At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from a...At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.展开更多
One of the challenges of remote sensing and computer vision lies in the three-dimensional(3-D)reconstruction of individual trees by using automated methods through very high-resolution(VHR)data sets.However,a successf...One of the challenges of remote sensing and computer vision lies in the three-dimensional(3-D)reconstruction of individual trees by using automated methods through very high-resolution(VHR)data sets.However,a successful and complete 3-D reconstruction relies on precise delineation of the trees in two dimensions.In this paper,we present an original approach to detect and delineate citrus trees using unmanned aerial vehicles based on photogrammetric digital surface models(DSMs).The symmetry of the citrus trees in a DSM is handled by an orientationbased radial symmetry transform which is computed in a unique way.Next,we propose an efficient strategy to accurately build influence regions of each tree,and then we delineate individual citrus trees through active contours by taking into account the influence region of each canopy.We also present two efficient strategies to filter out erroneously detected canopy regions without having any height thresholds.Experiments are carried out on eight test DSMs composed of different types of citrus orchards with varying densities and canopy sizes.Extensive comparisons to the state-of-the-art approaches reveal that our proposed approach provides superior detection and delineation performances through supporting a nice balance between precision and recall measures.展开更多
基金funded by the National Key Technologies R&D Program of China (Grants No. 2017YFC0505104)the Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying, Mapping and Geoinformation of China (Grants No. DM2016SC09)
文摘At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.
基金This work was supported by the Scientific and Technical Research Council of Turkey(TUBITAK)[grant number 114Y671].
文摘One of the challenges of remote sensing and computer vision lies in the three-dimensional(3-D)reconstruction of individual trees by using automated methods through very high-resolution(VHR)data sets.However,a successful and complete 3-D reconstruction relies on precise delineation of the trees in two dimensions.In this paper,we present an original approach to detect and delineate citrus trees using unmanned aerial vehicles based on photogrammetric digital surface models(DSMs).The symmetry of the citrus trees in a DSM is handled by an orientationbased radial symmetry transform which is computed in a unique way.Next,we propose an efficient strategy to accurately build influence regions of each tree,and then we delineate individual citrus trees through active contours by taking into account the influence region of each canopy.We also present two efficient strategies to filter out erroneously detected canopy regions without having any height thresholds.Experiments are carried out on eight test DSMs composed of different types of citrus orchards with varying densities and canopy sizes.Extensive comparisons to the state-of-the-art approaches reveal that our proposed approach provides superior detection and delineation performances through supporting a nice balance between precision and recall measures.
