The Experts below are selected from a list of 100098 Experts worldwide ranked by ideXlab platform
P. Loudjani - One of the best experts on this subject based on the ideXlab platform.
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tree height quantification using very high resolution Imagery acquired from an unmanned aerial vehicle uav and automatic 3d photo reconstruction methods
European Journal of Agronomy, 2014Co-Authors: Pablo J Zarcotejada, V Angileri, Ramon A Diazvarela, P. LoudjaniAbstract:Abstract This study provides insight into the assessment of canopy biophysical parameter retrieval using passive sensors and specifically into the quantification of tree height in a discontinuous canopy using a low-cost camera on board an unmanned aerial vehicle (UAV). The UAV was a 2-m wingspan fixed-wing platform with 5.8 kg take-off weight and 63 km/h ground speed. It carried a consumer-grade RGB camera modified for color-infrared detection (CIR) and synchronized with a GPS unit. In this study, the configuration of the electric UAV carrying the camera payload enabled the acquisition of 158 ha in one single flight. The camera system made it possible to acquire very high resolution (VHR) Imagery (5 cm pixel−1) to generate ortho-mosaics and digital surface models (DSMs) through automatic 3D reconstruction methods. The UAV followed pre-designed flight plans over each study site to ensure the acquisition of the Imagery with large across- and along-track overlaps (i.e. >80%) using a grid of parallel and perpendicular flight lines. The validation method consisted of taking field measurements of the height of a total of 152 trees in two different study areas using a GPS in real-time kinematic (RTK) mode. The results of the validation assessment conducted to estimate tree height from the VHR DSMs yielded R2 = 0.83, an overall root mean square error (RMSE) of 35 cm, and a relative root mean square error (R-RMSE) of 11.5% for trees with heights ranging between 1.16 and 4.38 m. An assessment conducted on the effects of the spatial resolution of the input images acquired by the UAV on the photo-reconstruction method and DSM generation demonstrated stable relationships for pixel resolutions between 5 and 30 cm that rapidly degraded for input images with pixel resolutions lower than 35 cm. RMSE and R-RMSE values obtained as a function of input pixel resolution showed errors in tree quantification below 15% when 30 cm pixel−1 resolution Imagery was used to generate the DSMs. The study conducted in two orchards with this UAV system and the photo-reconstruction method highlighted that an inexpensive approach based on consumer-grade cameras on board a hand-launched unmanned aerial platform can provide accuracies comparable to those of the expensive and computationally more complex light detection and ranging (LIDAR) systems currently operated for agricultural and environmental applications.
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Tree height quantification using very high resolution Imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods
European Journal of Agronomy, 2014Co-Authors: Pablo J. Zarco-tejada, R. Diaz-varela, V Angileri, P. LoudjaniAbstract:This study provides insight into the assessment of canopy biophysical parameter retrieval using passive sensors and specifically into the quantification of tree height in a discontinuous canopy using a low-cost camera on board an unmanned aerial vehicle (UAV). The UAV was a 2-m wingspan fixed-wing platform with 5.8kg take-off weight and 63km/h ground speed. It carried a consumer-grade RGB camera modified for color-infrared detection (CIR) and synchronized with a GPS unit. In this study, the configuration of the electric UAV carrying the camera payload enabled the acquisition of 158ha in one single flight. The camera system made it possible to acquire very high resolution (VHR) Imagery (5cmpixel-1) to generate ortho-mosaics and digital surface models (DSMs) through automatic 3D reconstruction methods. The UAV followed pre-designed flight plans over each study site to ensure the acquisition of the Imagery with large across- and along-track overlaps (i.e. >80%) using a grid of parallel and perpendicular flight lines. The validation method consisted of taking field measurements of the height of a total of 152 trees in two different study areas using a GPS in real-time kinematic (RTK) mode. The results of the validation assessment conducted to estimate tree height from the VHR DSMs yielded R2=0.83, an overall root mean square error (RMSE) of 35cm, and a relative root mean square error (R-RMSE) of 11.5% for trees with heights ranging between 1.16 and 4.38m. An assessment conducted on the effects of the spatial resolution of the input images acquired by the UAV on the photo-reconstruction method and DSM generation demonstrated stable relationships for pixel resolutions between 5 and 30cm that rapidly degraded for input images with pixel resolutions lower than 35cm. RMSE and R-RMSE values obtained as a function of input pixel resolution showed errors in tree quantification below 15% when 30cmpixel-1resolution Imagery was used to generate the DSMs. The study conducted in two orchards with this UAV system and the photo-reconstruction method highlighted that an inexpensive approach based on consumer-grade cameras on board a hand-launched unmanned aerial platform can provide accuracies comparable to those of the expensive and computationally more complex light detection and ranging (LIDAR) systems currently operated for agricultural and environmental applications. © 2014 Elsevier B.V.
V Angileri - One of the best experts on this subject based on the ideXlab platform.
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tree height quantification using very high resolution Imagery acquired from an unmanned aerial vehicle uav and automatic 3d photo reconstruction methods
European Journal of Agronomy, 2014Co-Authors: Pablo J Zarcotejada, V Angileri, Ramon A Diazvarela, P. LoudjaniAbstract:Abstract This study provides insight into the assessment of canopy biophysical parameter retrieval using passive sensors and specifically into the quantification of tree height in a discontinuous canopy using a low-cost camera on board an unmanned aerial vehicle (UAV). The UAV was a 2-m wingspan fixed-wing platform with 5.8 kg take-off weight and 63 km/h ground speed. It carried a consumer-grade RGB camera modified for color-infrared detection (CIR) and synchronized with a GPS unit. In this study, the configuration of the electric UAV carrying the camera payload enabled the acquisition of 158 ha in one single flight. The camera system made it possible to acquire very high resolution (VHR) Imagery (5 cm pixel−1) to generate ortho-mosaics and digital surface models (DSMs) through automatic 3D reconstruction methods. The UAV followed pre-designed flight plans over each study site to ensure the acquisition of the Imagery with large across- and along-track overlaps (i.e. >80%) using a grid of parallel and perpendicular flight lines. The validation method consisted of taking field measurements of the height of a total of 152 trees in two different study areas using a GPS in real-time kinematic (RTK) mode. The results of the validation assessment conducted to estimate tree height from the VHR DSMs yielded R2 = 0.83, an overall root mean square error (RMSE) of 35 cm, and a relative root mean square error (R-RMSE) of 11.5% for trees with heights ranging between 1.16 and 4.38 m. An assessment conducted on the effects of the spatial resolution of the input images acquired by the UAV on the photo-reconstruction method and DSM generation demonstrated stable relationships for pixel resolutions between 5 and 30 cm that rapidly degraded for input images with pixel resolutions lower than 35 cm. RMSE and R-RMSE values obtained as a function of input pixel resolution showed errors in tree quantification below 15% when 30 cm pixel−1 resolution Imagery was used to generate the DSMs. The study conducted in two orchards with this UAV system and the photo-reconstruction method highlighted that an inexpensive approach based on consumer-grade cameras on board a hand-launched unmanned aerial platform can provide accuracies comparable to those of the expensive and computationally more complex light detection and ranging (LIDAR) systems currently operated for agricultural and environmental applications.
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Tree height quantification using very high resolution Imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods
European Journal of Agronomy, 2014Co-Authors: Pablo J. Zarco-tejada, R. Diaz-varela, V Angileri, P. LoudjaniAbstract:This study provides insight into the assessment of canopy biophysical parameter retrieval using passive sensors and specifically into the quantification of tree height in a discontinuous canopy using a low-cost camera on board an unmanned aerial vehicle (UAV). The UAV was a 2-m wingspan fixed-wing platform with 5.8kg take-off weight and 63km/h ground speed. It carried a consumer-grade RGB camera modified for color-infrared detection (CIR) and synchronized with a GPS unit. In this study, the configuration of the electric UAV carrying the camera payload enabled the acquisition of 158ha in one single flight. The camera system made it possible to acquire very high resolution (VHR) Imagery (5cmpixel-1) to generate ortho-mosaics and digital surface models (DSMs) through automatic 3D reconstruction methods. The UAV followed pre-designed flight plans over each study site to ensure the acquisition of the Imagery with large across- and along-track overlaps (i.e. >80%) using a grid of parallel and perpendicular flight lines. The validation method consisted of taking field measurements of the height of a total of 152 trees in two different study areas using a GPS in real-time kinematic (RTK) mode. The results of the validation assessment conducted to estimate tree height from the VHR DSMs yielded R2=0.83, an overall root mean square error (RMSE) of 35cm, and a relative root mean square error (R-RMSE) of 11.5% for trees with heights ranging between 1.16 and 4.38m. An assessment conducted on the effects of the spatial resolution of the input images acquired by the UAV on the photo-reconstruction method and DSM generation demonstrated stable relationships for pixel resolutions between 5 and 30cm that rapidly degraded for input images with pixel resolutions lower than 35cm. RMSE and R-RMSE values obtained as a function of input pixel resolution showed errors in tree quantification below 15% when 30cmpixel-1resolution Imagery was used to generate the DSMs. The study conducted in two orchards with this UAV system and the photo-reconstruction method highlighted that an inexpensive approach based on consumer-grade cameras on board a hand-launched unmanned aerial platform can provide accuracies comparable to those of the expensive and computationally more complex light detection and ranging (LIDAR) systems currently operated for agricultural and environmental applications. © 2014 Elsevier B.V.
Marvin E Bauer - One of the best experts on this subject based on the ideXlab platform.
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mapping impervious surface area using high resolution Imagery a comparison of object based and per pixel classification
Annual Conference of the American Society for Photogrammetry and Remote Sensing 2006: Prospecting for Geospatial Information Integration ASPRS 2006, 2006Co-Authors: Fei Yuan, Marvin E BauerAbstract:Impervious surface area is a key indicator of environmental quality. Satellite remote sensing of impervious surface has focused on subpixel analysis via various forms of statistical estimation, subpixel classification, and spectral mixture analysis, using medium resolution Landsat TM or ETM+ data. Maps of impervious surface area from these studies provide useful inputs to planning and management activities at city to regional scales. However, for local studies, large-scale, higher resolution maps are preferred. This study investigates digital classification techniques of mapping of impervious surface area using high resolution Quickbird satellite data. Two methods – object-based and per pixel classification – are explored and compared. The results provide information for accurate impervious surface mapping and estimation in high resolution Imagery.
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extending satellite remote sensing to local scales land and water resource monitoring using high resolution Imagery
Remote Sensing of Environment, 2003Co-Authors: Kali E Sawaya, Leif G Olmanson, Nathan J Heinert, Patrick L Brezonik, Marvin E BauerAbstract:The potential of High-Resolution IKONOS and QuickBird satellite Imagery for mapping and analysis of land and water resources at local scales in Minnesota is assessed in a series of three applications. The applications and accuracies evaluated include: (1) classification of lake water clarity (r 2 =0.89), (2) mapping of urban impervious surface area (r 2 =0.98), and (3) aquatic vegetation surveys of emergent and submergent plant groups (80% accuracy). There were several notable findings from these applications. For example, modeling and estimation approaches developed for Landsat TM data for continuous variables such as lake water clarity and impervious surface area can be applied to High-Resolution satellite data. The rapid delivery of spatial data can be coupled with current GPS and field computer technologies to bring the Imagery into the field for cover type validation. We also found several limitations in working with this data type. For example, shadows can influence feature classification and their effects need to be evaluated. Nevertheless, High-Resolution satellite data has excellent potential to extend satellite remote sensing beyond what has been possible with aerial photography and Landsat data, and should be of interest to resource managers as a way to create timely and reliable assessments of land and water resources at a local scale.
Pablo J. Zarco-tejada - One of the best experts on this subject based on the ideXlab platform.
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Tree height quantification using very high resolution Imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods
European Journal of Agronomy, 2014Co-Authors: Pablo J. Zarco-tejada, R. Diaz-varela, V Angileri, P. LoudjaniAbstract:This study provides insight into the assessment of canopy biophysical parameter retrieval using passive sensors and specifically into the quantification of tree height in a discontinuous canopy using a low-cost camera on board an unmanned aerial vehicle (UAV). The UAV was a 2-m wingspan fixed-wing platform with 5.8kg take-off weight and 63km/h ground speed. It carried a consumer-grade RGB camera modified for color-infrared detection (CIR) and synchronized with a GPS unit. In this study, the configuration of the electric UAV carrying the camera payload enabled the acquisition of 158ha in one single flight. The camera system made it possible to acquire very high resolution (VHR) Imagery (5cmpixel-1) to generate ortho-mosaics and digital surface models (DSMs) through automatic 3D reconstruction methods. The UAV followed pre-designed flight plans over each study site to ensure the acquisition of the Imagery with large across- and along-track overlaps (i.e. >80%) using a grid of parallel and perpendicular flight lines. The validation method consisted of taking field measurements of the height of a total of 152 trees in two different study areas using a GPS in real-time kinematic (RTK) mode. The results of the validation assessment conducted to estimate tree height from the VHR DSMs yielded R2=0.83, an overall root mean square error (RMSE) of 35cm, and a relative root mean square error (R-RMSE) of 11.5% for trees with heights ranging between 1.16 and 4.38m. An assessment conducted on the effects of the spatial resolution of the input images acquired by the UAV on the photo-reconstruction method and DSM generation demonstrated stable relationships for pixel resolutions between 5 and 30cm that rapidly degraded for input images with pixel resolutions lower than 35cm. RMSE and R-RMSE values obtained as a function of input pixel resolution showed errors in tree quantification below 15% when 30cmpixel-1resolution Imagery was used to generate the DSMs. The study conducted in two orchards with this UAV system and the photo-reconstruction method highlighted that an inexpensive approach based on consumer-grade cameras on board a hand-launched unmanned aerial platform can provide accuracies comparable to those of the expensive and computationally more complex light detection and ranging (LIDAR) systems currently operated for agricultural and environmental applications. © 2014 Elsevier B.V.
Pablo J Zarcotejada - One of the best experts on this subject based on the ideXlab platform.
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tree height quantification using very high resolution Imagery acquired from an unmanned aerial vehicle uav and automatic 3d photo reconstruction methods
European Journal of Agronomy, 2014Co-Authors: Pablo J Zarcotejada, V Angileri, Ramon A Diazvarela, P. LoudjaniAbstract:Abstract This study provides insight into the assessment of canopy biophysical parameter retrieval using passive sensors and specifically into the quantification of tree height in a discontinuous canopy using a low-cost camera on board an unmanned aerial vehicle (UAV). The UAV was a 2-m wingspan fixed-wing platform with 5.8 kg take-off weight and 63 km/h ground speed. It carried a consumer-grade RGB camera modified for color-infrared detection (CIR) and synchronized with a GPS unit. In this study, the configuration of the electric UAV carrying the camera payload enabled the acquisition of 158 ha in one single flight. The camera system made it possible to acquire very high resolution (VHR) Imagery (5 cm pixel−1) to generate ortho-mosaics and digital surface models (DSMs) through automatic 3D reconstruction methods. The UAV followed pre-designed flight plans over each study site to ensure the acquisition of the Imagery with large across- and along-track overlaps (i.e. >80%) using a grid of parallel and perpendicular flight lines. The validation method consisted of taking field measurements of the height of a total of 152 trees in two different study areas using a GPS in real-time kinematic (RTK) mode. The results of the validation assessment conducted to estimate tree height from the VHR DSMs yielded R2 = 0.83, an overall root mean square error (RMSE) of 35 cm, and a relative root mean square error (R-RMSE) of 11.5% for trees with heights ranging between 1.16 and 4.38 m. An assessment conducted on the effects of the spatial resolution of the input images acquired by the UAV on the photo-reconstruction method and DSM generation demonstrated stable relationships for pixel resolutions between 5 and 30 cm that rapidly degraded for input images with pixel resolutions lower than 35 cm. RMSE and R-RMSE values obtained as a function of input pixel resolution showed errors in tree quantification below 15% when 30 cm pixel−1 resolution Imagery was used to generate the DSMs. The study conducted in two orchards with this UAV system and the photo-reconstruction method highlighted that an inexpensive approach based on consumer-grade cameras on board a hand-launched unmanned aerial platform can provide accuracies comparable to those of the expensive and computationally more complex light detection and ranging (LIDAR) systems currently operated for agricultural and environmental applications.
