Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning

We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only. This is achieved by extending recent ideas from learning of unsupervised image denoisers to unstructured 3D point clouds. Unsupervised image denoisers operate under the assumption that a noisy pixel observation is a random realization of a distribution around a clean pixel value, which allows appropriate learning on this distribution to eventually converge to the correct value. Regrettably, this assumption is not valid for unstructured points: 3D point clouds are subject to total noise, i.e. deviations in all coordinates, with no reliable pixel grid. Thus, an observation can be the realization of an entire manifold of clean 3D points, which makes the quality of a naive extension of unsupervised image denoisers to 3D point clouds unfortunately only little better than mean filtering. To overcome this, and to enable effective and unsupervised 3D point cloud denoising, we introduce a spatial prior term, that steers converges to the unique closest out of the many possible modes on the manifold. Our results demonstrate unsupervised denoising performance similar to that of supervised learning with clean data when given enough training examples - whereby we do not need any pairs of noisy and clean training data

Source identification and quantification of particulate matter emitted from livestock houses

There is need to identify and quantify the contribution of different sources to airborne particulate matter (PM) emissions from animal houses. To this end, we compared the chemical and morphological characteristics of fine and coarse PM from known sources collected from animal houses with the characteristics of on-farm fine and coarse airborne PM using two methods: classification rules based on decision trees and multiple linear regression. Fourteen different farms corresponding to seven different housing systems for poultry and pigs were sampled during winter. A total of 28 fine and 28 coarse on-farm airborne PM samples were collected, together with a representative sample of each known source per farm (56 known source samples in total). Source contributions were calculated as relative percentage contributions in particle numbers and then estimated in particle mass. Based on particle numbers, results showed that in poultry houses, most on-farm airborne PM originated from feathers (ranging from 4% to 43% in fine PM and from 6% to 35% in coarse PM) and manure (ranging from 9% to 85% in fine PM and from 30% to 94% in coarse PM). For pigs, most on-farm airborne PM originated from manure (ranging from 70% to 98% in fine PM and from 41% to 94% in coarse PM). Based on particle mass, for poultry most on-farm airborne PM still originated from feathers and manure; for pigs, however, most PM originated from skin and manure. Feed had a negligible contribution to on-farm airborne PM compared with other sources. Results presented in this study improve the understanding of sources of PM in different animal housing systems, which may be valuable when choosing optimal PM reduction technique

Deep-learning the Latent Space of Light Transport

We suggest a method to directly deep‐learn light transport, i. e., the mapping from a 3D geometry‐illumination‐material configuration to a shaded 2D image. While many previous learning methods have employed 2D convolutional neural networks applied to images, we show for the first time that light transport can be learned directly in 3D. The benefit of 3D over 2D is, that the former can also correctly capture illumination effects related to occluded and/or semi‐transparent geometry. To learn 3D light transport, we represent the 3D scene as an unstructured 3D point cloud, which is later, during rendering, projected to the 2D output image. Thus, we suggest a two‐stage operator comprising a 3D network that first transforms the point cloud into a latent representation, which is later on projected to the 2D output image using a dedicated 3D‐2D network in a second step. We will show that our approach results in improved quality in terms of temporal coherence while retaining most of the computational efficiency of common 2D methods. As a consequence, the proposed two stage‐operator serves as a valuable extension to modern deferred shading approaches

Deriving pseudo-vertical waveforms from small-footprint full-waveform LiDAR data

This is an author's accepted manuscript of an article published in “Remote Sensing Letters", Volume 5, Issue 4, 2014; copyright Taylor & Francis; available online at: http://www.tandfonline.com/doi/abs/10.1080/2150704X.2014.903350[EN] When processing scanning LiDAR data, it is commonly assumed that the extracted full-waveform LiDAR pulse registers truly vertical information of forest canopies. This assumption may lead to uncertain results for the spatiotemporal analysis of the waveforms due to off-nadir scanning angles and varying trajectories travelled by the pulses in overlapping strips. In this letter, we investigate these assumptions and undertake some preliminary analysis to overcome their impacts on forest-based LiDAR analyses. Our results demonstrate that for a standard LiDAR forest acquisition programme in Oregon, USA, most of the hits (83%) are acquired off-nadir, which leads to positional displacements on the ground of the full-waveforms of about 0.20 m for each 1-m height increment. We propose an approach to synthetize multiple waveform data into composite waveforms containing the vertical profile of vegetation for a given location. This approach is based on partitioning the aboveground vertical space into voxels and using the maximum full-waveform intensity value to construct new full-waveforms comprising the vertical information of the various waveforms crossing over a location. Our initial results indicate that deriving spatiotemporal metrics from the composite pseudo-vertical full-waveforms produces a more consistent response across adjacent height levels, which in turn enables a more complete characterization and more vegetation structure to be retrieved. We conclude that this type of pseudo-vertical full-waveform analysis is necessary to more fully understand the impact of the return signals from tree crownsThis paper was developed as a result of a visiting scholar grant funded by the Erasmus Mundus Programme of the European Commission under the Transatlantic Partnership for Excellence in Engineering – TEE Project. The authors also wish to thank the Generalitat Valenciana for the mobility grant [BEST/2012/235] and the Panther Creek Remote Sensing and Research cooperative programme for the data provided for this research.Hermosilla, T.; Coops, N.; Ruiz Fernández, LÁ.; Moskal, M. (2014). Deriving pseudo-vertical waveforms from small-footprint full-waveform LiDAR data. Remote Sensing Letters. 5(4):332-341. https://doi.org/10.1080/2150704X.2014.903350S33234154Baltsavias, E. . (1999). Airborne laser scanning: basic relations and formulas. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2-3), 199-214. doi:10.1016/s0924-2716(99)00015-5Blair, J. B., Rabine, D. L., & Hofton, M. A. (1999). The Laser Vegetation Imaging Sensor: a medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2-3), 115-122. doi:10.1016/s0924-2716(99)00002-7BOUDREAU, J., NELSON, R., MARGOLIS, H., BEAUDOIN, A., GUINDON, L., & KIMES, D. (2008). Regional aboveground forest biomass using airborne and spaceborne LiDAR in Québec. Remote Sensing of Environment, 112(10), 3876-3890. doi:10.1016/j.rse.2008.06.003Bretar, F., M. Pierrot-Deseilligny, and M. Roux. 2004. “Solving the Strip Adjustment Problem of 3D Airborne Lidar Data.” IEEE International Geoscience and Remote Sensing Symposium Proceedings, Anchorage, AK, September 20–24, 4734–4737.Buddenbaum, H., Seeling, S., & Hill, J. (2013). Fusion of full-waveform lidar and imaging spectroscopy remote sensing data for the characterization of forest stands. International Journal of Remote Sensing, 34(13), 4511-4524. doi:10.1080/01431161.2013.776721Carabajal, C. C., & Harding, D. J. (2005). ICESat validation of SRTM C-band digital elevation models. Geophysical Research Letters, 32(22), n/a-n/a. doi:10.1029/2005gl023957Drake, J. B., Dubayah, R. O., Clark, D. B., Knox, R. G., Blair, J. B., Hofton, M. A., … Prince, S. (2002). Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sensing of Environment, 79(2-3), 305-319. doi:10.1016/s0034-4257(01)00281-4Ferraz, A., G. Goncalves, P. Soares, M. Tome, C. Mallet, S. Jacquemoud, F. Bretar, and L. Pereira. 2012. “Comparing Small-footprint LiDAR and Forest Inventory Data for Single Strata Biomass Estimation – A Case Study over a Multi-layered Mediterranean Forest.” IEEE Geoscience and Remote Sensing Symposium (IGARSS), Munich, July 22–27, 6384–6387.Hall, S. A., Burke, I. C., Box, D. O., Kaufmann, M. R., & Stoker, J. M. (2005). Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. Forest Ecology and Management, 208(1-3), 189-209. doi:10.1016/j.foreco.2004.12.001Harding, D. J. (2005). ICESat waveform measurements of within-footprint topographic relief and vegetation vertical structure. Geophysical Research Letters, 32(21). doi:10.1029/2005gl023471Heinzel, J., & Koch, B. (2011). Exploring full-waveform LiDAR parameters for tree species classification. International Journal of Applied Earth Observation and Geoinformation, 13(1), 152-160. doi:10.1016/j.jag.2010.09.010Hermosilla, T., Ruiz, L. A., Kazakova, A. N., Coops, N. C., & Moskal, L. M. (2014). Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data. International Journal of Wildland Fire, 23(2), 224. doi:10.1071/wf13086Höfle, B., Hollaus, M., & Hagenauer, J. (2012). Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 134-147. doi:10.1016/j.isprsjprs.2011.12.003HYDE, P., DUBAYAH, R., PETERSON, B., BLAIR, J., HOFTON, M., HUNSAKER, C., … WALKER, W. (2005). Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems. Remote Sensing of Environment, 96(3-4), 427-437. doi:10.1016/j.rse.2005.03.005Kim, Y., Yang, Z., Cohen, W. B., Pflugmacher, D., Lauver, C. L., & Vankat, J. L. (2009). Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data. Remote Sensing of Environment, 113(11), 2499-2510. doi:10.1016/j.rse.2009.07.010Koetz, B., Morsdorf, F., Sun, G., Ranson, K. J., Itten, K., & Allgower, B. (2006). Inversion of a Lidar Waveform Model for Forest Biophysical Parameter Estimation. IEEE Geoscience and Remote Sensing Letters, 3(1), 49-53. doi:10.1109/lgrs.2005.856706Kronseder, K., Ballhorn, U., Böhm, V., & Siegert, F. (2012). Above ground biomass estimation across forest types at different degradation levels in Central Kalimantan using LiDAR data. International Journal of Applied Earth Observation and Geoinformation, 18, 37-48. doi:10.1016/j.jag.2012.01.010Lefsky, M. A., Cohen, W. B., Acker, S. A., Parker, G. G., Spies, T. A., & Harding, D. (1999). Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests. Remote Sensing of Environment, 70(3), 339-361. doi:10.1016/s0034-4257(99)00052-8Lefsky, M. A., Harding, D. J., Keller, M., Cohen, W. B., Carabajal, C. C., Del Bom Espirito-Santo, F., … de Oliveira, R. (2005). Estimates of forest canopy height and aboveground biomass using ICESat. Geophysical Research Letters, 32(22), n/a-n/a. doi:10.1029/2005gl023971Mallet, C., & Bretar, F. (2009). Full-waveform topographic lidar: State-of-the-art. ISPRS Journal of Photogrammetry and Remote Sensing, 64(1), 1-16. doi:10.1016/j.isprsjprs.2008.09.007Ni-Meister, W., Jupp, D. L. B., & Dubayah, R. (2001). Modeling lidar waveforms in heterogeneous and discrete canopies. IEEE Transactions on Geoscience and Remote Sensing, 39(9), 1943-1958. doi:10.1109/36.951085Pang, Y., Lefsky, M., Sun, G., & Ranson, J. (2011). Impact of footprint diameter and off-nadir pointing on the precision of canopy height estimates from spaceborne lidar. Remote Sensing of Environment, 115(11), 2798-2809. doi:10.1016/j.rse.2010.08.025Reitberger, J., Krzystek, P., & Stilla, U. (2008). Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees. International Journal of Remote Sensing, 29(5), 1407-1431. doi:10.1080/01431160701736448Reitberger, J., Schnörr, C., Krzystek, P., & Stilla, U. (2009). 3D segmentation of single trees exploiting full waveform LIDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6), 561-574. doi:10.1016/j.isprsjprs.2009.04.002Sarrazin, M. J. D., van Aardt, J. A. N., Asner, G. P., McGlinchy, J., Messinger, D. W., & Wu, J. (2012). Fusing small-footprint waveform LiDAR and hyperspectral data for canopy-level species classification and herbaceous biomass modeling in savanna ecosystems. Canadian Journal of Remote Sensing, 37(6), 653-665. doi:10.5589/m12-007SUN, G., RANSON, K., KIMES, D., BLAIR, J., & KOVACS, K. (2008). Forest vertical structure from GLAS: An evaluation using LVIS and SRTM data. Remote Sensing of Environment, 112(1), 107-117. doi:10.1016/j.rse.2006.09.036Van Leeuwen, M., & Nieuwenhuis, M. (2010). Retrieval of forest structural parameters using LiDAR remote sensing. European Journal of Forest Research, 129(4), 749-770. doi:10.1007/s10342-010-0381-4Wu, J., van Aardt, J. A. N., McGlinchy, J., & Asner, G. P. (2012). A Robust Signal Preprocessing Chain for Small-Footprint Waveform LiDAR. IEEE Transactions on Geoscience and Remote Sensing, 50(8), 3242-3255. doi:10.1109/tgrs.2011.2178420Yang, W., Ni-Meister, W., & Lee, S. (2011). Assessment of the impacts of surface topography, off-nadir pointing and vegetation structure on vegetation lidar waveforms using an extended geometric optical and radiative transfer model. Remote Sensing of Environment, 115(11), 2810-2822. doi:10.1016/j.rse.2010.02.02

Integrating Landsat pixel composites and change metrics with lidar plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada

Forest inventory and monitoring programs are needed to provide timely, spatially complete (i.e. mapped), and verifiable information to support forest management, policy formulation, and reporting obligations. Satellite images, in particular data from the Landsat Thematic Mapper and Enhanced Thematic Mapper (TM/ETM +) sensors, are often integrated with field plots from forest inventory programs, leveraging the complete spatial coverage of imagery with detailed ecological information from a sample of plots to spatially model forest conditions and resources. However, in remote and unmanaged areas such as Canada's northern forests, financial and logistic constraints can severely limit the availability of inventory plot data. Additionally, Landsat spectral information has known limitations for characterizing vertical vegetation structure and biomass; while clouds, snow, and short growing seasons can limit development of large area image mosaics that are spectrally and phenologically consistent across space and time. In this study we predict and map forest structure and aboveground biomass over 37 million ha of forestland in Saskatchewan, Canada. We utilize lidar plots—observations of forest structure collected from airborne discrete-return lidar transects acquired in 2010—as a surrogate for traditional field and photo plots. Mapped explanatory data included Tasseled Cap indices and multi-temporal change metrics derived from Landsat TM/ETM + pixel-based image composites. Maps of forest structure and total aboveground biomass were created using a Random Forest (RF) implementation of Nearest Neighbor (NN) imputation. The imputation model had moderate to high plot-level accuracy across all forest attributes (R2 values of 0.42–0.69), as well as reasonable attribute predictions and error estimates (for example, canopy cover above 2 m on validation plots averaged 35.77%, with an RMSE of 13.45%, while unsystematic and systematic agreement coefficients (ACuns and ACsys) had values of 0.63 and 0.97 respectively). Additionally, forest attributes displayed consistent trends in relation to the time since and magnitude of wildfires, indicating model predictions captured the dominant ecological patterns and processes in these forests. Acknowledging methodological and conceptual challenges based upon the use of lidar plots from transects, this study demonstrates that using lidar plots and pixel compositing in imputation mapping can provide forest inventory and monitoring information for regions lacking ongoing or up-to-date field data collection programs

Using street based metrics to characterize urban typologies

[EN] Urban spatial structures reflect local particularities produced during the development of a city. High spatial resolution imagery and LiDAR data are currently used to derive numerical attributes to describe in detail intra-urban structures and morphologies. Urban block boundaries have been frequently used to define the units for extracting metrics from remotely sensed data. In this paper, we propose to complement these metrics with a set of novel descriptors of the streets surrounding the urban blocks under consideration. These metrics numerically describe geometrical properties in addition to other distinctive aspects, such as presence and properties of vegetation and the relationship between the streets and buildings. For this purpose, we also introduce a methodology for partitioning the street area related to an urban block into polygons from which the street urban metrics are derived. We achieve the assessment of these metrics through application of a one-way ANOVA procedure, the winnowing technique, and a decision tree classifier. Our results suggest that street metrics, and particularly those describing the street geometry, are suitable for enhancing the discrimination of complex urban typologies and help to reduce the confusion between certain typologies. The overall classification accuracy increased from 72.7% to 81.1% after the addition street of descriptors. The results of this study demonstrate the usefulness of these metrics for describing street properties and complementing information derived from urban blocks to improve the description of urban areas. Street metrics are of particular use for the characterization of urban typologies and to study the dynamics of cities.The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation in the framework of the project CGL2010-19591/BTE, and the data made available by the Spanish Instituto Geográfico Nacional (IGN)Hermosilla, T.; Palomar-Vázquez, J.; Balaguer Beser, ÁA.; Balsa Barreiro, J.; Ruiz Fernández, LÁ. (2014). Using street based metrics to characterize urban typologies. Computers, Environment and Urban Systems. 44:68-79. https://doi.org/10.1016/j.compenvurbsys.2013.12.002S68794

Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds

Deep learning systems extensively use convolution operations to process input data. Though convolution is clearly defined for structured data such as 2D images or 3D volumes, this is not true for other data types such as sparse point clouds. Previous techniques have developed approximations to convolutions for restricted conditions. Unfortunately, their applicability is limited and cannot be used for general point clouds. We propose an efficient and effective method to learn convolutions for non-uniformly sampled point clouds, as they are obtained with modern acquisition techniques. Learning is enabled by four key novelties: first, representing the convolution kernel itself as a multilayer perceptron; second, phrasing convolution as a Monte Carlo integration problem, third, using this notion to combine information from multiple samplings at different levels; and fourth using Poisson disk sampling as a scalable means of hierarchical point cloud learning. The key idea across all these contributions is to guarantee adequate consideration of the underlying non-uniform sample distribution function from a Monte Carlo perspective. To make the proposed concepts applicable to real-world tasks, we furthermore propose an efficient implementation which significantly reduces the GPU memory required during the training process. By employing our method in hierarchical network architectures we can outperform most of the state-of-the-art networks on established point cloud segmentation, classification and normal estimation benchmarks. Furthermore, in contrast to most existing approaches, we also demonstrate the robustness of our method with respect to sampling variations, even when training with uniformly sampled data only. To support the direct application of these concepts, we provide a ready-to-use TensorFlow implementation of these layers at https://github.com/viscom-ulm/MCCNN

Change detection of isolated housing using a new hybrid approach based on object classification with optical and TerraSAR-X data

Optical and microwave high spatial resolution images are now available for a wide range of applications. In this work, they have been applied for the semi-automatic change detection of isolated housing in agricultural areas. This article presents a new hybrid methodology based on segmentation of high-resolution images and image differencing. This new approach mixes the main techniques used in change detection methods and it also adds a final segmentation process in order to classify the change detection product. First, isolated building classification is carried out using only optical data. Then, synthetic aperture radar (SAR) information is added to the classification process, obtaining excellent results with lower complexity cost. Since the first classification step is improved, the total change detection scheme is also enhanced when the radar data are used for classification. Finally, a comparison between the different methods is presented and some conclusions are extracted from the study. © 2011 Taylor & Francis.Vidal Pantaleoni, A.; Moreno Cambroreno, MDR. (2011). Change detection of isolated housing using a new hybrid approach based on object classification with optical and TerraSAR-X data. International Journal of Remote Sensing. 32(24):9621-9635. doi:10.1080/01431161.2011.571297S962196353224BLAES, X., VANHALLE, L., & DEFOURNY, P. (2005). Efficiency of crop identification based on optical and SAR image time series. Remote Sensing of Environment, 96(3-4), 352-365. doi:10.1016/j.rse.2005.03.010Chen, Y., Shi, P., Fung, T., Wang, J., & Li, X. (2007). Object‐oriented classification for urban land cover mapping with ASTER imagery. International Journal of Remote Sensing, 28(20), 4645-4651. doi:10.1080/01431160500444731Dalla Mura, M., Benediktsson, J. A., Bovolo, F., & Bruzzone, L. (2008). An Unsupervised Technique Based on Morphological Filters for Change Detection in Very High Resolution Images. IEEE Geoscience and Remote Sensing Letters, 5(3), 433-437. doi:10.1109/lgrs.2008.917726Dell’Acqua, F., & Gamba, P. (2006). Discriminating urban environments using multiscale texture and multiple SAR images. International Journal of Remote Sensing, 27(18), 3797-3812. doi:10.1080/01431160600557572Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610-621. doi:10.1109/tsmc.1973.4309314Im, J., Jensen, J. R., & Tullis, J. A. (2008). Object‐based change detection using correlation image analysis and image segmentation. International Journal of Remote Sensing, 29(2), 399-423. doi:10.1080/01431160601075582Lhomme, S., He, D., Weber, C., & Morin, D. (2009). A new approach to building identification from very‐high‐spatial‐resolution images. International Journal of Remote Sensing, 30(5), 1341-1354. doi:10.1080/01431160802509017LOBO, A., CHIC, O., & CASTERAD, A. (1996). Classification of Mediterranean crops with multisensor data: per-pixel versus per-object statistics and image segmentation. International Journal of Remote Sensing, 17(12), 2385-2400. doi:10.1080/01431169608948779Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365-2401. doi:10.1080/0143116031000139863Shimabukuro, Y. E., Almeida‐Filho, R., Kuplich, T. M., & de Freitas, R. M. (2007). Quantifying optical and SAR image relationships for tropical landscape features in the Amazônia. International Journal of Remote Sensing, 28(17), 3831-3840. doi:10.1080/01431160701236829Stramondo, S., Bignami, C., Chini, M., Pierdicca, N., & Tertulliani, A. (2006). Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies. International Journal of Remote Sensing, 27(20), 4433-4447. doi:10.1080/01431160600675895Yuan, D., & Elvidge, C. D. (1996). Comparison of relative radiometric normalization techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 51(3), 117-126. doi:10.1016/0924-2716(96)00018-

Prevalence and molecular identification of zoonotic Anisakis and Pseudoterranova species in fish destined to human consumption in Chile

Zoonotic larvae of the family Anisakidae found in several fish species represent a serious risk in public health since they may cause food-borne anisakidosis in humans. Chile has culinary preferences including eating raw fish in many traditional preparations. In the present study, a total of 180 fish specimens representing three different fish species, i.e., Chilean hake (Merluccius gayi), snoek (Thyrsites atun), and sea bream (Brama australis), were caught at central coast of Chile. Parasitological examination was performed on musculature and abdominal cavity for subsequent extraction and quantification of anisakid larvae. Estimation of infection parameters, such as prevalence, was performed indicating 100% (CI: 0.94-1.0) prevalence of anisakid L3 in Chilean hakes and snoeks. Moreover, sea breams reached a prevalence of 35% (CI: 0.23-0.48). Prevalence of anisakid larvae in muscle was also analyzed showing values of 18.6% (CI: 0.097-0.309) in Chilean hakes, 15% (CI: 0.07-0.26) in snoeks, and 1.7% (CI: 0-0.089) in sea breams. Meanwhile, prevalence of anisakid larvae in internal organs showed highest values for peritoneum (100% and 83.3%) for snoeks and Chilean hakes, respectively, for liver (96.7%) and gonads (86.6%) in Chilean hakes, and for intestine (98.3%) in snoeks. Molecular analysis of collected anisakid L3 unveiled presence of two potentially zoonotic nematode species, i.e., Pseudoterranova cattani and Anisakis pegreffii. P. cattani was found in Chilean hakes and snoeks being the first molecular host species report for Chilean snoeks. Besides, A. pegreffii was also identified in these species being the first molecular report on this regard. These findings are relevant for better understanding of epidemiology of anisakiasis in Chilean coasts and for public health issues considering potential risk of human population due to its culinary preferences in eating raw fish

Analysis of the factors affecting LiDAR DTM accuracy

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Digital Earth on 2011, available online: http://wwww.tandfonline.com/10.1080/17538947.2010.533201[EN] The creation of a quality Digital Terrain Model (DTM) is essential for representing and analyzing the Earth in a digital form. The continuous improvements in the acquisition and the potential of airborne Light Detection and Ranging (LiDAR) data are increasing the range of applications of this technique to the study of the Earth surface. The aim of this study was to determine the optimal parameters for calculating a DTM by using an iterative algorithm to select minimum elevations from LiDAR data in a steep mountain area with shrub vegetation. The parameters were: input data type, analysis window size, and height thresholds. The effects of slope, point density, and vegetation on DTM accuracy were also analyzed. The results showed that the lowest root mean square error (RMSE) was obtained with an analysis window size of 10 m, 5 m, and 2.5 m, rasterized data as input data, and height thresholds equal to or greater than 1.5 m. These parameters showed a RMSE of 0.19 m. When terrain slope varied from 0-10% to 50-60%, the RMSE increased by 0.11 m. The RMSE decreased by 0.06 m when point density was increased from 4 to 8 points/m2, and increased by 0.05 m in dense vegetation areas. © 2011 Taylor & Francis.This research has been supported by Vice-Rectorate for Research of Universidad Politecnica de Valencia (Grant PAID-06-08-3297).Estornell Cremades, J.; Ruiz Fernández, LÁ.; Velázquez Martí, B.; Hermosilla, T. (2011). Analysis of the factors affecting LiDAR DTM accuracy. International Journal of Digital Earth. 4(6):521-538. https://doi.org/10.1080/17538947.2010.533201S5215384