Genomic Characterization of DArT Markers Based on High-Density Linkage Analysis and Physical Mapping to the Eucalyptus Genome

PubMed Central

Petroli, César D.; Sansaloni, Carolina P.; Carling, Jason; Steane, Dorothy A.; Vaillancourt, René E.; Myburg, Alexander A.; da Silva, Orzenil Bonfim; Pappas, Georgios Joannis; Kilian, Andrzej; Grattapaglia, Dario

2012-01-01

Diversity Arrays Technology (DArT) provides a robust, high throughput, cost-effective method to query thousands of sequence polymorphisms in a single assay. Despite the extensive use of this genotyping platform for numerous plant species, little is known regarding the sequence attributes and genome-wide distribution of DArT markers. We investigated the genomic properties of the 7,680 DArT marker probes of a Eucalyptus array, by sequencing them, constructing a high density linkage map and carrying out detailed physical mapping analyses to the Eucalyptus grandis reference genome. A consensus linkage map with 2,274 DArT markers anchored to 210 microsatellites and a framework map, with improved support for ordering, displayed extensive collinearity with the genome sequence. Only 1.4 Mbp of the 75 Mbp of still unplaced scaffold sequence was captured by 45 linkage mapped but physically unaligned markers to the 11 main Eucalyptus pseudochromosomes, providing compelling evidence for the quality and completeness of the current Eucalyptus genome assembly. A highly significant correspondence was found between the locations of DArT markers and predicted gene models, while most of the 89 DArT probes unaligned to the genome correspond to sequences likely absent in E. grandis, consistent with the pan-genomic feature of this multi-Eucalyptus species DArT array. These comprehensive linkage-to-physical mapping analyses provide novel data regarding the genomic attributes of DArT markers in plant genomes in general and for Eucalyptus in particular. DArT markers preferentially target the gene space and display a largely homogeneous distribution across the genome, thereby providing superb coverage for mapping and genome-wide applications in breeding and diversity studies. Data reported on these ubiquitous properties of DArT markers will be particularly valuable to researchers working on less-studied crop species who already count on DArT genotyping arrays but for which no reference genome is yet available to allow such detailed characterization. PMID:22984541

Bridging gaps: On the performance of airborne LiDAR to model wood mouse-habitat structure relationships in pine forests.

PubMed

Jaime-González, Carlos; Acebes, Pablo; Mateos, Ana; Mezquida, Eduardo T

2017-01-01

LiDAR technology has firmly contributed to strengthen the knowledge of habitat structure-wildlife relationships, though there is an evident bias towards flying vertebrates. To bridge this gap, we investigated and compared the performance of LiDAR and field data to model habitat preferences of wood mouse (Apodemus sylvaticus) in a Mediterranean high mountain pine forest (Pinus sylvestris). We recorded nine field and 13 LiDAR variables that were summarized by means of Principal Component Analyses (PCA). We then analyzed wood mouse's habitat preferences using three different models based on: (i) field PCs predictors, (ii) LiDAR PCs predictors; and (iii) both set of predictors in a combined model, including a variance partitioning analysis. Elevation was also included as a predictor in the three models. Our results indicate that LiDAR derived variables were better predictors than field-based variables. The model combining both data sets slightly improved the predictive power of the model. Field derived variables indicated that wood mouse was positively influenced by the gradient of increasing shrub cover and negatively affected by elevation. Regarding LiDAR data, two LiDAR PCs, i.e. gradients in canopy openness and complexity in forest vertical structure positively influenced wood mouse, although elevation interacted negatively with the complexity in vertical structure, indicating wood mouse's preferences for plots with lower elevations but with complex forest vertical structure. The combined model was similar to the LiDAR-based model and included the gradient of shrub cover measured in the field. Variance partitioning showed that LiDAR-based variables, together with elevation, were the most important predictors and that part of the variation explained by shrub cover was shared. LiDAR derived variables were good surrogates of environmental characteristics explaining habitat preferences by the wood mouse. Our LiDAR metrics represented structural features of the forest patch, such as the presence and cover of shrubs, as well as other characteristics likely including time since perturbation, food availability and predation risk. Our results suggest that LiDAR is a promising technology for further exploring habitat preferences by small mammal communities.

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, Austria) and an agricultural maize crop stand (Heidelberg, Germany). This research demonstrates the potential and also limitations of fully automated, near real-time 4D LiDAR monitoring in geosciences.

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 data was used to create a map of shallow hazards to be avoided during the survey for the safety of the crew and the MBES equipment. While green LiDAR does not provide a single solution to all large river surveying problems, when combined with MBES it allows for more complete, more efficient, and safer surveys in a large river.

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...

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).

Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data.

PubMed

Nie, Sheng; Wang, Cheng; Xi, Xiaohuan; Luo, Shezhou; Li, Guoyuan; Tian, Jinyan; Wang, Hongtao

2018-05-14

The upcoming space-borne LiDAR satellite Ice, Cloud and land Elevation Satellite-2 (ICESat-2) is scheduled to launch in 2018. Different from the waveform LiDAR system onboard the ICESat, ICESat-2 will use a micro-pulse photon-counting LiDAR system. Thus new data processing algorithms are required to retrieve vegetation canopy height from photon-counting LiDAR data. The objective of this paper is to develop and validate an automated approach for better estimating vegetation canopy height. The new proposed method consists of three key steps: 1) filtering out the noise photons by an effective noise removal algorithm based on localized statistical analysis; 2) separating ground returns from canopy returns using an iterative photon classification algorithm, and then determining ground surface; 3) generating canopy-top surface and calculating vegetation canopy height based on canopy-top and ground surfaces. This automatic vegetation height estimation approach was tested to the simulated ICESat-2 data produced from Sigma Space LiDAR data and Multiple Altimeter Beam Experimental LiDAR (MABEL) data, and the retrieved vegetation canopy heights were validated by canopy height models (CHMs) derived from airborne discrete-return LiDAR data. Results indicated that the estimated vegetation canopy heights have a relatively strong correlation with the reference vegetation heights derived from airborne discrete-return LiDAR data (R 2 and RMSE values ranging from 0.639 to 0.810 and 4.08 m to 4.56 m respectively). This means our new proposed approach is appropriate for retrieving vegetation canopy height from micro-pulse photon-counting LiDAR data.

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.

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-...

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...

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

Automation of lidar-based hydrologic feature extraction workflows using GIS

NASA Astrophysics Data System (ADS)

Borlongan, Noel Jerome B.; de la Cruz, Roel M.; Olfindo, Nestor T.; Perez, Anjillyn Mae C.

2016-10-01

With the advent of LiDAR technology, higher resolution datasets become available for use in different remote sensing and GIS applications. One significant application of LiDAR datasets in the Philippines is in resource features extraction. Feature extraction using LiDAR datasets require complex and repetitive workflows which can take a lot of time for researchers through manual execution and supervision. The Development of the Philippine Hydrologic Dataset for Watersheds from LiDAR Surveys (PHD), a project under the Nationwide Detailed Resources Assessment Using LiDAR (Phil-LiDAR 2) program, created a set of scripts, the PHD Toolkit, to automate its processes and workflows necessary for hydrologic features extraction specifically Streams and Drainages, Irrigation Network, and Inland Wetlands, using LiDAR Datasets. These scripts are created in Python and can be added in the ArcGIS® environment as a toolbox. The toolkit is currently being used as an aid for the researchers in hydrologic feature extraction by simplifying the workflows, eliminating human errors when providing the inputs, and providing quick and easy-to-use tools for repetitive tasks. This paper discusses the actual implementation of different workflows developed by Phil-LiDAR 2 Project 4 in Streams, Irrigation Network and Inland Wetlands extraction.

Improving Measurement of Forest Structural Parameters by Co-Registering of High Resolution Aerial Imagery and Low Density LiDAR Data

PubMed Central

Huang, Huabing; Gong, Peng; Cheng, Xiao; Clinton, Nick; Li, Zengyuan

2009-01-01

Forest structural parameters, such as tree height and crown width, are indispensable for evaluating forest biomass or forest volume. LiDAR is a revolutionary technology for measurement of forest structural parameters, however, the accuracy of crown width extraction is not satisfactory when using a low density LiDAR, especially in high canopy cover forest. We used high resolution aerial imagery with a low density LiDAR system to overcome this shortcoming. A morphological filtering was used to generate a DEM (Digital Elevation Model) and a CHM (Canopy Height Model) from LiDAR data. The LiDAR camera image is matched to the aerial image with an automated keypoints search algorithm. As a result, a high registration accuracy of 0.5 pixels was obtained. A local maximum filter, watershed segmentation, and object-oriented image segmentation are used to obtain tree height and crown width. Results indicate that the camera data collected by the integrated LiDAR system plays an important role in registration with aerial imagery. The synthesis with aerial imagery increases the accuracy of forest structural parameter extraction when compared to only using the low density LiDAR data. PMID:22573971

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

NASA Astrophysics Data System (ADS)

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

2010-12-01

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

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...

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...

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

NASA Astrophysics Data System (ADS)

Suiter, Ashley Elizabeth

Multi-spectral imagery provides a robust and low-cost dataset for assessing wetland extent and quality over broad regions and is frequently used for wetland inventories. However in forested wetlands, hydrology is obscured by tree canopy making it difficult to detect with multi-spectral imagery alone. Because of this, classification of forested wetlands often includes greater errors than that of other wetlands types. Elevation and terrain derivatives have been shown to be useful for modelling wetland hydrology. But, few studies have addressed the use of LiDAR intensity data detecting hydrology in forested wetlands. Due the tendency of LiDAR signal to be attenuated by water, this research proposed the fusion of LiDAR intensity data with LiDAR elevation, terrain data, and aerial imagery, for the detection of forested wetland hydrology. We examined the utility of LiDAR intensity data and determined whether the fusion of Lidar derived data with multispectral imagery increased the accuracy of forested wetland classification compared with a classification performed with only multi-spectral image. Four classifications were performed: Classification A -- All Imagery, Classification B -- All LiDAR, Classification C -- LiDAR without Intensity, and Classification D -- Fusion of All Data. These classifications were performed using random forest and each resulted in a 3-foot resolution thematic raster of forested upland and forested wetland locations in Vermilion County, Illinois. The accuracies of these classifications were compared using Kappa Coefficient of Agreement. Importance statistics produced within the random forest classifier were evaluated in order to understand the contribution of individual datasets. Classification D, which used the fusion of LiDAR and multi-spectral imagery as input variables, had moderate to strong agreement between reference data and classification results. It was found that Classification A performed using all the LiDAR data and its derivatives (intensity, elevation, slope, aspect, curvatures, and Topographic Wetness Index) was the most accurate classification with Kappa: 78.04%, indicating moderate to strong agreement. However, Classification C, performed with LiDAR derivative without intensity data had less agreement than would be expected by chance, indicating that LiDAR contributed significantly to the accuracy of Classification B.

A DArT marker genetic map of perennial ryegrass (Lolium perenne L.) integrated with detailed comparative mapping information; comparison with existing DArT marker genetic maps of Lolium perenne, L. multiflorum and Festuca pratensis.

PubMed

King, Julie; Thomas, Ann; James, Caron; King, Ian; Armstead, Ian

2013-07-03

Ryegrasses and fescues (genera, Lolium and Festuca) are species of forage and turf grasses which are used widely in agricultural and amenity situations. They are classified within the sub-family Pooideae and so are closely related to Brachypodium distachyon, wheat, barley, rye and oats. Recently, a DArT array has been developed which can be used in generating marker and mapping information for ryegrasses and fescues. This represents a potential common marker set for ryegrass and fescue researchers which can be linked through to comparative genomic information for the grasses. A F2 perennial ryegrass genetic map was developed consisting of 7 linkage groups defined by 1316 markers and deriving a total map length of 683 cM. The marker set included 866 DArT and 315 gene sequence-based markers. Comparison with previous DArT mapping studies in perennial and Italian ryegrass (L. multiflorum) identified 87 and 105 DArT markers in common, respectively, of which 94% and 87% mapped to homoeologous linkage groups. A similar comparison with meadow fescue (F. pratensis) identified only 28 DArT markers in common, of which c. 50% mapped to non-homoelogous linkage groups. In L. perenne, the genetic distance spanned by the DArT markers encompassed the majority of the regions that could be described in terms of comparative genomic relationships with rice, Brachypodium distachyon, and Sorghum bicolor. DArT markers are likely to be a useful common marker resource for ryegrasses and fescues, though the success in aligning different populations through the mapping of common markers will be influenced by degrees of population interrelatedness. The detailed mapping of DArT and gene-based markers in this study potentially allows comparative relationships to be derived in future mapping populations characterised using solely DArT markers.

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

DArT Markers Effectively Target Gene Space in the Rye Genome.

PubMed

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.

Heterodimerization with beta2-adrenergic receptors promotes surface expression and functional activity of alpha1D-adrenergic receptors.

PubMed

Uberti, Michelle A; Hague, Chris; Oller, Heide; Minneman, Kenneth P; Hall, Randy A

2005-04-01

The alpha1D-adrenergic receptor (alpha1D-AR) is a G protein-coupled receptor (GPCR) that is poorly trafficked to the cell surface and largely nonfunctional when heterologously expressed by itself in a variety of cell types. We screened a library of approximately 30 other group I GPCRs in a quantitative luminometer assay for the ability to promote alpha1D-AR cell surface expression. Strikingly, these screens revealed only two receptors capable of inducing robust increases in the amount of alpha1D-AR at the cell surface: alpha1B-AR and beta2-AR. Confocal imaging confirmed that coexpression with beta2-AR resulted in translocation of alpha1D-AR from intracellular sites to the plasma membrane. Additionally, coimmunoprecipitation studies demonstrated that alpha1D-AR and beta2-AR specifically interact to form heterodimers when coexpressed in HEK-293 cells. Ligand binding studies revealed an increase in total alpha1D-AR binding sites upon coexpression with beta2-AR, but no apparent effect on the pharmacological properties of the receptors. In functional studies, coexpression with beta2-AR significantly enhanced the coupling of alpha1D-AR to norepinephrine-stimulated Ca2+ mobilization. Heterodimerization of beta2-AR with alpha1D-AR also conferred the ability of alpha1D-AR to cointernalize upon beta2-AR agonist stimulation, revealing a novel mechanism by which these different adrenergic receptor subtypes may regulate each other's activity. These findings demonstrate that the selective association of alpha1D-AR with other receptors is crucial for receptor surface expression and function and also shed light on a novel mechanism of cross talk between alpha1- and beta2-ARs that is mediated through heterodimerization and cross-internalization.

Important LiDAR metrics for discriminating forest tree species in Central Europe

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Diversity Arrays Technology (DArT) for Pan-Genomic Evolutionary Studies of Non-Model Organisms

PubMed Central

James, Karen E.; Schneider, Harald; Ansell, Stephen W.; Evers, Margaret; Robba, Lavinia; Uszynski, Grzegorz; Pedersen, Niklas; Newton, Angela E.; Russell, Stephen J.; Vogel, Johannes C.; Kilian, Andrzej

2008-01-01

Background High-throughput tools for pan-genomic study, especially the DNA microarray platform, have sparked a remarkable increase in data production and enabled a shift in the scale at which biological investigation is possible. The use of microarrays to examine evolutionary relationships and processes, however, is predominantly restricted to model or near-model organisms. Methodology/Principal Findings This study explores the utility of Diversity Arrays Technology (DArT) in evolutionary studies of non-model organisms. DArT is a hybridization-based genotyping method that uses microarray technology to identify and type DNA polymorphism. Theoretically applicable to any organism (even one for which no prior genetic data are available), DArT has not yet been explored in exclusively wild sample sets, nor extensively examined in a phylogenetic framework. DArT recovered 1349 markers of largely low copy-number loci in two lineages of seed-free land plants: the diploid fern Asplenium viride and the haploid moss Garovaglia elegans. Direct sequencing of 148 of these DArT markers identified 30 putative loci including four routinely sequenced for evolutionary studies in plants. Phylogenetic analyses of DArT genotypes reveal phylogeographic and substrate specificity patterns in A. viride, a lack of phylogeographic pattern in Australian G. elegans, and additive variation in hybrid or mixed samples. Conclusions/Significance These results enable methodological recommendations including procedures for detecting and analysing DArT markers tailored specifically to evolutionary investigations and practical factors informing the decision to use DArT, and raise evolutionary hypotheses concerning substrate specificity and biogeographic patterns. Thus DArT is a demonstrably valuable addition to the set of existing molecular approaches used to infer biological phenomena such as adaptive radiations, population dynamics, hybridization, introgression, ecological differentiation and phylogeography. PMID:18301759

LiDAR for data efficiency.

DOT National Transportation Integrated Search

2011-09-30

This report documents the AHMCT research project: LiDAR for Data Efficiency for the Washington State Department of Transportation (WSDOT). The research objective was to evaluate mobile LiDAR technology to enhance safety, determine efficiency ga...

Diversity arrays technology: a generic genome profiling technology on open platforms.

PubMed

Kilian, Andrzej; Wenzl, Peter; Huttner, Eric; Carling, Jason; Xia, Ling; Blois, Hélène; Caig, Vanessa; Heller-Uszynska, Katarzyna; Jaccoud, Damian; Hopper, Colleen; Aschenbrenner-Kilian, Malgorzata; Evers, Margaret; Peng, Kaiman; Cayla, Cyril; Hok, Puthick; Uszynski, Grzegorz

2012-01-01

In the last 20 years, we have observed an exponential growth of the DNA sequence data and simular increase in the volume of DNA polymorphism data generated by numerous molecular marker technologies. Most of the investment, and therefore progress, concentrated on human genome and genomes of selected model species. Diversity Arrays Technology (DArT), developed over a decade ago, was among the first "democratizing" genotyping technologies, as its performance was primarily driven by the level of DNA sequence variation in the species rather than by the level of financial investment. DArT also proved more robust to genome size and ploidy-level differences among approximately 60 organisms for which DArT was developed to date compared to other high-throughput genotyping technologies. The success of DArT in a number of organisms, including a wide range of "orphan crops," can be attributed to the simplicity of underlying concepts: DArT combines genome complexity reduction methods enriching for genic regions with a highly parallel assay readout on a number of "open-access" microarray platforms. The quantitative nature of the assay enabled a number of applications in which allelic frequencies can be estimated from DArT arrays. A typical DArT assay tests for polymorphism tens of thousands of genomic loci with the final number of markers reported (hundreds to thousands) reflecting the level of DNA sequence variation in the tested loci. Detailed DArT methods, protocols, and a range of their application examples as well as DArT's evolution path are presented.

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.

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

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

Highly reproducible surface-enhanced Raman scattering-active Au nanostructures prepared by simple electrodeposition: origin of surface-enhanced Raman scattering activity and applications as electrochemical substrates.

PubMed

Choi, Suhee; Ahn, Miri; Kim, Jongwon

2013-05-24

The fabrication of effective surface-enhanced Raman scattering (SERS) substrates has been the subject of intensive research because of their useful applications. In this paper, dendritic gold (Au) rod (DAR) structures prepared by simple one-step electrodeposition in a short time were examined as an effective SERS-active substrate. The SERS activity of the DAR surfaces was compared to that of other nanostructured Au surfaces with different morphologies, and its dependence on the structural variation of DAR structures was examined. These comparisonal investigations revealed that highly faceted sharp edge sites present on the DAR surfaces play a critical role in inducing a high SERS activity. The SERS enhancement factor was estimated to be greater than 10(5), and the detection limit of rhodamine 6G at DAR surfaces was 10(-8)M. The DAR surfaces exhibit excellent spot-to-spot and substrate-to-substrate SERS enhancement reproducibility, and their long-term stability is very good. It was also demonstrated that the DAR surfaces can be effectively utilized in electrochemical SERS systems, wherein a reversible SERS behavior was obtained during the cycling to cathodic potential regions. Considering the straightforward preparation of DAR substrates and the clean nature of SERS-active Au surfaces prepared in the absence of additives, we expect that DAR surfaces can be used as cost-effective SERS substrates in analytical and electrochemical applications. Copyright © 2013 Elsevier B.V. All rights reserved.

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...

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....

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.

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).

The first genetic map of pigeon pea based on diversity arrays technology (DArT) markers.

PubMed

Yang, Shi Ying; Saxena, Rachit K; Kulwal, Pawan L; Ash, Gavin J; Dubey, Anuja; Harper, John D I; Upadhyaya, Hari D; Gothalwal, Ragini; Kilian, Andrzej; Varshney, Rajeev K

2011-04-01

With an objective to develop a genetic map in pigeon pea (Cajanus spp.), a total of 554 diversity arrays technology (DArT) markers showed polymorphism in a pigeon pea F(2) mapping population of 72 progenies derived from an interspecific cross of ICP 28 (Cajanus cajan) and ICPW 94 (Cajanus scarabaeoides). Approximately 13% of markers did not conform to expected segregation ratio. The total number of DArT marker loci segregating in Mendelian manner was 405 with 73.1% (P > 0.001) of DArT markers having unique segregation patterns. Two groups of genetic maps were generated using DArT markers. While the maternal genetic linkage map had 122 unique DArT maternal marker loci, the paternal genetic linkage map has a total of 172 unique DArT paternal marker loci. The length of these two maps covered 270.0 cM and 451.6 cM, respectively. These are the first genetic linkage maps developed for pigeon pea, and this is the first report of genetic mapping in any grain legume using diversity arrays technology.

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.

Comparison of Precision of Biomass Estimates in Regional Field Sample Surveys and Airborne LiDAR-Assisted Surveys in Hedmark County, Norway

NASA Technical Reports Server (NTRS)

Naesset, Erik; Gobakken, Terje; Bollandsas, Ole Martin; Gregoire, Timothy G.; Nelson, Ross; Stahl, Goeran

2013-01-01

Airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool to provide auxiliary data for sample surveys aiming at estimation of above-ground tree biomass (AGB), with potential applications in REDD forest monitoring. For larger geographical regions such as counties, states or nations, it is not feasible to collect airborne LiDAR data continuously ("wall-to-wall") over the entire area of interest. Two-stage cluster survey designs have therefore been demonstrated by which LiDAR data are collected along selected individual flight-lines treated as clusters and with ground plots sampled along these LiDAR swaths. Recently, analytical AGB estimators and associated variance estimators that quantify the sampling variability have been proposed. Empirical studies employing these estimators have shown a seemingly equal or even larger uncertainty of the AGB estimates obtained with extensive use of LiDAR data to support the estimation as compared to pure field-based estimates employing estimators appropriate under simple random sampling (SRS). However, comparison of uncertainty estimates under SRS and sophisticated two-stage designs is complicated by large differences in the designs and assumptions. In this study, probability-based principles to estimation and inference were followed. We assumed designs of a field sample and a LiDAR-assisted survey of Hedmark County (HC) (27,390 km2), Norway, considered to be more comparable than those assumed in previous studies. The field sample consisted of 659 systematically distributed National Forest Inventory (NFI) plots and the airborne scanning LiDAR data were collected along 53 parallel flight-lines flown over the NFI plots. We compared AGB estimates based on the field survey only assuming SRS against corresponding estimates assuming two-phase (double) sampling with LiDAR and employing model-assisted estimators. We also compared AGB estimates based on the field survey only assuming two-stage sampling (the NFI plots being grouped in clusters) against corresponding estimates assuming two-stage sampling with the LiDAR and employing model-assisted estimators. For each of the two comparisons, the standard errors of the AGB estimates were consistently lower for the LiDAR-assisted designs. The overall reduction of the standard errors in the LiDAR-assisted estimation was around 40-60% compared to the pure field survey. We conclude that the previously proposed two-stage model-assisted estimators are inappropriate for surveys with unequal lengths of the LiDAR flight-lines and new estimators are needed. Some options for design of LiDAR-assisted sample surveys under REDD are also discussed, which capitalize on the flexibility offered when the field survey is designed as an integrated part of the overall survey design as opposed to previous LiDAR-assisted sample surveys in the boreal and temperate zones which have been restricted by the current design of an existing NFI.

High Density LiDAR and Orthophotography in UXO Wide Area Assessment

DTIC Science & Technology

2008-01-01

4 Figure 2. Helicopter-Mounted LiDAR and Orthophoto Sensor Equipment...4 Figure 3. LiDAR and Orthophoto System Installed on Helicopter........................................ 6 Figure 4. Kirtland Precision...17 Figure 11. Orthophoto Data Density Results

Cloning, sequence, and disruption of the Saccharomyces diastaticus DAR1 gene encoding a glycerol-3-phosphate dehydrogenase.

PubMed Central

Wang, H T; Rahaim, P; Robbins, P; Yocum, R R

1994-01-01

The Saccharomyces diastaticus DAR1 gene was cloned by complementation in an Escherichia coli strain auxogrophic for glycerol-3-phosphate. DAR1 encodes an NADH-dependent dihydroxyacetone phosphate reductase (sn-glycerol-3-phosphate dehydrogenase [G3PDase; EC 1.1.1.8]) homologous to several other eukaryotic G3PDases. DAR1 is distinct from GUT2, which encodes a glucose-repressed mitochondrial G3PDase, but is identical to GPD1 from S. cerevisiae, a close relative of S. diastaticus. The level of DAR1-encoded G3PDase was increased about threefold in a medium of high osmolarity. Disruption of DAR1 in a haploid S. cerevisiae was not lethal but led to a decrease in cytoplasmic NADH-dependent G3PDase activity, an increase in osmotic sensitivity, and a 25% reduction in glycerol secretion from cells grown anaerobically on glucose. PMID:7961476

Cloning, sequence, and disruption of the Saccharomyces diastaticus DAR1 gene encoding a glycerol-3-phosphate dehydrogenase.

PubMed

Wang, H T; Rahaim, P; Robbins, P; Yocum, R R

1994-11-01

The Saccharomyces diastaticus DAR1 gene was cloned by complementation in an Escherichia coli strain auxogrophic for glycerol-3-phosphate. DAR1 encodes an NADH-dependent dihydroxyacetone phosphate reductase (sn-glycerol-3-phosphate dehydrogenase [G3PDase; EC 1.1.1.8]) homologous to several other eukaryotic G3PDases. DAR1 is distinct from GUT2, which encodes a glucose-repressed mitochondrial G3PDase, but is identical to GPD1 from S. cerevisiae, a close relative of S. diastaticus. The level of DAR1-encoded G3PDase was increased about threefold in a medium of high osmolarity. Disruption of DAR1 in a haploid S. cerevisiae was not lethal but led to a decrease in cytoplasmic NADH-dependent G3PDase activity, an increase in osmotic sensitivity, and a 25% reduction in glycerol secretion from cells grown anaerobically on glucose.

Achieving Accuracy Requirements for Forest Biomass Mapping: A Data Fusion Method for Estimating Forest Biomass and LiDAR Sampling Error with Spaceborne Data

NASA Technical Reports Server (NTRS)

Montesano, P. M.; Cook, B. D.; Sun, G.; Simard, M.; Zhang, Z.; Nelson, R. F.; Ranson, K. J.; Lutchke, S.; Blair, J. B.

2012-01-01

The synergistic use of active and passive remote sensing (i.e., data fusion) demonstrates the ability of spaceborne light detection and ranging (LiDAR), synthetic aperture radar (SAR) and multispectral imagery for achieving the accuracy requirements of a global forest biomass mapping mission. This data fusion approach also provides a means to extend 3D information from discrete spaceborne LiDAR measurements of forest structure across scales much larger than that of the LiDAR footprint. For estimating biomass, these measurements mix a number of errors including those associated with LiDAR footprint sampling over regional - global extents. A general framework for mapping above ground live forest biomass (AGB) with a data fusion approach is presented and verified using data from NASA field campaigns near Howland, ME, USA, to assess AGB and LiDAR sampling errors across a regionally representative landscape. We combined SAR and Landsat-derived optical (passive optical) image data to identify forest patches, and used image and simulated spaceborne LiDAR data to compute AGB and estimate LiDAR sampling error for forest patches and 100m, 250m, 500m, and 1km grid cells. Forest patches were delineated with Landsat-derived data and airborne SAR imagery, and simulated spaceborne LiDAR (SSL) data were derived from orbit and cloud cover simulations and airborne data from NASA's Laser Vegetation Imaging Sensor (L VIS). At both the patch and grid scales, we evaluated differences in AGB estimation and sampling error from the combined use of LiDAR with both SAR and passive optical and with either SAR or passive optical alone. This data fusion approach demonstrates that incorporating forest patches into the AGB mapping framework can provide sub-grid forest information for coarser grid-level AGB reporting, and that combining simulated spaceborne LiDAR with SAR and passive optical data are most useful for estimating AGB when measurements from LiDAR are limited because they minimized forest AGB sampling errors by 15 - 38%. Furthermore, spaceborne global scale accuracy requirements were achieved. At least 80% of the grid cells at 100m, 250m, 500m, and 1km grid levels met AGB density accuracy requirements using a combination of passive optical and SAR along with machine learning methods to predict vegetation structure metrics for forested areas without LiDAR samples. Finally, using either passive optical or SAR, accuracy requirements were met at the 500m and 250m grid level, respectively.

A GUI visualization system for airborne lidar image data to reconstruct 3D city model

NASA Astrophysics Data System (ADS)

Kawata, Yoshiyuki; Koizumi, Kohei

2015-10-01

A visualization toolbox system with graphical user interfaces (GUIs) was developed for the analysis of LiDAR point cloud data, as a compound object oriented widget application in IDL (Interractive Data Language). The main features in our system include file input and output abilities, data conversion capability from ascii formatted LiDAR point cloud data to LiDAR image data whose pixel value corresponds the altitude measured by LiDAR, visualization of 2D/3D images in various processing steps and automatic reconstruction ability of 3D city model. The performance and advantages of our graphical user interface (GUI) visualization system for LiDAR data are demonstrated.

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...

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

DTIC Science & Technology

2008-01-01

Figure 11. Screenshot of OrthoPro seam lines (pink), tiles (blue), and photos (green)................ 26 Figure 12. Calibration craters (existing...with aerial targets for the orthophotography data collection, 1 per data collection tile (1 sq km). For the Phase I data collection, 9 LiDAR ground...Orthophotography data were collected concurrently with the LiDAR data collection. Based on the LiDAR flight line spacing parameters, the orthophoto images were

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. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data.

PubMed

Silva, Carlos Alberto; Hudak, Andrew Thomas; Klauberg, Carine; Vierling, Lee Alexandre; Gonzalez-Benecke, Carlos; de Padua Chaves Carvalho, Samuel; Rodriguez, Luiz Carlos Estraviz; Cardil, Adrián

2017-12-01

LiDAR remote sensing is a rapidly evolving technology for quantifying a variety of forest attributes, including aboveground carbon (AGC). Pulse density influences the acquisition cost of LiDAR, and grid cell size influences AGC prediction using plot-based methods; however, little work has evaluated the effects of LiDAR pulse density and cell size for predicting and mapping AGC in fast-growing Eucalyptus forest plantations. The aim of this study was to evaluate the effect of LiDAR pulse density and grid cell size on AGC prediction accuracy at plot and stand-levels using airborne LiDAR and field data. We used the Random Forest (RF) machine learning algorithm to model AGC using LiDAR-derived metrics from LiDAR collections of 5 and 10 pulses m -2 (RF5 and RF10) and grid cell sizes of 5, 10, 15 and 20 m. The results show that LiDAR pulse density of 5 pulses m -2 provides metrics with similar prediction accuracy for AGC as when using a dataset with 10 pulses m -2 in these fast-growing plantations. Relative root mean square errors (RMSEs) for the RF5 and RF10 were 6.14 and 6.01%, respectively. Equivalence tests showed that the predicted AGC from the training and validation models were equivalent to the observed AGC measurements. The grid cell sizes for mapping ranging from 5 to 20 also did not significantly affect the prediction accuracy of AGC at stand level in this system. LiDAR measurements can be used to predict and map AGC across variable-age Eucalyptus plantations with adequate levels of precision and accuracy using 5 pulses m -2 and a grid cell size of 5 m. The promising results for AGC modeling in this study will allow for greater confidence in comparing AGC estimates with varying LiDAR sampling densities for Eucalyptus plantations and assist in decision making towards more cost effective and efficient forest inventory.

Deformity Angular Ratio Describes the Severity of Spinal Deformity and Predicts the Risk of Neurologic Deficit in Posterior Vertebral Column Resection Surgery.

PubMed

Wang, Xiao-Bin; Lenke, Lawrence G; Thuet, Earl; Blanke, Kathy; Koester, Linda A; Roth, Michael

2016-09-15

Retrospective review of prospectively collected data. To assess the value of the deformity angular ratio (DAR, maximum Cobb measurement divided by number of vertebrae involved) in evaluating the severity of spinal deformity, and predicting the risk of neurologic deficit in posterior vertebral column resection (PVCR). Although the literature has demonstrated that PVCR in spinal deformity patients has achieved excellent outcomes, it is still high risk neurologically. This study, to our knowledge, is the largest series of PVCR patients from a single center, evaluating deformity severity, and potential neurologic deficit risk. A total of 202 consecutive pediatric and adult patients undergoing PVCRs from November 2002 to September 2014 were reviewed. The DAR (coronal DAR, sagittal DAR, and total DAR) was used to evaluate the complexity of the deformity. The incidence of spinal cord monitoring (SCM) events was 20.5%. Eight patients (4.0%) had new neurologic deficits. Patients with a high total DAR (≥25) were significantly younger (20.3 vs. 29.0 yr, P = 0.001), had more severe coronal and sagittal deformities, were more myelopathic (33.3% vs. 11.7%, P = 0.000), needed larger vertebral resections (1.8 vs. 1.3, P = 0.000), and had a significantly higher rate of SCM events than seen in the low total DAR (<25) patients (41.1% vs. 10.8%; P = 0.000). Patients with a high sagittal DAR (≥15) also had a significantly higher rate of SCM events (34.0% vs. 15.1%, P = 0.005) and a greater chance of neurologic deficits postoperatively (12.5% vs. 0, P = 0.000). For patients undergoing a PVCR, the DAR can be used to quantify the angularity of the spinal deformity, which is strongly correlated to the risk of neurologic deficits. Patients with a total DAR greater than or equal to 25 or sagittal DAR greater than or equal to 15 are at much higher risk for intraoperative SCM events and new neurologic deficits. 3.

Let’s agree on the casing of Lidar

USGS Publications Warehouse

Deering, Carol; Stoker, Jason M.

2014-01-01

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

Infrastructure Investment Protection with LiDAR

DOT National Transportation Integrated Search

2012-10-15

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

Assessing LiDAR elevation data for KDOT applications.

DOT National Transportation Integrated Search

2013-02-01

LiDAR-based elevation surveys are a cost-effective means for mapping topography over large areas. LiDAR : surveys use an airplane-mounted or ground-based laser radar unit to scan terrain. Post-processing techniques are : applied to remove vegetation ...

47 CFR 25.164 - Milestones.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Milestones. (a) Licensees of geostationary orbit satellite systems other than DBS and DARS satellite systems.... (b) Licensees of non-geostationary orbit satellite systems other than DBS and DARS satellite systems... both non-geostationary orbit satellites and geostationary orbit satellites, other than DBS and DARS...

47 CFR 25.164 - Milestones.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Milestones. (a) Licensees of geostationary orbit satellite systems other than DBS and DARS satellite systems.... (b) Licensees of non-geostationary orbit satellite systems other than DBS and DARS satellite systems... both non-geostationary orbit satellites and geostationary orbit satellites, other than DBS and DARS...

47 CFR 25.164 - Milestones.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Milestones. (a) Licensees of geostationary orbit satellite systems other than DBS and DARS satellite systems.... (b) Licensees of non-geostationary orbit satellite systems other than DBS and DARS satellite systems... both non-geostationary orbit satellites and geostationary orbit satellites, other than DBS and DARS...

47 CFR 25.164 - Milestones.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Milestones. (a) Licensees of geostationary orbit satellite systems other than DBS and DARS satellite systems.... (b) Licensees of non-geostationary orbit satellite systems other than DBS and DARS satellite systems... both non-geostationary orbit satellites and geostationary orbit satellites, other than DBS and DARS...

LiDAR for Air Quality Measurements

DOT National Transportation Integrated Search

2017-02-02

The overall goal of this research is to investigate a unique light detection and ranging (LiDAR) technology for ambient air quality measurement of particulate matter. The ODU team has recently received a state-of-the-art elastic LiDAR from NASA Langl...

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

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

Deflation-activated receptors, not classical inflation-activated receptors, mediate the Hering-Breuer deflation reflex.

PubMed

Yu, Jerry

2016-11-01

Many airway sensory units respond to both lung inflation and deflation. Whether those responses to opposite stimuli come from one sensor (one-sensor theory) or more than one sensor (multiple-sensor theory) is debatable. One-sensor theory is commonly presumed in the literature. This article proposes a multiple-sensor theory in which a sensory unit contains different sensors for sensing different forces. Two major types of mechanical sensors operate in the lung: inflation- and deflation-activated receptors (DARs). Inflation-activated sensors can be further divided into slowly adapting receptors (SARs) and rapidly adapting receptors (RARs). Many SAR and RAR units also respond to lung deflation because they contain DARs. Pure DARs, which respond to lung deflation only, are rare in large animals but are easily identified in small animals. Lung deflation-induced reflex effects previously attributed to RARs should be assigned to DARs (including pure DARs and DARs associated with SARs and RARs) if the multiple-sensor theory is accepted. Thus, based on the information, it is proposed that activation of DARs can attenuate lung deflation, shorten expiratory time, increase respiratory rate, evoke inspiration, and cause airway secretion and dyspnea.

The application of LiDAR to investigate foredune morphology and vegetation

NASA Astrophysics Data System (ADS)

Doyle, Thomas B.; Woodroffe, Colin D.

2018-02-01

LiDAR (Light Detection and Ranging) has been used to investigate coastal landform morphology, evolution, and change for almost a decade. Repeated airborne LiDAR surveys can provide the scientific community with significant observations of how shorelines have evolved, which may then enable forecasts of future patterns of change. However, there have been few studies that have considered the application of this new technology to the specific study of foredune morphology and vegetation. The accuracy and appropriateness of airborne LiDAR needs to be assessed, particularly where the density of vegetation may obscure the underlying topography, prior to interpreting derived geomorphic features. This study: i) tests the vertical accuracy of airborne LiDAR in 37 foredune systems along the coast of south-eastern Australia, and ii) demonstrates that it can be used to describe foredune morphology and vegetation in considerable detail. There was a strong correlation between the remotely-sensed LiDAR-derived elevation and field topographic and vegetation surveys (R2 = 0.96). A protocol for obtaining foredune geomorphic and botanical parameters is described. It enables widespread biogeomorphic characterisation along coasts for which LiDAR data is available, which can benefit both coastal managers and researchers alike.

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.

Assessing LiDAR elevation data for KDOT applications : [technical summary].

DOT National Transportation Integrated Search

2013-02-01

LiDAR-based elevation surveys : are a cost-effective means for : mapping topography over large : areas. LiDAR surveys use an : airplane-mounted or ground-based : laser radar unit to scan terrain. : Post-processing techniques are : applied to remove v...

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.

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.

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.

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.

Applicability Analysis of Cloth Simulation Filtering Algorithm for Mobile LIDAR Point Cloud

NASA Astrophysics Data System (ADS)

Cai, S.; Zhang, W.; Qi, J.; Wan, P.; Shao, J.; Shen, A.

2018-04-01

Classifying the original point clouds into ground and non-ground points is a key step in LiDAR (light detection and ranging) data post-processing. Cloth simulation filtering (CSF) algorithm, which based on a physical process, has been validated to be an accurate, automatic and easy-to-use algorithm for airborne LiDAR point cloud. As a new technique of three-dimensional data collection, the mobile laser scanning (MLS) has been gradually applied in various fields, such as reconstruction of digital terrain models (DTM), 3D building modeling and forest inventory and management. Compared with airborne LiDAR point cloud, there are some different features (such as point density feature, distribution feature and complexity feature) for mobile LiDAR point cloud. Some filtering algorithms for airborne LiDAR data were directly used in mobile LiDAR point cloud, but it did not give satisfactory results. In this paper, we explore the ability of the CSF algorithm for mobile LiDAR point cloud. Three samples with different shape of the terrain are selected to test the performance of this algorithm, which respectively yields total errors of 0.44 %, 0.77 % and1.20 %. Additionally, large area dataset is also tested to further validate the effectiveness of this algorithm, and results show that it can quickly and accurately separate point clouds into ground and non-ground points. In summary, this algorithm is efficient and reliable for mobile LiDAR point cloud.

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.

Airborne LiDAR reflective linear feature extraction for strip adjustment and horizontal accuracy determination.

DOT National Transportation Integrated Search

2009-02-01

ODOT's Office of Aerial Engineering (OAE) has been using an Opetch 30/70 ALTM airborne LiDAR system for about four years. The introduction of LiDAR technology was a major development towards improving the mapping operations. The overall experiences a...

Stationary LiDAR for traffic and safety applications - vehicles interpretation and tracking.

DOT National Transportation Integrated Search

2014-01-01

The goal of the T-Scan project is to develop a data processing module for a novel LiDAR-based traffic scanner to collect highly accurate microscopic traffic data at road intersections. : T-Scan uses Light Detection and Ranging (LiDAR) technology that...

Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico.

PubMed

Urbazaev, Mikhail; Thiel, Christian; Cremer, Felix; Dubayah, Ralph; Migliavacca, Mirco; Reichstein, Markus; Schmullius, Christiane

2018-02-21

Information on the spatial distribution of aboveground biomass (AGB) over large areas is needed for understanding and managing processes involved in the carbon cycle and supporting international policies for climate change mitigation and adaption. Furthermore, these products provide important baseline data for the development of sustainable management strategies to local stakeholders. The use of remote sensing data can provide spatially explicit information of AGB from local to global scales. In this study, we mapped national Mexican forest AGB using satellite remote sensing data and a machine learning approach. We modelled AGB using two scenarios: (1) extensive national forest inventory (NFI), and (2) airborne Light Detection and Ranging (LiDAR) as reference data. Finally, we propagated uncertainties from field measurements to LiDAR-derived AGB and to the national wall-to-wall forest AGB map. The estimated AGB maps (NFI- and LiDAR-calibrated) showed similar goodness-of-fit statistics (R 2 , Root Mean Square Error (RMSE)) at three different scales compared to the independent validation data set. We observed different spatial patterns of AGB in tropical dense forests, where no or limited number of NFI data were available, with higher AGB values in the LiDAR-calibrated map. We estimated much higher uncertainties in the AGB maps based on two-stage up-scaling method (i.e., from field measurements to LiDAR and from LiDAR-based estimates to satellite imagery) compared to the traditional field to satellite up-scaling. By removing LiDAR-based AGB pixels with high uncertainties, it was possible to estimate national forest AGB with similar uncertainties as calibrated with NFI data only. Since LiDAR data can be acquired much faster and for much larger areas compared to field inventory data, LiDAR is attractive for repetitive large scale AGB mapping. In this study, we showed that two-stage up-scaling methods for AGB estimation over large areas need to be analyzed and validated with great care. The uncertainties in the LiDAR-estimated AGB propagate further in the wall-to-wall map and can be up to 150%. Thus, when a two-stage up-scaling method is applied, it is crucial to characterize the uncertainties at all stages in order to generate robust results. Considering the findings mentioned above LiDAR can be used as an extension to NFI for example for areas that are difficult or not possible to access.

Airborne LiDAR reflective linear feature extraction for strip adjustment and horizontal accuracy determination : executive summary.

DOT National Transportation Integrated Search

2009-02-01

The Office of Aerial Engineering (OAE) has been : using an Optech 30/70 ALTM airborne LiDAR system : for about four years. The introduction of LiDAR : technology was a major development towards : improving the mapping operations, and the overall : ex...

LiDAR-guided Archaeological Survey of a Mediterranean Landscape: Lessons from the Ancient Greek Polis of Kolophon (Ionia, Western Anatolia).

PubMed

Grammer, Benedikt; Draganits, Erich; Gretscher, Martin; Muss, Ulrike

2017-01-01

In 2013, an airborne laser scan survey was conducted in the territory of the Ionian city of Kolophon near the western coast of modern Turkey as part of an archaeological survey project carried out by the Mimar Sinan University of Istanbul (Turkey) and the University of Vienna (Austria). Several light detection and ranging (LiDAR) studies have been carried out in the temperate climate zones of Europe, but only a few in Mediterranean landscapes. Our study is based on the first LiDAR survey carried out for an archaeological purpose in Turkey and one of the first in the Mediterranean that have been planned, measured and filtered especially for archaeological research questions. The interpretation of LiDAR data combined with ground-observations proved extremely useful for the detection and documentation of archaeological remains below Mediterranean evergreen vegetation and dense maquis. This article deals with the methodological aspects of interpreting LiDAR data, using the Kolophon data as a case study. We offer a discussion of the strengths and limitations of LiDAR as an archaeological remote sensing method and suggest a best practice model for interpreting LiDAR data in a Mediterranean context. © 2017 The Authors. Archaeological Prospection published by John Wiley & Sons Ltd.

[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.

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.

Diversity Arrays Technology (DArT) for whole-genome profiling of barley

PubMed Central

Wenzl, Peter; Carling, Jason; Kudrna, David; Jaccoud, Damian; Huttner, Eric; Kleinhofs, Andris; Kilian, Andrzej

2004-01-01

Diversity Arrays Technology (DArT) can detect and type DNA variation at several hundred genomic loci in parallel without relying on sequence information. Here we show that it can be effectively applied to genetic mapping and diversity analyses of barley, a species with a 5,000-Mbp genome. We tested several complexity reduction methods and selected two that generated the most polymorphic genomic representations. Arrays containing individual fragments from these representations generated DArT fingerprints with a genotype call rate of 98.0% and a scoring reproducibility of at least 99.8%. The fingerprints grouped barley lines according to known genetic relationships. To validate the Mendelian behavior of DArT markers, we constructed a genetic map for a cross between cultivars Steptoe and Morex. Nearly all polymorphic array features could be incorporated into one of seven linkage groups (98.8%). The resulting map comprised ≈385 unique DArT markers and spanned 1,137 centimorgans. A comparison with the restriction fragment length polymorphism-based framework map indicated that the quality of the DArT map was equivalent, if not superior, to that of the framework map. These results highlight the potential of DArT as a generic technique for genome profiling in the context of molecular breeding and genomics. PMID:15192146

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 lacks behind. We propose a novel concept, the LiDAR Vegetation Investigation and Signature Analysis System (LVISA), which shall enhance sharing of i) reference datasets of single vegetation objects with rich reference data (e.g., plant species, basic plant morphometric information) and ii) approaches for information extraction (e.g., single tree detection, tree species classification based on waveform LiDAR features). We will build an extensive LiDAR data repository for supporting the development and benchmarking of LiDAR-based object information extraction. The LiDAR Vegetation Investigation and Signature Analysis System (LVISA) uses international web service standards (Open Geospatial Consortium, OGC) for geospatial data access and also analysis (e.g., OGC Web Processing Services). This will allow the research community identifying plant object specific vegetation features from LiDAR data, while accounting for differences in LiDAR systems (e.g., beam divergence), settings (e.g., point spacing), and calibration techniques. It is the goal of LVISA to develop generic 3D information extraction approaches, which can be seamlessly transferred to other datasets, timestamps and also extraction tasks. The current prototype of LVISA can be visited and tested online via http://uni-heidelberg.de/lvisa. Video tutorials provide a quick overview and entry into the functionality of LVISA. We will present the current advances of LVISA and we will highlight future research and extension of LVISA, such as integrating low-cost LiDAR data and datasets acquired by highly temporal scanning of vegetation (e.g., continuous measurements). Everybody is invited to join the LVISA development and share datasets and analysis approaches in an interoperable way via the web-based LVISA geoportal.

Comparisons of Simultaneously Acquired Airborne Sfm Photogrammetry and Lidar

NASA Astrophysics Data System (ADS)

Larsen, C. F.

2014-12-01

Digital elevation models (DEMs) created using images from a consumer DSLR camera are compared against simultaneously acquired LiDAR on a number of airborne mapping projects across Alaska, California and Utah. The aircraft used is a Cessna 180, and is equipped with the University of Alaska Geophysical Institute (UAF-GI) scanning airborne LiDAR system. This LiDAR is the same as described in Johnson et al, 2013, and is the principal instrument used for NASA's Operation IceBridge flights in Alaska. The system has been in extensive use since 2009, and is particularly well characterized with dozens of calibration flights and a careful program of boresight angle determination and monitoring. The UAF-GI LiDAR has a precision of +/- 8 cm and accuracy of +/- 15 cm. The photogrammetry DEM simultaneously acquired with the LiDAR relies on precise shutter timing using an event marker input to the IMU associated with the LiDAR system. The photo positions are derived from the fully coupled GPS/IMU processing, which samples at 100 Hz and is able to directly calculate the antenna to image plane offset displacements from the full orientation data. This use of the GPS/IMU solution means that both the LiDAR and Cessna 180 photogrammetry DEM share trajectory input data, however no orientation data nor ground control is used for the photorammetry processing. The photogrammetry DEMs are overlaid on the LiDAR point cloud and analyzed for horizontal shifts or warps relative to the LiDAR. No warping or horizontal shifts have been detectable for a number of photogrammetry DEMs. Vertical offsets range from +/- 30 cm, with a typical standard deviation about that mean of 10 cm or better. LiDAR and photogrammetry function inherently differently over trees and brush, and direct comparisons between the two methods show much larger differences over vegetated areas. Finally, the differences in flight patterns associated with the two methods will be discussed, highlighting the photogrammetry requirements for a well planned grid pattern and the effects of significant end lap and side lap in the imagery coverage.

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.

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase one, volume 3 : use of scanning LiDAR in structural evaluation of bridges.

DOT National Transportation Integrated Search

2009-12-01

This volume introduces several applications of remote bridge inspection technologies studied in : this Integrated Remote Sensing and Visualization (IRSV) study using ground-based LiDAR : systems. In particular, the application of terrestrial LiDAR fo...

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...

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase two, volume 3 : advanced consideration in LiDAR technology for bridge evaluation.

DOT National Transportation Integrated Search

2012-03-01

This report describes Phase Two enhancement of terrestrial LiDAR scanning for bridge damage : evaluation that was initially developed in Phase One. Considering the spatial and reflectivity : information contained in LiDAR scans, two detection algorit...

Evaluation of the dimensions of anger reactions-5 (DAR-5) scale in combat veterans with posttraumatic stress disorder.

PubMed

Forbes, David; Alkemade, Nathan; Hopcraft, Dale; Hawthorne, Graeme; O'Halloran, Paul; Elhai, Jon D; McHugh, Tony; Bates, Glen; Novaco, Raymond W; Bryant, Richard; Lewis, Virginia

2014-12-01

After a traumatic event many people experience problems with anger which not only results in significant distress, but can also impede recovery. As such, there is value to include the assessment of anger in routine post-trauma screening procedures. The Dimensions of Anger Reactions-5 (DAR-5), as a concise measure of anger, was designed to meet such a need, its brevity minimizing the burden on client and practitioner. This study examined the psychometric properties of the DAR-5 with a sample of 163 male veterans diagnosed with Posttraumatic Stress Disorder. The DAR-5 demonstrated internal reliability (α=.86), along with convergent, concurrent and discriminant validity against a variety of established measures (e.g., HADS, PCL, STAXI). Support for the clinical cut-point score of 12 suggested by Forbes et al. (2014, Utility of the dimensions of anger reactions-5 (DAR-5) scale as a brief anger measure. Depression and Anxiety, 31, 166-173) was observed. The results support considering the DAR-5 as a preferred screening and assessment measure of problematic anger. Copyright © 2014 Elsevier Ltd. All rights reserved.

Simulated full-waveform lidar compared to Riegl VZ-400 terrestrial laser scans

NASA Astrophysics Data System (ADS)

Kim, Angela M.; Olsen, Richard C.; Béland, Martin

2016-05-01

A 3-D Monte Carlo ray-tracing simulation of LiDAR propagation models the reflection, transmission and ab- sorption interactions of laser energy with materials in a simulated scene. In this presentation, a model scene consisting of a single Victorian Boxwood (Pittosporum undulatum) tree is generated by the high-fidelity tree voxel model VoxLAD using high-spatial resolution point cloud data from a Riegl VZ-400 terrestrial laser scanner. The VoxLAD model uses terrestrial LiDAR scanner data to determine Leaf Area Density (LAD) measurements for small volume voxels (20 cm sides) of a single tree canopy. VoxLAD is also used in a non-traditional fashion in this case to generate a voxel model of wood density. Information from the VoxLAD model is used within the LiDAR simulation to determine the probability of LiDAR energy interacting with materials at a given voxel location. The LiDAR simulation is defined to replicate the scanning arrangement of the Riegl VZ-400; the resulting simulated full-waveform LiDAR signals compare favorably to those obtained with the Riegl VZ-400 terrestrial laser scanner.

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 locations.

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 classification results can be achieved by using the proposed approach.

A novel DARS2 mutation in a Japanese patient with leukoencephalopathy with brainstem and spinal cord involvement but no lactate elevation

PubMed Central

Shimojima, Keiko; Higashiguchi, Takafumi; Kishimoto, Kanako; Miyatake, Satoko; Miyake, Noriko; Takanashi, Jun-ichi; Matsumoto, Naomichi; Yamamoto, Toshiyuki

2017-01-01

The mitochondrial aspartyl-tRNA synthetase 2 gene (DARS2) is responsible for leukoencephalopathy with brainstem and spinal cord involvement and lactate elevation (LBSL). A Japanese patient with LBSL showed compound heterozygous DARS2 mutations c.358_359delinsTC (p.Gly120Ser) and c.228-15C>G (splicing error). This provides further evidence that most patients with LBSL show compound heterozygous mutations in DARS2 in association with a common splicing mutation in the splicing acceptor site of intron 2. PMID:29138691

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

PubMed Central

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

2014-01-01

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

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...

LiDAR and Image Point Cloud Comparison

DTIC Science & Technology

2014-09-01

UAV unmanned aerial vehicle USGS United States Geological Survey UTM Universal Transverse Mercator WGS 84 World Geodetic System 1984 WSI...19  1.  Physics of LiDAR Systems ................................................................20  III.  DATA AND SOFTWARE...ground control point GPS Global Positioning System IMU inertial measurements unit LiDAR light detection and ranging MI mutual information MVS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leo, Laura

The University of Notre Dame (ND) scanning LiDAR dataset used for the WFIP2 Campaign is provided. The LiDAR is a Halo Photonics Stream Line Scanning Doppler LiDAR. **It is highly recommended to discuss any planned use of these data with University of Notre Dame scientists**. For more information refer to the attached "WFIP2 Project (lidar.z07)" Readme file.

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, Liwang

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.

The Deformity Angular Ratio: Does It Correlate With High-Risk Cases for Potential Spinal Cord Monitoring Alerts in Pediatric 3-Column Thoracic Spinal Deformity Corrective Surgery?

PubMed

Lewis, Noah D H; Keshen, Sam G N; Lenke, Lawrence G; Zywiel, Michael G; Skaggs, David L; Dear, Taylor E; Strantzas, Samuel; Lewis, Stephen J

2015-08-01

A retrospective analysis. The purpose of this study was to determine whether the deformity angular ratio (DAR) can reliably assess the neurological risks of patients undergoing deformity correction. Identifying high-risk patients and procedures can help ensure that appropriate measures are taken to minimize neurological complications during spinal deformity corrections. Subjectively, surgeons look at radiographs and evaluate the riskiness of the procedure. However, 2 curves of similar magnitude and location can have significantly different risks of neurological deficit during surgery. Whether the curve spans many levels or just a few can significantly influence surgical strategies. Lenke et al have proposed the DAR, which is a measure of curve magnitude per level of deformity. The data from 35 pediatric spinal deformity correction procedures with thoracic 3-column osteotomies were reviewed. Measurements from preoperative radiographs were used to calculate the DAR. Binary logistic regression was used to model the relationship between DARs (independent variables) and presence or absence of an intraoperative alert (dependent variable). In patients undergoing 3-column osteotomies, sagittal curve magnitude and total curve magnitude were associated with increased incidence of transcranial motor evoked potential changes. Total DAR greater than 45° per level and sagittal DAR greater than 22° per level were associated with a 75% incidence of a motor evoked potential alert, with the incidence increasing to 90% with sagittal DAR of 28° per level. In patients undergoing 3-column osteotomies for severe spinal deformities, the DAR was predictive of patients developing intraoperative motor evoked potential alerts. Identifying accurate radiographical, patient, and procedural risk factors in the correction of severe deformities can help prepare the surgical team to improve safety and outcomes when carrying out complex spinal corrections. 3.

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.

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

NASA Astrophysics Data System (ADS)

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

2014-06-01

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

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.

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...

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...

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...

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...

Performance modelling of miniaturized flash-imaging lidars for future mars exploration missions

NASA Astrophysics Data System (ADS)

Mitev, V.; Pollini, A.; Haesler, J.; Pereira do Carmo, João.

2017-11-01

Future planetary exploration missions require the support of 3D vision in the GN&C during key spacecraft's proximity phases, namely: i) spacecraft precision and soft Landing on the planet's surface; ii) Rendezvous and Docking (RVD) between a Sample Canister (SC) and an orbiter spacecraft; iii) Rover Navigation (RN) on planetary surface. The imaging LiDARs are among the best candidate for such tasks [1-3]. The combination of measurement requirements and environmental conditions seems to find its optimum in the flash 3D LiDAR architecture. Here we present key steps is the evaluation of novelty light detectors and MOEMS (Micro-Opto- Electro-Mechanical Systems) technologies with respect to LiDAR system performance and miniaturization. The objectives of the project MILS (Miniaturized Imaging LiDAR System, Phase 1) concentrated on the evaluation of novel detection and scanning technologies for the miniaturization of 3D LiDARs intended for planetary mission. Preliminary designs for an elegant breadboard (EBB) for the three tasks stated above (Landing, RVD and RN) were proposed, based on results obtained with a numerical model developed in the project and providing the performances evaluation of imaging LiDARs.

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

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.

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.

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 field-inventory and airborne LiDAR are inextricably linked, and we encourage field inventory campaigns to strive for increased plot size and give greater attention to precise stem geolocation for better integration with remote sensing strategies.

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 respect to numerous engineering projects that are affected by geomechanical stability issues. The ability to efficiently and accurately map discontinuities, detect changes, and standardize roadside geomechanical stability analyses from remote locations will fundamentally change the state-of-practice of geotechnical investigation workflows and repeatable monitoring. This, in turn, will lead to earlier detection and definition of potential zones of instability, will allow for progressive monitoring and risk analysis, and will indicate the need for pro-active slope improvement and stabilization.

Estimating Above-Ground Carbon Biomass in a Newly Restored Coastal Plain Wetland Using Remote Sensing

PubMed Central

Riegel, Joseph B.; Bernhardt, Emily; Swenson, Jennifer

2013-01-01

Developing accurate but inexpensive methods for estimating above-ground carbon biomass is an important technical challenge that must be overcome before a carbon offset market can be successfully implemented in the United States. Previous studies have shown that LiDAR (light detection and ranging) is well-suited for modeling above-ground biomass in mature forests; however, there has been little previous research on the ability of LiDAR to model above-ground biomass in areas with young, aggrading vegetation. This study compared the abilities of discrete-return LiDAR and high resolution optical imagery to model above-ground carbon biomass at a young restored forested wetland site in eastern North Carolina. We found that the optical imagery model explained more of the observed variation in carbon biomass than the LiDAR model (adj-R2 values of 0.34 and 0.18 respectively; root mean squared errors of 0.14 Mg C/ha and 0.17 Mg C/ha respectively). Optical imagery was also better able to predict high and low biomass extremes than the LiDAR model. Combining both the optical and LiDAR improved upon the optical model but only marginally (adj-R2 of 0.37). These results suggest that the ability of discrete-return LiDAR to model above-ground biomass may be rather limited in areas with young, small trees and that high spatial resolution optical imagery may be the better tool in such areas. PMID:23840837

Identifying Populace Susceptible to Flooding Using ArcGIS and Remote Sensing Datasets

NASA Astrophysics Data System (ADS)

Fernandez, Sim Joseph; Milano, Alan

2016-07-01

Remote sensing technologies are growing vastly as with its various applications. The Department of Science and Technology (DOST), Republic of the Philippines, has made projects exploiting LiDAR datasets from remote sensing technologies. The Phil-LiDAR 1 project of DOST is a flood hazard mapping project. Among the project's objectives is the identification of building features which can be associated to the flood-exposed population. The extraction of building features from the LiDAR dataset is arduous as it requires manual identification of building features on an elevation map. The mapping of building footprints is made meticulous in order to compensate the accuracy between building floor area and building height both of which are crucial in flood decision making. A building identification method was developed to generate a LiDAR derivative which will serve as a guide in mapping building footprints. The method utilizes several tools of a Geographic Information System (GIS) software called ArcGIS which can operate on physical attributes of buildings such as roofing curvature, slope and blueprint area in order to obtain the LiDAR derivative from LiDAR dataset. The method also uses an intermediary process called building removal process wherein buildings and other features lying below the defined minimum building height - 2 meters in the case of Phil-LiDAR 1 project - are removed. The building identification method was developed in the hope to hasten the identification of building features especially when orthophotographs and/or satellite imageries are not made available.

Diversity arrays technology (DArT) markers in apple for genetic linkage maps.

PubMed

Schouten, Henk J; van de Weg, W Eric; Carling, Jason; Khan, Sabaz Ali; McKay, Steven J; van Kaauwen, Martijn P W; Wittenberg, Alexander H J; Koehorst-van Putten, Herma J J; Noordijk, Yolanda; Gao, Zhongshan; Rees, D Jasper G; Van Dyk, Maria M; Jaccoud, Damian; Considine, Michael J; Kilian, Andrzej

2012-03-01

Diversity Arrays Technology (DArT) provides a high-throughput whole-genome genotyping platform for the detection and scoring of hundreds of polymorphic loci without any need for prior sequence information. The work presented here details the development and performance of a DArT genotyping array for apple. This is the first paper on DArT in horticultural trees. Genetic mapping of DArT markers in two mapping populations and their integration with other marker types showed that DArT is a powerful high-throughput method for obtaining accurate and reproducible marker data, despite the low cost per data point. This method appears to be suitable for aligning the genetic maps of different segregating populations. The standard complexity reduction method, based on the methylation-sensitive PstI restriction enzyme, resulted in a high frequency of markers, although there was 52-54% redundancy due to the repeated sampling of highly similar sequences. Sequencing of the marker clones showed that they are significantly enriched for low-copy, genic regions. The genome coverage using the standard method was 55-76%. For improved genome coverage, an alternative complexity reduction method was examined, which resulted in less redundancy and additional segregating markers. The DArT markers proved to be of high quality and were very suitable for genetic mapping at low cost for the apple, providing moderate genome coverage. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11032-011-9579-5) contains supplementary material, which is available to authorized users.

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

PubMed

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

2014-12-01

In Escherichia coli, the ATP-bound form of DnaA (ATP-DnaA) promotes replication initiation. During replication, the bound ATP is hydrolyzed to ADP to yield the ADP-bound form (ADP-DnaA), which is inactive for initiation. The chromosomal site DARS2 facilitates the regeneration of ATP-DnaA by catalyzing nucleotide exchange between free ATP and ADP bound to DnaA. However, the regulatory mechanisms governing this exchange reaction are unclear. Here, using in vitro reconstituted experiments, we show that two nucleoid-associated proteins, IHF and Fis, bind site-specifically to DARS2 to activate coordinately the exchange reaction. The regenerated ATP-DnaA was fully active in replication initiation and underwent DnaA-ATP hydrolysis. ADP-DnaA formed heteromultimeric complexes with IHF and Fis on DARS2, and underwent nucleotide dissociation more efficiently than ATP-DnaA. Consistently, mutant analyses demonstrated that specific binding of IHF and Fis to DARS2 stimulates the formation of ATP-DnaA production, thereby promoting timely initiation. Moreover, we show that IHF-DARS2 binding is temporally regulated during the cell cycle, whereas Fis only binds to DARS2 in exponentially growing cells. These results elucidate the regulation of ATP-DnaA and replication initiation in coordination with the cell cycle and growth phase. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

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. This method provides seamlessly access to hillshaded imagery for both bare earth and first return terrain models with various angles of illumination. Seamless access to LiDAR-derived imagery in Google Earth has proven to be the most popular product available in the OpenTopography Portal. The hillshade KMZ files have been downloaded over 3000 times by users ranging from earthquake scientists to K-12 educators who wish to introduce cutting edge real world data into their earth science lessons. OpenTopography also provides dynamically generated KMZ visualizations of LiDAR data products produced when users choose to use the OpenTopography point cloud access and processing system. These Google Earth compatible products allow users to quickly visualize the custom terrain products they have generated without the burden of loading the data into a GIS environment. For users who have installed the Google Earth browser plug-in, these visualizations can be launched directly from the OpenTopography results page and viewed directly in the browser.

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...

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...

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase two, volume 2 : applications of LiDAR technology in structural evaluation under normal traffic operation and post blast loading.

DOT National Transportation Integrated Search

2012-03-01

This report focused on two potential applications of terrestrial LiDAR scans on highway : bridges: 1) vehicle crossing effects measured by3-D, terrestrial LiDAR scans of highway bridges : measuring clearance distance; and 2) bridge post-blast geometr...

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....

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...

Using Landsat Time-Series and LiDAR to Inform Aboveground Forest Biomass Baselines in Northern Minnesota, USA

Treesearch

Ram K. Deo; Matthew B. Russell; Grant M. Domke; Christopher W. Woodall; Michael J. Falkowski; Warren B. Cohen

2017-01-01

The publicly accessible archive of Landsat imagery and increasing regional-scale LiDAR acquisitions offer an opportunity to periodically estimate aboveground forest biomass (AGB) from 1990 to the present to alignwith the reporting needs ofNationalGreenhouseGas Inventories (NGHGIs). This study integrated Landsat time-series data, a state-wide LiDAR dataset, and a recent...

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...

Lidar - ND Halo Scanning Doppler, Boardman - Derived Data

DOE Data Explorer

Leo, Laura

2018-01-26

The University of Notre Dame (ND) scanning LiDAR dataset used for the WFIP2 Campaign is provided. The LiDAR is a Halo Photonics Stream Line Scanning Doppler LiDAR. **It is highly recommended to discuss any planned use of these data with University of Notre Dame scientists**. For more information refer to the attached "WFIP2 Project (lidar.z07)" Readme file.

Roles of dopamine receptors in mediating acute modulation of immunological responses in Macrobrachium rosenbergii.

PubMed

Chang, Zhong-Wen; Ke, Zhi-Han; Chang, Chin-Chyuan

2016-02-01

Dopamine (DA) was found to influence the immunological responses and resistance to pathogen infection in invertebrates. To clarify the possible modulation of DA through dopamine receptors (DAR) against acute environmental stress, the levels of DA, glucose and lactate in the haemolymph of Macrobrachium rosenbergii under hypo- and hyperthermal stresses were measured. The changes in immune parameters such as total haemocyte count (THC), differential haemocyte count (DHC), phenoloxidase (PO) activity, respiratory bursts (RBs), superoxide dismutase (SOD) and glutathione peroxidase (GPx) activities and phagocytic activity (PA) were evaluated in prawns which received DAR antagonists (SCH23390, SCH, D1 antagonist; domperidone, DOM, D2 antagonist; chlorpromazine, CH, D1+2 antagonist) followed by hypo- (15 °C) and hyperthermal (34 °C) stresses. In addition, pharmacological analysis of the effect DA modulation was studied in haemocytes incubated with DA and DAR antagonists. The results revealed a significant increase in haemolymph DA accompanied with upregulated levels of glucose and lactate in prawns exposed to both hypo- and hyperthermal stresses in 2 h. In addition, a significant decrease in RBs per haemocyte was noted in prawns which received DAR antagonists when they exposed to hyperthermal stress for 30 min. In in vitro test, antagonism on RBs, SOD and GPx activity of haemocytes were further evidenced through D1, D1, D1+D2 DARs, respectively, in the meantime, no significant difference in PO activity and PA was observed among the treatment groups. These results suggest that the upregulation of DA, glucose and lactate in haemolymph might be the response to acute thermal stress for the demand of energy, and the DAR occupied by its antagonistic action impart no effect on immunological responses except RBs in vivo even though the modulation mediated through D1 DAR was further evidenced in RBs, SOD and GPx activities in vitro. It is therefore concluded that thermal stress mediate stress responses not only through DAR but also via diverse pathways, and DA might modulate the levels of RBs, SOD and GPx activities mainly through D1 DAR. Copyright © 2016 Elsevier Ltd. All rights reserved.

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 work in LiDAR sensor measurement uncertainty must focus on the development of vegetative error models to create more robust error prediction algorithms. To achieve this objective, comprehensive empirical exploratory analysis is recommended to relate vegetative parameters to observed errors.

A high-density consensus map of barley linking DArT markers to SSR, RFLP and STS loci and agricultural traits

PubMed Central

Wenzl, Peter; Li, Haobing; Carling, Jason; Zhou, Meixue; Raman, Harsh; Paul, Edie; Hearnden, Phillippa; Maier, Christina; Xia, Ling; Caig, Vanessa; Ovesná, Jaroslava; Cakir, Mehmet; Poulsen, David; Wang, Junping; Raman, Rosy; Smith, Kevin P; Muehlbauer, Gary J; Chalmers, Ken J; Kleinhofs, Andris; Huttner, Eric; Kilian, Andrzej

2006-01-01

Background Molecular marker technologies are undergoing a transition from largely serial assays measuring DNA fragment sizes to hybridization-based technologies with high multiplexing levels. Diversity Arrays Technology (DArT) is a hybridization-based technology that is increasingly being adopted by barley researchers. There is a need to integrate the information generated by DArT with previous data produced with gel-based marker technologies. The goal of this study was to build a high-density consensus linkage map from the combined datasets of ten populations, most of which were simultaneously typed with DArT and Simple Sequence Repeat (SSR), Restriction Enzyme Fragment Polymorphism (RFLP) and/or Sequence Tagged Site (STS) markers. Results The consensus map, built using a combination of JoinMap 3.0 software and several purpose-built perl scripts, comprised 2,935 loci (2,085 DArT, 850 other loci) and spanned 1,161 cM. It contained a total of 1,629 'bins' (unique loci), with an average inter-bin distance of 0.7 ± 1.0 cM (median = 0.3 cM). More than 98% of the map could be covered with a single DArT assay. The arrangement of loci was very similar to, and almost as optimal as, the arrangement of loci in component maps built for individual populations. The locus order of a synthetic map derived from merging the component maps without considering the segregation data was only slightly inferior. The distribution of loci along chromosomes indicated centromeric suppression of recombination in all chromosomes except 5H. DArT markers appeared to have a moderate tendency toward hypomethylated, gene-rich regions in distal chromosome areas. On the average, 14 ± 9 DArT loci were identified within 5 cM on either side of SSR, RFLP or STS loci previously identified as linked to agricultural traits. Conclusion Our barley consensus map provides a framework for transferring genetic information between different marker systems and for deploying DArT markers in molecular breeding schemes. The study also highlights the need for improved software for building consensus maps from high-density segregation data of multiple populations. PMID:16904008

Dopamine receptor antagonists as new mode-of-action insecticide leads for control of Aedes and Culex mosquito vectors.

PubMed

Nuss, Andrew B; Ejendal, Karin F K; Doyle, Trevor B; Meyer, Jason M; Lang, Emma G; Watts, Val J; Hill, Catherine A

2015-03-01

New mode-of-action insecticides are sought to provide continued control of pesticide resistant arthropod vectors of neglected tropical diseases (NTDs). We previously identified antagonists of the AaDOP2 D1-like dopamine receptor (DAR) from the yellow fever mosquito, Aedes aegypti, with toxicity to Ae. aegypti larvae as leads for novel insecticides. To extend DAR-based insecticide discovery, we evaluated the molecular and pharmacological characteristics of an orthologous DAR target, CqDOP2, from Culex quinquefasciatus, the vector of lymphatic filariasis and West Nile virus. CqDOP2 has 94.7% amino acid identity to AaDOP2 and 28.3% identity to the human D1-like DAR, hD1. CqDOP2 and AaDOP2 exhibited similar pharmacological responses to biogenic amines and DAR antagonists in cell-based assays. The antagonists amitriptyline, amperozide, asenapine, chlorpromazine and doxepin were between 35 to 227-fold more selective at inhibiting the response of CqDOP2 and AaDOP2 in comparison to hD1. Antagonists were toxic to both C. quinquefasciatus and Ae. aegypti larvae, with LC50 values ranging from 41 to 208 μM 72 h post-exposure. Orthologous DOP2 receptors identified from the African malaria mosquito, Anopheles gambiae, the sand fly, Phlebotomus papatasi and the tsetse fly, Glossina morsitans, had high sequence similarity to CqDOP2 and AaDOP2. DAR antagonists represent a putative new insecticide class with activity against C. quinquefasciatus and Ae. aegypti, the two most important mosquito vectors of NTDs. There has been limited change in the sequence and pharmacological properties of the DOP2 DARs of these species since divergence of the tribes Culicini and Aedini. We identified antagonists selective for mosquito versus human DARs and observed a correlation between DAR pharmacology and the in vivo larval toxicity of antagonists. These data demonstrate that sequence similarity can be predictive of target potential. On this basis, we propose expanded insecticide discovery around orthologous DOP2 targets from additional dipteran vectors.

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.

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

LiDAR collection in August 2015 over the East River Watershed, Colorado, USA

DOE Data Explorer

Wainwright, Haruko [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Williams, Kenneth

2017-01-01

Airborne LiDAR data were acquired over the study area on August 10th, 2015, using a Riegl Q1560 dual-channel LiDAR system mounted on a Piper Navajo. The survey was performed by Quantum Spatial in collaboration with Eagle Mapping Ltd. The data complies with the USGS QL1 standard [Heideman, 2014] with the point density more than 8 pulse per m2.

Understanding the Unique Equatorial Density Irregularities

DTIC Science & Technology

2015-04-01

3 Bahir Dar, 79 ETHIOPIA EOARD GRANT #FA8655-13-1-3052 Report Date: April 2015 Final Report from 15 June 2013 to 30 March 2015 Air Force...ADDRESS(ES) Bahir Dar University Peda Straight, Region 3 Bahir Dar, 79 ETHIOPIA 8. PERFORMING ORGANIZATION REPORT NUMBER N/A 9. SPONSORING/MONITORING...satellite signal receivers and others in collaboration with institutions outside Ethiopia . This effort provided some support for the installation of the

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...

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...

LiDAR‐guided Archaeological Survey of a Mediterranean Landscape: Lessons from the Ancient Greek Polis of Kolophon (Ionia, Western Anatolia)

PubMed Central

Draganits, Erich; Gretscher, Martin; Muss, Ulrike

2017-01-01

Abstract In 2013, an airborne laser scan survey was conducted in the territory of the Ionian city of Kolophon near the western coast of modern Turkey as part of an archaeological survey project carried out by the Mimar Sinan University of Istanbul (Turkey) and the University of Vienna (Austria). Several light detection and ranging (LiDAR) studies have been carried out in the temperate climate zones of Europe, but only a few in Mediterranean landscapes. Our study is based on the first LiDAR survey carried out for an archaeological purpose in Turkey and one of the first in the Mediterranean that have been planned, measured and filtered especially for archaeological research questions. The interpretation of LiDAR data combined with ground‐observations proved extremely useful for the detection and documentation of archaeological remains below Mediterranean evergreen vegetation and dense maquis. This article deals with the methodological aspects of interpreting LiDAR data, using the Kolophon data as a case study. We offer a discussion of the strengths and limitations of LiDAR as an archaeological remote sensing method and suggest a best practice model for interpreting LiDAR data in a Mediterranean context. © 2017 The Authors. Archaeological Prospection published by John Wiley & Sons Ltd. PMID:29242700

a Novel Method for Automation of 3d Hydro Break Line Generation from LIDAR Data Using Matlab

NASA Astrophysics Data System (ADS)

Toscano, G. J.; Gopalam, U.; Devarajan, V.

2013-08-01

Water body detection is necessary to generate hydro break lines, which are in turn useful in creating deliverables such as TINs, contours, DEMs from LiDAR data. Hydro flattening follows the detection and delineation of water bodies (lakes, rivers, ponds, reservoirs, streams etc.) with hydro break lines. Manual hydro break line generation is time consuming and expensive. Accuracy and processing time depend on the number of vertices marked for delineation of break lines. Automation with minimal human intervention is desired for this operation. This paper proposes using a novel histogram analysis of LiDAR elevation data and LiDAR intensity data to automatically detect water bodies. Detection of water bodies using elevation information was verified by checking against LiDAR intensity data since the spectral reflectance of water bodies is very small compared with that of land and vegetation in near infra-red wavelength range. Detection of water bodies using LiDAR intensity data was also verified by checking against LiDAR elevation data. False detections were removed using morphological operations and 3D break lines were generated. Finally, a comparison of automatically generated break lines with their semi-automated/manual counterparts was performed to assess the accuracy of the proposed method and the results were discussed.

Adjustment of Measurements with Multiplicative Errors: Error Analysis, Estimates of the Variance of Unit Weight, and Effect on Volume Estimation from LiDAR-Type Digital Elevation Models

PubMed Central

Shi, Yun; Xu, Peiliang; Peng, Junhuan; Shi, Chuang; Liu, Jingnan

2014-01-01

Modern observation technology has verified that measurement errors can be proportional to the true values of measurements such as GPS, VLBI baselines and LiDAR. Observational models of this type are called multiplicative error models. This paper is to extend the work of Xu and Shimada published in 2000 on multiplicative error models to analytical error analysis of quantities of practical interest and estimates of the variance of unit weight. We analytically derive the variance-covariance matrices of the three least squares (LS) adjustments, the adjusted measurements and the corrections of measurements in multiplicative error models. For quality evaluation, we construct five estimators for the variance of unit weight in association of the three LS adjustment methods. Although LiDAR measurements are contaminated with multiplicative random errors, LiDAR-based digital elevation models (DEM) have been constructed as if they were of additive random errors. We will simulate a model landslide, which is assumed to be surveyed with LiDAR, and investigate the effect of LiDAR-type multiplicative error measurements on DEM construction and its effect on the estimate of landslide mass volume from the constructed DEM. PMID:24434880

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.

Light Detection and Ranging-Based Terrain Navigation: A Concept Exploration

NASA Technical Reports Server (NTRS)

Campbell, Jacob; UijtdeHaag, Maarten; vanGraas, Frank; Young, Steve

2003-01-01

This paper discusses the use of Airborne Light Detection And Ranging (LiDAR) equipment for terrain navigation. Airborne LiDAR is a relatively new technology used primarily by the geo-spatial mapping community to produce highly accurate and dense terrain elevation maps. In this paper, the term LiDAR refers to a scanning laser ranger rigidly mounted to an aircraft, as opposed to an integrated sensor system that consists of a scanning laser ranger integrated with Global Positioning System (GPS) and Inertial Measurement Unit (IMU) data. Data from the laser range scanner and IMU will be integrated with a terrain database to estimate the aircraft position and data from the laser range scanner will be integrated with GPS to estimate the aircraft attitude. LiDAR data was collected using NASA Dryden's DC-8 flying laboratory in Reno, NV and was used to test the proposed terrain navigation system. The results of LiDAR-based terrain navigation shown in this paper indicate that airborne LiDAR is a viable technology enabler for fully autonomous aircraft navigation. The navigation performance is highly dependent on the quality of the terrain databases used for positioning and therefore high-resolution (2 m post-spacing) data was used as the terrain reference.

Building block extraction and classification by means of aerial images fused with super-resolution reconstructed elevation data

NASA Astrophysics Data System (ADS)

Panagiotopoulou, Antigoni; Bratsolis, Emmanuel; Charou, Eleni; Perantonis, Stavros

2017-10-01

The detailed three-dimensional modeling of buildings utilizing elevation data, such as those provided by light detection and ranging (LiDAR) airborne scanners, is increasingly demanded today. There are certain application requirements and available datasets to which any research effort has to be adapted. Our dataset includes aerial orthophotos, with a spatial resolution 20 cm, and a digital surface model generated from LiDAR, with a spatial resolution 1 m and an elevation resolution 20 cm, from an area of Athens, Greece. The aerial images are fused with LiDAR, and we classify these data with a multilayer feedforward neural network for building block extraction. The innovation of our approach lies in the preprocessing step in which the original LiDAR data are super-resolution (SR) reconstructed by means of a stochastic regularized technique before their fusion with the aerial images takes place. The Lorentzian estimator combined with the bilateral total variation regularization performs the SR reconstruction. We evaluate the performance of our approach against that of fusing unprocessed LiDAR data with aerial images. We present the classified images and the statistical measures confusion matrix, kappa coefficient, and overall accuracy. The results demonstrate that our approach predominates over that of fusing unprocessed LiDAR data with aerial images.

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.

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 mission) with the goal of improving wildlife modeling for more locations across the globe. PMID:24324655

The effect of various factors on the dental arch relationship in non-syndromic unilateral cleft lip and palate children assessed by new approach: a retrospective study.

PubMed

Haque, Sanjida; Alam, Mohammad Khursheed; Khamis, Mohd Fadhli

2017-05-06

Cleft lip and palate (CLP) is one of the most common birth defects. Multiple factors are believed to be responsible for an unfavorable dental arch relationship in CLP. Facial growth (maxillary) retardation, which results in class III malocclusion, is the primary challenge that CLP patients face. Phenotype factors and postnatal treatment factors influence treatment outcomes in unilateral cleft lip and palate (UCLP) children, which has led to a great diversity in protocols and surgical techniques by various cleft groups worldwide. The aim of this study was to illustrate the dental arch relationship (DAR) and palatal morphology (PM) of UCLP in Bangladeshi children and to explore the various factors that are responsible for poor DAR and PM. Dental models of 84 subjects were taken before orthodontic treatment and alveolar bone grafting. The mean age was 7.69 (SD 2.46) years. The DAR and PM were assessed blindly by five raters using the EUROCRAN index (EI). Kappa statistics was used to evaluate the intra- and inter-examiner agreement, chi square was used to assess the associations, and logistic regression analysis was used to explore the responsible factors that affect DAR and PM. The mean EUROCRAN scores were 2.44 and 1.93 for DAR and PM, respectively. Intra- and inter-examiner agreement was moderate to very good. Using crude and stepwise backward regression analyses, significant associations were found between the modified Millard technique (P = 0.047, P = 0.034 respectively) of cheiloplasty and unfavorable DAR. Complete UCLP (P = 0.017) was also significantly correlated with unfavorable DAR. The PM showed a significant association with the type of cleft, type of cheiloplasty and type of palatoplasty. This multivariate study determined that the complete type of UCLP and the modified Millard technique of cheiloplasty had significantly unfavorable effects on both the DAR and PM.

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-scale forest assessment without compromising the accuracy of biomass estimates, and these estimates can be further improved using development density.

Forest structures retrieval from LiDAR onboard ULA

NASA Astrophysics Data System (ADS)

Shang, Xiaoxia; Chazette, Patrick; Totems, Julien; Marnas, Fabien; Sanak, Joseph

2013-04-01

Following the United Nations Framework Convention on Climate Change, the assessment of forest carbon stock is one of the main elements for a better understanding of the carbon cycle and its evolution following the climate change. The forests sequester 80% of the continental biospheric carbon and this efficiency is a function of the tree species and the tree health. The airborne backscatter LiDAR onboard the ultra light aircraft (ULA) can provide the key information on the forest vertical structures and evolution in the time. The most important structural parameter is the tree top height, which is directly linked to the above-ground biomass using non-linear relationships. In order to test the LiDAR capability for retrieving the tree top height, the LiDAR ULICE (Ultraviolet LIdar for Canopy Experiment) has been used over different forest types, from coniferous (maritime pins) to deciduous (oaks, hornbeams ...) trees. ULICE works at the wavelength of 355 nm with a sampling along the line-of-sight between 15 and 75 cm. According to the LiDAR signal to noise ratio (SNR), two different algorithms have been used in our study. The first algorithm is a threshold method directly based on the comparison between the LiDAR signal and the noise distributions, while the second one used a low pass filter by fitting a Gaussian curve family. In this paper, we will present these two algorithms and their evolution as a function of the SNR. The main error sources will be also discussed and assessed for each algorithm. The results show that these algorithms have great potential for ground-segment of future space borne LiDAR missions dedicated to the forest survey at the global scale. Acknowledgements: the canopy LiDAR system ULICE has been developed by CEA (Commissariat à l'Energie Atomique). It has been deployed with the support of CNES (Centre National d'Etude Spariales) and ANR (Agence Nationale de la Recherche). We acknowledge the ULA pilots Franck Toussaint for logistical help during the ULA campaign.

NASA Fluid Lensing & MiDAR: Next-Generation Remote Sensing Technologies for Aquatic Remote Sensing

NASA Technical Reports Server (NTRS)

Chirayath, Ved

2018-01-01

We present two recent instrument technology developments at NASA, Fluid Lensing and MiDAR, and their application to remote sensing of Earth's aquatic systems. Fluid Lensing is the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions. MiDAR is a next-generation active hyperspectral remote sensing and optical communications instrument capable of active fluid lensing. Fluid Lensing has been used to provide 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) to enable a new method for remote sensing of living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an objectâ€"TM"s non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Previously, MiDAR was developed into a TRL 2 laboratory instrument capable of imaging in thirty-two narrowband channels across the VNIR spectrum (400-950nm). Recently, MiDAR UV was raised to TRL4 and expanded to include five ultraviolet bands from 280-400nm, permitting UV remote sensing capabilities in UV A, B, and C bands and enabling mineral identification and stimulated fluorescence measurements of organic proteins and compounds, such as green fluorescent proteins in terrestrial and aquatic organics.

Evaluation of LiDAR Imagery as a Tool for Mapping the Northern San Andreas Fault in Heavily Forested Areas of Mendocino and Sonoma Counties, California

NASA Astrophysics Data System (ADS)

Prentice, C. S.; Koehler, R. D.; Baldwin, J. N.; Harding, D. J.

2004-12-01

We are mapping in detail active traces of the San Andreas Fault in Mendocino and Sonoma Counties in northern California, using recently acquired airborne LiDAR (also known as ALSM) data. The LiDAR data set provides a powerful new tool for mapping geomorphic features related to the San Andreas Fault because it can be used to produce high-resolution images of the ground surfaces beneath the forest canopy along the 70-km-long section of the fault zone encompassed by the data. Our effort represents the first use of LiDAR data to map active fault traces in a densely vegetated region along the San Andreas Fault. We are using shaded relief images generated from bare-earth DEMs to conduct detailed mapping of fault-related geomorphic features (e.g. scarps, offset streams, linear valleys, shutter ridges, and sag ponds) between Fort Ross and Point Arena. Initially, we map fault traces digitally, on-screen, based only on the geomorphology interpreted from LiDAR images. We then conduct field reconnaissance using the initial computer-based maps in order to verify and further refine our mapping. We found that field reconnaissance is of utmost importance in producing an accurate and detailed map of fault traces. Many lineaments identified as faults from the on-screen images were determined in the field to be old logging roads or other features unrelated to faulting. Also, in areas where the resolution of LiDAR data is poor, field reconnaissance, coupled with topographic maps and aerial photographs, permits a more accurate location of fault-related geomorphic features. LiDAR images are extremely valuable as a base for field mapping in this heavily forested area, and the use of LiDAR is far superior to traditional mapping techniques relying only on aerial photography and 7.5 minute USGS quadrangle topographic maps. Comparison with earlier mapping of the northern San Andreas fault (Brown and Wolfe, 1972) shows that in some areas the LiDAR data allow a correction of the fault trace location of up to several hundred meters. To date we have field checked approximately 24 km of the 70-km-long section of the fault for which LiDAR data is available. The remaining 46 km will be field checked in 2005. The result will be a much more accurate map of the active traces of the northern San Andreas Fault, which will be of great use for future fault studies.

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 other classed should be nearly zero. Therefore, SRC use the reconstruction error associated with each class to do data classification. A section of airborne LiDAR points of Vienna city is used and classified into 6classes: ground, roofs, vegetation, covered ground, walls and other points. Only 6 training samples from each class are taken. For the final classification result, ground and covered ground are merged into one same class(ground). The classification accuracy for ground is 94.60%, roof is 95.47%, vegetation is 85.55%, wall is 76.17%, other object is 20.39%.

The Uncertainty of Biomass Estimates from Modeled ICESat-2 Returns Across a Boreal Forest Gradient

NASA Technical Reports Server (NTRS)

Montesano, P. M.; Rosette, J.; Sun, G.; North, P.; Nelson, R. F.; Dubayah, R. O.; Ranson, K. J.; Kharuk, V.

2014-01-01

The Forest Light (FLIGHT) radiative transfer model was used to examine the uncertainty of vegetation structure measurements from NASA's planned ICESat-2 photon counting light detection and ranging (LiDAR) instrument across a synthetic Larix forest gradient in the taiga-tundra ecotone. The simulations demonstrate how measurements from the planned spaceborne mission, which differ from those of previous LiDAR systems, may perform across a boreal forest to non-forest structure gradient in globally important ecological region of northern Siberia. We used a modified version of FLIGHT to simulate the acquisition parameters of ICESat-2. Modeled returns were analyzed from collections of sequential footprints along LiDAR tracks (link-scales) of lengths ranging from 20 m-90 m. These link-scales traversed synthetic forest stands that were initialized with parameters drawn from field surveys in Siberian Larix forests. LiDAR returns from vegetation were compiled for 100 simulated LiDAR collections for each 10 Mg · ha(exp -1) interval in the 0-100 Mg · ha(exp -1) above-ground biomass density (AGB) forest gradient. Canopy height metrics were computed and AGB was inferred from empirical models. The root mean square error (RMSE) and RMSE uncertainty associated with the distribution of inferred AGB within each AGB interval across the gradient was examined. Simulation results of the bright daylight and low vegetation reflectivity conditions for collecting photon counting LiDAR with no topographic relief show that 1-2 photons are returned for 79%-88% of LiDAR shots. Signal photons account for approximately 67% of all LiDAR returns, while approximately 50% of shots result in 1 signal photon returned. The proportion of these signal photon returns do not differ significantly (p greater than 0.05) for AGB intervals greater than 20 Mg · ha(exp -1). The 50m link-scale approximates the finest horizontal resolution (length) at which photon counting LiDAR collection provides strong model fits and minimizes forest structure uncertainty in the synthetic Larix stands. At this link-scale AGB greater than 20 Mg · ha(exp -1) has AGB error from 20-50% at the 95% confidence level. These results suggest that the theoretical sensitivity of ICESat-2 photon counting LiDAR measurements alone lack the ability to consistently discern differences in inferred AGB at 10 Mg · ha(exp -1) intervals in sparse forests characteristic of the taiga-tundra ecotone.

KML-Based Access and Visualization of High Resolution LiDAR Topography

NASA Astrophysics Data System (ADS)

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

2008-12-01

Over the past decade, there has been dramatic growth in the acquisition of LiDAR (Light Detection And Ranging) high-resolution topographic data for earth science studies. Capable of providing digital elevation models (DEMs) more than an order of magnitude higher resolution than those currently available, LiDAR data allow earth scientists to study the processes that contribute to landscape evolution at resolutions not previously possible yet essential for their appropriate representation. These datasets also have significant implications for earth science education and outreach because they provide an accurate representation of landforms and geologic hazards. Unfortunately, the massive volume of data produced by LiDAR mapping technology can be a barrier to their use. To make these data available to a larger user community, we have been exploring the use of Keyhole Markup Language (KML) and Google Earth to provide access to LiDAR data products and visualizations. LiDAR digital elevation models are typically delivered in a tiled format that lends itself well to a KML-based distribution system. For LiDAR datasets hosted in the GEON OpenTopography Portal (www.opentopography.org) we have developed KML files that show the extent of available LiDAR DEMs and provide direct access to the data products. Users interact with these KML files to explore the extent of the available data and are able to select DEMs that correspond to their area of interest. Selection of a tile loads a download that the user can then save locally for analysis in their software of choice. The GEON topography system also has tools available that allow users to generate custom DEMs from LiDAR point cloud data. This system is powerful because it enables users to access massive volumes of raw LiDAR data and to produce DEM products that are optimized to their science applications. We have developed a web service that converts the custom DEM models produced by the system to a hillshade that is delivered to the user as a KML groundoverlay. The KML product enables users to quickly and easily visualize the DEMs in Google Earth. By combining internet-based LiDAR data processing with KML visualization products, users are able to execute computationally intensive data sub-setting, processing and visualization without having local access to computing resources, GIS software, or data processing expertise. Finally, GEON has partnered with the US Geological Survey to generate region-dependant network linked KML visualizations for large volumes of LiDAR derived hillshades of the Northern San Andreas fault system. These data, acquired by the NSF-funded GeoEarthScope project, offer an unprecedented look at active faults in the northern portion of the San Andreas system. Through the region-dependant network linked KML, users can seamlessly access 1 meter hillshades (both 315 and 45 degree sun angles) for the full 1400 square kilometer dataset, without downloading huge volumes of data. This type of data access has great utility for users ranging from earthquake scientists to K-12 educators who wish to introduce cutting edge real world data into their earth science lessons.

Delayed adverse reactions to the parenteral administration of iodinated contrast media.

PubMed

Egbert, Robert E; De Cecco, Carlo N; Schoepf, U Joseph; McQuiston, Andrew D; Meinel, Felix G; Katzberg, Richard W

2014-12-01

This article presents an overview of delayed adverse reactions (DARs) to parenteral iodinated contrast media and discusses the clinical nature, risk factors, mechanisms, and potential economic implications of these DARs. DARs to contrast media are not rare but are often not recognized as being linked to contrast administration and may be falsely ascribed to other drugs. These side effects are problematic because the patient is usually without medical supervision.

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

DTIC Science & Technology

2014-03-01

investigator’s objective. For example, if the sole objective is to identify geomorphic breaks in slope associated with the OHWM, points representing vegetation... geomorphic position. During the data-gathering stage of wetland delinea- tions, measurements made using LiDAR data should be considered esti- mates...field. Field validation of LiDAR topographic data is essential before using them as evidence of a secondary hydrology indicator, such as geomorphic

Mapping of past stand-level forest disturbances and estimation of time since disturbance using simulated spaceborne LiDAR data

NASA Astrophysics Data System (ADS)

Sanchez Lopez, N.; Hudak, A. T.; Boschetti, L.

2017-12-01

Explicit information on the location, the size or the time since disturbance (TSD) at the forest stand level complements field inventories, improves the monitoring of forest attributes and the estimation of biomass and carbon stocks. Even-aged stands display homogenous structural parameters that have often been used as a proxy of stand age. Consequently, performing object-oriented analysis on Light Detection and Ranging (LiDAR) data has potential to detect historical stand-replacing disturbances. Recent research has shown good results in the delineation of forest stands as well as in the prediction of disturbance occurrence and TSD using airborne LiDAR data. Nevertheless, the use of airborne LiDAR for systematic monitoring of forest stands is limited by the sporadic availability of data and its high cost compared to satellite instruments. NASA's forthcoming Global Ecosystem Dynamics Investigations (GEDI) mission will provide systematically data on the vertical structure of the vegetation, but its use presents some challenges compared to the common discrete-return airborne LiDAR. GEDI will be a waveform instrument, hence the summary metrics will be different to those obtained with airborne LiDAR, and the sampling configuration could limit the utility of the data, especially on heterogeneous landscapes. The potential use of GEDI data for forest characterization at the stand level would therefore depend on the predictive power of the GEDI footprint metrics, and on the density of point samples relative to forest stand size (i.e. the number of observation/footprints per stand).In this study, we assess the performance of simulated GEDI-derived metrics for stand characterization and estimation of TSD, and the point density needed to adequately identify forest stands, which translates - due to the fixed sampling configuration - into the minimum temporal interval needed to collect a sufficient number of points. The study area was located in the Clear Creek, Selway River, and Elk Creek watersheds ( 54,000 ha) within the Nez Perce-Clearwater National Forest in Idaho, where airborne LiDAR and reference maps on TSD were available. Simulated GEDI footprints and waveforms were obtained from airborne LiDAR point clouds and the results were compared to similar analysis performed with airborne LiDAR.

Evaluating UAV and LiDAR Retrieval of Snow Depth in a Coniferous Forest in Arizona

NASA Astrophysics Data System (ADS)

Van Leeuwen, W. J. D.; Broxton, P.; Biederman, J. A.

2017-12-01

Remote sensing of snow depth and cover in forested environments is challenging. Trees interfere with the remote sensing of snowpack below the canopy and cause large variations in the spatial distribution of the snowpack itself (e.g. between below canopy environments to shaded gaps to open clearings). The distribution of trees and topographic variation make it challenging to monitor the snowpack with in-situ observations. Airborne LiDAR has improved our ability to monitor snowpack over large areas in montane and forested environments because of its high sampling rate and ability to penetrate the canopy. However, these LiDAR flights can be too expensive and time-consuming to process, making it hard to use them for real-time snow monitoring. In this research, we evaluate Structure from Motion (SfM) as an alternative to Airborne LiDAR to generate high-resolution snow depth data in forested environments. This past winter, we conducted a snow field campaign over Arizona's Mogollon Rim where we acquired aerial LiDAR, multi-angle aerial photography from a UAV, and extensive field observations of snow depth at two sites. LiDAR and SFM derived snow depth maps were generated by comparing "snow-on" and "snow-off" LiDAR and SfM data. The SfM- and LiDAR-generated snow depth maps were similar at a site with fewer trees, though there were more discrepancies at a site with more trees. Both compared reasonably well with the field observations at the sparser forested site, with poorer agreement at the denser forested site. Finally, although the SfM produced point clouds with much higher point densities than the aerial LiDAR, the SfM was not able to produce meaningful snow depth estimates directly underneath trees and had trouble in areas with deep shadows. Based on these findings, we are optimizing our UAV data acquisition strategies for this upcoming field season. We are using these data, along with high-resolution hydrological modeling, to gain a better understanding of how different forest structural, climatic and topographic conditions affect the snowpack and consequently the water resources available to the Salt River Project, a water utility providing power and water to millions of customers in the Phoenix area

Determination of the Optimal Chromosomal Location(s) for a DNA Element in Escherichia coli Using a Novel Transposon-mediated Approach.

PubMed

Frimodt-Møller, Jakob; Charbon, Godefroid; Krogfelt, Karen A; Løbner-Olesen, Anders

2017-09-11

The optimal chromosomal position(s) of a given DNA element was/were determined by transposon-mediated random insertion followed by fitness selection. In bacteria, the impact of the genetic context on the function of a genetic element can be difficult to assess. Several mechanisms, including topological effects, transcriptional interference from neighboring genes, and/or replication-associated gene dosage, may affect the function of a given genetic element. Here, we describe a method that permits the random integration of a DNA element into the chromosome of Escherichia coli and select the most favorable locations using a simple growth competition experiment. The method takes advantage of a well-described transposon-based system of random insertion, coupled with a selection of the fittest clone(s) by growth advantage, a procedure that is easily adjustable to experimental needs. The nature of the fittest clone(s) can be determined by whole-genome sequencing on a complex multi-clonal population or by easy gene walking for the rapid identification of selected clones. Here, the non-coding DNA region DARS2, which controls the initiation of chromosome replication in E. coli, was used as an example. The function of DARS2 is known to be affected by replication-associated gene dosage; the closer DARS2 gets to the origin of DNA replication, the more active it becomes. DARS2 was randomly inserted into the chromosome of a DARS2-deleted strain. The resultant clones containing individual insertions were pooled and competed against one another for hundreds of generations. Finally, the fittest clones were characterized and found to contain DARS2 inserted in close proximity to the original DARS2 location.

Detailed Hydrographic Feature Extraction from High-Resolution LiDAR Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Danny L. Anderson

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 amore » 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.« less

Integrating LiDAR Data into Earth Science Education

NASA Astrophysics Data System (ADS)

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

2010-12-01

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

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.

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).

Modeling Diurnal and Seasonal 3D Light Profiles in Amazon Forests

NASA Astrophysics Data System (ADS)

Morton, D. C.; Rubio, J.; Gastellu-Etchegorry, J.; Cook, B. D.; Hunter, M. O.; Yin, T.; Nagol, J. R.; Keller, M. M.

2013-12-01

The complex horizontal and vertical structure in tropical forests generates a diversity of light environments for canopy and understory trees. These 3D light profiles are dynamic on diurnal and seasonal time scales based on changes in solar illumination and the fraction of diffuse light. Understanding this variability is critical for improving ecosystem models and interpreting optical and LiDAR remote sensing data from tropical forests. Here, we initialized the Discrete Anisotropic Radiative Transfer (DART) model using dense airborne LiDAR data (>20 returns m2) from three forest sites in the central and eastern Amazon. Forest scenes derived from airborne LiDAR data were tested using modeled and observed large-footprint LiDAR data from the ICESat-GLAS sensor. Next, diurnal and seasonal profiles of photosynthetically active radiation (PAR) for each forest site were simulated under clear sky and cloudy conditions using DART. Incident PAR was summarized for canopy, understory, and ground levels. Our study illustrates the importance of realistic canopy models for accurate representation of LiDAR and optical radiative transfer. In particular, canopy rugosity and ground topography information from airborne LiDAR data provided critical 3D information that cannot be recreated using stem maps and allometric relationships for crown dimensions. The spatial arrangement of canopy trees altered PAR availability, even for dominant individuals, compared to downwelling measurements from nearby eddy flux towers. Pseudo-realistic branch and leaf architecture was also essential for recreating multiple scattering within canopies at near-infrared wavelengths commonly used for LiDAR remote sensing and quantifying PAR attenuation from shading within and between canopies. These findings point to the need for more spatial information on forest structure to improve the representation of light availability in models of tropical forest productivity.

DTMs Assessment to the Definition of Shallow Landslides Prone Areas

NASA Astrophysics Data System (ADS)

Martins, Tiago D.; Oka-Fiori, Chisato; Carvalho Vieira, Bianca; Montgomery, David R.

2017-04-01

Predictive methods have been developed, especially since the 1990s, to identify landslide prone areas. One of the examples it is the physically based model SHALSTAB (Shallow Landsliding Stability Model), that calculate the potential instability for shallow landslides based on topography and physical soil properties. Normally, in such applications in Brazil, the Digital Terrain Model (DTM), is obtained mainly from conventional contour lines. However, recently the LiDAR (Light Detection and Ranging) system has been largely used in Brazil. Thus, this study aimed to evaluate different DTM's, generated from conventional data and LiDAR, and their influence in generating susceptibility maps to shallow landslides using SHALSTAB model. For that were analyzed the physical properties of soil, the response of the model when applying conventional topographical data and LiDAR's in the generation of DTM, and the shallow landslides susceptibility maps based on different topographical data. The selected area is in the urban perimeter of the municipality of Antonina (PR), affected by widespread landslides in March 2011. Among the results, it was evaluated different LiDAR data interpolation, using GIS tools, wherein the Triangulation/Natural Neighbor presented the best performance. It was also found that in one of evaluation indexes (Scars Concentration), the LiDAR derived DTM presented the best performance when compared with the one originated from contour lines, however, the Landslide Potential index, has presented a small increase. Consequently, it was possible to assess the DTM's, and the one derived from LiDAR improved very little the certitude percentage. It is also noted a gap in researches carried out in Brazil on the use of products generated from LiDAR data on geomorphological analysis.

Design, construction, and testing of the direct absorption receiver panel research experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chavez, J.M.; Rush, E.E.; Matthews, C.W.

1990-01-01

A panel research experiment (PRE) was designed, built, and tested as a scaled-down model of a direct absorption receiver (DAR). The PRE is a 3-MW{sub t}DAR experiment that will allow flow testing with molten nitrate salt and provide a test bed for DAR testing with actual solar heating. In a solar central receiver system DAR, the heat absorbing fluid (a blackened molten nitrate salt) flows in a thin film down a vertical panel (rather than through tubes as in conventional receiver designs) and absorbs the concentrated solar flux directly. The ability of the flowing salt film to absorb flux directly.more » The ability of the flowing salt film to absorb the incident solar flux depends on the panel design, hydraulic and thermal fluid flow characteristics, and fluid blackener properties. Testing of the PRE is being conducted to demonstrate the engineering feasibility of the DAR concept. The DAR concept is being investigated because it offers numerous potential performance and economic advantages for production of electricity when compared to other solar receiver designs. The PRE utilized a 1-m wide by 6-m long absorber panel. The salt flow tests are being used to investigate component performance, panel deformations, and fluid stability. Salt flow testing has demonstrated that all the DAR components work as designed and that there are fluid stability issues that need to be addressed. Future solar testing will include steady-state and transient experiments, thermal loss measurements, responses to severe flux and temperature gradients and determination of peak flux capability, and optimized operation. In this paper, we describe the design, construction, and some preliminary flow test results of the Panel Research Experiment. 11 refs., 8 figs., 2 tabs.« less

Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models

PubMed Central

2018-01-01

On-chip LiDAR sensors for vehicle collision avoidance are a rapidly expanding area of research and development. The assessment of reliable obstacle detection using data collected by LiDAR sensors has become a key issue that the scientific community is actively exploring. The design of a self-tuning methodology and its implementation are presented in this paper, to maximize the reliability of LiDAR sensors network for obstacle detection in the ‘Internet of Things’ (IoT) mobility scenarios. The Webots Automobile 3D simulation tool for emulating sensor interaction in complex driving environments is selected in order to achieve that objective. Furthermore, a model-based framework is defined that employs a point-cloud clustering technique, and an error-based prediction model library that is composed of a multilayer perceptron neural network, and k-nearest neighbors and linear regression models. Finally, a reinforcement learning technique, specifically a Q-learning method, is implemented to determine the number of LiDAR sensors that are required to increase sensor reliability for obstacle localization tasks. In addition, a IoT driving assistance user scenario, connecting a five LiDAR sensor network is designed and implemented to validate the accuracy of the computational intelligence-based framework. The results demonstrated that the self-tuning method is an appropriate strategy to increase the reliability of the sensor network while minimizing detection thresholds. PMID:29748521

Wind turbine wake characterization from temporally disjunct 3-D measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Doubrawa, Paula; Barthelmie, Rebecca J.; Wang, Hui

Scanning LiDARs can be used to obtain three-dimensional wind measurements in and beyond the atmospheric surface layer. In this work, metrics characterizing wind turbine wakes are derived from LiDAR observations and from large-eddy simulation (LES) data, which are used to recreate the LiDAR scanning geometry. The metrics are calculated for two-dimensional planes in the vertical and cross-stream directions at discrete distances downstream of a turbine under single-wake conditions. The simulation data are used to estimate the uncertainty when mean wake characteristics are quantified from scanning LiDAR measurements, which are temporally disjunct due to the time that the instrument takes tomore » probe a large volume of air. Based on LES output, we determine that wind speeds sampled with the synthetic LiDAR are within 10% of the actual mean values and that the disjunct nature of the scan does not compromise the spatial variation of wind speeds within the planes. We propose scanning geometry density and coverage indices, which quantify the spatial distribution of the sampled points in the area of interest and are valuable to design LiDAR measurement campaigns for wake characterization. Lastly, we find that scanning geometry coverage is important for estimates of the wake center, orientation and length scales, while density is more important when seeking to characterize the velocity deficit distribution.« less

Wind turbine wake characterization from temporally disjunct 3-D measurements

DOE PAGES

Doubrawa, Paula; Barthelmie, Rebecca J.; Wang, Hui; ...

2016-11-10

Scanning LiDARs can be used to obtain three-dimensional wind measurements in and beyond the atmospheric surface layer. In this work, metrics characterizing wind turbine wakes are derived from LiDAR observations and from large-eddy simulation (LES) data, which are used to recreate the LiDAR scanning geometry. The metrics are calculated for two-dimensional planes in the vertical and cross-stream directions at discrete distances downstream of a turbine under single-wake conditions. The simulation data are used to estimate the uncertainty when mean wake characteristics are quantified from scanning LiDAR measurements, which are temporally disjunct due to the time that the instrument takes tomore » probe a large volume of air. Based on LES output, we determine that wind speeds sampled with the synthetic LiDAR are within 10% of the actual mean values and that the disjunct nature of the scan does not compromise the spatial variation of wind speeds within the planes. We propose scanning geometry density and coverage indices, which quantify the spatial distribution of the sampled points in the area of interest and are valuable to design LiDAR measurement campaigns for wake characterization. Lastly, we find that scanning geometry coverage is important for estimates of the wake center, orientation and length scales, while density is more important when seeking to characterize the velocity deficit distribution.« less

Waveform fitting and geometry analysis for full-waveform lidar feature extraction

NASA Astrophysics Data System (ADS)

Tsai, Fuan; Lai, Jhe-Syuan; Cheng, Yi-Hsiu

2016-10-01

This paper presents a systematic approach that integrates spline curve fitting and geometry analysis to extract full-waveform LiDAR features for land-cover classification. The cubic smoothing spline algorithm is used to fit the waveform curve of the received LiDAR signals. After that, the local peak locations of the waveform curve are detected using a second derivative method. According to the detected local peak locations, commonly used full-waveform features such as full width at half maximum (FWHM) and amplitude can then be obtained. In addition, the number of peaks, time difference between the first and last peaks, and the average amplitude are also considered as features of LiDAR waveforms with multiple returns. Based on the waveform geometry, dynamic time-warping (DTW) is applied to measure the waveform similarity. The sum of the absolute amplitude differences that remain after time-warping can be used as a similarity feature in a classification procedure. An airborne full-waveform LiDAR data set was used to test the performance of the developed feature extraction method for land-cover classification. Experimental results indicate that the developed spline curve- fitting algorithm and geometry analysis can extract helpful full-waveform LiDAR features to produce better land-cover classification than conventional LiDAR data and feature extraction methods. In particular, the multiple-return features and the dynamic time-warping index can improve the classification results significantly.

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

NASA Astrophysics Data System (ADS)

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

2015-10-01

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

Nosql for Storage and Retrieval of Large LIDAR Data Collections

NASA Astrophysics Data System (ADS)

Boehm, J.; Liu, K.

2015-08-01

Developments in LiDAR technology over the past decades have made LiDAR to become a mature and widely accepted source of geospatial information. This in turn has led to an enormous growth in data volume. The central idea for a file-centric storage of LiDAR point clouds is the observation that large collections of LiDAR data are typically delivered as large collections of files, rather than single files of terabyte size. This split of the dataset, commonly referred to as tiling, was usually done to accommodate a specific processing pipeline. It makes therefore sense to preserve this split. A document oriented NoSQL database can easily emulate this data partitioning, by representing each tile (file) in a separate document. The document stores the metadata of the tile. The actual files are stored in a distributed file system emulated by the NoSQL database. We demonstrate the use of MongoDB a highly scalable document oriented NoSQL database for storing large LiDAR files. MongoDB like any NoSQL database allows for queries on the attributes of the document. As a specialty MongoDB also allows spatial queries. Hence we can perform spatial queries on the bounding boxes of the LiDAR tiles. Inserting and retrieving files on a cloud-based database is compared to native file system and cloud storage transfer speed.

Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models.

PubMed

Castaño, Fernando; Beruvides, Gerardo; Villalonga, Alberto; Haber, Rodolfo E

2018-05-10

On-chip LiDAR sensors for vehicle collision avoidance are a rapidly expanding area of research and development. The assessment of reliable obstacle detection using data collected by LiDAR sensors has become a key issue that the scientific community is actively exploring. The design of a self-tuning methodology and its implementation are presented in this paper, to maximize the reliability of LiDAR sensors network for obstacle detection in the 'Internet of Things' (IoT) mobility scenarios. The Webots Automobile 3D simulation tool for emulating sensor interaction in complex driving environments is selected in order to achieve that objective. Furthermore, a model-based framework is defined that employs a point-cloud clustering technique, and an error-based prediction model library that is composed of a multilayer perceptron neural network, and k-nearest neighbors and linear regression models. Finally, a reinforcement learning technique, specifically a Q-learning method, is implemented to determine the number of LiDAR sensors that are required to increase sensor reliability for obstacle localization tasks. In addition, a IoT driving assistance user scenario, connecting a five LiDAR sensor network is designed and implemented to validate the accuracy of the computational intelligence-based framework. The results demonstrated that the self-tuning method is an appropriate strategy to increase the reliability of the sensor network while minimizing detection thresholds.

Identification of a new fault and associated lineament features in Oregon's Summer Lake Valley using high-resolution LiDAR data

NASA Astrophysics Data System (ADS)

Bennett, L.; Madin, I.

2012-12-01

In 2012, the Oregon Department of Geology and Mineral Industries (DOGAMI) contracted WSI to co-acquire airborne Light Detecting and Ranging (LiDAR) and Thermal Infrared Imagery (TIR) data within the region surrounding Summer Lake, Oregon. The objective of this project was to detect surficial expressions of geothermal activity and associated geologic features. An analysis of the LiDAR data revealed one newly identified fault and several accompanying lineaments that strike northwest, similar to the trend of the Ana River, Brothers, and Eugene-Denio Fault Zones in Central Oregon. The age of the Ana River Fault Zone and Summer Lake bed is known to be within the Holocene epoch. Apparent scarp height observed from the LiDAR is up to 8 meters. While detailed analysis is ongoing, the data illustrated the effectiveness of using high resolution remote sensing data for surficial analysis of geologic displacement. This presentation will focus on direct visual detection of features in the Summer Lake, Oregon landscape using LiDAR data.

Real-time vehicle emissions monitoring using a compact LiDAR system and conventional instruments: first results of an experimental campaign in a suburban area in southern Italy

NASA Astrophysics Data System (ADS)

Parracino, Stefano; Richetta, Maria; Gelfusa, Michela; Malizia, Andrea; Bellecci, Carlo; De Leo, Leonardo; Perrimezzi, Carlo; Fin, Alessandro; Forin, Marco; Giappicucci, Francesca; Grion, Massimo; Marchese, Giuseppe; Gaudio, Pasquale

2016-10-01

Urban air pollution causes deleterious effects on human health and the environment. To meet stringent standards imposed by the European Commission, advanced measurement methods are required. Remote sensing techniques, such as light detection and ranging (LiDAR), can be a valuable option for evaluating particulate matter (PM), emitted by vehicles in urban traffic, with high sensitivity and in shorter time intervals. Since air quality problems persist not only in large urban areas, a measuring campaign was specifically performed in a suburban area of Crotone, Italy, using both a compact LiDAR system and conventional instruments for real-time vehicle emissions monitoring along a congested road. First results reported in this paper show a strong dependence between variations of LiDAR backscattering signals and traffic-related air pollution levels. Moreover, time-resolved LiDAR data averaged in limited regions, directly above conventional monitoring stations at the border of an intersection, were found to be linearly correlated to the PM concentration levels with a correlation coefficient between 0.75 and 0.84.

Optimization of non-linear gradient in hydrophobic interaction chromatography for the analytical characterization of antibody-drug conjugates.

PubMed

Bobály, Balázs; Randazzo, Giuseppe Marco; Rudaz, Serge; Guillarme, Davy; Fekete, Szabolcs

2017-01-20

The goal of this work was to evaluate the potential of non-linear gradients in hydrophobic interaction chromatography (HIC), to improve the separation between the different homologous species (drug-to-antibody, DAR) of commercial antibody-drug conjugates (ADC). The selectivities between Brentuximab Vedotin species were measured using three different gradient profiles, namely linear, power function based and logarithmic ones. The logarithmic gradient provides the most equidistant retention distribution for the DAR species and offers the best overall separation of cysteine linked ADC in HIC. Another important advantage of the logarithmic gradient, is its peak focusing effect for the DAR0 species, which is particularly useful to improve the quantitation limit of DAR0. Finally, the logarithmic behavior of DAR species of ADC in HIC was modelled using two different approaches, based on i) the linear solvent strength theory (LSS) and two scouting linear gradients and ii) a new derived equation and two logarithmic scouting gradients. In both cases, the retention predictions were excellent and systematically below 3% compared to the experimental values. Copyright © 2016 Elsevier B.V. All rights reserved.

Automatic extraction of building boundaries using aerial LiDAR data

NASA Astrophysics Data System (ADS)

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

2016-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2012-04-01

Field measurements of the wake flow produced from the interaction between atmospheric boundary layer and a wind turbine are performed with three wind LiDARs. The tested wind turbine is a 2 MW Enercon E-70 located in Collonges, Switzerland. First, accuracy of mean values and frequency resolution of the wind measurements are surveyed as a function of the number of laser rays emitted for each measurement. Indeed, measurements performed with one single ray allow maximizing sampling frequency, thus characterizing wake turbulence. On the other hand, if the number of emitted rays is increased accuracy of mean wind is increased due to the longer sampling period. Subsequently, two-dimensional measurements with a single LiDAR are carried out over vertical sections of the wind turbine wake and mean wake flow is obtained by averaging 2D measurements consecutively performed. The high spatial resolution of the used LiDAR allows characterizing in details velocity defect present in the central part of the wake and its downstream recovery. Single LiDAR measurements are also performed by staring the laser beam at fixed directions for a sampling period of about ten minutes and maximizing the sampling frequency in order to characterize wake turbulence. From these tests wind fluctuation peaks are detected in the wind turbine wake at blade top-tip height for different downstream locations. The magnitude of these turbulence peaks is generally reduced by moving downstream. This increased turbulence level at blade top-tip height observed for a real wind turbine has been already detected from previous wind tunnel tests and Large Eddy simulations, thus confirming the presence of a source of dangerous fatigue loads for following wind turbines within a wind farm. Furthermore, the proper characterization of wind fluctuations through LiDAR measurements is proved by the detection of the inertial subrange from spectral analysis of these velocity signals. Finally, simultaneous measurements with two LiDARs are performed over the mean vertical symmetry plane of the wind turbine wake, while a third LiDAR measures the incoming wind over a vertical plane parallel to the mean wind direction and lying outside of the wake. One LiDAR is placed in proximity of the wind turbine location and measures pointing downstream, whereas a second LiDAR is located along the mean wind direction at a downstream distance of 6.5 diameters and measures pointing upstream. For these measurements axial and vertical velocity components are retrieved only for measurement points where the two laser beams result to be roughly orthogonal. Statistics of the two velocity components show in the near wake at hub height strong flow fluctuations with magnitudes about 30% of the mean value, and a gradual reduction for downstream distances larger than three rotor diameters.

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

NASA Astrophysics Data System (ADS)

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

2010-05-01

Today, Airborne LiDAR derived digital terrain models (DTMs) are used for several aspects in different scientific disciplines, such as hydrology, geomorphology or archaeology. In the field of river geomorphology, LiDAR data sets can provide information on the riverine vegetation, the level and boundary of the water body, the elevation of the riparian foreland and their roughness. The LiDAR systems in use for topographic data acquisition mainly operate with wavelengths of at least 1064nm and, thus, are not able to penetrate water. LiDAR sensors with two wavelengths are available (bathymetric LiDAR), but they can only provide elevation information of riverbeds or lakes, if the water is clear and the minimum water depth exceeds 1.5m. In small and shallow rivers it is impossible to collect information of the riverbed, regardless of the used LiDAR sensor. In this article, we present a method to derive a high-resolution DTM of the riverbed and to combine it with the LiDAR DTM resulting in a watercourse DTM (DTM-W) as a basis for calculating the changes in the riverbed during several years. To obtain such a DTM-W we use river cross-sections acquired by terrestrial survey or echo-sounding. First, a differentiation between water and land has to be done. A highly accurate water surface can be derived by using a water surface delineation algorithm, which incorporates the amplitude information of the LiDAR point cloud and additional geometrical features (e.g. local surface roughness). The second step is to calculate a thalweg line, which is the lowest flow path in the riverbed. This is achieved by extracting the lowest point of each river cross section and by fitting a B-spline curve through those points. In the next step, the centerline of the river is calculated by applying a shrinking algorithm of the water boundary polygon. By averaging the thalweg line and the centerline, a main flow path line can be computed. Subsequently, a dense array of 2D-profiles perpendicular to the flow path line is defined and the heights are computed by linear interpolation of the original cross sections. Thus, a very dense 3D point cloud of the riverbed is obtained from which a grid model of the river bed can be calculated applying any suitable interpolation technique like triangulation, linear prediction or inverse distance mapping. In a final step, the river bed model and the LiDAR DTM are combined resulting in a watercourse DTM. By computing different DTM-Ws from multiple cross section data sets, the volume and the magnitude of changes in the riverbed can be estimated. Hence, the erosion or accumulation areas and their volume changes during several years can be quantified.

Designated Airworthiness Representatives

DOT National Transportation Integrated Search

1985-10-01

This advisory circular (AC) contains information and guidance concerning the : selection and appointment of Designated Airworthiness Representatives (DAR's) and identifies the specific functions which may be delegated to DAR's as authorized by the Fe...

Preliminary Results of Double-Sample Forest Inventory of Pine and Mixed Stands with High- and Low-Density LiDAR

Treesearch

Robert C. Parker; Patrick A. Glass

2004-01-01

LiDAR data (0.5 and 1 m postings) were used in a double-sample forest inventory on the Lee Experimental Forest, Louisiana. Phase 2 plots were established with DGPS. Tree d.b.h. (> 4.5 inches) and two sample heights were measured on every 10 th plot of the Phase 1 sample. Volume was computed for natural and planted pine and mixed hardwood species. LiDAR trees were...

Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast‑growing Eucalyptus forest plantation using airborne LiDAR data

Treesearch

Carlos Alberto Silva; Andrew Thomas Hudak; Carine Klauberg; Lee Alexandre Vierling; Carlos Gonzalez‑Benecke; Samuel de Padua Chaves Carvalho; Luiz Carlos Estraviz Rodriguez; Adrian Cardil

2017-01-01

LiDAR measurements can be used to predict and map AGC across variable-age Eucalyptus plantations with adequate levels of precision and accuracy using 5 pulses m− 2 and a grid cell size of 5 m. The promising results for AGC modeling in this study will allow for greater confidence in comparing AGC estimates with varying LiDAR sampling densities for Eucalyptus plantations...

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.

Lidar - ND Halo Scanning Doppler, Boardman - Reviewed Data

DOE Data Explorer

Otarola, Sebastian

2017-10-23

The University of Notre Dame (ND) scanning LiDAR dataset used for the WFIP2 Campaign is provided. The LiDAR is a Halo Photonics Stream Line Scanning Doppler LiDAR. **It is highly recommended to discuss any planned use of these data with University of Notre Dame scientists**. For more information refer to Section 4.c) in the updated version of the "WFIP2 Project (lidar.z07)" Readme file, where the lidar.z07.b0 dataset is fully explained.

High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR

PubMed Central

Jimenez-Berni, Jose A.; Deery, David M.; Rozas-Larraondo, Pablo; Condon, Anthony (Tony) G.; Rebetzke, Greg J.; James, Richard A.; Bovill, William D.; Furbank, Robert T.; Sirault, Xavier R. R.

2018-01-01

Crop improvement efforts are targeting increased above-ground biomass and radiation-use efficiency as drivers for greater yield. Early ground cover and canopy height contribute to biomass production, but manual measurements of these traits, and in particular above-ground biomass, are slow and labor-intensive, more so when made at multiple developmental stages. These constraints limit the ability to capture these data in a temporal fashion, hampering insights that could be gained from multi-dimensional data. Here we demonstrate the capacity of Light Detection and Ranging (LiDAR), mounted on a lightweight, mobile, ground-based platform, for rapid multi-temporal and non-destructive estimation of canopy height, ground cover and above-ground biomass. Field validation of LiDAR measurements is presented. For canopy height, strong relationships with LiDAR (r2 of 0.99 and root mean square error of 0.017 m) were obtained. Ground cover was estimated from LiDAR using two methodologies: red reflectance image and canopy height. In contrast to NDVI, LiDAR was not affected by saturation at high ground cover, and the comparison of both LiDAR methodologies showed strong association (r2 = 0.92 and slope = 1.02) at ground cover above 0.8. For above-ground biomass, a dedicated field experiment was performed with destructive biomass sampled eight times across different developmental stages. Two methodologies are presented for the estimation of biomass from LiDAR: 3D voxel index (3DVI) and 3D profile index (3DPI). The parameters involved in the calculation of 3DVI and 3DPI were optimized for each sample event from tillering to maturity, as well as generalized for any developmental stage. Individual sample point predictions were strong while predictions across all eight sample events, provided the strongest association with biomass (r2 = 0.93 and r2 = 0.92) for 3DPI and 3DVI, respectively. Given these results, we believe that application of this system will provide new opportunities to deliver improved genotypes and agronomic interventions via more efficient and reliable phenotyping of these important traits in large experiments. PMID:29535749

High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR.

PubMed

Jimenez-Berni, Jose A; Deery, David M; Rozas-Larraondo, Pablo; Condon, Anthony Tony G; Rebetzke, Greg J; James, Richard A; Bovill, William D; Furbank, Robert T; Sirault, Xavier R R

2018-01-01

Crop improvement efforts are targeting increased above-ground biomass and radiation-use efficiency as drivers for greater yield. Early ground cover and canopy height contribute to biomass production, but manual measurements of these traits, and in particular above-ground biomass, are slow and labor-intensive, more so when made at multiple developmental stages. These constraints limit the ability to capture these data in a temporal fashion, hampering insights that could be gained from multi-dimensional data. Here we demonstrate the capacity of Light Detection and Ranging (LiDAR), mounted on a lightweight, mobile, ground-based platform, for rapid multi-temporal and non-destructive estimation of canopy height, ground cover and above-ground biomass. Field validation of LiDAR measurements is presented. For canopy height, strong relationships with LiDAR ( r 2 of 0.99 and root mean square error of 0.017 m) were obtained. Ground cover was estimated from LiDAR using two methodologies: red reflectance image and canopy height. In contrast to NDVI, LiDAR was not affected by saturation at high ground cover, and the comparison of both LiDAR methodologies showed strong association ( r 2 = 0.92 and slope = 1.02) at ground cover above 0.8. For above-ground biomass, a dedicated field experiment was performed with destructive biomass sampled eight times across different developmental stages. Two methodologies are presented for the estimation of biomass from LiDAR: 3D voxel index (3DVI) and 3D profile index (3DPI). The parameters involved in the calculation of 3DVI and 3DPI were optimized for each sample event from tillering to maturity, as well as generalized for any developmental stage. Individual sample point predictions were strong while predictions across all eight sample events, provided the strongest association with biomass ( r 2 = 0.93 and r 2 = 0.92) for 3DPI and 3DVI, respectively. Given these results, we believe that application of this system will provide new opportunities to deliver improved genotypes and agronomic interventions via more efficient and reliable phenotyping of these important traits in large experiments.

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 suggests that reducing LiDAR point density is a viable solution to regional-scale forest biomass assessment without compromising the accuracy of estimation, which may further be improved using development density.

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.

LiDAR error estimation with WAsP engineering

NASA Astrophysics Data System (ADS)

Bingöl, F.; Mann, J.; Foussekis, D.

2008-05-01

The LiDAR measurements, vertical wind profile in any height between 10 to 150m, are based on assumption that the measured wind is a product of a homogenous wind. In reality there are many factors affecting the wind on each measurement point which the terrain plays the main role. To model LiDAR measurements and predict possible error in different wind directions for a certain terrain we have analyzed two experiment data sets from Greece. In both sites LiDAR and met, mast data have been collected and the same conditions are simulated with RisØ/DTU software, WAsP Engineering 2.0. Finally measurement data is compared with the model results. The model results are acceptable and very close for one site while the more complex one is returning higher errors at higher positions and in some wind directions.

Development and mapping of DArT markers within the Festuca - Lolium complex

PubMed Central

Kopecký, David; Bartoš, Jan; Lukaszewski, Adam J; Baird, James H; Černoch, Vladimír; Kölliker, Roland; Rognli, Odd Arne; Blois, Helene; Caig, Vanessa; Lübberstedt, Thomas; Studer, Bruno; Shaw, Paul; Doležel, Jaroslav; Kilian, Andrzej

2009-01-01

Background Grasses are among the most important and widely cultivated plants on Earth. They provide high quality fodder for livestock, are used for turf and amenity purposes, and play a fundamental role in environment protection. Among cultivated grasses, species within the Festuca-Lolium complex predominate, especially in temperate regions. To facilitate high-throughput genome profiling and genetic mapping within the complex, we have developed a Diversity Arrays Technology (DArT) array for five grass species: F. pratensis, F. arundinacea, F. glaucescens, L. perenne and L. multiflorum. Results The DArTFest array contains 7680 probes derived from methyl-filtered genomic representations. In a first marker discovery experiment performed on 40 genotypes from each species (with the exception of F. glaucescens for which only 7 genotypes were used), we identified 3884 polymorphic markers. The number of DArT markers identified in every single genotype varied from 821 to 1852. To test the usefulness of DArTFest array for physical mapping, DArT markers were assigned to each of the seven chromosomes of F. pratensis using single chromosome substitution lines while recombinants of F. pratensis chromosome 3 were used to allocate the markers to seven chromosome bins. Conclusion The resources developed in this project will facilitate the development of genetic maps in Festuca and Lolium, the analysis on genetic diversity, and the monitoring of the genomic constitution of the Festuca × Lolium hybrids. They will also enable marker-assisted selection for multiple traits or for specific genome regions. PMID:19832973

Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data.

PubMed

Garcia, Mariano; Saatchi, Sassan; Casas, Angeles; Koltunov, Alexander; Ustin, Susan; Ramirez, Carlos; Garcia-Gutierrez, Jorge; Balzter, Heiko

2017-02-01

Quantifying biomass consumption and carbon release is critical to understanding the role of fires in the carbon cycle and air quality. We present a methodology to estimate the biomass consumed and the carbon released by the California Rim fire by integrating postfire airborne LiDAR and multitemporal Landsat Operational Land Imager (OLI) imagery. First, a support vector regression (SVR) model was trained to estimate the aboveground biomass (AGB) from LiDAR-derived metrics over the unburned area. The selected model estimated AGB with an R 2 of 0.82 and RMSE of 59.98 Mg/ha. Second, LiDAR-based biomass estimates were extrapolated to the entire area before and after the fire, using Landsat OLI reflectance bands, Normalized Difference Infrared Index, and the elevation derived from LiDAR data. The extrapolation was performed using SVR models that resulted in R 2 of 0.73 and 0.79 and RMSE of 87.18 (Mg/ha) and 75.43 (Mg/ha) for the postfire and prefire images, respectively. After removing bias from the AGB extrapolations using a linear relationship between estimated and observed values, we estimated the biomass consumption from postfire LiDAR and prefire Landsat maps to be 6.58 ± 0.03 Tg (10 12  g), which translate into 12.06 ± 0.06 Tg CO2 e released to the atmosphere, equivalent to the annual emissions of 2.57 million cars.

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

NASA Astrophysics Data System (ADS)

Aktaruzzaman, Md.; Schmitt, Theo G.

2011-11-01

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

Estimating Stand Height and Tree Density in Pinus taeda plantations using in-situ data, airborne LiDAR and k-Nearest Neighbor Imputation.

PubMed

Silva, Carlos Alberto; Klauberg, Carine; Hudak, Andrew T; Vierling, Lee A; Liesenberg, Veraldo; Bernett, Luiz G; Scheraiber, Clewerson F; Schoeninger, Emerson R

2018-01-01

Accurate forest inventory is of great economic importance to optimize the entire supply chain management in pulp and paper companies. The aim of this study was to estimate stand dominate and mean heights (HD and HM) and tree density (TD) of Pinus taeda plantations located in South Brazil using in-situ measurements, airborne Light Detection and Ranging (LiDAR) data and the non- k-nearest neighbor (k-NN) imputation. Forest inventory attributes and LiDAR derived metrics were calculated at 53 regular sample plots and we used imputation models to retrieve the forest attributes at plot and landscape-levels. The best LiDAR-derived metrics to predict HD, HM and TD were H99TH, HSD, SKE and HMIN. The Imputation model using the selected metrics was more effective for retrieving height than tree density. The model coefficients of determination (adj.R2) and a root mean squared difference (RMSD) for HD, HM and TD were 0.90, 0.94, 0.38m and 6.99, 5.70, 12.92%, respectively. Our results show that LiDAR and k-NN imputation can be used to predict stand heights with high accuracy in Pinus taeda. However, furthers studies need to be realized to improve the accuracy prediction of TD and to evaluate and compare the cost of acquisition and processing of LiDAR data against the conventional inventory procedures.

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.

Registration of Panoramic/Fish-Eye Image Sequence and LiDAR Points Using Skyline Features

PubMed Central

Zhu, Ningning; Jia, Yonghong; Ji, Shunping

2018-01-01

We propose utilizing a rigorous registration model and a skyline-based method for automatic registration of LiDAR points and a sequence of panoramic/fish-eye images in a mobile mapping system (MMS). This method can automatically optimize original registration parameters and avoid the use of manual interventions in control point-based registration methods. First, the rigorous registration model between the LiDAR points and the panoramic/fish-eye image was built. Second, skyline pixels from panoramic/fish-eye images and skyline points from the MMS’s LiDAR points were extracted, relying on the difference in the pixel values and the registration model, respectively. Third, a brute force optimization method was used to search for optimal matching parameters between skyline pixels and skyline points. In the experiments, the original registration method and the control point registration method were used to compare the accuracy of our method with a sequence of panoramic/fish-eye images. The result showed: (1) the panoramic/fish-eye image registration model is effective and can achieve high-precision registration of the image and the MMS’s LiDAR points; (2) the skyline-based registration method can automatically optimize the initial attitude parameters, realizing a high-precision registration of a panoramic/fish-eye image and the MMS’s LiDAR points; and (3) the attitude correction values of the sequences of panoramic/fish-eye images are different, and the values must be solved one by one. PMID:29883431

Designing and Testing a UAV Mapping System for Agricultural Field Surveying

PubMed Central

Skovsen, Søren

2017-01-01

A Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV) can map the overflown environment in point clouds. Mapped canopy heights allow for the estimation of crop biomass in agriculture. The work presented in this paper contributes to sensory UAV setup design for mapping and textual analysis of agricultural fields. LiDAR data are combined with data from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors to conduct environment mapping for point clouds. The proposed method facilitates LiDAR recordings in an experimental winter wheat field. Crop height estimates ranging from 0.35–0.58 m are correlated to the applied nitrogen treatments of 0–300 kgNha. The LiDAR point clouds are recorded, mapped, and analysed using the functionalities of the Robot Operating System (ROS) and the Point Cloud Library (PCL). Crop volume estimation is based on a voxel grid with a spatial resolution of 0.04 × 0.04 × 0.001 m. Two different flight patterns are evaluated at an altitude of 6 m to determine the impacts of the mapped LiDAR measurements on crop volume estimations. PMID:29168783

Validation of the high-throughput marker technology DArT using the model plant Arabidopsis thaliana.

PubMed

Wittenberg, Alexander H J; van der Lee, Theo; Cayla, Cyril; Kilian, Andrzej; Visser, Richard G F; Schouten, Henk J

2005-08-01

Diversity Arrays Technology (DArT) is a microarray-based DNA marker technique for genome-wide discovery and genotyping of genetic variation. DArT allows simultaneous scoring of hundreds of restriction site based polymorphisms between genotypes and does not require DNA sequence information or site-specific oligonucleotides. This paper demonstrates the potential of DArT for genetic mapping by validating the quality and molecular basis of the markers, using the model plant Arabidopsis thaliana. Restriction fragments from a genomic representation of the ecotype Landsberg erecta (Ler) were amplified by PCR, individualized by cloning and spotted onto glass slides. The arrays were then hybridized with labeled genomic representations of the ecotypes Columbia (Col) and Ler and of individuals from an F(2) population obtained from a Col x Ler cross. The scoring of markers with specialized software was highly reproducible and 107 markers could unambiguously be ordered on a genetic linkage map. The marker order on the genetic linkage map coincided with the order on the DNA sequence map. Sequencing of the Ler markers and alignment with the available Col genome sequence confirmed that the polymorphism in DArT markers is largely a result of restriction site polymorphisms.

Registration of Laser Scanning Point Clouds: A Review.

PubMed

Cheng, Liang; Chen, Song; Liu, Xiaoqiang; Xu, Hao; Wu, Yang; Li, Manchun; Chen, Yanming

2018-05-21

The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.

Designing and Testing a UAV Mapping System for Agricultural Field Surveying.

PubMed

Christiansen, Martin Peter; Laursen, Morten Stigaard; Jørgensen, Rasmus Nyholm; Skovsen, Søren; Gislum, René

2017-11-23

A Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV) can map the overflown environment in point clouds. Mapped canopy heights allow for the estimation of crop biomass in agriculture. The work presented in this paper contributes to sensory UAV setup design for mapping and textual analysis of agricultural fields. LiDAR data are combined with data from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors to conduct environment mapping for point clouds. The proposed method facilitates LiDAR recordings in an experimental winter wheat field. Crop height estimates ranging from 0.35-0.58 m are correlated to the applied nitrogen treatments of 0-300 kg N ha . The LiDAR point clouds are recorded, mapped, and analysed using the functionalities of the Robot Operating System (ROS) and the Point Cloud Library (PCL). Crop volume estimation is based on a voxel grid with a spatial resolution of 0.04 × 0.04 × 0.001 m. Two different flight patterns are evaluated at an altitude of 6 m to determine the impacts of the mapped LiDAR measurements on crop volume estimations.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kumar, Jitendra; HargroveJr., William Walter; Norman, Steven P

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 andmore » 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.« less

Petrographic Analysis and Geochemical Source Correlation of Pigeon Peak, Sutter Buttes, CA

NASA Astrophysics Data System (ADS)

Novotny, N. M.; Hausback, B. P.

2013-12-01

The Sutter Buttes are a volcanic complex located in the center of the Great Valley north of Sacramento. They are comprised of numerous inter-intruding andesite and rhyolite lava domes of varying compositions surrounded by a shallow rampart of associated tephras. The Pigeon Peak block-and-ash flow sequence is located in the rampart and made up of a porphyritic Biotite bearing Hornblende Andesite. The andesite blocks demonstrate a high degree of propylization in hornblende crystals, highly zoned plagioclase, trace olivine, and display a red to gray color gradation. DAR is an andesite dome located less than one mile from Pigeon Peak. Of the 15 to 25 andesite lava domes within four miles from Pigeon Peak, only DAR displays trace olivine, red to grey color stratification, low biotite content, and propylitized hornblende. These characteristic similarities suggest that DAR may be the source for Pigeon Peak. My investigation used microprobe analysis of the DAR and Pigeon Peak feldspar crystals to identify the magmatic history of the magma body before emplacement. Correlation of the anorthite zoning within the feldspars from both locations support my hypothesis that DAR is the source of the Pigeon Peak block-and-ash flow.

Automatic registration of optical imagery with 3d lidar data using local combined mutual information

NASA Astrophysics Data System (ADS)

Parmehr, E. G.; Fraser, C. S.; Zhang, C.; Leach, J.

2013-10-01

Automatic registration of multi-sensor data is a basic step in data fusion for photogrammetric and remote sensing applications. The effectiveness of intensity-based methods such as Mutual Information (MI) for automated registration of multi-sensor image has been previously reported for medical and remote sensing applications. In this paper, a new multivariable MI approach that exploits complementary information of inherently registered LiDAR DSM and intensity data to improve the robustness of registering optical imagery and LiDAR point cloud, is presented. LiDAR DSM and intensity information has been utilised in measuring the similarity of LiDAR and optical imagery via the Combined MI. An effective histogramming technique is adopted to facilitate estimation of a 3D probability density function (pdf). In addition, a local similarity measure is introduced to decrease the complexity of optimisation at higher dimensions and computation cost. Therefore, the reliability of registration is improved due to the use of redundant observations of similarity. The performance of the proposed method for registration of satellite and aerial images with LiDAR data in urban and rural areas is experimentally evaluated and the results obtained are discussed.

Registration of Laser Scanning Point Clouds: A Review

PubMed Central

Cheng, Liang; Chen, Song; Xu, Hao; Wu, Yang; Li, Manchun

2018-01-01

The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles. PMID:29883397

Optimizing the Use of LiDAR for Hydraulic and Sediment Transport Model Development: Case Studies from Marin and Sonoma Counties, CA

NASA Astrophysics Data System (ADS)

Kobor, J. S.; O'Connor, M. D.; Sherwood, M. N.

2013-12-01

Effective floodplain management and restoration requires a detailed understanding of floodplain processes not readily achieved using standard one-dimensional hydraulic modeling approaches. The application of more advanced numerical models is, however, often limited by the relatively high costs of acquiring the high-resolution topographic data needed for model development using traditional surveying methods. The increasing availability of LiDAR data has the potential to significantly reduce these costs and thus facilitate application of multi-dimensional hydraulic models where budget constraints would have otherwise prohibited their use. The accuracy and suitability of LiDAR data for supporting model development can vary widely depending on the resolution of channel and floodplain features, the data collection density, and the degree of vegetation canopy interference among other factors. More work is needed to develop guidelines for evaluating LiDAR accuracy and determining when and how best the data can be used to support numerical modeling activities. Here we present two recent case studies where LiDAR datasets were used to support floodplain and sediment transport modeling efforts. One LiDAR dataset was collected with a relatively low point density and used to study a small stream channel in coastal Marin County and a second dataset was collected with a higher point density and applied to a larger stream channel in western Sonoma County. Traditional topographic surveying was performed at both sites which provided a quantitative means of evaluating the LiDAR accuracy. We found that with the lower point density dataset, the accuracy of the LiDAR varied significantly between the active stream channel and floodplain whereas the accuracy across the channel/floodplain interface was more uniform with the higher density dataset. Accuracy also varied widely as a function of the density of the riparian vegetation canopy. We found that coupled 1- and 2-dimensional hydraulic models whereby the active channel is simulated in 1-dimension and the floodplain in 2-dimensions provided the best means of utilizing the LiDAR data to evaluate existing conditions and develop alternative flood hazard mitigation and habitat restoration strategies. Such an approach recognizes the limitations of the LiDAR data within active channel areas with dense riparian cover and is cost-effective in that it allows field survey efforts to focus primarily on characterizing active stream channel areas. The multi-dimensional modeling approach also conforms well to the physical realties of the stream system whereby in-channel flows can generally be well-described as a one-dimensional flow problem and floodplain flows are often characterized by multiple and often poorly understood flowpaths. The multi-dimensional modeling approach has the additional advantages of allowing for accurate simulation of the effects of hydraulic structures using well-tested one-dimensional formulae and minimizing the computational burden of the models by not requiring the small spatial resolutions necessary to resolve the geometries of small stream channels in two-dimensions.

Mapping Understory Trees Using Airborne Discrete-Return LIDAR Data

NASA Astrophysics Data System (ADS)

Korpela, I.; Hovi, A.; Morsdorf, F.

2011-09-01

Understory trees in multi-layer stands are often ignored in forest inventories. Information about them would benefit silviculture, wood procurement and biodiversity management. Cost-efficient inventory methods for the assessment of the presence, density, species- and size-distributions are called for. LiDAR remote sensing is a promising addition to field work. Unlike in passive image data, in which the signals from multiple layers mix, the 3D position of each hot-spot reflection is known in LiDAR data. The overstory however prevents from obtaining a wall-to-wall sample of understory, and measurements are subject to transmission losses. Discriminating between the crowns of dominant and suppressed trees can also be challenging. We examined the potential of LiDAR for the mapping of the understory trees in Scots pine stands (62°N, 24°E), using carefully georeferenced reference data and several LiDAR data sets. We present results that highlight differences in echo-triggering between sensors that affect the near-ground height data. A conceptual model for the transmission losses in the overstory was created and formulated into simple compensation models that reduced the intensity variation in second- and third return data. The task is highly ill-posed in discrete-return LiDAR data, and our models employed the geometry of the overstory as well as the intensity of previous returns. We showed that even first-return data in the understory is subject to losses in the overstory that did not trigger an echo. Even with compensation of the losses, the intensity data was deemed of low value in species discrimination. Area-based LiDAR height metrics that were derived from the data belonging to the crown volume of the understory showed reasonable correlation with the density and mean height of the understory trees. Assessment of the species seems out of reach in discrete-return LiDAR data, which is a drastic drawback.

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

PubMed

Delagrange, Sylvain; Rochon, Pascal

2011-10-01

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 estimating 2-D and 3-D parameters of a hybrid poplar sapling. 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. 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. 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.

Novel Methods for Measuring LiDAR

NASA Astrophysics Data System (ADS)

Ayrey, E.; Hayes, D. J.; Fraver, S.; Weiskittel, A.; Cook, B.; Kershaw, J.

2017-12-01

The estimation of forest biometrics from airborne LiDAR data has become invaluable for quantifying forest carbon stocks, forest and wildlife ecology research, and sustainable forest management. The area-based approach is arguably the most common method for developing enhanced forest inventories from LiDAR. It involves taking a series of vertical height measurements of the point cloud, then using those measurements with field measured data to develop predictive models. Unfortunately, there is considerable variation in methodology for collecting point cloud data, which can vary in pulse density, seasonality, canopy penetrability, and instrument specifications. Today there exists a wealth of public LiDAR data, however the variation in acquisition parameters makes forest inventory prediction by traditional means unreliable across the different datasets. The goal of this project is to test a series of novel point cloud measurements developed along a conceptual spectrum of human interpretability, and then to use the best measurements to develop regional enhanced forest inventories on Northern New England's and Atlantic Canada's public LiDAR. Similarly to a field-based inventory, individual tree crowns are being segmented, and summary statistics are being used as covariates. Established competition and structural indices are being generated using each tree's relationship to one another, whilst existing allometric equations are being used to estimate diameter and biomass of each tree measured in the LiDAR. Novel metrics measuring light interception, clusteredness, and rugosity are also being measured as predictors. On the other end of the human interpretability spectrum, convolutional neural networks are being employed to directly measure both the canopy height model, and the point clouds by scanning each using two and three dimensional kernals trained to identify features useful for predicting biological attributes such as biomass. Predictive models will be trained and tested against one another using 28 different sites and over 42 different LiDAR acquisitions. The optimal model will then be used to generate regional wall-to-wall forest inventories at a 10 m resolution.

Features of Point Clouds Synthesized from Multi-View ALOS/PRISM Data and Comparisons with LiDAR Data in Forested Areas

NASA Technical Reports Server (NTRS)

Ni, Wenjian; Ranson, Kenneth Jon; Zhang, Zhiyu; Sun, Guoqing

2014-01-01

LiDAR waveform data from airborne LiDAR scanners (ALS) e.g. the Land Vegetation and Ice Sensor (LVIS) havebeen successfully used for estimation of forest height and biomass at local scales and have become the preferredremote sensing dataset. However, regional and global applications are limited by the cost of the airborne LiDARdata acquisition and there are no available spaceborne LiDAR systems. Some researchers have demonstrated thepotential for mapping forest height using aerial or spaceborne stereo imagery with very high spatial resolutions.For stereo imageswith global coverage but coarse resolution newanalysis methods need to be used. Unlike mostresearch based on digital surface models, this study concentrated on analyzing the features of point cloud datagenerated from stereo imagery. The synthesizing of point cloud data from multi-view stereo imagery increasedthe point density of the data. The point cloud data over forested areas were analyzed and compared to small footprintLiDAR data and large-footprint LiDAR waveform data. The results showed that the synthesized point clouddata from ALOSPRISM triplets produce vertical distributions similar to LiDAR data and detected the verticalstructure of sparse and non-closed forests at 30mresolution. For dense forest canopies, the canopy could be capturedbut the ground surface could not be seen, so surface elevations from other sourceswould be needed to calculatethe height of the canopy. A canopy height map with 30 m pixels was produced by subtracting nationalelevation dataset (NED) fromthe averaged elevation of synthesized point clouds,which exhibited spatial featuresof roads, forest edges and patches. The linear regression showed that the canopy height map had a good correlationwith RH50 of LVIS data with a slope of 1.04 and R2 of 0.74 indicating that the canopy height derived fromPRISM triplets can be used to estimate forest biomass at 30 m resolution.

Efficient LIDAR Point Cloud Data Managing and Processing in a Hadoop-Based Distributed Framework

NASA Astrophysics Data System (ADS)

Wang, C.; Hu, F.; Sha, D.; Han, X.

2017-10-01

Light Detection and Ranging (LiDAR) is one of the most promising technologies in surveying and mapping city management, forestry, object recognition, computer vision engineer and others. However, it is challenging to efficiently storage, query and analyze the high-resolution 3D LiDAR data due to its volume and complexity. In order to improve the productivity of Lidar data processing, this study proposes a Hadoop-based framework to efficiently manage and process LiDAR data in a distributed and parallel manner, which takes advantage of Hadoop's storage and computing ability. At the same time, the Point Cloud Library (PCL), an open-source project for 2D/3D image and point cloud processing, is integrated with HDFS and MapReduce to conduct the Lidar data analysis algorithms provided by PCL in a parallel fashion. The experiment results show that the proposed framework can efficiently manage and process big LiDAR data.

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.

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

The potential of LiDAR in ecohydrology is significant as characterising catchment vegetation is crucial to accurate estimation of evapotranspiration (ET). While this may be done at large scales for model parameterisation, stand-scale applications are equally appropriate where traditional methods of measurement of LAI or sapwood areas are time consuming and reliant on assumptions of representative sampling. This is particularly challenging in mountain forests where aspect, soil properties and energy budgets can vary significantly, reflected in the vegetation or where there are changes in the spatial distribution of structural attributes following disturbance. Recent research has investigated the spatial distribution of ET in a eucalypt forest in SE Australia using plot-scale sapflow, interception and forest floor ET measurements. LiDAR was used scale up these measurements. LiDAR (0.16 m scanner footprint) canopy indices were correlated via stepwise regression with 4 water use scalars: basal area (BA), sapwood area (SA), leaf area index (LAI) and canopy coverage (C), with Hmed, Hmean, H80, H95 the best predictors. Combining these indices with empirical relationships between SA and BA, and SA and transpiration (T), and inventory plot 'ground truthing' transpiration was estimated across the 1.3 km2 catchment. Interception was scaled via the Gash model with LiDAR derived inputs. The up-scaling showed a significant variability in the spatial distribution of ET, related to the distribution of SA. The use of LiDAR meant scaling could be achieved at an appropriate spatial scale (20 x 20 m) to the measurements. The second example is the use of airborne LiDAR in developing growth forest models for hydrologic modeling. LiDAR indices were used to stratify multilayered forests using mixed-effect models with a wide range of theoretical distribution functions. When combined with historical plot-scale inventory data we show demonstrated improved growth modeling over traditional inventory methods.These models can be used to parameterize hydrologic models to explore disturbance and age-related ET changes, and develop spatial-temporal maps of ET based on accurate representation of sapwood areas in complex terrain. The third example involves analyses of stand growth and long term streamflow response to thinning treatments in eucalyptus regnans forests. These forests have a strong age-streamflow relationship that can lead to streamflow declines as disturbed stands regrow. A set of thinning treatments in small experimental catchments (uniform, strip and understorey removal) were implemented in 1978-1982. The streamflow analysis supported early findings that flows increase and then relaxed, but also detected a flow decline below expected undisturbed levels for most catchments. Airborne LiDAR was used to analyse the structural recovery of treated stands, estimate LAI and canopy coverage via gap-fraction analysis, and scale ET measurements. The LiDAR data revealed the association of treatment type and regrowth and demonstrated that despite a net reduction in overstorey stem density, stand LAI had recovered and may explain the flow response. Finally, new terrestrial LiDAR instruments are being used in conjunction with eddy-covariance flux tower and sapflow measurement to measure fine temporal scale carbon-water dynamics. These instruments can be combined with airborne derived data to produce 3 dimensional canopy profile for linkage with ET processes.

An Online 3D Database System for Endangered Architectural and Archaeological Heritage in the South-Eastern Mediterranean

NASA Astrophysics Data System (ADS)

Abate, D.; Avgousti, A.; Faka, M.; Hermon, S.; Bakirtzis, N.; Christofi, P.

2017-10-01

This study compares performance of aerial image based point clouds (IPCs) and light detection and ranging (LiDAR) based point clouds in detection of thinnings and clear cuts in forests. IPCs are an appealing method to update forest resource data, because of their accuracy in forest height estimation and cost-efficiency of aerial image acquisition. We predicted forest changes over a period of three years by creating difference layers that displayed the difference in height or volume between the initial and subsequent time points. Both IPCs and LiDAR data were used in this process. The IPCs were constructed with the Semi-Global Matching (SGM) algorithm. Difference layers were constructed by calculating differences in fitted height or volume models or in canopy height models (CHMs) from both time points. The LiDAR-derived digital terrain model (DTM) was used to scale heights to above ground level. The study area was classified in logistic regression into the categories ClearCut, Thinning or NoChange with the values from the difference layers. We compared the predicted changes with the true changes verified in the field, and obtained at best a classification accuracy for clear cuts 93.1 % with IPCs and 91.7 % with LiDAR data. However, a classification accuracy for thinnings was only 8.0 % with IPCs. With LiDAR data 41.4 % of thinnings were detected. In conclusion, the LiDAR data proved to be more accurate method to predict the minor changes in forests than IPCs, but both methods are useful in detection of major changes.

A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

NASA Technical Reports Server (NTRS)

Jung, Jinha; Pasolli, Edoardo; Prasad, Saurabh; Tilton, James C.; Crawford, Melba M.

2014-01-01

Acquiring current, accurate land-use information is critical for monitoring and understanding the impact of anthropogenic activities on natural environments.Remote sensing technologies are of increasing importance because of their capability to acquire information for large areas in a timely manner, enabling decision makers to be more effective in complex environments. Although optical imagery has demonstrated to be successful for land cover classification, active sensors, such as light detection and ranging (LiDAR), have distinct capabilities that can be exploited to improve classification results. However, utilization of LiDAR data for land cover classification has not been fully exploited. Moreover, spatial-spectral classification has recently gained significant attention since classification accuracy can be improved by extracting additional information from the neighboring pixels. Although spatial information has been widely used for spectral data, less attention has been given to LiDARdata. In this work, a new framework for land cover classification using discrete return LiDAR data is proposed. Pseudo-waveforms are generated from the LiDAR data and processed by hierarchical segmentation. Spatial featuresare extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. The proposed framework is validated experimentally on a real dataset acquired in an urban area. Better classification results are exhibited by the proposed framework compared to the cases in which basic LiDAR products such as digital surface model and intensity image are used. Moreover, the proposed region-based feature extraction strategy results in improved classification accuracies in comparison with a more traditional window-based approach.

DATA ATTRIBUTE RATING SYSTEM

EPA Science Inventory

The paper discusses a data attribute rating system (DARS), developed by EPA to assist in evaluating data associated with emission inventories. he paper presents DARS for evaluation by the potential user community. ARS was originally conceived as a method for evaluating country-sp...

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 that native vegetation are responsive and resilient to high-severity fire, and show the usefulness of remote sensing tools such as LiDAR to monitor post-fire vegetation recovery over large areas in situ. © 2017 by the Ecological Society of America.

Development of DArT markers and assessment of diversity in Fusarium oxysporum f. sp. ciceris, wilt pathogen of chickpea (Cicer arietinum L.).

PubMed

Sharma, Mamta; Nagavardhini, Avuthu; Thudi, Mahendar; Ghosh, Raju; Pande, Suresh; Varshney, Rajeev K

2014-06-10

Fusarium oxysporum f. sp. ciceris (Foc), the causal agent of Fusarium wilt of chickpea is highly variable and frequent recurrence of virulent forms have affected chickpea production and exhausted valuable genetic resources. The severity and yield losses of Fusarium wilt differ from place to place owing to existence of physiological races among isolates. Diversity study of fungal population associated with a disease plays a major role in understanding and devising better disease control strategies. The advantages of using molecular markers to understand the distribution of genetic diversity in Foc populations is well understood. The recent development of Diversity Arrays Technology (DArT) offers new possibilities to study the diversity in pathogen population. In this study, we developed DArT markers for Foc population, analysed the genetic diversity existing within and among Foc isolates, compared the genotypic and phenotypic diversity and infer the race scenario of Foc in India. We report the successful development of DArT markers for Foc and their utility in genotyping of Foc collections representing five chickpea growing agro-ecological zones of India. The DArT arrays revealed a total 1,813 polymorphic markers with an average genotyping call rate of 91.16% and a scoring reproducibility of 100%. Cluster analysis, principal coordinate analysis and population structure indicated that the different isolates of Foc were partially classified based on geographical source. Diversity in Foc population was compared with the phenotypic variability and it was found that DArT markers were able to group the isolates consistent with its virulence group. A number of race-specific unique and rare alleles were also detected. The present study generated significant information in terms of pathogenic and genetic diversity of Foc which could be used further for development and deployment of region-specific resistant cultivars of chickpea. The DArT markers were proved to be a powerful diagnostic tool to study the genotypic diversity in Foc. The high number of DArT markers allowed a greater resolution of genetic differences among isolates and enabled us to examine the extent of diversity in the Foc population present in India, as well as provided support to know the changing race scenario in Foc population.

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 incorrect feature reconstruction; and 6) significant variability in both accuracy of LiDAR offset measurements (relative to field-based measurements) and reported uncertainty between workers, mostly based on differing interpretations of geomorphic complexity.

Modelisation de l'architecture des forets pour ameliorer la teledetection des attributs forestiers

NASA Astrophysics Data System (ADS)

Cote, Jean-Francois

The quality of indirect measurements of canopy structure, from in situ and satellite remote sensing, is based on knowledge of vegetation canopy architecture. Technological advances in ground-based, airborne or satellite remote sensing can now significantly improve the effectiveness of measurement programs on forest resources. The structure of vegetation canopy describes the position, orientation, size and shape of elements of the canopy. The complexity of the canopy in forest environments greatly limits our ability to characterize forest structural attributes. Architectural models have been developed to help the interpretation of canopy structural measurements by remote sensing. Recently, the terrestrial LiDAR systems, or TLiDAR (Terrestrial Light Detection and Ranging), are used to gather information on the structure of individual trees or forest stands. The TLiDAR allows the extraction of 3D structural information under the canopy at the centimetre scale. The methodology proposed in my Ph.D. thesis is a strategy to overcome the weakness in the structural sampling of vegetation cover. The main objective of the Ph.D. is to develop an architectural model of vegetation canopy, called L-Architect (LiDAR data to vegetation Architecture), and to focus on the ability to document forest sites and to get information on canopy structure from remote sensing tools. Specifically, L-Architect reconstructs the architecture of individual conifer trees from TLiDAR data. Quantitative evaluation of L-Architect consisted to investigate (i) the structural consistency of the reconstructed trees and (ii) the radiative coherence by the inclusion of reconstructed trees in a 3D radiative transfer model. Then, a methodology was developed to quasi-automatically reconstruct the structure of individual trees from an optimization algorithm using TLiDAR data and allometric relationships. L-Architect thus provides an explicit link between the range measurements of TLiDAR and structural attributes of individual trees. L-Architect has finally been applied to model the architecture of forest canopy for better characterization of vertical and horizontal structure with airborne LiDAR data. This project provides a mean to answer requests of detailed canopy architectural data, difficult to obtain, to reproduce a variety of forest covers. Because of the importance of architectural models, L-Architect provides a significant contribution for improving the capacity of parameters' inversion in vegetation cover for optical and lidar remote sensing. Mots-cles: modelisation architecturale, lidar terrestre, couvert forestier, parametres structuraux, teledetection.

Modelling vertical error in LiDAR-derived digital elevation models

NASA Astrophysics Data System (ADS)

Aguilar, Fernando J.; Mills, Jon P.; Delgado, Jorge; Aguilar, Manuel A.; Negreiros, J. G.; Pérez, José L.

2010-01-01

A hybrid theoretical-empirical model has been developed for modelling the error in LiDAR-derived digital elevation models (DEMs) of non-open terrain. The theoretical component seeks to model the propagation of the sample data error (SDE), i.e. the error from light detection and ranging (LiDAR) data capture of ground sampled points in open terrain, towards interpolated points. The interpolation methods used for infilling gaps may produce a non-negligible error that is referred to as gridding error. In this case, interpolation is performed using an inverse distance weighting (IDW) method with the local support of the five closest neighbours, although it would be possible to utilize other interpolation methods. The empirical component refers to what is known as "information loss". This is the error purely due to modelling the continuous terrain surface from only a discrete number of points plus the error arising from the interpolation process. The SDE must be previously calculated from a suitable number of check points located in open terrain and assumes that the LiDAR point density was sufficiently high to neglect the gridding error. For model calibration, data for 29 study sites, 200×200 m in size, belonging to different areas around Almeria province, south-east Spain, were acquired by means of stereo photogrammetric methods. The developed methodology was validated against two different LiDAR datasets. The first dataset used was an Ordnance Survey (OS) LiDAR survey carried out over a region of Bristol in the UK. The second dataset was an area located at Gador mountain range, south of Almería province, Spain. Both terrain slope and sampling density were incorporated in the empirical component through the calibration phase, resulting in a very good agreement between predicted and observed data (R2 = 0.9856 ; p < 0.001). In validation, Bristol observed vertical errors, corresponding to different LiDAR point densities, offered a reasonably good fit to the predicted errors. Even better results were achieved in the more rugged morphology of the Gador mountain range dataset. The findings presented in this article could be used as a guide for the selection of appropriate operational parameters (essentially point density in order to optimize survey cost), in projects related to LiDAR survey in non-open terrain, for instance those projects dealing with forestry applications.

New Visualization Techniques to Analyze Ultra-High Resolution Four-dimensional Surface Deformation Imagery Collected With Ground-based Tripod LiDAR

NASA Astrophysics Data System (ADS)

Kreylos, O.; Bawden, G. W.; Kellogg, L. H.

2005-12-01

We are developing a visualization application to display and interact with very large (tens of millions of points) four-dimensional point position datasets in an immersive environment such that point groups from repeated Tripod LiDAR (Light Detection And Ranging) surveys can be selected, measured, and analyzed for land surface change using 3D~interactions. Ground-based tripod or terrestrial LiDAR (T-LiDAR) can remotely collect ultra-high resolution (centimeter to subcentimeter) and accurate (± 4 mm) digital imagery of the scanned target, and at scanning rates of 2,000 (x, y, z, i) (3D~position~+ intensity) points per second over 7~million points can be collected for a given target in an hour. We developed a multiresolution point set data representation based on octrees to display large T-LiDAR point cloud datasets at the frame rates required for immersive display (between 60 Hz and 120 Hz). Data inside an observer's region of interest is shown in full detail, whereas data outside the field of view or far away from the observer is shown at reduced resolution to provide context. Using 3D input devices at the University of California Davis KeckCAVES, users can navigate large point sets, accurately select related point groups in two or more point sets by sweeping regions of space, and guide the software in deriving positional information from point groups to compute their displacements between surveys. We used this new software application in the KeckCAVES to analyze 4D T-LiDAR imagery from the June~1, 2005 Blue Bird Canyon landslide in Laguna Beach, southern California. Over 50~million (x, y, z, i) data points were collected between 10 and 21~days after the landslide to evaluate T-LiDAR as a natural hazards response tool. The visualization of the T-LiDAR scans within the immediate landslide showed minor readjustments in the weeks following the primarily landslide with no observable continued motion on the primary landslide. Recovery and demolition efforts across the landslide, such as the building of new roads and removal of unstable structures, are easily identified and assessed with the new software through the differencing of aligned imagery.

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.

A digital map of the high center (HC) and low center (LC) polygon boundaries delineated from high resolution LiDAR data for Barrow, Alaska

DOE Data Explorer

Gangodagamage, Chandana; Wullschleger, Stan

2014-07-03

This dataset represent a map of the high center (HC) and low center (LC) polygon boundaries delineated from high resolution LiDAR data for the arctic coastal plain at Barrow, Alaska. The polygon troughs are considered as the surface expression of the ice-wedges. The troughs are in lower elevations than the interior polygon. The trough widths were initially identified from LiDAR data, and the boundary between two polygons assumed to be located along the lowest elevations on trough widths between them.

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

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.

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

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

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

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.

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.

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

2018-03-01

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.

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.

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.

Octree-based indexing for 3D pointclouds within an Oracle Spatial DBMS

NASA Astrophysics Data System (ADS)

Schön, Bianca; Mosa, Abu Saleh Mohammad; Laefer, Debra F.; Bertolotto, Michela

2013-02-01

A large proportion of today's digital datasets have a spatial component. The effective storage and management of which poses particular challenges, especially with light detection and ranging (LiDAR), where datasets of even small geographic areas may contain several hundred million points. While in the last decade 2.5-dimensional data were prevalent, true 3-dimensional data are increasingly commonplace via LiDAR. They have gained particular popularity for urban applications including generation of city-scale maps, baseline data disaster management, and utility planning. Additionally, LiDAR is commonly used for flood plane identification, coastal-erosion tracking, and forest biomass mapping. Despite growing data availability, current spatial information systems do not provide suitable full support for the data's true 3D nature. Consequently, one system is needed to store the data and another for its processing, thereby necessitating format transformations. The work presented herein aims at a more cost-effective way for managing 3D LiDAR data that allows for storage and manipulation within a single system by enabling a new index within existing spatial database management technology. Implementation of an octree index for 3D LiDAR data atop Oracle Spatial 11g is presented, along with an evaluation showing up to an eight-fold improvement compared to the native Oracle R-tree index.

Geo-referenced digital data acquisition and processing system using LiDAR technology.

DOT National Transportation Integrated Search

2006-02-01

LiDAR technology, introduced in the late 90s, has received wide acceptance in airborne surveying as a leading : tool for obtaining high-quality surface data at decimeter-level vertical accuracy in an unprecedentedly short : turnaround time. State-of-...

Airborne LiDAR : a new source of traffic flow data : executive summary.

DOT National Transportation Integrated Search

2005-10-01

LiDAR (or airborne laser scanning) systems became a : dominant player in high-precision spatial data : acquisition in the late 90s. This new technology : quickly established itself as the main source of surface : information in commercial mapping,...

Airborne LiDAR : a new source of traffic flow data, executive summary report.

DOT National Transportation Integrated Search

2005-10-01

LiDAR (or airborne laser scanning) systems became a : dominant player in high-precision spatial data : acquisition in the late 90s. This new technology : quickly established itself as the main source of surface : information in commercial mapping,...

Airborne LiDAR : a new source of traffic flow data.

DOT National Transportation Integrated Search

2005-10-01

LiDAR (or airborne laser scanning) systems became a dominant player in high-precision spatial data acquisition : to efficiently create DEM/DSM in the late 90's. With increasing point density, new systems are now able to : support object extraction, s...

Airborne LiDAR : a new source of traffic flow data.

DOT National Transportation Integrated Search

2005-10-01

LiDAR (or airborne laser scanning) systems became a dominant player in high-precision spatial data acquisition : to efficiently create DEM/DSM in the late 90s. With increasing point density, new systems are now able to : support object extraction, ...

TScan : stationary LiDAR for traffic and safety studies : object detection and tracking.

DOT National Transportation Integrated Search

2016-08-01

The ability to accurately measure and cost-effectively collect traffic data at road intersections is needed to improve their safety and operations. This study investigates the feasibility of using laser ranging technology (LiDAR) for this purpose. Th...

Semi-automated stand delineation in Mediterranean Pinus sylvestris plantations through segmentation of LiDAR data: The influence of pulse density

NASA Astrophysics Data System (ADS)

Varo-Martínez, Mª Ángeles; Navarro-Cerrillo, Rafael M.; Hernández-Clemente, Rocío; Duque-Lazo, Joaquín

2017-04-01

Traditionally, forest-stand delineation has been assessed based on orthophotography. The application of LiDAR has improved forest management by providing high-spatial-resolution data on the vertical structure of the forest. The aim of this study was to develop and test a semi-automated algorithm for stands delineation in a plantation of Pinus sylvestris L. using LiDAR data. Three specific objectives were evaluated, i) to assess two complementary LiDAR metrics, Assmann dominant height and basal area, for the characterization of the structure of P. sylvestris Mediterranean forests based on object-oriented segmentation, ii) to evaluate the influence of the LiDAR pulse density on forest-stand delineation accuracy, and iii) to investigate the algorithmś effectiveness in the delineation of P. sylvestris stands for map prediction of Assmann dominant height and basal area. Our results show that it is possible to generate accurate P. sylvestris forest-stand segmentations using multiresolution or mean shift segmentation methods, even with low-pulse-density LiDAR - which is an important economic advantage for forest management. However, eCognition multiresolution methods provided better results than the OTB (Orfeo Tool Box) for stand delineation based on dominant height and basal area estimations. Furthermore, the influence of pulse density on the results was not statistically significant in the basal area calculations. However, there was a significant effect of pulse density on Assmann dominant height [F2,9595 = 5.69, p = 0.003].for low pulse density. We propose that the approach shown here should be considered for stand delineation in other large Pinus plantations in Mediterranean regions with similar characteristics.

Recruiting Conventional Tree Architecture Models into State-of-the-Art LiDAR Mapping for Investigating Tree Growth Habits in Structure.

PubMed

Lin, Yi; Jiang, Miao; Pellikka, Petri; Heiskanen, Janne

2018-01-01

Mensuration of tree growth habits is of considerable importance for understanding forest ecosystem processes and forest biophysical responses to climate changes. However, the complexity of tree crown morphology that is typically formed after many years of growth tends to render it a non-trivial task, even for the state-of-the-art 3D forest mapping technology-light detection and ranging (LiDAR). Fortunately, botanists have deduced the large structural diversity of tree forms into only a limited number of tree architecture models, which can present a-priori knowledge about tree structure, growth, and other attributes for different species. This study attempted to recruit Hallé architecture models (HAMs) into LiDAR mapping to investigate tree growth habits in structure. First, following the HAM-characterized tree structure organization rules, we run the kernel procedure of tree species classification based on the LiDAR-collected point clouds using a support vector machine classifier in the leave-one-out-for-cross-validation mode. Then, the HAM corresponding to each of the classified tree species was identified based on expert knowledge, assisted by the comparison of the LiDAR-derived feature parameters. Next, the tree growth habits in structure for each of the tree species were derived from the determined HAM. In the case of four tree species growing in the boreal environment, the tests indicated that the classification accuracy reached 85.0%, and their growth habits could be derived by qualitative and quantitative means. Overall, the strategy of recruiting conventional HAMs into LiDAR mapping for investigating tree growth habits in structure was validated, thereby paving a new way for efficiently reflecting tree growth habits and projecting forest structure dynamics.

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.

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.

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

Recruiting Conventional Tree Architecture Models into State-of-the-Art LiDAR Mapping for Investigating Tree Growth Habits in Structure

PubMed Central

Lin, Yi; Jiang, Miao; Pellikka, Petri; Heiskanen, Janne

2018-01-01

Mensuration of tree growth habits is of considerable importance for understanding forest ecosystem processes and forest biophysical responses to climate changes. However, the complexity of tree crown morphology that is typically formed after many years of growth tends to render it a non-trivial task, even for the state-of-the-art 3D forest mapping technology—light detection and ranging (LiDAR). Fortunately, botanists have deduced the large structural diversity of tree forms into only a limited number of tree architecture models, which can present a-priori knowledge about tree structure, growth, and other attributes for different species. This study attempted to recruit Hallé architecture models (HAMs) into LiDAR mapping to investigate tree growth habits in structure. First, following the HAM-characterized tree structure organization rules, we run the kernel procedure of tree species classification based on the LiDAR-collected point clouds using a support vector machine classifier in the leave-one-out-for-cross-validation mode. Then, the HAM corresponding to each of the classified tree species was identified based on expert knowledge, assisted by the comparison of the LiDAR-derived feature parameters. Next, the tree growth habits in structure for each of the tree species were derived from the determined HAM. In the case of four tree species growing in the boreal environment, the tests indicated that the classification accuracy reached 85.0%, and their growth habits could be derived by qualitative and quantitative means. Overall, the strategy of recruiting conventional HAMs into LiDAR mapping for investigating tree growth habits in structure was validated, thereby paving a new way for efficiently reflecting tree growth habits and projecting forest structure dynamics. PMID:29515616

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.

A LiDAR and IMU Integrated Indoor Navigation System for UAVs and Its Application in Real-Time Pipeline Classification

PubMed Central

Kumar, G. Ajay; Patil, Ashok Kumar; Patil, Rekha; Park, Seong Sill; Chai, Young Ho

2017-01-01

Mapping the environment of a vehicle and localizing a vehicle within that unknown environment are complex issues. Although many approaches based on various types of sensory inputs and computational concepts have been successfully utilized for ground robot localization, there is difficulty in localizing an unmanned aerial vehicle (UAV) due to variation in altitude and motion dynamics. This paper proposes a robust and efficient indoor mapping and localization solution for a UAV integrated with low-cost Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) sensors. Considering the advantage of the typical geometric structure of indoor environments, the planar position of UAVs can be efficiently calculated from a point-to-point scan matching algorithm using measurements from a horizontally scanning primary LiDAR. The altitude of the UAV with respect to the floor can be estimated accurately using a vertically scanning secondary LiDAR scanner, which is mounted orthogonally to the primary LiDAR. Furthermore, a Kalman filter is used to derive the 3D position by fusing primary and secondary LiDAR data. Additionally, this work presents a novel method for its application in the real-time classification of a pipeline in an indoor map by integrating the proposed navigation approach. Classification of the pipeline is based on the pipe radius estimation considering the region of interest (ROI) and the typical angle. The ROI is selected by finding the nearest neighbors of the selected seed point in the pipeline point cloud, and the typical angle is estimated with the directional histogram. Experimental results are provided to determine the feasibility of the proposed navigation system and its integration with real-time application in industrial plant engineering. PMID:28574474

Canadian and U.S. Cooperation for the development of standards and specifications for emerging mapping technologies

USGS Publications Warehouse

Habib, A.; Jarvis, A.; Al-Durgham, M. M.; Lay, J.; Quackenbush, P.; Stensaas, G.; Moe, D.

2007-01-01

The mapping community is witnessing significant advances in available sensors, such as medium format digital cameras (MFDC) and Light Detection and Ranging (LiDAR) systems. In this regard, the Digital Photogrammetry Research Group (DPRG) of the Department of Geomatics Engineering at the University of Calgary has been actively involved in the development of standards and specifications for regulating the use of these sensors in mapping activities. More specifically, the DPRG has been working on developing new techniques for the calibration and stability analysis of medium format digital cameras. This research is essential since these sensors have not been developed with mapping applications in mind. Therefore, prior to their use in Geomatics activies, new standards should be developed to ensure the quality of the developed products. In another front, the persistent improvement in direct geo-referencing technology has led to an expansion in the use of LiDAR systems for the acquisition of dense and accurate surface information. However, the processing of the raw LiDAR data (e.g., ranges, mirror angles, and navigation data) remains a non-transparent process that is proprietary to the manufacturers of LiDAR systems. Therefore, the DPRG has been focusing on the development of quality control procedures to quantify the accuracy of LiDAR output in the absence of initial system measurements. This paper presents a summary of the research conducted by the DPRG together with the British Columbia Base Mapping and Geomatic Services (BMGS) and the United States Geological Survey (USGS) for the development of quality assurance and quality control procedures for emerging mapping technologies. The outcome of this research will allow for the possiblity of introducing North American Standards and Specifications to regulate the use of MFDC and LiDAR systems in the mapping industry.

Mobile hybrid LiDAR & infrared sensing for natural gas pipeline monitoring compendium.

DOT National Transportation Integrated Search

2016-01-01

This item consists of several documents that were created throughout the Mobile Hybrid LiDAR & Infrared Sensing for Natural Gas Pipeline Monitoring project, No. RITARS-14-H-RUT, which was conducted from January 15, 2014 to June 30, 2016. Documents in...

47 CFR 25.164 - Milestones.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Milestones. (a) Licensees of geostationary orbit satellite systems other than DBS and DARS satellite systems...) Licensees of non-geostationary orbit satellite systems other than DBS and DARS satellite systems licensed on... placed in the authorized orbital location or non-geostationary orbit(s) and that in-orbit operation of...

Airborne LiDAR : a new source of traffic flow data, research implementation plan.

DOT National Transportation Integrated Search

2005-10-01

LiDAR (or airborne laser scanning) systems became a dominant player in high-precision spatial data acquisition in the late 90's. This new technology quickly established itself as the main source of surface information in commercial mapping, deliverin...

a Comparitive Study Using Geometric and Vertical Profile Features Derived from Airborne LIDAR for Classifying Tree Genera

NASA Astrophysics Data System (ADS)

Ko, C.; Sohn, G.; Remmel, T. K.

2012-07-01

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

Typical Applications of Airborne LIDAR Technolagy in Geological Investigation

NASA Astrophysics Data System (ADS)

Zheng, X.; Xiao, C.

2018-05-01

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

The deethylatrazine/atrazine ratio as an indicator of the onset of the spring flush of herbicides into surface water of the Midwestern United States

USGS Publications Warehouse

Thurman, E.M.; Fallon, J.D.

1996-01-01

The ratio of deethylatrazine to atrazine (DAR) may be used to record the first major runoff of herbicides from non-point-source corn fields to surface water in the Midwestern United States. The DAR dramatically decreases from ∼0.5 to < 0.1 upon application of herbicide and the first major runoff event of a basin. The DAR then gradually increases to values of approximately 0.4–0.6 during the harvest season. Furthermore, the DAR may be used in studies of surface water movement to give a temporal indicator of water moving into reservoirs for possible storage of herbicides. It is hypothesized that deethylatrazine, which accounts for only 6% of the degradation of atrazine, becomes a significant metabolite in surface water (∼ 50% of parent compound) because of its selective removal from soil. This removal process may be an important concept for consideration in studies of herbicide contamination of rivers and reservoirs.

The Registration and Segmentation of Heterogeneous Laser Scanning Data

NASA Astrophysics Data System (ADS)

Al-Durgham, Mohannad M.

Light Detection And Ranging (LiDAR) mapping has been emerging over the past few years as a mainstream tool for the dense acquisition of three dimensional point data. Besides the conventional mapping missions, LiDAR systems have proven to be very useful for a wide spectrum of applications such as forestry, structural deformation analysis, urban mapping, and reverse engineering. The wide application scope of LiDAR lead to the development of many laser scanning technologies that are mountable on multiple platforms (i.e., airborne, mobile terrestrial, and tripod mounted), this caused variations in the characteristics and quality of the generated point clouds. As a result of the increased popularity and diversity of laser scanners, one should address the heterogeneous LiDAR data post processing (i.e., registration and segmentation) problems adequately. Current LiDAR integration techniques do not take into account the varying nature of laser scans originating from various platforms. In this dissertation, the author proposes a methodology designed particularly for the registration and segmentation of heterogeneous LiDAR data. A data characterization and filtering step is proposed to populate the points' attributes and remove non-planar LiDAR points. Then, a modified version of the Iterative Closest Point (ICP), denoted by the Iterative Closest Projected Point (ICPP) is designed for the registration of heterogeneous scans to remove any misalignments between overlapping strips. Next, a region-growing-based heterogeneous segmentation algorithm is developed to ensure the proper extraction of planar segments from the point clouds. Validation experiments show that the proposed heterogeneous registration can successfully align airborne and terrestrial datasets despite the great differences in their point density and their noise level. In addition, similar testes have been conducted to examine the heterogeneous segmentation and it is shown that one is able to identify common planar features in airborne and terrestrial data without resampling or manipulating the data in any way. The work presented in this dissertation provides a framework for the registration and segmentation of airborne and terrestrial laser scans which has a positive impact on the completeness of the scanned feature. Therefore, the derived products from these point clouds have higher accuracy as seen in the full manuscript.

Near-Field Deformation Associated with the South Napa Earthquake (M 6.0) Using Differential Airborne LiDAR

NASA Astrophysics Data System (ADS)

Hudnut, K. W.; Glennie, C. L.; Brooks, B. A.; Hauser, D. L.; Ericksen, T.; Boatwright, J.; Rosinski, A.; Dawson, T. E.; Mccrink, T. P.; Mardock, D. K.; Hoirup, D. F., Jr.; Bray, J.

2014-12-01

Pre-earthquake airborne LiDAR coverage exists for the area impacted by the M 6.0 South Napa earthquake. The Napa watershed data set was acquired in 2003, and data sets were acquired in other portions of the impacted area in 2007, 2010 and 2014. The pre-earthquake data are being assessed and are of variable quality and point density. Following the earthquake, a coalition was formed to enable rapid acquisition of post-earthquake LiDAR. Coordination of this coalition took place through the California Earthquake Clearinghouse; consequently, a commercial contract was organized by Department of Water Resources that allowed for the main fault rupture and damaged Browns Valley area to be covered 16 days after the earthquake at a density of 20 points per square meter over a 20 square kilometer area. Along with the airborne LiDAR, aerial imagery was acquired and will be processed to form an orthomosaic using the LiDAR-derived DEM. The 'Phase I' airborne data were acquired using an Optech Orion M300 scanner, an Applanix 200 GPS-IMU, and a DiMac ultralight medium format camera by Towill. These new data, once delivered, will be differenced against the pre-earthquake data sets using a newly developed algorithm for point cloud matching, which is improved over prior methods by accounting for scan geometry error sources. Proposed additional 'Phase II' coverage would allow repeat-pass, post-earthquake coverage of the same area of interest as in Phase I, as well as an addition of up to 4,150 square kilometers that would potentially allow for differential LiDAR assessment of levee and bridge impacts at a greater distance from the earthquake source. Levee damage was reported up to 30 km away from the epicenter, and proposed LiDAR coverage would extend up to 50 km away and cover important critical lifeline infrastructure in the western Sacramento River delta, as well as providing full post-earthquake repeat-pass coverage of the Napa watershed to study transient deformation.

Gene expression analysis in zebrafish embryos: a potential approach to predict effect concentrations in the fish early life stage test.

PubMed

Weil, Mirco; Scholz, Stefan; Zimmer, Michaela; Sacher, Frank; Duis, Karen

2009-09-01

Based on the hypothesis that analysis of gene expression could be used to predict chronic fish toxicity, the zebrafish (Danio rerio) embryo test (DarT), developed as a replacement method for the acute fish test, was expanded to a gene expression D. rerio embryo test (Gene-DarT). The effects of 14 substances on lethal and sublethal endpoints of the DarT and on expression of potential marker genes were investigated: the aryl hydrocarbon receptor 2, cytochrome P450 1A (cypla), heat shock protein 70, fizzy-related protein 1, the transcription factors v-maf musculoaponeurotic fibrosarcoma oncogene family protein g (avian) 1 and NF-E2-p45-related factor, and heme oxygenase 1 (hmox1). After exposure of zebrafish embryos for 48 h, differential gene expression was evaluated using reverse transcriptase-polymerase chain reaction, gel electrophoresis, and densitometric analysis of the gels. All tested compounds significantly affected the expression of at least one potential marker gene, with cyp1a and hmox1 being most sensitive. Lowest-observed-effect concentrations (LOECs) for gene expression were below concentrations resulting in 10% lethal effects in the DarT. For 10 (3,4- and 3,5-dichloroaniline, 1,4-dichlorobenzene, 2,4-dinitrophenol, atrazine, parathion-ethyl, chlorotoluron, genistein, 4-nitroquinoline-1-oxide, and cadmium) out of the 14 tested substances, LOEC values derived with the Gene-DarT differ by a factor of less than 10 from LOEC values of fish early life stage tests with zebrafish. For pentachloroaniline and pentachlorobenzene, the Gene-DarT showed a 23- and 153-fold higher sensitivity, respectively, while for lindane, it showed a 13-fold lower sensitivity. For ivermectin, the Gene-DarT was by a factor of more than 1,000 less sensitive than the acute fish test. The results of the present study indicate that gene expression analysis in zebrafish embryos could principally be used to predict effect concentrations in the fish early life stage test.

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 Arctic river delta water surface and hydraulic attributes that would be challenging to acquire by other means. ?? 2011 John Wiley & Sons, Ltd.

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 the Iowa River corridor was developed using the United States Bureau of Reclamation SRH-2D hydraulic modeling software. The numerical model uses an unstructured numerical mesh and variable surface roughness, assigned according to observed land use and cover. The numerical model was calibrated using inundation extents and water surface elevations derived from the LiDAR data. It was also calibrated using high water marks and land survey data collected daily during the 2008 flood. The investigators compared the two calibrations to evaluate the benefit of high-resolution LiDAR data in improving the accuracy of a two-dimensional urban flood simulation.

High-resolution digital elevation dataset for Crater Lake National Park and vicinity, Oregon, based on LiDAR survey of August-September 2010 and bathymetric survey of July 2000

USGS Publications Warehouse

Robinson, Joel E.

2012-01-01

Crater Lake partially fills the caldera that formed approximately 7,700 years ago during the eruption of a 12,000-foot volcano known as Mount Mazama. The caldera-forming or climactic eruption of Mount Mazama devastated the surrounding landscape, left a thick deposit of pumice and ash in adjacent valleys, and spread a blanket of volcanic ash as far away as southern Canada. Because the Crater Lake region is potentially volcanically active, knowledge of past events is important to understanding hazards from future eruptions. Similarly, because the area is seismically active, documenting and evaluating geologic faults is critical to assessing hazards from earthquakes. As part of the American Recovery and Reinvestment Act (ARRA) of 2009, the U.S. Geological Survey was awarded funding for high-precision airborne LiDAR (Light Detection And Ranging) data collection at several volcanoes in the Cascade Range through the Oregon LiDAR Consortium, administered by the Oregon Department of Geology and Mineral Industries (DOGAMI). The Oregon LiDAR Consortium contracted with Watershed Sciences, Inc., to conduct the data collection surveys. Collaborating agencies participating with the Oregon LiDAR Consortium for data collection in the Crater Lake region include Crater Lake National Park (National Park Service) and the Federal Highway Administration. In the immediate vicinity of Crater Lake National Park, 798 square kilometers of LiDAR data were collected, providing a digital elevation dataset of the ground surface beneath forest cover with an average resolution of 1.6 laser returns/m2 and both vertical and horizontal accuracies of ±5 cm. The LiDAR data were mosaicked in this report with bathymetry of the lake floor of Crater Lake, collected in 2000 using high-resolution multibeam sonar in a collaborative effort between the U.S. Geological Survey, Crater Lake National Park, and the Center for Coastal and Ocean Mapping at the University of New Hampshire. The bathymetric survey collected 16 million soundings with a spatial resolution of 2 meters using an EM1002 system owned and operated by C&C Technologies, Inc. The combined LiDAR and bathymetric dataset has a cell size of 1 meter and will contribute to understanding past volcanic events and their deposits, recognizing of faults and volcanic landforms, and quantifying landscape modification during and after the next volcanic eruption at Crater Lake.

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.

A graph signal filtering-based approach for detection of different edge types on airborne lidar data

NASA Astrophysics Data System (ADS)

Bayram, Eda; Vural, Elif; Alatan, Aydin

2017-10-01

Airborne Laser Scanning is a well-known remote sensing technology, which provides a dense and highly accurate, yet unorganized point cloud of earth surface. During the last decade, extracting information from the data generated by airborne LiDAR systems has been addressed by many studies in geo-spatial analysis and urban monitoring applications. However, the processing of LiDAR point clouds is challenging due to their irregular structure and 3D geometry. In this study, we propose a novel framework for the detection of the boundaries of an object or scene captured by LiDAR. Our approach is motivated by edge detection techniques in vision research and it is established on graph signal filtering which is an exciting and promising field of signal processing for irregular data types. Due to the convenient applicability of graph signal processing tools on unstructured point clouds, we achieve the detection of the edge points directly on 3D data by using a graph representation that is constructed exclusively to answer the requirements of the application. Moreover, considering the elevation data as the (graph) signal, we leverage aerial characteristic of the airborne LiDAR data. The proposed method can be employed both for discovering the jump edges on a segmentation problem and for exploring the crease edges on a LiDAR object on a reconstruction/modeling problem, by only adjusting the filter characteristics.

Development of DArT-based PCR markers for selecting drought-tolerant spring barley.

PubMed

Fiust, Anna; Rapacz, Marcin; Wójcik-Jagła, Magdalena; Tyrka, Mirosław

2015-08-01

The tolerance of spring barley (Hordeum vulgare L.) cultivars to spring drought is an important agronomic trait affecting crop yield and quality in Poland. Therefore, breeders require new molecular markers to select plants with lower spring drought susceptibility. With the advent of genomic selection technology, simple molecular tools may still be applicable to screen material for markers of the most important traits and in-depth genome scanning. In previous studies, diversity arrays technology (DArT)-based genetic maps were constructed for F2 populations of Polish fodder and malt barley elite breeding lines, and 15 and 18 quantitative trait loci (QTLs) related to spring drought tolerance were identified, respectively. In this paper, we show the results of a conversion of 30 DArT markers corresponding to 11 QTLs into simple sequence repeat (SSR) and sequence tagged site (STS) markers. Twenty-two polymorphic markers were obtained, including 13 DArT-based SSRs. Additionally, 31 SSR markers, located in close proximity to the DArT markers, were selected from the GrainGenes database and tested. Further analyses of 24 advanced breeding lines with different drought tolerances confirmed that five out of the 30 converted markers, as well as three out of the 31 additional SSR markers, were effective in marker-assisted selection for drought tolerance. The possible function of clones related to these markers in drought tolerance is discussed.

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...

Guaranteed LiDAR-aided multi-object tracking at road intersections : USDOT Region V Regional University Transportation Center final report.

DOT National Transportation Integrated Search

2016-12-21

The ability to accurately measure and cost-effectively collect traffic data at road intersections is needed to improve their safety and operations. This study investigates the feasibility of using laser ranging technology (LiDAR) for this purpose. Th...

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...

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...

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…

Damage Assessment for Disaster Relief Efforts in Urban Areas Using Optical Imagery and LiDAR Data

NASA Astrophysics Data System (ADS)

Bahr, Thomas

2014-05-01

Imagery combined with LiDAR data and LiDAR-derived products provides a significant source of geospatial data which is of use in disaster mitigation planning. Feature rich building inventories can be constructed from tools with 3D rooftop extraction capabilities, and two dimensional outputs such as DSMs and DTMs can be used to generate layers to support routing efforts in Spatial Analyst and Network Analyst workflows. This allows us to leverage imagery and LiDAR tools for disaster mitigation or other scenarios. Software such as ENVI, ENVI LiDAR, and ArcGIS® Spatial and Network Analyst can therefore be used in conjunction to help emergency responders route ground teams in support of disaster relief efforts. This is exemplified by a case study against the background of the magnitude 7.0 earthquake that struck Haiti's capital city of Port-au-Prince on January 12, 2010. Soon after, both LiDAR data and an 8-band WorldView-2 scene were collected to map the disaster zone. The WorldView-2 scene was orthorectified and atmospherically corrected in ENVI prior to use. ENVI LiDAR was used to extract the DSM, DTM, buildings, and debris from the LiDAR data point cloud. These datasets provide a foundation for the 2D portion of the analysis. As the data was acquired over an area of dense urbanization, the majority of ground surfaces are roads, and standing buildings and debris are actually largely separable on the basis of elevation classes. To extract the road network of Port-au-Prince, the LiDAR-based feature height information was fused with the WorldView-2 scene, using ENVI's object-based feature extraction approach. This road network was converted to a network dataset for further analysis by the ArcGIS Network Analyst. For the specific case of Haiti, the distribution of blue tarps, used as accommodations for refugees, provided a spectrally distinct target. Pure blue tarp pixel spectra were selected from the WorldView-2 scene and input as a reference into ENVI's Spectral Angle Mapper (SAM) classification routine, together with a water-shadow mask to prevent false positives. The resulting blue tarp shape file was input into the ArcGIS Point Density tool, a feature of the Spatial Analyst toolbox. The final distribution map shows the density of blue tarps in Port-au-Prince and can be used to roughly delineate camps of refugees. Analogous, a debris density map was generated after separating the debris elevation class. The combination of this debris density map with the road network allowed to construct an intact road network of Port-au-Prince within the ArcGIS Network Analyst. Moderate density debris was used as a cost-increase barrier feature of the network dataset, and high density debris was used as a total obstruction barrier feature. Based on this information, two hypothetical routing scenarios were analyzed. One involved routing a ground team between two different refugee concentration zones. For the other, potential helicopter landing zones were computed from the LiDAR-derived products and added as facility features to the Network Analyst. Routes from the helicopter landing zones to refugee concentration access points were solved using closest facility logic, again making use of the obstructed network.

Three-dimensional feature extraction and geometric mappings for improved parameter estimation in forested terrain using airborne LiDAR data

NASA Astrophysics Data System (ADS)

Lee, Heezin

Scanning laser ranging technology is well suited for measuring point-to-point distances because of its ability to generate small beam divergences. As a result, many of the laser pulses emitted from airborne light detection and ranging (LiDAR) systems are able to reach the ground underneath tree canopies through small (10 cm scale) gaps in the foliage. Using high pulse rate lasers and fast optical scanners, airborne LiDAR systems can provide both high spatial resolution and canopy penetration, and these data have become more widely available in recent years for use in environmental and forestry applications. The small-footprint, discrete-return Airborne Laser Swath Mapping (ALSM) system at the University of Florida (UF) is used to directly measure ground surface elevations and the three-dimensional (3D) distribution of the vegetative material above the soil surface. Field of view geometric mappings are explored to find optical gaps inside forests. First, a method is developed to detect walking trails in natural forests that are obscured from above by the canopy. Several features are derived from the ALSM data and used to constrain the search space and infer the location of trails. Second, a robust and simple procedure for estimating intercepted photosynthetically active radiation (IPAR), which is an important measure of forest timber productivity and of daylight visibility in forested terrain, is presented. Simple scope functions that isolate the relevant LiDAR reflections between observer locations and the sun are defined and shown to give good agreement between the LiDAR-derived estimates and values of IPAR measured in situ. A conical scope function with an angular divergence from the centerline of +/-7° provided the best agreement with the in situ measurements. This scope function yielded remarkably consistent IPAR estimates for different pine species and growing conditions. The developed idea could be extended, through potential future work, to characterize the spatial distribution of attenuation of GPS (L-band) microwave signals and of detectability from the sky for military personnel operating in forested terrain. Measuring individual trees can provide valuable information about forests, and airborne LiDAR sensors have been recently used to identify individual trees and measure structural tree parameters. Past results, however, have been mixed because of reliance on interpolated (image) versions of the LiDAR measurements and search methods that do not adapt to variations in canopies. In this work, an adaptive clustering method is developed using 3D airborne LiDAR data acquired over two distinctly different managed pine forests in North-Central Florida, USA. A critical issue in isolating individual trees is determining the appropriate size of the moving window (search radius) when locating seed points. The proposed approach works directly on the 3D "cloud" of LiDAR points and adapts to irregular canopy sizes. The region growing step yields collectively exhaustive sets in an initial segmentation of tree canopies. An agglomerative clustering step is then used to merge clusters that represent parts of whole canopies using the locally varying height distribution. The overall tree detection accuracy achieved is 95.1% with no significant bias. The tree detection enables subsequent estimation of tree height and vertical crown length to an accuracy of better than 0.8 m and 1.5 m, respectively. Lastly, a compact representation of the different geometric characteristics of the segmented LiDAR points is introduced using spin images as a new tool that can potentially help tree detection in complex natural forests.

Promoting scientific collaboration and research through integrated social networking capabilities within the OpenTopography Portal

NASA Astrophysics Data System (ADS)

Nandigam, V.; Crosby, C. J.; Baru, C.

2009-04-01

LiDAR (Light Distance And Ranging) topography data offer earth scientists the opportunity to study the earth's surface at very high resolutions. As a result, the popularity of these data is growing dramatically. However, the management, distribution, and analysis of community LiDAR data sets is a challenge due to their massive size (multi-billion point, mutli-terabyte). We have also found that many earth science users of these data sets lack the computing resources and expertise required to process these data. We have developed the OpenTopography Portal to democratize access to these large and computationally challenging data sets. The OpenTopography Portal uses cyberinfrastructure technology developed by the GEON project to provide access to LiDAR data in a variety of formats. LiDAR data products available range from simple Google Earth visualizations of LiDAR-derived hillshades to 1 km2 tiles of standard digital elevation model (DEM) products as well as LiDAR point cloud data and user generated custom-DEMs. We have found that the wide spectrum of LiDAR users have variable scientific applications, computing resources and technical experience and thus require a data system with multiple distribution mechanisms and platforms to serve a broader range of user communities. Because the volume of LiDAR topography data available is rapidly expanding, and data analysis techniques are evolving, there is a need for the user community to be able to communicate and interact to share knowledge and experiences. To address this need, the OpenTopography Portal enables social networking capabilities through a variety of collaboration tools, web 2.0 technologies and customized usage pattern tracking. Fundamentally, these tools offer users the ability to communicate, to access and share documents, participate in discussions, and to keep up to date on upcoming events and emerging technologies. The OpenTopography portal achieves the social networking capabilities by integrating various software technologies and platforms. These include the Expression Engine Content Management System (CMS) that comes with pre-packaged collaboration tools like blogs and wikis, the Gridsphere portal framework that contains the primary GEON LiDAR System portlet with user job monitoring capabilities and a java web based discussion forum (Jforums) application all seamlessly integrated under one portal. The OpenTopography Portal also provides integrated authentication mechanism between the various CMS collaboration tools and the core gridsphere based portlets. The integration of these various technologies allows for enhanced user interaction capabilities within the portal. By integrating popular collaboration tools like discussion forums and blogs we can promote conversation and openness among users. The ability to ask question and share expertise in forum discussions allows users to easily find information and interact with users facing similar challenges. The OpenTopography Blog enables our domain experts to post ideas, news items, commentary, and other resources in order to foster discussion and information sharing. The content management capabilities of the portal allow for easy updates to information in the form of publications, documents, and news articles. Access to the most current information fosters better decision-making. As has become the standard for web 2.0 technologies, the OpenTopography Portal is fully RSS enabled to allow users of the portal to keep track of news items, forum discussions, blog updates, and system outages. We are currently exploring how the information captured by user and job monitoring components of the Gridsphere based GEON LiDAR System can be harnessed to provide a recommender system that will help users to identify appropriate processing parameters and to locate related documents and data. By seamlessly integrating the various platforms and technologies under one single portal, we can take advantage of popular online collaboration tools that are either stand alone or software platform restricted. The availability of these collaboration tools along with the data will foster more community interaction and increase the strength and vibrancy of the LiDAR topography user community.

Post-construction monitoring of a Core-Loc™ breakwater using tripod-based LiDAR

USGS Publications Warehouse

Podoski, Jessica H.; Bawden, Gerald W.; Bond, Sandra; Smith, Thomas D.; Foster, James

2010-01-01

The goal of the technology application described herein is to determine whether breakwater monitoring data collected using Tripod (or Terrestrial) Light Detection and Ranging (T-LiDAR) can give insight into processes such as how Core-Loc™ concrete armour units nest following construction, and in turn how settlement affects armour layer stability, concrete cap performance, and armour unit breakage.  A further objective is that this information can then be incorporated into the design of future projects using concrete armour units.  The results of this application of T-LiDAR, including the challenges encountered and the conclusions drawn regarding initial concrete armour unit movement will be presented in this paper.

Development of a LiDAR derived digital elevation model (DEM) as Input to a METRANS geographic information system (GIS).

DOT National Transportation Integrated Search

2011-05-01

This report describes an assessment of digital elevation models (DEMs) derived from : LiDAR data for a subset of the Ports of Los Angeles and Long Beach. A methodology : based on Monte Carlo simulation was applied to investigate the accuracy of DEMs ...

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...

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...

Molecular genetic characterization of lasquerella new industrial crop using DArTseq markers

USDA-ARS?s Scientific Manuscript database

DArTseq, a new SNP-based marker platform, was developed and used to analyze the genetic diversity of the US germplasm collection of lesquerella. Lesquerella is a new oilseed crop in the Brassica family found native in the American Southwest. The potential of the species as a domestic source of indu...

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...

Shift work and sleep disorder among textile mill workers in Bahir Dar, northwest Ethiopia.

PubMed

Abebe, Y; Fantahun, M

1999-07-01

To assess the length and quality of sleep among shift workers at Bahir Dar textile mill. A cross sectional study using structured questionnaire that contained sociodemographic variables, duration of work, work schedule, number of sleeping hours, sleep disorders, and associated reasons for such disorders. A textile mill in Bahir Dar, northwest Ethiopia. Three-hundred ninety four random sample of production workers of the mill. Sleep disorders, and the impact of external and home environment on sleep. The mean duration of work in the factory was 25.4 +/- 7.1 years. Ninety-seven per cent of the study population work in a rotating eight hourly shift system. The mean number of hours a worker sleeps after a worked shift was 5.1 +/- 2.3. Two hundred thirty (58.4%) claimed to experience a sleep disorder. Sleep disturbance was significantly associated with rotating shift work, external environmental noise, and working in the spinning department. The majority of the workers in Bahir Dar textile mill experienced sleep disturbances as detailed in the study methodology.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

The Application of Lidar to Synthetic Vision System Integrity

NASA Technical Reports Server (NTRS)

Campbell, Jacob L.; UijtdeHaag, Maarten; Vadlamani, Ananth; Young, Steve

2003-01-01

One goal in the development of a Synthetic Vision System (SVS) is to create a system that can be certified by the Federal Aviation Administration (FAA) for use at various flight criticality levels. As part of NASA s Aviation Safety Program, Ohio University and NASA Langley have been involved in the research and development of real-time terrain database integrity monitors for SVS. Integrity monitors based on a consistency check with onboard sensors may be required if the inherent terrain database integrity is not sufficient for a particular operation. Sensors such as the radar altimeter and weather radar, which are available on most commercial aircraft, are currently being investigated for use in a real-time terrain database integrity monitor. This paper introduces the concept of using a Light Detection And Ranging (LiDAR) sensor as part of a real-time terrain database integrity monitor. A LiDAR system consists of a scanning laser ranger, an inertial measurement unit (IMU), and a Global Positioning System (GPS) receiver. Information from these three sensors can be combined to generate synthesized terrain models (profiles), which can then be compared to the stored SVS terrain model. This paper discusses an initial performance evaluation of the LiDAR-based terrain database integrity monitor using LiDAR data collected over Reno, Nevada. The paper will address the consistency checking mechanism and test statistic, sensitivity to position errors, and a comparison of the LiDAR-based integrity monitor to a radar altimeter-based integrity monitor.

Real-time surveillance system for marine environment based on HLIF LiDAR

NASA Astrophysics Data System (ADS)

Babichenko, Sergey; Sobolev, Innokenti; Aleksejev, Valeri; Sõro, Oliver

2017-10-01

The operational monitoring of the risk areas of marine environment requires cost-effective solutions. One of the options is the use of sensor networks based on fixed installations and moving platforms (coastal boats, supply-, cargo-, and passenger vessels). Such network allows to gather environmental data in time and space with direct links to operational activities in the controlled area for further environmental risk assessment. Among many remote sensing techniques the LiDAR (Light Detection And Ranging) based on Light Induced Fluorescence (LIF) is the tool of direct assessment of water quality variations caused by chemical pollution, colored dissolved organic matter, and phytoplankton composition. The Hyperspectral LIF (HLIF) LiDAR acquires comprehensive LIF spectra and analyses them by spectral pattern recognition technique to detect and classify the substances in water remotely. Combined use of HLIF LiDARs with Real-Time Data Management System (RTDMS) provides the economically effective solution for the regular monitoring in the controlled area. OCEAN VISUALS in cooperation with LDI INNOVATION has developed Oil in Water Locator (OWL™) with RTDMS (OWL MAP™) based on HLIF LiDAR technique. This is a novel technical solution for monitoring of marine environment providing continuous unattended operations. OWL™ has been extensively tested on board of various vessels in the North Sea, Norwegian Sea, Barents Sea, Baltic Sea and Caribbean Sea. This paper describes the technology features, the results of its operational use in 2014-2017, and outlook for the technology development.

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.

Application of Airborne Hydrographic Laser Scanning for Mapping Shallow Water Riverine Environments in the Pacific Northwest, United States

NASA Astrophysics Data System (ADS)

Cooper, C.; Nayegandhi, A.; Faux, R.

2013-12-01

Small-footprint, green wavelength airborne LiDAR systems can provide seamless topography across the land-water interface at very high spatial resolution. These data have the potential to improve floodplain modeling, fisheries habitat assessments, stream restoration efforts, and other applications by continuously mapping shallow water depths that are difficult or impossible to measure using traditional ground-based or water-borne survey techniques. WSI (Corvallis, Oregon) in collaboration with Dewberry, (Tampa, Florida) and Riegl (Orlando, Florida), deployed the Riegl VQ-820-G hydrographic airborne laser scanner to map riverine and lacustrine environments from Oregon to Minnesota. Discussion will focus on the ability to accurately map depth and underwater structure, as well as riparian vegetation and terrain under different conditions. Results indicate that depth penetration varies with both water (i.e. clarity and surface conditions) and bottom conditions (i.e. substrate, depth, and landform). Depth penetration was typically limited to 1 Secchi depth or less across selected project areas. As an example, the green LiDAR system effectively mapped 83% of a shallow water river system, the Sandy River, with typical depths ranging from 0-2.5 meters. WSI will show quantitative comparisons of Green LiDAR surveys against more traditional methods such as rod or sonar surveys. WSI will also discuss advantages and limitations of Green LiDAR surveys for bathymetric modeling including survey accuracy, density, and efficiency along with data processing challenges not inherent with traditional NIR LiDAR processing.

Modeling Forest Biomass and Growth: Coupling Long-Term Inventory and Lidar Data

NASA Technical Reports Server (NTRS)

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

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 growth using LiDAR data. The proposed model accommodates temporal misalignment between field measurements and remotely sensed data-a problem pervasive in such settings-by including multiple time-indexed measurements at plot locations to estimate AGB growth. We pursue a Bayesian modeling framework that allows for appropriately complex parameter associations and uncertainty propagation through to prediction. Specifically, we identify a space-varying coefficients model to predict and map AGB and its associated growth simultaneously. The proposed model is assessed using LiDAR data acquired from NASA Goddard's LiDAR, Hyper-spectral & Thermal imager and field inventory data from the Penobscot Experimental Forest in Bradley, Maine. The proposed model outperformed the time-invariant counterpart models in predictive performance as indicated by a substantial reduction in root mean squared error. The proposed model adequately accounts for temporal misalignment through the estimation of forest AGB growth and accommodates residual spatial dependence. Results from this analysis suggest that future AGB models informed using remotely sensed data, such as LiDAR, may be improved by adapting traditional modeling frameworks to account for temporal misalignment and spatial dependence using random effects.

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.

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

NASA Astrophysics Data System (ADS)

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

2009-12-01

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

Diversity Arrays Technology (DArT) Marker Platforms for Diversity Analysis and Linkage Mapping in a Complex Crop, the Octoploid Cultivated Strawberry (Fragaria × ananassa)

PubMed Central

Sánchez-Sevilla, José F.; Horvath, Aniko; Botella, Miguel A.; Gaston, Amèlia; Folta, Kevin; Kilian, Andrzej; Denoyes, Beatrice; Amaya, Iraida

2015-01-01

Cultivated strawberry (Fragaria × ananassa) is a genetically complex allo-octoploid crop with 28 pairs of chromosomes (2n = 8x = 56) for which a genome sequence is not yet available. The diploid Fragaria vesca is considered the donor species of one of the octoploid sub-genomes and its available genome sequence can be used as a reference for genomic studies. A wide number of strawberry cultivars are stored in ex situ germplasm collections world-wide but a number of previous studies have addressed the genetic diversity present within a limited number of these collections. Here, we report the development and application of two platforms based on the implementation of Diversity Array Technology (DArT) markers for high-throughput genotyping in strawberry. The first DArT microarray was used to evaluate the genetic diversity of 62 strawberry cultivars that represent a wide range of variation based on phenotype, geographical and temporal origin and pedigrees. A total of 603 DArT markers were used to evaluate the diversity and structure of the population and their cluster analyses revealed that these markers were highly efficient in classifying the accessions in groups based on historical, geographical and pedigree-based cues. The second DArTseq platform took benefit of the complexity reduction method optimized for strawberry and the development of next generation sequencing technologies. The strawberry DArTseq was used to generate a total of 9,386 SNP markers in the previously developed ‘232’ × ‘1392’ mapping population, of which, 4,242 high quality markers were further selected to saturate this map after several filtering steps. The high-throughput platforms here developed for genotyping strawberry will facilitate genome-wide characterizations of large accessions sets and complement other available options. PMID:26675207

Dopamine D5 receptor modulates male and female sexual behavior in mice.

PubMed

Kudwa, A E; Dominguez-Salazar, E; Cabrera, D M; Sibley, D R; Rissman, E F

2005-07-01

Dopamine exerts its actions through at least five receptor (DAR) isoforms. In female rats, D5 DAR may be involved in expression of sexual behavior. We used a D5 knockout (D5KO) mouse to assess the role of D5 DAR in mouse sexual behavior. Both sexes of D5KO mice are fertile and exhibit only minor disruptions in exploratory locomotion, startle, and prepulse inhibition responses. This study was conducted to characterize the sexual behavior of male and female D5KO mice relative to their WT littermates. Female WT and D5KO littermates were ovariectomized and given a series of sexual behavior tests after treatment with estradiol benzoate (EB) and progesterone (P). Once sexual performance was optimal the dopamine agonist, apomorphine (APO), was substituted for P. Male mice were observed in pair- and trio- sexual behavior tests. To assess whether the D5 DAR is involved in rewarding aspects of sexual behavior, WT and D5KO male mice were tested for conditioned place preference. Both WT and D5KO females can display receptivity after treatment with EB and P, but APO was only able to facilitate receptivity in EB-primed WT, not in D5KO, mice. Male D5KO mice display normal masculine sexual behavior in mating tests. In conditioned preference tests, WT males formed a conditioned preference for context associated with either intromissions alone or ejaculation as the unconditioned stimulus. In contrast, D5KO males only showed a place preference when ejaculation was paired with the context. In females, the D5 DAR is essential for the actions of dopamine on receptivity. In males, D5 DAR influences rewarding aspects of intromissions. Taken together, the work suggests that the D5 receptor mediates dopamine's action on sexual behavior in both sexes, perhaps via a reward pathway.

Automatic 3d Building Model Generations with Airborne LiDAR Data

NASA Astrophysics Data System (ADS)

Yastikli, N.; Cetin, Z.

2017-11-01

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

Random forest regression modelling for forest aboveground biomass estimation using RISAT-1 PolSAR and terrestrial LiDAR data

NASA Astrophysics Data System (ADS)

Mangla, Rohit; Kumar, Shashi; Nandy, Subrata

2016-05-01

SAR and LiDAR remote sensing have already shown the potential of active sensors for forest parameter retrieval. SAR sensor in its fully polarimetric mode has an advantage to retrieve scattering property of different component of forest structure and LiDAR has the capability to measure structural information with very high accuracy. This study was focused on retrieval of forest aboveground biomass (AGB) using Terrestrial Laser Scanner (TLS) based point clouds and scattering property of forest vegetation obtained from decomposition modelling of RISAT-1 fully polarimetric SAR data. TLS data was acquired for 14 plots of Timli forest range, Uttarakhand, India. The forest area is dominated by Sal trees and random sampling with plot size of 0.1 ha (31.62m*31.62m) was adopted for TLS and field data collection. RISAT-1 data was processed to retrieve SAR data based variables and TLS point clouds based 3D imaging was done to retrieve LiDAR based variables. Surface scattering, double-bounce scattering, volume scattering, helix and wire scattering were the SAR based variables retrieved from polarimetric decomposition. Tree heights and stem diameters were used as LiDAR based variables retrieved from single tree vertical height and least square circle fit methods respectively. All the variables obtained for forest plots were used as an input in a machine learning based Random Forest Regression Model, which was developed in this study for forest AGB estimation. Modelled output for forest AGB showed reliable accuracy (RMSE = 27.68 t/ha) and a good coefficient of determination (0.63) was obtained through the linear regression between modelled AGB and field-estimated AGB. The sensitivity analysis showed that the model was more sensitive for the major contributed variables (stem diameter and volume scattering) and these variables were measured from two different remote sensing techniques. This study strongly recommends the integration of SAR and LiDAR data for forest AGB estimation.

Argon Diffusion Measured in Rhyolite Melt at 100 MPa

NASA Astrophysics Data System (ADS)

Weldon, N.; Edwards, P. M.; Watkins, J. M.; Lesher, C. E.

2016-12-01

Argon diffusivity (D_{Ar} ) controls the rate and length scale of argon exchange between melt and gas phases and is used as a parameter to model noble gas fractionation during magma degassing. D_{Ar} may also be useful in geochronology to estimate the distribution of excess (non-radiogenic) atmospheric argon in lavas. Our measurements of D_{Ar} in molten anhydrous rhyolite near 1000 °C and 100 MPa add to the existing dataset. Using a rapid-quench cold seal pressure apparatus we exposed cylindrical charges drilled from a Miocene rhyolite flow near Buck Mtn., CA to a pure argon atmosphere resulting in a gradually lengthening argon concentration gradient between the saturated surface and the argon poor interior. Argon concentration was measured by electron microprobe along radial transects from the center to the surface of bisected samples. D_{Ar} was calculated for each transect by fitting relative argon concentration (as a function of distance from the surface) to Green's function (given each experiment's specific temperature, pressure and runtime). Variability (σ = 1.202{μm }^{2} /s) was smaller than in previous studies, but still greater than what is likely due to analytical or experimental uncertainty. We observed a symmetric geometric bias in the distribution of argon in our samples, possibly related to advective redistribution of argon accompanying the deformation of cylindrical charges into spheroids driven by surface tension. Average diffusivity, D_{Ar} = 4.791{μm }^{2} /s, is close to the predicted value, D_{Ar} = {μm }^{2} /s ( σ_{ \\bar{x} } = 1.576 {μm }^{2} /s), suggesting that Behrens and Zhang's (2001) empirical model is valid for anhydrous rhyolite melts to relatively higher temperatures and lower pressures. Behrens, H. and Y. Zhang (2001). "Ar diffusion in hydrous silicic melts: implications for volatile diffusion mechanisms and fractionation." Earth and Planetary Science Letters 192: 363-376.

Contrasting Patterns of Damage and Recovery in Logged Amazon Forests From Small Footprint LiDAR Data

NASA Technical Reports Server (NTRS)

Morton, D. C.; Keller, M.; Cook, B. D.; Hunter, Maria; Sales, Marcio; Spinelli, L.; Victoria, D.; Andersen, H.-E.; Saleska, S.

2012-01-01

Tropical forests ecosystems respond dynamically to climate variability and disturbances on time scales of minutes to millennia. To date, our knowledge of disturbance and recovery processes in tropical forests is derived almost exclusively from networks of forest inventory plots. These plots typically sample small areas (less than or equal to 1 ha) in conservation units that are protected from logging and fire. Amazon forests with frequent disturbances from human activity remain under-studied. Ongoing negotiations on REDD+ (Reducing Emissions from Deforestation and Forest Degradation plus enhancing forest carbon stocks) have placed additional emphasis on identifying degraded forests and quantifying changing carbon stocks in both degraded and intact tropical forests. We evaluated patterns of forest disturbance and recovery at four -1000 ha sites in the Brazilian Amazon using small footprint LiDAR data and coincident field measurements. Large area coverage with airborne LiDAR data in 2011-2012 included logged and unmanaged areas in Cotriguacu (Mato Grosso), Fiona do Jamari (Rondonia), and Floresta Estadual do Antimary (Acre), and unmanaged forest within Reserva Ducke (Amazonas). Logging infrastructure (skid trails, log decks, and roads) was identified using LiDAR returns from understory vegetation and validated based on field data. At each logged site, canopy gaps from logging activity and LiDAR metrics of canopy heights were used to quantify differences in forest structure between logged and unlogged areas. Contrasting patterns of harvesting operations and canopy damages at the three logged sites reflect different levels of pre-harvest planning (i.e., informal logging compared to state or national logging concessions), harvest intensity, and site conditions. Finally, we used multi-temporal LiDAR data from two sites, Reserva Ducke (2009, 2012) and Antimary (2010, 2011), to evaluate gap phase dynamics in unmanaged forest areas. The rates and patterns of canopy gap formation at these sites illustrate potential issues for separating logging damages from natural forest disturbances over longer time scales. Multi-temporal airborne LiDAR data and coincident field measurements provide complementary perspectives on disturbance and recovery processes in intact and degraded Amazon forests. Compared to forest inventory plots, the large size of each individual site permitted analyses of landscape-scale processes that would require extremely high investments to study using traditional forest inventory methods.

A Fusion of GPR- and LiDAR-Data for Surveying and Visualisation of Archaeological Structures - a case example of an archaeological site in Strettweg, District of Murtal, Austria

NASA Astrophysics Data System (ADS)

Kamp, Nicole; Russ, Stefan; Sass, Oliver; Tiefengraber, Georg; Tiefengraber, Susanne

2014-05-01

Strettweg is a small community located in Upper Styria in the valley of the Mur. It is seen as one of the most outstanding prehistoric archaeological sites in Austria. In 1851 the "Strettweger Opferwagen" (~ 600 BC) was discovered and is considered one of the most important Hallstatt find of Austria. More than 160 years later Airborne LiDAR and modern geophysical methods like Ground Penetrating Radar (GPR) and/or Magnetics have made it possible to find additional burial mounds and map the largest prehistoric settlement in the southeastern Alps (Falkenberg). These modern techniques have provided an auxiliary tool for the archaeological team's project "Hallstattzeitlicher Fürstensitz Falkenberg/Strettweg". GPR allows for a fast and non-invasive surveying of structures and anomalies of the sub surface, by using electromagnetic radiation in the microwave range. The active remote sensing technique LiDAR (Light Detection and Ranging, also known as Laser Scanning), measures the runtime of discrete light pulses in order to map objects and structures on the surface of the earth. In the course of this archaeological project GPR (Mala ProEx - 500 MHz antenna) and terrestrial LiDAR (Riegl LMS Z620) were applied by the University of Graz, Department of Geography and Regional Science, ALADYN work group (Univ.-Prof. Dr. Oliver Sass) to collect data of a testing site with 2500 m². The existence of archaeological structures was crucial for choosing this area. The area is surrounded by fine sediments, which originated by fluviatile transportation, making the remnants of these archaeological structures easier to detect. A standard GPR-processing-workflow does not allow for a 3-dimensional visualisation of the results and complicates the detection of archaeological structures. Unlike, LiDAR which does allow for a 3-dimensional visualisation. A fusion of both techniques, by using Python scripts and the software packages REFLEXW - Sandmeier Scientific Software and LASTools - rapidlasso, applies the advantages and specialities of LiDAR and GPR, and allows to get a high-resolution 3-dimensional pointcloud. This simplifies the identification of ancient man-made near-surface structures, which enables both in the field and lab quick post-processing. The LiDAR pointcloud, when coupled with the GPR pointcloud, act as reference datasets and improve the accuracy, classification, and filtering of the GPR data.

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 of the derived ditches was improved by referring to the original LiDAR point cloud data. In each 1-m buffer of the preliminary detected network vertices, the lowest LiDAR point was taken as the vertex of an improved network, shifting the preliminary network into the lowest 'depressions' of the study area. A drawback of a morphological approach is that the connectivity of the network is usually poor since the pixels are processed separately. Therefore, the field observations on connectivity (including culverts) were used to develop an empirical model to estimate the probability of connectivity between LiDAR-derived ditch segments from auxiliary datasets and hydrological and morphological properties of the preliminary network. This allowed deriving a connectivity probability map. The underlying model was tested by cross-validating the field observations on connectivity.

Large off-nadir scan angle of airborne LiDAR can severely affect the estimates of forest structure metrics

NASA Astrophysics Data System (ADS)

Liu, Jing; Skidmore, Andrew K.; Jones, Simon; Wang, Tiejun; Heurich, Marco; Zhu, Xi; Shi, Yifang

2018-02-01

Gap fraction (Pgap) and vertical gap fraction profile (vertical Pgap profile) are important forest structural metrics. Accurate estimation of Pgap and vertical Pgap profile is therefore critical for many ecological applications, including leaf area index (LAI) mapping, LAI profile estimation and wildlife habitat modelling. Although many studies estimated Pgap and vertical Pgap profile from airborne LiDAR data, the scan angle was often overlooked and a nadir view assumed. However, the scan angle can be off-nadir and highly variable in the same flight strip or across different flight strips. In this research, the impact of off-nadir scan angle on Pgap and vertical Pgap profile was evaluated, for several forest types. Airborne LiDAR data from nadir (0°∼7°), small off-nadir (7°∼23°), and large off-nadir (23°∼38°) directions were used to calculate both Pgap and vertical Pgap profile. Digital hemispherical photographs (DHP) acquired during fieldwork were used as references for validation. Our results show that angular Pgap from airborne LiDAR correlates well with angular Pgap from DHP (R2 = 0.74, 0.87, and 0.67 for nadir, small off-nadir and large off-nadir direction). But underestimation of Pgap from LiDAR amplifies at large off-nadir scan angle. By comparing Pgap and vertical Pgap profiles retrieved from different directions, it is shown that scan angle impact on Pgap and vertical Pgap profile differs amongst different forest types. The difference is likely to be caused by different leaf angle distribution and canopy architecture in these forest types. Statistical results demonstrate that the scan angle impact is more severe for plots with discontinuous or sparse canopies. These include coniferous plots, and deciduous or mixed plots with between-crown gaps. In these discontinuous plots, Pgap and vertical Pgap profiles are maximum when observed from nadir direction, and then rapidly decrease with increasing scan angle. The results of this research have many important practical implications. First, it is suggested that large off-nadir scan angle of airborne LiDAR should be avoided to ensure a more accurate Pgap and LAI estimation. Second, the angular dependence of vertical Pgap profiles observed from airborne LiDAR should be accounted for, in order to improve the retrieval of LAI profiles, and other quantitative canopy structural metrics. This is especially necessary when using multi-temporal datasets in discontinuous forest types. Third, the anisotropy of Pgap and vertical Pgap profile observed by airborne LiDAR, can potentially help to resolve the anisotropic behavior of canopy reflectance, and refine the inversion of biophysical and biochemical properties from passive multispectral or hyperspectral data.

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 the consistency of the developed ontologies, and logical reasoning is performed to infer implicit relations between defined concepts. The ontology for the definition of building is specified using the Ontology Web Language (OWL). It is the most widely used ontology language that is based on Description Logics (DL). DL allows the description of internal properties of modelled concepts (roof typology, shape, area, height etc.) and relationships between objects (IS_A, MEMBER_OF/INSTANCE_OF). It captures terminological knowledge (TBox) as well as assertional knowledge (ABox) - that represents facts about concept instances, i.e. the buildings in airborne LiDAR data. To assess the classification accuracy, ground truth data generated by visual interpretation and calculated classification results in terms of precision and recall are used. The advantages of this approach are: (i) flexibility, (ii) transferability, and (iii) extendibility - i.e. ontology can be extended with further concepts, data properties and object properties.

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...

Accessible light detection and ranging: estimating large tree density for habitat identification

Treesearch

Heather A. Kramer; Brandon M. Collins; Claire V. Gallagher; John Keane; Scott L. Stephens; Maggi Kelly

2016-01-01

Large trees are important to a wide variety of wildlife, including many species of conservation concern, such as the California spotted owl (Strix occidentalis occidentalis). Light detection and ranging (LiDAR) has been successfully utilized to identify the density of large-diameter trees, either by segmenting the LiDAR point cloud into...

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...

Diversity, genetic mapping, and signatures of domestication in the carrot (Daucus carota L.) genome, as revealed by Diversity Arrays Technology (DArT) markers

USDA-ARS?s Scientific Manuscript database

Carrot is one of the most economically important vegetables worldwide, however, genetic and genomic resources supporting carrot breeding remain limited. We developed a Diversity Arrays Technology (DArT) platform for wild and cultivated carrot and used it to investigate genetic diversity and to devel...

Computational Modeling of River Flow, Sediment Transport, and Bed Evolution Using Remotely Sensed Data

DTIC Science & Technology

2009-01-01

Ranch. Bathymetric LiDAR was collected over a 40-mile reach from Lewiston Lake to the North Fork of the Trinity, which includes the sites above. Initial...bathymetric LiDAR flight is planned for the Kootenai River near Bonner’s Ferry, Idaho for next year. Detailed multibeam acoustic surveys already exist for the

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...

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…

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...

Linking LiDAR with streamwater biogeochemistry in coastal temperate rainforest watersheds

Treesearch

Jason B. Fellman; Brian Buma; Eran Hood; Richard T. Edwards; David V. D’Amore

2017-01-01

The goal of this study was to use watershed characteristics derived from light detection and ranging (LiDAR) data to predict stream biogeochemistry in Perhumid Coastal Temperate Rainforest (PCTR) watersheds. Over a 2-day period, we sampled 37 streams for concentrations of dissolved C, N, P, major cations, and measures of dissolved organic matter quality (specific...

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...

LiDAR Point Cloud and Stereo Image Point Cloud Fusion

DTIC Science & Technology

2013-09-01

LiDAR point cloud (right) highlighting linear edge features ideal for automatic registration...point cloud (right) highlighting linear edge features ideal for automatic registration. Areas where topography is being derived, unfortunately, do...with the least amount of automatic correlation errors was used. The following graphic (Figure 12) shows the coverage of the WV1 stereo triplet as

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.…

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…

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…

A Decade of Technology Enhanced Learning at the University of Dar es Salaam, Tanzania: Challenges, Achievements, and Opportunities

ERIC Educational Resources Information Center

Mtebe, Joel S.; Raphael, Christina

2017-01-01

For a decade past, integration of technology in teaching and learning has been received with both apprehension and skeptism from academics and student majority at the University of Dar es Salaam (UDSM). The study recounts real, professional and practical experiences, challenges, and opportunities of integrating educational technologies using…

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...

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...

Terrain Classification Using Multi-Wavelength Lidar Data

DTIC Science & Technology

2015-09-01

Figure 9. Pseudo- NDVI of three layers within the vertical structure of the forest. (Top) First return from the LiDAR instrument, including the ground...in NDVI throughout the vertical canopy. ........................................................17 Figure 10. Optech Titan operating wavelengths...and Ranging LMS LiDAR Mapping Suite ML Maximum Likelihood NIR Near Infrared N-D VIS n-Dimensional Visualizer NDVI Normalized Difference

Draft Genome Sequence of Chryseobacterium sp. JV274 Isolated from Maize Rhizosphere

PubMed Central

Vacheron, Jordan; Dubost, Audrey; Chapulliot, David; Prigent-Combaret, Claire

2017-01-01

ABSTRACT We report the draft genome sequence of Chryseobacterium sp. JV274. This strain was isolated from the rhizosphere of maize during a greenhouse experiment. JV274 harbors genes involved in flexirubin production (darA and darB genes), bacterial competition (type VI secretion system), and gliding (bacterial motility; type IX secretion system). PMID:28408666

Real-time computational photon-counting LiDAR

NASA Astrophysics Data System (ADS)

Edgar, Matthew; Johnson, Steven; Phillips, David; Padgett, Miles

2018-03-01

The availability of compact, low-cost, and high-speed MEMS-based spatial light modulators has generated widespread interest in alternative sampling strategies for imaging systems utilizing single-pixel detectors. The development of compressed sensing schemes for real-time computational imaging may have promising commercial applications for high-performance detectors, where the availability of focal plane arrays is expensive or otherwise limited. We discuss the research and development of a prototype light detection and ranging (LiDAR) system via direct time of flight, which utilizes a single high-sensitivity photon-counting detector and fast-timing electronics to recover millimeter accuracy three-dimensional images in real time. The development of low-cost real time computational LiDAR systems could have importance for applications in security, defense, and autonomous vehicles.

Terbuthylazine and deethylterbuthylazine in rain and surface water - Determination by enzyme immunoassay and gas chromatography/mass spectrometry

USGS Publications Warehouse

Dankwardt, A.; Thurman, E.M.; Hock, B.

1997-01-01

Rain and surface water samples from Southern Germany were investigated from 1991 to 1995 for terbuthylazine and one of its major metabolites, deethylterbuthylazine. The concentrations observed were compared to the concentrations found for atrazine and deethylatrazine in the same water samples. Concentrations ranged from <0.02 ??g/L to 0.7 ??g/L for terbuthylazine and from <0.02 ??g/L to 0.6 ??g/L for deethylterbuthylazine, compared to concentrations of <0.02 ??g/L to 3 ??g/L and <0.02 ??g/L to 0.5 ??g/L for atrazine and deethylatrazine, respectively. The ratios of metabolite concentrations to parent compound concentrations were calculated for deethylterbuthylazine to terbuthylazine (DTR) and deethylatrazine to atrazine (DAR). In rain water, DTR of 0.8...3.0 and DAR of 0.3...1.9 were determined with mean values of 0.9...1.7 for DTR and 0.6...0.9 for DAR in the different years. The ratios increased during summer periods. The highest ratios were observed in samples from forest stands, showing that degradation of the herbicide has occurred during transport between the source and the sampling site. The DTR in rain water were about 50...100% higher than the DAR. This indicates a higher degradation rate of terbuthylazine during atmospheric transport. In surface water, DTR of 0.3...1.2 with mean values of 0.5...0.8 and DAR of 0.2...2.2 with mean values of 0.2...1.3 were observed. The ratios increased from June to September.

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.

Real-time full-motion color Flash lidar for target detection and identification

NASA Astrophysics Data System (ADS)

Nelson, Roy; Coppock, Eric; Craig, Rex; Craner, Jeremy; Nicks, Dennis; von Niederhausern, Kurt

2015-05-01

Greatly improved understanding of areas and objects of interest can be gained when real time, full-motion Flash LiDAR is fused with inertial navigation data and multi-spectral context imagery. On its own, full-motion Flash LiDAR provides the opportunity to exploit the z dimension for improved intelligence vs. 2-D full-motion video (FMV). The intelligence value of this data is enhanced when it is combined with inertial navigation data to produce an extended, georegistered data set suitable for a variety of analysis. Further, when fused with multispectral context imagery the typical point cloud now becomes a rich 3-D scene which is intuitively obvious to the user and allows rapid cognitive analysis with little or no training. Ball Aerospace has developed and demonstrated a real-time, full-motion LIDAR system that fuses context imagery (VIS to MWIR demonstrated) and inertial navigation data in real time, and can stream these information-rich geolocated/fused 3-D scenes from an airborne platform. In addition, since the higher-resolution context camera is boresighted and frame synchronized to the LiDAR camera and the LiDAR camera is an array sensor, techniques have been developed to rapidly interpolate the LIDAR pixel values creating a point cloud that has the same resolution as the context camera, effectively creating a high definition (HD) LiDAR image. This paper presents a design overview of the Ball TotalSight™ LIDAR system along with typical results over urban and rural areas collected from both rotary and fixed-wing aircraft. We conclude with a discussion of future work.

3D turbulence measurements in inhomogeneous boundary layers with three wind LiDARs

NASA Astrophysics Data System (ADS)

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

2014-05-01

One of the most challenging tasks in atmospheric anemometry is obtaining reliable turbulence measurements of inhomogeneous boundary layers at heights or in locations where is not possible or convenient to install tower-based measurement systems, e.g. mountainous terrain, cities, wind farms, etc. Wind LiDARs are being extensively used for the measurement of averaged vertical wind profiles, but they can only successfully accomplish this task under the limiting conditions of flat terrain and horizontally homogeneous flow. Moreover, it has been shown that common scanning strategies introduce large systematic errors in turbulence measurements, regardless of the characteristics of the flow addressed. From the point of view of research, there exist a variety of techniques and scanning strategies to estimate different turbulence quantities but most of them rely in the combination of raw measurements with atmospheric models. Most of those models are only valid under the assumption of horizontal homogeneity. The limitations stated above can be overcome by a new triple LiDAR technique which uses simultaneous measurements from three intersecting Doppler wind LiDARs. It allows for the reconstruction of the three-dimensional velocity vector in time as well as local velocity gradients without the need of any turbulence model and with minimal assumptions [EGU2013-9670]. The triple LiDAR technique has been applied to the study of the flow over the campus of EPFL in Lausanne (Switzerland). The results show the potential of the technique for the measurement of turbulence in highly complex boundary layer flows. The technique is particularly useful for micrometeorology and wind engineering studies.

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.

Significant effect of topographic normalization of airborne LiDAR data on the retrieval of plant area index profile in mountainous forests

NASA Astrophysics Data System (ADS)

Liu, Jing; Skidmore, Andrew K.; Heurich, Marco; Wang, Tiejun

2017-10-01

As an important metric for describing vertical forest structure, the plant area index (PAI) profile is used for many applications including biomass estimation and wildlife habitat assessment. PAI profiles can be estimated with the vertically resolved gap fraction from airborne LiDAR data. Most research utilizes a height normalization algorithm to retrieve local or relative height by assuming the terrain to be flat. However, for many forests this assumption is not valid. In this research, the effect of topographic normalization of airborne LiDAR data on the retrieval of PAI profile was studied in a mountainous forest area in Germany. Results show that, although individual tree height may be retained after topographic normalization, the spatial arrangement of trees is changed. Specifically, topographic normalization vertically condenses and distorts the PAI profile, which consequently alters the distribution pattern of plant area density in space. This effect becomes more evident as the slope increases. Furthermore, topographic normalization may also undermine the complexity (i.e., canopy layer number and entropy) of the PAI profile. The decrease in PAI profile complexity is not solely determined by local topography, but is determined by the interaction between local topography and the spatial distribution of each tree. This research demonstrates that when calculating the PAI profile from airborne LiDAR data, local topography needs to be taken into account. We therefore suggest that for ecological applications, such as vertical forest structure analysis and modeling of biodiversity, topographic normalization should not be applied in non-flat areas when using LiDAR data.

Assessment of seismic loading on structures based on airborne LiDAR data from the Kalochori urban area (N. Greece)

NASA Astrophysics Data System (ADS)

Rovithis, Emmanouil; Kirtas, Emmanouil; Marini, Eleftheria; Bliziotis, Dimitris; Maltezos, Evangelos; Pitilakis, Dimitris; Makra, Konstantia; Savvaidis, Alexandros

2016-08-01

Airborne LiDAR monitoring integrated with field data is employed to assess the fundamental period and the seismic loading of structures composing an urban area under prescribed earthquake scenarios. Α piecewise work-flow is adopted by combining geometrical data of the building stock derived from a LiDAR-based 3D city model, structural data from in-situ inspections on representative city blocks and results of soil response analyses. The procedure is implemented in the residential area of Kalochori, (west of Thessaloniki in Northern Greece). Special attention is paid to the in-situ inspection of the building stock in order to discriminate recordings between actual buildings and man-made constructions that do not conform to seismic design codes and to acquire additional building stock data on structural materials, typologies and number of stories which is not feasible by the LiDAR process. The processed LiDAR and field data are employed to compute the fundamental period of each building by means of code-defined formulas. Knowledge of soil conditions in the Kalochoti area allows for soil response analyses to obtain free-field at ground surface under earthquake scenarios with varying return period. Upon combining the computed vibrational characteristics of the structures with the free-field response spectra, the seismic loading imposed on the structures of the urban area under investigation is derived for each one of the prescribed seismic motions. Results are presented in GIS environment in the form of spatially distributed spectral accelerations with direct implications in seismic vulnerability studies of an urban area.

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.

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.

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...

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...

Analysis of spatial correlation in predictive models of forest variables that use LiDAR auxiliary information

Treesearch

F. Mauro; Vicente J. Monleon; H. Temesgen; L.A. Ruiz

2017-01-01

Accounting for spatial correlation of LiDAR model errors can improve the precision of model-based estimators. To estimate spatial correlation, sample designs that provide close observations are needed, but their implementation might be prohibitively expensive. To quantify the gains obtained by accounting for the spatial correlation of model errors, we examined (

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...

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)

Relationships among vegetation structure, canopy composition, and avian richness patterns across an aspen-conifer forest gradient

Treesearch

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

2017-01-01

Ecologists have a long-term interest in understanding the relative influence of vegetation composition and vegetation structure on avian diversity. LiDAR remote sensing is useful in studying local patterns of avian diversity because it characterizes fine-scale vegetation structure across broad extents. We used LiDAR, aerial and satellite imagery, and avian field data...

Effects of Family Educational Background, Dwelling and Parenting Style on Students' Academic Achievement: The Case of Secondary Schools in Bahir Dar

ERIC Educational Resources Information Center

Mekonnen, Melaku Anteneh

2017-01-01

This study predominantly focuses on investigating the respective impacts of family educational background, dwelling background and parenting styles on students' overall academic performance with respect to governmental secondary schools in Bahir Dar town, Ethiopia. A descriptive survey method was employed. A 42 items questionnaire was constructed…

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…

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...

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...

Post-Fire Changes in Forest Biomass Retrieved by Airborne LiDAR in Amazonia

Treesearch

Luciane Sato; Vitor Gomes; Yosio Shimabukuro; Michael Keller; Egidio Arai; Maiza dos-Santos; Irving Brown; Luiz Aragão

2016-01-01

Fire is one of the main factors directly impacting Amazonian forest biomass and dynamics. Because of Amazonia’s large geographical extent, remote sensing techniques are required for comprehensively assessing forest fire impacts at the landscape level. In this context, Light Detection and Ranging (LiDAR) stands out as a technology capable of retrieving direct...

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...

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....

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...

Teacher Trainers' and Trainees' Perceptions, Practices, and Constraints to Active Learning Methods: The Case of English Department in Bahir Dar University

ERIC Educational Resources Information Center

Engidaw, Berhanu

2014-01-01

This study is on teacher trainers and teacher trainees' perceptions and practices of active learning and the constraints to implementing them in the English Department of Bahir Dar University. A mixed study approach that involves a quantitative self administered questionnaire, a semi-structured lesson observation guide, and qualitative in depth…

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...

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...

Use of diversity arrays technology markers for integration into a cotton reference map and anchoring to a recombinant inbred line map

USDA-ARS?s Scientific Manuscript database

A DArT marker platform is developed for the cotton genome to evaluate the use of DArT markers compared to AFLPs in mapping, and transferability across the mapping populations. We used a reference genetic map of tetraploid Gossypium that already contained ~5000 loci which coalesced into 26 chromosom...

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…

Landscape-scale parameterization of a tree-level forest growth model: a k-nearest neighbor imputation approach incorporating LiDAR data

Treesearch

Michael J. Falkowski; Andrew T. Hudak; Nicholas L. Crookston; Paul E. Gessler; Edward H. Uebler; Alistair M. S. Smith

2010-01-01

Sustainable forest management requires timely, detailed forest inventory data across large areas, which is difficult to obtain via traditional forest inventory techniques. This study evaluated k-nearest neighbor imputation models incorporating LiDAR data to predict tree-level inventory data (individual tree height, diameter at breast height, and...

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).

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.

Hydrography change detection: the usefulness of surface channels derived From LiDAR DEMs for updating mapped hydrography

USGS Publications Warehouse

Poppenga, Sandra K.; Gesch, Dean B.; Worstell, Bruce B.

2013-01-01

The 1:24,000-scale high-resolution National Hydrography Dataset (NHD) mapped hydrography flow lines require regular updating because land surface conditions that affect surface channel drainage change over time. Historically, NHD flow lines were created by digitizing surface water information from aerial photography and paper maps. Using these same methods to update nationwide NHD flow lines is costly and inefficient; furthermore, these methods result in hydrography that lacks the horizontal and vertical accuracy needed for fully integrated datasets useful for mapping and scientific investigations. Effective methods for improving mapped hydrography employ change detection analysis of surface channels derived from light detection and ranging (LiDAR) digital elevation models (DEMs) and NHD flow lines. In this article, we describe the usefulness of surface channels derived from LiDAR DEMs for hydrography change detection to derive spatially accurate and time-relevant mapped hydrography. The methods employ analyses of horizontal and vertical differences between LiDAR-derived surface channels and NHD flow lines to define candidate locations of hydrography change. These methods alleviate the need to analyze and update the nationwide NHD for time relevant hydrography, and provide an avenue for updating the dataset where change has occurred.

Phylogenetic Relationships between Four Salix L. Species Based on DArT Markers

PubMed Central

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

2013-01-01

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. PMID:24336112

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.

The value of Doppler LiDAR systems to monitor turbulence intensity during storm events in order to enhance aviation safety in Iceland

NASA Astrophysics Data System (ADS)

Yang, Shu; Nína Petersen, Guðrún; Finger, David C.

2017-04-01

Turbulence and wind shear are a major natural hazards for aviation safety in Iceland. The temporal and spatial scale of atmospheric turbulence is very dynamic, requiring an adequate method to detect and monitor turbulence with high resolution. The Doppler Light Detection and Ranging (LiDAR) system can provide continuous information about the wind field using the Doppler effect form emitted light signals. In this study, we use a Leosphere Windcube 200s LiDAR systems stationed near Reykjavik city Airport and at Keflavik International Airport, Iceland, to evaluate turbulence intensity by estimating eddy dissipation rate (EDR). For this purpose, we retrieved radial wind velocity observations from Velocity Azimuth Display (VAD) scans (360°scans at 15° and 75° elevation angle) to compute EDR. The method was used to monitor and characterize storm events in fall 2016 and the following winter. The preliminary result reveal that the LiDAR observations can detect and quantify atmospheric turbulence with high spatial and temporal resolution. This finding is an important step towards enhanced aviation safety in subpolar climate characterized by sever wind turbulence.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

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 considering an Optimizer Algorithm based on Particle Swarm (PS) and a matching procedure which takes the position and the height of the extracted trees respect to the measured ones and iteratively tries to improve the candidate solution changing the models' parameters. Example of application of the LESTO tools will be presented on test sites. Test area consists in a series of circular sampling plots randomly selected from a 50x50 m regular grid within a buffer zone of 150 m from the forest road. Other studies on the same sites take as reference measurements of position, diameter, species and height and proposed allometric relationships. These allometric relationship were obtained for each species deriving the stem volume of single trees based on height and diameter at breast height. LESTO is integrated in the JGrassTools project and available for download at www.jgrasstools.org. A simple and easy to use graphical interface to run the models is available at https://github.com/moovida/STAGE/releases.

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 bands) with their CASI 1500 hyperspectral sensor. The ground survey was conducted by CMGL. The team used an airboat to collect in-situ radiometer measurements of sky irradiance and surface water reflectance at different locations in the bay. The team also collected water samples, GPS position, and depth. A follow-up survey was conducted to acquire ground-truth data of benthic type at over 80 locations within the bay. Two complementary approaches were developed to detect and map the seagrass cover over the study area - automated classification algorithms were validated with high spatial resolution hyperspectral imagery, and a continuous wavelet based signal processing and pulse broadening analysis of the digitized returns was performed with the full waveform of the bathymetric LiDAR. The two approaches were compared to the collected ground truth data of seagrass type, height, and location. Results of the evaluation will be presented, along with a preliminary discussion of the fusion of the LiDAR and hyperspectral imagery for improved overall classification accuracy.

Mangrove classification through the use of object oriented classification and support vector machine of lidar datasets: a case study in Naawan and Manticao, Misamis Oriental, Philippines

NASA Astrophysics Data System (ADS)

Jalbuena, Rey L.; Peralta, Rudolph V.; Tamondong, Ayin M.

2016-10-01

Mangroves are trees or shrubs that grows at the surface between the land and the sea in tropical and sub-tropical latitudes. Mangroves are essential in supporting various marine life, thus, it is important to preserve and manage these areas. There are many approaches in creating Mangroves maps, one of which is through the use of Light Detection and Ranging (LiDAR). It is a remote sensing technique which uses light pulses to measure distances and to generate three-dimensional point clouds of the Earth's surface. In this study, the topographic LiDAR Data will be used to analyze the geophysical features of the terrain and create a Mangrove map. The dataset that we have were first pre-processed using the LAStools software. It is a software that is used to process LiDAR data sets and create different layers such as DSM, DTM, nDSM, Slope, LiDAR Intensity, LiDAR number of first returns, and CHM. All the aforementioned layers together was used to derive the Mangrove class. Then, an Object-based Image Analysis (OBIA) was performed using eCognition. OBIA analyzes a group of pixels with similar properties called objects, as compared to the traditional pixel-based which only examines a single pixel. Multi-threshold and multiresolution segmentation were used to delineate the different classes and split the image into objects. There are four levels of classification, first is the separation of the Land from the Water. Then the Land class was further dived into Ground and Non-ground objects. Furthermore classification of Nonvegetation, Mangroves, and Other Vegetation was done from the Non-ground objects. Lastly Separation of the mangrove class was done through the Use of field verified training points which was then run into a Support Vector Machine (SVM) classification. Different classes were separated using the different layer feature properties, such as mean, mode, standard deviation, geometrical properties, neighbor-related properties, and textural properties. Accuracy assessment was done using a different set of field validation points. This workflow was applied in the classification of Mangroves to a LiDAR dataset of Naawan and Manticao, Misamis Oriental, Philippines. The process presented in this study shows that LiDAR data and its derivatives can be used in extracting and creating Mangrove maps, which can be helpful in managing coastal environment.

Estimating forest structural characteristics using the airborne LiDAR scanning system and a near-real time profiling laser system

NASA Astrophysics Data System (ADS)

Zhao, Kaiguang

LiDAR (Light Detection and Ranging) directly measures canopy vertical structures, and provides an effective remote sensing solution to accurate and spatially-explicit mapping of forest characteristics, such as canopy height and Leaf Area Index. However, many factors, such as large data volume and high costs for data acquisition, precludes the operational and practical use of most currently available LiDARs for frequent and large-scale mapping. At the same time, a growing need is arising for real-time remote sensing platforms, e.g., to provide timely information for urgent applications. This study aims to develop an airborne profiling LiDAR system, featured with on-the-fly data processing, for near real- or real-time forest inventory. The development of such a system involves implementing the on-board data processing and analysis as well as building useful regression-based models to relate LiDAR measurements with forest biophysical parameters. This work established a paradigm for an on-the-fly airborne profiling LiDAR system to inventory regional forest resources in real- or near real-time. The system was developed based on an existing portable airborne laser system (PALS) that has been previously assembled at NASA by Dr. Ross Nelson. Key issues in automating PALS as an on-the-fly system were addressed, including the design of an archetype for the system workflow, the development of efficient and robust algorithms for automatic data processing and analysis, the development of effective regression models to predict forest biophysical parameters from LiDAR measurements, and the implementation of an integrated software package to incorporate all the above development. This work exploited the untouched potential of airborne laser profilers for real-time forest inventory, and therefore, documented an initial step toward developing airborne-laser-based, on-the-fly, real-time, forest inventory systems. Results from this work demonstrated the utility and effectiveness of airborne scanning or profiling laser systems for remotely measuring various forest structural attributes at a range of scales, i.e., from individual tree, plot, stand and up to regional levels. The system not only provides a regional assessment tool, one that can be used to repeatedly, remotely measure hundreds or thousands of square kilometers with little/no analyst interaction or interpretation, but also serves as a paradigm for future efforts in building more advanced airborne laser systems such as real-time laser scanners.

Quantifying Soil Carbon Change from Wildfires in Peatland Ecosystems of the Eastern United States Using Repeat LiDAR

NASA Astrophysics Data System (ADS)

Reddy, A.; Hawbaker, T. J.; Zhu, Z.; Ward, S.; Wurster, F.; Newcomb, D.

2013-12-01

Wildfires are an increasing concern in peatland ecosystems along the coastal plains of the Eastern US. Human- and climate-induced changes to the ecosystems' hydrology can leave the soils, heavy with organic matter, susceptible to combustion in wildfires. This results in large losses of carbon that took many years to accumulate. However, accurately quantifying carbon losses in peatlands from wildfires is challenging because field data collection over extensive areas is difficult. For this study, our first objective was to evaluate the use of pre- and post-fire LiDAR data to quantify changes in surface elevations and soil carbon stocks for the 2011 Lateral West fire, which occurred in the Great Dismal Swamp National Wildlife Refuge (GDSNWR), Virginia, USA. Our second objective was to use a Monte Carlo approach to estimate how the vertical error in LiDAR points affected our calculation of soil carbon emissions. Bare-earth LiDAR points from 2010 and 2012 were obtained for GDSNWR with densities of 2 pulses/m2 and vertical elevation RMSE of 9 and 7 cm, respectively. Monte Carlo replicates were used to perturb individual bare-earth LiDAR points and generate probability distributions of elevation change within 10 m grid cells. Change in soil carbon were calculated within the Monte Carlo replicates by multiplying the LiDAR-derived volume of soil loss by depth-specific published values of soil bulk density, organic matter content, and carbon content. The 5th, 50th and 95th percentiles of the elevation and carbon change distributions were outputted as raster layers. Loss in soil volume ranged from 10,820,000 to 13,190,000 m3 based on vertical error. Carbon loss within the entire area burned by the Lateral West fire perimeter (32.1 km2), based on the 5th, 50th and 95th percentiles was 0.64, 0.96, and 1.33 Tg C, respectively. Our study demonstrated a method to use LiDAR data to quantify carbon loss following fires in peatland ecosystems and incorporate elevation errors to calculate uncertainties in the estimates. The results of our analysis showed that soil carbon lost from the 2011 Lateral West fire were not insignificant, and suggest that the hydrology of these ecosystems should be managed to preserve and restore peatlands and their carbon stocks where possible, as they can play an important role in mitigating and offsetting fossil-fuel emissions.

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...

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...

Occupancy of red-naped sapsuckers in a coniferous forest: Using LiDAR to understand effects of vegetation structure and disturbance

Treesearch

Joseph D. Holbrook; Kerri T. Vierling; Lee A. Vierling; Andrew T. Hudak; Patrick Adam

2015-01-01

Red-naped sapsuckers (Sphyrapicus nuchalis) are functionally important because they create sapwells and cavities that other species use for food and nesting. Red-naped sapsucker ecology within aspen (Populus tremuloides) has been well studied, but relatively little is known about red-naped sapsuckers in conifer forests. We used light detection and ranging (LiDAR) data...

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…

Practical Use of ICT in Science and Mathematics Teachers' Training at Dar es Salaam University College of Education: An Analysis of Prospective Teachers' Technological Pedagogical Content Knowledge

ERIC Educational Resources Information Center

Kafyulilo, Ayoub C.

2010-01-01

This study investigated the ways through which pre-service science and mathematics teachers at Dar es Salaam University College of Education (DUCE) can acquire competencies for integrating technology pedagogy and content in teaching. Specifically the study investigated the preservice teachers' ICT integration competencies; practices that can be…

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...

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…

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...

A bayesian hierarchical model for spatio-temporal prediction and uncertainty assessment using repeat LiDAR acquisitions for the Kenai Peninsula, AK, USA

Treesearch

Chad Babcock; Hans Andersen; Andrew O. Finley; Bruce D. Cook

2015-01-01

Models leveraging repeat LiDAR and field collection campaigns may be one possible mechanism to monitor carbon flux in remote forested regions. Here, we look to the spatio-temporally data-rich Kenai Peninsula in Alaska, USA to examine the potential for Bayesian spatio-temporal mapping of terrestrial forest carbon storage and uncertainty.

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...

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...

Processing and utilization of LiDAR data as a support for a good management of DDBR

NASA Astrophysics Data System (ADS)

Nichersu, I.; Grigoras, I.; Constantinescu, A.; Mierla, M.; Tifanov, C.

2012-04-01

Danube Delta Biosphere Reserve (DDBR) has 5,800 km2 as surface and it is situated in the South-East of Europe, in the East of Romania. The paper is taking into account the data related to the elevation surfaces of the DDBR (Digital Terrain Model DTM and Digital Surface Model DSM). To produce such kind of models of elevation for the entire area of DDBR it was used the most modern method that utilizes the Light Detection And Ranging (LiDAR). The raw LiDAR data (x, y, z) for each point were transformed into grid formats for DTM and DSM. Based on these data multiple GIS analyses can be done for management purposes : hydraulic modeling 1D2D scenarios, flooding regime and protection, biomass volume estimation, GIS biodiversity processing. These analyses are very useful in the management planning process. The hydraulic modeling 1D2D scenarios are used by the administrative authority to predict the sense of the fluvial water flow and also to predict the places where the flooding could occur. Also it can be predicted the surface of the terrain that will be occupied by the water from floods. Flooding regime gives information about the frequency of the floods and also the intensity of these. In the same time it could be predicted the time of water remanence period. The protection face of the flooding regime is in direct relation with the socio-cultural communities and all their annexes those that are in risk of being flooded. This raises the problem of building dykes and other flooding protection systems. The biomass volume contains information derived from the LiDAR cloud points that describes only the vegetation. The volume of biomass is an important item in the management of a Biosphere Reserve. Also the LiDAR cloud points that refer to vegetation could help in identifying the groups of vegetal association. All these information corroborated with other information build good premises for a good management. Keywords: Danube Delta Biosphere Reserve, LiDAR data, DTM, DSM, flooding, management

Ground-Truthing of Airborne LiDAR Using RTK-GPS Surveyed Data in Coastal Louisiana's Wetlands

NASA Astrophysics Data System (ADS)

Lauve, R. M.; Alizad, K.; Hagen, S. C.

2017-12-01

Airborne LiDAR (Light Detection and Ranging) data are used by engineers and scientists to create bare earth digital elevation models (DEM), which are essential to modeling complex coastal, ecological, and hydrological systems. However, acquiring accurate bare earth elevations in coastal wetlands is difficult due to the density of marsh grasses that prevent the sensors reflection off the true ground surface. Previous work by Medeiros et al. [2015] developed a technique to assess LiDAR error and adjust elevations according to marsh vegetation density and index. The aim of this study is the collection of ground truth points and the investigation on the range of potential errors found in existing LiDAR datasets within coastal Louisiana's wetlands. Survey grids were mapped out in an area dominated by Spartina alterniflora and a survey-grade Trimble Real Time Kinematic (RTK) GPS device was employed to measure bare earth ground elevations in the marsh system adjacent to Terrebonne Bay, LA. Elevations were obtained for 20 meter-spaced surveyed grid points and were used to generate a DEM. The comparison between LiDAR derived and surveyed data DEMs yield an average difference of 23 cm with a maximum difference of 68 cm. Considering the local tidal range of 45 cm, these differences can introduce substantial error when the DEM is used for ecological modeling [Alizad et al., 2016]. Results from this study will be further analyzed and implemented in order to adjust LiDAR-derived DEMs closer to their true elevation across Louisiana's coastal wetlands. ReferencesAlizad, K., S. C. Hagen, J. T. Morris, S. C. Medeiros, M. V. Bilskie, and J. F. Weishampel (2016), Coastal wetland response to sea-level rise in a fluvial estuarine system, Earth's Future, 4(11), 483-497, 10.1002/2016EF000385. Medeiros, S., S. Hagen, J. Weishampel, and J. Angelo (2015), Adjusting Lidar-Derived Digital Terrain Models in Coastal Marshes Based on Estimated Aboveground Biomass Density, Remote Sensing, 7(4), 3507-3525, 10.3390/rs70403507.

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 towards areas where the methods disagree, highlighting how each technique should be properly weighted over these areas to increase the classification accuracy of water. The conclusions and techniques developed in this study are applicable to other areas where similar environmental conditions and data availability exist.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

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, Landslides, Oregon, Inventory, Hazard

High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates.

PubMed

Madec, Simon; Baret, Fred; de Solan, Benoît; Thomas, Samuel; Dutartre, Dan; Jezequel, Stéphane; Hemmerlé, Matthieu; Colombeau, Gallian; Comar, Alexis

2017-01-01

The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide plant height estimates as a high-throughput plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Frequent LiDAR measurements were performed along the growth cycle using a phénomobile unmanned ground vehicle. UAV equipped with a high resolution RGB camera was flying the experiment several times to retrieve the digital surface model from structure from motion techniques. Both techniques provide a 3D dense point cloud from which the plant height can be estimated. Plant height first defined as the z -value for which 99.5% of the points of the dense cloud are below. This provides good consistency with manual measurements of plant height (RMSE = 3.5 cm) while minimizing the variability along each microplot. Results show that LiDAR and structure from motion plant height values are always consistent. However, a slight under-estimation is observed for structure from motion techniques, in relation with the coarser spatial resolution of UAV imagery and the limited penetration capacity of structure from motion as compared to LiDAR. Very high heritability values ( H 2 > 0.90) were found for both techniques when lodging was not present. The dynamics of plant height shows that it carries pertinent information regarding the period and magnitude of the plant stress. Further, the date when the maximum plant height is reached was found to be very heritable ( H 2 > 0.88) and a good proxy of the flowering stage. Finally, the capacity of plant height as a proxy for total above ground biomass and yield is discussed.

To the Application of LiDAR to Detect the Geological Structures in Sulphurets Property, British Columbia, Canada

NASA Astrophysics Data System (ADS)

Koohzare, A.; Rezaeian, M.; McIntosh, A.

2009-05-01

The Kerr Sulphurets property in North Western British Columbia has been explored primarily as a placer gold holding since the 1880s; and, potentially includes one of Canada's largest gold deposits (e.g. the Mitchell Zone). The Sulphurets camp has been classified by Taylor in 2007 as a prominent global epithermal high-sulphidation subtype with 10 million tonnes of ore (reserves + production) containing approximately 10 g/t gold. The geological and geophysical observations of this deposit indicate intrusion- related mineralized veins which are known to overlap as the result of structural complexities. Faulting predates mineralization and alteration and dramatically dominates the location of the mineralization for this porphyry- epithermal high-sulphidation deposit (Britton and Alldrick 1988, British Columbia Ministry of Energy, Mines and Petroleum Resources, 1992; Margolis, 1993). However, the surface trace of these structures and lineaments within the site is obscured by vegetation, glacial cover and steep topographic relief. We used high resolution LiDAR airborne bare-earth sensing (vegetative data deleted) in an effort to detect the surface geological features and lineaments in the Kerr Sulphurets site. The LiDAR flight was designed to acquire high density data with 2 points per square meter using a 150 kHz multipulse system. High resolution LiDAR data provides a level of detail not achievable by other digital terrain modelling techniques, whether extracted from aerial photography, low-resolution topographic contour maps, 10-30 meter USGS, or SRTM digital elevation models. LiDAR bare-earth data spectacularly revealed hidden geological structures within the property district, which in turn assisted in identifying the high potential zones for mineralization in Sulphurets.

Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data

USGS Publications Warehouse

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

2015-01-01

Modern operational forest inventory often uses remotely sensed data that cover the whole inventory area to produce spatially explicit estimates of forest properties through statistical models. The data obtained by airborne light detection and ranging (LiDAR) correlate well with many forest inventory variables, such as the tree height, the timber volume, and the biomass. To construct an accurate model over thousands of hectares, LiDAR data must be supplemented with several hundred field sample measurements of forest inventory variables. This can be costly and time consuming. Different LiDAR-data-based and spatial-data-based sampling designs can reduce the number of field sample plots needed. However, problems arising from the features of the LiDAR data, such as a large number of predictors compared with the sample size (overfitting) or a strong correlation among predictors (multicollinearity), may decrease the accuracy and precision of the estimates and predictions. To overcome these problems, a Bayesian linear model with the singular value decomposition of predictors, combined with regularization, is proposed. The model performance in predicting different forest inventory variables is verified in ten inventory areas from two continents, where the number of field sample plots is reduced using different sampling designs. The results show that, with an appropriate field plot selection strategy and the proposed linear model, the total relative error of the predicted forest inventory variables is only 5%–15% larger using 50 field sample plots than the error of a linear model estimated with several hundred field sample plots when we sum up the error due to both the model noise variance and the model’s lack of fit.

Mapping SOC (Soil Organic Carbon) using LiDAR-derived vegetation indices in a random forest regression model

NASA Astrophysics Data System (ADS)

Will, R. M.; Glenn, N. F.; Benner, S. G.; Pierce, J. L.; Spaete, L.; Li, A.

2015-12-01

Quantifying SOC (Soil Organic Carbon) storage in complex terrain is challenging due to high spatial variability. Generally, the challenge is met by transforming point data to the entire landscape using surrogate, spatially-distributed, variables like elevation or precipitation. In many ecosystems, remotely sensed information on above-ground vegetation (e.g. NDVI) is a good predictor of below-ground carbon stocks. In this project, we are attempting to improve this predictive method by incorporating LiDAR-derived vegetation indices. LiDAR provides a mechanism for improved characterization of aboveground vegetation by providing structural parameters such as vegetation height and biomass. In this study, a random forest model is used to predict SOC using a suite of LiDAR-derived vegetation indices as predictor variables. The Reynolds Creek Experimental Watershed (RCEW) is an ideal location for a study of this type since it encompasses a strong elevation/precipitation gradient that supports lower biomass sagebrush ecosystems at low elevations and forests with more biomass at higher elevations. Sagebrush ecosystems composed of Wyoming, Low and Mountain Sagebrush have SOC values ranging from .4 to 1% (top 30 cm), while higher biomass ecosystems composed of aspen, juniper and fir have SOC values approaching 4% (top 30 cm). Large differences in SOC have been observed between canopy and interspace locations and high resolution vegetation information is likely to explain plot scale variability in SOC. Mapping of the SOC reservoir will help identify underlying controls on SOC distribution and provide insight into which processes are most important in determining SOC in semi-arid mountainous regions. In addition, airborne LiDAR has the potential to characterize vegetation communities at a high resolution and could be a tool for improving estimates of SOC at larger scales.

Genetic Structure, Linkage Disequilibrium and Signature of Selection in Sorghum: Lessons from Physically Anchored DArT Markers

PubMed Central

Bouchet, Sophie; Pot, David; Deu, Monique; Rami, Jean-François; Billot, Claire; Perrier, Xavier; Rivallan, Ronan; Gardes, Laëtitia; Xia, Ling; Wenzl, Peter; Kilian, Andrzej; Glaszmann, Jean-Christophe

2012-01-01

Population structure, extent of linkage disequilibrium (LD) as well as signatures of selection were investigated in sorghum using a core sample representative of worldwide diversity. A total of 177 accessions were genotyped with 1122 informative physically anchored DArT markers. The properties of DArTs to describe sorghum genetic structure were compared to those of SSRs and of previously published RFLP markers. Model-based (STRUCTURE software) and Neighbor-Joining diversity analyses led to the identification of 6 groups and confirmed previous evolutionary hypotheses. Results were globally consistent between the different marker systems. However, DArTs appeared more robust in terms of data resolution and bayesian group assignment. Whole genome linkage disequilibrium as measured by mean r2 decreased from 0.18 (between 0 to 10 kb) to 0.03 (between 100 kb to 1 Mb), stabilizing at 0.03 after 1 Mb. Effects on LD estimations of sample size and genetic structure were tested using i. random sampling, ii. the Maximum Length SubTree algorithm (MLST), and iii. structure groups. Optimizing population composition by the MLST reduced the biases in small samples and seemed to be an efficient way of selecting samples to make the best use of LD as a genome mapping approach in structured populations. These results also suggested that more than 100,000 markers may be required to perform genome-wide association studies in collections covering worldwide sorghum diversity. Analysis of DArT markers differentiation between the identified genetic groups pointed out outlier loci potentially linked to genes controlling traits of interest, including disease resistance genes for which evidence of selection had already been reported. In addition, evidence of selection near a homologous locus of FAR1 concurred with sorghum phenotypic diversity for sensitivity to photoperiod. PMID:22428056

Mapping the spatial pattern of temperate forest above ground biomass by integrating airborne lidar with Radarsat-2 imagery via geostatistical models

NASA Astrophysics Data System (ADS)

Li, Wang; Niu, Zheng; Gao, Shuai; Wang, Cheng

2014-11-01

Light Detection and Ranging (LiDAR) and Synthetic Aperture Radar (SAR) are two competitive active remote sensing techniques in forest above ground biomass estimation, which is important for forest management and global climate change study. This study aims to further explore their capabilities in temperate forest above ground biomass (AGB) estimation by emphasizing the spatial auto-correlation of variables obtained from these two remote sensing tools, which is a usually overlooked aspect in remote sensing applications to vegetation studies. Remote sensing variables including airborne LiDAR metrics, backscattering coefficient for different SAR polarizations and their ratio variables for Radarsat-2 imagery were calculated. First, simple linear regression models (SLR) was established between the field-estimated above ground biomass and the remote sensing variables. Pearson's correlation coefficient (R2) was used to find which LiDAR metric showed the most significant correlation with the regression residuals and could be selected as co-variable in regression co-kriging (RCoKrig). Second, regression co-kriging was conducted by choosing the regression residuals as dependent variable and the LiDAR metric (Hmean) with highest R2 as co-variable. Third, above ground biomass over the study area was estimated using SLR model and RCoKrig model, respectively. The results for these two models were validated using the same ground points. Results showed that both of these two methods achieved satisfactory prediction accuracy, while regression co-kriging showed the lower estimation error. It is proved that regression co-kriging model is feasible and effective in mapping the spatial pattern of AGB in the temperate forest using Radarsat-2 data calibrated by airborne LiDAR metrics.

High-resolution mapping of forest carbon stocks in the Colombian Amazon

NASA Astrophysics Data System (ADS)

Asner, G. P.; Clark, J. K.; Mascaro, J.; Galindo García, G. A.; Chadwick, K. D.; Navarrete Encinales, D. A.; Paez-Acosta, G.; Cabrera Montenegro, E.; Kennedy-Bowdoin, T.; Duque, Á.; Balaji, A.; von Hildebrand, P.; Maatoug, L.; Bernal, J. F. Phillips; Yepes Quintero, A. P.; Knapp, D. E.; García Dávila, M. C.; Jacobson, J.; Ordóñez, M. F.

2012-07-01

High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or light detection and ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high-resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (> 40%) of the Colombian Amazon - a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon maps have 14% uncertainty at 1 ha resolution, and the regional map based on stratification has 28% uncertainty in any given hectare. High-resolution approaches with quantifiable pixel-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.

High-resolution Mapping of Forest Carbon Stocks in the Colombian Amazon

NASA Astrophysics Data System (ADS)

Asner, G. P.; Clark, J. K.; Mascaro, J.; Galindo García, G. A.; Chadwick, K. D.; Navarrete Encinales, D. A.; Paez-Acosta, G.; Cabrera Montenegro, E.; Kennedy-Bowdoin, T.; Duque, Á.; Balaji, A.; von Hildebrand, P.; Maatoug, L.; Bernal, J. F. Phillips; Knapp, D. E.; García Dávila, M. C.; Jacobson, J.; Ordóñez, M. F.

2012-03-01

High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or Light Detection and Ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (>40 %) of the Colombian Amazon - a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon mapping samples had 14.6 % uncertainty at 1 ha resolution, and regional maps based on stratification and regression approaches had 25.6 % and 29.6 % uncertainty, respectively, in any given hectare. High-resolution approaches with reported local-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision-makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.

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.

Genetic structure, linkage disequilibrium and signature of selection in Sorghum: lessons from physically anchored DArT markers.

PubMed

Bouchet, Sophie; Pot, David; Deu, Monique; Rami, Jean-François; Billot, Claire; Perrier, Xavier; Rivallan, Ronan; Gardes, Laëtitia; Xia, Ling; Wenzl, Peter; Kilian, Andrzej; Glaszmann, Jean-Christophe

2012-01-01

Population structure, extent of linkage disequilibrium (LD) as well as signatures of selection were investigated in sorghum using a core sample representative of worldwide diversity. A total of 177 accessions were genotyped with 1122 informative physically anchored DArT markers. The properties of DArTs to describe sorghum genetic structure were compared to those of SSRs and of previously published RFLP markers. Model-based (STRUCTURE software) and Neighbor-Joining diversity analyses led to the identification of 6 groups and confirmed previous evolutionary hypotheses. Results were globally consistent between the different marker systems. However, DArTs appeared more robust in terms of data resolution and bayesian group assignment. Whole genome linkage disequilibrium as measured by mean r(2) decreased from 0.18 (between 0 to 10 kb) to 0.03 (between 100 kb to 1 Mb), stabilizing at 0.03 after 1 Mb. Effects on LD estimations of sample size and genetic structure were tested using i. random sampling, ii. the Maximum Length SubTree algorithm (MLST), and iii. structure groups. Optimizing population composition by the MLST reduced the biases in small samples and seemed to be an efficient way of selecting samples to make the best use of LD as a genome mapping approach in structured populations. These results also suggested that more than 100,000 markers may be required to perform genome-wide association studies in collections covering worldwide sorghum diversity. Analysis of DArT markers differentiation between the identified genetic groups pointed out outlier loci potentially linked to genes controlling traits of interest, including disease resistance genes for which evidence of selection had already been reported. In addition, evidence of selection near a homologous locus of FAR1 concurred with sorghum phenotypic diversity for sensitivity to photoperiod.

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

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

Optimizing placements of ground-based snow sensors for areal snow cover estimation using a machine-learning algorithm and melt-season snow-LiDAR data

NASA Astrophysics Data System (ADS)

Oroza, C.; Zheng, Z.; Glaser, S. D.; Bales, R. C.; Conklin, M. H.

2016-12-01

We present a structured, analytical approach to optimize ground-sensor placements based on time-series remotely sensed (LiDAR) data and machine-learning algorithms. We focused on catchments within the Merced and Tuolumne river basins, covered by the JPL Airborne Snow Observatory LiDAR program. First, we used a Gaussian mixture model to identify representative sensor locations in the space of independent variables for each catchment. Multiple independent variables that govern the distribution of snow depth were used, including elevation, slope, and aspect. Second, we used a Gaussian process to estimate the areal distribution of snow depth from the initial set of measurements. This is a covariance-based model that also estimates the areal distribution of model uncertainty based on the independent variable weights and autocorrelation. The uncertainty raster was used to strategically add sensors to minimize model uncertainty. We assessed the temporal accuracy of the method using LiDAR-derived snow-depth rasters collected in water-year 2014. In each area, optimal sensor placements were determined using the first available snow raster for the year. The accuracy in the remaining LiDAR surveys was compared to 100 configurations of sensors selected at random. We found the accuracy of the model from the proposed placements to be higher and more consistent in each remaining survey than the average random configuration. We found that a relatively small number of sensors can be used to accurately reproduce the spatial patterns of snow depth across the basins, when placed using spatial snow data. Our approach also simplifies sensor placement. At present, field surveys are required to identify representative locations for such networks, a process that is labor intensive and provides limited guarantees on the networks' representation of catchment independent variables.

Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing.

PubMed

Hakkenberg, C R; Peet, R K; Urban, D L; Song, C

2018-01-01

In light of the need to operationalize the mapping of forest composition at landscape scales, this study uses multi-scale nested vegetation sampling in conjunction with LiDAR-hyperspectral remotely sensed data from the G-LiHT airborne sensor to map vascular plant compositional turnover in a compositionally and structurally complex North Carolina Piedmont forest. Reflecting a shift in emphasis from remotely sensing individual crowns to detecting aggregate optical-structural properties of forest stands, predictive maps reflect the composition of entire vascular plant communities, inclusive of those species smaller than the resolution of the remotely sensed imagery, intertwined with proximate taxa, or otherwise obscured from optical sensors by dense upper canopies. Stand-scale vascular plant composition is modeled as community continua: where discrete community-unit classes at different compositional resolutions provide interpretable context for continuous gradient maps that depict n-dimensional compositional complexity as a single, consistent RGB color combination. In total, derived remotely sensed predictors explain 71%, 54%, and 48% of the variation in the first three components of vascular plant composition, respectively. Among all remotely sensed environmental gradients, topography derived from LiDAR ground returns, forest structure estimated from LiDAR all returns, and morphological-biochemical traits determined from hyperspectral imagery each significantly correspond to the three primary axes of floristic composition in the study site. Results confirm the complementarity of LiDAR and hyperspectral sensors for modeling the environmental gradients constraining landscape turnover in vascular plant composition and hold promise for predictive mapping applications spanning local land management to global ecosystem modeling. © 2017 by the Ecological Society of America.

A Whole Genome DArTseq and SNP Analysis for Genetic Diversity Assessment in Durum Wheat from Central Fertile Crescent

PubMed Central

Shahid, Muhammad Qasim; Çiftçi, Vahdettin; E. Sáenz de Miera, Luis; Aasim, Muhammad; Nadeem, Muhammad Azhar; Aktaş, Husnu; Özkan, Hakan; Hatipoğlu, Rüştü

2017-01-01

Until now, little attention has been paid to the geographic distribution and evaluation of genetic diversity of durum wheat from the Central Fertile Crescent (modern-day Turkey and Syria). Turkey and Syria are considered as primary centers of wheat diversity, and thousands of locally adapted wheat landraces are still present in the farmers’ small fields. We planned this study to evaluate the genetic diversity of durum wheat landraces from the Central Fertile Crescent by genotyping based on DArTseq and SNP analysis. A total of 39,568 DArTseq and 20,661 SNP markers were used to characterize the genetic characteristic of 91 durum wheat land races. Clustering based on Neighbor joining analysis, principal coordinate as well as Bayesian model implemented in structure, clearly showed that the grouping pattern is not associated with the geographical distribution of the durum wheat due to the mixing of the Turkish and Syrian landraces. Significant correlation between DArTseq and SNP markers was observed in the Mantel test. However, we detected a non-significant relationship between geographical coordinates and DArTseq (r = -0.085) and SNP (r = -0.039) loci. These results showed that unconscious farmer selection and lack of the commercial varieties might have resulted in the exchange of genetic material and this was apparent in the genetic structure of durum wheat in Turkey and Syria. The genomic characterization presented here is an essential step towards a future exploitation of the available durum wheat genetic resources in genomic and breeding programs. The results of this study have also depicted a clear insight about the genetic diversity of wheat accessions from the Central Fertile Crescent. PMID:28099442

High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates

PubMed Central

Madec, Simon; Baret, Fred; de Solan, Benoît; Thomas, Samuel; Dutartre, Dan; Jezequel, Stéphane; Hemmerlé, Matthieu; Colombeau, Gallian; Comar, Alexis

2017-01-01

The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide plant height estimates as a high-throughput plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Frequent LiDAR measurements were performed along the growth cycle using a phénomobile unmanned ground vehicle. UAV equipped with a high resolution RGB camera was flying the experiment several times to retrieve the digital surface model from structure from motion techniques. Both techniques provide a 3D dense point cloud from which the plant height can be estimated. Plant height first defined as the z-value for which 99.5% of the points of the dense cloud are below. This provides good consistency with manual measurements of plant height (RMSE = 3.5 cm) while minimizing the variability along each microplot. Results show that LiDAR and structure from motion plant height values are always consistent. However, a slight under-estimation is observed for structure from motion techniques, in relation with the coarser spatial resolution of UAV imagery and the limited penetration capacity of structure from motion as compared to LiDAR. Very high heritability values (H2> 0.90) were found for both techniques when lodging was not present. The dynamics of plant height shows that it carries pertinent information regarding the period and magnitude of the plant stress. Further, the date when the maximum plant height is reached was found to be very heritable (H2> 0.88) and a good proxy of the flowering stage. Finally, the capacity of plant height as a proxy for total above ground biomass and yield is discussed. PMID:29230229

Challenges and Opportunities: One Stop Processing of Automatic Large-Scale Base Map Production Using Airborne LIDAR Data Within GIS Environment. Case Study: Makassar City, Indonesia

NASA Astrophysics Data System (ADS)

Widyaningrum, E.; Gorte, B. G. H.

2017-05-01

LiDAR data acquisition is recognized as one of the fastest solutions to provide basis data for large-scale topographical base maps worldwide. Automatic LiDAR processing is believed one possible scheme to accelerate the large-scale topographic base map provision by the Geospatial Information Agency in Indonesia. As a progressive advanced technology, Geographic Information System (GIS) open possibilities to deal with geospatial data automatic processing and analyses. Considering further needs of spatial data sharing and integration, the one stop processing of LiDAR data in a GIS environment is considered a powerful and efficient approach for the base map provision. The quality of the automated topographic base map is assessed and analysed based on its completeness, correctness, quality, and the confusion matrix.

Automated On-tip Affinity Capture Coupled with Mass Spectrometry to Characterize Intact Antibody-Drug Conjugates from Blood

NASA Astrophysics Data System (ADS)

Li, Ke Sherry; Chu, Phillip Y.; Fourie-O'Donohue, Aimee; Srikumar, Neha; Kozak, Katherine R.; Liu, Yichin; Tran, John C.

2018-05-01

Antibody-drug conjugates (ADCs) present unique challenges for ligand-binding assays primarily due to the dynamic changes of the drug-to-antibody ratio (DAR) distribution in vivo and in vitro. Here, an automated on-tip affinity capture platform with subsequent mass spectrometry analysis was developed to accurately characterize the DAR distribution of ADCs from biological matrices. A variety of elution buffers were tested to offer optimal recovery, with trastuzumab serving as a surrogate to the ADCs. High assay repeatability (CV 3%) was achieved for trastuzumab antibody when captured below the maximal binding capacity of 7.5 μg. Efficient on-tip deglycosylation was also demonstrated in 1 h followed by affinity capture. Moreover, this tip-based platform affords higher throughput for DAR characterization when compared with a well-characterized bead-based method.

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*}]).

San Clemente Island Baseline LiDAR Mapping Final Report

DTIC Science & Technology

2016-12-01

automatically detect individual shrubs , though this method was more successful in some places than others. Finally, the hyperspectral data cubes, with...for California coastal shrub land, likely because the LiDAR tends not to penetrate the dense shrub , resulting in nearly identical first and last...subclasses of these structures. Using the aerial photographs in conjunction with the ground truth data, we automated the identification of the shrub Rhus

Predicting diameter at breast height from total height and crown length

Treesearch

Quang V. Cao; Thomas J. Dean

2013-01-01

Tree diameter at breast height (d.b.h.) is often predicted from total height (model 1a) or both total height and number of trees per acre (model 1b). These approaches are useful when Light Detection and Ranging (LiDAR) data are available. LiDAR height data can be employed to predict tree d.b.h., and consequently individual tree volumes and volume/ ha can be obtained...

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...

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...

Use of airborne near-infrared LiDAR for determining channel cross-section characteristics and monitoring aquatic habitat in Pacific Northwest rivers: A preliminary analysis [Chapter 6

Treesearch

Russell N. Faux; John M. Buffington; M. German Whitley; Steve H. Lanigan; Brett B. Roper

2009-01-01

Aquatic habitat monitoring is being conducted by numerous organizations in many parts of the Pacific Northwest to document physical and biological conditions of stream reaches as part of legal- and policy-mandated environmental assessments. Remote sensing using discrete-return, near-infrared, airborne LiDAR (Light Detection and Ranging) and high-resolution digital...

An Exploration of the Viability of Partnership between "Dar Al-Ulum" and Higher Education Institutions in North West England Focusing upon Pedagogy and Relevance

ERIC Educational Resources Information Center

Geaves, Ron

2015-01-01

The article explores possibilities of collaboration between Muslim providers of traditional education ("dar al-ulums") and HE/FE institutions in close geographical proximity in the North West England. It reports the outcomes of a project carried out in 2011/2012 influenced by the findings of the Makadam/Scott-Baumann report in 2010, in…

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…

Factors Contributing to the Accumulation of Primary Teacher's Debts to the Government of Tanzania: A Case Study for Dar Es Salaam Region

ERIC Educational Resources Information Center

Kombo, Ibun

2015-01-01

This paper presents the findings of the study which was conducted to determine factors contributing to the accumulation of primary school teacher's debts to the Government of Tanzania, a case study of Dar es Salaam Region in its three municipalities namely, Ilala, Kinondoni and Temeke. Data was obtained through sampling method which also helped to…

Small Purchases.

DTIC Science & Technology

1982-01-01

excusable delay under "general contract law ". The Board went on to state that had the usual DAR default clause been present, it would have...that in Michigan the Board, in applying its "general contract law ", referenced the "usual DAR default clause" after the Board specifically recognized...Provisions through its "general contract law " principles in appropriate cases. In future cases the appropriate remedy may not be found in the Additional

Physiological and pharmacological characterization of the larval Anopheles albimanus rectum supports a change in protein distribution and/or function in varying salinities

PubMed Central

Smith, Kristin E.; Raymond, Steven L.; Valenti, Micheala L.; Smith, Peter J.S.; Linser, Paul J.

2010-01-01

Ion regulation is a biological process crucial to the survival of mosquito larvae and a major organ responsible for this regulation is the rectum. The recta of anopheline larvae are distinct from other subfamilies of mosquitoes in several ways, yet have not yet been characterized extensively. Here we characterize the two major cell types of the anopheline rectum, DAR and non-DAR cells, using histological, physiological, and pharmacological analyses. Proton flux was measured at the basal membrane of 2%- and 50%-artificial sea water-reared An. albimanus larvae using self-referencing ion-selective microelectrodes, and the two cell types were found to differ in basal membrane proton flux. Additionally, differences in the response of that flux to pharmacological inhibitors in larvae reared in 2% versus 50% ASW indicate changes in protein function between the two rearing conditions. Finally, histological analyses suggest that the non-DAR cells are structurally suited for mediating ion transport. These data support a model of rectal ion regulation in which the non-DAR cells have a resorptive function in freshwater-reared larvae and a secretive function in saline water-reared larvae. In this way, anopheline larvae may adapt to varying salinities. PMID:20460167

Seasonal changes in the larvel populations of Aedes aegypti in two biotopes in Dar es Salaam, Tanzania

PubMed Central

Trpis, Milan

1972-01-01

The seasonal dynamics of larval populations of Aedes aegypti was studied in two different biotopes in Dar es Salaam, Tanzania. The first biotope was located on the Msasani peninsula on the coast 6 km north of Dar es Salaam, where A. aegypti breeds exclusively in coral rock holes. The population dynamics was studied during both the rainy and the dry season. Seasonal changes in the density of A. aegypti larvae depend primarily on variation in rainfall. The population of larvae dropped to zero only for a short time during the driest period while the adult population was maintained at a low level. The second biotope was in an automobile dump in a Dar es Salaam suburb, where A. aegypti breeds in artificial containers such as tires, automobile parts, tins, coconut shells, and snail shells. The greater part of the A. aegypti population of this biotope is maintained in the egg stage during the dry season. It serves as a focal point for breeding during the dry season: with the coming of the rains, the population expands into the surrounding residential areas. More than 70% of the larval population developed in tires, 20% in tins, 5% in coconut shells, and 1% in snail shells. PMID:4539415

Rapid topographic and bathymetric reconnaissance using airborne LiDAR

NASA Astrophysics Data System (ADS)

Axelsson, Andreas

2010-10-01

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

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.

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.

Conjugated Polymers Via Direct Arylation Polymerization in Continuous Flow: Minimizing the Cost and Batch-to-Batch Variations for High-Throughput Energy Conversion.

PubMed

Gobalasingham, Nemal S; Carlé, Jon E; Krebs, Frederik C; Thompson, Barry C; Bundgaard, Eva; Helgesen, Martin

2017-11-01

Continuous flow methods are utilized in conjunction with direct arylation polymerization (DArP) for the scaled synthesis of the roll-to-roll compatible polymer, poly[(2,5-bis(2-hexyldecyloxy)phenylene)-alt-(4,7-di(thiophen-2-yl)-benzo[c][1,2,5]thiadiazole)] (PPDTBT). PPDTBT is based on simple, inexpensive, and scalable monomers using thienyl-flanked benzothiadiazole as the acceptor, which is the first β-unprotected substrate to be used in continuous flow via DArP, enabling critical evaluation of the suitability of this emerging synthetic method for minimizing defects and for the scaled synthesis of high-performance materials. To demonstrate the usefulness of the method, DArP-prepared PPDTBT via continuous flow synthesis is employed for the preparation of indium tin oxide (ITO)-free and flexible roll-coated solar cells to achieve a power conversion efficiency of 3.5% for 1 cm 2 devices, which is comparable to the performance of PPDTBT polymerized through Stille cross coupling. These efforts demonstrate the distinct advantages of the continuous flow protocol with DArP avoiding use of toxic tin chemicals, reducing the associated costs of polymer upscaling, and minimizing batch-to-batch variations for high-quality material. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

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.

Understanding patterns of vegetation structure and distribution across Great Smoky Mountains National Park using LiDAR and meteorology data

NASA Astrophysics Data System (ADS)

Kumar, J.; Hargrove, W. W.; Norman, S. P.; Hoffman, F. M.

2017-12-01

Great Smoky Mountains National Park (GSMNP) in Tennessee is a biodiversity hotspot and home to a large number of plant, animal and bird species. Driven by gradients of climate (ex. temperature, precipitation regimes), topography (ex. elevation, slope, aspect), geology (ex. soil types, textures, depth), hydrology (ex. drainage, moisture availability) etc. GSMNP offers a diverse composition and distribution of vegetation which in turn supports an array of wildlife. Understanding the vegetation canopy structure is critical to understand, monitor and manage the complex forest ecosystems like the Great Smoky Mountain National Park (GSMNP). Vegetation canopies not only help understand the vegetation, but are also a critically important habitat characteristics of many threatened and endangered animal and bird species that GSMNP is home to. Using airborne Light Detection and Ranging (LiDAR) we characterize the three-dimensional structure of the vegetation. LiDAR based analysis gives detailed insight in the canopy structure (overstory and understory) and its spatial variability within and across forest types. Vegetation structure and spatial distribution show strong correlation with climate, topographic, and edaphic variables and our multivariate analysis not just mines rich and large LiDAR data but presents ecological insights and data for vegetation within the park that can be useful to forest managers in their management and conservation efforts.

Identifying buried segments of active faults in the northern Rio Grande Rift using aeromagnetic, LiDAR,and gravity data, south-central Colorado, USA

USGS Publications Warehouse

Grauch, V.J.S.; Ruleman, Chester A.

2013-01-01

Combined interpretation of aeromagnetic and LiDAR data builds on the strength of the aeromagnetic method to locate normal faults with significant offset under cover and the strength of LiDAR interpretation to identify the age and sense of motion of faults. Each data set helps resolve ambiguities in interpreting the other. In addition, gravity data can be used to infer the sense of motion for totally buried faults inferred solely from aeromagnetic data. Combined interpretation to identify active faults at the northern end of the San Luis Basin of the northern Rio Grande rift has confirmed general aspects of previous geologic mapping but has also provided significant improvements. The interpretation revises and extends mapped fault traces, confirms tectonic versus fluvial origins of steep stream banks, and gains additional information on the nature of active and potentially active partially and totally buried faults. Detailed morphology of surfaces mapped from the LiDAR data helps constrain ages of the faults that displace the deposits. The aeromagnetic data provide additional information about their extents in between discontinuous scarps and suggest that several totally buried, potentially active faults are present on both sides of the valley.

The special role of item-context associations in the direct-access region of working memory.

PubMed

Campoy, Guillermo

2017-09-01

The three-embedded-component model of working memory (WM) distinguishes three representational states corresponding to three WM regions: activated long-term memory, direct-access region (DAR), and focus of attention. Recent neuroimaging research has revealed that access to the DAR is associated with enhanced hippocampal activity. Because the hippocampus mediates the encoding and retrieval of item-context associations, it has been suggested that this hippocampal activation is a consequence of the fact that item-context associations are particularly strong and accessible in the DAR. This study provides behavioral evidence for this view using an item-recognition task to assess the effect of non-intentional encoding and maintenance of item-location associations across WM regions. Five pictures of human faces were sequentially presented in different screen locations followed by a recognition probe. Visual cues immediately preceding the probe indicated the location thereof. When probe stimuli appeared in the same location that they had been presented within the memory set, the presentation of the cue was expected to elicit the activation of the corresponding WM representation through the just-established item-location association, resulting in faster recognition. Results showed this same-location effect, but only for items that, according to their serial position within the memory set, were held in the DAR.

Comparison of the filtering models for airborne LiDAR data by three classifiers with exploration on model transfer

NASA Astrophysics Data System (ADS)

Ma, Hongchao; Cai, Zhan; Zhang, Liang

2018-01-01

This paper discusses airborne light detection and ranging (LiDAR) point cloud filtering (a binary classification problem) from the machine learning point of view. We compared three supervised classifiers for point cloud filtering, namely, Adaptive Boosting, support vector machine, and random forest (RF). Nineteen features were generated from raw LiDAR point cloud based on height and other geometric information within a given neighborhood. The test datasets issued by the International Society for Photogrammetry and Remote Sensing (ISPRS) were used to evaluate the performance of the three filtering algorithms; RF showed the best results with an average total error of 5.50%. The paper also makes tentative exploration in the application of transfer learning theory to point cloud filtering, which has not been introduced into the LiDAR field to the authors' knowledge. We performed filtering of three datasets from real projects carried out in China with RF models constructed by learning from the 15 ISPRS datasets and then transferred with little to no change of the parameters. Reliable results were achieved, especially in rural area (overall accuracy achieved 95.64%), indicating the feasibility of model transfer in the context of point cloud filtering for both easy automation and acceptable accuracy.

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.

3D Lasers Increase Efficiency, Safety of Moving Machines

NASA Technical Reports Server (NTRS)

2015-01-01

Canadian company Neptec Design Group Ltd. developed its Laser Camera System, used by shuttles to render 3D maps of their hulls for assessing potential damage. Using NASA funding, the firm incorporated LiDAR technology and created the TriDAR 3D sensor. Its commercial arm, Neptec Technologies Corp., has sold the technology to Orbital Sciences, which uses it to guide its Cygnus spacecraft during rendezvous and dock operations at the International Space Station.

Mapping forest structure and composition from low-density LiDAR for informed forest, fuel, and fire management at Eglin Air Force Base, Florida, USA

Treesearch

Andrew T. Hudak; Benjamin C. Bright; Scott M. Pokswinski; E. Louise Loudermilk; Joseph J. O' Brien; Benjamin S. Hornsby; Carine Klauberg; Carlos A. Silva

2016-01-01

Eglin Air Force Base (AFB) in Florida, in the United States, conserves a large reservoir of native longleaf pine (Pinus palustris Mill.) stands that land managers maintain by using frequent fires. We predicted tree density, basal area, and dominant tree species from 195 forest inventory plots, low-density airborne LiDAR, and Landsat data available across the entirety...

Fast tracking of wind speed with a differential absorption LiDAR system: first results of an experimental campaign at Stromboli volcano

NASA Astrophysics Data System (ADS)

Parracino, Stefano; Santoro, Simone; Maio, Giovanni; Nuvoli, Marcello; Aiuppa, Alessandro; Fiorani, Luca

2017-04-01

Carbon dioxide (CO2) is considered a precursor gas of volcanic eruptions by volcanologists. Monitoring the anomalous release of this parameter, we can retrieve useful information for the mitigation of volcanic hazards, such as for air traffic security. From a dataset collected during the Stromboli volcano field campaign, an assessment of the wind speed, in both horizontal and vertical paths, performing a fast tracking of this parameter was retrieved. This was determined with a newly designed shot-per-shot differential absorption LiDAR system operated in the near-infrared spectral region due to the simultaneous reconstruction of CO2 concentrations and wind speeds, using the same sample of LiDAR returns. A correlation method was used for the wind speed retrieval in which the transport of the spatial inhomogeneities of the aerosol backscattering coefficient, along the optical path of the system, was analyzed.

a Voxel-Based Filtering Algorithm for Mobile LIDAR Data

NASA Astrophysics Data System (ADS)

Qin, H.; Guan, G.; Yu, Y.; Zhong, L.

2018-04-01

This paper presents a stepwise voxel-based filtering algorithm for mobile LiDAR data. In the first step, to improve computational efficiency, mobile LiDAR points, in xy-plane, are first partitioned into a set of two-dimensional (2-D) blocks with a given block size, in each of which all laser points are further organized into an octree partition structure with a set of three-dimensional (3-D) voxels. Then, a voxel-based upward growing processing is performed to roughly separate terrain from non-terrain points with global and local terrain thresholds. In the second step, the extracted terrain points are refined by computing voxel curvatures. This voxel-based filtering algorithm is comprehensively discussed in the analyses of parameter sensitivity and overall performance. An experimental study performed on multiple point cloud samples, collected by different commercial mobile LiDAR systems, showed that the proposed algorithm provides a promising solution to terrain point extraction from mobile point clouds.

Performance testing of 3D point cloud software

NASA Astrophysics Data System (ADS)

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

2013-10-01

LiDAR systems are being used widely in recent years for many applications in the engineering field: civil engineering, cultural heritage, mining, industry and environmental engineering. One of the most important limitations of this technology is the large computational requirements involved in data processing, especially for large mobile LiDAR datasets. Several software solutions for data managing are available in the market, including open source suites, however, users often unknown methodologies to verify their performance properly. In this work a methodology for LiDAR software performance testing is presented and four different suites are studied: QT Modeler, VR Mesh, AutoCAD 3D Civil and the Point Cloud Library running in software developed at the University of Vigo (SITEGI). The software based on the Point Cloud Library shows better results in the loading time of the point clouds and CPU usage. However, it is not as strong as commercial suites in working set and commit size tests.

Contribution to understanding the post-mining landscape - Application of airborn LiDAR and historical maps at the example from Silesian Upland (Poland)

NASA Astrophysics Data System (ADS)

Gawior, D.; Rutkiewicz, P.; Malik, I.; Wistuba, M.

2017-11-01

LiDAR data provide new insights into the historical development of mining industry recorded in the topography and landscape. In the study on the lead ore mining in the 13th-17th century we identified remnants of mining activity in relief that are normally obscured by dense vegetation. The industry in Tarnowice Plateau was based on exploitation of galena from the bedrock. New technologies, including DEM from airborne LiDAR provide show that present landscape and relief of post-mining area under study developed during several, subsequent phases of exploitation when different techniques of exploitation were used and probably different types of ores were exploited. Study conducted on the Tarnowice Plateau proved that combining GIS visualization techniques with historical maps, among all geological maps, is a promising approach in reconstructing development of anthropogenic relief and landscape..

Current clinical efficacy of chloroquine for the treatment of Plasmodium falciparum infections in urban Dar es Salaam, United Republic of Tanzania.

PubMed Central

Premji, Z.; Makwaya, C.; Minjas, J. N.

1999-01-01

Reported is the use of a 14-day WHO protocol, which takes into account the clinical, parasitological and haematological responses to antimalarial drugs, to determine the efficacy of chloroquine in the treatment of uncomplicated malaria in young children (n = 200) in urban Dar es Salaam. Chloroquine failure was found in 43% of the children. Of these, 12.5% were considered to be early treatment failures and were given a single dose of sulfadoxine-pyrimethamine. Fever subsided in all children treated with sulfadoxine-pyrimethamine and there were no parasitological failures. In addition, children treated with sulfadoxine-pyrimethamine because of early treatment failure with chloroquine had better haematological recovery than the chloroquine-sensitive group. It is concluded that chloroquine can no longer be considered an effective therapy for P. falciparum malaria in young children in Dar es Salaam. PMID:10534897

Complex Building Detection Through Integrating LIDAR and Aerial Photos

NASA Astrophysics Data System (ADS)

Zhai, R.

2015-02-01

This paper proposes a new approach on digital building detection through the integration of LiDAR data and aerial imagery. It is known that most building rooftops are represented by different regions from different seed pixels. Considering the principals of image segmentation, this paper employs a new region based technique to segment images, combining both the advantages of LiDAR and aerial images together. First, multiple seed points are selected by taking several constraints into consideration in an automated way. Then, the region growing procedures proceed by combining the elevation attribute from LiDAR data, visibility attribute from DEM (Digital Elevation Model), and radiometric attribute from warped images in the segmentation. Through this combination, the pixels with similar height, visibility, and spectral attributes are merged into one region, which are believed to represent the whole building area. The proposed methodology was implemented on real data and competitive results were achieved.

Vibration and acoustic noise emitted by dry-type air-core reactors under PWM voltage excitation

NASA Astrophysics Data System (ADS)

Li, Jingsong; Wang, Shanming; Hong, Jianfeng; Yang, Zhanlu; Jiang, Shengqian; Xia, Shichong

2018-05-01

According to coupling way between the magnetic field and the structural order, structure mode is discussed by engaging finite element (FE) method and both natural frequency and modal shape for a dry-type air-core reactor (DAR) are obtained in this paper. On the basis of harmonic response analysis, electromagnetic force under PWM (Pulse Width Modulation) voltage excitation is mapped with the structure mesh, the vibration spectrum is gained and the consequences represents that the whole structure vibration predominates in the radial direction, with less axial vibration. Referring to the test standard of reactor noise, the rules of emitted noise of the DAR are measured and analyzed at chosen switching frequency matches the sample resonant frequency and the methods of active vibration and noise reduction are put forward. Finally, the low acoustic noise emission of a prototype DAR is verified by measurement.

Stability of anti-reflection coatings via the self-assembly encapsulation of silica nanoparticles by diazo-resins

NASA Astrophysics Data System (ADS)

Metzman, Jonathan S.; Ridley, Jason I.; Khalifa, Moataz B.; Heflin, James R.

2015-12-01

A modified silica nanoparticle (MSNP) solution was formed by the encapsulation of negatively charged silica nanoparticles by the UV-crosslinkable polycation oligomer diazo-resin (DAR). Appropriate DAR encapsulation concentrations were determined by use of zeta-potential and dynamic light scattering measurements. The MSNPs were used in conjunction with poly(styrene sulfonate) (PSS) to grow homogenous ionic self-assembled multilayer anti-reflection coatings. Stability was induced within the films by the exposure of UV-irradiation that allowed for crosslinking of the DAR and PSS. The films were characterized by UV/vis/IR spectroscopy and field emission scanning electron microscopy. The transmission and reflection levels were >98.5% and <0.05%, respectively. The refractive indices resided in the 1.25-1.26 range. The solvent stability was tested by sonication of the films in a ternary solvent (H2O/DMF/ZnCl2 3:5:2 w/w/w).

Remote measurement of surface roughness, surface reflectance, and body reflectance with LiDAR.

PubMed

Li, Xiaolu; Liang, Yu

2015-10-20

Light detection and ranging (LiDAR) intensity data are attracting increasing attention because of the great potential for use of such data in a variety of remote sensing applications. To fully investigate the data potential for target classification and identification, we carried out a series of experiments with typical urban building materials and employed our reconstructed built-in-lab LiDAR system. Received intensity data were analyzed on the basis of the derived bidirectional reflectance distribution function (BRDF) model and the established integration method. With an improved fitting algorithm, parameters involved in the BRDF model can be obtained to depict the surface characteristics. One of these parameters related to surface roughness was converted to a most used roughness parameter, the arithmetical mean deviation of the roughness profile (R_a), which can be used to validate the feasibility of the BRDF model in surface characterizations and performance evaluations.

Estimating Aboveground Forest Carbon Stock of Major Tropical Forest Land Uses Using Airborne Lidar and Field Measurement Data in Central Sumatra

NASA Astrophysics Data System (ADS)

Thapa, R. B.; Watanabe, M.; Motohka, T.; Shiraishi, T.; shimada, M.

2013-12-01

Tropical forests are providing environmental goods and services including carbon sequestration, energy regulation, water fluxes, wildlife habitats, fuel, and building materials. Despite the policy attention, the tropical forest reserve in Southeast Asian region is releasing vast amount of carbon to the atmosphere due to deforestation. Establishing quality forest statistics and documenting aboveground forest carbon stocks (AFCS) are emerging in the region. Airborne and satellite based large area monitoring methods are developed to compliment conventional plot based field measurement methods as they are costly, time consuming, and difficult to implement for large regions. But these methods still require adequate ground measurements for calibrating accurate AFCS model. Furthermore, tropical region comprised of varieties of natural and plantation forests capping higher variability of forest structures and biomass volumes. To address this issue and the needs for ground data, we propose the systematic collection of ground data integrated with airborne light detection and ranging (LiDAR) data. Airborne LiDAR enables accurate measures of vertical forest structure, including canopy height and volume demanding less ground measurement plots. Using an appropriate forest type based LiDAR sampling framework, structural properties of forest can be quantified and treated similar to ground measurement plots, producing locally relevant information to use independently with satellite data sources including synthetic aperture radar (SAR). In this study, we examined LiDAR derived forest parameters with field measured data and developed general and specific AFCS models for tropical forests in central Sumatra. The general model is fitted for all types of natural and plantation forests while the specific model is fitted to the specific forest type. The study region consists of natural forests including peat swamp and dry moist forests, regrowth, and mangrove and plantation forests including rubber, acacia, oil palm, and coconut. To cover these variations of forest type, eight LiDAR transacts crossing 60 (1-ha size) field plots were acquired for calibrating the models. The field plots consisted of AFCS ranging from 4 - 161 Mg /ha. The calibrated LiDAR to AFCS general model enabled to predict the AFCS with R2 = 0.87 and root mean square errors (RMSE) = 17.4 Mg /ha. The specific AFCS models provided carbon estimates, varied by forest types, with R2 ranging from 0.72 - 0.97 and uncertainty (RMSE) ranging from 1.4 - 10.7 Mg /ha. Using these models, AFCS maps were prepared for the LiDAR coverage that provided AFCS estimates for 8,000 ha offering larger ground sampling measurements for calibration of SAR based carbon mapping model to wider region of Sumatra.

Integration of airborne LiDAR data and voxel-based ray tracing to determine high-resolution solar radiation dynamics at the forest floor: implications for improving stand-scale distributed snowmelt models

NASA Astrophysics Data System (ADS)

Musselman, K. N.; Molotch, N. P.; Margulis, S. A.

2012-12-01

Forest architecture dictates sub-canopy solar irradiance and the resulting patterns can vary seasonally and over short spatial distances. These radiation dynamics are thought to have significant implications on snowmelt processes, regional hydrology, and remote sensing signatures. The variability calls into question many assumptions inherent in traditional canopy models (e.g. Beer's Law) when applied at high resolution (i.e. 1 m). We present a method of estimating solar canopy transmissivity using airborne LiDAR data. The canopy structure is represented in 3-D voxel space (i.e. a cubic discretization of a 3-D domain analogous to a pixel representation of a 2-D space). The solar direct beam canopy transmissivity (DBT) is estimated with a ray-tracing algorithm and the diffuse component is estimated from LiDAR-derived effective LAI. Results from one year at five-minute temporal and 1 m spatial resolutions are presented from Sequoia National Park. Compared to estimates from 28 hemispherical photos, the ray-tracing model estimated daily mean DBT with a 10% average error, while the errors from a Beer's-type DBT estimate exceeded 20%. Compared to the ray-tracing estimates, the Beer's-type transmissivity method was unable to resolve complex spatial patterns resulting from canopy gaps, individual tree canopies and boles, and steep variable terrain. The snowmelt model SNOWPACK was applied at locations of ultrasonic snow depth sensors. Two scenarios were tested; 1) a nominal case where canopy model parameters were obtained from hemispherical photographs, and 2) an explicit scenario where the model was modified to accept LiDAR-derived time-variant DBT. The bulk canopy treatment was generally unable to simulate the sub-canopy snowmelt dynamics observed at the depth sensor locations. The explicit treatment reduced error in the snow disappearance date by one week and both positive and negative melt-season SWE biases were reduced. The results highlight the utility of LiDAR canopy measurements and physically based snowmelt models to simulate spatially distributed stand- and slope-scale snowmelt dynamics at resolutions necessary to capture the inherent underlying variability.iDAR-derived solar direct beam canopy transmissivity computed as the daily average for March 1st and May 1st.

Volumetric LiDAR scanning of a wind turbine wake and comparison with a 3D analytical wake model

NASA Astrophysics Data System (ADS)

Carbajo Fuertes, Fernando; Porté-Agel, Fernando

2016-04-01

A correct estimation of the future power production is of capital importance whenever the feasibility of a future wind farm is being studied. This power estimation relies mostly on three aspects: (1) a reliable measurement of the wind resource in the area, (2) a well-established power curve of the future wind turbines and, (3) an accurate characterization of the wake effects; the latter being arguably the most challenging one due to the complexity of the phenomenon and the lack of extensive full-scale data sets that could be used to validate analytical or numerical models. The current project addresses the problem of obtaining a volumetric description of a full-scale wake of a 2MW wind turbine in terms of velocity deficit and turbulence intensity using three scanning wind LiDARs and two sonic anemometers. The characterization of the upstream flow conditions is done by one scanning LiDAR and two sonic anemometers, which have been used to calculate incoming vertical profiles of horizontal wind speed, wind direction and an approximation to turbulence intensity, as well as the thermal stability of the atmospheric boundary layer. The characterization of the wake is done by two scanning LiDARs working simultaneously and pointing downstream from the base of the wind turbine. The direct LiDAR measurements in terms of radial wind speed can be corrected using the upstream conditions in order to provide good estimations of the horizontal wind speed at any point downstream of the wind turbine. All this data combined allow for the volumetric reconstruction of the wake in terms of velocity deficit as well as turbulence intensity. Finally, the predictions of a 3D analytical model [1] are compared to the 3D LiDAR measurements of the wind turbine. The model is derived by applying the laws of conservation of mass and momentum and assuming a Gaussian distribution for the velocity deficit in the wake. This model has already been validated using high resolution wind-tunnel measurements and large-eddy simulation (LES) data of miniature wind turbine wakes, as well as LES data of real-scale wind-turbine wakes, but not yet with full-scale wind turbine wake measurements. [1] M. Bastankhah and F. Porté-Agel. A New Analytical Model For Wind-Turbine Wakes, in Renewable Energy, vol. 70, p. 116-123, 2014.

High resolution t-LiDAR scanning of an active bedrock fault scarp for palaeostress analysis

NASA Astrophysics Data System (ADS)

Reicherter, Klaus; Wiatr, Thomas; Papanikolaou, Ioannis; Fernández-Steeger, Tomas

2013-04-01

Palaeostress analysis of an active bedrock normal fault scarp based on kinematic indicators is carried applying terrestrial laser scanning (t-LiDAR or TLS). For this purpose three key elements are necessary for a defined region on the fault plane: (i) the orientation of the fault plane, (ii) the orientation of the slickenside lineation or other kinematic indicators and (iii) the sense of motion of the hanging wall. We present a workflow to obtain palaeostress data from point cloud data using terrestrial laser scanning. The entire case-study was performed on a continuous limestone bedrock normal fault scarp on the island of Crete, Greece, at four different locations along the WNW-ESE striking Spili fault. At each location we collected data with a mobile terrestrial light detection and ranging system and validated the calculated three-dimensional palaeostress results by comparison with the conventional palaeostress method with compass at three of the locations. Numerous kinematics indicators for normal faulting were discovered on the fault plane surface using t-LiDAR data and traditional methods, like Riedel shears, extensional break-outs, polished corrugations and many more. However, the kinematic indicators are more or less unidirectional and almost pure dip-slip. No oblique reactivations have been observed. But, towards the tips of the fault, inclination of the striation tends to point towards the centre of the fault. When comparing all reconstructed palaeostress data obtained from t-LiDAR to that obtained through manual compass measurements, the degree of fault plane orientation divergence is around ±005/03 for dip direction and dip. The degree of slickenside lineation variation is around ±003/03 for dip direction and dip. Therefore, the percentage threshold error of the individual vector angle at the different investigation site is lower than 3 % for the dip direction and dip for planes, and lower than 6 % for strike. The maximum mean variation of the complete calculated palaeostress tensors is ±005/03. So, technically t-LiDAR measurements are in the error range of conventional compass measurements. The advantages is that remote palaeostress analysis is possible. Further steps in our research will be studying reactivated faults planes with multiple kinematic indicators or striations with t-LiDAR.

Genome-wide DArT and SNP scan for QTL associated with resistance to stripe rust (Puccinia striiformis f. sp. tritici) in elite ICARDA wheat (Triticum aestivum L.) germplasm.

PubMed

Jighly, Abdulqader; Oyiga, Benedict C; Makdis, Farid; Nazari, Kumarse; Youssef, Omran; Tadesse, Wuletaw; Abdalla, Osman; Ogbonnaya, Francis C

2015-07-01

Identified DArT and SNP markers including a first reported QTL on 3AS, validated large effect APR on 3BS. The different genes can be used to incorporate stripe resistance in cultivated varieties. Stripe rust [yellow rust, caused by Puccinia striiformis f. sp. tritici (Pst)] is a serious disease in wheat (Triticum aestivum). This study employed genome-wide association mapping (GWAM) to identify markers linked to stripe rust resistance genes using Diversity Arrays Technology (DArT(®)) and single-nucleotide polymorphism (SNP) Infinium 9K assays in 200 ICARDA wheat genotypes, phenotyped for seedling and adult plant resistance in two sites over two growing seasons in Syria. Only 25.8 % of the genotypes showed resistance at seedling stage while about 33 and 44 % showed moderate resistance and resistance response, respectively. Mixed-linear model adjusted for false discovery rate at p < 0.05 identified 12 DArT and 29 SNP markers on chromosome arms 3AS, 3AL, 1AL, 2AL, 2BS, 2BL, 3BS, 3BL, 5BL, 6AL, and 7DS significantly linked to Pst resistance genes. Of these, the locus on 3AS has not been previously reported to confer resistance to stripe rust in wheat. The QTL on 3AS, 3AL, 1AL, 2AL, and 2BS were effective at seedling and adult plant growth stages while those on 3BS, 3BL, 5BL, 6AL and 7DS were effective at adult plant stage. The 3BS QTL was validated in Cham-6 × Cham-8 recombinant inbred line population; composite interval analysis identified a stripe resistance QTL flanked by the DArT marker, wPt-798970, contributed by Cham-6 parent which accounted for 31.2 % of the phenotypic variation. The DArT marker "wPt-798970" lies 1.6 cM away from the 3BS QTL detected within GWAM. Epistatic interactions were also investigated; only the QTL on 1AL, 3AS and 6AL exhibited interactions with other loci. These results suggest that GWAM can be an effective approach for identifying and improving resistance to stripe rust in wheat.

Digital topographic map showing the extents of glacial ice and perennial snowfields at Mount Rainier, Washington, based on the LiDAR survey of September 2007 to October 2008

USGS Publications Warehouse

Robinson, Joel E.; Sisson, Thomas W.; Swinney, Darin D.

2010-01-01

In response to severe flooding in November 2006, the National Park Service contracted for a high-resolution aerial Light Detection and Ranging (LiDAR) topographic survey of Mount Rainier National Park, Washington. Due to inclement weather, this survey was performed in two stages: early September 2007 and September-October 2008. The total surveyed area of 241,585 acres includes an approximately 100-m-wide buffer zone around the Park to ensure complete coverage and adequate point densities at survey edges. Final results averaged 5.73 laser first return points/m2 over forested and high-elevation terrain, with a vertical accuracy of 3.7 cm on bare road surfaces and mean relative accuracy of 11 cm, based on comparisons between flightlines. Bare-earth topography, as developed by the contractor, is included in this release. A map of the 2007-2008 limits of glaciers and perennial snowfields was developed by digitizing 1:2,000 to 1:5,000 slope and shaded-relief images derived from the LiDAR topography. Edges of snow and exposed ice are readily seen in such images as sharp changes in surface roughness and slope. Ice mantled by moraine can be distinguished by the moraine's distinctly high roughness due to ice motion and melting, local exposures of smooth ice, and commonly by the presence of crevasses and shear boundaries. A map of the 1970 limits of ice and perennial snow was also developed by digitizing the snow and ice perimeters as depicted on the hydrologic separates used to produce the 1:24,000 topographic maps of the Mount Rainier region. These maps, produced in 1971, were derived from September 1970 aerial photographs. Boundaries between adjacent glacier systems were estimated and mapped from drainage divides, including partly emergent rock ridges, lines of diverging slope, and medial moraines. This data release contains the bare-earth LiDAR data as an ESRI grid file (DS549-Rainier_LiDAR.zip), the glacial limits derived from the USGS 1970 aerial photographs of the Mount Rainier vicinity as a shapefile, and the glacial limits derived from the 2007 to 2008 LiDAR survey as a shapefile (both shapefiles contained in DS549-Glacial_Limits.zip). These geospatial data files require GIS software for viewing.

Physical partner violence, women’s economic status and help-seeking behaviour in Dar es Salaam and Mbeya, Tanzania

PubMed Central

Vyas, Seema; Mbwambo, Jessie

2017-01-01

ABSTRACT Background: Women’s responses to partner violence are influenced by a complex constellation of factors including: psychological attachment to the partner; context of the abuse; and structural factors, all of which shape available options for women outside of the relationship. Objective: To describe women’s responses to physical partner violence; and to understand the role of women’s economic resources on their responses. Methods: Cross-sectional data from Dar es Salaam and Mbeya, Tanzania. Multivariate logistic regression was used to explore the relationship between women’s economic resources and their responses to violence. Results: In both sites, among physically abused women, over one-half experienced severe violence; approximately two-thirds had disclosed the violence; and approximately 40% had sought help. Abused women were more likely to have sought help from health services, the police and religious leaders in Dar es Salaam, and from local leaders in Mbeya. Economic resources did not facilitate women’s ability to leave violent partners in Dar es Salaam. In Mbeya, women who jointly owned capital assets were less likely to have left. In both sites, women’s sole ownership of capital assets facilitated help-seeking. Conclusion: Although support services are being scaled-up in Tanzania, efforts are needed to increase the acceptability of accessing such services. PMID:28485667

Estimation of carbon storage based on individual tree detection in Pinus densiflora stands using a fusion of aerial photography and LiDAR data.

PubMed

Kim, So-Ra; Kwak, Doo-Ahn; Lee, Woo-Kyun; oLee, Woo-Kyun; Son, Yowhan; Bae, Sang-Won; Kim, Choonsig; Yoo, Seongjin

2010-07-01

The objective of this study was to estimate the carbon storage capacity of Pinus densiflora stands using remotely sensed data by combining digital aerial photography with light detection and ranging (LiDAR) data. A digital canopy model (DCM), generated from the LiDAR data, was combined with aerial photography for segmenting crowns of individual trees. To eliminate errors in over and under-segmentation, the combined image was smoothed using a Gaussian filtering method. The processed image was then segmented into individual trees using a marker-controlled watershed segmentation method. After measuring the crown area from the segmented individual trees, the individual tree diameter at breast height (DBH) was estimated using a regression function developed from the relationship observed between the field-measured DBH and crown area. The above ground biomass of individual trees could be calculated by an image-derived DBH using a regression function developed by the Korea Forest Research Institute. The carbon storage, based on individual trees, was estimated by simple multiplication using the carbon conversion index (0.5), as suggested in guidelines from the Intergovernmental Panel on Climate Change. The mean carbon storage per individual tree was estimated and then compared with the field-measured value. This study suggested that the biomass and carbon storage in a large forest area can be effectively estimated using aerial photographs and LiDAR data.

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

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.

Harnessing Satellite Imageries in Feature Extraction Using Google Earth Pro

NASA Astrophysics Data System (ADS)

Fernandez, Sim Joseph; Milano, Alan

2016-07-01

Climate change has been a long-time concern worldwide. Impending flooding, for one, is among its unwanted consequences. The Phil-LiDAR 1 project of the Department of Science and Technology (DOST), Republic of the Philippines, has developed an early warning system in regards to flood hazards. The project utilizes the use of remote sensing technologies in determining the lives in probable dire danger by mapping and attributing building features using LiDAR dataset and satellite imageries. A free mapping software named Google Earth Pro (GEP) is used to load these satellite imageries as base maps. Geotagging of building features has been done so far with the use of handheld Global Positioning System (GPS). Alternatively, mapping and attribution of building features using GEP saves a substantial amount of resources such as manpower, time and budget. Accuracy-wise, geotagging by GEP is dependent on either the satellite imageries or orthophotograph images of half-meter resolution obtained during LiDAR acquisition and not on the GPS of three-meter accuracy. The attributed building features are overlain to the flood hazard map of Phil-LiDAR 1 in order to determine the exposed population. The building features as obtained from satellite imageries may not only be used in flood exposure assessment but may also be used in assessing other hazards and a number of other uses. Several other features may also be extracted from the satellite imageries.

Water Mapping Using Multispectral Airborne LIDAR Data

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

Foliar and woody materials discriminated using terrestrial LiDAR in a mixed natural forest

NASA Astrophysics Data System (ADS)

Zhu, Xi; Skidmore, Andrew K.; Darvishzadeh, Roshanak; Niemann, K. Olaf; Liu, Jing; Shi, Yifang; Wang, Tiejun

2018-02-01

Separation of foliar and woody materials using remotely sensed data is crucial for the accurate estimation of leaf area index (LAI) and woody biomass across forest stands. In this paper, we present a new method to accurately separate foliar and woody materials using terrestrial LiDAR point clouds obtained from ten test sites in a mixed forest in Bavarian Forest National Park, Germany. Firstly, we applied and compared an adaptive radius near-neighbor search algorithm with a fixed radius near-neighbor search method in order to obtain both radiometric and geometric features derived from terrestrial LiDAR point clouds. Secondly, we used a random forest machine learning algorithm to classify foliar and woody materials and examined the impact of understory and slope on the classification accuracy. An average overall accuracy of 84.4% (Kappa = 0.75) was achieved across all experimental plots. The adaptive radius near-neighbor search method outperformed the fixed radius near-neighbor search method. The classification accuracy was significantly higher when the combination of both radiometric and geometric features was utilized. The analysis showed that increasing slope and understory coverage had a significant negative effect on the overall classification accuracy. Our results suggest that the utilization of the adaptive radius near-neighbor search method coupling both radiometric and geometric features has the potential to accurately discriminate foliar and woody materials from terrestrial LiDAR data in a mixed natural forest.

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

NASA Astrophysics Data System (ADS)

Morsy, S.; Shaker, A.; El-Rabbany, A.

2017-09-01

With the evolution of the LiDAR technology, multispectral airborne laser scanning systems are currently available. The first operational multispectral airborne LiDAR sensor, the Optech Titan, acquires LiDAR point clouds at three different wavelengths (1.550, 1.064, 0.532 μm), allowing the acquisition of different spectral information of land surface. Consequently, the recent studies are devoted to use the radiometric information (i.e., intensity) of the LiDAR data along with the geometric information (e.g., height) for classification purposes. In this study, a data clustering method, based on Gaussian decomposition, is presented. First, a ground filtering mechanism is applied to separate non-ground from ground points. Then, three normalized difference vegetation indices (NDVIs) are computed for both non-ground and ground points, followed by histograms construction from each NDVI. The Gaussian function model is used to decompose the histograms into a number of Gaussian components. The maximum likelihood estimate of the Gaussian components is then optimized using Expectation - Maximization algorithm. The intersection points of the adjacent Gaussian components are subsequently used as threshold values, whereas different classes can be clustered. This method is used to classify the terrain of an urban area in Oshawa, Ontario, Canada, into four main classes, namely roofs, trees, asphalt and grass. It is shown that the proposed method has achieved an overall accuracy up to 95.1 % using different NDVIs.

Boundary Layer Temporal Evolution Observed by Doppler LiDAR Upwind of a Lake-Effect Snow Event

NASA Astrophysics Data System (ADS)

King, D.; Kristovich, D.

2017-12-01

Lake-effect snow (LES) annually affects the Great Lakes region. It can impact communities economically, recreationally and perhaps result in fatalities. Previous studies have shown that the upwind shore of a LES system tends to be a region for mesoscale downdrafts. This study intends to show how the depth of the boundary (BL) on the upwind shore and how it could influence a LES event downstream. From December 7-10, 2016, we deployed a Halo-Photonics Streamline pulsed Doppler LiDAR at Illinois Beach State Park in Zion, Illinois, to observe the evolving BL wind structure and depth upwind of the growing LES over eastern Lake Michigan. The LiDAR scans included vertical stare, velocity-azimuth display (VAD), and range height indicator (RHI) modes to display the BL depth as well as LES cloud band structure. The BL depth was observed by turbulent velocities and backscatter profiles from the LiDAR. The BL was found to be approximately one kilometer during the day, and reduced to near surface at night. The BL depth, overall, increased from the 8th to the 9th, while snowfall rate decreased on the downwind shore. This suggests that local BL dynamics have less influence on downwind convection and snow production than originally anticipated. The larger scale environment appears to play a larger role in the multi-day BL evolution.

Carbon Emissions from Residue Burn Piles Estimated Using LiDAR or Ground Based Measurements of Pile Volumes in a Coastal Douglas-Fir Forest

NASA Astrophysics Data System (ADS)

Trofymow, J. A.; Coops, N.; Hayhurst, D.

2012-12-01

Following forest harvest, residues left on site and roadsides are often disposed of to reduce fire risk and free planting space. In coastal British Columbia burn piles are the main method of disposal, particularly for accumulations from log processing. Quantification of residue wood in piles is required for: smoke emission estimates, C budget calculations, billable waste assessment, harvest efficiency monitoring, and determination of bioenergy potentials. A second-growth Douglas-fir dominated (DF1949) site on eastern Vancouver Island and subject of C flux and budget studies since 1998, was clearcut in winter 2011, residues piled in spring and burned in fall. Prior to harvest, the site was divided into 4 blocks to account for harvest plans and ecosite conditions. Total harvested wood volume was scaled for each block. Residue pile wood volume was determined by a standard Waste and Residue Survey (WRS) using field estimates of pile base area and plot density (wood volume / 0.005 ha plot) on 2 piles per block, by a smoke emissions geometric method with pile volumes estimated as ellipsoidal paraboloids and packing ratios (wood volume / pile volume) for 2 piles per block, as well as by five other GIS methods using pile volumes and areas from LiDAR and orthophotography flown August 2011, a LiDAR derived digital elevation model (DEM) from 2008, and total scaled wood volumes of 8 sample piles disassembled November 2011. A weak but significant negative relationship was found between pile packing ratio and pile volume. Block level avoidable+unavoidable residue pile wood volumes from the WRS method (20.0 m3 ha-1 SE 2.8) were 30%-50% of the geometric (69.0 m3 ha-1 SE 18.0) or five GIS/LiDAR (48.0 to 65.7 m3 ha-1 ) methods. Block volumes using the 2008 LiDAR DEM (unshifted 48.0 m3 ha-1 SE 3.9, shifted 53.6 m3 ha-1 SE 4.2) to account for pre-existing humps or hollows beneath piles were not different from those using the 2011 LiDAR DEM (50.3 m3 ha-1 SE 4.0). The block volume ratio (total residue pile / harvest scale, wood volumes x 100) for the WRS method (3.3% SE 0.45) was lower than for LiDAR 2011 method (8.1% SE 0.31). Using wood densities from in situ samples and LiDAR 2011 method wood volumes, total residue pile wood biomass in the blocks was 21.5 t dry mass ha-1 (SE 1.9). Post-burn charred residues were ~1.5 t dry mass ha-1 resulting in C emission estimates of 10 t C ha-1 (SE 0.91), assuming 50% C, and equivalent to 2 - 3 years of pre-harvest stand C uptake (NEP 4.8 t C ha-1 y-1 SE 0.58). Results suggest the WRS method may underestimate residue pile wood volumes, while the geometric method may overestimate depending on packing ratio used. While remote sensing methods reduce uncertainty in estimating volumes or areas of all piles in a block, quantification of packing ratios remains a significant source of uncertainty in determining block level residue pile wood volumes. Additional studies are needed for other forest and harvest types to determine the wider applicability of these findings.

Integrated Airborne and In-Situ Measurements over Land-Fast Ice near Barrow, AK

NASA Astrophysics Data System (ADS)

Brozena, J. M.; Gardner, J. M.; Liang, R.; Vermillion, M.; Ball, D.; Stoudt, C. A.; Geiger, C. A.; Woods, J. E.; Samluk, J.; Deliberty, T. L.

2013-12-01

During March of 2013, the Naval Research Laboratory, the University of Delaware and the US Naval Academy collected an integrated set of measurements over the largely floating, but land-fast ice near the coast of Barrow, AK. The purpose of the collection was to compare airborne remote sensing methods of collection to in-situ ground-truth measurements. Airborne measurements include scanning LiDAR (Riegl Q 680i), digital photogrammetry (Applanix DSS-439) and a short-pulse (~ 1 nsec) 10 GHz radar altimeter. The LiDAR measures total freeboard (ice + snow) referenced to leads in the ice. The radar measures approximate ice freeboard with the difference with the LiDAR providing an estimate of snow thickness. The freeboard measurements are aimed at estimating ice thickness via estimates of densities and isostasy. The photogrammetry was used to measure ice motion over free-floating sea-ice, but provided only a velocity calibration and general situational awareness over the land-fast ice. Ground measurements were collected along a transect, and included boreholes, snow-thickness (Magnaprobe), and ice thickness (EM31). Airborne data were collected on six overflights of this transect over a three week period. LiDAR swath widths ranged from 200-300m (depending on altitude) and encompassed three grounded ridges which remained essentially stationary over the collection period, that together with the shoreline, provided fixed reference points to compare the heights of the floating ice that varied with the tide (and to some extent the snow conditions). Sampling size or 'footprint' plays a critical role in the attempt to compare in-situ measurements with airborne (or satellite) measurements. Boreholes are point measurements and are difficult enough to obtain, that only a limited number are practical during a survey period. EM31 and Magnaprobe instrumentation allows collection of snow and ice thickness along one-dimensional profiles, and several adjacent profiles can be collected to approximate a two-dimensional swath, assuming that the spacing between profiles does not lead to unacceptable aliasing. For this project we collected two em31 profiles roughly 3-5m apart and two profiles of Magnaprobe snow thickness with separation varying from 1-20 m. The radar footprint is ~ 10-15m at our survey altitudes, and at least somewhat comparable. The LiDAR had a ground point spacing of ~25 cm and so easily encompassed the EM31, Magnaprobe and radar data. Measured snow thickness was minimal, averaging 9 cm on the date of the first collection and 12 cm on the second. Airborne radar data were compared to the LiDAR by applying a circular, weighted kernel to the LiDAR measurements surrounding the radar profile and commensurate in diameter to the radar footprint. Estimated snow thickness is then obtained from the difference of the radar and averaged LiDAR. Ice thickness was then calculated from the freeboard measurements and compared to the boreholes. Using these data sets we hope to address important questions such as: How can we improve co-registration between ground and airborne campaigns by taking advantage of land-fast ice as a non-moving ice field? How can we improve co-registration on drift ice by building from such activities? Is there spatial aliasing of sea ice at different resolutions and if so, what is the impact on sea ice volume and ice thickness distribution?

Integration of Heterogenous Digital Surface Models

NASA Astrophysics Data System (ADS)

Boesch, R.; Ginzler, C.

2011-08-01

The application of extended digital surface models often reveals, that despite an acceptable global accuracy for a given dataset, the local accuracy of the model can vary in a wide range. For high resolution applications which cover the spatial extent of a whole country, this can be a major drawback. Within the Swiss National Forest Inventory (NFI), two digital surface models are available, one derived from LiDAR point data and the other from aerial images. Automatic photogrammetric image matching with ADS80 aerial infrared images with 25cm and 50cm resolution is used to generate a surface model (ADS-DSM) with 1m resolution covering whole switzerland (approx. 41000 km2). The spatially corresponding LiDAR dataset has a global point density of 0.5 points per m2 and is mainly used in applications as interpolated grid with 2m resolution (LiDAR-DSM). Although both surface models seem to offer a comparable accuracy from a global view, local analysis shows significant differences. Both datasets have been acquired over several years. Concerning LiDAR-DSM, different flight patterns and inconsistent quality control result in a significantly varying point density. The image acquisition of the ADS-DSM is also stretched over several years and the model generation is hampered by clouds, varying illumination and shadow effects. Nevertheless many classification and feature extraction applications requiring high resolution data depend on the local accuracy of the used surface model, therefore precise knowledge of the local data quality is essential. The commercial photogrammetric software NGATE (part of SOCET SET) generates the image based surface model (ADS-DSM) and delivers also a map with figures of merit (FOM) of the matching process for each calculated height pixel. The FOM-map contains matching codes like high slope, excessive shift or low correlation. For the generation of the LiDAR-DSM only first- and last-pulse data was available. Therefore only the point distribution can be used to derive a local accuracy measure. For the calculation of a robust point distribution measure, a constrained triangulation of local points (within an area of 100m2) has been implemented using the Open Source project CGAL. The area of each triangle is a measure for the spatial distribution of raw points in this local area. Combining the FOM-map with the local evaluation of LiDAR points allows an appropriate local accuracy evaluation of both surface models. The currently implemented strategy ("partial replacement") uses the hypothesis, that the ADS-DSM is superior due to its better global accuracy of 1m. If the local analysis of the FOM-map within the 100m2 area shows significant matching errors, the corresponding area of the triangulated LiDAR points is analyzed. If the point density and distribution is sufficient, the LiDAR-DSM will be used in favor of the ADS-DSM at this location. If the local triangulation reflects low point density or the variance of triangle areas exceeds a threshold, the investigated location will be marked as NODATA area. In a future implementation ("anisotropic fusion") an anisotropic inverse distance weighting (IDW) will be used, which merges both surface models in the point data space by using FOM-map and local triangulation to derive a quality weight for each of the interpolation points. The "partial replacement" implementation and the "fusion" prototype for the anisotropic IDW make use of the Open Source projects CGAL (Computational Geometry Algorithms Library), GDAL (Geospatial Data Abstraction Library) and OpenCV (Open Source Computer Vision).

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

Improving representation of riparian vegetation shading in a regional stream temperature model using LiDAR data.

PubMed

Loicq, Pierre; Moatar, Florentina; Jullian, Yann; Dugdale, Stephen J; Hannah, David M

2018-05-15

Modelling river temperature at the catchment scale is needed to understand how aquatic communities may adapt to current and projected climate change. In small and medium rivers, riparian vegetation can greatly reduce maximum water temperature by providing shade. It is thus important that river temperature models are able to correctly characterise the impact of this riparian shading. In this study, we describe the use of a spatially-explicit method using LiDAR-derived data for computing the riparian shading on direct and diffuse solar radiation. The resulting data are used in the T-NET one-dimensional stream temperature model to simulate water temperature from August 2007 to July 2014 for 270km of the Loir River, an indirect tributary of the Loire River (France). Validation is achieved with 4 temperature monitoring stations spread along the Loir River. The vegetation characterised with the LiDAR approach provides a cooling effect on maximum daily temperature (T max ) ranging from 3.0°C (upstream) to 1.3°C (downstream) in late August 2009. Compared to two other riparian shading routines that are less computationally-intensive, the use of our LiDAR-based methodology improves the bias of T max simulated by the T-NET model by 0.62°C on average between April and September. However, difference between the shading routines reaches up to 2°C (monthly average) at the upstream-most station. Standard deviation of errors on T max is not improved. Computing the impact of riparian vegetation at the hourly timescale using reach-averaged parameters provides results close to the LiDAR-based approach, as long as it is supplied with accurate vegetation cover data. Improving the quality of riparian vegetation data should therefore be a priority to increase the accuracy of stream temperature modelling at the regional scale. Copyright © 2017 Elsevier B.V. All rights reserved.

Novel SSR Markers from BAC-End Sequences, DArT Arrays and a Comprehensive Genetic Map with 1,291 Marker Loci for Chickpea (Cicer arietinum L.)

PubMed Central

Nayak, Spurthi N.; Varghese, Nicy; Shah, Trushar M.; Penmetsa, R. Varma; Thirunavukkarasu, Nepolean; Gudipati, Srivani; Gaur, Pooran M.; Kulwal, Pawan L.; Upadhyaya, Hari D.; KaviKishor, Polavarapu B.; Winter, Peter; Kahl, Günter; Town, Christopher D.; Kilian, Andrzej; Cook, Douglas R.; Varshney, Rajeev K.

2011-01-01

Chickpea (Cicer arietinum L.) is the third most important cool season food legume, cultivated in arid and semi-arid regions of the world. The goal of this study was to develop novel molecular markers such as microsatellite or simple sequence repeat (SSR) markers from bacterial artificial chromosome (BAC)-end sequences (BESs) and diversity arrays technology (DArT) markers, and to construct a high-density genetic map based on recombinant inbred line (RIL) population ICC 4958 (C. arietinum)×PI 489777 (C. reticulatum). A BAC-library comprising 55,680 clones was constructed and 46,270 BESs were generated. Mining of these BESs provided 6,845 SSRs, and primer pairs were designed for 1,344 SSRs. In parallel, DArT arrays with ca. 15,000 clones were developed, and 5,397 clones were found polymorphic among 94 genotypes tested. Screening of newly developed BES-SSR markers and DArT arrays on the parental genotypes of the RIL mapping population showed polymorphism with 253 BES-SSR markers and 675 DArT markers. Segregation data obtained for these polymorphic markers and 494 markers data compiled from published reports or collaborators were used for constructing the genetic map. As a result, a comprehensive genetic map comprising 1,291 markers on eight linkage groups (LGs) spanning a total of 845.56 cM distance was developed (http://cmap.icrisat.ac.in/cmap/sm/cp/thudi/). The number of markers per linkage group ranged from 68 (LG 8) to 218 (LG 3) with an average inter-marker distance of 0.65 cM. While the developed resource of molecular markers will be useful for genetic diversity, genetic mapping and molecular breeding applications, the comprehensive genetic map with integrated BES-SSR markers will facilitate its anchoring to the physical map (under construction) to accelerate map-based cloning of genes in chickpea and comparative genome evolution studies in legumes. PMID:22102885

Characteristics and geographic distribution of HIV-positive women diagnosed with cervical cancer in Dar es Salaam, Tanzania.

PubMed

Lovgren, Kathleen; Soliman, Amr S; Ngoma, Twalib; Kahesa, Crispin; Meza, Jane

2016-10-01

Cervical cancer is the leading incident cancer and the main cause of cancer-related mortality among women in sub-Saharan Africa. Furthermore, HIV-infected women are at a higher risk of developing cervical cancer than HIV-negative women. The purpose of this study was to distinguish differences in characteristics of HIV-positive and HIV-negative patients with cervical cancer in Dar es Salaam, Tanzania. The HIV status of cervical cancer patients diagnosed and/or treated at Ocean Road Cancer Institute in Dar es Salaam, Tanzania, during the period 2007-2011 was abstracted from the medical records. Additional abstracted information included patient's name, age, place of residence, occupation, education, marital status, age at marriage, gravidity, and screening clinic visit results. Ocean Road Cancer Institute patients came from two sources: the screening clinic followed by treatment clinic or the treatment clinic without prior screening. HIV-positive and HIV-negative patients were compared regarding the above-listed clinical and epidemiologic factors. Multivariable analysis was also performed to assess the risk factors associated with cervical cancer treatment without prior screening at Ocean Road Cancer Institute. HIV-positive cervical cancer patients tended to be younger, with higher education and lower parity. Patients screened for cervical cancer prior to treatment were more likely to be HIV-positive (OR: 2.09, 95% CI: 1.36, 3.21), less likely to have higher disease stages (OR: 0.64, 95% CI: 0.43, 0.94), and less likely to reside outside of Dar es Salaam (OR: 0.44, 95% CI: 0.30, 0.65). Screening for cervical cancer at Ocean Road Cancer Institute is utilised by more HIV-positive patients from Dar es Salaam. Future studies should focus on identifying the reasons for lower utilisation of screening by HIV-negative patients and patients from other distant rural regions in Tanzania. © The Author(s) 2016.

Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm

NASA Astrophysics Data System (ADS)

Ahmed, Oumer S.; Franklin, Steven E.; Wulder, Michael A.; White, Joanne C.

2015-03-01

Many forest management activities, including the development of forest inventories, require spatially detailed forest canopy cover and height data. Among the various remote sensing technologies, LiDAR (Light Detection and Ranging) offers the most accurate and consistent means for obtaining reliable canopy structure measurements. A potential solution to reduce the cost of LiDAR data, is to integrate transects (samples) of LiDAR data with frequently acquired and spatially comprehensive optical remotely sensed data. Although multiple regression is commonly used for such modeling, often it does not fully capture the complex relationships between forest structure variables. This study investigates the potential of Random Forest (RF), a machine learning technique, to estimate LiDAR measured canopy structure using a time series of Landsat imagery. The study is implemented over a 2600 ha area of industrially managed coastal temperate forests on Vancouver Island, British Columbia, Canada. We implemented a trajectory-based approach to time series analysis that generates time since disturbance (TSD) and disturbance intensity information for each pixel and we used this information to stratify the forest land base into two strata: mature forests and young forests. Canopy cover and height for three forest classes (i.e. mature, young and mature and young (combined)) were modeled separately using multiple regression and Random Forest (RF) techniques. For all forest classes, the RF models provided improved estimates relative to the multiple regression models. The lowest validation error was obtained for the mature forest strata in a RF model (R2 = 0.88, RMSE = 2.39 m and bias = -0.16 for canopy height; R2 = 0.72, RMSE = 0.068% and bias = -0.0049 for canopy cover). This study demonstrates the value of using disturbance and successional history to inform estimates of canopy structure and obtain improved estimates of forest canopy cover and height using the RF algorithm.

The application of GBS markers for extending the dense genetic map of rye (Secale cereale L.) and the localization of the Rfc1 gene restoring male fertility in plants with the C source of sterility-inducing cytoplasm.

PubMed

Milczarski, Paweł; Hanek, Monika; Tyrka, Mirosław; Stojałowski, Stefan

2016-11-01

Genotyping by sequencing (GBS) is an efficient method of genotyping in numerous plant species. One of the crucial steps toward the application of GBS markers in crop improvement is anchoring them on particular chromosomes. In rye (Secale cereale L.), chromosomal localization of GBS markers has not yet been reported. In this paper, the application of GBS markers generated by the DArTseq platform for extending the high-density map of rye is presented. Additionally, their application is used for the localization of the Rfc1 gene that restores male fertility in plants with the C source of sterility-inducing cytoplasm. The total number of markers anchored on the current version of the map is 19,081, of which 18,132 were obtained from the DArTseq platform. Numerous markers co-segregated within the studied mapping population, so, finally, only 3397 unique positions were located on the map of all seven rye chromosomes. The total length of the map is 1593 cM and the average distance between markers is 0.47 cM. In spite of the resolution of the map being not very high, it should be a useful tool for further studies of the Secale cereale genome because of the presence on this map of numerous GBS markers anchored for the first time on rye chromosomes. The Rfc1 gene was located on high-density maps of the long arm of the 4R chromosome obtained for two mapping populations. Genetic maps were composed of DArT, DArTseq, and PCR-based markers. Consistent mapping results were obtained and DArTs tightly linked to the Rfc1 gene were successfully applied for the development of six new PCR-based markers useful in marker-assisted selection.

Archaeological application of airborne LiDAR to examine social changes in the Ceibal region of the Maya lowlands.

PubMed

Inomata, Takeshi; Triadan, Daniela; Pinzón, Flory; Burham, Melissa; Ranchos, José Luis; Aoyama, Kazuo; Haraguchi, Tsuyoshi

2018-01-01

Although the application of LiDAR has made significant contributions to archaeology, LiDAR only provides a synchronic view of the current topography. An important challenge for researchers is to extract diachronic information over typically extensive LiDAR-surveyed areas in an efficient manner. By applying an architectural chronology obtained from intensive excavations at the site center and by complementing it with surface collection and test excavations in peripheral zones, we analyze LiDAR data over an area of 470 km2 to trace social changes through time in the Ceibal region, Guatemala, of the Maya lowlands. We refine estimates of structure counts and populations by applying commission and omission error rates calculated from the results of ground-truthing. Although the results of our study need to be tested and refined with additional research in the future, they provide an initial understanding of social processes over a wide area. Ceibal appears to have served as the only ceremonial complex in the region during the transition to sedentism at the beginning of the Middle Preclassic period (c. 1000 BC). As a more sedentary way of life was accepted during the late part of the Middle Preclassic period and the initial Late Preclassic period (600-300 BC), more ceremonial assemblages were constructed outside the Ceibal center, possibly symbolizing the local groups' claim to surrounding agricultural lands. From the middle Late Preclassic to the initial Early Classic period (300 BC-AD 300), a significant number of pyramidal complexes were probably built. Their high concentration in the Ceibal center probably reflects increasing political centralization. After a demographic decline during the rest of the Early Classic period, the population in the Ceibal region reached the highest level during the Late and Terminal Classic periods, when dynastic rule was well established (AD 600-950).

Differential Absorption Radar: An Emerging Technology for Remote Sounding of Water Vapor Within Clouds

NASA Astrophysics Data System (ADS)

Lebsock, M. D.; Millan Valle, L. F.; Cooper, K. B.; Siles, J.; Monje, R.

2017-12-01

We present the results of our efforts to build and demonstrate the first Differential Absorption Radar (DAR), which will provide unique capabilities to remotely sound for water vapor within cloudy and precipitating atmospheres. The approach leverages multiple radar channels located near the 183 GHz water vapor absorption feature to simultaneously derive microphysical and water vapor profiles. The DAR technique has the potential to neatly complement existing water vapor sounding techniques such as infrared and microwave sounding and GPS radio occultation. These precisions rival those of existing water vapor remote sensing instruments. The approach works best from above clouds because the water vapor burden and line width increases towards the Earth surface allowing increased sampling from the top-down compared with bottom-up. From an airborne or satellite platform channels can be selected that target either upper-tropospheric or lower-tropospheric clouds. Our theoretical studies suggest that the water vapor concentration can be retrieved to within 1-3 gm-3 and the column integrated water vapor can be retrieved to within 1 kgm-2. The high-frequency radar is only recently enabled by technological advances that have allowed us to demonstrate 0.5 W of continuous power near 183 GHz. We are currently developing an airborne DAR using a Frequency Modulated Continuous Wave (FMCW) architecture with a quasi-optical duplexer providing 80 dB of transmit/receive isolation. A prototype of this instrument recently made the first ever range resolved DAR measurements of humidity out to several hundred meters during a light rain event at JPL. The spectral dependence of the attenuation was in excellent agreement with the predicted attenuation based on nearby weather stations, proving for the first time the feasibility of the concept. A major impediment to implementing DAR is the international regulation of radio-frequency transmissions below 300 GHz. The major roadblocks and potential paths forward towards a spaceborne instruments will be presented.

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.

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 definition of pool areas dominated by weak bottom reflections. These processing techniques are also being tested for EAARL surveys collected along the Platte and Klamath Rivers where environmental conditions have resulted in suppressed or convolved bottom reflections.

Archaeological application of airborne LiDAR to examine social changes in the Ceibal region of the Maya lowlands

PubMed Central

Triadan, Daniela; Pinzón, Flory; Burham, Melissa; Ranchos, José Luis; Aoyama, Kazuo; Haraguchi, Tsuyoshi

2018-01-01

Although the application of LiDAR has made significant contributions to archaeology, LiDAR only provides a synchronic view of the current topography. An important challenge for researchers is to extract diachronic information over typically extensive LiDAR-surveyed areas in an efficient manner. By applying an architectural chronology obtained from intensive excavations at the site center and by complementing it with surface collection and test excavations in peripheral zones, we analyze LiDAR data over an area of 470 km2 to trace social changes through time in the Ceibal region, Guatemala, of the Maya lowlands. We refine estimates of structure counts and populations by applying commission and omission error rates calculated from the results of ground-truthing. Although the results of our study need to be tested and refined with additional research in the future, they provide an initial understanding of social processes over a wide area. Ceibal appears to have served as the only ceremonial complex in the region during the transition to sedentism at the beginning of the Middle Preclassic period (c. 1000 BC). As a more sedentary way of life was accepted during the late part of the Middle Preclassic period and the initial Late Preclassic period (600–300 BC), more ceremonial assemblages were constructed outside the Ceibal center, possibly symbolizing the local groups’ claim to surrounding agricultural lands. From the middle Late Preclassic to the initial Early Classic period (300 BC-AD 300), a significant number of pyramidal complexes were probably built. Their high concentration in the Ceibal center probably reflects increasing political centralization. After a demographic decline during the rest of the Early Classic period, the population in the Ceibal region reached the highest level during the Late and Terminal Classic periods, when dynastic rule was well established (AD 600–950). PMID:29466384

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 78.43% accuracy improvement for the HDL-32E can be achieved using the proposed calibration method.

A reference linkage map for Eucalyptus

PubMed Central

2012-01-01

Background Genetic linkage maps are invaluable resources in plant research. They provide a key tool for many genetic applications including: mapping quantitative trait loci (QTL); comparative mapping; identifying unlinked (i.e. independent) DNA markers for fingerprinting, population genetics and phylogenetics; assisting genome sequence assembly; relating physical and recombination distances along the genome and map-based cloning of genes. Eucalypts are the dominant tree species in most Australian ecosystems and of economic importance globally as plantation trees. The genome sequence of E. grandis has recently been released providing unprecedented opportunities for genetic and genomic research in the genus. A robust reference linkage map containing sequence-based molecular markers is needed to capitalise on this resource. Several high density linkage maps have recently been constructed for the main commercial forestry species in the genus (E. grandis, E. urophylla and E. globulus) using sequenced Diversity Arrays Technology (DArT) and microsatellite markers. To provide a single reference linkage map for eucalypts a composite map was produced through the integration of data from seven independent mapping experiments (1950 individuals) using a marker-merging method. Results The composite map totalled 1107 cM and contained 4101 markers; comprising 3880 DArT, 213 microsatellite and eight candidate genes. Eighty-one DArT markers were mapped to two or more linkage groups, resulting in the 4101 markers being mapped to 4191 map positions. Approximately 13% of DArT markers mapped to identical map positions, thus the composite map contained 3634 unique loci at an average interval of 0.31 cM. Conclusion The composite map represents the most saturated linkage map yet produced in Eucalyptus. As the majority of DArT markers contained on the map have been sequenced, the map provides a direct link to the E. grandis genome sequence and will serve as an important reference for progressing eucalypt research. PMID:22702473

An investigation of upland erosion and sources of fine-sediment using aerial and terrestrial LiDAR, mineralogy, geochemistry, and particle-size, Humbug Creek and Malakoff Diggings State Historic Park, California

NASA Astrophysics Data System (ADS)

Curtis, J.; Alpers, C. N.; Howle, J.; Monohan, C.; Ward, J.; Bailey, T. L.; Walck, C.

2015-12-01

One of the largest hydraulic mines (1.6 km2) is located in California's Sierra Nevada within the Humbug Creek watershed and Malakoff Diggins State Historic Park (MDSHP). Previous work indicates typical annual discharge from Humbug Creek of > 500,000 kg of sediment and > 100 g of mercury. This study uses photogrammetry and repeat high-resolution topographic surveys to quantify erosion rates and geomorphic processes, and sediment "fingerprinting" to quantify contributions of fine-sediment sources. The headwaters of Humbug Creek are underlain by volcanic mudflows, whereas MDSHP's denuded and dissected landscape is composed of weathered auriferous sediments susceptible to chronic rill and gully erosion with block failures and debris flows occurring in more cohesive terrain. Aerial LiDAR (November 2014) was used to create a 1-meter digital elevation model (DEM); photogrammetry will be used to create a pre-1997 DEM from historic aerial photographs. DEM differencing will provide an integrated estimate of long-term erosion averaged over ~20 years in unvegetated areas. Finer-resolution (1-cm) terrestrial LiDAR (T-LiDAR) scans were made in late 2014 at four pit locations and will be repeated in the fall of 2015 and 2016. The T-LiDAR time series will provide annual erosion rates under modern conditions, allowing assessment of relative contributions from shallow surface processes and deeper gravity-driven processes. In 2014‒15 we collected storm runoff and in-situ hillslope samples. Sediment fingerprints (mineralogy, major elements, trace elements, and particle size) for source sediments will be used to assess relative contributions from fine-sediment sources using a statistical mixing model. We will present our approach, preliminary results, and discuss how this study supports selection and implementation of management and remediation strategies to ameliorate the discharge of sediment and mercury and mitigate downstream water-quality impacts.

Using LiDAR datasets to improve HSPF water quality modeling in the Red River of the North Basin

NASA Astrophysics Data System (ADS)

Burke, M. P.; Foreman, C. S.

2013-12-01

The Red River of the North Basin (RRB), located in the lakebed of ancient glacial Lake Agassiz, comprises one of the flattest landscapes in North America. The topography of the basin, coupled with the Red River's direction of flow from south to north results in a system that is highly susceptible to flooding. The magnitude and frequency of flood events in the RRB has prompted several multijurisdictional projects and mitigation efforts. In response to the devastating 1997 flood, an International Joint Commission sponsored task force established the need for accurate elevation data to help improve flood forecasting and better understand risks. This led to the International Water Institute's Red River Basin Mapping Initiative, and the acquisition LiDAR Data for the entire US portion of the RRB. The resulting 1 meter bare earth digital elevation models have been used to improve hydraulic and hydrologic modeling within the RRB, with focus on flood prediction and mitigation. More recently, these LiDAR datasets have been incorporated into Hydrological Simulation Program-FORTRAN (HSPF) model applications to improve water quality predictions in the MN portion of the RRB. RESPEC is currently building HSPF model applications for five of MN's 8-digit HUC watersheds draining to the Red River, including: the Red Lake River, Clearwater River, Sandhill River, Two Rivers, and Tamarac River watersheds. This work is being conducted for the Minnesota Pollution Control Agency (MPCA) as part of MN's statewide watershed approach to restoring and protecting water. The HSPF model applications simulate hydrology (discharge, stage), as well as a number of water quality constituents (sediment, temperature, organic and inorganic nitrogen, total ammonia, organic and inorganic phosphorus, dissolved oxygen and biochemical oxygen demand, and algae) continuously for the period 1995-2009 and are formulated to provide predictions at points of interest within the watersheds, such as observation gages, management boundaries, compliance points, and impaired water body endpoints. Incorporation of the LiDAR datasets has been critical to representing the topographic characteristics that impact hydrologic and water quality processes in the extremely flat, heavily drained sub-basins of the RRB. Beyond providing more detailed elevation and slope measurements, the high resolution LiDAR datasets have helped to identify drainage alterations due to agricultural practices, as well as improve representation of channel geometry. Additionally, when available, LiDAR based hydraulic models completed as part of the RRB flood mitigation efforts, are incorporated to further improve flow routing. The MPCA will ultimately use these HSPF models to aid in Total Maximum Daily Load (TMDL) development, permit development/compliance, analysis of Best Management Practice (BMP) implementation scenarios, and other watershed planning and management objectives. LiDAR datasets are an essential component of the water quality models build for the watersheds within the RRB and would greatly benefit water quality modeling efforts in similarly characterized areas.

On Feature Extraction from Large Scale Linear LiDAR Data

NASA Astrophysics Data System (ADS)

Acharjee, Partha Pratim

Airborne light detection and ranging (LiDAR) can generate co-registered elevation and intensity map over large terrain. The co-registered 3D map and intensity information can be used efficiently for different feature extraction application. In this dissertation, we developed two algorithms for feature extraction, and usages of features for practical applications. One of the developed algorithms can map still and flowing waterbody features, and another one can extract building feature and estimate solar potential on rooftops and facades. Remote sensing capabilities, distinguishing characteristics of laser returns from water surface and specific data collection procedures provide LiDAR data an edge in this application domain. Furthermore, water surface mapping solutions must work on extremely large datasets, from a thousand square miles, to hundreds of thousands of square miles. National and state-wide map generation/upgradation and hydro-flattening of LiDAR data for many other applications are two leading needs of water surface mapping. These call for as much automation as possible. Researchers have developed many semi-automated algorithms using multiple semi-automated tools and human interventions. This reported work describes a consolidated algorithm and toolbox developed for large scale, automated water surface mapping. Geometric features such as flatness of water surface, higher elevation change in water-land interface and, optical properties such as dropouts caused by specular reflection, bimodal intensity distributions were some of the linear LiDAR features exploited for water surface mapping. Large-scale data handling capabilities are incorporated by automated and intelligent windowing, by resolving boundary issues and integrating all results to a single output. This whole algorithm is developed as an ArcGIS toolbox using Python libraries. Testing and validation are performed on a large datasets to determine the effectiveness of the toolbox and results are presented. Significant power demand is located in urban areas, where, theoretically, a large amount of building surface area is also available for solar panel installation. Therefore, property owners and power generation companies can benefit from a citywide solar potential map, which can provide available estimated annual solar energy at a given location. An efficient solar potential measurement is a prerequisite for an effective solar energy system in an urban area. In addition, the solar potential calculation from rooftops and building facades could open up a wide variety of options for solar panel installations. However, complex urban scenes make it hard to estimate the solar potential, partly because of shadows cast by the buildings. LiDAR-based 3D city models could possibly be the right technology for solar potential mapping. Although, most of the current LiDAR-based local solar potential assessment algorithms mainly address rooftop potential calculation, whereas building facades can contribute a significant amount of viable surface area for solar panel installation. In this paper, we introduce a new algorithm to calculate solar potential of both rooftop and building facades. Solar potential received by the rooftops and facades over the year are also investigated in the test area.

Assessing the Transferability of Statistical Predictive Models for Leaf Area Index Between Two Airborne Discrete Return LiDAR Sensor Designs Within Multiple Intensely Managed Loblolly Pine Forest Locations in the South-Eastern USA

NASA Technical Reports Server (NTRS)

Sumnall, Matthew; Peduzzi, Alicia; Fox, Thomas R.; Wynne, Randolph H.; Thomas, Valerie A.; Cook, Bruce

2016-01-01

Leaf area is an important forest structural variable which serves as the primary means of mass and energy exchange within vegetated ecosystems. The objective of the current study was to determine if leaf area index (LAI) could be estimated accurately and consistently in five intensively managed pine plantation forests using two multiple-return airborne LiDAR datasets. Field measurements of LAI were made using the LiCOR LAI2000 and LAI2200 instruments within 116 plots were established of varying size and within a variety of stand conditions (i.e. stand age, nutrient regime and stem density) in North Carolina and Virginia in 2008 and 2013. A number of common LiDAR return height and intensity distribution metrics were calculated (e.g. average return height), in addition to ten indices, with two additional variants, utilized in the surrounding literature which have been used to estimate LAI and fractional cover, were calculated from return heights and intensity, for each plot extent. Each of the indices was assessed for correlation with each other, and was used as independent variables in linear regression analysis with field LAI as the dependent variable. All LiDAR derived metrics were also entered into a forward stepwise linear regression. The results from each of the indices varied from an R2 of 0.33 (S.E. 0.87) to 0.89 (S.E. 0.36). Those indices calculated using ratios of all returns produced the strongest correlations, such as the Above and Below Ratio Index (ABRI) and Laser Penetration Index 1 (LPI1). The regression model produced from a combination of three metrics did not improve correlations greatly (R2 0.90; S.E. 0.35). The results indicate that LAI can be predicted over a range of intensively managed pine plantation forest environments accurately when using different LiDAR sensor designs. Those indices which incorporated counts of specific return numbers (e.g. first returns) or return intensity correlated poorly with field measurements. There were disparities between the number of different types of returns and intensity values when comparing the results from two LiDAR sensors, indicating that predictive models developed using such metrics are not transferable between datasets with different acquisition parameters. Each of the indices were significantly correlated with one another, with one exception (LAI proxy), in particular those indices calculated from all returns, which indicates similarities in information content for those indices. It can then be argued that LiDAR indices have reached a similar stage in development to those calculated from optical-spectral sensors, but which offer a number of advantages, such as the reduction or removal of saturation issues in areas of high biomass.

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.

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.

Comparative study of building footprint estimation methods from LiDAR point clouds

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

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

NASA Astrophysics Data System (ADS)

Abera, F. N.

2017-12-01

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

Ultra-high resolution four dimensional geodetic imaging of engineered structures for stability assessment

USGS Publications Warehouse

Bawden, Gerald W.; Bond, Sandra; Podoski, J. H.; Kreylos, O.; Kellogg, L. H.

2012-01-01

We used ground-based Tripod LiDAR (T-LiDAR) to assess the stability of two engineered structures: a bridge spanning the San Andreas fault following the M6.0 Parkfield earthquake in Central California and a newly built coastal breakwater located at the Kaumālapa`u Harbor Lana'i, Hawaii. In the 10 weeks following the earthquake, we found that the surface under the bridge shifted 7.1 cm with an additional 2.6 cm of motion in the subsequent 13 weeks, which deflected the bridge's northern I-beam support 4.3 cm and 2.1 respectively; the bridge integrity remained intact. T-LiDAR imagery was collected after the completion of armored breakwater with 817 35-ton interlocking concrete armor units, Core-Locs®, in the summers of 2007, 2008 and 2010. We found a wide range of motion of individual Core-Locs, from a few centimeters to >110 cm along the ocean side of the breakwater, with lesser movement along the harbor side.

Adolescents' Communication with Parents, Other Adult Family Members and Teachers on Sexuality: Effects of School-Based Interventions in South Africa and Tanzania.

PubMed

Namisi, Francis; Aarø, Leif Edvard; Kaaya, Sylvia; Kajula, Lusajo J; Kilonzo, Gad P; Onya, Hans; Wubs, Annegreet; Mathews, Catherine

2015-12-01

Cluster-randomized controlled trials were carried out to examine effects on sexual practices of school-based interventions among adolescents in three sites in sub-Saharan Africa. In this publication, effects on communication about sexuality with significant adults (including parents) and such communication as a mediator of other outcomes were examined. Belonging to the intervention group was significantly associated with fewer reported sexual debuts in Dar es Salaam only (OR 0.648). Effects on communication with adults about sexuality issues were stronger for Dar es Salaam than for the other sites. In Dar, increase in communication with adults proved to partially mediate associations between intervention and a number of social cognition outcomes. The hypothesized mediational effect of communication on sexual debut was not confirmed. Promoting intergenerational communication on sexuality issues is associated with several positive outcomes and therefore important. Future research should search for mediating factors influencing behavior beyond those examined in the present study.

Construction of integrated linkage map of a recombinant inbred line population of white lupin (Lupinus albus L.)

PubMed Central

Vipin, Cina Ann; Luckett, David J.; Harper, John D.I.; Ash, Gavin J.; Kilian, Andrzej; Ellwood, Simon R.; Phan, Huyen T.T.; Raman, Harsh

2013-01-01

We report the development of a Diversity Arrays Technology (DArT) marker panel and its utilisation in the development of an integrated genetic linkage map of white lupin (Lupinus albus L.) using an F8 recombinant inbred line population derived from Kiev Mutant/P27174. One hundred and thirty-six DArT markers were merged into the first genetic linkage map composed of 220 amplified fragment length polymorphisms (AFLPs) and 105 genic markers. The integrated map consists of 38 linkage groups of 441 markers and spans a total length of 2,169 cM, with an average interval size of 4.6 cM. The DArT markers exhibited good genome coverage and were associated with previously identified genic and AFLP markers linked with quantitative trait loci for anthracnose resistance, flowering time and alkaloid content. The improved genetic linkage map of white lupin will aid in the identification of markers for traits of interest and future syntenic studies. PMID:24273424

What is the effect of LiDAR-derived DEM resolution on large-scale watershed model results?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ping Yang; Daniel B. Ames; Andre Fonseca

This paper examines the effect of raster cell size on hydrographic feature extraction and hydrological modeling using LiDAR derived DEMs. LiDAR datasets for three experimental watersheds were converted to DEMs at various cell sizes. Watershed boundaries and stream networks were delineated from each DEM and were compared to reference data. Hydrological simulations were conducted and the outputs were compared. Smaller cell size DEMs consistently resulted in less difference between DEM-delineated features and reference data. However, minor differences been found between streamflow simulations resulted for a lumped watershed model run at daily simulations aggregated at an annual average. These findings indicatemore » that while higher resolution DEM grids may result in more accurate representation of terrain characteristics, such variations do not necessarily improve watershed scale simulation modeling. Hence the additional expense of generating high resolution DEM's for the purpose of watershed modeling at daily or longer time steps may not be warranted.« less

Influence of Waveform Characteristics on LiDAR Ranging Accuracy and Precision

PubMed Central

Yang, Bingwei; Xie, Xinhao; Li, Duan

2018-01-01

Time of flight (TOF) based light detection and ranging (LiDAR) is a technology for calculating distance between start/stop signals of time of flight. In lab-built LiDAR, two ranging systems for measuring flying time between start/stop signals include time-to-digital converter (TDC) that counts time between trigger signals and analog-to-digital converter (ADC) that processes the sampled start/stop pulses waveform for time estimation. We study the influence of waveform characteristics on range accuracy and precision of two kinds of ranging system. Comparing waveform based ranging (WR) with analog discrete return system based ranging (AR), a peak detection method (WR-PK) shows the best ranging performance because of less execution time, high ranging accuracy, and stable precision. Based on a novel statistic mathematical method maximal information coefficient (MIC), WR-PK precision has a high linear relationship with the received pulse width standard deviation. Thus keeping the received pulse width of measuring a constant distance as stable as possible can improve ranging precision. PMID:29642639

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.

Mapping standing dead trees (snags) in the aftermath of the 2013 Rim Fire using airborne LiDAR data.

NASA Astrophysics Data System (ADS)

Casas Planes, Á.; Garcia-Alonso, M.; Koltunov, A.; Ustin, S.; Falk, M.; Ramirez, C.; Siegel, R.

2014-12-01

Abundance and spatial distribution of standing dead trees (snags) are key indicators of forest biodiversity and ecosystem health and represent a critical component of habitat for various wildlife species, including the great grey owl and the black-backed woodpecker. In this work we assess the potential of light detection and ranging (LiDAR) to discriminate snags from the live trees and map their distribution. The study area encompasses the burn perimeter of the Rim Fire, the third largest wildfire in California's recorded history (~104.000 ha) and represents a heterogeneous mosaic of mixed conifer forests, hardwood, and meadows. The snags mapping procedure is based on a 3D single tree detection using a Watershed algorithm and the extraction of height and intensity metrics within each segment. Variables selected using Gaussian processes form a feature space for a classifier to distinguish between dead trees and live trees. Finally, snag density and snag diameter classes that are relevant for avian species are mapped. This work shows the use of LiDAR metrics to quantify ecological variables related to the vertical heterogeneity of the forest canopy that are important in the identification of snags, for example, fractional cover. We observed that intensity-related variables are critical to the successful identification of snags and their distribution. Our study highlights the importance of high-density LiDAR for characterizing the forest structural variables that contribute to the assessment of wildlife habitat suitability.

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

Automated method for measuring the extent of selective logging damage with airborne LiDAR data

NASA Astrophysics Data System (ADS)

Melendy, L.; Hagen, S. C.; Sullivan, F. B.; Pearson, T. R. H.; Walker, S. M.; Ellis, P.; Kustiyo; Sambodo, Ari Katmoko; Roswintiarti, O.; Hanson, M. A.; Klassen, A. W.; Palace, M. W.; Braswell, B. H.; Delgado, G. M.

2018-05-01

Selective logging has an impact on the global carbon cycle, as well as on the forest micro-climate, and longer-term changes in erosion, soil and nutrient cycling, and fire susceptibility. Our ability to quantify these impacts is dependent on methods and tools that accurately identify the extent and features of logging activity. LiDAR-based measurements of these features offers significant promise. Here, we present a set of algorithms for automated detection and mapping of critical features associated with logging - roads/decks, skid trails, and gaps - using commercial airborne LiDAR data as input. The automated algorithm was applied to commercial LiDAR data collected over two logging concessions in Kalimantan, Indonesia in 2014. The algorithm results were compared to measurements of the logging features collected in the field soon after logging was complete. The automated algorithm-mapped road/deck and skid trail features match closely with features measured in the field, with agreement levels ranging from 69% to 99% when adjusting for GPS location error. The algorithm performed most poorly with gaps, which, by their nature, are variable due to the unpredictable impact of tree fall versus the linear and regular features directly created by mechanical means. Overall, the automated algorithm performs well and offers significant promise as a generalizable tool useful to efficiently and accurately capture the effects of selective logging, including the potential to distinguish reduced impact logging from conventional logging.

A Hierarchical Object-oriented Urban Land Cover Classification Using WorldView-2 Imagery and Airborne LiDAR data

NASA Astrophysics Data System (ADS)

Wu, M. F.; Sun, Z. C.; Yang, B.; Yu, S. S.

2016-11-01

In order to reduce the “salt and pepper” in pixel-based urban land cover classification and expand the application of fusion of multi-source data in the field of urban remote sensing, WorldView-2 imagery and airborne Light Detection and Ranging (LiDAR) data were used to improve the classification of urban land cover. An approach of object- oriented hierarchical classification was proposed in our study. The processing of proposed method consisted of two hierarchies. (1) In the first hierarchy, LiDAR Normalized Digital Surface Model (nDSM) image was segmented to objects. The NDVI, Costal Blue and nDSM thresholds were set for extracting building objects. (2) In the second hierarchy, after removing building objects, WorldView-2 fused imagery was obtained by Haze-ratio-based (HR) fusion, and was segmented. A SVM classifier was applied to generate road/parking lot, vegetation and bare soil objects. (3) Trees and grasslands were split based on an nDSM threshold (2.4 meter). The results showed that compared with pixel-based and non-hierarchical object-oriented approach, proposed method provided a better performance of urban land cover classification, the overall accuracy (OA) and overall kappa (OK) improved up to 92.75% and 0.90. Furthermore, proposed method reduced “salt and pepper” in pixel-based classification, improved the extraction accuracy of buildings based on LiDAR nDSM image segmentation, and reduced the confusion between trees and grasslands through setting nDSM threshold.

Determinants of acceptance of cervical cancer screening in Dar es Salaam, Tanzania.

PubMed

Kahesa, Crispin; Kjaer, Susanne; Mwaiselage, Julius; Ngoma, Twalib; Tersbol, Britt; Dartell, Myassa; Rasch, Vibeke

2012-12-19

To describe how demographic characteristics and knowledge of cervical cancer influence screening acceptance among women living in Dar es Salaam, Tanzania. Multistage cluster sampling was carried out in 45 randomly selected streets in Dar es Salaam. Women between the ages of 25-59 who lived in the sampled streets were invited to a cervical cancer screening; 804 women accepted and 313 rejected the invitation. Information on demographic characteristics and knowledge of cervical cancer were obtained through structured questionnaire interviews. Women aged 35-44 and women aged 45-59 had increased ORs of 3.52 and 7.09, respectively, for accepting screening. Increased accepting rates were also found among single women (OR 2.43) and among women who had attended primary or secondary school (ORs of 1.81 and 1.94). Women who had 0-2 children were also more prone to accept screening in comparison with women who had five or more children (OR 3.21). Finally, knowledge of cervical cancer and awareness of the existing screening program were also associated with increased acceptance rates (ORs of 5.90 and 4.20). There are identifiable subgroups where cervical cancer screening can be increased in Dar es Salaam. Special attention should be paid to women of low education and women of high parity. In addition, knowledge and awareness raising campaigns that goes hand in hand with culturally acceptable screening services will likely lead to an increased uptake of cervical cancer screening.

lidar change detection using building models

NASA Astrophysics Data System (ADS)

Kim, Angela M.; Runyon, Scott C.; Jalobeanu, Andre; Esterline, Chelsea H.; Kruse, Fred A.

2014-06-01

Terrestrial LiDAR scans of building models collected with a FARO Focus3D and a RIEGL VZ-400 were used to investigate point-to-point and model-to-model LiDAR change detection. LiDAR data were scaled, decimated, and georegistered to mimic real world airborne collects. Two physical building models were used to explore various aspects of the change detection process. The first model was a 1:250-scale representation of the Naval Postgraduate School campus in Monterey, CA, constructed from Lego blocks and scanned in a laboratory setting using both the FARO and RIEGL. The second model at 1:8-scale consisted of large cardboard boxes placed outdoors and scanned from rooftops of adjacent buildings using the RIEGL. A point-to-point change detection scheme was applied directly to the point-cloud datasets. In the model-to-model change detection scheme, changes were detected by comparing Digital Surface Models (DSMs). The use of physical models allowed analysis of effects of changes in scanner and scanning geometry, and performance of the change detection methods on different types of changes, including building collapse or subsistence, construction, and shifts in location. Results indicate that at low false-alarm rates, the point-to-point method slightly outperforms the model-to-model method. The point-to-point method is less sensitive to misregistration errors in the data. Best results are obtained when the baseline and change datasets are collected using the same LiDAR system and collection geometry.

Automatic Registration of TLS-TLS and TLS-MLS Point Clouds Using a Genetic Algorithm

PubMed Central

Yan, Li; Xie, Hong; Chen, Changjun

2017-01-01

Registration of point clouds is a fundamental issue in Light Detection and Ranging (LiDAR) remote sensing because point clouds scanned from multiple scan stations or by different platforms need to be transformed to a uniform coordinate reference frame. This paper proposes an efficient registration method based on genetic algorithm (GA) for automatic alignment of two terrestrial LiDAR scanning (TLS) point clouds (TLS-TLS point clouds) and alignment between TLS and mobile LiDAR scanning (MLS) point clouds (TLS-MLS point clouds). The scanning station position acquired by the TLS built-in GPS and the quasi-horizontal orientation of the LiDAR sensor in data acquisition are used as constraints to narrow the search space in GA. A new fitness function to evaluate the solutions for GA, named as Normalized Sum of Matching Scores, is proposed for accurate registration. Our method is divided into five steps: selection of matching points, initialization of population, transformation of matching points, calculation of fitness values, and genetic operation. The method is verified using a TLS-TLS data set and a TLS-MLS data set. The experimental results indicate that the RMSE of registration of TLS-TLS point clouds is 3~5 mm, and that of TLS-MLS point clouds is 2~4 cm. The registration integrating the existing well-known ICP with GA is further proposed to accelerate the optimization and its optimizing time decreases by about 50%. PMID:28850100

Automatic Registration of TLS-TLS and TLS-MLS Point Clouds Using a Genetic Algorithm.

PubMed

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

2017-08-29

Registration of point clouds is a fundamental issue in Light Detection and Ranging (LiDAR) remote sensing because point clouds scanned from multiple scan stations or by different platforms need to be transformed to a uniform coordinate reference frame. This paper proposes an efficient registration method based on genetic algorithm (GA) for automatic alignment of two terrestrial LiDAR scanning (TLS) point clouds (TLS-TLS point clouds) and alignment between TLS and mobile LiDAR scanning (MLS) point clouds (TLS-MLS point clouds). The scanning station position acquired by the TLS built-in GPS and the quasi-horizontal orientation of the LiDAR sensor in data acquisition are used as constraints to narrow the search space in GA. A new fitness function to evaluate the solutions for GA, named as Normalized Sum of Matching Scores, is proposed for accurate registration. Our method is divided into five steps: selection of matching points, initialization of population, transformation of matching points, calculation of fitness values, and genetic operation. The method is verified using a TLS-TLS data set and a TLS-MLS data set. The experimental results indicate that the RMSE of registration of TLS-TLS point clouds is 3~5 mm, and that of TLS-MLS point clouds is 2~4 cm. The registration integrating the existing well-known ICP with GA is further proposed to accelerate the optimization and its optimizing time decreases by about 50%.

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).

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.

VPAC1 targeted 64Cu-TP3805 PET imaging of prostate cancer: preliminary evaluation in man

PubMed Central

Tripathi, Sushil; Trabulsi, Edouard J; Gomella, Leonard; Kim, Sung; McCue, Peter; Intenzo, Charles; Birbe, Ruth; Gandhe, Ashish; Kumar, Pardeep; Thakur, Mathew

2015-01-01

Objectives To evaluate 64Cu-TP3805 as a novel biomolecule, to PET image prostate cancer (PC), at the onset of which VPAC1, the superfamily of G-protein coupled receptors, is expressed in high density on PC cells, but not on normal cells. Methods 25 patients undergoing radical prostatectomy were PET/CT imaged preoperatively with 64Cu-TP3805. Standardized uptake values (SUVmax) were determined, malignant lesions (SUV > 1.0) counted, and compared with histologic findings. Whole mount pathology slides from 6 VPAC1 PET imaged patients, 3 BPH patients, one malignant and one benign lymph node underwent digital autoradiography (DAR) after 64Cu-TP3805 incubation and compared to H&E stained slides. Results In 25 patient PET imaging, 212 prostate gland lesions had SUVmax > 1.0 vs.127 lesions identified by histology of biopsy tissues. The status of the additional 85 PET identified prostate lesions remains to be determined. In 68 histological slides from 6 PET imaged patients, DAR identified 105/107 PC foci, 19/19 HGPIN, and ejaculatory ducts and verumontanum involved with cancer. Additionally, DAR found 9 PC lesions not previously identified histologically. The positive and negative lymph nodes were correctly identified and in 3/3 BPH patients and 5/5 cysts, DAR was negative. Conclusion This feasibility study demonstrated that 64Cu-TP3805 delineates PC in vivo and ex vivo, provided normal images for benign masses, and is worthy of further studies. PMID:26519886

Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data

PubMed Central

Saatchi, Sassan; Casas, Angeles; Koltunov, Alexander; Ustin, Susan; Ramirez, Carlos; Garcia‐Gutierrez, Jorge; Balzter, Heiko

2017-01-01

Abstract Quantifying biomass consumption and carbon release is critical to understanding the role of fires in the carbon cycle and air quality. We present a methodology to estimate the biomass consumed and the carbon released by the California Rim fire by integrating postfire airborne LiDAR and multitemporal Landsat Operational Land Imager (OLI) imagery. First, a support vector regression (SVR) model was trained to estimate the aboveground biomass (AGB) from LiDAR‐derived metrics over the unburned area. The selected model estimated AGB with an R 2 of 0.82 and RMSE of 59.98 Mg/ha. Second, LiDAR‐based biomass estimates were extrapolated to the entire area before and after the fire, using Landsat OLI reflectance bands, Normalized Difference Infrared Index, and the elevation derived from LiDAR data. The extrapolation was performed using SVR models that resulted in R 2 of 0.73 and 0.79 and RMSE of 87.18 (Mg/ha) and 75.43 (Mg/ha) for the postfire and prefire images, respectively. After removing bias from the AGB extrapolations using a linear relationship between estimated and observed values, we estimated the biomass consumption from postfire LiDAR and prefire Landsat maps to be 6.58 ± 0.03 Tg (1012 g), which translate into 12.06 ± 0.06 Tg CO2e released to the atmosphere, equivalent to the annual emissions of 2.57 million cars. PMID:28405539

Intergration of LiDAR Data with Aerial Imagery for Estimating Rooftop Solar Photovoltaic Potentials in City of Cape Town

NASA Astrophysics Data System (ADS)

Adeleke, A. K.; Smit, J. L.

2016-06-01

Apart from the drive to reduce carbon dioxide emissions by carbon-intensive economies like South Africa, the recent spate of electricity load shedding across most part of the country, including Cape Town has left electricity consumers scampering for alternatives, so as to rely less on the national grid. Solar energy, which is adequately available in most part of Africa and regarded as a clean and renewable source of energy, makes it possible to generate electricity by using photovoltaics technology. However, before time and financial resources are invested into rooftop solar photovoltaic systems in urban areas, it is important to evaluate the potential of the building rooftop, intended to be used in harvesting the solar energy. This paper presents methodologies making use of LiDAR data and other ancillary data, such as high-resolution aerial imagery, to automatically extract building rooftops in City of Cape Town and evaluate their potentials for solar photovoltaics systems. Two main processes were involved: (1) automatic extraction of building roofs using the integration of LiDAR data and aerial imagery in order to derive its' outline and areal coverage; and (2) estimating the global solar radiation incidence on each roof surface using an elevation model derived from the LiDAR data, in order to evaluate its solar photovoltaic potential. This resulted in a geodatabase, which can be queried to retrieve salient information about the viability of a particular building roof for solar photovoltaic installation.

[DARS mutations responsible for hypomyelination with brain stem and spinal cord involvement and leg spasticity: report of two cases and review of literature].

PubMed

Zhang, J; Liu, M; Zhou, L; Zhang, Z B; Wang, J M; Jiang, Y W; Wu, Y

2018-03-02

Objective: To analyze the clinical and imaging features of hypomyelination with brain stem and spinal cord involvement and leg spasticity (HBSL) due to mutations in DARS, and to identify DARS mutations responsible for HBSL. Methods: Data on 2 HBSL patients who were admitted to the pediatric department of Peking University First Hospital from January 2009 through December 2016 were reviewed and the 2 patients were followed up. Targeted next generation sequencing, whole exome sequencing and Sanger sequencing were employed to identify potential genetic variations of the children and their parents. The clinical manifestations, MRI features and genotypic characteristics of two patients were reviewed, and the literature was reviewed. HBSL reported cases were searched with"leukoencephalopathies, DARS"on databases of PubMed, Wanfang, China National Knowledge Infrastructure and VIP from 1975 to 2017. The clinical manifestations and molecular features were analyzed. Results: Both patients showed delayed motor development, but had normal cognitive development. At the age of 8 years, case 1 reached the most significant motor development milestone of only standing with help during the last follow-up. At the age of 9, case 2 could walk independently during the last follow-up. On physical examination, both showed leg spastcity, active tendon reflex, positive Babinski sign. Both patients had brain MRI findings of high T2WI signal in bilateral deep cerebral white matter, slightly lower T1WI, and no abnormal DWI signal. Lesions of case 1 were relatively extensive and involved subcortical white matter, corpus callosum and internal capsule. Spinal MRI scans for both patients showed no abnormal signals. Novel mutations in DARS gene-namely, c.1498_1499insTCA (p.500_501insIle) and c.1210A>G (p.Met404Val) , c.1432A>G (p.Met478Val) and c.1210A>G (p.Met404Val) were identified in case 1 and case 2 respectively. On the database, 2 reports involving 13 foreign patients were retrieved. The age of disease onset was from 4 months to 18 years, and their initial symptoms were development delay or regression. Most of them presented with progressive lower extremity spasm, and the brain magnetic resonance imaging was characterized by hypomyelination in white matter. Clinical phenotypes of different age groups were significantly different. Conclusion: We have reported two patients with HBSL in China, and 3 novel mutations in DARS, which is helpful for the diagnosis and genetic counseling of HBSL.

QUANTIFYING FOREST ABOVEGROUND CARBON POOLS AND FLUXES USING MULTI-TEMPORAL LIDAR A report on field monitoring, remote sensing MMV, GIS integration, and modeling results for forestry field validation test to quantify aboveground tree biomass and carbon

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee Spangler; Lee A. Vierling; Eva K. Stand

2012-04-01

Sound policy recommendations relating to the role of forest management in mitigating atmospheric carbon dioxide (CO{sub 2}) depend upon establishing accurate methodologies for quantifying forest carbon pools for large tracts of land that can be dynamically updated over time. Light Detection and Ranging (LiDAR) remote sensing is a promising technology for achieving accurate estimates of aboveground biomass and thereby carbon pools; however, not much is known about the accuracy of estimating biomass change and carbon flux from repeat LiDAR acquisitions containing different data sampling characteristics. In this study, discrete return airborne LiDAR data was collected in 2003 and 2009 acrossmore » {approx}20,000 hectares (ha) of an actively managed, mixed conifer forest landscape in northern Idaho, USA. Forest inventory plots, established via a random stratified sampling design, were established and sampled in 2003 and 2009. The Random Forest machine learning algorithm was used to establish statistical relationships between inventory data and forest structural metrics derived from the LiDAR acquisitions. Aboveground biomass maps were created for the study area based on statistical relationships developed at the plot level. Over this 6-year period, we found that the mean increase in biomass due to forest growth across the non-harvested portions of the study area was 4.8 metric ton/hectare (Mg/ha). In these non-harvested areas, we found a significant difference in biomass increase among forest successional stages, with a higher biomass increase in mature and old forest compared to stand initiation and young forest. Approximately 20% of the landscape had been disturbed by harvest activities during the six-year time period, representing a biomass loss of >70 Mg/ha in these areas. During the study period, these harvest activities outweighed growth at the landscape scale, resulting in an overall loss in aboveground carbon at this site. The 30-fold increase in sampling density between the 2003 and 2009 did not affect the biomass estimates. Overall, LiDAR data coupled with field reference data offer a powerful method for calculating pools and changes in aboveground carbon in forested systems. The results of our study suggest that multitemporal LiDAR-based approaches are likely to be useful for high quality estimates of aboveground carbon change in conifer forest systems.« less

Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD.

PubMed

Mitchell, Anthea L; Rosenqvist, Ake; Mora, Brice

2017-12-01

Forest degradation is a global phenomenon and while being an important indicator and precursor to further forest loss, carbon emissions due to degradation should also be accounted for in national reporting within the frame of UN REDD+. At regional to country scales, methods have been progressively developed to detect and map forest degradation, with these based on multi-resolution optical, synthetic aperture radar (SAR) and/or LiDAR data. However, there is no one single method that can be applied to monitor forest degradation, largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. The review assesses two main approaches to monitoring forest degradation: first, where detection is indicated by a change in canopy cover or proxies, and second, the quantification of loss (or gain) in above ground biomass (AGB). The discussion only considers degradation that has a visible impact on the forest canopy and is thus detectable by remote sensing. The first approach encompasses methods that characterise the type of degradation and track disturbance, detect gaps in, and fragmentation of, the forest canopy, and proxies that provide evidence of forestry activity. Progress in these topics has seen the extension of methods to higher resolution (both spatial and temporal) data to better capture the disturbance signal, distinguish degraded and intact forest, and monitor regrowth. Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data. The second approach exploits EO sensors with known sensitivity to forest structure and biomass and discusses monitoring efforts using repeat LiDAR and SAR data. There has been progress in the capacity to discriminate forest age and growth stage using data fusion methods and LiDAR height metrics. Interferometric SAR and LiDAR have found new application in linking forest structure change to degradation in tropical forests. Estimates of AGB change have been demonstrated at national level using SAR and LiDAR-assisted approaches. Future improvements are anticipated with the availability of next generation LiDAR sensors. Improved access to relevant satellite data and best available methods are key to operational forest degradation monitoring. Countries will need to prioritise their monitoring efforts depending on the significance of the degradation, balanced against available resources. A better understanding of the drivers and impacts of degradation will help guide monitoring and restoration efforts. Ultimately we want to restore ecosystem service and function in degraded forests before the change is irreversible.

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 elevation and azimuth angles. Following this analysis, different LiDAR layouts have been tested, e.g., one vertical beam and the other two almost horizontal, all three equidistant with the same elevation angle, etc. Preliminary results show different degrees of agreement between the proposed technique and the sonic anemometers depending on the LiDARs layout, which is in agreement with the uncertainty analysis carried out. The best configurations show a good agreement for the three components of the velocity and turbulence spectra, thus proving the ability of the technique to measure accurately atmospheric turbulence, consolidating it as a very interesting alternative to meteorological masts for many different applications.

Dynamic association rules for gene expression data analysis.

PubMed

Chen, Shu-Chuan; Tsai, Tsung-Hsien; Chung, Cheng-Han; Li, Wen-Hsiung

2015-10-14

The purpose of gene expression analysis is to look for the association between regulation of gene expression levels and phenotypic variations. This association based on gene expression profile has been used to determine whether the induction/repression of genes correspond to phenotypic variations including cell regulations, clinical diagnoses and drug development. Statistical analyses on microarray data have been developed to resolve gene selection issue. However, these methods do not inform us of causality between genes and phenotypes. In this paper, we propose the dynamic association rule algorithm (DAR algorithm) which helps ones to efficiently select a subset of significant genes for subsequent analysis. The DAR algorithm is based on association rules from market basket analysis in marketing. We first propose a statistical way, based on constructing a one-sided confidence interval and hypothesis testing, to determine if an association rule is meaningful. Based on the proposed statistical method, we then developed the DAR algorithm for gene expression data analysis. The method was applied to analyze four microarray datasets and one Next Generation Sequencing (NGS) dataset: the Mice Apo A1 dataset, the whole genome expression dataset of mouse embryonic stem cells, expression profiling of the bone marrow of Leukemia patients, Microarray Quality Control (MAQC) data set and the RNA-seq dataset of a mouse genomic imprinting study. A comparison of the proposed method with the t-test on the expression profiling of the bone marrow of Leukemia patients was conducted. We developed a statistical way, based on the concept of confidence interval, to determine the minimum support and minimum confidence for mining association relationships among items. With the minimum support and minimum confidence, one can find significant rules in one single step. The DAR algorithm was then developed for gene expression data analysis. Four gene expression datasets showed that the proposed DAR algorithm not only was able to identify a set of differentially expressed genes that largely agreed with that of other methods, but also provided an efficient and accurate way to find influential genes of a disease. In the paper, the well-established association rule mining technique from marketing has been successfully modified to determine the minimum support and minimum confidence based on the concept of confidence interval and hypothesis testing. It can be applied to gene expression data to mine significant association rules between gene regulation and phenotype. The proposed DAR algorithm provides an efficient way to find influential genes that underlie the phenotypic variance.

Coastal monitoring solutions of the geomorphological response of beach-dune systems using multi-temporal LiDAR datasets (Vendée coast, France)

NASA Astrophysics Data System (ADS)

Le Mauff, Baptiste; Juigner, Martin; Ba, Antoine; Robin, Marc; Launeau, Patrick; Fattal, Paul

2018-03-01

Three beach and dune systems located in the northeastern part of the Bay of Biscay in France were monitored over 5 years with a time series of three airborne LiDAR datasets. The three study sites illustrate a variety of morphological beach types found in this region. Reproducible monitoring solutions adapted to basic and complex beach and dune morphologies using LiDAR time series were investigated over two periods bounded by the three surveys. The first period (between May 2008 and August 2010) is characterized by a higher prevalence of storm events, and thus has a greater potential for eroding the coast, than the second period (between August 2010 and September 2013). During the first period, the central and northeastern part of the Bay of Biscay was notably impacted by Storm Xynthia, with water levels and wave heights exceeding the 10-year return period and 1-year return period, respectively. Despite differences in dune morphology between the sites, the dune crest (Dhigh) and the dune base (Dlow) are efficiently extracted from each DEM. Based on the extracted dune base, an original shoreline mobility indicator is built displaying a combination of the horizontal and vertical migrations of this geomorphic indicator between two LiDAR datasets. A 'Geomorphic Change Detection' is also completed by computing DEMs of Difference (DoD) resulting in segregated maps of erosion and deposition and sediment budgets. Accounting for the accuracy of LiDAR datasets, a probabilistic approach at a 95% confidence interval is used as a threshold for the Geomorphic Change Detection showing more reliable results. However, caution should be taken when interpreting thresholded maps of changes and sediment budgets because some beach processes may be masked, especially on wide tidal beaches, by only keeping the most significant changes. The results of the shoreline mobility and Geomorphic Change Detection show a high variability in the beach responses between and within the three study sites, explained mainly by beach orientation and local factors. Despite variable site-specific mechanisms, the recovery processes redistribute the available sand more on the upper parts of the beach, producing significant deposition generally in the form of embryo dunes. The monitoring of the beach and dune systems with airborne LiDAR datasets reveals that the three study sites show diverse behaviours during the first period likely associated with storms, while the analysis show more homogenous beach responses during the second period likely associated with a recovery phase.

Spatial patterns of water quality parameters in upper layer of the Kara Sea in summer 2016 based on laser remote sensing

NASA Astrophysics Data System (ADS)

Osokina, Varvara; Pelevin, Vadim; Shatravin, Alexander; Belyaev, Nikolay; Demidov, Andrey; Redzhepova, Zuleyha

2017-04-01

The paper represents results of remote sensing by means of Laser Induced Fluorescence LiDAR during the expedition in Kara Sea in summer 2016. The expedition took place in Western and Southern parts of Kara Sea including Ob and Yenisei areas from June, 14 to August, 20 2016. The LiDAR observations were obtained from the research vessel Mstislav Keldysh and included 4600 km of almost continuous measurements and 94 complex stations. As a result now there is a vast LiDAR database available for scientific purposes. The data were processed and recalculated providing a set of high resolution maps of distribution of main oceanographic water quality parameters including chlorophyll "a", total organic carbon and total suspended matter in surface layer. The proceeded maps give a precise information about the location of frontal zones between Ob and Yenisei waters and Kara Sea waters, provide a detailed picture of complex surface water structure in central Kara Sea and other locations and present data about spatial distinction of concentrations of measured water parameters. The LiDAR measurements were afterwards compared to data, obtained by underway flow-through CTD measuring system and satellite images providing adjunct information on water parameters' distribution features. The instruments of UFL (Ultraviolet fluorescent LiDAR) series were developed by the Shirshov Institute of Oceanology, Moscow, Russia, and have been successfully used in lots of scientific expeditions in different water areas. UFL LiDARs take measurements with sampling rate up to 2 Hz from the vessel under way in any weather or sunlight conditions. The measurements are linked to a GPS, and so all data are geo-tagged and can be used to create interpolated maps of the measured parameters. The instrument analyses backward signal from dual excitation (355, 532 nm) laser pulses emitted at 2 Hz. The signal is detected across 11 bands in series (355, 385, 404, 424, 440, 460, 499, 532, 620, 651, 685 nm) on stations, and across 4 bands at the same time (355, 404, 440, 685 nm) in transect mode while the vessel is moving. Fluorescence intensities at 440 and 685 nm and backscattering of the 355 nm laser pulse are normalized to Raman scattering at 404 nm, and then calibrated using a set of laboratory-measured concentrations to derive total organic carbon, chlorophyll a and total suspended matter concentrations.

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 industrial emission sources; 3) under the heavy polluted condition, the height of planetary boundary layer (PBL) reduced to 500m. This experimental measurement suggests that mobile LiDAR is capable of detecting the time and area dependent air pollution episode in regional scale. Especially, LiDAR offers active remote sensing of aerosol vertical properties, which makes it feasible to detect the PBL evolution playing a crucial role in the haze formation. With regular weekly/monthly/quarterly mobile detection, some hidden emission sources could be detected and the air pollution local pattern would be revealed.

A Study of 3-D Visualization and Knowledge-Based Mission Planning and Control for the NPS Model 2 Autonomous Underwater Vehicle

DTIC Science & Technology

1989-12-01

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