98 resultados para DIGITAL ELEVATION MODELS


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An efficient technique to cut polygonal meshes as a step in the geometric modeling of topographic and geological data has been developed. In boundary represented models of outcropping strata and faulted horizons polygonal meshes often intersect each other. TRICUT determines the line of intersection and re-triangulates the area of contact. Along this line the mesh is split in two or more parts which can be selected for removal. The user interaction takes place in the 3D-model space. The intersection, selection and removal are under graphic control. The visualization of outcropping geological structures in digital terrain models is improved by determining intersections against a slightly shifted terrain model. Thus, the outcrop line becomes a surface which overlaps the terrain in its initial position. The area of this overlapping surface changes with respect to the strike and dip of the structure, the morphology and the offset. Some applications of TRICUT on different real datasets are shown. TRICUT is implemented in C+ + using the Visualization Toolkit in conjunction with the RAPID and TRIANGLE libraries. The program runs under LINUX and UNIX using the MESA OpenGL library. This work gives an example of solving a complex 3D geometric problem by integrating available robust public domain software. (C) 2002 Elsevier B.V. Ltd. All rights reserved.

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The digital elevation model is important to determine the slope and land use capability, therefore, a proposal of methodology for acquisition of elevation data contemplating an efficient algorithm to generate a slope map was developed. Thus, it was aimed to obtain and evaluate a digital elevation model without the vetorization of the contours on planialtimetric charts. The area for acquisition of elevation data was Sao Manuel, SP. The data were collected by two methods: level contour vetorization and the gathering of elevation points on the level contour with maximum elevation points. The elevation data were analyzed by geostatistical techniques. Inspite of wide difference in the number of collected points between two methods, the variograms were adjusted to the exponential model and showed a range of approximately 1500 m, which does not justify the wide difficulty in vetorization of the planialtimetric charts, once the data points collected in the area were appropriately distributed, they represented rightly the terrain surface.

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In this paper is presented a region-based methodology for Digital Elevation Model segmentation obtained from laser scanning data. The methodology is based on two sequential techniques, i.e., a recursive splitting technique using the quad tree structure followed by a region merging technique using the Markov Random Field model. The recursive splitting technique starts splitting the Digital Elevation Model into homogeneous regions. However, due to slight height differences in the Digital Elevation Model, region fragmentation can be relatively high. In order to minimize the fragmentation, a region merging technique based on the Markov Random Field model is applied to the previously segmented data. The resulting regions are firstly structured by using the so-called Region Adjacency Graph. Each node of the Region Adjacency Graph represents a region of the Digital Elevation Model segmented and two nodes have connectivity between them if corresponding regions share a common boundary. Next it is assumed that the random variable related to each node, follows the Markov Random Field model. This hypothesis allows the derivation of the posteriori probability distribution function whose solution is obtained by the Maximum a Posteriori estimation. Regions presenting high probability of similarity are merged. Experiments carried out with laser scanning data showed that the methodology allows to separate the objects in the Digital Elevation Model with a low amount of fragmentation.

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Structures transverse/sub-transverse to the shoreline have been identified and characterized on the precambrian basement outcrop on the continent adjacent to the northern Santos Basin. These structures were analyzed from images of digital elevation model SRTM 90m by extracting NW-SE lineaments that intersect the NE-SW foliation. The lineaments were selected, classified into 48 segments that extend toward offshore, and correlated with basin structures. In the basin 25 2D seismic sections, 12 well logs and data from potential methods were interpreted, identifying the key stratigraphic levels and the major structures. Structural maps of each horizon were generated. Six transfer faults (FTs) were recognized and named FT-1 to FT-6, whose extensions correspond to continental lineaments named FC1 to FC6. The FTs are related to the basin deformation and evolution. In seismic sections, these faults have lateral slip in flower structures, displacement inversions from normal at the top to reverse at the base, abrupt changes in thickness or even disappearance of the seismic reflectors. The structural map of the Basement and Top of the Rift shows control of some depocenters by faults and displacements in some areas. The maps of potential methods indicate that there are pronounced anomaly shifts in some areas, associated with FTs. Some seismic sections indicate reactivation of FTs when they intersect horizons from the basement until the most recent layers. The 3D integration of data facilitated the observation of the FT extensions in the continent discontinuity.

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Sugarcane is one of the most important crops of the Brazilian agribusiness and this importance justifies the use of techniques that allow the implementation of cultivation systems capable of reducing the variability of soil characteristics and the establishment of efficient agricultural planning. The aim of the present work was the planning and implementation of cultivation systems for sugarcane using geostatistical techniques, in the Pereira Barreto region, SP. An area of 505 ha was mapped using the global positioning system (GPS) and a Digital Elevation Model was elaborated (MDE). Soil samples were collected for 0-0.25 m depth, in each 7 ha, for their chemical attributes and texture characterization. Data were analyzed by descriptive statistics and geostatistics. The determination of the spatial distribution of soil granulometric and chemical attributes allowed the allocation of the studied sugarcane varieties according to soil fertility and clay content. The kriging maps of soil granulometric and chemical attributes brought useful information to the establishment of production environments with different soil and crop managements. The identification of different environments by means of geostatistical techniques allowed the precise planning of the sugarcane cultivation, as well as the adequacy of fertilization practices and the allocation of suitable sugarcane varieties adapted to the conditions imposed by differences in the soil attributes.

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Variations in soil attributes occur based on relief forms and parent material. The objective of this research was to study the influences of geomorphic surfaces on the spatial distribution of soil attributes in a sugarcane cultivation area. First, 530,67 hectares were mapped by using a Global Positioning System. Then, a Digital Elevation Model was developed and geomorphic surfaces were identified and delimited, according to topographic and stratigraphic criteria based on detailed field investigations. Soil samples were collected every 7 ha, at 0.0-0.25 m and 0.80-1.00 m depths, totalizing 134 samples. Texture, Ca, K, Mg, SB, CEC, V%, pH, and OMS analyses were carried out as well as descriptive statistics and geostatistics analysis. It was concluded that the geostatistic techniques and digital elevation model helped to notice that soil attributes presented limits close to the geomorphic surfaces ones.

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This paper proposes a method for the automatic extraction of building roof contours from a LiDAR-derived digital surface model (DSM). The method is based on two steps. First, to detect aboveground objects (buildings, trees, etc.), the DSM is segmented through a recursive splitting technique followed by a region merging process. Vectorization and polygonization are used to obtain polyline representations of the detected aboveground objects. Second, building roof contours are identified from among the aboveground objects by optimizing a Markov-random-field-based energy function that embodies roof contour attributes and spatial constraints. Preliminary results have shown that the proposed methodology works properly.

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This work has the objective of correlating altitude with the bauxite profiles in the Poços de Caldas Plateau in order to identify paleosurfaces. Then, bauxite profiles found in the Plateau have been classified and mapped through DEM. Three different types of bauxite profiles are highlighted in the area: (1) Rim Profiles which are well-developed and thick and are located in the areas of highest altitude in the Plateau; (2) Plateau Profiles which are not very thick and very clayey, normally appear in the lower portion of the landscape; (3) Reworked Profiles, characterized by bauxite reworked fragments composed by similar materials found in the tops of Rim and Plateau Profiles. After relating altitude to the constituting materials of the bauxite profiles, it is noticed that there are occurrences of Rim Bauxite Profiles located in altitudes of Plateau Profiles and Reworked Profiles located over Rim Profiles, above 1,400 meters of altitude. In the opposite direction, tops of the same elevation are found, where there should be Rim Profiles, there are rocky outcrops observed in field, without any evidence of laterite profiles. It is concluded, therefore, that the elevation rates by themselves, at least in this area, are not good enough references to determine the existence and distribution of paleosurfaces.

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Predicting and mapping productivity areas allows crop producers to improve their planning of agricultural activities. The primary aims of this work were the identification and mapping of specific management areas allowing coffee bean quality to be predicted from soil attributes and their relationships to relief. The study area was located in the Southeast of the Minas Gerais state, Brazil. A grid containing a total of 145 uniformly spaced nodes 50 m apart was established over an area of 31. 7 ha from which samples were collected at depths of 0. 00-0. 20 m in order to determine physical and chemical attributes of the soil. These data were analysed in conjunction with plant attributes including production, proportion of beans retained by different sieves and drink quality. The results of principal component analysis (PCA) in combination with geostatistical data showed the attributes clay content and available iron to be the best choices for identifying four crop production environments. Environment A, which exhibited high clay and available iron contents, and low pH and base saturation, was that providing the highest yield (30. 4l ha-1) and best coffee beverage quality (61 sacks ha-1). Based on the results, we believe that multivariate analysis, geostatistics and the soil-relief relationships contained in the digital elevation model (DEM) can be effectively used in combination for the hybrid mapping of areas of varying suitability for coffee production. © 2012 Springer Science+Business Media New York.

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This paper proposes a method by simulated annealing for building roof contours identification from LiDAR-derived digital elevation model. Our method is based on the concept of first extracting aboveground objects and then identifying those objects that are building roof contours. First, to detect aboveground objects (buildings, trees, etc.), the digital elevation model is segmented through a recursive splitting technique followed by a region merging process. Vectorization and polygonization are used to obtain polyline representations of the detected aboveground objects. Second, building roof contours are identified from among the aboveground objects by optimizing a Markov-random-field-based energy function that embodies roof contour attributes and spatial constraints. The solution of this function is a polygon set corresponding to building roof contours and is found by using a minimization technique, like the Simulated Annealing algorithm. Experiments carried out with laser scanning digital elevation model showed that the methodology works properly, as it provides roof contour information with approximately 90% shape accuracy and no verified false positives.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Ciências Cartográficas - FCT

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)