Suchergebnisse

The motorway Ourense – Celanova will become the next years in one of the main roads of inland Galicia (northwest region of Spain) that will connect quickly with the cities of Northern Portugal. This highway is projected as a public – private partnership between the regional government of Xunta de Galicia and the construction companies Copasa SA and Extraco SA. There are currently under construction the 19 km of this road and presents a number of structures as viaducts, overpasses and underpasses. The viaducts are part of the main road, allowing passage of the vehicles at conventional speed. Overpasses are mainly used in the connection of the highway with secondary roads. Moreover, the underpasses are better suited for the passage of wildlife animals, persons or agricultural machinery. The underpass arch-shape structures used for this project consist of two reinforced concrete voussoirs placed on two small concrete walls. For each set of voussoirs there are three joining points, two between the walls and the voussoirs and one between the both voussoirs at the top of the structure. These underpasses suffer significant mechanical stress during construction, because during the backfilling process asymmetric loads are applied to both sides. Thus, it is very important the monitoring of the structure using geodetic techniques as total stations, levels or laser scanners The underpass selected for this study is located at the kilometric point 4.9 of the highway, with a total length of 50.38 m, maximum span of 13.30 m and rise of 7.23 m. Voussoirs has a thickness of 0.35 m and a length of 2.52 m. The small lateral walls exhibit a height of 2.35 m and thickness of 0.85 m. The underpass presents a slope of approximately 4 % and the maximum height of the backfill over the top of the structure is 3.80 m. The foundation consists of a concrete slab arch-shape (curvature opposite the main arch) with a thickness of 0.7 m. The geodetic technology used for the deformation monitoring is a Optech Lynx mobile LiDAR. This laser scanner is based on time of flight technology and presents an accuracy of 6 mm in the determination of the geometrical coordinates. This accuracy can be improved to around 1 mm using fitting post-processing techniques and makes this technology very useful for studies related with deformation monitoring. The laser scanner, in comparison with other geodetic techniques as total stations, allows the control of all the structure, including unexpected deformations. Reflective targets are permanently positioned over the small walls of the structure to allow the 3D orientation of the different scans. Two main scans are made for this study, before and after the backfilling process. Backfilling takes about 10 days for the construction companies. The scans need a time of approximately 12 minutes. Construction works do not need to be interrupted during the scans. Point clouds are then post-processed using QT Modeler Software. First, the point cloud is cleaned to use only the data directly related with the structure under study. Then, using the target coordinates, both point clouds are moved to the same coordinate system. Finally, the deformation of the underpass is studied using two algorithms specifically developed using Matlab software. First algorithm fits a geometrical surface to the point cloud of the first scan and evaluates the residuals of both scans for this fitting surface. Differences in the residuals give the deformation map of the structure. Second algorithm takes a portion of the point cloud from the top of the structure, where it is located the joining point between the voussoirs. The joining between two voussoirs shows a height step that in an ideal case must tend to zero. Deformations produced by the loading of the structure are measured as a comparison between the steps before and after the backfilling process. The analysis of the results show as some deformation occurs in the structure in the joining point of the voussoirs ranging between 1 mm and 5 mm. These deformations are under the tolerances predicted by the structure and confirm the success in the construction works developed. The laser scanning and the post-processing algorithms here developed appear as an easy methodology to make deformation monitoring of underpass structures and guarantee the load capacity of the structure.

The motorway Ourense – Celanova will become the next years in one of the main roads of inland Galicia (northwest region of Spain) that will connect quickly with the cities of Northern Portugal. This highway is projected as a public – private partnership between the regional government of Xunta de Galicia and the construction companies Copasa SA and Extraco SA. There are currently under construction the 19 km of this road and presents a number of structures as viaducts, overpasses and underpasses. The viaducts are part of the main road, allowing passage of the vehicles at conventional speed. Overpasses are mainly used in the connection of the highway with secondary roads. Moreover, the underpasses are better suited for the passage of wildlife animals, persons or agricultural machinery. The underpass arch-shape structures used for this project consist of two reinforced concrete voussoirs placed on two small concrete walls. For each set of voussoirs there are three joining points, two between the walls and the voussoirs and one between the both voussoirs at the top of the structure. These underpasses suffer significant mechanical stress during construction, because during the backfilling process asymmetric loads are applied to both sides. Thus, it is very important the monitoring of the structure using geodetic techniques as total stations, levels or laser scanners The underpass selected for this study is located at the kilometric point 4.9 of the highway, with a total length of 50.38 m, maximum span of 13.30 m and rise of 7.23 m. Voussoirs has a thickness of 0.35 m and a length of 2.52 m. The small lateral walls exhibit a height of 2.35 m and thickness of 0.85 m. The underpass presents a slope of approximately 4 % and the maximum height of the backfill over the top of the structure is 3.80 m. The foundation consists of a concrete slab arch-shape (curvature opposite the main arch) with a thickness of 0.7 m. The geodetic technology used for the deformation monitoring is a Optech Lynx mobile LiDAR. This laser scanner is based on time of flight technology and presents an accuracy of 6 mm in the determination of the geometrical coordinates. This accuracy can be improved to around 1 mm using fitting post-processing techniques and makes this technology very useful for studies related with deformation monitoring. The laser scanner, in comparison with other geodetic techniques as total stations, allows the control of all the structure, including unexpected deformations. Reflective targets are permanently positioned over the small walls of the structure to allow the 3D orientation of the different scans. Two main scans are made for this study, before and after the backfilling process. Backfilling takes about 10 days for the construction companies. The scans need a time of approximately 12 minutes. Construction works do not need to be interrupted during the scans. Point clouds are then post-processed using QT Modeler Software. First, the point cloud is cleaned to use only the data directly related with the structure under study. Then, using the target coordinates, both point clouds are moved to the same coordinate system. Finally, the deformation of the underpass is studied using two algorithms specifically developed using Matlab software. First algorithm fits a geometrical surface to the point cloud of the first scan and evaluates the residuals of both scans for this fitting surface. Differences in the residuals give the deformation map of the structure. Second algorithm takes a portion of the point cloud from the top of the structure, where it is located the joining point between the voussoirs. The joining between two voussoirs shows a height step that in an ideal case must tend to zero. Deformations produced by the loading of the structure are measured as a comparison between the steps before and after the backfilling process. The analysis of the results show as some deformation occurs in the structure in the joining point of the voussoirs ranging between 1 mm and 5 mm. These deformations are under the tolerances predicted by the structure and confirm the success in the construction works developed. The laser scanning and the post-processing algorithms here developed appear as an easy methodology to make deformation monitoring of underpass structures and guarantee the load capacity of the structure.

Image classification stands as an essential tool for automated mapping, that is demanded by agencies and stakeholders dealing with geospatial information. Decreasing costs or UAV-based surveying and open access to high resolution satellite images such as that provided by European Union's Copernicus programme are the basis for multi-temporal landscape analysis and monitoring. Besides that, invasive alien species are considered a risk for biodiversity and their inventory is needed for further control and eradication. In this work, a methodology for semi-automatic detection of invasive alien species through UAV surveying and Sentinel 2 satellite monitoring is presented and particularized for Acacia dealbata Link species in the province of Pontevedra, in NW Spain. We selected a scenario with notable invasion of Acaciae and performed a UAS surveying to outline feasible training areas. Such areas were used as bounds for obtaining a spectral response of the cover from Sentinel 2 images with a level of processing 2A, that was used for invasive area detection. Sparse detected areas were treated as a seed for a region growing step to obtain the final map of alien species.

Image classification stands as an essential tool for automated mapping, that is demanded by agencies and stakeholders dealing with geospatial information. Decreasing costs or UAV-based surveying and open access to high resolution satellite images such as that provided by European Union's Copernicus programme are the basis for multi-temporal landscape analysis and monitoring. Besides that, invasive alien species are considered a risk for biodiversity and their inventory is needed for further control and eradication. In this work, a methodology for semi-automatic detection of invasive alien species through UAV surveying and Sentinel 2 satellite monitoring is presented and particularized for Acacia dealbata Link species in the province of Pontevedra, in NW Spain. We selected a scenario with notable invasion of Acaciae and performed a UAS surveying to outline feasible training areas. Such areas were used as bounds for obtaining a spectral response of the cover from Sentinel 2 images with a level of processing 2A, that was used for invasive area detection. Sparse detected areas were treated as a seed for a region growing step to obtain the final map of alien species.

There is a complex relation between roads and fires. Several major wildfires were ignited near to roads (Morrison 2007) and how they progressed is an important role to understand the importance to forest management in this environment. Nowadays, a sustainable forest management is necessary both for environment and politics. One of the reasons of road management is that these infrastructures provide an effective firewall in case of forest fires and an escape route for the population. Forest management optimization in road surroundings would improve wildfires prevention and mitigate their effects. One of the main indicators of road safety is the distance between road and vegetation. The aim of this work is to develop a methodology to determine what areas do not obey current laws about safety distances between forest and roads. The acquisition of LiDAR data is done by Lynx Mobile Mapper System from University of Vigo. The methodology is automated using LiDAR data processing. The developed algorithms are based in height and length segmentation of the road. The objective is classifying vegetation groups by height and calculate the distance to the edges of road. The vegetation is divided in groups of height of 5, 10, 15 and 30 m. The minimum distance calculation is 2 m, for the vegetation of 5 m height and a maximum of 60 m for vegetation 30 m height. The height of vegetation has a directly relation with the distance separation with the road.

There is a complex relation between roads and fires. Several major wildfires were ignited near to roads (Morrison 2007) and how they progressed is an important role to understand the importance to forest management in this environment. Nowadays, a sustainable forest management is necessary both for environment and politics. One of the reasons of road management is that these infrastructures provide an effective firewall in case of forest fires and an escape route for the population. Forest management optimization in road surroundings would improve wildfires prevention and mitigate their effects. One of the main indicators of road safety is the distance between road and vegetation. The aim of this work is to develop a methodology to determine what areas do not obey current laws about safety distances between forest and roads. The acquisition of LiDAR data is done by Lynx Mobile Mapper System from University of Vigo. The methodology is automated using LiDAR data processing. The developed algorithms are based in height and length segmentation of the road. The objective is classifying vegetation groups by height and calculate the distance to the edges of road. The vegetation is divided in groups of height of 5, 10, 15 and 30 m. The minimum distance calculation is 2 m, for the vegetation of 5 m height and a maximum of 60 m for vegetation 30 m height. The height of vegetation has a directly relation with the distance separation with the road.