inventory

    This study used remotely-sensed Light Detection and Ranging (LiDAR) data to estimate potential water storage capacity of isolated wetlands in north central Florida. The data were used to calculate the water storage potential of >8500 polygons identified as isolated wetlands. We f...

  11. Application of LiDAR data for hydrologic assessments of low-gradient coastal watershed drainage characteristics

    Treesearch

    Devendra Amatya; Carl Trettin; Sudhanshu Panda; Herbert. Ssegane

    2013-01-01

    Documenting the recovery of hydrologic functions following perturbations of a landscape/watershed is important to address issues associated with land use change and ecosystem restoration. High resolution LiDAR data for the USDA Forest Service Santee Experimental Forest in coastal South Carolina,USA was used to delineate the remnant historical water management...

  12. Testing the Suitability of a Terrestrial 2D LiDAR Scanner for Canopy Characterization of Greenhouse Tomato Crops.

    PubMed

    Llop, Jordi; Gil, Emilio; Llorens, Jordi; Miranda-Fuentes, Antonio; Gallart, Montserrat

    2016-09-06

    Canopy characterization is essential for pesticide dosage adjustment according to vegetation volume and density. It is especially important for fresh exportable vegetables like greenhouse tomatoes. These plants are thin and tall and are planted in pairs, which makes their characterization with electronic methods difficult. Therefore, the accuracy of the terrestrial 2D LiDAR sensor is evaluated for determining canopy parameters related to volume and density and established useful correlations between manual and electronic parameters for leaf area estimation. Experiments were performed in three commercial tomato greenhouses with a paired plantation system. In the electronic characterization, a LiDAR sensor scanned the plant pairs from both sides. The canopy height, canopy width, canopy volume, and leaf area were obtained. From these, other important parameters were calculated, like the tree row volume, leaf wall area, leaf area index, and leaf area density. Manual measurements were found to overestimate the parameters compared with the LiDAR sensor. The canopy volume estimated with the scanner was found to be reliable for estimating the canopy height, volume, and density. Moreover, the LiDAR scanner could assess the high variability in canopy density along rows and hence is an important tool for generating canopy maps.

  13. Computing Risk to West Coast Intertidal Rocky Habitat due to Sea Level Rise using LiDAR Topobathy

    EPA Science Inventory

    Compared to marshes, little information is available on the potential for rocky intertidal habitats to migrate upward in response to sea level rise (SLR). To address this gap, we utilized topobathy LiDAR digital elevation models (DEMs) downloaded from NOAA’s Digital Coast G...

  14. Provision of Vocational Skills Education to Orphans: Lessons from Orphanage Centres in Dar es Salaam City, Tanzania

    ERIC Educational Resources Information Center

    Meli, Benjamin Mbeba

    2015-01-01

    This paper utilises data from a study that investigated the efficacy of vocational skills training provided to orphans from three orphanages in Temeke District, Dar es Salaam. The three orphanage centres that were studied are Kurasini National Children Home, Saudia and Don Bosco Vocational Centre. The sample comprised of 45 orphans, an official…

  15. Computing Risk to West Coast Intertidal Rocky Habitat due to Sea Level Rise using LiDAR Topobathy

    EPA Science Inventory

    Compared to marshes, little information is available on the potential for rocky intertidal habitats to migrate upward in response to sea level rise (SLR). To address this gap, we utilized topobathy LiDAR digital elevation models (DEMs) downloaded from NOAA’s Digital Coast G...

  16. Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurments of height and crown dimensions

    Treesearch

    Scott D. Roberts; Thomas J. Dean; David L. Evans; John W. McCombs; Richard L. Harrington; Partick A. Glass

    2005-01-01

    Accurate estimates of leaf area index (LAI) could provide useful information to forest managers, but due to difficulties in measurement, leaf area is rarely used in decision-making. A reliable approach to remotely estimating LA1 would greatly facilitate its use in forest management. This study investigated the potential for using small-footprint iDAR, a laser-based...

  17. Leaf area index, biomass carbon and growth rate of radiata pine genetic types and relationships with LiDAR

    Treesearch

    Peter N. Beets; Stephen Reutebuch; Mark O. Kimberley; Graeme R. Oliver; Stephen H. Pearce; Robert J. McGaughey

    2011-01-01

    Relationships between discrete-return light detection and ranging (LiDAR) data and radiata pine leaf area index (LAI), stem volume, above ground carbon, and carbon sequestration were developed using 10 plots with directly measured biomass and leaf area data, and 36 plots with modelled carbon data. The plots included a range of genetic types established on north- and...

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

    Treesearch

    Jitendra Kumar; Jon Weiner; William W. Hargrove; Steve Norman; Forrest M. Hoffman; Doug Newcomb

    2016-01-01

    Vegetation canopy structure is a critically important habitat characteristic for many threatened and endangered birds and other animal species, and it is key information needed by forest and wildlife managers for monitoring and managing forest resources, conservation planning and fostering biodiversity. Advances in Light Detection and Ranging (LiDAR) technologies have...

  19. Analysis of LiDAR point data and derived elevation models for mapping and characterizing bouldery landforms

    NASA Astrophysics Data System (ADS)

    Maxwell, Aaron Edward

    This thesis assessed the viability of using LiDAR-derived elevation data in accurately mapping and characterizing bouldery geomorphic features in a study area in the Allegheny Mountains. This study showed that the ground returns classification process conducted by the Canaan Valley Institute (CVI) for their property using the TerraScan software generally removed 5 to 10 m scale local topographic variability and bouldery landforms in creating the CVI classified ground returns data. In open areas, last returns elevation and intensity data were successfully used in this study to map bouldery landforms in the study area. Identifying and describing boulders under a tree canopy required a relatively reliable ground classification of LiDAR points. This study's classifications conducted within Prologic LiDAR Explorer provided a more useful representation than the CVI classified ground data for mapping bouldery landforms and generalized rugged topography. Index overlay for likelihood of presence of bouldery landforms using supervised classified aerial imagery and LiDAR-derived parameters in a raster environment was explored as an alternative means of detecting bouldery landforms because hillshade imagery derived from CVI classified ground data were inadequate for mapping bouldery landforms.

  20. Applying inventory methods to estimate aboveground biomass from satellite light detection and ranging (LiDAR) forest height data

    Treesearch

    Sean P. Healey; Paul L. Patterson; Sassan Saatchi; Michael A. Lefsky; Andrew J. Lister; Elizabeth A. Freeman; Gretchen G. Moisen

    2012-01-01

    Light Detection and Ranging (LiDAR) returns from the spaceborne Geoscience Laser Altimeter (GLAS) sensor may offer an alternative to solely field-based forest biomass sampling. Such an approach would rely upon model-based inference, which can account for the uncertainty associated with using modeled, instead of field-collected, measurements. Model-based methods have...

  1. Approaching a more Complete Picture of Rockfall Activity: Seismic and LiDAR Detection, Loaction and Volume Estimates

    NASA Astrophysics Data System (ADS)

    Dietze, Michael; Mohadjer, Solmaz; Turowski, Jens; Ehlers, Todd; Hovius, Niels

    2016-04-01

    Rockfall activity in steep alpine landscapes is often difficult to survey due to its infrequent nature. Classic approaches are limited by temporal and spatial resolution. In contrast, seismic monitoring provides access to catchment-wide analysis of activity patterns in rockfall-dominated environments. The deglaciated U-shaped Lauterbrunnen Valley in the Bernese Oberland, Switzerland, is a perfect example of such landscapes. It was instrumented with up to six broadband seismometers and repeatedly surveyed by terrestrial LiDAR to provide independent validation data. During August-October 2014 and April-June 2015 more than 23 (LiDAR) to hundred (seismic) events were detected. Their volumes range from < 0.01 to 5.80 cubic metres as detected by LiDAR. The evolution of individual events (i.e., precursor activity, detachment, falling phase, impact, talus cone activity) can be quantified in terms of location and duration. For events that consist of single detachments rather than a series of releases, volume scaling relationships are possible. Seismic monitoring approaches are well-suited for studying not only the rockfall process but also for understanding the geomorphic framework and boundary conditions that control such processes in a comprehensive way. Taken together, the combined LiDAR and seismic monitoring approach provides high fidelity spatial and temporal resolution of individual events.

  2. Diversity arrays technology (DArT) for studying the genetic polymorphism of flue-cured tobacco (Nicotiana tabacum).

    PubMed

    Lu, Xiu-Ping; Xiao, Bing-Guang; Li, Yong-Ping; Gui, Yi-Jie; Wang, Yu; Fan, Long-Jiang

    2013-07-01

    Diversity arrays technology (DArT) is a microarray-based marker system that achieves high throughput by reducing the complexity of the genome. A DArT chip has recently been developed for tobacco. In this study, we genotyped 267 flue-cured cultivars/landraces, including 121 Chinese accessions over five decades from widespread geographic regions in China, 103 from the Americas, and 43 other foreign cultivars, using the newly developed chip. Three hundred and thirty polymorphic DArT makers were selected and used for a phylogenetic analysis, which suggested that the 267 accessions could be classified into two subgroups, which could each be further divided into 2‒4 sections. Eight elite cultivars, which account for 83% of the area of Chinese tobacco production, were all found in one subgroup. Two high-quality cultivars, HHDJY and Cuibi1, were grouped together in one section, while six other high-yield cultivars were grouped into another section. The 330 DArT marker clones were sequenced and close to 95% of them are within non-repetitive regions. Finally, the implications of this study for Chinese flue-cured tobacco breeding and production programs were discussed.

  3. Diversity arrays technology (DArT) for studying the genetic polymorphism of flue-cured tobacco (Nicotiana tabacum)* #

    PubMed Central

    Lu, Xiu-ping; Xiao, Bing-guang; Li, Yong-ping; Gui, Yi-jie; Wang, Yu; Fan, Long-jiang

    2013-01-01

    Diversity arrays technology (DArT) is a microarray-based marker system that achieves high throughput by reducing the complexity of the genome. A DArT chip has recently been developed for tobacco. In this study, we genotyped 267 flue-cured cultivars/landraces, including 121 Chinese accessions over five decades from widespread geographic regions in China, 103 from the Americas, and 43 other foreign cultivars, using the newly developed chip. Three hundred and thirty polymorphic DArT makers were selected and used for a phylogenetic analysis, which suggested that the 267 accessions could be classified into two subgroups, which could each be further divided into 2‒4 sections. Eight elite cultivars, which account for 83% of the area of Chinese tobacco production, were all found in one subgroup. Two high-quality cultivars, HHDJY and Cuibi1, were grouped together in one section, while six other high-yield cultivars were grouped into another section. The 330 DArT marker clones were sequenced and close to 95% of them are within non-repetitive regions. Finally, the implications of this study for Chinese flue-cured tobacco breeding and production programs were discussed. PMID:23825142

  4. National and Global: A History of Scholars' Experiences with Research at the University of Dar Es Salaam, Tanzania (1961-Present)

    ERIC Educational Resources Information Center

    Jamison, Amy J.

    2010-01-01

    In this dissertation, I draw on research carried out at the University of Dar es Salaam (UDSM), Tanzania in 2008 to examine Tanzanian academics' experience with research throughout the history of this institution. This dissertation is designed as an historical case study and investigates how economic and political changes in Tanzania's…

  5. A categorical, improper probability method for combining NDVI and LiDAR elevation information for potential cotton precision agricultural applications

    USDA-ARS?s Scientific Manuscript database

    An algorithm is presented to fuse the Normalized Difference Vegetation Index (NDVI) with Light Detection and Ranging (LiDAR) elevation data to produce a map potentially useful for the site-specific scouting and pest management of several insect pests. In cotton, these pests include the Tarnished Pl...

  6. Vegetation and slope effects on accuracy of a LiDAR-derived DEM in the sagebrush steppe

    USDA-ARS?s Scientific Manuscript database

    This study analyzed the errors associated with vegetation cover type and slope on a LiDAR derived DEM in a semiarid environment in southwestern Idaho, USA. Reference data were collected over a range of vegetation cover types and slopes. Reference data were compared to ground raster values and Root...

  7. Testing the Suitability of a Terrestrial 2D LiDAR Scanner for Canopy Characterization of Greenhouse Tomato Crops

    PubMed Central

    Llop, Jordi; Gil, Emilio; Llorens, Jordi; Miranda-Fuentes, Antonio; Gallart, Montserrat

    2016-01-01

    Canopy characterization is essential for pesticide dosage adjustment according to vegetation volume and density. It is especially important for fresh exportable vegetables like greenhouse tomatoes. These plants are thin and tall and are planted in pairs, which makes their characterization with electronic methods difficult. Therefore, the accuracy of the terrestrial 2D LiDAR sensor is evaluated for determining canopy parameters related to volume and density and established useful correlations between manual and electronic parameters for leaf area estimation. Experiments were performed in three commercial tomato greenhouses with a paired plantation system. In the electronic characterization, a LiDAR sensor scanned the plant pairs from both sides. The canopy height, canopy width, canopy volume, and leaf area were obtained. From these, other important parameters were calculated, like the tree row volume, leaf wall area, leaf area index, and leaf area density. Manual measurements were found to overestimate the parameters compared with the LiDAR sensor. The canopy volume estimated with the scanner was found to be reliable for estimating the canopy height, volume, and density. Moreover, the LiDAR scanner could assess the high variability in canopy density along rows and hence is an important tool for generating canopy maps. PMID:27608025

  8. Estimating urban tree aboveground carbon storage using LiDAR and field measurement

    NASA Astrophysics Data System (ADS)

    Yu, Q.; Pu, R.; Landry, S.

    2016-12-01

    As a hotspot of human-environment interaction, urban trees are an important component of local and global carbon cycle. Accurate and efficient mapping and estimating of urban tree carbon storage can help us better understand the role of urban trees in mitigating local urban heat islands and global warming. Given the highly complex and fragmented landscapes and restricted accessibility in urban environment, studies in urban tree carbon storage mapping are limited and are greatly lagging behind similar studies in natural environments. In this context, the aim of this study is to better map urban tree aboveground carbon storage, and evaluate the impacts of different land use/ land cover (LULC) classes on mapping accuracies in the City of Tampa, Florida, USA. Our study used digital surface model (DSM) derived from aerial LiDAR (1-m spatial resolution), field inventory data (diameter at breast height (DBH), and tree species) of 111 plots (0.1 acre each), and i-Tree derived tree aboveground carbon storage. We linked a series of height-related metrics within plots (i.e., lowest top of canopy height (TCH), highest TCH, mean TCH, and standard deviation of TCH) to their basal area (BA) and found that the mean TCH could best predict BA (R2=0.51). We then used predicted BA and other height-related metrics (besides mean TCH) to construct the optimum plot-aggregate allometric model through stepwise regression analysis. The optimum allometric model, consisting of predicted BA, lowest TCH, and standard deviation of TCH, could predict aboveground carbon storage with a R2 of 0.60. But its predicting capacity varies with LULC background and species composition. This paper illustrates the potential of using LiDAR to accurately and reliably estimate urban tree aboveground carbon storage and the need to involve other variables such as LULC. As a hotspot of human-environment interaction, urban trees are an important component of local and global carbon cycle. Accurate and efficient

  9. G-LiHT: Goddard's LiDAR, Hyperspectral and Thermal Airborne Imager

    NASA Technical Reports Server (NTRS)

    Cook, Bruce; Corp, Lawrence; Nelson, Ross; Morton, Douglas; Ranson, Kenneth J.; Masek, Jeffrey; Middleton, Elizabeth

    2012-01-01

    Scientists at NASA's Goddard Space Flight Center have developed an ultra-portable, low-cost, multi-sensor remote sensing system for studying the form and function of terrestrial ecosystems. G-LiHT integrates two LIDARs, a 905 nanometer single beam profiler and 1550 nm scanner, with a narrowband (1.5 nanometers) VNIR imaging spectrometer and a broadband (8-14 micrometers) thermal imager. The small footprint (approximately 12 centimeters) LIDAR data and approximately 1 meter ground resolution imagery are advantageous for high resolution applications such as the delineation of canopy crowns, characterization of canopy gaps, and the identification of sparse, low-stature vegetation, which is difficult to detect from space-based instruments and large-footprint LiDAR. The hyperspectral and thermal imagery can be used to characterize species composition, variations in biophysical variables (e.g., photosynthetic pigments), surface temperature, and responses to environmental stressors (e.g., heat, moisture loss). Additionally, the combination of LIDAR optical, and thermal data from G-LiHT is being used to assess forest health by sensing differences in foliage density, photosynthetic pigments, and transpiration. Low operating costs (approximately $1 ha) have allowed us to evaluate seasonal differences in LiDAR, passive optical and thermal data, which provides insight into year-round observations from space. Canopy characteristics and tree allometry (e.g., crown height:width, canopy:ground reflectance) derived from G-LiHT data are being used to generate realistic scenes for radiative transfer models, which in turn are being used to improve instrument design and ensure continuity between LiDAR instruments. G-LiHT has been installed and tested in aircraft with fuselage viewports and in a custom wing-mounted pod that allows G-LiHT to be flown on any Cessna 206, a common aircraft in use throughout the world. G-LiHT is currently being used for forest biomass and growth estimation

  10. Fine-scale ignimbrite morphology revealed in LiDAR at Crater Lake, OR

    NASA Astrophysics Data System (ADS)

    Robinson, J. E.; Bacon, C. R.; Wright, H. M.

    2011-12-01

    Mount Mazama erupted ~7,700 years ago resulting in the collapse of Crater Lake caldera, ash fall across the Pacific Northwest, and emplacement of compositionally zoned ignimbrite. Early climactic ignimbrite contains uniform rhyodacitic pumice and traveled far from the vent, whereas late, less mobile ignimbrite is dominated by crystal-rich andesitic scoria and mafic crystal mush. Funded by the USGS, NPS, and FHWA, the DOGAMI-led Oregon LiDAR Consortium contracted with Watershed Services to collect ~800 km2 of LiDAR over Crater Lake National Park from Aug 2010 to Sept 2010. Ground laser returns have an average density of 1.63 returns/m2 over the heavily forested area of interest. The data have a lateral RMSE and vertical accuracy of 0.05 m. A bare earth terrain model allows a virtual removal of the forest, revealing fine-scale surface morphology, notably in the climactic ignimbrite. Secondary pyroclastic flows, explosion craters, erosion by water, and compaction-related deformation modified the originally smooth ignimbrite surface. Distinct pyroclastic flow fronts are evident in the LiDAR in Annie Creek valley. Leveed flows stand approximately 5 m above the lower ignimbrite surface, and individual toes are about 1-2 m high. Preliminary field checking indicates that rhyodacitic pumice dominates the lower ignimbrite surface, but the leveed flows are a subequal mix of locally oxidized rhyodacitic pumice and andesitic scoria. We hypothesize that these deposits were secondary pyroclastic flows formed by gravitational failure of late ignimbrite. In the Castle Creek valley, is a 2-meter collapse scarp that may have spawned a small secondary pyroclastic flow; several such headwall scarps are present in Sand Creek valley. Differential compaction features are common in many thick ignimbrites. We suggest this caused the deformation of the ignimbrite apparent in the LiDAR. In Annie Creek valley are a series of flow parallel asymmetric ridges, with shallower slopes toward the

  11. Fusion of terrestrial LiDAR and tomographic mapping data for 3D karst landform investigation

    NASA Astrophysics Data System (ADS)

    Höfle, B.; Forbriger, M.; Siart, C.; Nowaczinski, E.

    2012-04-01

    Highly detailed topographic information has gained in importance for studying Earth surface landforms and processes. LiDAR has evolved into the state-of-the-art technology for 3D data acquisition on various scales. This multi-sensor system can be operated on several platforms such as airborne LS (ALS), mobile LS (MLS) from moving vehicles or stationary on ground (terrestrial LS, TLS). In karst research the integral investigation of surface and subsurface components of solution depressions (e.g. sediment-filled dolines) is required to gather and quantify the linked geomorphic processes such as sediment flux and limestone dissolution. To acquire the depth of the different subsurface layers, a combination of seismic refraction tomography (SRT) and electrical resistivity tomography (ERT) is increasingly applied. This multi-method approach allows modeling the extension of different subsurface media (i.e. colluvial fill, epikarst zone and underlying basal bedrock). Subsequent fusion of the complementary techniques - LiDAR surface and tomographic subsurface data - first-time enables 3D prospection and visualization as well as quantification of geomorphometric parameters (e.g. depth, volume, slope and aspect). This study introduces a novel GIS-based method for semi-automated fusion of TLS and geophysical data. The study area is located in the Dikti Mountains of East Crete and covers two adjacent dolines. The TLS data was acquired with a Riegl VZ-400 scanner from 12 scan positions located mainly at the doline divide. The scan positions were co-registered using the iterative closest point (ICP) algorithm of RiSCAN PRO. For the digital elevation rasters a resolution of 0.5 m was defined. The digital surface model (DSM) of the study was derived by moving plane interpolation of all laser points (including objects) using the OPALS software. The digital terrain model (DTM) was generated by iteratively "eroding" objects in the DSM by minimum filter, which additionally accounts for

  12. Understanding household behavioral risk factors for diarrheal disease in Dar es Salaam: a photovoice community assessment.

    PubMed

    Badowski, Natalie; Castro, Cynthia M; Montgomery, Maggie; Pickering, Amy J; Mamuya, Simon; Davis, Jennifer

    2011-01-01

    Whereas Tanzania has seen considerable improvements in water and sanitation infrastructure over the past 20 years, the country still faces high rates of childhood morbidity from diarrheal diseases. This study utilized a qualitative, cross-sectional, modified Photovoice method to capture daily activities of Dar es Salaam mothers. A total of 127 photographs from 13 households were examined, and 13 interviews were conducted with household mothers. The photographs and interviews revealed insufficient hand washing procedures, unsafe disposal of wastewater, uncovered household drinking water containers, a lack of water treatment prior to consumption, and inappropriate toilets for use by small children. The interviews revealed that mothers were aware and knowledgeable of the risks of certain household practices and understood safer alternatives, yet were restricted by the perceived impracticality and financial constraints to make changes. The results draw attention to the real economic and behavioral challenges faced in reducing the spread of disease.

  13. Climate change induced risk analysis of Dar es Salaam city (Tanzania)

    NASA Astrophysics Data System (ADS)

    Topa, Maria Elena; Herslund, Lise; Cavan, Gina; Printz, Andreas; Simonis, Ingo; Bucchignani, Edoardo; Jean-Baptiste, Nathalie; Hellevik, Siri; Johns, Regina; Kibassa, Deusdedit; Kweka, Clara; Magina, Fredrick; Mangula, Alpha; Mbuya, Elinorata; Uhinga, Guido; Kassenga, Gabriel; Kyessi, Alphonce; Shemdoe, Riziki; Kombe, Wilbard

    2013-04-01

    CLUVA (CLimate change and Urban Vulnerability in Africa; http://www.cluva.eu/) is a 3 years project, funded by the European Commission in 2010. The main objective of CLUVA is to develop context-centered methods and knowledge to be applied to African cities to assess vulnerabilities and increase knowledge on managing climate related risks. The project estimates the impacts of climate changes in the next 40 years at urban scale and downscales IPCC climate projections to evaluate specific threats to selected African test cities. These are mainly from floods, sea-level rise, droughts, heat waves, and desertification. The project evaluates and links: social vulnerability; urban green structures and ecosystem services; urban-rural interfaces; vulnerability of urban built environment and lifelines; and related institutional and governance dimensions of adaptation. The multi-scale and multi-disciplinary qualitative, quantitative and probabilistic approach of CLUVA is currently being applied to selected African test cities (Addis Ababa - Ethiopia; Dar es Salaam - Tanzania; Douala - Cameroun; Ouagadougou - Burkina Faso; St. Louis - Senegal). In particular, the poster will present preliminary findings for the Dar es Salaam case study. Dar es Salaam, which is Tanzania's largest coastal city, is exposed to floods, coastal erosion, droughts and heat waves, and highly vulnerable to impacts as a result of ineffective urban planning (about 70% unplanned settlements), poverty and lack of basic infrastructure (e.g. lack of or poor quality storm water drainage systems). Climate change could exacerbate the current situation increasing hazard-exposure alongside the impacts of development pressures which act to increase urban vulnerability for example because of informal (unregulated) urbanization. The CLUVA research team - composed of climate and environmental scientists, risk management experts, urban planners and social scientists from both European and African institutions - has

  14. Analysis of airborne LiDAR as a basis for digital soil mapping in Alpine areas

    NASA Astrophysics Data System (ADS)

    Kringer, K.; Tusch, M.; Geitner, C.; Meißl, G.; Rutzinger, M.

    2009-04-01

    Especially in mountainous regions like the Alps the formation of soil is highly influenced by relief characteristics. Among all factors included in Jenny's (1941) model for soil development, relief is the one most commonly used in approaches to create digital soil maps and to derive soil properties from secondary data sources (McBratney et al. 2003). Elevation data, first order (slope, aspect) and second order derivates (plan, profile and cross-sectional curvature) as well as complex morphometric parameters (various landform classifications, e.g., Wood 1996) and compound indices (e.g., topographic wetness indices, vertical distance to drainage network, insolation) can be calculated from digital elevation models (DEM). However, while being an important source of information for digital soil mapping on small map scales, "conventional" DEMs are of limited use for the design of large scale conceptual soil maps for small areas due to rather coarse raster resolutions with cell sizes ranging from 20 to 100 meters. Slight variations in elevation and small landform features might not be discernible even though they might have a significant effect to soil formation, e.g., regarding the influence of groundwater in alluvial soils or the extent of alluvial fans. Nowadays, Airborne LiDAR (Light Detection And Ranging) provides highly accurate data for the elaboration of high-resolution digital terrain models (DTM) even in forested areas. In the project LASBO (Laserscanning in der Bodenkartierung) the applicability of digital terrain models derived from LiDAR for the identification of soil-relevant geomorphometric parameter is investigated. Various algorithms which were initially designed for coarser raster data are applied on high-resolution DTMs. Test areas for LASBO are located in the region of Bruneck (Italy) and near the municipality of Kramsach in the Inn Valley (Austria). The freely available DTM for Bruneck has a raster resolution of 2.5 meters while in Kramsach a DTM with

  15. A temperature calibration method for CDOM fluorescence LIF LiDAR

    NASA Astrophysics Data System (ADS)

    Chen, Peng; Mao, Zhihua; Huang, Haiqing; Bai, Yan; Wang, Tianyu

    2016-10-01

    The influence of temperature variations on the determined concentrations of dissolved organic matter (DOM) in water was investigated by laser induced fluorescence (LIF) technique in laboratory. The effect of temperature on CDOM fluorescence was investigated in freshwaters of Xixi River and in aqueous standards. The total luminescence spectra (TLS) of CDOM in several types of water samples with laser-induced fluorescence (LIF) measurements using a 405 nm wavelength excitation source were measured in the laboratory. A temperature calibration equation was derived to standardize CDOM fluorescence measurements to a specific reference temperature. Laboratory experiments with a portable LIF LiDAR showed that CDOM fluorescence intensity decreased as ambient water temperature increased. High correlation (R2=0.91) was observed between concentration of CDOM and fluorescence normalized to water Raman scattering with the temperature calibration method. The results demonstrated that temperature calibration is a necessary and important aspect of CDOM monitoring using in situ fluorescence sensors.

  16. Understanding Household Behavioral Risk Factors for Diarrheal Disease in Dar es Salaam: A Photovoice Community Assessment

    PubMed Central

    Badowski, Natalie; Castro, Cynthia M.; Montgomery, Maggie; Pickering, Amy J.; Mamuya, Simon; Davis, Jennifer

    2011-01-01

    Whereas Tanzania has seen considerable improvements in water and sanitation infrastructure over the past 20 years, the country still faces high rates of childhood morbidity from diarrheal diseases. This study utilized a qualitative, cross-sectional, modified Photovoice method to capture daily activities of Dar es Salaam mothers. A total of 127 photographs from 13 households were examined, and 13 interviews were conducted with household mothers. The photographs and interviews revealed insufficient hand washing procedures, unsafe disposal of wastewater, uncovered household drinking water containers, a lack of water treatment prior to consumption, and inappropriate toilets for use by small children. The interviews revealed that mothers were aware and knowledgeable of the risks of certain household practices and understood safer alternatives, yet were restricted by the perceived impracticality and financial constraints to make changes. The results draw attention to the real economic and behavioral challenges faced in reducing the spread of disease. PMID:21969836

  17. Potential Landslide Detection with Fractal and Roughness by LiDAR Data in Taiwan

    NASA Astrophysics Data System (ADS)

    Cheng, Youg-Sin; Yu, Teng-To

    2015-04-01

    The purpose of this study is to detect the potential landslides since they would be triggered by heavy rain, earthquake and/or larger degree of geomorphology alteration under different terrain characteristics. Not only the newly area but also the past landslide area would generate landslide after serious events. To gather the newly landslides and past landslides overwhelmed by thick vegetation, LiDAR could produce the high resolution DEM, denote actual surface terrain information and identify landform with a spatial resolution of 1m in different time interval. The 1-m interval DEM of Laonong watershed of southern Taiwan is utilized by fractal and roughness calculating with MATLAB code. DEM, aspect, and slope images are adopted to improve the accuracy of potential landslide detection with the random forest (RF) classifier. In present study, we provide the analysis results of the potential landslide area including these features calculation.

  18. A gradient-constrained morphological filtering algorithm for airborne LiDAR

    NASA Astrophysics Data System (ADS)

    Li, Yong; Wu, Huayi; Xu, Hanwei; An, Ru; Xu, Jia; He, Qisheng

    2013-12-01

    This paper presents a novel gradient-constrained morphological filtering algorithm for bare-earth extraction from light detection and ranging (LiDAR) data. Based on the gradient feature points determined by morphological half-gradients, the potential object points are located prior to filtering. Innovative gradient-constrained morphological operations are created, which are executed only for the potential object points. Compared with the traditional morphological operations, the new operations reduce many meaningless operations for object removal and consequently decrease the possibility of losing terrain to a great extent. The applicability and reliability of this algorithm are demonstrated by evaluating the filtering performance for fifteen sample datasets in various complex scenes. The proposed algorithm is found to achieve a high level of accuracy compared with eight other filtering algorithms tested by the International Society for Photogrammetry and Remote Sensing. Moreover, the proposed algorithm has minimal error oscillation for different landscapes, which is important for quality control of digital terrain model generation.

  19. Joint Temperature-Lasing Mode Compensation for Time-of-Flight LiDAR Sensors

    PubMed Central

    Alhashimi, Anas; Varagnolo, Damiano; Gustafsson, Thomas

    2015-01-01

    We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser’s datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three. PMID:26690445

  20. How integrating 3D LiDAR data in the dike surveillance protocol: The French case

    NASA Astrophysics Data System (ADS)

    Bretar, F.; Mériaux, P.; Fauchard, C.

    2012-04-01

    carried out. A LiDAR system is able to acquire data on a dike structure of up to 80 km per day, which makes the use of this technique also valuable in case of emergency situations. It provides additional valuable products like precious information on dike slopes and crest or their near environment (river banks, etc.). Moreover, in case of vegetation, LiDAR data makes possible to study hidden structures or defaults from images like the erosion of riverbanks under forestry vegetation. The possibility of studying the vegetation is also of high importance: the development of woody vegetation near or onto the dike is a major risk factor. Surface singularities are often signs of disorder or suspected disorder in the dike itself: for example a subsidence or a sinkhole on a ridge may result from internal erosion collapse. Finally, high resolution topographic data contribute to build specific geomechanical model of the dike that, after incorporating data provided by geophysical and geotechnical surveys, are integrated in the calculations of the structure stability. Integrating the regular use of LiDAR data in the dike surveillance protocol is not yet operational in France. However, the high number of French stakeholders at the national level (on average, there is one stakeholder for only 8-9km of dike !) and the real added value of LiDAR data makes a spatial data infrastructure valuable (webservices for processing the data, consulting and filling the database on the field when performing the local diagnosis)

  1. Joint Temperature-Lasing Mode Compensation for Time-of-Flight LiDAR Sensors.

    PubMed

    Alhashimi, Anas; Varagnolo, Damiano; Gustafsson, Thomas

    2015-12-11

    We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser's datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three.

  2. Subtropical Forest Biomass Estimation Using Airborne LiDAR and Hyperspectral Data

    NASA Astrophysics Data System (ADS)

    Pang, Yong; Li, Zengyuan

    2016-06-01

    Forests have complex vertical structure and spatial mosaic pattern. Subtropical forest ecosystem consists of vast vegetation species and these species are always in a dynamic succession stages. It is very challenging to characterize the complexity of subtropical forest ecosystem. In this paper, CAF's (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System was used to collect waveform Lidar and hyperspectral data in Puer forest region, Yunnan province in the Southwest of China. The study site contains typical subtropical species of coniferous forest, evergreen broadleaf forest, and some other mixed forests. The hypersectral images were orthorectified and corrected into surface reflectance with support of Lidar DTM product. The fusion of Lidar and hyperspectral can classify dominate forest types. The lidar metrics improved the classification accuracy. Then forest biomass estimation was carried out for each dominate forest types using waveform Lidar data, which get improved than single Lidar data source.

  3. On the use of airborne LiDAR for braided river monitoring and water surface delineation

    NASA Astrophysics Data System (ADS)

    Vetter, M.; Höfle, B.; Pfeifer, N.; Rutzinger, M.; Stötter, J.

    2009-04-01

    Airborne LiDAR is an established technology for Earth surface surveying. With LiDAR data sets it is possible to derive maps with different land use classes, which are important for hydraulic simulations. We present a 3D point cloud based method for automatic water surface delineation using single as well as multitemporal LiDAR data sets. With the developed method it is possible to detect the location of the water surface with high planimetric accuracy. The multitemporal analysis of different LiDAR data sets makes it possible to visualize, monitor and quantify the changes of the flow path of braided rivers as well as derived water surface land use classes. The reflection properties from laser beams (1064 nm wavelength) on water surfaces are characterized by strong absorption or specular reflection resulting in a dominance of low signal amplitude values and a high number of laser shot dropouts (i.e. non-recorded laser echoes). The occurrence of dropouts is driven by (i) the incidence angle, (ii) the surface reflectance and (iii) the roughness of the water body. The input data of the presented delineation method are the modeled dropouts and the point cloud attributes of geometry and signal amplitude. A terrestrial orthophoto is used to explore the point cloud in order to find proper information about the geometry and amplitude attributes that are characteristic for water surfaces. The delineation method is divided into five major steps. (a) We compute calibrated amplitude values by reducing the atmospheric, topographic influences and the scan geometry for each laser echo. (b) Then, the dropouts are modeled by using the information from the time stamps, the pulse repetition frequency, the inertial measurement unit and the GPS information of the laser shots and the airplane. The next step is to calculate the standard deviation of the heights for all reflections and all modeled dropouts (c) in a specific radius around the points. (d) We compute the amplitude ratio

  4. Tropical Airborne LiDAR for Landslide Assessment in Malaysia: a technical perspective

    NASA Astrophysics Data System (ADS)

    Abd Manap, Mohamad; Azhari Razak, Khamarrul; Mohamad, Zakaria; Ahmad, Azhari; Ahmad, Ferdaus; Mohamad Zin, Mazlan; A'zad Rosle, Qalam

    2015-04-01

    Malaysia has faced a substantial number of landslide events every year. Cameron Highlands, Pahang is one of the badly areas affected by slope failures characterized by extreme climate, rugged topographic and weathered geological structures in a tropical environment. A high frequency of landslide occurrence in the hilly areas is predominantly due to the geological materials, tropical monsoon seasons and uncontrolled agricultural activities. Therefore the Government of Malaysia through the Prime Minister Department has allocated a special budget to conduct national level hazard and risk mapping project through Minerals and Geoscience Department Malaysia, the Ministry of Natural Resources and Environment. The primary aim of this project is to provide slope hazard risk information for a better slope management in Malaysia. In addition this project will establish national infrastructure for geospatial information on the geological terrain and slope by emphasizing the disaster risk throughout the country. The areas of interest are located in the three different selected areas i.e. Cameron Highlands (275 square kilometers), Ipoh (200 square kilometers) and Cheras Kajang -- Batang kali (650 square kilometers). These areas are selected based on National Slope Master Plan (2009 -- 2023) that endorsed by Malaysia Government Cabinet. The national hazard and risk mapping project includes six parts of major tasks: (1) desk study and mobilization, (2) airborne LiDAR data acquisition and analysis, (3) field data acquisition and verification, (4) hazard and risk for natural terrain, (5) hazard and risk analysis for man-made slope and (6) Man-made slope mitigation/preventive measures. The project was authorized in September, 2014 and will be ended in March, 2016. In this paper, the main focus is to evaluate the suitability of integrated capability of airborne- and terrestrial LiDAR data acquisition and analysis, and also digital photography for regional landslide assessment. The

  5. Urban mosquitoes, situational publics, and the pursuit of interspecies separation in Dar es Salaam

    PubMed Central

    KELLY, ANN H.; LEZAUN, JAVIER

    2014-01-01

    Recent work in anthropology points to the recognition of multispecies entanglements as the grounds for a more ethical politics. In this article, we examine efforts to control mosquitoes in Dar es Salaam, Tanzania, as an example of the laborious tasks of disentanglement that characterize public health interventions. The mosquito surveillance and larval elimination practices of an urban malaria control program offer an opportunity to observe how efforts to create distance between species relate to the physical and civic textures of the city. Seen in the particular context of the contemporary African metropolis, the work of public health appears less a matter of control than a commitment to constant urban maintenance and political mobilization. PMID:25429167

  6. Application of High-resolution Aerial LiDAR Data in Calibration of a Two-dimensional Urban Flood Simulation

    NASA Astrophysics Data System (ADS)

    Piotrowski, J.; Goska, R.; Chen, B.; Krajewski, W. F.; Young, N.; Weber, L.

    2009-12-01

    In June 2008, the state of Iowa experienced an unprecedented flood event which resulted in an economic loss of approximately $2.88 billion. Flooding in the Iowa River corridor, which exceeded the previous flood of record by 3 feet, devastated several communities, including Coralville and Iowa City, home to the University of Iowa. Recognizing an opportunity to capture a unique dataset detailing the impacts of the historic flood, the investigators contacted the National Center for Airborne Laser Mapping (NCALM), which performed an aerial Light Detection and Ranging (LiDAR) survey along the Iowa River. The survey, conducted immediately following the flood peak, provided coverage of a 60-mile reach. The goal of the present research is to develop a process by which flood extents and water surface elevations can be accurately extracted from the LiDAR data set and to evaluate the benefit of such data in calibrating one- and two-dimensional hydraulic models. Whereas data typically available for model calibration include sparsely distributed point observations and high water marks, the LiDAR data used in the present study provide broad-scale, detailed, and continuous information describing the spatial extent and depth of flooding. Initial efforts were focused on a 10-mile, primarily urban reach of the Iowa River extending from Coralville Reservoir, a United States Army Corps of Engineers flood control project, downstream through the Coralville and Iowa City. Spatial extent and depth of flooding were estimated from the LiDAR data. At a given cross-sectional location, river channel and floodplain measurements were compared. When differences between floodplain and river channel measurements were less than a standard deviation of the vertical uncertainty in the LiDAR survey, floodplain measurements were classified as flooded. A flood water surface DEM was created using measurements classified as flooded. A two-dimensional, depth-averaged numerical model of a 10-mile reach of

  7. Mapping and exploring variation in post-fire vegetation recovery following mixed severity wildfire using airborne LiDAR.

    PubMed

    Gordon, Christopher E; Price, Owen F; Tasker, Elizabeth M

    2017-07-01

    There is a public perception that large high-severity wildfires decrease biodiversity and increase fire hazard by homogenizing vegetation composition and increasing the cover of mid-story vegetation. But a growing literature suggests that vegetation responses are nuanced. LiDAR technology provides a promising remote sensing tool to test hypotheses about post-fire vegetation regrowth because vegetation cover can be quantified within different height strata at fine scales over large areas. We assess the usefulness of airborne LiDAR data for measuring post-fire mid-story vegetation regrowth over a range of spatial resolutions (10 × 10 m, 30 × 30 m, 50 × 50 m, 100 × 100 m cell size) and investigate the effect of fire severity on regrowth amount and spatial pattern following a mixed severity wildfire in Warrumbungle National Park, Australia. We predicted that recovery would be more vigorous in areas of high fire severity, because park managers observed dense post-fire regrowth in these areas. Moderate to strong positive associations were observed between LiDAR and field surveys of mid-story vegetation cover between 0.5-3.0 m. Thus our LiDAR survey was an apt representation of on-ground vegetation cover. LiDAR-derived mid-story vegetation cover was 22-40% lower in areas of low and moderate than high fire severity. Linear mixed-effects models showed that fire severity was among the strongest biophysical predictors of mid-story vegetation cover irrespective of spatial resolution. However much of the variance associated with these models was unexplained, presumably because soil seed banks varied at finer scales than our LiDAR maps. Dense patches of mid-story vegetation regrowth were small (median size 0.01 ha) and evenly distributed between areas of low, moderate and high fire severity, demonstrating that high-severity fires do not homogenize vegetation cover. Our results are relevant for ecosystem conservation and fire management because they: indicate

  8. Urban flood modelling combining top-view LiDAR data with ground-view SfM observations

    NASA Astrophysics Data System (ADS)

    Meesuk, Vorawit; Vojinovic, Zoran; Mynett, Arthur E.; Abdullah, Ahmad F.

    2015-01-01

    Remote Sensing technologies are capable of providing high-resolution spatial data needed to set up advanced flood simulation models. Amongst them, aerial Light Detection and Ranging (LiDAR) surveys or Airborne Laser Scanner (ALS) systems have long been used to provide digital topographic maps. Nowadays, Remote Sensing data are commonly used to create Digital Terrain Models (DTMs) for detailed urban-flood modelling. However, the difficulty of relying on top-view LiDAR data only is that it cannot detect whether passages for floodwaters are hidden underneath vegetated areas or beneath overarching structures such as roads, railroads, and bridges. Such (hidden) small urban features can play an important role in urban flood propagation. In this paper, a complex urban area of Kuala Lumpur, Malaysia was chosen as a study area to simulate the extreme flooding event that occurred in 2003. Three different DTMs were generated and used as input for a two-dimensional (2D) urban flood model. A top-view LiDAR approach was used to create two DTMs: (i) a standard LiDAR-DTM and (ii) a Filtered LiDAR-DTM taking into account specific ground-view features. In addition, a Structure from Motion (SfM) approach was used to detect hidden urban features from a sequence of ground-view images; these ground-view SfM data were then combined with top-view Filtered LiDAR data to create (iii) a novel Multidimensional Fusion of Views-Digital Terrain Model (MFV-DTM). These DTMs were then used as a basis for the 2D urban flood model. The resulting dynamic flood maps are compared with observations at six measurement locations. It was found that when applying only top-view DTMs as input data, the flood simulation results appear to have mismatches in both floodwater depths and flood propagation patterns. In contrast, when employing the top-ground-view fusion approach (MFV-DTM), the results not only show a good agreement in floodwater depth, but also simulate more correctly the floodwater dynamics around

  9. Effects of LiDAR point density and landscape context on the retrieval of urban forest biomass

    NASA Astrophysics Data System (ADS)

    Singh, K. K.; Chen, G.; McCarter, J. B.; Meentemeyer, R. K.

    2014-12-01

    Light Detection and Ranging (LiDAR), as an alternative to conventional optical remote sensing, is being increasingly used 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 better data accuracies, which however pose challenges to the procurement and processing of LiDAR data for large-area assessments. Reducing point density cuts data acquisition costs and overcome computational challenges for broad-scale forest management. However, how does that impact the accuracy of biomass estimation in an urban environment containing a great level of anthropogenic disturbances? The main goal of this study is to evaluate the effects of LiDAR point density on the biomass estimation of remnant forests in the rapidly urbanizing regions of Charlotte, North Carolina, USA. We used multiple linear regression to establish the statistical relationship between field-measured biomass and predictor variables (PVs) derived from LiDAR point clouds with varying densities. We compared the estimation accuracies between the general Urban Forest models (no discrimination of forest type) and the Forest Type models (evergreen, deciduous, and mixed), which was followed by quantifying the degree to which landscape context influenced biomass estimation. The explained biomass variance of Urban Forest models, adjusted R2, was fairly 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 using two 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, signifying the distance impact of development on biomass estimation. Our evaluation

  10. How much does the time lag between wildlife field-data collection and LiDAR-data acquisition matter for studies of animal distributions? A case study using bird communities

    Treesearch

    Kerri T. Vierling; Charles E. Swift; Andrew T. Hudak; Jody C. Vogeler; Lee A. Vierling

    2014-01-01

    Vegetation structure quantified by light detection and ranging (LiDAR) can improve understanding of wildlife occupancy and species-richness patterns. However, there is often a time lag between the collection of LiDAR data and wildlife data. We investigated whether a time lag between the LiDAR acquisition and field-data acquisition affected mapped wildlife distributions...

  11. Tree Canopy Cover Mapping Using LiDAR in Urban Barangays of Cebu City, Central Philippines

    NASA Astrophysics Data System (ADS)

    Ejares, J. A.; Violanda, R. R.; Diola, A. G.; Dy, D. T.; Otadoy, J. B.; Otadoy, R. E. S.

    2016-06-01

    This paper investigates tree canopy cover mapping of urban barangays (smallest administrative division in the Philippines) in Cebu City using LiDAR (Light Detection and Ranging). Object-Based Image Analysis (OBIA) was used to extract tree canopy cover. Multi-resolution segmentation and a series of assign-class algorithm in eCognition software was also performed to extract different land features. Contextual features of tree canopies such as height, area, roundness, slope, length-width and elliptic fit were also evaluated. The results showed that at the time the LiDAR data was collected (June 24, 2014), the tree cover was around 25.11 % (or 15,674,341.8 m2) of the city's urban barangays (or 62,426,064.6 m2). Among all urban barangays in Cebu City, Barangay Busay had the highest cover (55.79 %) while barangay Suba had the lowest (0.8 %). The 16 barangays with less than 10 % tree cover were generally located in the coastal area, presumably due to accelerated urbanization. Thirty-one barangays have tree cover ranging from 10.59--27.3 %. Only 3 barangays (i.e., Lahug, Talamban, and Busay) have tree cover greater than 30 %. The overall accuracy of the analysis was 96.6 % with the Kappa Index of Agreement or KIA of 0.9. From the study, a grouping can be made of the city's urban barangays with regards to tree cover. The grouping will be useful to urban planners not only in allocating budget to the tree planting program of the city but also in planning and creation of urban parks and playgrounds.

  12. Open-Source Digital Elevation Model (DEMs) Evaluation with GPS and LiDAR Data

    NASA Astrophysics Data System (ADS)

    Khalid, N. F.; Din, A. H. M.; Omar, K. M.; Khanan, M. F. A.; Omar, A. H.; Hamid, A. I. A.; Pa'suya, M. F.

    2016-09-01

    Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM), Shuttle Radar Topography Mission (SRTM), and Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) are freely available Digital Elevation Model (DEM) datasets for environmental modeling and studies. The quality of spatial resolution and vertical accuracy of the DEM data source has a great influence particularly on the accuracy specifically for inundation mapping. Most of the coastal inundation risk studies used the publicly available DEM to estimated the coastal inundation and associated damaged especially to human population based on the increment of sea level. In this study, the comparison between ground truth data from Global Positioning System (GPS) observation and DEM is done to evaluate the accuracy of each DEM. The vertical accuracy of SRTM shows better result against ASTER and GMTED10 with an RMSE of 6.054 m. On top of the accuracy, the correlation of DEM is identified with the high determination of coefficient of 0.912 for SRTM. For coastal zone area, DEMs based on airborne light detection and ranging (LiDAR) dataset was used as ground truth data relating to terrain height. In this case, the LiDAR DEM is compared against the new SRTM DEM after applying the scale factor. From the findings, the accuracy of the new DEM model from SRTM can be improved by applying scale factor. The result clearly shows that the value of RMSE exhibit slightly different when it reached 0.503 m. Hence, this new model is the most suitable and meets the accuracy requirement for coastal inundation risk assessment using open source data. The suitability of these datasets for further analysis on coastal management studies is vital to assess the potentially vulnerable areas caused by coastal inundation.

  13. Using LiDAR, RADAR, and Optical data to improve a NFMS in Kalimantan, Indonesia

    NASA Astrophysics Data System (ADS)

    Hagen, S. C.; Saatchi, S. S.; Braswell, B. H., Jr.; Palace, M. W.; Salas, W.; Walker, S.; Hoekman, D.; Ipsan, C.; Brown, S.; Sullivan, F.

    2014-12-01

    Around the world, governments are establishing national forest monitoring systems (NFMS) that use a combination of remote sensing and ground-based forest carbon inventory approaches to estimate anthropogenic forest-related greenhouse gas emissions and removals. The NFMS forms the link between historical assessments and current/future assessments of forests, enabling consistency in the data and information to support the implementation of REDD+ activities. The creation of a reliable, transparent, and comprehensive NFMS is currently limited by a dearth of relevant data that are accurate, low-cost, and spatially resolved at subnational scales. With funding from a 3-year NASA Carbon Monitoring System project beginning in September 2013, we are developing, evaluating, and validating several critical components of an NFMS in Kalimantan, Indonesia, focusing on the use of LiDAR and radar imagery for improved carbon stock and forest degradation information. Here, we present results from an initial analysis of a spatially extensive set of LiDAR data collected across the Indonesian provinces on the island of Borneo together with RADAR and optical data. Our objectives are to evaluate sensor and platform tradeoffs systematically against in situ investments, as well as provide detailed tracking and characterization of uncertainty in a cost-benefit framework. Kalimantan is an ideal area to evaluate the use of remote sensing methods because measuring forest carbon stocks and their human caused changes with a high degree of certainty on the ground can be difficult. While our work focuses at the subnational scale for Kalimantan, we are targeting these methods for applicability across broader geographies and for implementation at various scales.

  14. Guild-specific responses of avian species richness to LiDAR-derived habitat heterogeneity

    NASA Astrophysics Data System (ADS)

    Weisberg, Peter J.; Dilts, Thomas E.; Becker, Miles E.; Young, Jock S.; Wong-Kone, Diane C.; Newton, Wesley E.; Ammon, Elisabeth M.

    2014-08-01

    Ecological niche theory implies that more heterogeneous habitats have the potential to support greater biodiversity. Positive heterogeneity-diversity relationships have been found for most studies investigating animal taxa, although negative relationships also occur and the scale dependence of heterogeneity-diversity relationships is little known. We investigated multi-scale, heterogeneity-diversity relationships for bird communities in a semi-arid riparian landscape, using airborne LiDAR data to derive key measures of structural habitat complexity. Habitat heterogeneity-diversity relationships were generally positive, although the overall strength of relationships varied across avian life history guilds (R2 range: 0.03-0.41). Best predicted were the species richness indices of cavity nesters, habitat generalists, woodland specialists, and foliage foragers. Heterogeneity-diversity relationships were also strongly scale-dependent, with strongest associations at the 200-m scale (4 ha) and weakest associations at the 50-m scale (0.25 ha). Our results underscore the value of LiDAR data for fine-grained quantification of habitat structure, as well as the need for biodiversity studies to incorporate variation among life-history guilds and to simultaneously consider multiple guild functional types (e.g. nesting, foraging, habitat). Results suggest that certain life-history guilds (foliage foragers, cavity nesters, woodland specialists) are more susceptible than others (ground foragers, ground nesters, low nesters) to experiencing declines in local species richness if functional elements of habitat heterogeneity are lost. Positive heterogeneity-diversity relationships imply that riparian conservation efforts need to not only provide high-quality riparian habitat locally, but also to provide habitat heterogeneity across multiple scales.

  15. Terrestrial LiDAR monitoring of rock slope-channel coupling

    NASA Astrophysics Data System (ADS)

    Bell, R.; Blöthe, J. H.; Meyer, N. K.; Hoffmann, T.; Hoffert, H.; Kreiner, D.; Elverfeldt, K. V.

    2009-04-01

    In steep terrain, various types of landslides (e.g. rock falls, debris flows and slides) are important erosional processes which often have a major impact on fluvial systems. On the one hand, they may divert river channels to opposite slopes or even block entire river channels, leading to the formation of landslide-dammed lakes. On the other hand, rivers prepare or even trigger landslides by undercutting slopes, which again will have an impact on the river channel. Our focus is on two study areas. One of them, the Schlichem Valley, is located in the Swabian Alb (SW-Germany), a lower mountain range consisting of Jurassic sedimentary rocks forming a cuesta landscape. There, the focus is on a larger landslide complex which blocked the river Schlichem three times during the 18th century and which is still active. Recent activity, especially at the location where the landslide enters the fluvial system, is investigated using Terrestrial LiDAR monitoring. The second study area is located in the Gesaeuse National Park in the Austrian Alps. There, various geomorphic environments are investigated by Terrestrial LiDAR including a vertical rock face in Dachstein limestone, which talus slope is directly coupled to the river Enns. The talus slope is built up by rock fall deposits, eroded mainly through smaller debris flow events. Furthermore, the talus slope is undercut by flood events of the river Enns. In this study a concept and first results are presented. They suggest how rock slope processes and their interactions with river channels can be monitored.

  16. Quantifying Urban Forest Structure Using Crown-Level Fusion of Imaging Spectroscopy and LiDAR

    NASA Astrophysics Data System (ADS)

    Alonzo, M.; Bookhagen, B.; McFadden, J. P.; Roberts, D. A.

    2013-12-01

    The magnitude and distribution of ecosystem services provided by urban trees depend largely on canopy fractional cover, leaf area index, and species. Most efforts to quantify the structure and function of urban forests have been limited to measuring canopy extent or extrapolation of forest structure and function from plot sample inventories. Hyperspectral remote sensing has shown promise as a means for discriminating tree species. However, in many urban settings, tree species diversity and within-class spectral variability are both high, resulting in low classification accuracies. Canopy structural variables derived from LiDAR can provide additional information, such as tree height and crown width, that do not duplicate the information contained in the spectral variables. In this research we use crown-level fusion of hyperspectral and airborne LiDAR data to map 29 common tree species in Santa Barbara, California. From a discretized, full-waveform lidar dataset, we isolate canopy and, using watershed segmentation, delineate individual crowns. The crown segments are overlaid on Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data and all suitable vegetation spectra are extracted. These same segments are used to extract lidar variables. The two datasets are fused at the crown-object level and classified using canonical discriminant analysis. Overall accuracy for the 29 species, based on correctly classified canopy area, is 83%. When including species outside of the training set, the overall classification accuracy to the tree type level was 90%. At the pixel level, using only spectral data, the classification accuracy of the trained species was 68%. These results indicate the potential for wall-to-wall mapping of an urban forest to the species or tree type level, depending on species diversity and availability of training data. Further, we find that imperfect segmentation is not an insurmountable obstacle to crown-level analysis.

  17. Genetic Map of Triticale Integrating Microsatellite, DArT and SNP Markers

    PubMed Central

    Tyrka, Mirosław; Tyrka, Dorota; Wędzony, Maria

    2015-01-01

    Triticale (×Triticosecale Wittm) is an economically important crop for fodder and biomass production. To facilitate the identification of markers for agronomically important traits and for genetic and genomic characteristics of this species, a new high-density genetic linkage map of triticale was constructed using doubled haploid (DH) population derived from a cross between cultivars ‘Hewo’ and ‘Magnat’. The map consists of 1615 bin markers, that represent 50 simple sequence repeat (SSR), 842 diversity array technology (DArT), and 16888 DArTseq markers mapped onto 20 linkage groups assigned to the A, B, and R genomes of triticale. No markers specific to chromosome 7R were found, instead mosaic linkage group composed of 1880 highly distorted markers (116 bins) from 10 wheat chromosomes was identified. The genetic map covers 4907 cM with a mean distance between two bins of 3.0 cM. Comparative analysis in respect to published maps of wheat, rye and triticale revealed possible deletions in chromosomes 4B, 5A, and 6A, as well as inversion in chromosome 7B. The number of bin markers in each chromosome varied from 24 in chromosome 3R to 147 in chromosome 6R. The length of individual chromosomes ranged between 50.7 cM for chromosome 2R and 386.2 cM for chromosome 7B. A total of 512 (31.7%) bin markers showed significant (P < 0.05) segregation distortion across all chromosomes. The number of 8 the segregation distorted regions (SDRs) were identified on 1A, 7A, 1B, 2B, 7B (2 SDRs), 5R and 6R chromosomes. The high-density genetic map of triticale will facilitate fine mapping of quantitative trait loci, the identification of candidate genes and map-based cloning. PMID:26717308

  18. LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

    USGS Publications Warehouse

    Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.

    2015-01-01

    Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.

  19. Evaluating dryland ecological and river restoration using repeat LiDAR and hydrological monitoring

    NASA Astrophysics Data System (ADS)

    Henderson, W. M.; DeLong, S.

    2012-12-01

    Recent improvements in the collection of multitemporal, high-resolution topographic data such as Light Detection and Ranging (LiDAR) have done a great deal to increase our ability to quantify the details of landscape change. Both Terrestrial Laser Scanning (TLS) and Airborne Laser Swath Mapping (ALSM) can be used to easily assess how Earth surface processes affect landscape form to a level of precision that was previously more difficult to attain. A comprehensive approach using ALSM, TLS-TLS comparison, and hydrological monitoring is being used to assess the effectiveness of a large scale ecological and river restoration effort by the Cuenca los Ojos Foundation at San Bernardino Ranch near Agua Prieta, Sonora, Mexico. In the study area, historical arroyo cutting and changes in land use led to the abandonment of a ciénega wetland and resulted in widespread ecological destruction. The current land managers have employed engineering methods in order to restore stream and ciénega ecology, including the installation of large rock gabions, earthen berms, and concrete spillways along channels. Our goal is to test the hypothesis that the use of dam and gabion structures leads to stream aggradation, flash flood dampening, and ultimately, increased available water and reestablishment of historic wetland plant and animal communities. We present results from LiDAR change detection that includes 2007-2011 ALSM to TLS change, and several 2011-2012 TLS-TLS comparisons. We also present results from streamflow monitoring, field observation, and monitoring of shallow groundwater and soil moisture conditions. Preliminary results show that channel aggradation occurs rapidly upstream of engineered structures. However, the apparent dampening of sediment transport by the structures leads to less aggradation and even incision immediately downstream of structures. Peak flood flows are decreased by the reservoirs formed behind large earthen berms. After several years of water retention

  20. Determinants of high blood pressure and barriers to diagnosis and treatment in Dar es Salaam, Tanzania.

    PubMed

    Zack, Rachel M; Irema, Kahema; Kazonda, Patrick; Leyna, Germana H; Liu, Enju; Spiegelman, Donna; Fawzi, Wafaie; Njelekela, Marina; Killewo, Japhet; Danaei, Goodarz

    2016-12-01

    We assessed the prevalence and determinants of high blood pressure (BP), and barriers to diagnosis and treatment, in Dar es Salaam, Tanzania. We surveyed and screened 2174 community-dwelling adults aged at least 40 years in 2014 and conducted a follow-up after 1 year. Median BP was 131/81 mmHg, and hypertension prevalence was 37%. Mean adjusted difference in SBP was 4.0 mmHg for overweight, 6.3 mmHg for obese class I, and 10.5 mmHg for obese class II/III compared with normal weight participants. Those who were physically inactive had 4.8 mmHg higher SBP compared with those with more than 24 h of moderate or vigorous activity per week. Drinkers of at least 10 g of alcohol per day had 4.5 mmHg higher SBP than did nondrinkers. Among hypertensive participants, 48% were previously diagnosed, 22% were treated, and 10% were controlled. Hypertensive participants without health insurance were 12% less likely to have been previously diagnosed than insured hypertensive participants. Of referred participants, 68% sought care, but only 27% were on treatment and 8% had controlled BP at follow-up. Reasons for not seeking care included lack of symptoms, cost of visit, and lack of time. Reasons for not being on treatment included lack of symptoms, not being prescribed treatment, and having finished one course of treatment. Major risk factors for hypertension in Dar es Salaam are overweight, obesity, inadequate physical activity, and limited access to quality medical care. Increased insurance coverage and community-based screening, along with quality medical care and patient education, may help control this burgeoning epidemic.

  1. Quantifying landscape change in an arctic coastal lowland using repeat airborne LiDAR

    NASA Astrophysics Data System (ADS)

    Jones, Benjamin M.; Stoker, Jason M.; Gibbs, Ann E.; Grosse, Guido; Romanovsky, Vladimir E.; Douglas, Thomas A.; Kinsman, Nicole E. M.; Richmond, Bruce M.

    2013-12-01

    Increases in air, permafrost, and sea surface temperature, loss of sea ice, the potential for increased wave energy, and higher river discharge may all be interacting to escalate erosion of arctic coastal lowland landscapes. Here we use airborne light detection and ranging (LiDAR) data acquired in 2006 and 2010 to detect landscape change in a 100 km2 study area on the Beaufort Sea coastal plain of northern Alaska. We detected statistically significant change (99% confidence interval), defined as contiguous areas (>10 m2) that had changed in height by at least 0.55 m, in 0.3% of the study region. Erosional features indicative of ice-rich permafrost degradation were associated with ice-bonded coastal, river, and lake bluffs, frost mounds, ice wedges, and thermo-erosional gullies. These features accounted for about half of the area where vertical change was detected. Inferred thermo-denudation and thermo-abrasion of coastal and river bluffs likely accounted for the dominant permafrost-related degradational processes with respect to area (42%) and volume (51%). More than 300 thermokarst pits significantly subsided during the study period, likely as a result of storm surge flooding of low-lying tundra (<1.4 m asl) as well as the lasting impact of warm summers in the late-1980s and mid-1990s. Our results indicate that repeat airborne LiDAR can be used to detect landscape change in arctic coastal lowland regions at large spatial scales over sub-decadal time periods.

  2. Effect of slope on treetop detection using a LiDAR Canopy Height Model

    NASA Astrophysics Data System (ADS)

    Khosravipour, Anahita; Skidmore, Andrew K.; Wang, Tiejun; Isenburg, Martin; Khoshelham, Kourosh

    2015-06-01

    Canopy Height Models (CHMs) or normalized Digital Surface Models (nDSM) derived from LiDAR data have been applied to extract relevant forest inventory information. However, generating a CHM by height normalizing the raw LiDAR points is challenging if trees are located on complex terrain. On steep slopes, the raw elevation values located on either the downhill or the uphill part of a tree crown are height-normalized with parts of the digital terrain model that may be much lower or higher than the tree stem base, respectively. In treetop detection, a highest crown return located in the downhill part may prove to be a "false" local maximum that is distant from the true treetop. Based on this observation, we theoretically and experimentally quantify the effect of slope on the accuracy of treetop detection. The theoretical model presented a systematic horizontal displacement of treetops that causes tree height to be systematically displaced as a function of terrain slope and tree crown radius. Interestingly, our experimental results showed that the effect of CHM distortion on treetop displacement depends not only on the steepness of the slope but more importantly on the crown shape, which is species-dependent. The influence of the systematic error was significant for Scots pine, which has an irregular crown pattern and weak apical dominance, but not for mountain pine, which has a narrow conical crown with a distinct apex. Based on our findings, we suggest that in order to minimize the negative effect of steep slopes on the CHM, especially in heterogeneous forest with multiple species or species which change their morphological characteristics as they mature, it is best to use raw elevation values (i.e., use the un-normalized DSM) and compute the height after treetop detection.

  3. Guild-specific responses of avian species richness to LiDAR-derived habitat heterogeneity

    USGS Publications Warehouse

    Weisberg, Peter J.; Dilts, Thomas E.; Becker, Miles E.; Young, Jock S.; Wong-Kone, Diane C.; Newton, Wesley E.; Ammon, Elisabeth M.

    2014-01-01

    Ecological niche theory implies that more heterogeneous habitats have the potential to support greater biodiversity. Positive heterogeneity-diversity relationships have been found for most studies investigating animal taxa, although negative relationships also occur and the scale dependence of heterogeneity-diversity relationships is little known. We investigated multi-scale, heterogeneity-diversity relationships for bird communities in a semi-arid riparian landscape, using airborne LiDAR data to derive key measures of structural habitat complexity. Habitat heterogeneity-diversity relationships were generally positive, although the overall strength of relationships varied across avian life history guilds (R2 range: 0.03–0.41). Best predicted were the species richness indices of cavity nesters, habitat generalists, woodland specialists, and foliage foragers. Heterogeneity-diversity relationships were also strongly scale-dependent, with strongest associations at the 200-m scale (4 ha) and weakest associations at the 50-m scale (0.25 ha). Our results underscore the value of LiDAR data for fine-grained quantification of habitat structure, as well as the need for biodiversity studies to incorporate variation among life-history guilds and to simultaneously consider multiple guild functional types (e.g. nesting, foraging, habitat). Results suggest that certain life-history guilds (foliage foragers, cavity nesters, woodland specialists) are more susceptible than others (ground foragers, ground nesters, low nesters) to experiencing declines in local species richness if functional elements of habitat heterogeneity are lost. Positive heterogeneity-diversity relationships imply that riparian conservation efforts need to not only provide high-quality riparian habitat locally, but also to provide habitat heterogeneity across multiple scales.

  4. Assessing and Correcting Topographic Effects on Forest Canopy Height Retrieval Using Airborne LiDAR Data

    PubMed Central

    Duan, Zhugeng; Zhao, Dan; Zeng, Yuan; Zhao, Yujin; Wu, Bingfang; Zhu, Jianjun

    2015-01-01

    Topography affects forest canopy height retrieval based on airborne Light Detection and Ranging (LiDAR) data a lot. This paper proposes a method for correcting deviations caused by topography based on individual tree crown segmentation. The point cloud of an individual tree was extracted according to crown boundaries of isolated individual trees from digital orthophoto maps (DOMs). Normalized canopy height was calculated by subtracting the elevation of centres of gravity from the elevation of point cloud. First, individual tree crown boundaries are obtained by carrying out segmentation on the DOM. Second, point clouds of the individual trees are extracted based on the boundaries. Third, precise DEM is derived from the point cloud which is classified by a multi-scale curvature classification algorithm. Finally, a height weighted correction method is applied to correct the topological effects. The method is applied to LiDAR data acquired in South China, and its effectiveness is tested using 41 field survey plots. The results show that the terrain impacts the canopy height of individual trees in that the downslope side of the tree trunk is elevated and the upslope side is depressed. This further affects the extraction of the location and crown of individual trees. A strong correlation was detected between the slope gradient and the proportions of returns with height differences more than 0.3, 0.5 and 0.8 m in the total returns, with coefficient of determination R2 of 0.83, 0.76, and 0.60 (n = 41), respectively. PMID:26016907

  5. Deconstructing a Polygenetic Landscape Using LiDAR and Multi-Resolution Analysis

    NASA Astrophysics Data System (ADS)

    Houser, C.; Barrineau, C. P.; Dobreva, I. D.; Bishop, M. P.

    2015-12-01

    In many earth surface systems characteristic morphologies are associated with various regimes both past and present. Aeolian systems contain a variety of features differentiated largely by morphometric differences, which in turn reflect age and divergent process regimes. Using quantitative analysis of high-resolution elevation data to generate detailed information regarding these characteristic morphometries enables geomorphologists to effectively map process regimes from a distance. Combined with satellite imagery and other types of remotely sensed data, the outputs can even help to delineate phases of activity within aeolian systems. The differentiation of regimes and identification of relict features together enables a greater level of rigor to analyses leading to field-based investigations, which are highly dependent on site-specific historical contexts that often obscure distinctions between separate process-form regimes. We present results from a Principal Components Analysis (PCA) performed on a LiDAR-derived elevation model of a largely stabilized aeolian system in South Texas. The resulting components are layered and classified to generate a map of aeolian morphometric signatures for a portion of the landscape. Several of these areas do not immediately appear to be aeolian in nature in satellite imagery or LiDAR-derived models, yet field observations and historical imagery reveal the PCA did in fact identify stabilized and relict dune features. This methodology enables researchers to generate a morphometric classification of the land surface. We believe this method is a valuable and innovative tool for researchers identifying process regimes within a study area, particularly in field-based investigations that rely heavily on site-specific context.

  6. Tracking geomorphic signatures of watershed suburbanization with multitemporal LiDAR

    NASA Astrophysics Data System (ADS)

    Jones, Daniel K.; Baker, Matthew E.; Miller, Andrew J.; Jarnagin, S. Taylor; Hogan, Dianna M.

    2014-08-01

    Urban development practices redistribute surface materials through filling, grading, and terracing, causing drastic changes to the geomorphic organization of the landscape. Many studies document the hydrologic, biologic, or geomorphic consequences of urbanization using space-for-time comparisons of disparate urban and rural landscapes. However, no previous studies have documented geomorphic changes from development using multiple dates of high-resolution topographic data at the watershed scale. This study utilized a time series of five sequential light detection and ranging (LiDAR) derived digital elevation models (DEMs) to track watershed geomorphic changes within two watersheds throughout development (2002-2008) and across multiple spatial scales (0.01-1 km2). Development-induced changes were compared against an undeveloped forested watershed during the same time period. Changes in elevations, slopes, hypsometry, and surface flow pathways were tracked throughout the development process to assess watershed geomorphic alterations. Results suggest that development produced an increase in sharp topographic breaks between relatively flat surfaces and steep slopes, replacing smoothly varying hillslopes and leading to greater variation in slopes. Examinations of flowpath distributions highlight systematic modifications that favor rapid convergence in unchanneled upland areas. Evidence of channel additions in the form of engineered surface conduits is apparent in comparisons of pre- and post-development stream maps. These results suggest that topographic modification, in addition to impervious surfaces, contributes to altered hydrologic dynamics observed in urban systems. This work highlights important considerations for the use of repeat LiDAR flights in analyzing watershed change through time. Novel methods introduced here may allow improved understanding and targeted mitigation of the processes driving geomorphic changes during development and help guide future

  7. A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data

    NASA Astrophysics Data System (ADS)

    Strîmbu, Victor F.; Strîmbu, Bogdan M.

    2015-06-01

    This work proposes a segmentation method that isolates individual tree crowns using airborne LiDAR data. The proposed approach captures the topological structure of the forest in hierarchical data structures, quantifies topological relationships of tree crown components in a weighted graph, and finally partitions the graph to separate individual tree crowns. This novel bottom-up segmentation strategy is based on several quantifiable cohesion criteria that act as a measure of belief on weather two crown components belong to the same tree. An added flexibility is provided by a set of weights that balance the contribution of each criterion, thus effectively allowing the algorithm to adjust to different forest structures. The LiDAR data used for testing was acquired in Louisiana, inside the Clear Creek Wildlife management area with a RIEGL LMS-Q680i airborne laser scanner. Three 1 ha forest areas of different conditions and increasing complexity were segmented and assessed in terms of an accuracy index (AI) accounting for both omission and commission. The three areas were segmented under optimum parameterization with an AI of 98.98%, 92.25% and 74.75% respectively, revealing the excellent potential of the algorithm. When segmentation parameters are optimized locally using plot references the AI drops to 98.23%, 89.24%, and 68.04% on average with plot sizes of 1000 m2 and 97.68%, 87.78% and 61.1% on average with plot sizes of 500 m2. More than introducing a segmentation algorithm, this paper proposes a powerful framework featuring flexibility to support a series of segmentation methods including some of those recurring in the tree segmentation literature. The segmentation method may extend its applications to any data of topological nature or data that has a topological equivalent.

  8. Quantifying landscape change in an arctic coastal lowland using repeat airborne LiDAR

    USGS Publications Warehouse

    Jones, Benjamin M.; Stoker, Jason M.; Gibbs, Ann E.; Grosse, Guido; Romanovsky, Vladimir E.; Douglas, Thomas A.; Kinsman, Nichole E.M.; Richmond, Bruce M.

    2013-01-01

    Increases in air, permafrost, and sea surface temperature, loss of sea ice, the potential for increased wave energy, and higher river discharge may all be interacting to escalate erosion of arctic coastal lowland landscapes. Here we use airborne light detection and ranging (LiDAR) data acquired in 2006 and 2010 to detect landscape change in a 100 km2 study area on the Beaufort Sea coastal plain of northern Alaska. We detected statistically significant change (99% confidence interval), defined as contiguous areas (>10 m2) that had changed in height by at least 0.55 m, in 0.3% of the study region. Erosional features indicative of ice-rich permafrost degradation were associated with ice-bonded coastal, river, and lake bluffs, frost mounds, ice wedges, and thermo-erosional gullies. These features accounted for about half of the area where vertical change was detected. Inferred thermo-denudation and thermo-abrasion of coastal and river bluffs likely accounted for the dominant permafrost-related degradational processes with respect to area (42%) and volume (51%). More than 300 thermokarst pits significantly subsided during the study period, likely as a result of storm surge flooding of low-lying tundra (<1.4 m asl) as well as the lasting impact of warm summers in the late-1980s and mid-1990s. Our results indicate that repeat airborne LiDAR can be used to detect landscape change in arctic coastal lowland regions at large spatial scales over sub-decadal time periods.

  9. Underwater monitoring experiment using hyperspectral sensor, LiDAR and high resolution satellite imagery

    NASA Astrophysics Data System (ADS)

    Yang, Chan-Su; Kim, Sun-Hwa

    2014-10-01

    In general, hyper-spectral sensor, LiDAR and high spatial resolution satellite imagery for underwater monitoring are dependent on water clarity or water transparency that can be measured using a Secchi disk or satellite ocean color data. Optical properties in the sea waters of South Korea are influenced mainly by a strong tide and oceanic currents, diurnal, daily and seasonal variations of water transparency. The satellite-based Secchi depth (ZSD) analysis showed the applicability of hyper-spectral sensor, LiDAR and optical satellite, determined by the location connected with the local distribution of Case 1 and 2 waters. The southeast coastal areas of Jeju Island are selected as test sites for a combined underwater experiment, because those areas represent Case 1 water. Study area is a small port (<15m) in the southeast area of the island and linear underwater target used by sewage pipe is located in this area. Our experiments are as follows: 1. atmospheric and sun-glint correction methods to improve the underwater monitoring ability; 2. intercomparison of water depths obtained from three different sensors. Three sensors used here are the CASI-1500 (Wide-Array Airborne Hyperspectral VNIR Imager (0.38-1.05 microns), the Coastal Zone Mapping and Imaging Lidar (CZMIL) and Korean Multi-purpose Satellite-3 (KOMPSAT-3) with 2.8 meter multi-spectral resolution. The experimental results were affected by water clarity and surface condition, and the bathymetric results of three sensors show some differences caused by sensor-itself, bathymetric algorithm and tide level. It is shown that CASI-1500 was applicable for bathymetry and underwater target detection in this area, but KOMPSAT-3 should be improved for Case 1 water. Although this experiment was designed to compare underwater monitoring ability of LIDAR, CASI-1500, KOMPSAT-3 data, this paper was based on initial results and suggested only results about the bathymetry and underwater target detection.

  10. Detecting Montane Meadows in the Tahoe National Forest Using LiDAR and ASTER Imagery

    NASA Astrophysics Data System (ADS)

    Lorenz, A.; Blesius, L.; Davis, J. D.

    2016-12-01

    In the Sierra Nevada mountains, meadows provide numerous hydraulic and ecosystem functions such as flood attenuation, groundwater storage, and wildlife habitat. However, many meadows have been degraded from historical land use such as water diversion, grazing, and logging. Land managers have altered management strategies for restoration purposes, but there is a lack of comprehensive data on meadow locations. Previous attempts to inventory Sierra Nevada meadows have included several remote sensing techniques including heads up digitizing and pixel based image analysis, but this has been challenging due to geographic variability, seasonal changes, and meadow health. I present a remote sensing method using multiple return LiDAR (Light Detection and Ranging) and ASTER imagery to detect montane meadows in a subset of the Tahoe National Forest. The project used LiDAR data to create a digital terrain model and digital surface model. From these models, I derived canopy height, surface slope, and watercourse for the entire study area. Literature queries returned known values for canopy height and surface slope characteristic of montane meadows. These values were used to select for possible meadows within the study area. To filter out noise, only contiguous areas greater than one acre that satisfied the queries were used. Finally, 15-meter ASTER imagery was used to de-select for areas such as dirt patches or gravel bars that might have satisfied the previous queries and meadow criteria. When using high resolution aerial imagery to assess model accuracy, preliminary results show user accuracy of greater than 80%. Further validation is still needed to improve the accuracy of modeled meadow delineation. This method allows for meadows to be inventoried without discriminating based on geographic variability, seasonal changes, or meadow health.

  11. Coastal change analysis of Lovells Island using high resolution ground based LiDAR imagery

    NASA Astrophysics Data System (ADS)

    Ly, Jennifer K.

    Many methods have been employed to study coastline change. These methods range from historical map analysis to GPS surveys to modern airborne LiDAR and satellite imagery. These previously used methods can be time consuming, labor intensive, and expensive and have varying degrees of accuracy and temporal coverage. Additionally, it is often difficult to apply such techniques in direct response to an isolated event within an appropriate temporal framework. Here we utilize a new ground based Canopy Biomass LiDAR (CBL) system built at The University of Massachusetts Boston (in collaboration with the Rochester Institute of Technology) in order to identify and analyze coastal change on Lovells Island, Boston Harbor. Surveys of a bluff developing in an eroding drumlin and beach cusps on a high-energy cobble beach on Lovells Island were conducted in June, September and December of 2013. At each site for each survey, the CBL was set up and multiple scans of each feature were taken on a predetermined transect that was established parallel to the high-water mark at distances relative to the scale of the bluff and cusps. The scans from each feature were compiled, integrated and visualized using Meshlab. Results from our surveys indicate that the highly portable and easy to deploy CBL system produces images of exceptional clarity, with the capacity to resolve small-scale changes to coastal features and systems. The CBL, while still under development (and coastal surveying protocols with it are just being established), appears to be an ideal tool for analyzing coastal geological features and is anticipated to prove to be a useful tool for the observation and analysis of coastal change. Furthermore, there is significant potential for utilizing the low cost ultra-portable CBL in frequent deployments to develop small-scale erosion rate and sediment budget analyses.

  12. Use of Remotely Piloted Aircraft System and LiDAR for Alpine forested Landslide

    NASA Astrophysics Data System (ADS)

    Borgniet, Laurent; Lachenal, Philippe; Berger, Frédéric

    2017-04-01

    In the last decade Remotely Piloted Aircraft Systems (RPAS) technologies considerably evolved, improving flight stability, GPS positioning and payload. Recent researches shown that RPAS-SfM framework, combining high volumes data acquisition and fast treatments capacity, make it suitable for environmental monitoring. However, monitoring, in a short period, an active landslide with major land displacements in a context of unstable and vegetated mountainous area still represent a real challenge. In this study, we aimed at developing a reproducible and optimized cost-efficiency method to accurately survey active terrain movements. The combined use of two RPAS allows to i)better visualize at large scale (1km2) the phenomenon dimensions and velocity in order to ii) focus our efforts on a safe topographic and photogrammetric data acquisition. The study area is a re-activated landslide previously reported in 1966 by forest management services located near Beaufort in the French Alps. For six time steps between April and September 2017, we acquired aerial photos with two reflex camera (Visible and Near Infra-Red Bands) mounted on a hexacopter with a payload up to 4kg. A validation campaign with aerial LiDAR and Terrestrial Laser Scanner took place on June 2017. Comparison of the digital Surface models and orthophotos derived from RPAS flights gave satisfactory results. Spatial analysis in a GIS allowed a quantitative evaluation of heterogeneous behaviors and dynamic distributions of materials (mineral and vegetal) along the slope. Estimations of displaced volumes (500 000 m3) constitute a precious information for improving in emergency crisis the calibration of deposits place in order to avoid jam and flood on the road network. In this research, we demonstrate the feasibility of a repetitive RPAS based data acquisition method but some limitations still remain. Research efforts will now focus on DEM under vegetation cover determination combining RPAS adapted LiDAR, improved

  13. Rapid Urban Malaria Appraisal (RUMA) II: epidemiology of urban malaria in Dar es Salaam (Tanzania).

    PubMed

    Wang, Shr-Jie; Lengeler, Christian; Mtasiwa, Deodatus; Mshana, Thomas; Manane, Lusinge; Maro, Godson; Tanner, Marcel

    2006-04-04

    The thinking behind malaria research and control strategies stems largely from experience gained in rural areas and needs to be adapted to the urban environment. A rapid assessment of urban malaria was conducted in Dar es Salaam in June-August, 2003 using a standard Rapid Urban Malaria Appraisal (RUMA) methodology. This study was part of a multi-site study in sub-Saharan Africa supported by the Roll Back Malaria Partnership. Overall, around one million cases of malaria are reported every year by health facilities. However, school surveys in Dar es Salaam during a dry spell in 2003 showed that the prevalence of malaria parasites was low: 0.8%, 1.4%, 2.7% and 3.7% in the centre, intermediate, periphery and surrounding rural areas, respectively. Health facilities surveys showed that only 37/717 (5.2%) of presenting fever cases and 22/781 (2.8%) of non-fever cases were positive by blood slide. As a result, malaria-attributable fractions for fever episodes were low in all age groups and there was an important over-reporting of malaria cases. Increased malarial infection rates were seen in persons who travelled to rural areas within the past three months. A remarkably high coverage of insecticide-treated nets and a corresponding reduction in malarial infection risk were found. The number of clinical malaria cases was much lower than routine reporting suggested. Improved malaria diagnosis and re-defined clinical guidelines are urgently required to avoid over-treatment with antimalarials.

  14. Full-waveform LiDAR echo decomposition based on wavelet decomposition and particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Li, Duan; Xu, Lijun; Li, Xiaolu

    2017-04-01

    To measure the distances and properties of the objects within a laser footprint, a decomposition method for full-waveform light detection and ranging (LiDAR) echoes is proposed. In this method, firstly, wavelet decomposition is used to filter the noise and estimate the noise level in a full-waveform echo. Secondly, peak and inflection points of the filtered full-waveform echo are used to detect the echo components in the filtered full-waveform echo. Lastly, particle swarm optimization (PSO) is used to remove the noise-caused echo components and optimize the parameters of the most probable echo components. Simulation results show that the wavelet-decomposition-based filter is of the best improvement of SNR and decomposition success rates than Wiener and Gaussian smoothing filters. In addition, the noise level estimated using wavelet-decomposition-based filter is more accurate than those estimated using other two commonly used methods. Experiments were carried out to evaluate the proposed method that was compared with our previous method (called GS-LM for short). In experiments, a lab-build full-waveform LiDAR system was utilized to provide eight types of full-waveform echoes scattered from three objects at different distances. Experimental results show that the proposed method has higher success rates for decomposition of full-waveform echoes and more accurate parameters estimation for echo components than those of GS-LM. The proposed method based on wavelet decomposition and PSO is valid to decompose the more complicated full-waveform echoes for estimating the multi-level distances of the objects and measuring the properties of the objects in a laser footprint.

  15. First LiDAR images of the Alpine Fault, central South Island, New Zealand

    NASA Astrophysics Data System (ADS)

    Langridge, R. M.; Toy, V. G.; Barth, N.; de Pascale, G. P.; Sutherland, R.; Farrier, T.

    2010-12-01

    In central South Island, New Zealand, the dextral-reverse Alpine fault forms the principal component of the Australia-Pacific plate boundary. The fault typically accommodates slip rates of the order of ~27-29 mm/yr (dextral) and up to 6-11 mm/yr (reverse), mostly uplifting Pacific plate rocks that form the Southern Alps. However, the associated high relief, rapid uplift and erosion and high rainfall and accompanying dense temperate rainforest along the western side of the island has typically hampered geological efforts to better understand the neotectonics of the Alpine fault. LiDAR data have been acquired over a 34 km stretch of the fault between Whataroa in the northeast and Franz Josef in the southwest to test the viability of this technique under dense vegetation and in steep, dissected terrain. LiDAR has been collected from a fixed wing base (1300m above ground level) at a frequency of 70k Hz, with 33.5 Hz scan frequency and a 39° field of view. We employed a strategy of flying a dense pattern of 6 flight lines across a swath width of 1.3 km. This creates areas of both single and double overlap coverage that have allowed for accurate landscape models to be created. Results show that this strategy has provided an optimum level of forest penetration and ground returns. Initial results show remarkable level of detail in DEM’s of the landscape along the Alpine fault. Examples of results presented here include: Franz Josef, where the fault traverses the township; and Gaunt Creek, where a Deep Fault Drilling Project will be sited in early 2011.

  16. Automatic extraction of insulators from 3D LiDAR data of an electrical substation

    NASA Astrophysics Data System (ADS)

    Arastounia, M.; Lichti, D. D.

    2013-10-01

    A considerable percentage of power outages are caused by animals that come into contact with conductive elements of electrical substations. These can be prevented by insulating conductive electrical objects, for which a 3D as-built plan of the substation is crucial. This research aims to create such a 3D as-built plan using terrestrial LiDAR data while in this paper the aim is to extract insulators, which are key objects in electrical substations. This paper proposes a segmentation method based on a new approach of finding the principle direction of points' distribution. This is done by forming and analysing the distribution matrix whose elements are the range of points in 9 different directions in 3D space. Comparison of the computational performance of our method with PCA (principal component analysis) shows that our approach is 25% faster since it utilizes zero-order moments while PCA computes the first- and second-order moments, which is more time-consuming. A knowledge-based approach has been developed to automatically recognize points on insulators. The method utilizes known insulator properties such as diameter and the number and the spacing of their rings. The results achieved indicate that 24 out of 27 insulators could be recognized while the 3 un-recognized ones were highly occluded. Check point analysis was performed by manually cropping all points on insulators. The results of check point analysis show that the accuracy, precision and recall of insulator recognition are 98%, 86% and 81%, respectively. It is concluded that automatic object extraction from electrical substations using only LiDAR data is not only possible but also promising. Moreover, our developed approach to determine the directional distribution of points is computationally more efficient for segmentation of objects in electrical substations compared to PCA. Finally our knowledge-based method is promising to recognize points on electrical objects as it was successfully applied for

  17. Tracking geomorphic signatures of watershed suburbanization with multi-temporal LiDAR

    USGS Publications Warehouse

    Jones, Daniel K.; Baker, Matthew E.; Miller, Andrew J.; Jarnagin, S. Taylor; Hogan, Dianna M.

    2014-01-01

    Urban development practices redistribute surface materials through filling, grading, and terracing, causing drastic changes to the geomorphic organization of the landscape. Many studies document the hydrologic, biologic, or geomorphic consequences of urbanization using space-for-time comparisons of disparate urban and rural landscapes. However, no previous studies have documented geomorphic changes from development using multiple dates of high-resolution topographic data at the watershed scale. This study utilized a time series of five sequential light detection and ranging (LiDAR) derived digital elevation models (DEMs) to track watershed geomorphic changes within two watersheds throughout development (2002–2008) and across multiple spatial scales (0.01–1 km2). Development-induced changes were compared against an undeveloped forested watershed during the same time period. Changes in elevations, slopes, hypsometry, and surface flow pathways were tracked throughout the development process to assess watershed geomorphic alterations. Results suggest that development produced an increase in sharp topographic breaks between relatively flat surfaces and steep slopes, replacing smoothly varying hillslopes and leading to greater variation in slopes. Examinations of flowpath distributions highlight systematic modifications that favor rapid convergence in unchanneled upland areas. Evidence of channel additions in the form of engineered surface conduits is apparent in comparisons of pre- and post-development stream maps. These results suggest that topographic modification, in addition to impervious surfaces, contributes to altered hydrologic dynamics observed in urban systems. This work highlights important considerations for the use of repeat LiDAR flights in analyzing watershed change through time. Novel methods introduced here may allow improved understanding and targeted mitigation of the processes driving geomorphic changes during development and help guide future

  18. Data Archiving and Distribution of LiDAR and Derived Datasets in the Philippines

    NASA Astrophysics Data System (ADS)

    Tupas, M. E. A.; Lat, S. C.; Magturo, R. A.

    2016-06-01

    LiDAR programs in the Philippines have been generating valuable resource and hazard information for most of the country at a substantial rate since 2012. Significant progress have been made due to the programs design of engaging 16 Universities and research institutions spatially distributed across the country. Because of this, data has been accumulating at a brisk rate which poses significant technical and logistic issues. While a central node, the University of the Philippines, Diliman, handles data acquisition, pre-processing, and quality checking, processing and ground validation are devolved to the various nodes. For this setup to be successful, an efficient data access and distribution system should be in place. In this paper, we discuss the spatial data infrastructure and data access protocols implemented by the program. At the center of the data access and distribution operations is LiPAD or our LiDAR portal for archiving and distribution. LiPAD is built on open source technologies, established web standards, and protocols. At its back-end a central data archive has been established using state of the art Object Storage technology to store both raw, processed Lidar and derived data sets. Catalog of available data sets ranging from data acquisition foot prints, to DEM coverages, to derived products such as flood hazard, and crop suitability are viewable and accessible on the main site based on the popular GeoNode application. Data exchange is performed using varying protocols to address various logistical problems. Given the various challenges the program is successful in distributing data sets not just to partner processing nodes but to other stakeholders where main requesters include national agencies and general research and academic institutions.

  19. Multispectral, hyperspectral, and LiDAR remote sensing and geographic information fusion for improved earthquake response

    NASA Astrophysics Data System (ADS)

    Kruse, F. A.; Kim, A. M.; Runyon, S. C.; Carlisle, Sarah C.; Clasen, C. C.; Esterline, C. H.; Jalobeanu, A.; Metcalf, J. P.; Basgall, P. L.; Trask, D. M.; Olsen, R. C.

    2014-06-01

    The Naval Postgraduate School (NPS) Remote Sensing Center (RSC) and research partners have completed a remote sensing pilot project in support of California post-earthquake-event emergency response. The project goals were to dovetail emergency management requirements with remote sensing capabilities to develop prototype map products for improved earthquake response. NPS coordinated with emergency management services and first responders to compile information about essential elements of information (EEI) requirements. A wide variety of remote sensing datasets including multispectral imagery (MSI), hyperspectral imagery (HSI), and LiDAR were assembled by NPS for the purpose of building imagery baseline data; and to demonstrate the use of remote sensing to derive ground surface information for use in planning, conducting, and monitoring post-earthquake emergency response. Worldview-2 data were converted to reflectance, orthorectified, and mosaicked for most of Monterey County; CA. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired at two spatial resolutions were atmospherically corrected and analyzed in conjunction with the MSI data. LiDAR data at point densities from 1.4 pts/m2 to over 40 points/ m2 were analyzed to determine digital surface models. The multimodal data were then used to develop change detection approaches and products and other supporting information. Analysis results from these data along with other geographic information were used to identify and generate multi-tiered products tied to the level of post-event communications infrastructure (internet access + cell, cell only, no internet/cell). Technology transfer of these capabilities to local and state emergency respo