Geophysical Analysis of a Central Florida Karst Terrain using Light Detection and Ranging (LIDAR) and Ground Penetrating Radar (GPR) Derived Surfaces

Airborne LIDAR (Light Detecting and Ranging) is a relatively new technique that rapidly and accurately measures micro-topographic features. This study compares topography derived from LIDAR with subsurface karst structures mapped in 3-dimensions with ground penetrating radar (GPR). Over 500 km of LIDAR data were collected in 1995 by the NASA ATM instrument. The LIDAR data was processed and analyzed to identify closed depressions. A GPR survey was then conducted at a 200 by 600 m site to determine if the target features are associated with buried karst structures. The GPR survey resolved two major depressions in the top of a clay rich layer at ~10m depth. These features are interpreted as buried dolines and are associated spatially with subtle (< 1m) trough-like depressions in the topography resolved from the LIDAR data. This suggests that airborne LIDAR may be a useful tool for indirectly detecting subsurface features associated with sinkhole hazard.

A framework for automatic feature extraction from airborne light detection and ranging data

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. ^ In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance. ^

Remote Sensing Detection of atmospheric pollutants using Lidar, Sodar and correlation with air quality data in an industrial area

Optical remote sensing techniques have obvious advantages for monitoring gas and aerosol emissions, since they enable the operation over large distances, far from hostile environments, and fast processing of the measured signal. In this study two remote sensing devices, namely a Lidar (Light Detection and Ranging) for monitoring the vertical profile of backscattered light intensity, and a Sodar (Acoustic Radar, Sound Detection and Ranging) for monitoring the vertical profile of the wind vector were operated during specific periods. The acquired data were processed and compared with data of air quality obtained from ground level monitoring stations, in order to verify the possibility of using the remote sensing techniques to monitor industrial emissions. The campaigns were carried out in the area of the Environmental Research Center (Cepema) of the University of São Paulo, in the city of Cubatão, Brazil, a large industrial site, where numerous different industries are located, including an oil refinery, a steel plant, as well as fertilizer, cement and chemical/petrochemical plants. The local environmental problems caused by the industrial activities are aggravated by the climate and topography of the site, unfavorable to pollutant dispersion. Results of a campaign are presented for a 24- hour period, showing data of a Lidar, an air quality monitoring station and a Sodar. © 2011 SPIE.

Unsupervised segmentation using Gabor wavelets and statistical features in LIDAR data analysis

In this paper, we address issues in segmentation Of remotely sensed LIDAR (LIght Detection And Ranging) data. The LIDAR data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5D) terrain surface height information, e.g. houses, vegetation, flat field, river, basin, etc. Our aim in this paper is to segment ground (flat field)from non-ground (houses and high vegetation) in hilly urban areas. By projecting the 2.5D data onto a surface, we obtain a texture map as a grey-level image. Based on the image, Gabor wavelet filters are applied to generate Gabor wavelet features. These features are then grouped into various windows. Among these windows, a combination of their first and second order of statistics is used as a measure to determine the surface properties. The test results have shown that ground areas can successfully be segmented from LIDAR data. Most buildings and high vegetation can be detected. In addition, Gabor wavelet transform can partially remove hill or slope effects in the original data by tuning Gabor parameters.

Comparació de la tecnologia LiDAR vers els mètodes tradicionals d’inventari forestal, a la finca La Vall de Benifallet, al Baix Ebre

L’inventari forestal és una eina molt important per obtenir la informació necessària, sobre una massa arbrada, respecte a la seva situació actual i la seva possible evolució en el temps, a fi i efecte de poder prendre les decisions necessàries sobre la seva planificació i gestió. Amb aquest treball s’ha volgut avaluar la possible millora que es pot obtenir aplicant les noves tecnologies en la realització dels inventaris forestals, com la tecnologia LiDAR (Light Detection and Ranging). El mètode tradicional de realitzar un inventari forestal, consisteix en anar a camp i prendre dades d’unes mostres representatives, de les variables dasomètriques que caracteritzen una massa forestal. La tecnologia LiDAR és un sistema de teledetecció que calcula distàncies a partir de, la mesura del temps entre l’emissió d’un làser polsat i el seu retorn desprès de la seva reflexió en tocar terra. El resultat és un núvol de punts a diferents alçades, amb el qual s’aconsegueix un Model Digital del Terreny (MDT) i un Model Digital de Superfície (MDS). De la resta d’aquests dos models s’obté una imatge de l’estructura vertical de la vegetació, a partir de la qual es poden deduir dades bàsiques del bosc amb mesures per tot el territori. L’àrea d’estudi on es va dur a terme el present treball, és una finca del terme municipal de Benifallet, al Baix Ebre, província de Tarragona. L’estudi ha consistit en la comparació dels dos mètodes, tradicional i LiDAR, a l’hora d’obtenir les variables de densitat, alçada i fracció de cabuda coberta (FCC). El mètode tradicional consisteix en mesurar les variables en 24 parcel•les representatives i posteriorment, en extrapolar-les als estrats, que són les unitats en que es divideix la part de la finca on es realitza l’inventari. En el mètode utilitzant la tecnologia LiDAR, s’utilitzen dos tipus de resolucions (8 píxels i 24 píxels) a l’hora de treballar amb les dades

Maneig de les dades d'un escàner làser (LiDAR) per a l'obtenció de paràmetres vegetatius en fruiters (perera) i vinya

Aquest projecte té com a finalitat desenvolupar un sistema no destructiu per a la caracterització de les plantacions de vinya i d’arbres fruiters mitjançant la utilització d’un sensor làser (LiDAR - Light Detection and Ranging). La informació obtinguda ha de permetre estudiar la resposta del cultiu a determinades accions (poda, reg, adobs, etc.); i també realitzar tractaments fitosanitaris adaptats a la densitat foliar del cultiu. La posada a punt del sistema (software i hardware) es va realitzar a escala reduïda mitjançant proves de laboratori sobre un arbre ornamental. Obtenint la configuració del sensor LiDAR més adequada i la calibració de tot el sistema. L’any 2004 van realitzar assajos en plantacions de pomera, perera, cítrics i vinya. L’objectiu era posar a prova el sistema i obtenir dades dels cultius. Amb la introducció de canvis i millores en el sistema i en la metodologia de treball, l’any 2005 es van realitzar nous assajos, però només en perera Blanquilla i en vinya Merlot. En tots els assajos s’escanejaven unes franges de vegetació concretes i posteriorment es desfullaven manualment per a calcular-ne l’Índex d’Àrea Foliar (IAF). Les dades obtingudes amb el sensor LiDAR s’han analitzat mitjançant l’aplicació de la metodologia desenvolupada per Walklate et al.(2002) i s’han obtingut determinats paràmetres vegetatius de cultiu, que posteriorment s’han correlacionat amb l’Índex d’Àrea Foliar (IAF) obtingut de forma experimental. La capacitat de predicció de l’Índex d’Àrea Foliar (IAF) per part dels diferents paràmetres calculats es diferent en cada cultiu, essent necessàries més proves i major nombre de dades a fi d’obtenir un model fiable per a l’estimació de l’IAF a partir de les lectures del sensor LiDAR. L’estudi de la variabilitat de la vegetació mitjançant l’anàlisi de la variabilitat del Tree Area Index (TAI) al llarg de la fila ha permès determinar el nombre mínim necessari d’escanejades acumulades per a l’estimació fiable de l’Índex d’Àrea Foliar. Finalment s’ha estudiat la incidència de l’alçada de col•locació del sensor LiDAR respecte la vegetació.

GRASS GIS for the distinction of vegetation from buildings using LiDAR altimetric data

La tecnología LiDAR (Light Detection and Ranging), basada en el escaneado del territorio por un telémetro láser aerotransportado, permite la construcción de Modelos Digitales de Superficie (DSM) mediante una simple interpolación, así como de Modelos Digitales del Terreno (DTM) mediante la identificación y eliminación de los objetos existentes en el terreno (edificios, puentes o árboles). El Laboratorio de Geomática del Politécnico de Milán – Campus de Como- desarrolló un algoritmo de filtrado de datos LiDAR basado en la interpolación con splines bilineares y bicúbicas con una regularización de Tychonov en una aproximación de mínimos cuadrados. Sin embargo, en muchos casos son todavía necesarios modelos más refinados y complejos en los cuales se hace obligatorio la diferenciación entre edificios y vegetación. Este puede ser el caso de algunos modelos de prevención de riesgos hidrológicos, donde la vegetación no es necesaria; o la modelización tridimensional de centros urbanos, donde la vegetación es factor problemático. (...)

LiDAR data filtering with GRASS GIS for the determination of digital terrain models

In the last years, the use of every type of Digital Elevation Models has iimproved. The LiDAR (Light Detection and Ranging) technology, based on the scansion of the territory b airborne laser telemeters, allows the construction of digital Surface Models (DSM), in an easy way by a simple data interpolation

Detection of flooded urban areas in high resolution Synthetic Aperture Radar images using double scattering

Flooding is a particular hazard in urban areas worldwide due to the increased risks to life and property in these regions. Synthetic Aperture Radar (SAR) sensors are often used to image flooding because of their all-weather day-night capability, and now possess sufficient resolution to image urban flooding. The flood extents extracted from the images may be used for flood relief management and improved urban flood inundation modelling. A difficulty with using SAR for urban flood detection is that, due to its side-looking nature, substantial areas of urban ground surface may not be visible to the SAR due to radar layover and shadow caused by buildings and taller vegetation. This paper investigates whether urban flooding can be detected in layover regions (where flooding may not normally be apparent) using double scattering between the (possibly flooded) ground surface and the walls of adjacent buildings. The method estimates double scattering strengths using a SAR image in conjunction with a high resolution LiDAR (Light Detection and Ranging) height map of the urban area. A SAR simulator is applied to the LiDAR data to generate maps of layover and shadow, and estimate the positions of double scattering curves in the SAR image. Observations of double scattering strengths were compared to the predictions from an electromagnetic scattering model, for both the case of a single image containing flooding, and a change detection case in which the flooded image was compared to an un-flooded image of the same area acquired with the same radar parameters. The method proved successful in detecting double scattering due to flooding in the single-image case, for which flooded double scattering curves were detected with 100% classification accuracy (albeit using a small sample set) and un-flooded curves with 91% classification accuracy. The same measures of success were achieved using change detection between flooded and un-flooded images. Depending on the particular flooding situation, the method could lead to improved detection of flooding in urban areas.

The potential of flood forecasting using a variable-resolution global Digital Terrain Model and flood extents from Synthetic Aperture Radar images

A basic data requirement of a river flood inundation model is a Digital Terrain Model (DTM) of the reach being studied. The scale at which modeling is required determines the accuracy required of the DTM. For modeling floods in urban areas, a high resolution DTM such as that produced by airborne LiDAR (Light Detection And Ranging) is most useful, and large parts of many developed countries have now been mapped using LiDAR. In remoter areas, it is possible to model flooding on a larger scale using a lower resolution DTM, and in the near future the DTM of choice is likely to be that derived from the TanDEM-X Digital Elevation Model (DEM). A variable-resolution global DTM obtained by combining existing high and low resolution data sets would be useful for modeling flood water dynamics globally, at high resolution wherever possible and at lower resolution over larger rivers in remote areas. A further important data resource used in flood modeling is the flood extent, commonly derived from Synthetic Aperture Radar (SAR) images. Flood extents become more useful if they are intersected with the DTM, when water level observations (WLOs) at the flood boundary can be estimated at various points along the river reach. To illustrate the utility of such a global DTM, two examples of recent research involving WLOs at opposite ends of the spatial scale are discussed. The first requires high resolution spatial data, and involves the assimilation of WLOs from a real sequence of high resolution SAR images into a flood model to update the model state with observations over time, and to estimate river discharge and model parameters, including river bathymetry and friction. The results indicate the feasibility of such an Earth Observation-based flood forecasting system. The second example is at a larger scale, and uses SAR-derived WLOs to improve the lower-resolution TanDEM-X DEM in the area covered by the flood extents. The resulting reduction in random height error is significant.

Aplicação de técnicas de processamento e análise de imagens para detecção de edificações e vegetação a partir de dados LiDAR

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