Análise e monitoramento geoambiental da praia de Genipabu/RN

Issues concerning coastal regions, especially the beaches have sparked quite complex because studies are there that most people in the world has secured housing, mainly from the half of the last century, without concern for the natural dynamics of these environments, which have complex interactions of continental and oceanic, coastal responsible for changes in locations that can be perceived in a few years and sometimes even a few days or hours. The search took as main goal, analyze the Genipabu Beach, in the municipality of Extremoz/RN, fragile environment and rapid momentum, which has been occupied in a disorderly and unplanned. Carried out a beach monitoring through profiles beach environments: defined stages morphodynamics; realization of characterize hydrodynamic processes; identification of changes in the landscape. To this end, made necessary a survey from the bibliographic collection for theoretical and conceptual rationale. An empirical step for conducting the environmental characterization of hydrodynamics, leveling and topographic analysis of sediments (in laboratory), for observation of changes in features, influenced, and natural dynamics, anthropic action that increasingly comes taking the characteristics from the natural landscape. Underlines therefore the importance of academic studies in several areas in these environments, for setting up a coastal zoning giving public subsidies for managers for managing and planning the use and occupation of the coast in their areas

Estimativas do IWV utilizando receptores GPS em bases terrestres no Brasil: sinergia entre a geodésia e a meteorologia

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Monitoramento das irregularidades ionosféricas e cintilação dos sinais GNSS: em tempo real e pós-processado

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data.

Agroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology for mapping aboveground biomass (AGB) over landscape levels. However, little research in the past has been done to study AGB of agroforestry systems using airborne lidar data. Focusing on an agroforestry system in the Brazilian Amazon, this study first predicted plot-level AGB using fixed-effects regression models that assumed the regression coefficients to be constants. The model prediction errors were then analyzed from the perspectives of tree DBH (diameter at breast height)?height relationships and plot-level wood density, which suggested the need for stratifying agroforestry fields to improve plot-level AGB modeling. We separated teak plantations from other agroforestry types and predicted AGB using mixed-effects models that can incorporate the variation of AGB-height relationship across agroforestry types. We found that, at the plot scale, mixed-effects models led to better model prediction performance (based on leave-one-out cross-validation) than the fixed-effects models, with the coefficient of determination (R2) increasing from 0.38 to 0.64. At the landscape level, the difference between AGB densities from the two types of models was ~10% on average and up to ~30% at the pixel level. This study suggested the importance of stratification based on tree AGB allometry and the utility of mixed-effects models in modeling and mapping AGB of agroforestry systems.

Classification of Airborne LIDAR Intensity Data Using Statistical Analysis and Hough Transform with Application to Power Line Corridors

Light Detection and Ranging (LIDAR) has great potential to assist vegetation management in power line corridors by providing more accurate geometric information of the power line assets and vegetation along the corridors. However, the development of algorithms for the automatic processing of LIDAR point cloud data, in particular for feature extraction and classification of raw point cloud data, is in still in its infancy. In this paper, we take advantage of LIDAR intensity and try to classify ground and non-ground points by statistically analyzing the skewness and kurtosis of the intensity data. Moreover, the Hough transform is employed to detected power lines from the filtered object points. The experimental results show the effectiveness of our methods and indicate that better results were obtained by using LIDAR intensity data than elevation data.

Building a USDL repository as a service using MDE

The decision to represent the USDL abstract syntax as a metamodel, shown as a set of UML diagrams, has two main benefits: the ability to show a well- understood standard graphical representation of the concepts and their relation- ships to one another, and the ability to use object-oriented frameworks such as Eclipse Modeling Framework (EMF) to assist in the automated generation of tool support for USDL service descriptions.

An integrated approach for precise road reconstruction from aerial imagery and LiDAR data

Accurate and detailed road models play an important role in a number of geospatial applications, such as infrastructure planning, traffic monitoring, and driver assistance systems. In this thesis, an integrated approach for the automatic extraction of precise road features from high resolution aerial images and LiDAR point clouds is presented. A framework of road information modeling has been proposed, for rural and urban scenarios respectively, and an integrated system has been developed to deal with road feature extraction using image and LiDAR analysis. For road extraction in rural regions, a hierarchical image analysis is first performed to maximize the exploitation of road characteristics in different resolutions. The rough locations and directions of roads are provided by the road centerlines detected in low resolution images, both of which can be further employed to facilitate the road information generation in high resolution images. The histogram thresholding method is then chosen to classify road details in high resolution images, where color space transformation is used for data preparation. After the road surface detection, anisotropic Gaussian and Gabor filters are employed to enhance road pavement markings while constraining other ground objects, such as vegetation and houses. Afterwards, pavement markings are obtained from the filtered image using the Otsu's clustering method. The final road model is generated by superimposing the lane markings on the road surfaces, where the digital terrain model (DTM) produced by LiDAR data can also be combined to obtain the 3D road model. As the extraction of roads in urban areas is greatly affected by buildings, shadows, vehicles, and parking lots, we combine high resolution aerial images and dense LiDAR data to fully exploit the precise spectral and horizontal spatial resolution of aerial images and the accurate vertical information provided by airborne LiDAR. Objectoriented image analysis methods are employed to process the feature classiffcation and road detection in aerial images. In this process, we first utilize an adaptive mean shift (MS) segmentation algorithm to segment the original images into meaningful object-oriented clusters. Then the support vector machine (SVM) algorithm is further applied on the MS segmented image to extract road objects. Road surface detected in LiDAR intensity images is taken as a mask to remove the effects of shadows and trees. In addition, normalized DSM (nDSM) obtained from LiDAR is employed to filter out other above-ground objects, such as buildings and vehicles. The proposed road extraction approaches are tested using rural and urban datasets respectively. The rural road extraction method is performed using pan-sharpened aerial images of the Bruce Highway, Gympie, Queensland. The road extraction algorithm for urban regions is tested using the datasets of Bundaberg, which combine aerial imagery and LiDAR data. Quantitative evaluation of the extracted road information for both datasets has been carried out. The experiments and the evaluation results using Gympie datasets show that more than 96% of the road surfaces and over 90% of the lane markings are accurately reconstructed, and the false alarm rates for road surfaces and lane markings are below 3% and 2% respectively. For the urban test sites of Bundaberg, more than 93% of the road surface is correctly reconstructed, and the mis-detection rate is below 10%.

Learned ultra-wideband RADAR sensor model for augmented LIDAR-based traversability mapping in vegetated environments

In vegetated environments, reliable obstacle detection remains a challenge for state-of-the-art methods, which are usually based on geometrical representations of the environment built from LIDAR and/or visual data. In many cases, in practice field robots could safely traverse through vegetation, thereby avoiding costly detours. However, it is often mistakenly interpreted as an obstacle. Classifying vegetation is insufficient since there might be an obstacle hidden behind or within it. Some Ultra-wide band (UWB) radars can penetrate through vegetation to help distinguish actual obstacles from obstacle-free vegetation. However, these sensors provide noisy and low-accuracy data. Therefore, in this work we address the problem of reliable traversability estimation in vegetation by augmenting LIDAR-based traversability mapping with UWB radar data. A sensor model is learned from experimental data using a support vector machine to convert the radar data into occupancy probabilities. These are then fused with LIDAR-based traversability data. The resulting augmented traversability maps capture the fine resolution of LIDAR-based maps but clear safely traversable foliage from being interpreted as obstacle. We validate the approach experimentally using sensors mounted on two different mobile robots, navigating in two different environments.

LiDAR Augmented Optimal Path Planning for Unmanned Aerial Vehicle during Remote Sensing at Low Altitude and Network Sensor Data Acquisition

This technical report describes a Light Detection and Ranging (LiDAR) augmented optimal path planning at low level flight methodology for remote sensing and sampling Unmanned Aerial Vehicles (UAV). The UAV is used to perform remote air sampling and data acquisition from a network of sensors on the ground. The data that contains information on the terrain is in the form of a 3D point clouds maps is processed by the algorithms to find an optimal path. The results show that the method and algorithm are able to use the LiDAR data to avoid obstacles when planning a path from a start to a target point. The report compares the performance of the method as the resolution of the LIDAR map is increased and when a Digital Elevation Model (DEM) is included. From a practical point of view, the optimal path plan is loaded and works seemingly with the UAV ground station and also shows the UAV ground station software augmented with more accurate LIDAR data.

Vertical structure and horizontal gradients of aerosol extinction coefficients over coastal India inferred from airborne lidar measurements during the Integrated Campaign for Aerosol, Gases and Radiation Budget (ICARB) field campaign

Quantitative estimates of the vertical structure and the spatial gradients of aerosol extinction coefficients have been made from airborne lidar measurements across the coastline into offshore oceanic regions along the east and west coasts of India. The vertical structure revealed the presence of strong, elevated aerosol layers in the altitude region of similar to 2-4 km, well above the atmospheric boundary layer (ABL). Horizontal gradients also showed a vertical structure, being sharp with the e(-1) scaling distance (D-0H) as small as similar to 150 km in the well-mixed regions mostly under the influence of local source effects. Above the ABL, where local effects are subdued, the gradients were much shallower (similar to 600-800 km); nevertheless, they were steep compared to the value of similar to 1500-2500 km reported for columnar AOD during winter. The gradients of these elevated layers were steeper over the east coast of India than over the west coast. Near-simultaneous radio sonde (Vaisala, Inc., Finland) ascents made over the northern Bay of Bengal showed the presence of convectively unstable regions, first from surface to similar to 750-1000 m and the other extending from 1750 to 3000 m separated by a stable region in between. These can act as a conduit for the advection of aerosols and favor the transport of continental aerosols in the higher levels (> 2 km) into the oceans without entering the marine boundary layer below. Large spatial gradient in aerosol optical and hence radiative impacts between the coastal landmass and the adjacent oceans within a short distance of < 300 km (even at an altitude of 3 km) during summer and the premonsoon is of significance to the regional climate.

Lidar heat map based channel sounding for complex wireless environments

This thesis is a development of a methodology to predict the radio transmitter signal attenuation, via vertical density profiling of digitised objects, through the use of Light Detection and Ranging (LiDaR) measurements. The resulting map of indexed signal attenuation is useful for dynamic radio transmitter placement within the geospatial data set without expensive and tedious radio measurements.

Vertical distribution of aerosols over the east coast of India inferred from airborne LIDAR measurements

The information on altitude distribution of aerosols in the atmosphere is essential in assessing the impact of aerosol warming on thermal structure and stability of the atmosphere.In addition, aerosol altitude distribution is needed to address complex problems such as the radiative interaction of aerosols in the presence of clouds. With this objective,an extensive, multi-institutional and multi-platform field experiment (ICARB-Integrated Campaign for Aerosols, gases and Radiation Budget) was carried out under the Geosphere Biosphere Programme of the Indian Space Research Organization (ISRO-GBP) over continental India and adjoining oceans during March to May 2006. Here, we present airborne LIDAR measurements carried out over the east Coast of the India during the ICARB field campaign. An increase in aerosol extinction (scattering + absorption) was observed from the surface upwards with a maximum around 2 to 4 km. Aerosol extinction at higher atmospheric layers (>2 km) was two to three times larger compared to that of the surface. A large fraction (75-85%) of aerosol column optical depth was contributed by aerosols located above 1 km. The aerosol layer heights (defined in this paper as the height at which the gradient in extinction coefficient changes sign) showed a gradual decrease with an increase in the offshore distance. A large fraction (60-75%) of aerosol was found located above clouds indicating enhanced aerosol absorption above clouds. Our study implies that a detailed statistical evaluation of the temporal frequency and spatial extent of elevated aerosol layers is necessary to assess their significance to the climate. This is feasible using data from space-borne lidars such as CALIPSO,which fly in formation with other satellites like MODIS AQUA and MISR, as part of the A-Train constellation.

Comments on hydrographic and topographic LIDAR acquisition and merging with multibeam sounding data acquired in the Olympic Coast National Marine Sanctuary

In April 2005, a SHOALS 1000T LIDAR system was used as an efficient alternative for safely acquiring data to describe the existing conditions of nearshore bathymetry and the intertidal zone over an approximately 40.7 km2 (11.8 nm2) portion of hazardous coastline within the Olympic Coast National Marine Sanctuary (OCNMS). Data were logged from 1,593 km (860 nm) of track lines in just over 21 hours of flight time. Several islands and offshore rocks were also surveyed, and over 24,000 geo-referenced digital still photos were captured to assist with data cleaning and QA/QC. The 1 kHz bathymetry laser obtained a maximum water depth of 22.2 meters. Floating kelp beds, breaking surf lines and turbid water were all challenges to the survey. Although sea state was favorable for this time of the year, recent heavy rainfall and a persistent low-lying layer of fog reduced acquisition productivity. The existence of a completed VDatum model covering this same geographic region permitted the LIDAR data to be vertically transformed and merged with existing shallow water multibeam data and referenced to the mean lower low water (MLLW) tidal datum. Analysis of a multibeam bathymetry-LIDAR difference surface containing over 44,000 samples indicated surface deviations from –24.3 to 8.48 meters, with a mean difference of –0.967 meters, and standard deviation of 1.762 meters. Errors in data cleaning and false detections due to interference from surf, kelp, and turbidity likely account for the larger surface separations, while the remaining general surface difference trend could partially be attributed to a more dense data set, and shoal-biased cleaning, binning and gridding associated with the multibeam data for maintaining conservative least depths important for charting dangers to navigation. (PDF contains 27 pages.)

Dinâmica populacional de Halodule wrightii Ascherson e sua fauna e flora associada em Cabo Frio RJ

Fanerógamas marinhas (gramas marinhas) são plantas com flores adaptadas ao ambiente marinho costeiro da maioria dos continentes do mundo. As gramas marinhas formam extensos bancos e proveem valiosos recursos em águas costeiras rasas em todo o mundo, servindo de alimento e berçário para espécies importantes de pescados comerciais e recreacionais. Nesse estudo foi realizada uma revisão sobre o estado de conhecimento das fanerógamas marinhas no Brasil até o presente momento; avaliou-se a importância do monitoramento em longo prazo e a influência de fatores ambientais, como o número de manchas solares; pesquisou-se também a distribuição espacial da grama marinha, bem como a fauna e flora associada; e o crescimento de Halodule wrightii em duas condições ambientais extremas (exposta no ciclo de maré baixa e permanentemente submersa). A revisão bibliográfica sobre as gramas marinhas foi abrangente e verificou a existência de algumas lacunas no conhecimento. Através do monitoramento a longo prazo pôde ser observado que o número de manchas solares tem forte relação negativa sobre a altura do dossel das gramas marinhas de região entre marés. A variação de marés na região de mediolitoral está relacionada diretamente com a distribuição espacial de Halodule wrightii e, consequentemente na distribuição da fauna e flora associada. A diferença de crescimento nos eixos de Halodule wrightii em condições ambientais diferentes é compensada pelas variações nas características de distribuição da planta no ambiente, tais como a altura do dossel, a densidade e biomassa de eixos. O monitoramento a longo prazo pode permitir a tomada de ações que auxiliem no manejo e na recuperação desses importantes habitats costeiros.

Monitoramento microbiológico e físico-químico de tanques de armazenamento de óleo e água

A injeção da água do mar nos campos marítimos (offshore), processo este conhecido como recuperação secundária de petróleo, gera muitos resíduos e efluentes. Dentre estes, pode-se destacar a água produzida, que consiste de água de formação, água naturalmente presente na formação geológica do reservatório de petróleo, e água de injeção, aquela normalmente injetada no reservatório para aumento de produção. Sete tanques de armazenamento de água/óleo de um terminal foram monitorados quanto à presença de micro-organismos e teores de sulfato, sulfeto, pH e condutividade. Particularmente, as bactérias redutoras de sulfato (BRS), que agem às expensas da atividade de outras espécies, reduzindo sulfato à sulfeto, constituindo-se num problema-chave. Os tanques de óleo codificados como Verde, Ciano, Roxo, Cinza, Vermelho, Amarelo e Azul, apresentaram comportamentos distintos quanto aos parâmetros microbiológicos e físico-químicos. Após este monitoramento, de acordo com valores referência adotados, e levando-se em conta como principais parâmetros classificatórios concentrações de BRS, bactérias anaeróbias totais e sulfeto, os dois tanques considerados mais limpos do monitoramento foram os tanques roxo e ciano. Analogamente, por apresentarem os piores desempenhos frente aos três principais parâmetros, os tanques amarelo e cinza foram considerados os mais sujos de todo o monitoramento. Após esta segregação, esses três principais parâmetros, mais a concentração de sulfato, foram inter-relacionados a fim de se corroborar esta classificação. Foi possível observar que o sulfeto instantâneo não foi o parâmetro mais adequado para se avaliar o potencial metabólico de uma amostra. Por este motivo, foram verificados os perfis metabólicos das BRS presentes nas amostras, confirmando a segregação dos tanques, baseada em parâmetros em batelada