Suchergebnisse

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.