Esta pesquisa tem por objeto apresentar os resultados dos sistemas de perfilamento a LASER aerotransportado em experiências práticas para mapeamento de dutos. Para embasar as reflexões pretendidas, o trabalho desenvolve-se em dois momentos que se relacionam – o conceitual e o empírico. Num primeiro momento é feita uma revisão do referencial conceitual que compreende a criação dos primeiros equipamentos LASER comerciais até às vantagens advindas das inovações tecnológicas, para mapeamento de dutos. Num segundo momento, apresenta-se um panorama de experiências técnicas no mapeamento de dutos, que constatam inovações advindas das novas tecnologias dos sistemas a LASER, tais como: as melhorias no delineamento do terreno, através da grande quantidade de pontos emitidos pelo sistema; o aumento significativo da largura das faixas de vôo LASER em função dos equipamentos permitirem maior altura de vôo; o ganho de qualidade horizontal, na medição das coordenadas planimétricas e o ganho de qualidade das imagens obtidas a partir dos pontos do levantamento LASER. Por fim, constata-se a agilidade apresentada pelo mercado de LASER aerotransportado.

Ordering the imagery with right parameters is crucial for getting the best value for your investment. Multi-spectral Imagery contains rich spectral content that can be exploited for variety of applications in upstream as well as down stream applications in the Oil & Gas industry, for encroachment monitoring for pipelines, extracting impervious surfaces for storm water billing and watershed modeling, change detection for environmental studies, vegetation analysis for agriculture and forestry, and others. When ordering imagery, users are often presented with myriad of options or not enough parameters to select the right imagery for their application. The three order parameters that will ensure you order the right imagery are: 1. Number of spectral bands 2. Radiometric Resolution (i.e. 8 bit or 16 bits) and 3. File format and tile size

Aerial LiDAR is accepted as the most efficient and cost-effective means to create accurate digital elevation and terrain data. It has become the standard for flood mapping and many other applications requiring fast, accurate, inexpensive Digital Terrain Models (DTMs), Digital Elevation Models (DEMs) and other geospatial features. But exactly how those final deliverables such as DTMs, DEMs and topographic features are derived has been a mystery to many LiDAR customers and data users.

Lidar data have become a major source of digital terrain information for use in many applications including hydraulic modeling and flood plane mapping. Based on established relationships between sampling intensity and error, nominal post-spacing likely contributes significantly to the error budget.

Building boundary is necessary for the real estate industry, flood management, and homeland security applications. The extraction of building boundary is also a crucial and difficult step towards generating city models. This study presents an approach to the tracing and regularization of building boundary from raw lidar point clouds.

The effects of land cover and surface slope on lidar-derived elevation data were examined for a watershed in the piedmont of North Carolina. Lidar data were collected over the study area in a winter (leaf-off) overflight. Survey-grade elevation points (1,225) for six different land cover classes were used as reference points. Root mean squared error (RMSE) for land cover classes ranged from 14.5 cm to 36.1. Land cover with taller canopy vegetation exhibited the largest errors.

ASPRS released version 1.1 of the ASPRS Lidar Data Exchange Format Standard (LAS) at its 71^ST Annual ASPRS Conference and Exhibition held in Baltimore, Maryland in March. This binary data exchange format is an industry standard for the exchange of lidar data between various hardware manufacturers, software developers, data providers and end users.

Object reconstruction has attracted great attention from both computer vision and photogrammetry communities, and new technologies are being introduced into this research society. Lidar (Light Detection And Ranging) has become well recognized in the geomatics community since the late 1990s. Compared with traditional photogrammetry, lidar has advantages in measuring surface in terms of accuracy and density, automatic, and fast delivery time.

This paper explores the effects of terrain morphology, sampling density, and interpolation methods for scattered sample data on the accuracy of interpolated heights in grid Digital Elevation Models (DEM). Sampled data were collected with a 2 by 2 meters sampling interval from seven different morphologies, applying digital photogrammetric methods to large scale aerial stereo imagery (1:5000).

The use of airborne laser profiling has increased in a varlety of surveying and mapping applications. Concurrently with this increase in usage, the performance of airborne laser profiling systems has increased dramatically over the past several years. Nonetheless, users of these systems have recognized that there are still applications in which airborne surveys provide inadequate results (compared to ground-based systems) despite current high performance levels. In this article we will review the history and convergence of airborne - and ground-based lidar technologies. We will also demonstrate how the two technologies can be employed either 1) separately ot 2) jointly in a complementary fashion.

The introduction of a standart file format (LAS 1.0) for lidar data in 2002 has been very successful as evidenced by the fact that all major software vendors have adopted the standart and customers are routinely requiring that lidar data be delivered according to this standard. As with any standard, usage has made it apparent that changes need to be made both due to omissions in the original design as well as continuing maturity of the lidar industry.

As lidar (light detection and ranging) techonology matures, more applications are being explored by U.S. Geological Survey (USGS) scientists throughout the Nation, both in collaboration with other Federal agencies and alone in support of USGS natural-hazards research (Crane et al., 2004). As the techonology continues to improve and evolve, USGS scientists are finding new and unique methods to use and represent high-resolution lidar data, and new ways to make these data and derived information publicly available. Different lidar sensors and configurations have offered opportunities to use high-resolution elevation data for a variety of projects across all disciplines of the USGS. The following examples are just a few of the diverse projects in the USGS where lidar data is being used.

To ensure the safety of the national airspace system, the Federal Aviation Administration (FAA) oversees surveying programs with the good of geolocating vertical features that penetrate 3D Obstruction Identification Surfaces (OIS) around airfileds. These OIS are defined mathematically and are based on the layout of the runways, the types of eletronic navigation equipment used for each runway, and other factors. Under a series of interagency agreements, the National Geodetic Survey (NGS) is tasked with supplying obstruction survey data to the FAA.

Em levantamentos geodésicos, a integração GPS/INS enfrenta desafios em relação a questões de alto custo dos instrumentos e ausência de recursos computacionais específicos. A utilização de dispositivos de baixo custo, baseados em tecnologia MEMS (MicroElectroMechanical Systems), procura a redução do custo instrumental. Desta forma, utilizou-se um dispositivo de baixo custo contendo acelerômetros baseados em MEMS. Entretanto, os sensores inerciais foram observados apenas em modo estático, em laboratório. Neste caso, as observações representam ruído que pode ser utilizado como parâmetro no processo de integração GPS/INS. O monitoramento de vibrações e da inclinação de estruturas são exemplos de aplicações que podem utilizar sensores operando desta forma. Conclui-se que os dispositivos MEMS são uma realidade viável para a pesquisa de baixo custo. Além disso, é esperado o desenvolvimento de novos sensores inerciais baseados em NEMS (NanoElectroMechanical Systems) considerando-se os atuais investimentos em Nanotecnologia.

As one of the tools for rapid topographic feature extraction, the commercial use of airborne laser scanning (ALS) has gained wider acceptance in the last few years as more reliable and accurate systems are developed. While airborne laser scanning systems have come a long way, the choice of appropriate data processing techniques for particular applications is still being researched. The tasks in data processing include the “modeling of systematic errors”, “filtering”, “feature detection” and “thinning”. Of these tasks manual classification (filtering) and quality control pose the greatest challenges, consuming an estimated 60 to 80% of processing time and thus underlining the necessity for research in this are

A técnica ALS (Airborne Laser Scanning), ou VLA (Varredura Laser Aerotransportada), permite a coleta de nuvens de pontos semi-aleatoriamente distribuídos sobre a superfície do terreno e, a partir do processamento off-line destas nuvens, a obtenção das coordenadas 3D georreferenciadas dos pontos coletados e a conseqüente geração de modelos digitais 3D (DEMs, DTMs e DSMs) da superfície varrida.

O presente trabalho é resultado de uma pesquisa bibliográfica elaborada como parte do desenvolvimento da tese de doutorado da autora sobre equipamento laser aerotransportado. A pesquisa estendeu-se também aos equipamentos laser terrestres dada a importância de conhecer os padrões utilizados para sua classificação, em função do tipo de laser utilizado e dos riscos potenciais que representam à saúde humana.

Uma das atividades que mais consomem tempo na Fotogrametria é a obtenção de Modelos Digitais de Elevação ou do Terreno (MDE ou MDT). Este subproduto cartográfico normalmente é utilizado para a retificação diferencial de ortofotos ou para obtenção automática de curvas de nível. O Perfilamento a LASER é uma tecnologia que está revolucionando esta metodologia, permitindo a obtenção do MDE de maneira mais direta, evitando processos fotogramétricos ou levantamentos com outras técnicas como o GPS. Este trabalho apresenta uma comparação de resultados na obtenção de MDT derivados de Correlação de Imagem, Curvas de Nível e Perfilamento a LASER. Também são enfocados os aspectos de remoção automática de camada vegetal no MDT, uma das características principais deste sistema.

This is an oblique view of a point cloud of LIDAR points. The red portion of the image shows ground points, and the green portion of the image shows the vegetation (tree canopy) points. Software can usually define the difference through nearest-neighbor comparisons.

Since the mid-1990s the science of laser altimetry has been actively adopted by the remote sensing community as a tool for the rapid generation of accurate, high-resolution, digital terrain models. The fundamental science behind laser altimetry has been studied for decades, going back to the 1960s, but it is only with the increased availability of off-the-shelf sensors and the emergence of commercial data providers that this tool has seen wider deployment and acceptance by geospatial data users. The advantages of laser altimetry include rapid turn-around times, generation of relatively high-accuracy, high-density data sets and the ability to map in areas of low contrast, low relief or relatively dense vegetation cover. The disadvantages include relatively high cost on a project basis, a lack of direct object-oriented information (imagery, spectral information), data processing issues related to robust, efficient feature extraction (bare earth, break lines) and a lack of common standards and professional practices. As a result laser altimetry, or lidar mapping as it is more commonly referred to in the mapping community, is increasingly used in conjunction with other sensors ranging from traditional film imagery to integrated digital cameras, synthetic aperture radar or hyperspectral scanners.

Hours after the September 11.2001. attack on the Word Trade Center, the New York Office for Technology (NYSOFT) was tasked with gathering and managing the airborne data collection over Ground Zero. On September 14, they enlisted the help of Earthdata, an airborne mapping and remote sensing company headquartered in Washington, D.C.

Airborne laserscanning (or lidar) has become a very important technique for the acquisition of digital terrain model data. Beyond this, the technique is increasingly being used for the acquisition of point clouds for 3D modeling of a wide range of objects, such as buildings, vegetation, or electrical power lines. As an active technique is often specified to be on the order of one to two decimeters. By reason of its primary use in digital terrain madeling, examinations of the precision potential of airborne laserscanning have so for been concentrated on the height precision. With the use of the technique for general 3D reconstruction tasks and the increasing resolution of laserscanner systems, the planimetric precision of laserscanner point clouds becomes an important issue.

Airborne laser mapping integrates three technologies into a single system to produce accurate digital terrain models (DTM) of the earth’s surface. The three technologies, Light Detection and Ranging (LIDAR) using laser, Global Positioning System (GPS) satellites, and Inertial Navigation Systems (INS), have all been available for several years. Developments in all three technologies have allowed the integrated system to be utilized in an airborne environment with increasing levels of accuracy.

The requirements for digital elevation data for flood insurance studies should be determined early in the process. Typically, for hydraulic modeling, elevation data equivalent to 4' contours (RMSE = 37 cm) are appropriate for rolling to hilly terrain, and elevation data equivalent to 2' contours (RMSE = 18.5 cm) are appropriate for flat terrain. North Carolina specified a RMSE of 20 cm for coastal counties and RMSE of 25 cm for inland counties.

Airborne laser scanning is widely used for the derivation of terrain information in wooded or open areas but also for the production of building models in cities. For this, the generation of a digital terrain model (DTM) is also required. In this paper laser scanning, and the filtering and classification of laser scanner data with a specific algorithm are described. The results for test data sets are presented.

Report on Airborne Laser Scanning (ALS) trials conducted over the Dunwich Irrigation Area on North Stradbroke Island.A digital terrain model was required under dense vegetation. Our client, Redland Shire Council, considered two options. One approach was to use existing 1:10,000 aerial photography, exposed just after a fire had reduced the vegetation on the site. The second approach was to use Airborne Laser Scanning. The existence of aerial photography exposed with little vegetation over the site, plus ALS data captured through dense vegetation offered an interesting comparison of the two survey methods.

A comparison between data acquisition and processing from passive optical sensors and airborne laser scanning is presented. A short overview and the major differences between the two technologies are outlined. Advantages and disadvantages with respect to various aspects are discussed, like sensors, platforms, flight planning, data acquisition conditions, imaging, object reflectance, automation, accuracy, flexibility and maturity, production time and costs. A more detailed comparison is presented with respect to DTM and DSM generation. Strengths of laser scanning with respect to certain applications are outlined. Although airborne laser scanning competes to a certain extent with photogrammetry and will replace it in certain cases, the two technologies are fairly complementary and their integration can lead to more accurate and complete products, and open up new areas of application. q1999 Elsevier Science B.V. All rights reserved.

Airborne laser scanning is widely used for the derivation of terrain information in wooded or open areas but also for the production of building models in cities. For this, the generation of a digital terrain model (DTM) is also required. In this paper the filtering and classification of laser scanner data with iterative robust linear prediction in a hierarchical fashion using data pyramids is described. The coarse-to-fine approach is advantageous because it strengthens the robustness of the method and makes it faster. The results for test data sets of the OEEPE are presented.

A methodology for forest inventory in south-east Queensland utilising airborne laser scanning was assessed. Trials were conducted in open dry sclerophyll forest in St Marys State Forest near Maryborough and dense wet sclerophyll and complex notophyll vine forest near Springbrook in the Gold Coast hinterland. Use of a laser scanner for the production of digital terrain models (DTM’s) of the ground and derivation of canopy height and foliage density was evaluated.

For certain applications irregularly distributed scanning laser altimeter data need to be rasterised - such as for use in GIS systems and for creating DEMs. Also, least squares matching on a raster grid can enable the measurement of planimetric and height shifts between overlapping strips of laser data. The shifts are a manifestation of errors in the laser altimeter, most of which are caused by the positioning elements of the system (GPS and/or INS). These translations form the input into a block adjustment to correct for relative and absolute errors. Here a discussion of the issues related to deriving a regular grid of 2.5D points from the original data is presented, with particular reference to the interpolation method, grid size, and quantisation level. An interpolation method based on a TIN of the original points with a grid size that relates as closely as possible to the point density at acquisition is found to give the best results. 8-bit quantisation is found to be sufficient for height differences of up to 100m.

Every once in a while a technology suddenly seems to catch peoples’ imagination and you start hearing about it all the time. That’s been happening with LIDAR recently. What is LIDAR? Well, like radar, it’s an acronym, except in this case it stands for LIght Detection And Ranging. What does that mean? It is the technology, which uses light, specifically a laser light, to measure distance.