WorldWideScience

Sample records for monochromatic aerial imagery

  1. Aerial Photography and Imagery, Uncorrected, historic aerial imagery; 1931-1990, Published in 2006, Washoe County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Uncorrected dataset, was produced all or in part from Hardcopy Maps information as of 2006. It is described as 'historic aerial...

  2. Advanced Image Processing of Aerial Imagery

    Science.gov (United States)

    Woodell, Glenn; Jobson, Daniel J.; Rahman, Zia-ur; Hines, Glenn

    2006-01-01

    Aerial imagery of the Earth is an invaluable tool for the assessment of ground features, especially during times of disaster. Researchers at the NASA Langley Research Center have developed techniques which have proven to be useful for such imagery. Aerial imagery from various sources, including Langley's Boeing 757 Aries aircraft, has been studied extensively. This paper discusses these studies and demonstrates that better-than-observer imagery can be obtained even when visibility is severely compromised. A real-time, multi-spectral experimental system will be described and numerous examples will be shown.

  3. D Surface Generation from Aerial Thermal Imagery

    Science.gov (United States)

    Khodaei, B.; Samadzadegan, F.; Dadras Javan, F.; Hasani, H.

    2015-12-01

    Aerial thermal imagery has been recently applied to quantitative analysis of several scenes. For the mapping purpose based on aerial thermal imagery, high accuracy photogrammetric process is necessary. However, due to low geometric resolution and low contrast of thermal imaging sensors, there are some challenges in precise 3D measurement of objects. In this paper the potential of thermal video in 3D surface generation is evaluated. In the pre-processing step, thermal camera is geometrically calibrated using a calibration grid based on emissivity differences between the background and the targets. Then, Digital Surface Model (DSM) generation from thermal video imagery is performed in four steps. Initially, frames are extracted from video, then tie points are generated by Scale-Invariant Feature Transform (SIFT) algorithm. Bundle adjustment is then applied and the camera position and orientation parameters are determined. Finally, multi-resolution dense image matching algorithm is used to create 3D point cloud of the scene. Potential of the proposed method is evaluated based on thermal imaging cover an industrial area. The thermal camera has 640×480 Uncooled Focal Plane Array (UFPA) sensor, equipped with a 25 mm lens which mounted in the Unmanned Aerial Vehicle (UAV). The obtained results show the comparable accuracy of 3D model generated based on thermal images with respect to DSM generated from visible images, however thermal based DSM is somehow smoother with lower level of texture. Comparing the generated DSM with the 9 measured GCPs in the area shows the Root Mean Square Error (RMSE) value is smaller than 5 decimetres in both X and Y directions and 1.6 meters for the Z direction.

  4. Aerial Photography and Imagery, Oblique, Pictometry Imagery, Published in 2009, North Georgia College and State University.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset, was produced all or in part from Orthoimagery information as of 2009. It is described as 'Pictometry Imagery'....

  5. NAIP Aerial Imagery (Resampled), Salton Sea - 2005 [ds425

    Data.gov (United States)

    California Department of Resources — NAIP 2005 aerial imagery that has been resampled from 1-meter source resolution to approximately 30-meter resolution. This is a mosaic composed from several NAIP...

  6. Aerial Photography and Imagery, Ortho-Corrected, Aerial Photography and Imagery, Ortho-Corrected -, Published in unknown, Not Applicable scale, FREAC.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at Not Applicable scale as of unknown. It is described as 'Aerial Photography and Imagery,...

  7. Converting aerial imagery to application maps

    Science.gov (United States)

    Over the last couple of years in Agricultural Aviation and at the 2014 and 2015 NAAA conventions, we have written about and presented both single-camera and two-camera imaging systems for use on agricultural aircraft. Many aerial applicators have shown a great deal of interest in the imaging systems...

  8. Aerial Photography and Imagery, Ortho-Corrected, 2007 imagery over Eureka Township, Published in 2007, Eureka County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, was produced all or in part from Orthoimagery information as of 2007. It is described as '2007 imagery...

  9. Encoding and analyzing aerial imagery using geospatial semantic graphs

    Energy Technology Data Exchange (ETDEWEB)

    Watson, Jean-Paul; Strip, David R.; McLendon, William Clarence,; Parekh, Ojas D.; Diegert, Carl F.; Martin, Shawn Bryan; Rintoul, Mark Daniel

    2014-02-01

    While collection capabilities have yielded an ever-increasing volume of aerial imagery, analytic techniques for identifying patterns in and extracting relevant information from this data have seriously lagged. The vast majority of imagery is never examined, due to a combination of the limited bandwidth of human analysts and limitations of existing analysis tools. In this report, we describe an alternative, novel approach to both encoding and analyzing aerial imagery, using the concept of a geospatial semantic graph. The advantages of our approach are twofold. First, intuitive templates can be easily specified in terms of the domain language in which an analyst converses. These templates can be used to automatically and efficiently search large graph databases, for specific patterns of interest. Second, unsupervised machine learning techniques can be applied to automatically identify patterns in the graph databases, exposing recurring motifs in imagery. We illustrate our approach using real-world data for Anne Arundel County, Maryland, and compare the performance of our approach to that of an expert human analyst.

  10. Aerial Photography and Imagery, Ortho-Corrected - Montana 2013 NAIP Orthophotos

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains imagery from the National Agriculture Imagery Program (NAIP). These data are digital aerial photos, at one meter resolution, of the entire...

  11. LA0801 Ortho-rectified Aerial Imagery of Terrebonne and Timbalier Bays Barrier Islands, Louisiana.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — AERO-METRIC, INC. (AME) was provided aerial photographic imagery collected by NOAA along the shoreline of Louisiana. The purpose of the imagery was to provide...

  12. Oblique Aerial Imagery for NMA - Some best Practices

    Science.gov (United States)

    Remondino, F.; Toschi, I.; Gerke, M.; Nex, F.; Holland, D.; McGill, A.; Talaya Lopez, J.; Magarinos, A.

    2016-06-01

    Oblique airborne photogrammetry is rapidly maturing and being offered by service providers as a good alternative or replacement of the more traditional vertical imagery and for very different applications (Fig.1). EuroSDR, representing European National Mapping Agencies (NMAs) and research organizations of most EU states, is following the development of oblique aerial cameras since 2013, when an ongoing activity was created to continuously update its members on the developments in this technology. Nowadays most European NMAs still rely on the traditional workflow based on vertical photography but changes are slowly taking place also at production level. Some NMAs have already run some tests internally to understand the potential for their needs whereas other agencies are discussing on the future role of this technology and how to possibly adapt their production pipelines. At the same time, some research institutions and academia demonstrated the potentialities of oblique aerial datasets to generate textured 3D city models or large building block models. The paper provides an overview of tests, best practices and considerations coming from the R&D community and from three European NMAs concerning the use of oblique aerial imagery.

  13. High resolution channel geometry from repeat aerial imagery

    Science.gov (United States)

    King, T.; Neilson, B. T.; Jensen, A.; Torres-Rua, A. F.; Winkelaar, M.; Rasmussen, M. T.

    2015-12-01

    River channel cross sectional geometry is a key attribute for controlling the river energy balances where surface heat fluxes dominate and discharge varies significantly over short time periods throughout the open water season. These dynamics are seen in higher gradient portions of Arctic rivers where surface heat fluxes can dominates river energy balances and low hillslope storage produce rapidly varying hydrographs. Additionally, arctic river geometry can be highly dynamic in the face of thermal erosion of permafrost landscape. While direct in-situ measurements of channel cross sectional geometry are accurate, they are limited in spatial resolution and coverage, and can be access limited in remote areas. Remote sensing can help gather data at high spatial resolutions and large areas, however techniques for extracting channel geometry is often limited to the banks and flood plains adjacent to river, as the water column inhibits sensing of the river bed itself. Green light LiDAR can be used to map bathymetry, however this is expensive, difficult to obtain at large spatial scales, and dependent on water quality. Alternatively, 3D photogrammetry from aerial imagery can be used to analyze the non-wetted portion of the river channel, but extracting full cross sections requires extrapolation into the wetted portion of the river. To bridge these gaps, an approach for using repeat aerial imagery surveys with visual (RGB) and near infrared (NIR) to extract high resolution channel geometry for the Kuparuk River in the Alaskan Arctic was developed. Aerial imagery surveys were conducted under multiple flow conditions and water surface geometry (elevation and width) were extracted through photogrammetry. Channel geometry was extracted by combining water surface widths and elevations from multiple flights. The accuracy of these results were compared against field surveyed cross sections at many locations throughout the study reach and a digital elevation model created under

  14. 3D SURFACE GENERATION FROM AERIAL THERMAL IMAGERY

    Directory of Open Access Journals (Sweden)

    B. Khodaei

    2015-12-01

    Full Text Available Aerial thermal imagery has been recently applied to quantitative analysis of several scenes. For the mapping purpose based on aerial thermal imagery, high accuracy photogrammetric process is necessary. However, due to low geometric resolution and low contrast of thermal imaging sensors, there are some challenges in precise 3D measurement of objects. In this paper the potential of thermal video in 3D surface generation is evaluated. In the pre-processing step, thermal camera is geometrically calibrated using a calibration grid based on emissivity differences between the background and the targets. Then, Digital Surface Model (DSM generation from thermal video imagery is performed in four steps. Initially, frames are extracted from video, then tie points are generated by Scale-Invariant Feature Transform (SIFT algorithm. Bundle adjustment is then applied and the camera position and orientation parameters are determined. Finally, multi-resolution dense image matching algorithm is used to create 3D point cloud of the scene. Potential of the proposed method is evaluated based on thermal imaging cover an industrial area. The thermal camera has 640×480 Uncooled Focal Plane Array (UFPA sensor, equipped with a 25 mm lens which mounted in the Unmanned Aerial Vehicle (UAV. The obtained results show the comparable accuracy of 3D model generated based on thermal images with respect to DSM generated from visible images, however thermal based DSM is somehow smoother with lower level of texture. Comparing the generated DSM with the 9 measured GCPs in the area shows the Root Mean Square Error (RMSE value is smaller than 5 decimetres in both X and Y directions and 1.6 meters for the Z direction.

  15. Aerial Photography and Imagery, Oblique, 2008 Aerial Oblique imagery for Iredell County, NC, Published in 2008, 1:2400 (1in=200ft) scale, Iredell County GIS.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Other information as of 2008. It is...

  16. Aerial Photography and Imagery, Ortho-Corrected, Pictometry Imagery, Published in 2009, North Georgia College and State University.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, was produced all or in part from Orthoimagery information as of 2009. It is described as 'Pictometry...

  17. Automatic Sea Bird Detection from High Resolution Aerial Imagery

    Science.gov (United States)

    Mader, S.; Grenzdörffer, G. J.

    2016-06-01

    Great efforts are presently taken in the scientific community to develop computerized and (fully) automated image processing methods allowing for an efficient and automatic monitoring of sea birds and marine mammals in ever-growing amounts of aerial imagery. Currently the major part of the processing, however, is still conducted by especially trained professionals, visually examining the images and detecting and classifying the requested subjects. This is a very tedious task, particularly when the rate of void images regularly exceeds the mark of 90%. In the content of this contribution we will present our work aiming to support the processing of aerial images by modern methods from the field of image processing. We will especially focus on the combination of local, region-based feature detection and piecewise global image segmentation for automatic detection of different sea bird species. Large image dimensions resulting from the use of medium and large-format digital cameras in aerial surveys inhibit the applicability of image processing methods based on global operations. In order to efficiently handle those image sizes and to nevertheless take advantage of globally operating segmentation algorithms, we will describe the combined usage of a simple performant feature detector based on local operations on the original image with a complex global segmentation algorithm operating on extracted sub-images. The resulting exact segmentation of possible candidates then serves as a basis for the determination of feature vectors for subsequent elimination of false candidates and for classification tasks.

  18. High-biomass sorghum yield estimate with aerial imagery

    Science.gov (United States)

    Sui, Ruixiu; Hartley, Brandon E.; Gibson, John M.; Yang, Chenghai; Thomasson, J. Alex; Searcy, Stephen W.

    2011-01-01

    To reach the goals laid out by the U.S. Government for displacing fossil fuels with biofuels, high-biomass sorghum is well-suited to achieving this goal because it requires less water per unit dry biomass and can produce very high biomass yields. In order to make biofuels economically competitive with fossil fuels it is essential to maximize production efficiency throughout the system. The goal of this study was to use remote sensing technologies to optimize the yield and harvest logistics of high-biomass sorghum with respect to production costs based on spatial variability within and among fields. Specific objectives were to compare yield to aerial multispectral imagery and develop predictive relationships. A 19.2-ha high-biomass sorghum field was selected as a study site and aerial multispectral images were acquired with a four-camera imaging system on July 17, 2009. Sorghum plant samples were collected at predetermined geographic coordinates to determine biomass yield. Aerial images were processed to find relationships between image reflectance and yield of the biomass sorghum. Results showed that sorghum biomass yield in early August was closely related (R2 = 0.76) to spectral reflectance. However, in the late season the correlations between the biomass yield and spectral reflectance were not as positive as in the early season. The eventual outcome of this work could lead to predicted-yield maps based on remotely sensed images, which could be used in developing field management practices to optimize yield and harvest logistics.

  19. Aerial Photography and Imagery, Uncorrected, Oconto County 1975 Aerial Photographs, Published in 1975, 1:9600 (1in=800ft) scale, Bay-Lake Regional Planning Commission.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Uncorrected dataset, published at 1:9600 (1in=800ft) scale, was produced all or in part from Uncorrected Imagery information as...

  20. Aerial Photography and Imagery, Ortho-Corrected, Published in 2009, SWGRC.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, was produced all or in part from Orthoimagery information as of 2009. Data by this publisher are often...

  1. Aerial Photography and Imagery, Ortho-Corrected, Published in unknown, Park County GIS Department.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, was produced all or in part from Orthoimagery information as of unknown. Data by this publisher are...

  2. Aerial Photography and Imagery, Ortho-Corrected - 2011 Digital Orthophotos - Lee County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International January 2nd and March 21st, 2011. Automatic aerial...

  3. Aerial Photography and Imagery, Ortho-Corrected, Urban areas, Published in 2009, Boone County Government.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, was produced all or in part from Orthoimagery information as of 2009. It is described as 'Urban areas'....

  4. Hurricane Gustav Aerial Photography: Rapid ResponseImagery of the Surrounding Regions After Landfall

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is of the surrounding regionsafter Hurricane Gustav made landfall. The aerial photography missions wereconducted by the NOAA Remote...

  5. Aerial Photography and Imagery, Ortho-Corrected, Published in 2005, Ashland County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, was produced all or in part from Orthoimagery information as of 2005. Data by this publisher are often...

  6. Aerial Photography and Imagery, Ortho-Corrected, Published in unknown, BRANCH COUNTY.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, was produced all or in part from Orthoimagery information as of unknown. Data by this publisher are...

  7. Aerial Photography and Imagery, Ortho-Corrected - 2011 Digital Orthophotos - Lee County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International January 2nd and March 21st, 2011. Automatic aerial...

  8. Aerial Photography and Imagery, Ortho-Corrected, Published in Not Provided, Phillips County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset as of Not Provided. Data by this publisher are often provided in State Plane coordinate system; in a...

  9. Aerial Photography and Imagery, Ortho-Corrected, Published in 2007, Towns County E911 Mapping.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, was produced all or in part from Orthoimagery information as of 2007. Data by this publisher are often...

  10. Hurricane Ike Aerial Photography: Rapid ResponseImagery of the Surrounding Regions After Landfall

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is of the surrounding regionsafter Hurricane Ike made landfall. The aerial photography missions wereconducted by the NOAA Remote...

  11. Aerial Photography and Imagery, Ortho-Corrected, Urban area resolution, Published in 2006, Mahaska County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, was produced all or in part from Orthoimagery information as of 2006. It is described as 'Urban area...

  12. LA0801 Ortho-rectified Aerial Imagery of Terrebonne and Timbalier Bays Barrier Islands, Louisiana.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — AERO-METRIC, INC. (AME) was provided aerial photographic imagery along the shoreline of Louisiana under Contract DG133C-06-CO-0055, Task Order T0006. The purpose of...

  13. APPLICABILITY EVALUATION OF OBJECT DETECTION METHOD TO SATELLITE AND AERIAL IMAGERIES

    Directory of Open Access Journals (Sweden)

    K. Kamiya

    2016-06-01

    Full Text Available Since satellite and aerial imageries are recently widely spread and frequently observed, combination of them are expected to complement spatial and temporal resolution each other. One of the prospective applications is traffic monitoring, where objects of interest, or vehicles, need to be recognized automatically. Techniques that employ object detection before object recognition can save a computational time and cost, and thus take a significant role. However, there is not enough knowledge whether object detection method can perform well on satellite and aerial imageries. In addition, it also has to be studied how characteristics of satellite and aerial imageries affect the object detection performance. This study employ binarized normed gradients (BING method that runs significantly fast and is robust to rotation and noise. For our experiments, 11-bits BGR-IR satellite imageries from WorldView-3, and BGR-color aerial imageries are used respectively, and we create thousands of ground truth samples. We conducted several experiments to compare the performances with different images, to verify whether combination of different resolution images improved the performance, and to analyze the applicability of mixing satellite and aerial imageries. The results showed that infrared band had little effect on the detection rate, that 11-bit images performed less than 8-bit images and that the better spatial resolution brought the better performance. Another result might imply that mixing higher and lower resolution images for training dataset could help detection performance. Furthermore, we found that aerial images improved the detection performance on satellite images.

  14. Applicability Evaluation of Object Detection Method to Satellite and Aerial Imageries

    Science.gov (United States)

    Kamiya, K.; Fuse, T.; Takahashi, M.

    2016-06-01

    Since satellite and aerial imageries are recently widely spread and frequently observed, combination of them are expected to complement spatial and temporal resolution each other. One of the prospective applications is traffic monitoring, where objects of interest, or vehicles, need to be recognized automatically. Techniques that employ object detection before object recognition can save a computational time and cost, and thus take a significant role. However, there is not enough knowledge whether object detection method can perform well on satellite and aerial imageries. In addition, it also has to be studied how characteristics of satellite and aerial imageries affect the object detection performance. This study employ binarized normed gradients (BING) method that runs significantly fast and is robust to rotation and noise. For our experiments, 11-bits BGR-IR satellite imageries from WorldView-3, and BGR-color aerial imageries are used respectively, and we create thousands of ground truth samples. We conducted several experiments to compare the performances with different images, to verify whether combination of different resolution images improved the performance, and to analyze the applicability of mixing satellite and aerial imageries. The results showed that infrared band had little effect on the detection rate, that 11-bit images performed less than 8-bit images and that the better spatial resolution brought the better performance. Another result might imply that mixing higher and lower resolution images for training dataset could help detection performance. Furthermore, we found that aerial images improved the detection performance on satellite images.

  15. First results for an image processing workflow for hyperspatial imagery acquired with a low-cost unmanned aerial vehicle (UAV).

    Science.gov (United States)

    Very high-resolution images from unmanned aerial vehicles (UAVs) have great potential for use in rangeland monitoring and assessment, because the imagery fills the gap between ground-based observations and remotely sensed imagery from aerial or satellite sensors. However, because UAV imagery is ofte...

  16. Aerial Photography and Imagery, Ortho-Corrected, 2004 imagery over Eureka Township and Crescent Valley, unknown resolution, Published in 2004, Eureka County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, was produced all or in part from Orthoimagery information as of 2004. It is described as '2004 imagery...

  17. Aerial Photography and Imagery, Oblique, Pictometry Oblique Imagery for the 10-County Atlanta Regional Commission Region, Published in 2007, Atlanta Regional Commission.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset, was produced all or in part from Other information as of 2007. It is described as 'Pictometry Oblique Imagery...

  18. Texture and scale in object-based analysis of subdecimeter resolution unmanned aerial vehicle (UAV) imagery

    Science.gov (United States)

    Imagery acquired with unmanned aerial vehicles (UAVs) has great potential for incorporation into natural resource monitoring protocols due to their ability to be deployed quickly and repeatedly and to fly at low altitudes. While the imagery may have high spatial resolution, the spectral resolution i...

  19. Aerial Photography and Imagery, Ortho-Corrected, Historic 1958 black and white aerial photography for Wicomico County, Maryland. Imagery was scanned from historic hard copy images and georeferenced to current imagery. This data is available via map service., Published in 2010, 1:12000 (1in=1000ft) scale, Eastern Shore Regional GIS Cooperative.

    Data.gov (United States)

    NSGIC Regional | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2010. Historic 1958 black and white aerial photography for Wicomico County, Maryland. Imagery...

  20. Aerial Photography and Imagery, Oblique, This was done along the entire Southshore of Lake Superior., Published in 2003, Bayfield County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset, was produced all or in part from Uncorrected Imagery information as of 2003. It is described as 'This was done...

  1. LA0801 Ortho-rectified Aerial Imagery of Terrebonne and Timbalier Bays Barrier Islands, Louisiana (NODC Accession 0075828)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — AERO-METRIC, INC. (AME) was provided aerial photographic imagery collected by NOAA along the shoreline of Louisiana. The purpose of the imagery was to provide...

  2. Aerial Photography and Imagery, Ortho-Corrected, Digital Aerial Photography 1970. Limited georeferencing. See metadata for additional information., Published in 1970, 1:4800 (1in=400ft) scale, Washington County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 1970. Digital Aerial Photography 1970. Limited georeferencing. See metadata for additional...

  3. Aerial Photography and Imagery, Ortho-Corrected, This data set contains imagery from the National Agriculture Imagery Program (NAIP). NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S. NAIP imagery may contain as much as 10% cloud cover per tile. This fil, Published in 2005, 1:63360 (1in=1mile) scale, University of Georgia.

    Data.gov (United States)

    NSGIC Education | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2005. This data set contains imagery from the National Agriculture Imagery Program (NAIP). NAIP...

  4. Aerial Photography and Imagery, Ortho-Corrected, 2009 Aerial Orthophotography for Iredell County, NC, Published in 2005, 1:2400 (1in=200ft) scale, Iredell County GIS.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  5. Aerial Photography and Imagery, Ortho-Corrected, Aerial Photography of Lowndes County, GA, Published in 2007, 1:1200 (1in=100ft) scale, Southern Georgia Regional Commission.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  6. Aerial Photography and Imagery, Ortho-Corrected, Olympus Aerial Survey bound, Published in 2009, 1:24000 (1in=2000ft) scale, Washington County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Other information as of 2009....

  7. Aerial Photography and Imagery, Ortho-Corrected - VOLUSIA 2006 Orthophotography

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — 2006, 6 inch Pixel Color Orthophotography - - Panchromatic, red, green, blue and near infrared imagery was acquired using the Leica ADS40 multi-spectral scanner (see...

  8. Aerial Photography and Imagery, Ortho-Corrected - VOLUSIA 2006 Orthophotography

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — 2006, 6 inch Pixel Color Orthophotography - - Panchromatic, red, green, blue and near infrared imagery was acquired using the Leica ADS40 multi-spectral scanner (see...

  9. Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring

    Science.gov (United States)

    In this paper, we examine the potential of using a small unmanned aerial vehicle (UAV) for rangeland inventory, assessment and monitoring. Imagery with 8-cm resolution was acquired over 290 ha in southwestern Idaho. We developed a semi-automated orthorectification procedure suitable for handling lar...

  10. Developing a framework for monitoring coastal habitats using aerial imagery and object-based image analysis

    DEFF Research Database (Denmark)

    Juel, Anders

    of decreased habitat dynamics exists. A valuable source of land cover changes are historical aerial imagery of which Denmark has unique datasets.This poster presents an object-based image analysis approach for mapping and monitoring af coastal habitat stucture, which integrates the high spectral resolution...

  11. Quantifying the Effect of Aerial Imagery Resolution in Automated Hydromorphological River Characterisation

    Directory of Open Access Journals (Sweden)

    Monica Rivas Casado

    2016-08-01

    Full Text Available Existing regulatory frameworks aiming to improve the quality of rivers place hydromorphology as a key factor in the assessment of hydrology, morphology and river continuity. The majority of available methods for hydromorphological characterisation rely on the identification of homogeneous areas (i.e., features of flow, vegetation and substrate. For that purpose, aerial imagery is used to identify existing features through either visual observation or automated classification techniques. There is evidence to believe that the success in feature identification relies on the resolution of the imagery used. However, little effort has yet been made to quantify the uncertainty in feature identification associated with the resolution of the aerial imagery. This paper contributes to address this gap in knowledge by contrasting results in automated hydromorphological feature identification from unmanned aerial vehicles (UAV aerial imagery captured at three resolutions (2.5 cm, 5 cm and 10 cm along a 1.4 km river reach. The results show that resolution plays a key role in the accuracy and variety of features identified, with larger identification errors observed for riffles and side bars. This in turn has an impact on the ecological characterisation of the river reach. The research shows that UAV technology could be essential for unbiased hydromorphological assessment.

  12. Incremental road discovery from aerial imagery using curvilinear spanning tree (CST) search

    Science.gov (United States)

    Wang, Guozhi; Huang, Yuchun; Xie, Rongchang; Zhang, Hongchang

    2016-10-01

    Robust detection of road network in aerial imagery is a challenging task since roads have different pavement texture, road-side surroundings, as well as grades. Roads of different grade have different curvilinear saliency in the aerial imagery. This paper is motivated to incrementally extract roads and construct the topology of the road network of aerial imagery from the higher-grade-first perspective. Inspired by the spanning tree technique, the proposed method starts from the robust extraction of the most salient road segment(s) of the road network, and incrementally connects segments of less saliency of curvilinear structure until all road segments in the network are extracted. The proposed algorithm includes: curvilinear path-based road morphological enhancement, extraction of road segments, and spanning tree search for the incremental road discovery. It is tested on a diverse set of aerial imagery acquired in the city and inter-city areas. Experimental results show that the proposed curvilinear spanning tree (CST) can detect roads efficiently and construct the topology of the road network effectively. It is promising for the change detection of the road network.

  13. Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery.

    Science.gov (United States)

    Casado, Monica Rivas; Gonzalez, Rocio Ballesteros; Kriechbaumer, Thomas; Veal, Amanda

    2015-11-04

    European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management.

  14. Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery

    Directory of Open Access Journals (Sweden)

    Monica Rivas Casado

    2015-11-01

    Full Text Available European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management.

  15. Aerial Photography and Imagery, Ortho-Corrected, Hi-Resolution Ortho Imagery, Published in 2008, 1:1200 (1in=100ft) scale, Department of Information Technology.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  16. Aerial Photography and Imagery, Ortho-Corrected, 2004 Satellite Imagery, Published in 2004, 1:12000 (1in=1000ft) scale, Anne Arundel County, OIT GIS.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:12000 (1in=1000ft) scale, was produced all or in part from Orthoimagery information as...

  17. Aerial Photography and Imagery, Ortho-Corrected, Mecklenburg County 2007 Digital Ortho Imagery, Published in 2007, 1:2400 (1in=200ft) scale, Mecklenburg County GIS.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  18. Aerial Photography and Imagery, Ortho-Corrected, County wide imagery, Published in 2007, 1:1200 (1in=100ft) scale, Norton County Appraisal Office.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  19. AUTOMATIC EXTRACTION OF BUILDING OUTLINE FROM HIGH RESOLUTION AERIAL IMAGERY

    Directory of Open Access Journals (Sweden)

    Y. Wang

    2016-06-01

    Full Text Available In this paper, a new approach for automated extraction of building boundary from high resolution imagery is proposed. The proposed approach uses both geometric and spectral properties of a building to detect and locate buildings accurately. It consists of automatic generation of high quality point cloud from the imagery, building detection from point cloud, classification of building roof and generation of building outline. Point cloud is generated from the imagery automatically using semi-global image matching technology. Buildings are detected from the differential surface generated from the point cloud. Further classification of building roof is performed in order to generate accurate building outline. Finally classified building roof is converted into vector format. Numerous tests have been done on images in different locations and results are presented in the paper.

  20. Automatic Extraction of Building Outline from High Resolution Aerial Imagery

    Science.gov (United States)

    Wang, Yandong

    2016-06-01

    In this paper, a new approach for automated extraction of building boundary from high resolution imagery is proposed. The proposed approach uses both geometric and spectral properties of a building to detect and locate buildings accurately. It consists of automatic generation of high quality point cloud from the imagery, building detection from point cloud, classification of building roof and generation of building outline. Point cloud is generated from the imagery automatically using semi-global image matching technology. Buildings are detected from the differential surface generated from the point cloud. Further classification of building roof is performed in order to generate accurate building outline. Finally classified building roof is converted into vector format. Numerous tests have been done on images in different locations and results are presented in the paper.

  1. Aerial Photography and Imagery, Ortho-Corrected, 1997 Ortho Imagery collected by Abrams Aerial Survey, Published in 1997, 1:2400 (1in=200ft) scale, CITY OF PORTAGE.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  2. Aerial Photography and Imagery, Ortho-Corrected, MFRDC has aerial imagery for Dooly, Crisp, Macon, Taylor, Schley, Marion and Webster counties., Published in 2006, 1:1200 (1in=100ft) scale, Middle Flint RDC.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Other information as of 2006....

  3. Onboard Algorithms for Data Prioritization and Summarization of Aerial Imagery

    Science.gov (United States)

    Chien, Steve A.; Hayden, David; Thompson, David R.; Castano, Rebecca

    2013-01-01

    Many current and future NASA missions are capable of collecting enormous amounts of data, of which only a small portion can be transmitted to Earth. Communications are limited due to distance, visibility constraints, and competing mission downlinks. Long missions and high-resolution, multispectral imaging devices easily produce data exceeding the available bandwidth. To address this situation computationally efficient algorithms were developed for analyzing science imagery onboard the spacecraft. These algorithms autonomously cluster the data into classes of similar imagery, enabling selective downlink of representatives of each class, and a map classifying the terrain imaged rather than the full dataset, reducing the volume of the downlinked data. A range of approaches was examined, including k-means clustering using image features based on color, texture, temporal, and spatial arrangement

  4. Geomorphic habitat units derived from 2011 aerial imagery and elevation data for the Elwha River estuary, Washington

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 03 September 2011 0.3 meter resolution Microsoft/Digital Globe aerial imagery;...

  5. Aerial Photography and Imagery, Ortho-Corrected, Published in 2005, 1:4800 (1in=400ft) scale, Sauk County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  6. Aerial Photography and Imagery, Ortho-Corrected, Published in 2008, 1:2400 (1in=200ft) scale, Sampson County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  7. Aerial Photography and Imagery, Ortho-Corrected, Published in 2008, 1:1200 (1in=100ft) scale, Warren County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  8. Aerial Photography and Imagery, Ortho-Corrected, Scheduled for new flights Spring 2010, Published in 2010, Washington County, Iowa GIS.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, was produced all or in part from Orthoimagery information as of 2010. It is described as 'Scheduled...

  9. Aerial Photography and Imagery, Oblique, Pictometry done Spring 2009, not rectified into GIS yet, Published in 2009, Jones County GIS.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset as of 2009. It is described as 'Pictometry done Spring 2009, not rectified into GIS yet'. Data by this publisher...

  10. Aerial Photography and Imagery, Oblique, Published in 2009, 1:4800 (1in=400ft) scale, Cerro Gordo County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset, published at 1:4800 (1in=400ft) scale as of 2009. Data by this publisher are often provided in State Plane...

  11. Aerial Photography and Imagery, Ortho-Corrected, Published in 2008, 1:1200 (1in=100ft) scale, Greenwood County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  12. Aerial Photography and Imagery, Oblique, Published in 2010, 1:1200 (1in=100ft) scale, Brown County, WI.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of 2010....

  13. Pseudo natural colour aerial imagery for urban and suburban mapping

    DEFF Research Database (Denmark)

    Knudsen, Thomas

    2005-01-01

    Due to their near-infrared data channel, digital airborne four-channel imagers provide a potentially good discrimination between vegetation and human-made materials, which is very useful in automated mapping. Due to their red, green and blue data channels, they also provide natural colour images......, which are very useful in traditional (manual) mapping. In this paper, an algorithm is described which provides an approximation to the spectral capabilities of the four-channel imagers by using a colour-infrared aerial photo as input. The algorithm is tailored to urban/suburban surroundings, where...

  14. Exterior Orientation Estimation of Oblique Aerial Imagery Using Vanishing Points

    Science.gov (United States)

    Verykokou, Styliani; Ioannidis, Charalabos

    2016-06-01

    In this paper, a methodology for the calculation of rough exterior orientation (EO) parameters of multiple large-scale overlapping oblique aerial images, in the case that GPS/INS information is not available (e.g., for old datasets), is presented. It consists of five main steps; (a) the determination of the overlapping image pairs and the single image in which four ground control points have to be measured; (b) the computation of the transformation parameters from every image to the coordinate reference system; (c) the rough estimation of the camera interior orientation parameters; (d) the estimation of the true horizon line and the nadir point of each image; (e) the calculation of the rough EO parameters of each image. A developed software suite implementing the proposed methodology is tested using a set of UAV multi-perspective oblique aerial images. Several tests are performed for the assessment of the errors and show that the estimated EO parameters can be used either as initial approximations for a bundle adjustment procedure or as rough georeferencing information for several applications, like 3D modelling, even by non-photogrammetrists, because of the minimal user intervention needed. Finally, comparisons with a commercial software are made, in terms of automation and correctness of the computed EO parameters.

  15. An Automated Approach to Extracting River Bank Locations from Aerial Imagery Using Image Texture

    Science.gov (United States)

    2015-11-04

    12·98) (e) FORM CANCELS AND SUPERSEOES AU. PREVIOUS VERSIONS RIVER RESEARCH AND APPLICATIONS River Res. Applic. (2013) Published online in Wiley ... Online Library (wileyonlinelibrary.com) DOI: 10.1002/rra.2701AN AUTOMATED APPROACH TO EXTRACTING RIVER BANK LOCATIONS FROM AERIAL IMAGERY USING IMAGE...Computer Science. Vol. 2. Pratt W. 1991. Digital Image Processing. John Wiley and Sons: New York. Sera J. 1983. Image Analysis and Mathematical Morphology

  16. Canopy Density Mapping on Ultracam-D Aerial Imagery in Zagros Woodlands, Iran

    Science.gov (United States)

    Erfanifard, Y.; Khodaee, Z.

    2013-09-01

    Canopy density maps express different characteristics of forest stands, especially in woodlands. Obtaining such maps by field measurements is so expensive and time-consuming. It seems necessary to find suitable techniques to produce these maps to be used in sustainable management of woodland ecosystems. In this research, a robust procedure was suggested to obtain these maps by very high spatial resolution aerial imagery. It was aimed to produce canopy density maps by UltraCam-D aerial imagery, newly taken in Zagros woodlands by Iran National Geographic Organization (NGO), in this study. A 30 ha plot of Persian oak (Quercus persica) coppice trees was selected in Zagros woodlands, Iran. The very high spatial resolution aerial imagery of the plot purchased from NGO, was classified by kNN technique and the tree crowns were extracted precisely. The canopy density was determined in each cell of different meshes with different sizes overlaid on the study area map. The accuracy of the final maps was investigated by the ground truth obtained by complete field measurements. The results showed that the proposed method of obtaining canopy density maps was efficient enough in the study area. The final canopy density map obtained by a mesh with 30 Ar (3000 m2) cell size had 80% overall accuracy and 0.61 KHAT coefficient of agreement which shows a great agreement with the observed samples. This method can also be tested in other case studies to reveal its capability in canopy density map production in woodlands.

  17. A procedure for orthorectification of sub-decimeter resolution imagery obtained with an unmanned aerial vehicle (UAV)

    Science.gov (United States)

    Digital aerial photography acquired with unmanned aerial vehicles (UAVs) has great value for resource management due to the flexibility and relatively low cost for image acquisition, and very high resolution imagery (5 cm) which allows for mapping bare soil and vegetation types, structure and patter...

  18. Pseudo natural colour aerial imagery for urban and suburban mapping

    DEFF Research Database (Denmark)

    Knudsen, Thomas

    2005-01-01

    Due to their near-infrared data channel, digital airborne four-channel imagers provide a potentially good discrimination between vegetation and human-made materials, which is very useful in automated mapping. Due to their red, green and blue data channels, they also provide natural colour images......, which are very useful in traditional (manual) mapping. In this paper, an algorithm is described which provides an approximation to the spectral capabilities of the four-channel imagers by using a colour-infrared aerial photo as input. The algorithm is tailored to urban/suburban surroundings, where...... the quality of the generated (pseudo) natural colour images are fully acceptable for manual mapping. This brings the combined availability of near-infrared and (pseudo) natural colours within reach for mapping projects based on traditional photogrammetry, which is valuable since traditional analytical cameras...

  19. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Leon County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  20. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Taylor County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  1. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Palm Beach County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  2. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Madison County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  3. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Indian River County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  4. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Volusia County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  5. Aerial Photography and Imagery, Ortho-Corrected - FL Bay Ortho Imagery Project Spring 2013

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This file references a single orthogonal imagery tile produced from nadir images captured by Pictometry International during the period of December 30th, 2012 and...

  6. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Okeechobee County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  7. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Walton County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  8. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Hillsborough County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  9. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Sumter County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  10. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Duval County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  11. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Hernando County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  12. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Baker County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  13. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Hernando County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  14. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Pasco County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  15. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Miami-Dade County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  16. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Washington County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  17. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Glades County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  18. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Martin County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  19. Aerial Photography and Imagery, Ortho-Corrected - FL Bay Ortho Imagery Project Spring 2013

    Data.gov (United States)

    NSGIC Education | GIS Inventory — This file references a single orthogonal imagery tile produced from nadir images captured by Pictometry International during the period of December 30th, 2012 and...

  20. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Brevard County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  1. Aerial Photography and Imagery, Ortho-Corrected - 2010 NAIP Imagery - Bradford County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — This data set contains polygons delineating the seams boundary between acquired imagery used in the creation of DOQQs and compressed county mosaic (CCM). The DOQQ...

  2. Unmanned Aerial Vehicles Produce High-Resolution Seasonally-Relevant Imagery for Classifying Wetland Vegetation

    Science.gov (United States)

    Marcaccio, J. V.; Markle, C. E.; Chow-Fraser, P.

    2015-08-01

    With recent advances in technology, personal aerial imagery acquired with unmanned aerial vehicles (UAVs) has transformed the way ecologists can map seasonal changes in wetland habitat. Here, we use a multi-rotor (consumer quad-copter, the DJI Phantom 2 Vision+) UAV to acquire a high-resolution (determine if a UAV image and SWOOP (Southwestern Ontario Orthoimagery Project) image (collected in spring 2010) differ in their classification of type of dominant vegetation type and percent cover of three plant classes: submerged aquatic vegetation, floating aquatic vegetation, and emergent vegetation. The UAV imagery was more accurate than available SWOOP imagery for mapping percent cover of submergent and floating vegetation categories, but both were able to accurately determine the dominant vegetation type and percent cover of emergent vegetation. Our results underscore the value and potential for affordable UAVs (complete quad-copter system < 3,000 CAD) to revolutionize the way ecologists obtain imagery and conduct field research. In Canada, new UAV regulations make this an easy and affordable way to obtain multiple high-resolution images of small (< 1.0 km2) wetlands, or portions of larger wetlands throughout a year.

  3. Vehicle detection from very-high-resolution (VHR) aerial imagery using attribute belief propagation (ABP)

    Science.gov (United States)

    Wang, Yanli; Li, Ying; Zhang, Li; Huang, Yuchun

    2016-10-01

    With the popularity of very-high-resolution (VHR) aerial imagery, the shape, color, and context attribute of vehicles are better characterized. Due to the various road surroundings and imaging conditions, vehicle attributes could be adversely affected so that vehicle is mistakenly detected or missed. This paper is motivated to robustly extract the rich attribute feature for detecting the vehicles of VHR imagery under different scenarios. Based on the hierarchical component tree of vehicle context, attribute belief propagation (ABP) is proposed to detect salient vehicles from the statistical perspective. With the Max-tree data structure, the multi-level component tree around the road network is efficiently created. The spatial relationship between vehicle and its belonging context is established with the belief definition of vehicle attribute. To effectively correct single-level belief error, the inter-level belief linkages enforce consistency of belief assignment between corresponding components at different levels. ABP starts from an initial set of vehicle belief calculated by vehicle attribute, and then iterates through each component by applying inter-level belief passing until convergence. The optimal value of vehicle belief of each component is obtained via minimizing its belief function iteratively. The proposed algorithm is tested on a diverse set of VHR imagery acquired in the city and inter-city areas of the West and South China. Experimental results show that the proposed algorithm can detect vehicle efficiently and suppress the erroneous effectively. The proposed ABP framework is promising to robustly classify the vehicles from VHR Aerial imagery.

  4. Aerial Photography and Imagery, Ortho-Corrected, 4 inch aerial photography (color, infrared, and color oblique) in urban areas, 1 foot in national forest, Published in 2006, 1:600 (1in=50ft) scale, Los Angeles County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2006. 4 inch aerial photography (color, infrared, and color oblique) in urban areas, 1 foot in...

  5. Vectorization of Road Data Extracted from Aerial and Uav Imagery

    Science.gov (United States)

    Bulatov, Dimitri; Häufel, Gisela; Pohl, Melanie

    2016-06-01

    Road databases are essential instances of urban infrastructure. Therefore, automatic road detection from sensor data has been an important research activity during many decades. Given aerial images in a sufficient resolution, dense 3D reconstruction can be performed. Starting at a classification result of road pixels from combined elevation and optical data, we present in this paper a fivestep procedure for creating vectorized road networks. These main steps of the algorithm are: preprocessing, thinning, polygonization, filtering, and generalization. In particular, for the generalization step, which represents the principal area of innovation, two strategies are presented. The first strategy corresponds to a modification of the Douglas-Peucker-algorithm in order to reduce the number of vertices while the second strategy allows a smoother representation of street windings by Bezir curves, which results in reduction - to a decimal power - of the total curvature defined for the dataset. We tested our approach on three datasets with different complexity. The quantitative assessment of the results was performed by means of shapefiles from OpenStreetMap data. For a threshold of 6 m, completeness and correctness values of up to 85% were achieved.

  6. Aerial Photography and Imagery, Ortho-Corrected, 1-meter natural color imagery imagery over 13 of 15 counties (excluding Maricopa and Cochise), Published in 2005, 1:12000 (1in=1000ft) scale, Arizona State Cartographer's Office.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:12000 (1in=1000ft) scale, was produced all or in part from Orthoimagery information as...

  7. Aerial Photography and Imagery, Oblique, This data set was acquired through a federal grant with Pictometry International. The imagery is either 4" or 9" resolution., Published in 2011, Not Applicable scale, Chippewa County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Oblique dataset current as of 2011. This data set was acquired through a federal grant with Pictometry International. The imagery is...

  8. Effective delineation of urban flooded areas based on aerial ortho-photo imagery

    Science.gov (United States)

    Zhang, Ying; Guindon, Bert; Raymond, Don; Hong, Gang

    2016-10-01

    The combination of rapid global urban growth and climate change has resulted in increased occurrence of major urban flood events across the globe. The distribution of flooded area is one of the key information layers for applications of emergency planning and response management. While SAR systems and technologies have been widely used for flood area delineation, radar images suffer from range ambiguities arising from corner reflection effects and shadowing in dense urban settings. A new mapping framework is proposed for the extraction and quantification of flood extent based on aerial optical multi-spectral imagery and ancillary data. This involves first mapping of flood areas directly visible to the sensor. Subsequently, the complete area of submergence is estimated from this initial mapping and inference techniques based on baseline data such as land cover and GIS information such as available digital elevation models. The methodology has been tested and proven effective using aerial photography for the case of the 2013 flood in Calgary, Canada.

  9. Environmental waste site characterization utilizing aerial photographs and satellite imagery: Three sites in New Mexico, USA

    Energy Technology Data Exchange (ETDEWEB)

    Van Eeckhout, E.; Pope, P.; Becker, N.; Wells, B. [Los Alamos National Lab., NM (United States); Lewis, A.; David, N. [Environmental Research Inst. of Michigan, Santa Fe, NM (United States)

    1996-04-01

    The proper handling and characterization of past hazardous waste sites is becoming more and more important as world population extends into areas previously deemed undesirable. Historical photographs, past records, current aerial satellite imagery can play an important role in characterizing these sites. These data provide clear insight into defining problem areas which can be surface samples for further detail. Three such areas are discussed in this paper: (1) nuclear wastes buried in trenches at Los Alamos National Laboratory, (2) surface dumping at one site at Los Alamos National Laboratory, and (3) the historical development of a municipal landfill near Las Cruces, New Mexico.

  10. Estimation of walrus populations on sea ice with infrared imagery and aerial photography

    Science.gov (United States)

    Udevitz, M.S.; Burn, D.M.; Webber, M.A.

    2008-01-01

    Population sizes of ice-associated pinnipeds have often been estimated with visual or photographic aerial surveys, but these methods require relatively slow speeds and low altitudes, limiting the area they can cover. Recent developments in infrared imagery and its integration with digital photography could allow substantially larger areas to be surveyed and more accurate enumeration of individuals, thereby solving major problems with previous survey methods. We conducted a trial survey in April 2003 to estimate the number of Pacific walruses (Odobenus rosmarus divergens) hauled out on sea ice around St. Lawrence Island, Alaska. The survey used high altitude infrared imagery to detect groups of walruses on strip transects. Low altitude digital photography was used to determine the number of walruses in a sample of detected groups and calibrate the infrared imagery for estimating the total number of walruses. We propose a survey design incorporating this approach with satellite radio telemetry to estimate the proportion of the population in the water and additional low-level flights to estimate the proportion of the hauled-out population in groups too small to be detected in the infrared imagery. We believe that this approach offers the potential for obtaining reliable population estimates for walruses and other ice-associated pinnipeds. ?? 2007 by the Society for Marine Mammalogy.

  11. A semi-automated single day image differencing technique to identify animals in aerial imagery.

    Directory of Open Access Journals (Sweden)

    Pat Terletzky

    Full Text Available Our research presents a proof-of-concept that explores a new and innovative method to identify large animals in aerial imagery with single day image differencing. We acquired two aerial images of eight fenced pastures and conducted a principal component analysis of each image. We then subtracted the first principal component of the two pasture images followed by heuristic thresholding to generate polygons. The number of polygons represented the number of potential cattle (Bos taurus and horses (Equus caballus in the pasture. The process was considered semi-automated because we were not able to automate the identification of spatial or spectral thresholding values. Imagery was acquired concurrently with ground counts of animal numbers. Across the eight pastures, 82% of the animals were correctly identified, mean percent commission was 53%, and mean percent omission was 18%. The high commission error was due to small mis-alignments generated from image-to-image registration, misidentified shadows, and grouping behavior of animals. The high probability of correctly identifying animals suggests short time interval image differencing could provide a new technique to enumerate wild ungulates occupying grassland ecosystems, especially in isolated or difficult to access areas. To our knowledge, this was the first attempt to use standard change detection techniques to identify and enumerate large ungulates.

  12. A semi-automated single day image differencing technique to identify animals in aerial imagery.

    Science.gov (United States)

    Terletzky, Pat; Ramsey, Robert Douglas

    2014-01-01

    Our research presents a proof-of-concept that explores a new and innovative method to identify large animals in aerial imagery with single day image differencing. We acquired two aerial images of eight fenced pastures and conducted a principal component analysis of each image. We then subtracted the first principal component of the two pasture images followed by heuristic thresholding to generate polygons. The number of polygons represented the number of potential cattle (Bos taurus) and horses (Equus caballus) in the pasture. The process was considered semi-automated because we were not able to automate the identification of spatial or spectral thresholding values. Imagery was acquired concurrently with ground counts of animal numbers. Across the eight pastures, 82% of the animals were correctly identified, mean percent commission was 53%, and mean percent omission was 18%. The high commission error was due to small mis-alignments generated from image-to-image registration, misidentified shadows, and grouping behavior of animals. The high probability of correctly identifying animals suggests short time interval image differencing could provide a new technique to enumerate wild ungulates occupying grassland ecosystems, especially in isolated or difficult to access areas. To our knowledge, this was the first attempt to use standard change detection techniques to identify and enumerate large ungulates.

  13. Influence of Gsd for 3d City Modeling and Visualization from Aerial Imagery

    Science.gov (United States)

    Alrajhi, Muhamad; Alam, Zafare; Afroz Khan, Mohammad; Alobeid, Abdalla

    2016-06-01

    Ministry of Municipal and Rural Affairs (MOMRA), aims to establish solid infrastructure required for 3D city modelling, for decision making to set a mark in urban development. MOMRA is responsible for the large scale mapping 1:1,000; 1:2,500; 1:10,000 and 1:20,000 scales for 10cm, 20cm and 40 GSD with Aerial Triangulation data. As 3D city models are increasingly used for the presentation exploration, and evaluation of urban and architectural designs. Visualization capabilities and animations support of upcoming 3D geo-information technologies empower architects, urban planners, and authorities to visualize and analyze urban and architectural designs in the context of the existing situation. To make use of this possibility, first of all 3D city model has to be created for which MOMRA uses the Aerial Triangulation data and aerial imagery. The main concise for 3D city modelling in the Kingdom of Saudi Arabia exists due to uneven surface and undulations. Thus real time 3D visualization and interactive exploration support planning processes by providing multiple stakeholders such as decision maker, architects, urban planners, authorities, citizens or investors with a three - dimensional model. Apart from advanced visualization, these 3D city models can be helpful for dealing with natural hazards and provide various possibilities to deal with exotic conditions by better and advanced viewing technological infrastructure. Riyadh on one side is 5700m above sea level and on the other hand Abha city is 2300m, this uneven terrain represents a drastic change of surface in the Kingdom, for which 3D city models provide valuable solutions with all possible opportunities. In this research paper: influence of different GSD (Ground Sample Distance) aerial imagery with Aerial Triangulation is used for 3D visualization in different region of the Kingdom, to check which scale is more sophisticated for obtaining better results and is cost manageable, with GSD (7.5cm, 10cm, 20cm and 40cm

  14. INFLUENCE OF GSD FOR 3D CITY MODELING AND VISUALIZATION FROM AERIAL IMAGERY

    Directory of Open Access Journals (Sweden)

    M. Alrajhi

    2016-06-01

    Full Text Available Ministry of Municipal and Rural Affairs (MOMRA, aims to establish solid infrastructure required for 3D city modelling, for decision making to set a mark in urban development. MOMRA is responsible for the large scale mapping 1:1,000; 1:2,500; 1:10,000 and 1:20,000 scales for 10cm, 20cm and 40 GSD with Aerial Triangulation data. As 3D city models are increasingly used for the presentation exploration, and evaluation of urban and architectural designs. Visualization capabilities and animations support of upcoming 3D geo-information technologies empower architects, urban planners, and authorities to visualize and analyze urban and architectural designs in the context of the existing situation. To make use of this possibility, first of all 3D city model has to be created for which MOMRA uses the Aerial Triangulation data and aerial imagery. The main concise for 3D city modelling in the Kingdom of Saudi Arabia exists due to uneven surface and undulations. Thus real time 3D visualization and interactive exploration support planning processes by providing multiple stakeholders such as decision maker, architects, urban planners, authorities, citizens or investors with a three – dimensional model. Apart from advanced visualization, these 3D city models can be helpful for dealing with natural hazards and provide various possibilities to deal with exotic conditions by better and advanced viewing technological infrastructure. Riyadh on one side is 5700m above sea level and on the other hand Abha city is 2300m, this uneven terrain represents a drastic change of surface in the Kingdom, for which 3D city models provide valuable solutions with all possible opportunities. In this research paper: influence of different GSD (Ground Sample Distance aerial imagery with Aerial Triangulation is used for 3D visualization in different region of the Kingdom, to check which scale is more sophisticated for obtaining better results and is cost manageable, with GSD (7.5cm

  15. Aerial Photography and Imagery, Ortho-Corrected, Black & White imagery flown in 2000 leaf-off season, countywide 400 scale imagery, 200 scale imagery in fast growing portion of the county in the south west, and 100 scale imagery in the towns and Lake Royale., Published in 2000, 1:4800 (1in=400ft) scale, Franklin County G.I.S..

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  16. Application of Unmanned Aerial Systems in Spatial Downscaling of Landsat VIR imageries of Agricultural Fields

    Science.gov (United States)

    Torres, A.; Hassan Esfahani, L.; Ebtehaj, A.; McKee, M.

    2016-12-01

    While coarse space-time resolution of satellite observations in visible to near infrared (VIR) is a serious limiting factor for applications in precision agriculture, high resolution remotes sensing observation by the Unmanned Aerial Systems (UAS) systems are also site-specific and still practically restrictive for widespread applications in precision agriculture. We present a modern spatial downscaling approach that relies on new sparse approximation techniques. The downscaling approach learns from a large set of coincident low- and high-resolution satellite and UAS observations to effectively downscale the satellite imageries in VIR bands. We focus on field experiments using the AggieAirTM platform and Landsat 7 ETM+ and Landsat 8 OLI observations obtained in an intensive field campaign in 2013 over an agriculture field in Scipio, Utah. The results show that the downscaling methods can effectively increase the resolution of Landsat VIR imageries by the order of 2 to 4 from 30 m to 15 and 7.5 m, respectively. Specifically, on average, the downscaling method reduces the root mean squared errors up to 26%, considering bias corrected AggieAir imageries as the reference.

  17. Spatially explicit rangeland erosion monitoring using high-resolution digital aerial imagery

    Science.gov (United States)

    Gillan, Jeffrey K.; Karl, Jason W.; Barger, Nichole N.; Elaksher, Ahmed; Duniway, Michael C.

    2016-01-01

    Nearly all of the ecosystem services supported by rangelands, including production of livestock forage, carbon sequestration, and provisioning of clean water, are negatively impacted by soil erosion. Accordingly, monitoring the severity, spatial extent, and rate of soil erosion is essential for long-term sustainable management. Traditional field-based methods of monitoring erosion (sediment traps, erosion pins, and bridges) can be labor intensive and therefore are generally limited in spatial intensity and/or extent. There is a growing effort to monitor natural resources at broad scales, which is driving the need for new soil erosion monitoring tools. One remote-sensing technique that can be used to monitor soil movement is a time series of digital elevation models (DEMs) created using aerial photogrammetry methods. By geographically coregistering the DEMs and subtracting one surface from the other, an estimate of soil elevation change can be created. Such analysis enables spatially explicit quantification and visualization of net soil movement including erosion, deposition, and redistribution. We constructed DEMs (12-cm ground sampling distance) on the basis of aerial photography immediately before and 1 year after a vegetation removal treatment on a 31-ha Piñon-Juniper woodland in southeastern Utah to evaluate the use of aerial photography in detecting soil surface change. On average, we were able to detect surface elevation change of ± 8−9cm and greater, which was sufficient for the large amount of soil movement exhibited on the study area. Detecting more subtle soil erosion could be achieved using the same technique with higher-resolution imagery from lower-flying aircraft such as unmanned aerial vehicles. DEM differencing and process-focused field methods provided complementary information and a more complete assessment of soil loss and movement than any single technique alone. Photogrammetric DEM differencing could be used as a technique to

  18. Vegetation habitat units derived from 2011 aerial imagery and field data for the Elwha River estuary, Washington

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Estuary vegetation cover delineated from 3 September 2011 0.3-meter-resolution aerial imagery (Microsoft/Digital Globe) at a scale of 1:1500. Image date of 3-Sep...

  19. Historical aerial imagery reveals rapid frontal retreat following the 1920’s warming in southeast Greenland

    DEFF Research Database (Denmark)

    Bjørk, Anders Anker; Kjær, Kurt H.; Korsgaard, Niels Jákup;

    sea surface and air temperatures. However, little is known about the long term glacier history prior to the satellite era. Here we show a unique record of the frontal history of 132 glaciers in southeast Greenland based on historical aerial and satellite imagery from 1933 to 2010. Our results...

  20. Assessment of the Quality of Digital Terrain Model Produced from Unmanned Aerial System Imagery

    Science.gov (United States)

    Kosmatin Fras, M.; Kerin, A.; Mesarič, M.; Peterman, V.; Grigillo, D.

    2016-06-01

    Production of digital terrain model (DTM) is one of the most usual tasks when processing photogrammetric point cloud generated from Unmanned Aerial System (UAS) imagery. The quality of the DTM produced in this way depends on different factors: the quality of imagery, image orientation and camera calibration, point cloud filtering, interpolation methods etc. However, the assessment of the real quality of DTM is very important for its further use and applications. In this paper we first describe the main steps of UAS imagery acquisition and processing based on practical test field survey and data. The main focus of this paper is to present the approach to DTM quality assessment and to give a practical example on the test field data. For data processing and DTM quality assessment presented in this paper mainly the in-house developed computer programs have been used. The quality of DTM comprises its accuracy, density, and completeness. Different accuracy measures like RMSE, median, normalized median absolute deviation and their confidence interval, quantiles are computed. The completeness of the DTM is very often overlooked quality parameter, but when DTM is produced from the point cloud this should not be neglected as some areas might be very sparsely covered by points. The original density is presented with density plot or map. The completeness is presented by the map of point density and the map of distances between grid points and terrain points. The results in the test area show great potential of the DTM produced from UAS imagery, in the sense of detailed representation of the terrain as well as good height accuracy.

  1. Unsupervised building detection from irregularly spaced LiDAR and aerial imagery

    Science.gov (United States)

    Shorter, Nicholas Sven

    As more data sources containing 3-D information are becoming available, an increased interest in 3-D imaging has emerged. Among these is the 3-D reconstruction of buildings and other man-made structures. A necessary preprocessing step is the detection and isolation of individual buildings that subsequently can be reconstructed in 3-D using various methodologies. Applications for both building detection and reconstruction have commercial use for urban planning, network planning for mobile communication (cell phone tower placement), spatial analysis of air pollution and noise nuisances, microclimate investigations, geographical information systems, security services and change detection from areas affected by natural disasters. Building detection and reconstruction are also used in the military for automatic target recognition and in entertainment for virtual tourism. Previously proposed building detection and reconstruction algorithms solely utilized aerial imagery. With the advent of Light Detection and Ranging (LiDAR) systems providing elevation data, current algorithms explore using captured LiDAR data as an additional feasible source of information. Additional sources of information can lead to automating techniques (alleviating their need for manual user intervention) as well as increasing their capabilities and accuracy. Several building detection approaches surveyed in the open literature have fundamental weaknesses that hinder their use; such as requiring multiple data sets from different sensors, mandating certain operations to be carried out manually, and limited functionality to only being able to detect certain types of buildings. In this work, a building detection system is proposed and implemented which strives to overcome the limitations seen in existing techniques. The developed framework is flexible in that it can perform building detection from just LiDAR data (first or last return), or just nadir, color aerial imagery. If data from both LiDAR and

  2. Mapping snow depth in complex alpine terrain with close range aerial imagery - estimating the spatial uncertainties of repeat autonomous aerial surveys over an active rock glacier

    Science.gov (United States)

    Goetz, Jason; Marcer, Marco; Bodin, Xavier; Brenning, Alexander

    2017-04-01

    Snow depth mapping in open areas using close range aerial imagery is just one of the many cases where developments in structure-from-motion and multi-view-stereo (SfM-MVS) 3D reconstruction techniques have been applied for geosciences - and with good reason. Our ability to increase the spatial resolution and frequency of observations may allow us to improve our understanding of how snow depth distribution varies through space and time. However, to ensure accurate snow depth observations from close range sensing we must adequately characterize the uncertainty related to our measurement techniques. In this study, we explore the spatial uncertainties of snow elevation models for estimation of snow depth in a complex alpine terrain from close range aerial imagery. We accomplish this by conducting repeat autonomous aerial surveys over a snow-covered active-rock glacier located in the French Alps. The imagery obtained from each flight of an unmanned aerial vehicle (UAV) is used to create an individual digital elevation model (DEM) of the snow surface. As result, we obtain multiple DEMs of the snow surface for the same site. These DEMs are obtained from processing the imagery with the photogrammetry software Agisoft Photoscan. The elevation models are also georeferenced within Photoscan using the geotagged imagery from an onboard GNSS in combination with ground targets placed around the rock glacier, which have been surveyed with highly accurate RTK-GNSS equipment. The random error associated with multi-temporal DEMs of the snow surface is estimated from the repeat aerial survey data. The multiple flights are designed to follow the same flight path and altitude above the ground to simulate the optimal conditions of repeat survey of the site, and thus try to estimate the maximum precision associated with our snow-elevation measurement technique. The bias of the DEMs is assessed with RTK-GNSS survey observations of the snow surface elevation of the area on and surrounding

  3. Extracting Semantically Annotated 3d Building Models with Textures from Oblique Aerial Imagery

    Science.gov (United States)

    Frommholz, D.; Linkiewicz, M.; Meissner, H.; Dahlke, D.; Poznanska, A.

    2015-03-01

    This paper proposes a method for the reconstruction of city buildings with automatically derived textures that can be directly used for façade element classification. Oblique and nadir aerial imagery recorded by a multi-head camera system is transformed into dense 3D point clouds and evaluated statistically in order to extract the hull of the structures. For the resulting wall, roof and ground surfaces high-resolution polygonal texture patches are calculated and compactly arranged in a texture atlas without resampling. The façade textures subsequently get analyzed by a commercial software package to detect possible windows whose contours are projected into the original oriented source images and sparsely ray-casted to obtain their 3D world coordinates. With the windows being reintegrated into the previously extracted hull the final building models are stored as semantically annotated CityGML "LOD-2.5" objects.

  4. Accurate Estimation of Orientation Parameters of Uav Images Through Image Registration with Aerial Oblique Imagery

    Science.gov (United States)

    Onyango, F. A.; Nex, F.; Peter, M. S.; Jende, P.

    2017-05-01

    Unmanned Aerial Vehicles (UAVs) have gained popularity in acquiring geotagged, low cost and high resolution images. However, the images acquired by UAV-borne cameras often have poor georeferencing information, because of the low quality on-board Global Navigation Satellite System (GNSS) receiver. In addition, lightweight UAVs have a limited payload capacity to host a high quality on-board Inertial Measurement Unit (IMU). Thus, orientation parameters of images acquired by UAV-borne cameras may not be very accurate. Poorly georeferenced UAV images can be correctly oriented using accurately oriented airborne images capturing a similar scene by finding correspondences between the images. This is not a trivial task considering the image pairs have huge variations in scale, perspective and illumination conditions. This paper presents a procedure to successfully register UAV and aerial oblique imagery. The proposed procedure implements the use of the AKAZE interest operator for feature extraction in both images. Brute force is implemented to find putative correspondences and later on Lowe's ratio test (Lowe, 2004) is used to discard a significant number of wrong matches. In order to filter out the remaining mismatches, the putative correspondences are used in the computation of multiple homographies, which aid in the reduction of outliers significantly. In order to increase the number and improve the quality of correspondences, the impact of pre-processing the images using the Wallis filter (Wallis, 1974) is investigated. This paper presents the test results of different scenarios and the respective accuracies compared to a manual registration of the finally computed fundamental and essential matrices that encode the orientation parameters of the UAV images with respect to the aerial images.

  5. ACCURATE ESTIMATION OF ORIENTATION PARAMETERS OF UAV IMAGES THROUGH IMAGE REGISTRATION WITH AERIAL OBLIQUE IMAGERY

    Directory of Open Access Journals (Sweden)

    F. A. Onyango

    2017-05-01

    Full Text Available Unmanned Aerial Vehicles (UAVs have gained popularity in acquiring geotagged, low cost and high resolution images. However, the images acquired by UAV-borne cameras often have poor georeferencing information, because of the low quality on-board Global Navigation Satellite System (GNSS receiver. In addition, lightweight UAVs have a limited payload capacity to host a high quality on-board Inertial Measurement Unit (IMU. Thus, orientation parameters of images acquired by UAV-borne cameras may not be very accurate. Poorly georeferenced UAV images can be correctly oriented using accurately oriented airborne images capturing a similar scene by finding correspondences between the images. This is not a trivial task considering the image pairs have huge variations in scale, perspective and illumination conditions. This paper presents a procedure to successfully register UAV and aerial oblique imagery. The proposed procedure implements the use of the AKAZE interest operator for feature extraction in both images. Brute force is implemented to find putative correspondences and later on Lowe’s ratio test (Lowe, 2004 is used to discard a significant number of wrong matches. In order to filter out the remaining mismatches, the putative correspondences are used in the computation of multiple homographies, which aid in the reduction of outliers significantly. In order to increase the number and improve the quality of correspondences, the impact of pre-processing the images using the Wallis filter (Wallis, 1974 is investigated. This paper presents the test results of different scenarios and the respective accuracies compared to a manual registration of the finally computed fundamental and essential matrices that encode the orientation parameters of the UAV images with respect to the aerial images.

  6. Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses

    Directory of Open Access Journals (Sweden)

    Yuhua Xu

    2016-03-01

    Full Text Available The mosaicking of Unmanned Aerial Vehicle (UAV imagery usually requires information from additional sensors, such as Global Position System (GPS and Inertial Measurement Unit (IMU, to facilitate direct orientation, or 3D reconstruction approaches (e.g., structure-from-motion to recover the camera poses. In this paper, we propose a novel mosaicking method for UAV imagery in which neither direct nor indirect orientation procedures are required. Inspired by the embedded deformation model, a widely used non-rigid mesh deformation model, we present a novel objective function for image mosaicking. Firstly, we construct a feature correspondence energy term that minimizes the sum of the squared distances between matched feature pairs to align the images geometrically. Secondly, we model a regularization term that constrains the image transformation parameters directly by keeping all transformations as rigid as possible to avoid global distortion in the final mosaic. Experimental results presented herein demonstrate that the accuracy of our method is twice as high as an existing (purely image-based approach, with the associated benefits of significantly faster processing times and improved robustness with respect to reference image selection.

  7. Deploying a quantum annealing processor to detect tree cover in aerial imagery of California

    Science.gov (United States)

    Basu, Saikat; Ganguly, Sangram; Michaelis, Andrew; Mukhopadhyay, Supratik; Nemani, Ramakrishna R.

    2017-01-01

    Quantum annealing is an experimental and potentially breakthrough computational technology for handling hard optimization problems, including problems of computer vision. We present a case study in training a production-scale classifier of tree cover in remote sensing imagery, using early-generation quantum annealing hardware built by D-wave Systems, Inc. Beginning within a known boosting framework, we train decision stumps on texture features and vegetation indices extracted from four-band, one-meter-resolution aerial imagery from the state of California. We then impose a regulated quadratic training objective to select an optimal voting subset from among these stumps. The votes of the subset define the classifier. For optimization, the logical variables in the objective function map to quantum bits in the hardware device, while quadratic couplings encode as the strength of physical interactions between the quantum bits. Hardware design limits the number of couplings between these basic physical entities to five or six. To account for this limitation in mapping large problems to the hardware architecture, we propose a truncation and rescaling of the training objective through a trainable metaparameter. The boosting process on our basic 108- and 508-variable problems, thus constituted, returns classifiers that incorporate a diverse range of color- and texture-based metrics and discriminate tree cover with accuracies as high as 92% in validation and 90% on a test scene encompassing the open space preserves and dense suburban build of Mill Valley, CA. PMID:28241028

  8. Aerial Photography and Imagery, Ortho-Corrected, Color and NIR aerial orthoimagery 2006-07, Published in 2007, 1:2400 (1in=200ft) scale, Office of Management and Budget.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  9. Aerial Photography and Imagery, Ortho-Corrected, Aerial Photography for Emporia & Metro Planning Area, Published in 2007, 1:600 (1in=50ft) scale, City of Emporia/Lyon County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  10. Aerial Photography and Imagery, Ortho-Corrected, Lyon County Aerial Photography Rural:1', Black and White; Cities: 6", Color, Published in 2003, 1:4800 (1in=400ft) scale, City of Emporia/Lyon County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  11. Aerial Photography and Imagery, Ortho-Corrected, 4 inch aerial photography (color, infrared, and color oblique) in urban areas, 1 foot in national forest, Published in 2006, 1:600 (1in=50ft) scale, County of Los Angeles.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  12. The Photo-Mosaic Assistant: Incorporating Historic Aerial Imagery into Modern Research Projects

    Science.gov (United States)

    Flathers, E.

    2013-12-01

    One challenge that researchers face as data organization and analysis shift into the digital realm is the incorporation of 'dirty' data from analog back-catalogs into current projects. Geospatial data collections in university libraries, government data repositories, and private industry contain historic data such as aerial photographs that may be stored as negatives, prints, and as scanned digital image files. A typical aerial imagery series is created by taking photos of the ground from an aircraft along a series of parallel flight lines. The raw photos can be assembled into a mosaic that represents the full geographic area of the collection, but each photo suffers from individual distortion according to the attitude and altitude of the collecting aircraft at the moment of acquisition, so there is a process of orthorectification needed in order to produce a planimetric composite image that can be used to accurately refer to locations on the ground. Historic aerial photo collections often need significant preparation for consumption by a GIS: they may need to be digitized, often lack any explicit spatial coordinates, and may not include information about flight line patterns. Many collections lack even such basic information as index numbers for the photos, so it may be unclear in what order the photos were acquired. When collections contain large areas of, for example, forest or agricultural land, any given photo may have few visual cues to assist in relating it to the other photos or to an area on the ground. The Photo-Mosaic Assistant (PMA) is a collection of tools designed to assist in the organization of historic aerial photo collections and the preparation of collections for orthorectification and use in modern research applications. The first tool is a light table application that allows a user to take advantage of visual cues within photos to organize and explore the collection, potentially building a rough image mosaic by hand. The second tool is a set of

  13. Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV Imagery

    Directory of Open Access Journals (Sweden)

    Arko Lucieer

    2012-05-01

    Full Text Available Sensor miniaturisation, improved battery technology and the availability of low-cost yet advanced Unmanned Aerial Vehicles (UAV have provided new opportunities for environmental remote sensing. The UAV provides a platform for close-range aerial photography. Detailed imagery captured from micro-UAV can produce dense point clouds using multi-view stereopsis (MVS techniques combining photogrammetry and computer vision. This study applies MVS techniques to imagery acquired from a multi-rotor micro-UAV of a natural coastal site in southeastern Tasmania, Australia. A very dense point cloud ( < 1–3 cm point spacing is produced in an arbitrary coordinate system using full resolution imagery, whereas other studies usually downsample the original imagery. The point cloud is sparse in areas of complex vegetation and where surfaces have a homogeneous texture. Ground control points collected with Differential Global Positioning System (DGPS are identified and used for georeferencing via a Helmert transformation. This study compared georeferenced point clouds to a Total Station survey in order to assess and quantify their geometric accuracy. The results indicate that a georeferenced point cloud accurate to 25–40 mm can be obtained from imagery acquired from 50 m. UAV-based image capture provides the spatial and temporal resolution required to map and monitor natural landscapes. This paper assesses the accuracy of the generated point clouds based on field survey points. Based on our key findings we conclude that sub-decimetre terrain change (in this case coastal erosion can be monitored.

  14. Aerial Photography and Imagery, Ortho-Corrected, Published in 2005, 1:4800 (1in=400ft) scale, County of Grant.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  15. Aerial Photography and Imagery, Ortho-Corrected, Published in 2007, 1:2400 (1in=200ft) scale, Aiken County Govt.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  16. Aerial Photography and Imagery, Ortho-Corrected, National Agricultural Inventory Program - Orthoimagery Statewide, Published in 2005, 1:24000 (1in=2000ft) scale, Office of Shared Solutions.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Orthoimagery information as...

  17. Aerial Photography and Imagery, Ortho-Corrected, Published in 2007, 1:2400 (1in=200ft) scale, Chautauqua County/Elk County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  18. Aerial Photography and Imagery, Ortho-Corrected, June 2007 NAIP DOQQ, Published in 2007, 1:12000 (1in=1000ft) scale, Cochise County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:12000 (1in=1000ft) scale, was produced all or in part from Orthoimagery information as...

  19. Aerial Photography and Imagery, Ortho-Corrected, Published in 2008, 1:2400 (1in=200ft) scale, City of Benson, Arizona.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  20. Aerial Photography and Imagery, Ortho-Corrected, Allegany County 1997 Orthophotography, Published in 1997, 1:2400 (1in=200ft) scale, Allegany County Government.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  1. Aerial Photography and Imagery, Ortho-Corrected, Published in 2005, 1:1200 (1in=100ft) scale, Walker County Government.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  2. Aerial Photography and Imagery, Ortho-Corrected, ortho05, Published in 2005, 1:1200 (1in=100ft) scale, Jasper County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Field Observation information...

  3. Aerial Photography and Imagery, Ortho-Corrected, 2007 6 Inch Color Orthophoto, Published in 2007, 1:1200 (1in=100ft) scale, Dunn County, WI.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  4. Aerial Photography and Imagery, Ortho-Corrected, Published in 2009, 1:4800 (1in=400ft) scale, Heart of Georgia Altamaha RC.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  5. Aerial Photography and Imagery, Ortho-Corrected, Published in 2008, 1:1200 (1in=100ft) scale, Johnson County, Iowa.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale as of 2008. Data by this publisher are often provided in State...

  6. Aerial Photography and Imagery, Oblique, 24" resolution for entire county, Published in 2010, 1:4800 (1in=400ft) scale, CLAY COUNTY.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of 2010. It...

  7. Aerial Photography and Imagery, Ortho-Corrected, Maui Digital Raster Graphic, Published in 2004, 1:24000 (1in=2000ft) scale, U.S. Geological Survey.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Orthoimagery information as...

  8. Aerial Photography and Imagery, Ortho-Corrected, Molokai Digital Raster Graphic, Published in 2004, 1:24000 (1in=2000ft) scale, U.S. Geological Survey.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Hardcopy Maps information as...

  9. Aerial Photography and Imagery, Ortho-Corrected, Maui County Digital Orthophoto Mosaic, Published in 2003, 1:24000 (1in=2000ft) scale, U.S. Geological Survey.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Hardcopy Maps information as...

  10. Aerial Photography and Imagery, Ortho-Corrected, Urban layer has 4" resolution, Published in 2009, 1:1200 (1in=100ft) scale, Cerro Gordo County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  11. Aerial Photography and Imagery, Ortho-Corrected, 2010 Orthophotography, Published in 2010, 1:2400 (1in=200ft) scale, Walworth County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Other information as of 2010....

  12. Aerial Photography and Imagery, Ortho-Corrected, Published in 2005, 1:4800 (1in=400ft) scale, Churchill County, NV.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  13. Aerial Photography and Imagery, Ortho-Corrected, 6-inch color orthoimagery, Published in 2007, 1:600 (1in=50ft) scale, Sheridan County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  14. Aerial Photography and Imagery, Ortho-Corrected, Oahu Digital Orthophoto Mosaic, Published in 2003, 1:24000 (1in=2000ft) scale, U.S. Geological Survey.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Orthoimagery information as...

  15. Aerial Photography and Imagery, Ortho-Corrected, true-color leaf-off orthophotography, Published in 2007, 1:2400 (1in=200ft) scale, Rockingham County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  16. Aerial Photography and Imagery, Ortho-Corrected, Horry County, SC 6" RGB orthophotography, Published in 2008, 1:2400 (1in=200ft) scale, Horry County GIS.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  17. Aerial Photography and Imagery, Ortho-Corrected, 2000 Color 1 Foot Orthoimagery, Published in 2000, 1:2400 (1in=200ft) scale, Winnebago County GIS.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  18. Aerial Photography and Imagery, Ortho-Corrected, USDA-NAIP, Published in 2006, 1:12000 (1in=1000ft) scale, Office of Geographic Information.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:12000 (1in=1000ft) scale, was produced all or in part from Orthoimagery information as...

  19. Aerial Photography and Imagery, Ortho-Corrected, Published in 2006, 1:4800 (1in=400ft) scale, Thomas County BOC.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  20. Aerial Photography and Imagery, Ortho-Corrected, Published in 2009, 1:100000 (1in=8333ft) scale, City of Americus & Sumter County, GA GIS.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:100000 (1in=8333ft) scale, was produced all or in part from Orthoimagery information as...

  1. Aerial Photography and Imagery, Ortho-Corrected, Color Orthophotography at 12-inch pixel resolution, Published in 2010, 1:1200 (1in=100ft) scale, Pierce County Wisconsin.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  2. Aerial Photography and Imagery, Ortho-Corrected, Harnett.Orthos_2008, Published in 2008, 1:2400 (1in=200ft) scale, Harnett County GIS.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Other information as of 2008....

  3. Aerial Photography and Imagery, Ortho-Corrected, USDA-NAIP, Published in 2004, 1:12000 (1in=1000ft) scale, Office of Geographic Information.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:12000 (1in=1000ft) scale, was produced all or in part from Orthoimagery information as...

  4. Aerial Photography and Imagery, Ortho-Corrected, 6 inch pixel orthophotography by Pictometry, Published in 2008, 1:600 (1in=50ft) scale, Fulton County Government.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  5. Aerial Photography and Imagery, Ortho-Corrected, 6" Black and White Cities, Published in 2000, 1:1200 (1in=100ft) scale, Rice County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  6. Aerial Photography and Imagery, Ortho-Corrected, Orthoimagery for Oconee County, Georgia, Published in 2006, 1:4800 (1in=400ft) scale, Northeast Georgia Regional Commission.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  7. Aerial Photography and Imagery, Ortho-Corrected, Mr. Sid file, Published in unknown, 1:600 (1in=50ft) scale, Board of Tax Assessors.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  8. Aerial Photography and Imagery, Ortho-Corrected, Oahu Digital Raster Graphic, Published in 2003, 1:24000 (1in=2000ft) scale, U.S. Geological Survey.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Hardcopy Maps information as...

  9. Aerial Photography and Imagery, Ortho-Corrected, Published in 2008, 1:600 (1in=50ft) scale, Weld County GIS.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  10. Aerial Photography and Imagery, Ortho-Corrected, Rural areas, Published in 2005, 1:4800 (1in=400ft) scale, Benton County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  11. Aerial Photography and Imagery, Ortho-Corrected, WROC 2010, Published in 2010, 1:4800 (1in=400ft) scale, Sawyer County Surveyors Department.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Field Observation information...

  12. Aerial Photography and Imagery, Ortho-Corrected, 6" ground resolution, black & white, Published in 2005, 1:1200 (1in=100ft) scale, Brown County, WI.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  13. Aerial Photography and Imagery, Ortho-Corrected, color infrared DOP washoe county, Published in 2006, 1:1200 (1in=100ft) scale, Washoe County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  14. Aerial Photography and Imagery, Ortho-Corrected, natural color DOP washoe county, Published in 2006, 1:1200 (1in=100ft) scale, Washoe County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  15. Aerial Photography and Imagery, Ortho-Corrected, 2008 Orthogonal Images from Pictometry, Published in 2008, 1:1200 (1in=100ft) scale, Jefferson County Land Information Office.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  16. Aerial Photography and Imagery, Ortho-Corrected, NAIP 2006, Published in 2006, 1:24000 (1in=2000ft) scale, Washington County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Other information as of 2006....

  17. Aerial Photography and Imagery, Ortho-Corrected, 1993 1 meter resolution 7.5 minute quadrangle, Published in 1993, Rock County Planning, Economic, and Community Development Agency.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, was produced all or in part from Orthoimagery information as of 1993. It is described as '1993 1 meter...

  18. Aerial Photography and Imagery, Ortho-Corrected, 6" Color Ortho for Elkhart. Rolla, Richfield, Published in 2007, 1:1200 (1in=100ft) scale, Morton County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  19. Aerial Photography and Imagery, Ortho-Corrected, Color Orthophotos for all of Dickinson County, Published in 2007, 1:2400 (1in=200ft) scale, Dickinson County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  20. Aerial Photography and Imagery, Ortho-Corrected, Urban areas, Published in 2006, 1:1200 (1in=100ft) scale, Carroll County, Iowa.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  1. Aerial Photography and Imagery, Ortho-Corrected, 1 meter rural and 6 inch urban, Published in 2009, 1:600 (1in=50ft) scale, Franklin County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  2. Aerial Photography and Imagery, Ortho-Corrected, 2006 NAIPs for all counties we serve., Published in 2006, Smaller than 1:100000 scale, Prairie Land Electric COOP, Inc..

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at Smaller than 1:100000 scale, was produced all or in part from Orthoimagery information as...

  3. Aerial Photography and Imagery, Ortho-Corrected, 2005 1-Foot Resolution Digital Imagery, Published in 2005, 1:2400 (1in=200ft) scale, Dane County Land Information Office.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  4. Aerial Photography and Imagery, Ortho-Corrected, Six-inch orthoimagery of Washburn County, Wisconsin. The imagery was collected May 5, 2004 by ImageAmerica, Published in 2004, 1:4800 (1in=400ft) scale, Washburn County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  5. Aerial Photography and Imagery, Ortho-Corrected, Color 1 meter NAIP --This data set contains imagery from the National Agricultural, Published in 2006, 1:24000 (1in=2000ft) scale, State of Utah Automated Geographic Reference Center.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Orthoimagery information as...

  6. Aerial Photography and Imagery, Oblique, Prince George's County Oblique Imagery captured through Pictometry International, Published in 2005, 1:2400 (1in=200ft) scale, Prince George's County Office of Information Technology and Communications.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Other information as of 2005. It is...

  7. Aerial Photography and Imagery, Ortho-Corrected, Effingham County Imagery - 1 pixel = 6 inches / We also have a MrSID, Published in 2008, 1:600 (1in=50ft) scale, Effingham County Board Of Commissioners.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  8. Aerial Photography and Imagery, Oblique, Prince George's County Oblique Imagery captured through Pictometry International, Published in 2009, 1:2400 (1in=200ft) scale, Prince George's County Office of Information Technology and Communications.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Other information as of 2009. It is...

  9. Detecting methane ebullition on thermokarst lake ice using high resolution optical aerial imagery

    Directory of Open Access Journals (Sweden)

    P. R. Lindgren

    2015-05-01

    Full Text Available Thermokarst lakes are important emitters of methane, a potent greenhouse gas. However, accurate estimation of methane flux from thermokarst lakes is difficult due to their remoteness and observational challenges associated with the heterogeneous nature of ebullition (bubbling. We used multi-temporal high-resolution (9–11 cm aerial images of an interior Alaskan thermokarst lake, Goldstream Lake, acquired 2 and 4 days following freeze-up in 2011 and 2012, respectively, to characterize methane ebullition seeps and to estimate whole-lake ebullition. Bubbles impeded by the lake ice sheet form distinct white patches as a function of bubbling rate vs. time as ice thickens. Our aerial imagery thus captured in a single snapshot the ebullition events that occurred before the image acquisition. Image analysis showed that low-flux A- and B-type seeps are associated with low brightness patches and are statistically distinct from high-flux C-type and Hotspot seeps associated with high brightness patches. Mean whole-lake ebullition based on optical image analysis in combination with bubble-trap flux measurements was estimated to be 174 ± 28 and 216 ± 33 mL gas m−2 d−1 for the years 2011 and 2012, respectively. A large number of seeps demonstrated spatio-temporal stability over our two-year study period. A strong inverse exponential relationship (R2 ≥ 0.79 was found between percent surface area of lake ice covered with bubble patches and distance from the active thermokarst lake margin. Our study shows that optical remote sensing is a powerful tool to map ebullition seeps on lake ice, to identify their relative strength of ebullition and to assess their spatio-temporal variability.

  10. Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery

    Science.gov (United States)

    Michez, Adrien; Piégay, Hervé; Jonathan, Lisein; Claessens, Hugues; Lejeune, Philippe

    2016-02-01

    Riparian zones are key landscape features, representing the interface between terrestrial and aquatic ecosystems. Although they have been influenced by human activities for centuries, their degradation has increased during the 20th century. Concomitant with (or as consequences of) these disturbances, the invasion of exotic species has increased throughout the world's riparian zones. In our study, we propose a easily reproducible methodological framework to map three riparian invasive taxa using Unmanned Aerial Systems (UAS) imagery: Impatiens glandulifera Royle, Heracleum mantegazzianum Sommier and Levier, and Japanese knotweed (Fallopia sachalinensis (F. Schmidt Petrop.), Fallopia japonica (Houtt.) and hybrids). Based on visible and near-infrared UAS orthophoto, we derived simple spectral and texture image metrics computed at various scales of image segmentation (10, 30, 45, 60 using eCognition software). Supervised classification based on the random forests algorithm was used to identify the most relevant variable (or combination of variables) derived from UAS imagery for mapping riparian invasive plant species. The models were built using 20% of the dataset, the rest of the dataset being used as a test set (80%). Except for H. mantegazzianum, the best results in terms of global accuracy were achieved with the finest scale of analysis (segmentation scale parameter = 10). The best values of overall accuracies reached 72%, 68%, and 97% for I. glandulifera, Japanese knotweed, and H. mantegazzianum respectively. In terms of selected metrics, simple spectral metrics (layer mean/camera brightness) were the most used. Our results also confirm the added value of texture metrics (GLCM derivatives) for mapping riparian invasive species. The results obtained for I. glandulifera and Japanese knotweed do not reach sufficient accuracies for operational applications. However, the results achieved for H. mantegazzianum are encouraging. The high accuracies values combined to

  11. Aerial Photography and Imagery, Ortho-Corrected, 2003 NAIP Digital Orthophotographs of Rhode Island; This data set contains imagery from the National Agricultural Imagery Program (NAIP). NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S., Published in 2004, 1:24000 (1in=2000ft) scale, State of Rhode Island and Providence Plantations.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Orthoimagery information as...

  12. Highway Traffic Incident Detection using High-Resolution Aerial Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Seyed M.M. Kahaki

    2011-01-01

    -fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} ° and the performance for learning system in order to vehicle detection is 86%. This performance achived in testing algorithm on 45 highway aerial images. Conclusion: In order to consider with the high complexity of this kind of imagery, we integrate knowledge about roadways using formulated scale-dependent models. The intensity images are used for the extraction of road from satellite images. Threshold techniques, neural network and Radon transform are used for the road extraction, vehicle detection and incident detection. Results indicated that in most aerial images the incident can be detect by applying the angle algorithm.

  13. Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping.

    Science.gov (United States)

    Borra-Serrano, Irene; Peña, José Manuel; Torres-Sánchez, Jorge; Mesas-Carrascosa, Francisco Javier; López-Granados, Francisca

    2015-08-12

    Unmanned aerial vehicles (UAVs) combined with different spectral range sensors are an emerging technology for providing early weed maps for optimizing herbicide applications. Considering that weeds, at very early phenological stages, are similar spectrally and in appearance, three major components are relevant: spatial resolution, type of sensor and classification algorithm. Resampling is a technique to create a new version of an image with a different width and/or height in pixels, and it has been used in satellite imagery with different spatial and temporal resolutions. In this paper, the efficiency of resampled-images (RS-images) created from real UAV-images (UAV-images; the UAVs were equipped with two types of sensors, i.e., visible and visible plus near-infrared spectra) captured at different altitudes is examined to test the quality of the RS-image output. The performance of the object-based-image-analysis (OBIA) implemented for the early weed mapping using different weed thresholds was also evaluated. Our results showed that resampling accurately extracted the spectral values from high spatial resolution UAV-images at an altitude of 30 m and the RS-image data at altitudes of 60 and 100 m, was able to provide accurate weed cover and herbicide application maps compared with UAV-images from real flights.

  14. Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping

    Directory of Open Access Journals (Sweden)

    Irene Borra-Serrano

    2015-08-01

    Full Text Available Unmanned aerial vehicles (UAVs combined with different spectral range sensors are an emerging technology for providing early weed maps for optimizing herbicide applications. Considering that weeds, at very early phenological stages, are similar spectrally and in appearance, three major components are relevant: spatial resolution, type of sensor and classification algorithm. Resampling is a technique to create a new version of an image with a different width and/or height in pixels, and it has been used in satellite imagery with different spatial and temporal resolutions. In this paper, the efficiency of resampled-images (RS-images created from real UAV-images (UAV-images; the UAVs were equipped with two types of sensors, i.e., visible and visible plus near-infrared spectra captured at different altitudes is examined to test the quality of the RS-image output. The performance of the object-based-image-analysis (OBIA implemented for the early weed mapping using different weed thresholds was also evaluated. Our results showed that resampling accurately extracted the spectral values from high spatial resolution UAV-images at an altitude of 30 m and the RS-image data at altitudes of 60 and 100 m, was able to provide accurate weed cover and herbicide application maps compared with UAV-images from real flights.

  15. Discrimination of Deciduous Tree Species from Time Series of Unmanned Aerial System Imagery.

    Directory of Open Access Journals (Sweden)

    Jonathan Lisein

    Full Text Available Technology advances can revolutionize Precision Forestry by providing accurate and fine forest information at tree level. This paper addresses the question of how and particularly when Unmanned Aerial System (UAS should be used in order to efficiently discriminate deciduous tree species. The goal of this research is to determine when is the best time window to achieve an optimal species discrimination. A time series of high resolution UAS imagery was collected to cover the growing season from leaf flush to leaf fall. Full benefit was taken of the temporal resolution of UAS acquisition, one of the most promising features of small drones. The disparity in forest tree phenology is at the maximum during early spring and late autumn. But the phenology state that optimized the classification result is the one that minimizes the spectral variation within tree species groups and, at the same time, maximizes the phenologic differences between species. Sunlit tree crowns (5 deciduous species groups were classified using a Random Forest approach for monotemporal, two-date and three-date combinations. The end of leaf flushing was the most efficient single-date time window. Multitemporal datasets definitely improve the overall classification accuracy. But single-date high resolution orthophotomosaics, acquired on optimal time-windows, result in a very good classification accuracy (overall out of bag error of 16%.

  16. Random Forest and Objected-Based Classification for Forest Pest Extraction from Uav Aerial Imagery

    Science.gov (United States)

    Yuan, Yi; Hu, Xiangyun

    2016-06-01

    Forest pest is one of the most important factors affecting the health of forest. However, since it is difficult to figure out the pest areas and to predict the spreading ways just to partially control and exterminate it has not effective enough so far now. The infected areas by it have continuously spreaded out at present. Thus the introduction of spatial information technology is highly demanded. It is very effective to examine the spatial distribution characteristics that can establish timely proper strategies for control against pests by periodically figuring out the infected situations as soon as possible and by predicting the spreading ways of the infection. Now, with the UAV photography being more and more popular, it has become much cheaper and faster to get UAV images which are very suitable to be used to monitor the health of forest and detect the pest. This paper proposals a new method to effective detect forest pest in UAV aerial imagery. For an image, we segment it to many superpixels at first and then we calculate a 12-dimension statistical texture information for each superpixel which are used to train and classify the data. At last, we refine the classification results by some simple rules. The experiments show that the method is effective for the extraction of forest pest areas in UAV images.

  17. Historical aerial imagery reveals rapid frontal retreat following the 1920's warming in southeast Greenland

    Science.gov (United States)

    Bjork, A. A.; Kjaer, K. H.; Korsgaard, N. J.; Khan, S. A.; Kjeldsen, K. K.; Andresen, C. S.

    2011-12-01

    The Greenland ice sheet (GIS) is undergoing massive changes in its frontal positions, velocity structure, and overall mass balance. Since 2000, marine and terrestrial terminating glaciers in southeast Greenland have experienced dramatic frontal retreat and dynamic thinning in response to increased sea surface and air temperatures. However, little is known about the long term glacier history prior to the satellite era. Here we show a unique record of the frontal history of 132 glaciers in southeast Greenland based on historical aerial and satellite imagery from 1933 to 2010. Our results demonstrate decadal sensitivity to temperature changes with rapid retreat following the early century warming (1919-1932) and glacial advance during a minor, but profound mid century cooling (1955-1972) succeeded by the present warming again leading to massive retreat. One significant finding lies in the similarity of the retreat following the early century warming and the latest decade, with a majority of the 132 glaciers exhibiting larger retreat rates in the early period. Furthermore, during the mid century cooling glaciers in southeast Greenland showed a surprisingly rapid response to the cooling, indicating that stabilization and subsequent advance can occur with a short cooling period. Marine terminating glaciers originating from the GIS experienced the largest frontal fluctuations often out of sync with local glaciers and ice caps indicating a larger coherence with ocean temperatures, whereas local glaciers and ice caps and GIS terrestrial terminating glaciers shows frontal fluctuations closely related to the air temperature.

  18. Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery

    Science.gov (United States)

    Wu, Qiusheng; Lane, Charles R.

    2017-07-01

    In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features with seasonal to permanent inundation patterning characterized by nested hierarchical structures and dynamic filling-spilling-merging surface-water hydrological processes. Differentiating and appropriately processing such ecohydrologically meaningful features remains a major technical terrain-processing challenge, particularly as high-resolution spatial data are increasingly used to support modeling and geographic analysis needs. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution lidar data and aerial imagery. The graph-theory-based contour tree method was used to delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost-path algorithm. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow simulation and hydrologic connectivity analysis.

  19. Aerial_Shorelines_1940_2015.shp - Dauphin Island, Alabama Shoreline Data Derived from Aerial Imagery from 1940 to 2015

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Aerial_WDL_Shorelines.zip features digitized historic shorelines for the Dauphin Island coastline from October 1940 to November 2015. This dataset contains 10 Wet...

  20. Exploration towards the modeling of gable-roofed buildings using a combination of aerial and street-level imagery

    Science.gov (United States)

    Creusen, Ivo; Hazelhoff, Lykele; de With, Peter H. N.

    2015-03-01

    Extraction of residential building properties is helpful for numerous applications, such as computer-guided feasibility analysis for solar panel placement, determination of real-estate taxes and assessment of real-estate insurance policies. Therefore, this work explores the automated modeling of buildings with a gable roof (the most common roof type within Western Europe), based on a combination of aerial imagery and street-level panoramic images. This is a challenging task, since buildings show large variations in shape, dimensions and building extensions, and may additionally be captured under non-ideal lighting conditions. The aerial images feature a coarse overview of the building due to the large capturing distance. The building footprint and an initial estimate of the building height is extracted based on the analysis of stereo aerial images. The estimated model is then refined using street-level images, which feature higher resolution and enable more accurate measurements, however, displaying a single building side only. Initial experiments indicate that the footprint dimensions of the main building can be accurately extracted from aerial images, while the building height is extracted with slightly less accuracy. By combining aerial and street-level images, we have found that the accuracies of these height measurements are significantly increased, thereby improving the overall quality of the extracted building model, and resulting in an average inaccuracy of the estimated volume below 10%.

  1. Aerial Photography and Imagery, Uncorrected, as part of the original impervious surface project in 1993, Published in 1989, 1:2400 (1in=200ft) scale, City of Fort Wayne.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Uncorrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Uncorrected Imagery information as...

  2. Aerial Photography and Imagery, Oblique, Digital photos taken of PA's Lake Erie shoreline and Presque Isle at an oblique angle at about 1,500-2,000' altitude., Published in 2017, Not Applicable scale, Pennsylvania Coastal Resources Management Program.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset, published at Not Applicable scale, was produced all or in part from Uncorrected Imagery information as of 2017....

  3. Aerial Photography and Imagery, Oblique, Digital photos taken of PA's Lake Erie shoreline and Presque Isle at an oblique angle at about 1,500-2,000' altitude., Published in 2015, Not Applicable scale, Pennsylvania Coastal Resources Management Program.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset, published at Not Applicable scale, was produced all or in part from Uncorrected Imagery information as of 2015....

  4. Aerial Photography and Imagery, Uncorrected, various years are available, including 1948, 1954, 1967, and others from the 1970s, 80s and 90s, Published in 1976, 1:24000 (1in=2000ft) scale, Brown County, WI.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Uncorrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Uncorrected Imagery information...

  5. Aerial Photography and Imagery, Oblique, Oblique by Pictometry for Eastern Half of York County, Published in 2007, 1:2400 (1in=200ft) scale, York County Government, SC.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Uncorrected Imagery information as of...

  6. Aerial Photography and Imagery, Ortho-Corrected, True Color Orthophotography for Cambridge, Hurlock, Secretary, and Vienna - 4" pixles, 2006., Published in 2006, 1:600 (1in=50ft) scale, Eastern Shore Regional GIS Cooperative.

    Data.gov (United States)

    NSGIC Regional | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2006. True Color Orthophotography for Cambridge, Hurlock, Secretary, and Vienna - 4" pixles,...

  7. Advanced Tie Feature Matching for the Registration of Mobile Mapping Imaging Data and Aerial Imagery

    Science.gov (United States)

    Jende, P.; Peter, M.; Gerke, M.; Vosselman, G.

    2016-06-01

    Mobile Mapping's ability to acquire high-resolution ground data is opposing unreliable localisation capabilities of satellite-based positioning systems in urban areas. Buildings shape canyons impeding a direct line-of-sight to navigation satellites resulting in a deficiency to accurately estimate the mobile platform's position. Consequently, acquired data products' positioning quality is considerably diminished. This issue has been widely addressed in the literature and research projects. However, a consistent compliance of sub-decimetre accuracy as well as a correction of errors in height remain unsolved. We propose a novel approach to enhance Mobile Mapping (MM) image orientation based on the utilisation of highly accurate orientation parameters derived from aerial imagery. In addition to that, the diminished exterior orientation parameters of the MM platform will be utilised as they enable the application of accurate matching techniques needed to derive reliable tie information. This tie information will then be used within an adjustment solution to correct affected MM data. This paper presents an advanced feature matching procedure as a prerequisite to the aforementioned orientation update. MM data is ortho-projected to gain a higher resemblance to aerial nadir data simplifying the images' geometry for matching. By utilising MM exterior orientation parameters, search windows may be used in conjunction with a selective keypoint detection and template matching. Originating from different sensor systems, however, difficulties arise with respect to changes in illumination, radiometry and a different original perspective. To respond to these challenges for feature detection, the procedure relies on detecting keypoints in only one image. Initial tests indicate a considerable improvement in comparison to classic detector/descriptor approaches in this particular matching scenario. This method leads to a significant reduction of outliers due to the limited availability

  8. Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery.

    Science.gov (United States)

    Ma, Yalong; Wu, Xinkai; Yu, Guizhen; Xu, Yongzheng; Wang, Yunpeng

    2016-03-26

    Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM) with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT) features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness.

  9. Low-Level Tie Feature Extraction of Mobile Mapping Data (mls/images) and Aerial Imagery

    Science.gov (United States)

    Jende, P.; Hussnain, Z.; Peter, M.; Oude Elberink, S.; Gerke, M.; Vosselman, G.

    2016-03-01

    Mobile Mapping (MM) is a technique to obtain geo-information using sensors mounted on a mobile platform or vehicle. The mobile platform's position is provided by the integration of Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS). However, especially in urban areas, building structures can obstruct a direct line-of-sight between the GNSS receiver and navigation satellites resulting in an erroneous position estimation. Therefore, derived MM data products, such as laser point clouds or images, lack the expected positioning reliability and accuracy. This issue has been addressed by many researchers, whose aim to mitigate these effects mainly concentrates on utilising tertiary reference data. However, current approaches do not consider errors in height, cannot achieve sub-decimetre accuracy and are often not designed to work in a fully automatic fashion. We propose an automatic pipeline to rectify MM data products by employing high resolution aerial nadir and oblique imagery as horizontal and vertical reference, respectively. By exploiting the MM platform's defective, and therefore imprecise but approximate orientation parameters, accurate feature matching techniques can be realised as a pre-processing step to minimise the MM platform's three-dimensional positioning error. Subsequently, identified correspondences serve as constraints for an orientation update, which is conducted by an estimation or adjustment technique. Since not all MM systems employ laser scanners and imaging sensors simultaneously, and each system and data demands different approaches, two independent workflows are developed in parallel. Still under development, both workflows will be presented and preliminary results will be shown. The workflows comprise of three steps; feature extraction, feature matching and the orientation update. In this paper, initial results of low-level image and point cloud feature extraction methods will be discussed as well as an outline of

  10. Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery

    Science.gov (United States)

    Ma, Yalong; Wu, Xinkai; Yu, Guizhen; Xu, Yongzheng; Wang, Yunpeng

    2016-01-01

    Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM) with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT) features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness. PMID:27023564

  11. LOW-LEVEL TIE FEATURE EXTRACTION OF MOBILE MAPPING DATA (MLS/IMAGES AND AERIAL IMAGERY

    Directory of Open Access Journals (Sweden)

    P. Jende

    2016-03-01

    Full Text Available Mobile Mapping (MM is a technique to obtain geo-information using sensors mounted on a mobile platform or vehicle. The mobile platform’s position is provided by the integration of Global Navigation Satellite Systems (GNSS and Inertial Navigation Systems (INS. However, especially in urban areas, building structures can obstruct a direct line-of-sight between the GNSS receiver and navigation satellites resulting in an erroneous position estimation. Therefore, derived MM data products, such as laser point clouds or images, lack the expected positioning reliability and accuracy. This issue has been addressed by many researchers, whose aim to mitigate these effects mainly concentrates on utilising tertiary reference data. However, current approaches do not consider errors in height, cannot achieve sub-decimetre accuracy and are often not designed to work in a fully automatic fashion. We propose an automatic pipeline to rectify MM data products by employing high resolution aerial nadir and oblique imagery as horizontal and vertical reference, respectively. By exploiting the MM platform’s defective, and therefore imprecise but approximate orientation parameters, accurate feature matching techniques can be realised as a pre-processing step to minimise the MM platform’s three-dimensional positioning error. Subsequently, identified correspondences serve as constraints for an orientation update, which is conducted by an estimation or adjustment technique. Since not all MM systems employ laser scanners and imaging sensors simultaneously, and each system and data demands different approaches, two independent workflows are developed in parallel. Still under development, both workflows will be presented and preliminary results will be shown. The workflows comprise of three steps; feature extraction, feature matching and the orientation update. In this paper, initial results of low-level image and point cloud feature extraction methods will be discussed

  12. Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery

    Directory of Open Access Journals (Sweden)

    Yalong Ma

    2016-03-01

    Full Text Available Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs, more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG and Discrete Cosine Transform (DCT features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness.

  13. Aerial Photography and Imagery, Ortho-Corrected, HOGARDC has MrSID NAIP Aerial Imagery for Appling, Bleckley, Candler, Dodge, Emanuel, Evans, Jeff Davis, Johnson, Laurens, Montgomery, Tattnall, Telfair, Toombs, Treutlen, Wayne, Wheeler, and Wilcox Counties., Published in 2007, 1:4800 (1in=400ft) scale, Heart of Georgia Altamaha RDC.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  14. Aerial Photography and Imagery, Ortho-Corrected - MO 2012 Cole NAIP (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seamline boundaries of imagery acquired as part of the National Agriculture Imagery Program (NAIP), and used in the...

  15. Aerial Photography and Imagery, Ortho-Corrected - MO 2012 St. Charles NAIP (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seamline boundaries of imagery acquired as part of the National Agriculture Imagery Program (NAIP), and used in the...

  16. Aerial Photography and Imagery, Ortho-Corrected - MO 2012 McDonald NAIP (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seamline boundaries of imagery acquired as part of the National Agriculture Imagery Program (NAIP), and used in the...

  17. Aerial Photography and Imagery, Ortho-Corrected - MO 2012 Phelps NAIP (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seamline boundaries of imagery acquired as part of the National Agriculture Imagery Program (NAIP), and used in the...

  18. Aerial Photography and Imagery, Ortho-Corrected - MO 2012 Nodaway NAIP (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seamline boundaries of imagery acquired as part of the National Agriculture Imagery Program (NAIP), and used in the...

  19. Aerial Photography and Imagery, Ortho-Corrected - MO 2012 Jasper NAIP (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seamline boundaries of imagery acquired as part of the National Agriculture Imagery Program (NAIP), and used in the...

  20. Aerial Photography and Imagery, Ortho-Corrected - 2013 Digital Orthophotos - Franklin County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital ortho imagery covering Franklin County, FL. This 1"=200' scale imagery is comprised of 24 bit natural color orthophotography with...

  1. Aerial Photography and Imagery, Ortho-Corrected - MO 2012 Scotland NAIP (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seamline boundaries of imagery acquired as part of the National Agriculture Imagery Program (NAIP), and used in the...

  2. Aerial Photography and Imagery, Ortho-Corrected - MO 2012 St. Louis NAIP (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seamline boundaries of imagery acquired as part of the National Agriculture Imagery Program (NAIP), and used in the...

  3. Aerial Photography and Imagery, Ortho-Corrected - MO 2012 Cass NAIP (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seamline boundaries of imagery acquired as part of the National Agriculture Imagery Program (NAIP), and used in the...

  4. Aerial Photography and Imagery, Ortho-Corrected - 2013 Digital Orthophotos - Liberty County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital ortho imagery covering Liberty County, FL. This 1"=200' scale imagery is comprised of 24 bit natural color orthophotography with...

  5. Aerial Photography and Imagery, Ortho-Corrected - MO 2012 Christian NAIP (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seamline boundaries of imagery acquired as part of the National Agriculture Imagery Program (NAIP), and used in the...

  6. Aerial Photography and Imagery, Ortho-Corrected - MO 2012 Franklin NAIP (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seamline boundaries of imagery acquired as part of the National Agriculture Imagery Program (NAIP), and used in the...

  7. Aerial Photography and Imagery, Ortho-Corrected - 2010 Digital Orthophotos - Liberty County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital ortho imagery covering Liberty County, FL. This 1"=200' scale imagery is comprised of natural color orthophotography with a GSD...

  8. Aerial Photography and Imagery, Ortho-Corrected - 2008 Digital Orthophotos - Lake County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital ortho imagery for Lake County, FL. This 1":100' scale imagery is comprised of natural color orthophotography with a GSD (Ground...

  9. Aerial Photography and Imagery, Ortho-Corrected - 2013 Digital Orthophotos - Calhoun County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital ortho imagery covering Calhoun and Gulf Counties, FL. This 1"=200' scale imagery is comprised of natural color orthoimagery with...

  10. Aerial Photography and Imagery, Ortho-Corrected - 2010 Digital Orthophotos - Franklin County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital ortho imagery covering Franklin County, FL. This 1"=200' scale imagery is comprised of natural color orthophotography with a GSD...

  11. Aerial Photography and Imagery, Ortho-Corrected - 2010 Digital Orthophotos - Gulf County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital ortho imagery covering Gulf County, FL. This 1"=200' scale imagery is comprised of natural color orthophotography with a GSD...

  12. Aerial Photography and Imagery, Ortho-Corrected - 2009 Digital Orthophotos - Bradford County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital ortho imagery covering Bradford County, FL. This 1"=200' scale imagery is comprised of natural color orthophotography with a GSD...

  13. Aerial Photography and Imagery, Ortho-Corrected - 2009 Digital Orthophotos - Glades County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital ortho imagery covering Glades County, FL. This 1":200' scale imagery is comprised of natural color orthophotography with a GSD...

  14. Aerial Photography and Imagery, Ortho-Corrected - 2013 Digital Orthophotos - Gulf County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital ortho imagery covering Calhoun and Gulf Counties, FL. This 1"=200' scale imagery is comprised of natural color orthoimagery with...

  15. Aerial Photography and Imagery, Ortho-Corrected - 2008 Digital Orthophotos - HIghlands County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital ortho imagery for Highlands County, FL. This 1":100' scale imagery is comprised of natural color orthophotography with a GSD...

  16. Aerial Photography and Imagery, Ortho-Corrected - 2010 Digital Orthophotos - Calhoun County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital ortho imagery covering Calhoun County, FL. This 1"=200' scale imagery is comprised of natural color orthophotography with a GSD...

  17. Aerial Photography and Imagery, Ortho-Corrected - 2010 Digital Orthophotos - Union County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital ortho imagery covering Union County, FL. This 1"=200' scale imagery is comprised of natural color orthophotography with a GSD...

  18. Aerial Photography and Imagery, Ortho-Corrected - 2008 Digital Orthophotos - Glades County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital ortho imagery covering Glades and Hendry Counties, FL. This 1":200' scale imagery is comprised of natural color orthophotography...

  19. Aerial Photography and Imagery, Ortho-Corrected - MO 2012 Gentry NAIP (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seamline boundaries of imagery acquired as part of the National Agriculture Imagery Program (NAIP), and used in the...

  20. Aerial Photography and Imagery, Ortho-Corrected - MO 2012 Cape Giradeau NIAP (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seamline boundaries of imagery acquired as part of the National Agriculture Imagery Program (NAIP), and used in the...

  1. Aerial Photography and Imagery, Ortho-Corrected - MO 2012 Laclede NAIP (SHP)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set contains polygons delineating the seamline boundaries of imagery acquired as part of the National Agriculture Imagery Program (NAIP), and used in the...

  2. Aerial Photography and Imagery, Ortho-Corrected, Digital orthophographs (DOPs) were derived from black and white aerial photographs taken in the spring of 2000. The DOP scale is 1:4800 (1" = 400') rectified to 18" pixels., Published in 2000, 1:4800 (1in=400ft) scale, Manitowoc County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2000. Digital orthophographs (DOPs) were derived from black and white aerial photographs taken...

  3. Automatic geolocation of targets tracked by aerial imaging platforms using satellite imagery

    OpenAIRE

    Shukla, P. K.; Goel, S.; Singh, P.; B. Lohani

    2014-01-01

    Tracking of targets from aerial platforms is an important activity in several applications, especially surveillance. Knowled ge of geolocation of these targets adds additional significant and useful information to the application. This paper determines the geolocation of a target being tracked from an aerial platform using the technique of image registration. Current approaches utilize a POS to determine the location of the aerial platform and then use the same for geolocation of the...

  4. Aerial Photography and Imagery, Uncorrected, Scanned imagery dating back to 1944. The data is not always complete for all areas. The years were also not consistently flown., Published in 2003, 1:63360 (1in=1mile) scale, City of Emporia/Lyon County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Uncorrected dataset, published at 1:63360 (1in=1mile) scale, was produced all or in part from Uncorrected Imagery information as...

  5. Aerial Photography and Imagery, Ortho-Corrected, We have new imagery from Pictometry's AccuPlus flown in March 2010 and to be delivered in October 2010., Published in 2010, 1:600 (1in=50ft) scale, Augusta-Richmond County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2010. We have new imagery from Pictometry's AccuPlus flown in March 2010 and to be delivered in...

  6. Aerial Photography and Imagery, Ortho-Corrected, This dataset contains imagery of Prince George's County in RGB format. The primary goal was to acquire Countywide Digital Orthoimagery at 6" ground pixel resolution., Published in 2009, 1:1200 (1in=100ft) scale, Maryland National Capital Park and Planning Commission.

    Data.gov (United States)

    NSGIC Non-Profit | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2009. This dataset contains imagery of Prince George's County in RGB format. The primary goal...

  7. Aerial Photography and Imagery, Ortho-Corrected, This imagery was acquired through a Federal Grant with Pictometry International. The resolution is 6" in more densly populated areas and 1' in the other areas., Published in 2011, Not Applicable scale, Chippewa County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2011. This imagery was acquired through a Federal Grant with Pictometry International. The...

  8. Open-Source Processing and Analysis of Aerial Imagery Acquired with a Low-Cost Unmanned Aerial System to Support Invasive Plant Management

    Directory of Open Access Journals (Sweden)

    Jan R. K. Lehmann

    2017-07-01

    Full Text Available Remote sensing by Unmanned Aerial Systems (UAS is a dynamic evolving technology. UAS are particularly useful in environmental monitoring and management because they have the capability to provide data at high temporal and spatial resolutions. Moreover, data acquisition costs are lower than those of conventional methods such as extensive ground sampling, manned airplanes, or satellites. Small fixed-wing UAS in particular offer further potential benefits as they extend the operational coverage of the area under study at lower operator risks and accelerate data deployment times. Taking these aspects into account, UAS might be an effective tool to support management of invasive plant based on early detection and regular monitoring. A straightforward UAS approach to map invasive plant species is presented in this study with the intention of providing ready-to-use field maps essential for action-oriented management. Our UAS utilizes low-cost sensors, free-of-charge software for mission planning and an affordable, commercial aerial platform to reduce operational costs, reducing expenses with personnel while increasing overall efficiency. We illustrate our approach using a real example of invasion by Acacia mangium in a Brazilian Savanna ecosystem. A. mangium was correctly identified with an overall accuracy of 82.7% from the analysis of imagery. This approach provides land management authorities and practitioners with new prospects for environmental restoration in areas where invasive plant species are present.

  9. Bridging Estimates of Greenness in an Arid Grassland Using Field Observations, Phenocams, and Time Series Unmanned Aerial System (UAS) Imagery

    Science.gov (United States)

    Browning, D. M.; Tweedie, C. E.; Rango, A.

    2013-12-01

    Spatially extensive grasslands and savannas in arid and semi-arid ecosystems (i.e., rangelands) require cost-effective, accurate, and consistent approaches for monitoring plant phenology. Remotely sensed imagery offers these capabilities; however contributions of exposed soil due to modest vegetation cover, susceptibility of vegetation to drought, and lack of robust scaling relationships challenge biophysical retrievals using moderate- and coarse-resolution satellite imagery. To evaluate methods for characterizing plant phenology of common rangeland species and to link field measurements to remotely sensed metrics of land surface phenology, we devised a hierarchical study spanning multiple spatial scales. We collect data using weekly standardized field observations on focal plants, daily phenocam estimates of vegetation greenness, and very high spatial resolution imagery from an Unmanned Aerial System (UAS) throughout the growing season. Field observations of phenological condition and vegetation cover serve to verify phenocam greenness indices along with indices derived from time series UAS imagery. UAS imagery is classified using object-oriented image analysis to identify species-specific image objects for which greenness indices are derived. Species-specific image objects facilitate comparisons with phenocam greenness indices and scaling spectral responses to footprints of Landsat and MODIS pixels. Phenocam greenness curves indicated rapid canopy development for the widespread deciduous shrub Prosopis glandulosa over 14 (in April 2012) to 16 (in May 2013) days. The modest peak in greenness for the dominant perennial grass Bouteloua eriopoda occurred in October 2012 following peak summer rainfall. Weekly field estimates of canopy development closely coincided with daily patterns in initial growth and senescence for both species. Field observations improve the precision of the timing of phenophase transitions relative to inflection points calculated from phenocam

  10. INTERGRATION OF LiDAR DATA WITH AERIAL IMAGERY FOR ESTIMATING ROOFTOP SOLAR PHOTOVOLTAIC POTENTIALS IN CITY OF CAPE TOWN

    Directory of Open Access Journals (Sweden)

    A. K. Adeleke

    2016-06-01

    Full Text Available Apart from the drive to reduce carbon dioxide emissions by carbon-intensive economies like South Africa, the recent spate of electricity load shedding across most part of the country, including Cape Town has left electricity consumers scampering for alternatives, so as to rely less on the national grid. Solar energy, which is adequately available in most part of Africa and regarded as a clean and renewable source of energy, makes it possible to generate electricity by using photovoltaics technology. However, before time and financial resources are invested into rooftop solar photovoltaic systems in urban areas, it is important to evaluate the potential of the building rooftop, intended to be used in harvesting the solar energy. This paper presents methodologies making use of LiDAR data and other ancillary data, such as high-resolution aerial imagery, to automatically extract building rooftops in City of Cape Town and evaluate their potentials for solar photovoltaics systems. Two main processes were involved: (1 automatic extraction of building roofs using the integration of LiDAR data and aerial imagery in order to derive its’ outline and areal coverage; and (2 estimating the global solar radiation incidence on each roof surface using an elevation model derived from the LiDAR data, in order to evaluate its solar photovoltaic potential. This resulted in a geodatabase, which can be queried to retrieve salient information about the viability of a particular building roof for solar photovoltaic installation.

  11. Detection of two intermixed invasive woody species using color infrared aerial imagery and the support vector machine classifier

    Science.gov (United States)

    Mirik, Mustafa; Chaudhuri, Sriroop; Surber, Brady; Ale, Srinivasulu; James Ansley, R.

    2013-01-01

    Both the evergreen redberry juniper (Juniperus pinchotii Sudw.) and deciduous honey mesquite (Prosopis glandulosa Torr.) are destructive and aggressive invaders that affect rangelands and grasslands of the southern Great Plains of the United States. However, their current spatial extent and future expansion trends are unknown. This study was aimed at: (1) exploring the utility of aerial imagery for detecting and mapping intermixed redberry juniper and honey mesquite while both are in full foliage using the support vector machine classifier at two sites in north central Texas and, (2) assessing and comparing the mapping accuracies between sites. Accuracy assessments revealed that the overall accuracies were 90% with the associated kappa coefficient of 0.86% and 89% with the associated kappa coefficient of 0.85 for sites 1 and 2, respectively. Z-statistics (0.102<1.96) used to compare the classification results for both sites indicated an insignificant difference between classifications at 95% probability level. In most instances, juniper and mesquite were identified correctly with <7% being mistaken for the other woody species. These results indicated that assessment of the current infestation extent and severity of these two woody species in a spatial context is possible using aerial remote sensing imagery.

  12. DECISION LEVEL FUSION OF LIDAR DATA AND AERIAL COLOR IMAGERY BASED ON BAYESIAN THEORY FOR URBAN AREA CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    H. Rastiveis

    2015-12-01

    Full Text Available Airborne Light Detection and Ranging (LiDAR generates high-density 3D point clouds to provide a comprehensive information from object surfaces. Combining this data with aerial/satellite imagery is quite promising for improving land cover classification. In this study, fusion of LiDAR data and aerial imagery based on Bayesian theory in a three-level fusion algorithm is presented. In the first level, pixel-level fusion, the proper descriptors for both LiDAR and image data are extracted. In the next level of fusion, feature-level, using extracted features the area are classified into six classes of “Buildings”, “Trees”, “Asphalt Roads”, “Concrete roads”, “Grass” and “Cars” using Naïve Bayes classification algorithm. This classification is performed in three different strategies: (1 using merely LiDAR data, (2 using merely image data, and (3 using all extracted features from LiDAR and image. The results of three classifiers are integrated in the last phase, decision level fusion, based on Naïve Bayes algorithm. To evaluate the proposed algorithm, a high resolution color orthophoto and LiDAR data over the urban areas of Zeebruges, Belgium were applied. Obtained results from the decision level fusion phase revealed an improvement in overall accuracy and kappa coefficient.

  13. Intergration of LiDAR Data with Aerial Imagery for Estimating Rooftop Solar Photovoltaic Potentials in City of Cape Town

    Science.gov (United States)

    Adeleke, A. K.; Smit, J. L.

    2016-06-01

    Apart from the drive to reduce carbon dioxide emissions by carbon-intensive economies like South Africa, the recent spate of electricity load shedding across most part of the country, including Cape Town has left electricity consumers scampering for alternatives, so as to rely less on the national grid. Solar energy, which is adequately available in most part of Africa and regarded as a clean and renewable source of energy, makes it possible to generate electricity by using photovoltaics technology. However, before time and financial resources are invested into rooftop solar photovoltaic systems in urban areas, it is important to evaluate the potential of the building rooftop, intended to be used in harvesting the solar energy. This paper presents methodologies making use of LiDAR data and other ancillary data, such as high-resolution aerial imagery, to automatically extract building rooftops in City of Cape Town and evaluate their potentials for solar photovoltaics systems. Two main processes were involved: (1) automatic extraction of building roofs using the integration of LiDAR data and aerial imagery in order to derive its' outline and areal coverage; and (2) estimating the global solar radiation incidence on each roof surface using an elevation model derived from the LiDAR data, in order to evaluate its solar photovoltaic potential. This resulted in a geodatabase, which can be queried to retrieve salient information about the viability of a particular building roof for solar photovoltaic installation.

  14. Monitoring the invasion of Spartina alterniflora using very high resolution unmanned aerial vehicle imagery in Beihai, Guangxi (China).

    Science.gov (United States)

    Wan, Huawei; Wang, Qiao; Jiang, Dong; Fu, Jingying; Yang, Yipeng; Liu, Xiaoman

    2014-01-01

    Spartina alterniflora was introduced to Beihai, Guangxi (China), for ecological engineering purposes in 1979. However, the exceptional adaptability and reproductive ability of this species have led to its extensive dispersal into other habitats, where it has had a negative impact on native species and threatens the local mangrove and mudflat ecosystems. To obtain the distribution and spread of Spartina alterniflora, we collected HJ-1 CCD imagery from 2009 and 2011 and very high resolution (VHR) imagery from the unmanned aerial vehicle (UAV). The invasion area of Spartina alterniflora was 357.2 ha in 2011, which increased by 19.07% compared with the area in 2009. A field survey was conducted for verification and the total accuracy was 94.0%. The results of this paper show that VHR imagery can provide details on distribution, progress, and early detection of Spartina alterniflora invasion. OBIA, object based image analysis for remote sensing (RS) detection method, can enable control measures to be more effective, accurate, and less expensive than a field survey of the invasive population.

  15. Monitoring the Invasion of Spartina alterniflora Using Very High Resolution Unmanned Aerial Vehicle Imagery in Beihai, Guangxi (China

    Directory of Open Access Journals (Sweden)

    Huawei Wan

    2014-01-01

    Full Text Available Spartina alterniflora was introduced to Beihai, Guangxi (China, for ecological engineering purposes in 1979. However, the exceptional adaptability and reproductive ability of this species have led to its extensive dispersal into other habitats, where it has had a negative impact on native species and threatens the local mangrove and mudflat ecosystems. To obtain the distribution and spread of Spartina alterniflora, we collected HJ-1 CCD imagery from 2009 and 2011 and very high resolution (VHR imagery from the unmanned aerial vehicle (UAV. The invasion area of Spartina alterniflora was 357.2 ha in 2011, which increased by 19.07% compared with the area in 2009. A field survey was conducted for verification and the total accuracy was 94.0%. The results of this paper show that VHR imagery can provide details on distribution, progress, and early detection of Spartina alterniflora invasion. OBIA, object based image analysis for remote sensing (RS detection method, can enable control measures to be more effective, accurate, and less expensive than a field survey of the invasive population.

  16. Cedar Breaks National Monument Vegetation Mapping Project - True Color Aerial Imagery

    Data.gov (United States)

    National Park Service, Department of the Interior — Color aerial photography was collected, in stereo with 60 percent forward overlap and 40 percent side overlap on 6-27-02. The flight height was 20,000 feet above...

  17. Aerial Photography and Imagery, Ortho-Corrected - 2013 Digital Orthophotos - Suwannee County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International during the period of December 30th, 2012 to March...

  18. Hurricane Ophelia Aerial Photography: High-Resolution Imagery of the North Carolina Coast After Landfall

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is of the North Carolina coast after Hurricane Ophelia made landfall. The regions photographed range from Hubert, North Carolina to...

  19. Coastal Bend Texas Benthic Habitat Mapping Reprocessed DOQQ Aerial Imagery (NODC Accession 0086051)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — In 2006 and 2007 the NOAA Coastal Services Center purchased services to reprocess existing digital multi-spectral imagery (ADS-40) and create digital benthic habitat...

  20. Aerial Photography and Imagery, Ortho-Corrected - 2012 Digital Orthophotos - Okeechobee County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This file references a single orthogonal imagery tile produced from nadir images captured by Pictometry International during the period of January 12th, 2011 to...

  1. Aerial Photography and Imagery, Ortho-Corrected - 2013 Digital Orthophotos - Okaloosa County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This file references a single orthogonal imagery tile produced from nadir images captured by Pictometry International during the period of December 21st, 2012 to...

  2. Aerial Photography and Imagery, Ortho-Corrected - 2009 Digital Orthophotos - Hernando County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set is one component of a digital orthophoto imagery (DOI) coverage over the Southwest Florida Water Management District (SWFWMD) North District Area, for...

  3. Aerial Photography and Imagery, Ortho-Corrected - 2010 Digital Orthophotos - Hernando County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set is one component of a digital orthophoto imagery (DOI) coverage over the Southwest Florida Water Management District (SWFWMD) North District Area, for...

  4. Hurricane Wilma Aerial Photography: High-Resolution Imagery of the Florida Coast After Landfall

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is of the Florida coast after Hurricane Wilma made landfall. The regions photographed range from Key West to Sixmile Bend, Florida....

  5. Aerial Photography and Imagery, Ortho-Corrected - 2012 Digital Orthophotos - Monroe County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital orthoimagery covering Monroe County, FL. This 1"=100' scale imagery is comprised of natural color orthoimagery with a GSD (Ground...

  6. Aerial Photography and Imagery, Ortho-Corrected - 2011 Digital Orthophotos - Highlands County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital orthoimagery covering Highlands County, FL. This 1"=100' scale imagery is comprised of natural color orthoimagery with a GSD...

  7. Aerial Photography and Imagery, Ortho-Corrected - 2011 Digital Orthophotos - Hendry County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital orthoimagery covering Hendry County, FL. This 1"=200' scale imagery is comprised of natural color orthoimagery with a GSD (Ground...

  8. Aerial Photography and Imagery, Ortho-Corrected - 2012 Digital Orthophotos - Monroe County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This metadata describes the digital orthoimagery covering the area of the Everglades in Monroe County, FL as defined by the FL DOR. This 1"=100' scale imagery is...

  9. Aerial Photography and Imagery, Ortho-Corrected - 2009 Digital Orthophotos - Citrus County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set is one component of a digital orthophoto imagery (DOI) coverage over the Southwest Florida Water Management District (SWFWMD) North District Area, for...

  10. Aerial Photography and Imagery, Ortho-Corrected - 2009 Digital Orthophotos - Levy County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set is one component of a digital orthophoto imagery (DOI) coverage over the Southwest Florida Water Management District (SWFWMD) North District Area, for...

  11. Aerial Photography and Imagery, Ortho-Corrected - 2009 Digital Orthophotos - Marion County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set is one component of a digital orthophoto imagery (DOI) coverage over the Southwest Florida Water Management District (SWFWMD) North District Area, for...

  12. Aerial Photography and Imagery, Ortho-Corrected - 2010 Digital Orthophotos - Marion County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set is one component of a digital orthophoto imagery (DOI) coverage over the Southwest Florida Water Management District (SWFWMD) North District Area, for...

  13. Aerial Photography and Imagery, Ortho-Corrected - 2010 Digital Orthophotos - Pasco County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set is one component of a digital orthophoto imagery (DOI) coverage over the Southwest Florida Water Management District (SWFWMD) North District Area, for...

  14. Aerial Photography and Imagery, Ortho-Corrected - 2010 Digital Orthophotos - Lake County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set is one component of a digital orthophoto imagery (DOI) coverage over the Southwest Florida Water Management District (SWFWMD) North District Area, for...

  15. Aerial Photography and Imagery, Ortho-Corrected - 2009 Digital Orthophotos - Pasco County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set is one component of a digital orthophoto imagery (DOI) coverage over the Southwest Florida Water Management District (SWFWMD) North District Area, for...

  16. Aerial Photography and Imagery, Ortho-Corrected - 2010 Digital Orthophotos - Sumter County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set is one component of a digital orthophoto imagery (DOI) coverage over the Southwest Florida Water Management District (SWFWMD) North District Area, for...

  17. Aerial Photography and Imagery, Ortho-Corrected - 2010 Digital Orthophotos - Citrus County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set is one component of a digital orthophoto imagery (DOI) coverage over the Southwest Florida Water Management District (SWFWMD) North District Area, for...

  18. Aerial Photography and Imagery, Ortho-Corrected - 2009 Digital Orthophotos - Sumter County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set is one component of a digital orthophoto imagery (DOI) coverage over the Southwest Florida Water Management District (SWFWMD) North District Area, for...

  19. Aerial Photography and Imagery, Ortho-Corrected - 2010 Digital Orthophotos - Levy County

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This data set is one component of a digital orthophoto imagery (DOI) coverage over the Southwest Florida Water Management District (SWFWMD) North District Area, for...

  20. Hurricane Jeanne Aerial Photography: High-Resolution Imagery of the Atlantic Coast of Florida After Landfall

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is of the Atlantic coast of Florida after Hurricane Jeanne made landfall. The regions photographed range along a 100-mile stretch...

  1. Aerial Photography and Imagery, Ortho-Corrected - 2013 Digital Orthophotos - Suwannee County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International during the period of December 30th, 2012 to March...

  2. Aerial Photography and Imagery, Ortho-Corrected - 2013 Digital Orthophotos - Okaloosa County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — This file references a single orthogonal imagery tile produced from nadir images captured by Pictometry International during the period of December 21st, 2012 to...

  3. Aerial Photography and Imagery, Ortho-Corrected - 2010 Digital Orthophotos - Lake County

    Data.gov (United States)

    NSGIC Education | GIS Inventory — This data set is one component of a digital orthophoto imagery (DOI) coverage over the Southwest Florida Water Management District (SWFWMD) North District Area, for...

  4. Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland

    Science.gov (United States)

    Lu, Bing; He, Yuhong

    2017-06-01

    Investigating spatio-temporal variations of species composition in grassland is an essential step in evaluating grassland health conditions, understanding the evolutionary processes of the local ecosystem, and developing grassland management strategies. Space-borne remote sensing images (e.g., MODIS, Landsat, and Quickbird) with spatial resolutions varying from less than 1 m to 500 m have been widely applied for vegetation species classification at spatial scales from community to regional levels. However, the spatial resolutions of these images are not fine enough to investigate grassland species composition, since grass species are generally small in size and highly mixed, and vegetation cover is greatly heterogeneous. Unmanned Aerial Vehicle (UAV) as an emerging remote sensing platform offers a unique ability to acquire imagery at very high spatial resolution (centimetres). Compared to satellites or airplanes, UAVs can be deployed quickly and repeatedly, and are less limited by weather conditions, facilitating advantageous temporal studies. In this study, we utilize an octocopter, on which we mounted a modified digital camera (with near-infrared (NIR), green, and blue bands), to investigate species composition in a tall grassland in Ontario, Canada. Seven flight missions were conducted during the growing season (April to December) in 2015 to detect seasonal variations, and four of them were selected in this study to investigate the spatio-temporal variations of species composition. To quantitatively compare images acquired at different times, we establish a processing flow of UAV-acquired imagery, focusing on imagery quality evaluation and radiometric correction. The corrected imagery is then applied to an object-based species classification. Maps of species distribution are subsequently used for a spatio-temporal change analysis. Results indicate that UAV-acquired imagery is an incomparable data source for studying fine-scale grassland species composition

  5. Automatic geolocation of targets tracked by aerial imaging platforms using satellite imagery

    Science.gov (United States)

    Shukla, P. K.; Goel, S.; Singh, P.; Lohani, B.

    2014-11-01

    Tracking of targets from aerial platforms is an important activity in several applications, especially surveillance. Knowled ge of geolocation of these targets adds additional significant and useful information to the application. This paper determines the geolocation of a target being tracked from an aerial platform using the technique of image registration. Current approaches utilize a POS to determine the location of the aerial platform and then use the same for geolocation of the targets using the principle of photogrammetry. The constraints of cost and low-payload restrict the applicability of this approach using UAV platforms. This paper proposes a methodology for determining the geolocation of a target tracked from an aerial platform in a partially GPS devoid environment. The method utilises automatic feature based registration technique of a georeferenced satellite image with an ae rial image which is already stored in UAV's database to retrieve the geolocation of the target. Since it is easier to register subsequent aerial images due to similar viewing parameters, the subsequent overlapping images are registered together sequentially thus resulting in the registration of each of the images with georeferenced satellite image thus leading to geolocation of the target under interest. Using the proposed approach, the target can be tracked in all the frames in which it is visible. The proposed concept is verified experimentally and the results are found satisfactory. Using the proposed method, a user can obtain location of target of interest as well features on ground without requiring any POS on-board the aerial platform. The proposed approach has applications in surveillance for target tracking, target geolocation as well as in disaster management projects like search and rescue operations.

  6. Aerial video and ladar imagery fusion for persistent urban vehicle tracking

    Science.gov (United States)

    Cho, Peter; Greisokh, Daniel; Anderson, Hyrum; Sandland, Jessica; Knowlton, Robert

    2007-04-01

    We assess the impact of supplementing two-dimensional video with three-dimensional geometry for persistent vehicle tracking in complex urban environments. Using recent video data collected over a city with minimal terrain content, we first quantify erroneous sources of automated tracking termination and identify those which could be ameliorated by detailed height maps. They include imagery misregistration, roadway occlusion and vehicle deceleration. We next develop mathematical models to analyze the tracking value of spatial geometry knowledge in general and high resolution ladar imagery in particular. Simulation results demonstrate how 3D information could eliminate large numbers of false tracks passing through impenetrable structures. Spurious track rejection would permit Kalman filter coasting times to be significantly increased. Track lifetimes for vehicles occluded by trees and buildings as well as for cars slowing down at corners and intersections could consequently be prolonged. We find high resolution 3D imagery can ideally yield an 83% reduction in the rate of automated tracking failure.

  7. A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery

    Directory of Open Access Journals (Sweden)

    Philippe Lejeune

    2013-11-01

    Full Text Available The recent development of operational small unmanned aerial systems (UASs opens the door for their extensive use in forest mapping, as both the spatial and temporal resolution of UAS imagery better suit local-scale investigation than traditional remote sensing tools. This article focuses on the use of combined photogrammetry and “Structure from Motion” approaches in order to model the forest canopy surface from low-altitude aerial images. An original workflow, using the open source and free photogrammetric toolbox, MICMAC (acronym for Multi Image Matches for Auto Correlation Methods, was set up to create a digital canopy surface model of deciduous stands. In combination with a co-registered light detection and ranging (LiDAR digital terrain model, the elevation of vegetation was determined, and the resulting hybrid photo/LiDAR canopy height model was compared to data from a LiDAR canopy height model and from forest inventory data. Linear regressions predicting dominant height and individual height from plot metrics and crown metrics showed that the photogrammetric canopy height model was of good quality for deciduous stands. Although photogrammetric reconstruction significantly smooths the canopy surface, the use of this workflow has the potential to take full advantage of the flexible revisit period of drones in order to refresh the LiDAR canopy height model and to collect dense multitemporal canopy height series.

  8. Seasonal surface velocities of a Himalayan glacier derived by automated correlation of unmanned aerial vehicle imagery

    NARCIS (Netherlands)

    Kraaijenbrink, Philip; Meijer, Sander W.; Shea, Joseph M.; Pellicciotti, Francesca; De Jong, Steven M.; Immerzeel, Walter W.

    2016-01-01

    Debris-covered glaciers play an important role in the high-altitude water cycle in the Himalaya, yet their dynamics are poorly understood, partly because of the difficult fieldwork conditions. In this study we therefore deploy an unmanned aerial vehicle (UAV) three times (May 2013, October 2013 and

  9. Seasonal surface velocities of a Himalayan glacier derived by automated correlation of unmanned aerial vehicle imagery

    NARCIS (Netherlands)

    Kraaijenbrink, Philip; Meijer, Sander W.; Shea, Joseph M.; Pellicciotti, Francesca; De Jong, Steven M.|info:eu-repo/dai/nl/120221306; Immerzeel, Walter W.|info:eu-repo/dai/nl/290472113

    2016-01-01

    Debris-covered glaciers play an important role in the high-altitude water cycle in the Himalaya, yet their dynamics are poorly understood, partly because of the difficult fieldwork conditions. In this study we therefore deploy an unmanned aerial vehicle (UAV) three times (May 2013, October 2013 and

  10. Automatic urban debris zone extraction from post-hurricane very high-resolution satellite and aerial imagery

    Directory of Open Access Journals (Sweden)

    Shasha Jiang

    2016-05-01

    Full Text Available Automated remote sensing methods have not gained widespread usage for damage assessment after hurricane events, especially for low-rise buildings, such as individual houses and small businesses. Hurricane wind, storm surge with waves, and inland flooding have unique damage signatures, further complicating the development of robust automated assessment methodologies. As a step toward realizing automated damage assessment for multi-hazard hurricane events, this paper presents a mono-temporal image classification methodology that quickly and accurately differentiates urban debris from non-debris areas using post-event images. Three classification approaches are presented: spectral, textural, and combined spectral–textural. The methodology is demonstrated for Gulfport, Mississippi, using IKONOS panchromatic satellite and NOAA aerial colour imagery collected after 2005 Hurricane Katrina. The results show that multivariate texture information significantly improves debris class detection performance by decreasing the confusion between debris and other land cover types, and the extracted debris zone accurately captures debris distribution. Additionally, the extracted debris boundary is approximately equivalent regardless of imagery type, demonstrating the flexibility and robustness of the debris mapping methodology. While the test case presents results for hurricane hazards, the proposed methodology is generally developed and expected to be effective in delineating debris zones for other natural hazards, including tsunamis, tornadoes, and earthquakes.

  11. Surface Temperature Mapping of the University of Northern Iowa Campus Using High Resolution Thermal Infrared Aerial Imageries.

    Science.gov (United States)

    Savelyev, Alexander; Sugumaran, Ramanathan

    2008-08-25

    The goal of this project was to map the surface temperature of the University of Northern Iowa campus using high-resolution thermal infrared aerial imageries. A thermal camera with a spectral bandwidth of 3.0-5.0 μm was flown at the average altitude of 600 m, achieving ground resolution of 29 cm. Ground control data was used to construct the pixelto-temperature conversion model, which was later used to produce temperature maps of the entire campus and also for validation of the model. The temperature map then was used to assess the building rooftop conditions and steam line faults in the study area. Assessment of the temperature map revealed a number of building structures that may be subject to insulation improvement due to their high surface temperatures leaks. Several hot spots were also identified on the campus for steam pipelines faults. High-resolution thermal infrared imagery proved highly effective tool for precise heat anomaly detection on the campus, and it can be used by university facility services for effective future maintenance of buildings and grounds.

  12. Mapping trees outside forests using high-resolution aerial imagery: a comparison of pixel- and object-based classification approaches.

    Science.gov (United States)

    Meneguzzo, Dacia M; Liknes, Greg C; Nelson, Mark D

    2013-08-01

    Discrete trees and small groups of trees in nonforest settings are considered an essential resource around the world and are collectively referred to as trees outside forests (ToF). ToF provide important functions across the landscape, such as protecting soil and water resources, providing wildlife habitat, and improving farmstead energy efficiency and aesthetics. Despite the significance of ToF, forest and other natural resource inventory programs and geospatial land cover datasets that are available at a national scale do not include comprehensive information regarding ToF in the United States. Additional ground-based data collection and acquisition of specialized imagery to inventory these resources are expensive alternatives. As a potential solution, we identified two remote sensing-based approaches that use free high-resolution aerial imagery from the National Agriculture Imagery Program (NAIP) to map all tree cover in an agriculturally dominant landscape. We compared the results obtained using an unsupervised per-pixel classifier (independent component analysis-[ICA]) and an object-based image analysis (OBIA) procedure in Steele County, Minnesota, USA. Three types of accuracy assessments were used to evaluate how each method performed in terms of: (1) producing a county-level estimate of total tree-covered area, (2) correctly locating tree cover on the ground, and (3) how tree cover patch metrics computed from the classified outputs compared to those delineated by a human photo interpreter. Both approaches were found to be viable for mapping tree cover over a broad spatial extent and could serve to supplement ground-based inventory data. The ICA approach produced an estimate of total tree cover more similar to the photo-interpreted result, but the output from the OBIA method was more realistic in terms of describing the actual observed spatial pattern of tree cover.

  13. Automated Identification of Rivers and Shorelines in Aerial Imagery Using Image Texture

    Science.gov (United States)

    2011-01-01

    defining the criteria for segmenting the image. For these cases certain automated, unsupervised (or minimally supervised), image classification ...banks, image analysis, edge finding, photography, satellite, texture, entropy 16. SECURITY CLASSIFICATION OF: a. REPORT Unclassified b. ABSTRACT...high resolution bank geometry. Much of the globe is covered by various sorts of multi- or hyperspectral imagery and numerous techniques have been

  14. Improving Measurement of Forest Structural Parameters by Co-Registering of High Resolution Aerial Imagery and Low Density LiDAR Data.

    Science.gov (United States)

    Huang, Huabing; Gong, Peng; Cheng, Xiao; Clinton, Nick; Li, Zengyuan

    2009-01-01

    Forest structural parameters, such as tree height and crown width, are indispensable for evaluating forest biomass or forest volume. LiDAR is a revolutionary technology for measurement of forest structural parameters, however, the accuracy of crown width extraction is not satisfactory when using a low density LiDAR, especially in high canopy cover forest. We used high resolution aerial imagery with a low density LiDAR system to overcome this shortcoming. A morphological filtering was used to generate a DEM (Digital Elevation Model) and a CHM (Canopy Height Model) from LiDAR data. The LiDAR camera image is matched to the aerial image with an automated keypoints search algorithm. As a result, a high registration accuracy of 0.5 pixels was obtained. A local maximum filter, watershed segmentation, and object-oriented image segmentation are used to obtain tree height and crown width. Results indicate that the camera data collected by the integrated LiDAR system plays an important role in registration with aerial imagery. The synthesis with aerial imagery increases the accuracy of forest structural parameter extraction when compared to only using the low density LiDAR data.

  15. The Importance of CIR Aerial Imagery in Inventory, Monitoring and Predicting Forest Condition

    Directory of Open Access Journals (Sweden)

    Jelena Kolić

    2015-07-01

    Full Text Available Background and Purpose: The main goal of this paper was to highlight the importance of colour infrared (CIR aerial photographs for efficient inventory, monitoring and predicting the health status of forests in changed site conditions. CIR aerial photographs from two aerial surveys conducted in 1989 and 2008 were used to identify and analyze the damage in lowland pedunculated oak (Quercus robur L. forests during each period, as well as to obtain a dieback trend in the observed period. Material and Methods: The research was conducted in lowland pedunculated oak forests of Josip Kozarac management unit. CIR aerial photographs (1989 of the research area were taken with a classical camera, while aerial images in 2008 were taken with a digital camera and then converted from digital to analogue form (contact copy - photograph in order to perform photointerpretation with a SOKKIA MS27 Carl ZEISS Jena mirror stereoscope, magnified by 8x. The health status of particular trees (crowns was assessed by means of photointerpretation keys in a stereomodel over a systematic 100x100 m sample grid on both 1989 and 2008 aerial photographs. The degree of damage of 4 individual trees was assessed at every grid point in the surveying strips covering the surveyed area. Damage indicators were calculated and thematic maps were constructed on the basis of the interpretation of data for all the grid points. Results: For the research area a damage index (IO of 68.36% for oak was determined by photointerpreting individual trees (2008; in other words, this percentage of pedunculate oak trees in the surveyed area was found to be in the damage degree of 2.1 and more. Of 68.36% trees classified in the damage degree of 2.1 or more, mean damage (SO1 amounted to 52.16% and could be classified in the damage degree of 2.2. In 1989, the mean damage index (IO for pedunculate oak was 48.00%, and pedunculate oak trees with mean damage degree of 2.1 or more (SO1 amounted to 36.03%. The

  16. Automatic Feature Detection, Description and Matching from Mobile Laser Scanning Data and Aerial Imagery

    Science.gov (United States)

    Hussnain, Zille; Oude Elberink, Sander; Vosselman, George

    2016-06-01

    In mobile laser scanning systems, the platform's position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of nadir aerial and MLSPC ortho images. In the data pre-processing step the MLSPC is patch-wise cropped and converted to ortho images. Furthermore, each aerial image patch covering the area of the corresponding MLSPC patch is also cropped from the aerial image. For feature detection, we implemented an adaptive variant of Harris-operator to automatically detect corner feature points on the vertices of road markings. In feature description phase, we used the LATCH binary descriptor, which is robust to data from different sensors. For descriptor matching, we developed an outlier filtering technique, which exploits the arrangements of relative Euclidean-distances and angles between corresponding sets of feature points. We found that the positioning accuracy of the computed correspondence has achieved the pixel level accuracy, where the image resolution is 12cm. Furthermore, the developed approach is reliable when enough road markings are available in the data sets. We conclude that, in urban areas, the developed approach can reliably extract features necessary to improve the MLSPC accuracy to pixel level.

  17. A surge of Perseibreen, Svalbard, examined using aerial photography and ASTER high resolution satellite imagery

    OpenAIRE

    Dowdeswell, Julian A.; Benham, Toby J.

    2003-01-01

    The identification of surge activity is important in assessing the duration of the active and quiescent phases of the surge cycle of Svalbard glaciers. Satellite and aerial photographic images are used to identify and describe the form and flow of Perseibreen, a valley glacier of 59 km2 on the east coast of Spitsbergen. Heavy surface crevassing and a steep ice front, indicative of surge activity, were first observed on Perseibreen in April 2002. Examination of high resolution (15 m) Advanced ...

  18. OVERVIEW OF MODERN RESEARCH OF LANDSLIDES ACCORDING TO AERIAL AND SATELLITE IMAGERY

    Directory of Open Access Journals (Sweden)

    K. M. Lyapishev

    2015-01-01

    Full Text Available This article is an overview of researches of landslides using remote sensing methods such as aerial photography, satellite images, radar interferometry, and their combination with the use of GIS technology. Modern methods of investigation of landslides are very diverse. The authors propose different approaches to the identification, classification and monitoring of landslides. Data analysis techniques can help in creating more sophisticated approach to the analysis of landslides.

  19. Aerial Photography and Imagery, Ortho-Corrected, 1991 DOQQs in Grid format on CD-ROM, Published in 1991, 1:12000 (1in=1000ft) scale, Reno County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:12000 (1in=1000ft) scale, was produced all or in part from Orthoimagery information as...

  20. Aerial Photography and Imagery, Ortho-Corrected, Lafayette County Mr. Sids and TIF files, Published in 2005, 1:24000 (1in=2000ft) scale, Lafayette County Land Records.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Orthoimagery information as...

  1. Aerial Photography and Imagery, Ortho-Corrected, True Color Orthophotography for Wicomico County, Maryland - 6" pixles, 2006., Published in 2006, 1:1200 (1in=100ft) scale, Eastern Shore Regional GIS Cooperative.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  2. Aerial Photography and Imagery, Ortho-Corrected, 2005 natural color orthoimagery for alameda county geotiff format, Published in 2005, 1:2400 (1in=200ft) scale, US Geological Survey.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  3. Aerial Photography and Imagery, Ortho-Corrected, One meter black and white digital orthophotographs created in concert with the U.S.G.S., Published in 1992, 1:9600 (1in=800ft) scale, Manitowoc County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 1992. One meter black and white digital orthophotographs created in concert with the U.S.G.S..

  4. Aerial Photography and Imagery, Ortho-Corrected, Washburn County had ortho/oblique photography flight done in April of 2009. Pictometry was contracted for the project., Published in 2009, 1:4800 (1in=400ft) scale, Washburn County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  5. Aerial Photography and Imagery, Ortho-Corrected, True Color Orthophotography for Centerville, Grasonville, Kent Island, and Queenstown - 4" pixles, 2004., Published in 2004, 1:600 (1in=50ft) scale, Eastern Shore Regional GIS Cooperative.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  6. Aerial Photography and Imagery, Ortho-Corrected, Washington County, NC true color orhophotography - 1/2 foot resolution over selected areas, Published in 2009, 1:2400 (1in=200ft) scale, Washington County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2009. Washington County, NC true color orhophotography - 1/2 foot resolution over selected areas.

  7. Aerial Photography and Imagery, Ortho-Corrected, Washington County, NC true color orthophotography - 1/4 foot resolution over selected areas, Published in 2009, 1:1200 (1in=100ft) scale, Washington County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2009. Washington County, NC true color orthophotography - 1/4 foot resolution over selected...

  8. Aerial Photography and Imagery, Ortho-Corrected, Washington County, NC true color orthophotography - 1 foot resolution in the remainder of the county, Published in 2009, 1:4800 (1in=400ft) scale, Washington County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2009. Washington County, NC true color orthophotography - 1 foot resolution in the remainder of...

  9. Aerial Photography and Imagery, Ortho-Corrected, Buffalo County, WI countywide orthoimagery flown April 14,17.2006, Published in 2006, 1:600 (1in=50ft) scale, Buffalo County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  10. Aerial Photography and Imagery, Ortho-Corrected, 2002 Buffalo County, WI Color orthophotography flown April 16/17, Published in 2002, 1:600 (1in=50ft) scale, Buffalo County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  11. Aerial Photography and Imagery, Ortho-Corrected, Have done 6" color resolution of entire County including the City of Ulysses, Published in 2007, 1:4800 (1in=400ft) scale, Grant County Emergency Management.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  12. Aerial Photography and Imagery, Ortho-Corrected, Chowan County, NC true color orthophotography - 1/4 foot resolution over selected areas, Published in 2009, 1:1200 (1in=100ft) scale, Chowan County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2009. Chowan County, NC true color orthophotography - 1/4 foot resolution over selected areas.

  13. Aerial Photography and Imagery, Ortho-Corrected, Bertie County, NC true color orthophotography - 1 foot resolution over the remainder of the county, Published in 2009, 1:4800 (1in=400ft) scale, Bertie County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2009. Bertie County, NC true color orthophotography - 1 foot resolution over the remainder of...

  14. Aerial Photography and Imagery, Oblique, Pictometry/Microsoft Virtual Earth is the source of most of this., Published in 2007, 1:12000 (1in=1000ft) scale, Brown County, WI.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset, published at 1:12000 (1in=1000ft) scale, was produced all or in part from Other information as of 2007. It is...

  15. Aerial Photography and Imagery, Ortho-Corrected, True Color Orthophotography for Cambridge, Hurlock, Secretary, and Vienna - 4" pixles, 2006., Published in 2006, 1:600 (1in=50ft) scale, Eastern Shore Regional GIS Cooperative.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  16. Aerial Photography and Imagery, Ortho-Corrected, Gates County, NC true color orthophotography - 1/2 foot resolution over selected areas, Published in unknown, 1:2400 (1in=200ft) scale, Gates County Tax Department.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  17. Aerial Photography and Imagery, Ortho-Corrected, Frederick County, 6" pixel orthophotography, Maryland State Highway, Published in 2007, 1:600 (1in=50ft) scale, Frederick County, MD Enterprise GIS.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  18. Aerial Photography and Imagery, Ortho-Corrected, 6 in orthophotography for cities of Lakin and Deerfield, Published in 2007, 1:1200 (1in=100ft) scale, Kearny County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Not Provided information as of...

  19. Aerial Photography and Imagery, Ortho-Corrected, Herford County, NC true color orthophotography - 1/4 foot resolution over selected areas, Published in 2009, 1:1200 (1in=100ft) scale, Herford County, NC Land Records.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  20. Aerial Photography and Imagery, Ortho-Corrected, 2005 Color 1 Foot and 6 Inch Orthoimagery, Published in 2005, 1:2400 (1in=200ft) scale, Winnebago County GIS.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  1. Aerial Photography and Imagery, Ortho-Corrected, 1998 orthophotography for the city of Hutchinson in TIFF format. Obtained from the City of Hutchinson., Published in 1998, 1:1200 (1in=100ft) scale, Reno County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  2. Aerial Photography and Imagery, Oblique, For rural areas Pictometry flown 11/2009, not yet delivered as of 2/2010, Published in 2009, 1:4800 (1in=400ft) scale, Greene County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of 2009....

  3. Aerial Photography and Imagery, Ortho-Corrected, NAIP 2007 Orthoimagery 1-meter natural color (RGBIR) Statewide coverage., Published in 2007, 1:12000 (1in=1000ft) scale, Arizona State Land Department.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:12000 (1in=1000ft) scale, was produced all or in part from Orthoimagery information as...

  4. Aerial Photography and Imagery, Ortho-Corrected, USGS/USDA/State of Oklahoma partnership, Published in 1995, 1:12000 (1in=1000ft) scale, Office of Geographic Information.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:12000 (1in=1000ft) scale, was produced all or in part from Orthoimagery information as...

  5. Aerial Photography and Imagery, Ortho-Corrected, Washburn County had ortho/oblique photography flight done in April of 2009. Pictometry was contracted for the project., Published in 2009, 1:4800 (1in=400ft) scale, Washburn County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2009. Washburn County had ortho/oblique photography flight done in April of 2009. Pictometry...

  6. Aerial Photography and Imagery, Oblique, Washburn County had oblique photography flight done in April of 2009. Pictometry was contracted for the project., Published in 2009, 1:4800 (1in=400ft) scale, Washburn County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Aerial Photography and Imagery, Oblique dataset current as of 2009. Washburn County had oblique photography flight done in April of 2009. Pictometry was contracted...

  7. Aerial Photography and Imagery, Ortho-Corrected, Hawaii (Big Island) Digital Raster Graphic, Published in 2004, 1:24000 (1in=2000ft) scale, U.S. Geological Survey.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Hardcopy Maps information as...

  8. Aerial Photography and Imagery, Ortho-Corrected, Orthoimagery for Athens-Clarke County, Georgia, Published in 2005, 1:4800 (1in=400ft) scale, Northeast Georgia Regional Commission.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  9. Aerial Photography and Imagery, Ortho-Corrected, 2005 DOQQs in MrSID format obtained from DASC, Published in 2005, 1:12000 (1in=1000ft) scale, Reno County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:12000 (1in=1000ft) scale, was produced all or in part from Orthoimagery information as...

  10. Aerial Photography and Imagery, Ortho-Corrected, 2007 Color Orthophotos (MrSID and TIFF formats), Published in 2007, 1:2400 (1in=200ft) scale, CHATHAM COUNTY GIS.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  11. Aerial Photography and Imagery, Ortho-Corrected, True Color Orthos at 1:4800 & 1:2400 3/2006, Published in 2006, 1:4800 (1in=400ft) scale, Hyde County Emergency Management.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  12. Aerial Photography and Imagery, Ortho-Corrected, naturla color 1-ft orthoinagery for fresno urban area public domain, Published in 2007, 1:4800 (1in=400ft) scale, US Geological Survey.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  13. Aerial Photography and Imagery, Ortho-Corrected, Color orthophotos of Eastern York County and the municipalities of Urbanized Eastern York County flown at 200 scale, Published in 2000, 1:2400 (1in=200ft) scale, York County Government, SC.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  14. Aerial Photography and Imagery, Ortho-Corrected, san bernardino urban area natural color orthoimagery 2006, Published in 2006, 1:2400 (1in=200ft) scale, US Geological Survey.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  15. Aerial Photography and Imagery, Ortho-Corrected, NAIP statewide natural color geotiff image tiles, 2005, Published in 2005, 1:12000 (1in=1000ft) scale, US Geological Survey.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:12000 (1in=1000ft) scale, was produced all or in part from Orthoimagery information as...

  16. Aerial Photography and Imagery, Ortho-Corrected, Spring 2010 leaf-off natural color 12 inch ground pixel digital orthophotograpy, Published in 2010, 1:4800 (1in=400ft) scale, Vernon County Wisconsin.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  17. Aerial Photography and Imagery, Ortho-Corrected, Rural areas are 12 inch and urban areas are 6in, Published in 2007, 1:24000 (1in=2000ft) scale, Johnson County GIS Department.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Orthoimagery information as...

  18. Aerial Photography and Imagery, Ortho-Corrected, Bertie County, NC true color orthophotography - 1 foot resolution over the remainder of the county, Published in 2009, 1:4800 (1in=400ft) scale, Bertie County - Tax Mapping.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Orthoimagery information as of...

  19. Aerial Photography and Imagery, Ortho-Corrected, USGS High Resolution Orthoimage, Salt Lake City - Ogden, UT, Published in 2003, 1:24000 (1in=2000ft) scale, State of Utah Automated Geographic Reference Center.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Orthoimagery information as...

  20. Mapping Urban Tree Canopy Coverage and Structure using Data Fusion of High Resolution Satellite Imagery and Aerial Lidar

    Science.gov (United States)

    Elmes, A.; Rogan, J.; Williams, C. A.; Martin, D. G.; Ratick, S.; Nowak, D.

    2015-12-01

    Urban tree canopy (UTC) coverage is a critical component of sustainable urban areas. Trees provide a number of important ecosystem services, including air pollution mitigation, water runoff control, and aesthetic and cultural values. Critically, urban trees also act to mitigate the urban heat island (UHI) effect by shading impervious surfaces and via evaporative cooling. The cooling effect of urban trees can be seen locally, with individual trees reducing home HVAC costs, and at a citywide scale, reducing the extent and magnitude of an urban areas UHI. In order to accurately model the ecosystem services of a given urban forest, it is essential to map in detail the condition and composition of these trees at a fine scale, capturing individual tree crowns and their vertical structure. This paper presents methods for delineating UTC and measuring canopy structure at fine spatial resolution (HVAC benefits from UTC for individual homes, and for assessing the ecosystem services for entire urban areas. Such maps have previously been made using a variety of methods, typically relying on high resolution aerial or satellite imagery. This paper seeks to contribute to this growing body of methods, relying on a data fusion method to combine the information contained in high resolution WorldView-3 satellite imagery and aerial lidar data using an object-based image classification approach. The study area, Worcester, MA, has recently undergone a large-scale tree removal and reforestation program, following a pest eradication effort. Therefore, the urban canopy in this location provides a wide mix of tree age class and functional type, ideal for illustrating the effectiveness of the proposed methods. Early results show that the object-based classifier is indeed capable of identifying individual tree crowns, while continued research will focus on extracting crown structural characteristics using lidar-derived metrics. Ultimately, the resulting fine resolution UTC map will be

  1. Low aerial imagery - an assessment of georeferencing errors and the potential for use in environmental inventory

    Science.gov (United States)

    Smaczyński, Maciej; Medyńska-Gulij, Beata

    2017-06-01

    Unmanned aerial vehicles are increasingly being used in close range photogrammetry. Real-time observation of the Earth's surface and the photogrammetric images obtained are used as material for surveying and environmental inventory. The following study was conducted on a small area (approximately 1 ha). In such cases, the classical method of topographic mapping is not accurate enough. The geodetic method of topographic surveying, on the other hand, is an overly precise measurement technique for the purpose of inventorying the natural environment components. The author of the following study has proposed using the unmanned aerial vehicle technology and tying in the obtained images to the control point network established with the aid of GNSS technology. Georeferencing the acquired images and using them to create a photogrammetric model of the studied area enabled the researcher to perform calculations, which yielded a total root mean square error below 9 cm. The performed comparison of the real lengths of the vectors connecting the control points and their lengths calculated on the basis of the photogrammetric model made it possible to fully confirm the RMSE calculated and prove the usefulness of the UAV technology in observing terrain components for the purpose of environmental inventory. Such environmental components include, among others, elements of road infrastructure, green areas, but also changes in the location of moving pedestrians and vehicles, as well as other changes in the natural environment that are not registered on classical base maps or topographic maps.

  2. Improving Measurement of Forest Structural Parameters by Co-Registering of High Resolution Aerial Imagery and Low Density LiDAR Data

    OpenAIRE

    Huang, Huabing; Gong, Peng; CHENG, XIAO; Clinton, Nick; Li, Zengyuan

    2009-01-01

    Forest structural parameters, such as tree height and crown width, are indispensable for evaluating forest biomass or forest volume. LiDAR is a revolutionary technology for measurement of forest structural parameters, however, the accuracy of crown width extraction is not satisfactory when using a low density LiDAR, especially in high canopy cover forest. We used high resolution aerial imagery with a low density LiDAR system to overcome this shortcoming. A morphological filtering was used to ...

  3. Blanding’s Turtle (Emydoidea blandingii Potential Habitat Mapping Using Aerial Orthophotographic Imagery and Object Based Classification

    Directory of Open Access Journals (Sweden)

    Douglas J. King

    2012-01-01

    Full Text Available Blanding’s turtle (Emydoidea blandingii is a threatened species under Canada’s Species at Risk Act. In southern Québec, field based inventories are ongoing to determine its abundance and potential habitat. The goal of this research was to develop means for mapping of potential habitat based on primary habitat attributes that can be detected with high-resolution remotely sensed imagery. Using existing spring leaf-off 20 cm resolution aerial orthophotos of a portion of Gatineau Park where some Blanding’s turtle observations had been made, habitat attributes were mapped at two scales: (1 whole wetlands; (2 within wetland habitat features of open water, vegetation (used for camouflage and thermoregulation, and logs (used for spring sun-basking. The processing steps involved initial pixel-based classification to eliminate most areas of non-wetland, followed by object-based segmentations and classifications using a customized rule sequence to refine the wetland map and to map the within wetland habitat features. Variables used as inputs to the classifications were derived from the orthophotos and included image brightness, texture, and segmented object shape and area. Independent validation using field data and visual interpretation showed classification accuracy for all habitat attributes to be generally over 90% with a minimum of 81.5% for the producer’s accuracy of logs. The maps for each attribute were combined to produce a habitat suitability map for Blanding’s turtle. Of the 115 existing turtle observations, 92.3% were closest to a wetland of the two highest suitability classes. High-resolution imagery combined with object-based classification and habitat suitability mapping methods such as those presented provide a much more spatially explicit representation of detailed habitat attributes than can be obtained through field work alone. They can complement field efforts to document and track turtle activities and can contribute to

  4. Aerial Photography and Imagery, Ortho-Corrected, RIGIS03/04 Digital Orthophotos of Rhode Island 2003-2004; For this dataset, the natural color orthoimages were produced at 2 foot pixel resolution. Each compressed and mosaiced orthoimage provides imagery for a 20000- by 20000-foot block on the ground., Published in 2005, 1:4800 (1in=400ft) scale, State of Rhode Island and Providence Plantations.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:4800 (1in=400ft) scale, was produced all or in part from Uncorrected Imagery information...

  5. The influence of the in situ camera calibration for direct georeferencing of aerial imagery

    Science.gov (United States)

    Mitishita, E.; Barrios, R.; Centeno, J.

    2014-11-01

    The direct determination of exterior orientation parameters (EOPs) of aerial images via GNSS/INS technologies is an essential prerequisite in photogrammetric mapping nowadays. Although direct sensor orientation technologies provide a high degree of automation in the process due to the GNSS/INS technologies, the accuracies of the obtained results depend on the quality of a group of parameters that models accurately the conditions of the system at the moment the job is performed. One sub-group of parameters (lever arm offsets and boresight misalignments) models the position and orientation of the sensors with respect to the IMU body frame due to the impossibility of having all sensors on the same position and orientation in the airborne platform. Another sub-group of parameters models the internal characteristics of the sensor (IOP). A system calibration procedure has been recommended by worldwide studies to obtain accurate parameters (mounting and sensor characteristics) for applications of the direct sensor orientation. Commonly, mounting and sensor characteristics are not stable; they can vary in different flight conditions. The system calibration requires a geometric arrangement of the flight and/or control points to decouple correlated parameters, which are not available in the conventional photogrammetric flight. Considering this difficulty, this study investigates the feasibility of the in situ camera calibration to improve the accuracy of the direct georeferencing of aerial images. The camera calibration uses a minimum image block, extracted from the conventional photogrammetric flight, and control point arrangement. A digital Vexcel UltraCam XP camera connected to POS AV TM system was used to get two photogrammetric image blocks. The blocks have different flight directions and opposite flight line. In situ calibration procedures to compute different sets of IOPs are performed and their results are analyzed and used in photogrammetric experiments. The IOPs

  6. Aerial Photography and Imagery, Ortho-Corrected, Polk County retained Ayres Associates to acquire digital aerial photography during the spring of 2010 suitable for the production of color orthophotography at a 12-inch ground pixel resolution (approximately 956 sq. miles). The photography was obtained du, Published in 2010, 1:2400 (1in=200ft) scale, Polk County, Wisconsin.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  7. Aerial Photography and Imagery, Ortho-Corrected, Aerial photography and scans acquired for a 359 square mile area encompassing the City and County of Denver, City of Glendale, City of Littleton, and the Denver Water Service Area., Published in 2004, 1:7200 (1in=600ft) scale, City & County of Denver.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:7200 (1in=600ft) scale, was produced all or in part from Orthoimagery information as of...

  8. Parameter optimization of image classification techniques to delineate crowns of coppice trees on UltraCam-D aerial imagery in woodlands

    Science.gov (United States)

    Erfanifard, Yousef; Stereńczak, Krzysztof; Behnia, Negin

    2014-01-01

    Estimating the optimal parameters of some classification techniques becomes their negative aspect as it affects their performance for a given dataset and reduces classification accuracy. It was aimed to optimize the combination of effective parameters of support vector machine (SVM), artificial neural network (ANN), and object-based image analysis (OBIA) classification techniques by the Taguchi method. The optimized techniques were applied to delineate crowns of Persian oak coppice trees on UltraCam-D very high spatial resolution aerial imagery in Zagros semiarid woodlands, Iran. The imagery was classified and the maps were assessed by receiver operating characteristic curve and other performance metrics. The results showed that Taguchi is a robust approach to optimize the combination of effective parameters in these image classification techniques. The area under curve (AUC) showed that the optimized OBIA could well discriminate tree crowns on the imagery (AUC=0.897), while SVM and ANN yielded slightly less AUC performances of 0.819 and 0.850, respectively. The indices of accuracy (0.999) and precision (0.999) and performance metrics of specificity (0.999) and sensitivity (0.999) in the optimized OBIA were higher than with other techniques. The optimization of effective parameters of image classification techniques by the Taguchi method, thus, provided encouraging results to discriminate the crowns of Persian oak coppice trees on UltraCam-D aerial imagery in Zagros semiarid woodlands.

  9. Supervised classification of aerial imagery and multi-source data fusion for flood assessment

    Science.gov (United States)

    Sava, E.; Harding, L.; Cervone, G.

    2015-12-01

    Floods are among the most devastating natural hazards and the ability to produce an accurate and timely flood assessment before, during, and after an event is critical for their mitigation and response. Remote sensing technologies have become the de-facto approach for observing the Earth and its environment. However, satellite remote sensing data are not always available. For these reasons, it is crucial to develop new techniques in order to produce flood assessments during and after an event. Recent advancements in data fusion techniques of remote sensing with near real time heterogeneous datasets have allowed emergency responders to more efficiently extract increasingly precise and relevant knowledge from the available information. This research presents a fusion technique using satellite remote sensing imagery coupled with non-authoritative data such as Civil Air Patrol (CAP) and tweets. A new computational methodology is proposed based on machine learning algorithms to automatically identify water pixels in CAP imagery. Specifically, wavelet transformations are paired with multiple classifiers, run in parallel, to build models discriminating water and non-water regions. The learned classification models are first tested against a set of control cases, and then used to automatically classify each image separately. A measure of uncertainty is computed for each pixel in an image proportional to the number of models classifying the pixel as water. Geo-tagged tweets are continuously harvested and stored on a MongoDB and queried in real time. They are fused with CAP classified data, and with satellite remote sensing derived flood extent results to produce comprehensive flood assessment maps. The final maps are then compared with FEMA generated flood extents to assess their accuracy. The proposed methodology is applied on two test cases, relative to the 2013 floods in Boulder CO, and the 2015 floods in Texas.

  10. Aerial Photography and Imagery, Ortho-Corrected, We have new imagery from Pictometry's AccuPlus flown in March 2010 and to be delivered in October 2010., Published in 2010, 1:600 (1in=50ft) scale, Augusta-Richmond County.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  11. Aerial Photography and Imagery, Ortho-Corrected, This dataset contains imagery of Prince George's County in RGB format. The primary goal was to acquire Countywide Digital Orthoimagery at 6" ground pixel resolution., Published in 2009, 1:1200 (1in=100ft) scale, The Maryland-National Capital Park and Planning Commission.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  12. Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots

    Directory of Open Access Journals (Sweden)

    Frédéric Baret

    2008-05-01

    Full Text Available This paper outlines how light Unmanned Aerial Vehicles (UAV can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships.

  13. Automatic Road Extraction Based on Integration of High Resolution LIDAR and Aerial Imagery

    Science.gov (United States)

    Rahimi, S.; Arefi, H.; Bahmanyar, R.

    2015-12-01

    In recent years, the rapid increase in the demand for road information together with the availability of large volumes of high resolution Earth Observation (EO) images, have drawn remarkable interest to the use of EO images for road extraction. Among the proposed methods, the unsupervised fully-automatic ones are more efficient since they do not require human effort. Considering the proposed methods, the focus is usually to improve the road network detection, while the roads' precise delineation has been less attended to. In this paper, we propose a new unsupervised fully-automatic road extraction method, based on the integration of the high resolution LiDAR and aerial images of a scene using Principal Component Analysis (PCA). This method discriminates the existing roads in a scene; and then precisely delineates them. Hough transform is then applied to the integrated information to extract straight lines; which are further used to segment the scene and discriminate the existing roads. The roads' edges are then precisely localized using a projection-based technique, and the round corners are further refined. Experimental results demonstrate that our proposed method extracts and delineates the roads with a high accuracy.

  14. Tree detection in urban regions from aerial imagery and DSM based on local maxima points

    Science.gov (United States)

    Korkmaz, Özgür; Yardımcı ćetin, Yasemin; Yilmaz, Erdal

    2017-05-01

    In this study, we propose an automatic approach for tree detection and classification in registered 3-band aerial images and associated digital surface models (DSM). The tree detection results can be used in 3D city modelling and urban planning. This problem is magnified when trees are in close proximity to each other or other objects such as rooftops in the scenes. This study presents a method for locating individual trees and estimation of crown size based on local maxima from DSM accompanied by color and texture information. For this purpose, segment level classifier trained for 10 classes and classification results are improved by analyzing the class probabilities of neighbour segments. Later, the tree classes under a certain height were eliminated using the Digital Terrain Model (DTM). For the tree classes, local maxima points are obtained and the tree radius estimate is made from the vertical and horizontal height profiles passing through these points. The final tree list containing the centers and radius of the trees is obtained by selecting from the list of tree candidates according to the overlapping and selection parameters. Although the limited number of train sets are used in this study, tree classification and localization results are competitive.

  15. Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle

    Directory of Open Access Journals (Sweden)

    Nancy F. Glenn

    2012-09-01

    Full Text Available In the summer of 2010, an Unmanned Aerial Vehicle (UAV hyperspectral calibration and characterization experiment of the Resonon PIKA II imaging spectrometer was conducted at the US Department of Energy’s Idaho National Laboratory (INL UAV Research Park. The purpose of the experiment was to validate the radiometric calibration of the spectrometer and determine the georegistration accuracy achievable from the on-board global positioning system (GPS and inertial navigation sensors (INS under operational conditions. In order for low-cost hyperspectral systems to compete with larger systems flown on manned aircraft, they must be able to collect data suitable for quantitative scientific analysis. The results of the in-flight calibration experiment indicate an absolute average agreement of 96.3%, 93.7% and 85.7% for calibration tarps of 56%, 24%, and 2.5% reflectivity, respectively. The achieved planimetric accuracy was 4.6 m (based on RMSE with a flying height of 344 m above ground level (AGL.

  16. Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles’ Imagery on Glaciers

    Directory of Open Access Journals (Sweden)

    Saskia Gindraux

    2017-02-01

    Full Text Available The use of Unmanned Aerial Vehicles (UAV for photogrammetric surveying has recently gained enormous popularity. Images taken from UAVs are used for generating Digital Surface Models (DSMs and orthorectified images. In the glaciological context, these can serve for quantifying ice volume change or glacier motion. This study focuses on the accuracy of UAV-derived DSMs. In particular, we analyze the influence of the number and disposition of Ground Control Points (GCPs needed for georeferencing the derived products. A total of 1321 different DSMs were generated from eight surveys distributed on three glaciers in the Swiss Alps during winter, summer and autumn. The vertical and horizontal accuracy was assessed by cross-validation with thousands of validation points measured with a Global Positioning System. Our results show that the accuracy increases asymptotically with increasing number of GCPs until a certain density of GCPs is reached. We call this the optimal GCP density. The results indicate that DSMs built with this optimal GCP density have a vertical (horizontal accuracy ranging between 0.10 and 0.25 m (0.03 and 0.09 m across all datasets. In addition, the impact of the GCP distribution on the DSM accuracy was investigated. The local accuracy of a DSM decreases when increasing the distance to the closest GCP, typically at a rate of 0.09 m per 100-m distance. The impact of the glacier’s surface texture (ice or snow was also addressed. The results show that besides cases with a surface covered by fresh snow, the surface texture does not significantly influence the DSM accuracy.

  17. KNOWLEDGE BASED 3D BUILDING MODEL RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS FROM LIDAR AND AERIAL IMAGERIES

    Directory of Open Access Journals (Sweden)

    F. Alidoost

    2016-06-01

    Full Text Available In recent years, with the development of the high resolution data acquisition technologies, many different approaches and algorithms have been presented to extract the accurate and timely updated 3D models of buildings as a key element of city structures for numerous applications in urban mapping. In this paper, a novel and model-based approach is proposed for automatic recognition of buildings’ roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical learning of features that are extracted from both LiDAR and aerial ortho-photos. The main steps of this approach include building segmentation, feature extraction and learning, and finally building roof labeling in a supervised pre-trained Convolutional Neural Network (CNN framework to have an automatic recognition system for various types of buildings over an urban area. In this framework, the height information provides invariant geometric features for convolutional neural network to localize the boundary of each individual roofs. CNN is a kind of feed-forward neural network with the multilayer perceptron concept which consists of a number of convolutional and subsampling layers in an adaptable structure and it is widely used in pattern recognition and object detection application. Since the training dataset is a small library of labeled models for different shapes of roofs, the computation time of learning can be decreased significantly using the pre-trained models. The experimental results highlight the effectiveness of the deep learning approach to detect and extract the pattern of buildings’ roofs automatically considering the complementary nature of height and RGB information.

  18. Knowledge Based 3d Building Model Recognition Using Convolutional Neural Networks from LIDAR and Aerial Imageries

    Science.gov (United States)

    Alidoost, F.; Arefi, H.

    2016-06-01

    In recent years, with the development of the high resolution data acquisition technologies, many different approaches and algorithms have been presented to extract the accurate and timely updated 3D models of buildings as a key element of city structures for numerous applications in urban mapping. In this paper, a novel and model-based approach is proposed for automatic recognition of buildings' roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical learning of features that are extracted from both LiDAR and aerial ortho-photos. The main steps of this approach include building segmentation, feature extraction and learning, and finally building roof labeling in a supervised pre-trained Convolutional Neural Network (CNN) framework to have an automatic recognition system for various types of buildings over an urban area. In this framework, the height information provides invariant geometric features for convolutional neural network to localize the boundary of each individual roofs. CNN is a kind of feed-forward neural network with the multilayer perceptron concept which consists of a number of convolutional and subsampling layers in an adaptable structure and it is widely used in pattern recognition and object detection application. Since the training dataset is a small library of labeled models for different shapes of roofs, the computation time of learning can be decreased significantly using the pre-trained models. The experimental results highlight the effectiveness of the deep learning approach to detect and extract the pattern of buildings' roofs automatically considering the complementary nature of height and RGB information.

  19. A 184-year record of river meander migration from tree rings, aerial imagery, and cross sections

    Science.gov (United States)

    Schook, Derek M.; Rathburn, Sara L.; Friedman, Jonathan M.; Wolf, J. Marshall

    2017-09-01

    Channel migration is the primary mechanism of floodplain turnover in meandering rivers and is essential to the persistence of riparian ecosystems. Channel migration is driven by river flows, but short-term records cannot disentangle the effects of land use, flow diversion, past floods, and climate change. We used three data sets to quantify nearly two centuries of channel migration on the Powder River in Montana. The most precise data set came from channel cross sections measured an average of 21 times from 1975 to 2014. We then extended spatial and temporal scales of analysis using aerial photographs (1939-2013) and by aging plains cottonwoods along transects (1830-2014). Migration rates calculated from overlapping periods across data sets mostly revealed cross-method consistency. Data set integration revealed that migration rates have declined since peaking at 5 m/year in the two decades after the extreme 1923 flood (3000 m3/s). Averaged over the duration of each data set, cross section channel migration occurred at 0.81 m/year, compared to 1.52 m/year for the medium-length air photo record and 1.62 m/year for the lengthy cottonwood record. Powder River peak annual flows decreased by 48% (201 vs. 104 m3/s) after the largest flood of the post-1930 gaged record (930 m3/s in 1978). Declining peak discharges led to a 53% reduction in channel width and a 29% increase in sinuosity over the 1939-2013 air photo record. Changes in planform geometry and reductions in channel migration make calculations of floodplain turnover rates dependent on the period of analysis. We found that the intensively studied last four decades do not represent the past two centuries.

  20. A 184-year record of river meander migration from tree rings, aerial imagery, and cross sections

    Science.gov (United States)

    Schook, Derek M.; Rathburn, Sara L.; Friedman, Jonathan M.; Wolf, J. Marshall

    2017-01-01

    Channel migration is the primary mechanism of floodplain turnover in meandering rivers and is essential to the persistence of riparian ecosystems. Channel migration is driven by river flows, but short-term records cannot disentangle the effects of land use, flow diversion, past floods, and climate change. We used three data sets to quantify nearly two centuries of channel migration on the Powder River in Montana. The most precise data set came from channel cross sections measured an average of 21 times from 1975 to 2014. We then extended spatial and temporal scales of analysis using aerial photographs (1939–2013) and by aging plains cottonwoods along transects (1830–2014). Migration rates calculated from overlapping periods across data sets mostly revealed cross-method consistency. Data set integration revealed that migration rates have declined since peaking at 5 m/year in the two decades after the extreme 1923 flood (3000 m3/s). Averaged over the duration of each data set, cross section channel migration occurred at 0.81 m/year, compared to 1.52 m/year for the medium-length air photo record and 1.62 m/year for the lengthy cottonwood record. Powder River peak annual flows decreased by 48% (201 vs. 104 m3/s) after the largest flood of the post-1930 gaged record (930 m3/s in 1978). Declining peak discharges led to a 53% reduction in channel width and a 29% increase in sinuosity over the 1939–2013 air photo record. Changes in planform geometry and reductions in channel migration make calculations of floodplain turnover rates dependent on the period of analysis. We found that the intensively studied last four decades do not represent the past two centuries

  1. Aerial Photography and Imagery, Oblique, Digital photos taken of PA's Delaware River shoreline, Little Tinicum Island, and tidal Schuylkill River at an oblique angle at about 1,500-2,000' altitude., Published in 2016, Not Applicable scale, Pennsylvania Coastal Resources Management Program.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Oblique dataset, published at Not Applicable scale, was produced all or in part from Uncorrected Imagery information as of 2016....

  2. Automated 3D modelling of buildings from aerial and space imagery using image understanding techniques

    Science.gov (United States)

    Kim, Taejung

    The development of a fully automated mapping system is one of the fundamental goals in photogrammetry and remote sensing. As an approach towards this goal, this thesis describes the work carried out in the automated 3D modelling of buildings in urban scenes. The whole work is divided into three parts: the development of an automated height extraction system, the development of an automated building detection system, and the combination of these two systems. After an analysis of the key problems of urban-area imagery for stereo matching, buildings were found to create isolated regions and blunders. From these findings, an automated building height extraction system was developed. This stereoscopic system is based on a pyramidal (area-based) matching algorithm with automatic seed points and a tile-based control strategy. To remove possible blunders and extract buildings from other background objects, a series of "smart" operations using linear elements from buildings were also applied. A new monoscopic building detection system was developed based on a graph constructed from extracted lines and their relations. After extracting lines from a single image using low-level image processing techniques, line relations are searched for and a graph constructed. By finding closed loops in the graph, building hypotheses are generated. These are then merged and verified using shadow analysis and perspective geometry. After verification, each building hypothesis indicates either a building or a part of a building. By combining results from these two systems, 3D building roofs can be modelled automatically. The modelling is performed using height information obtained from the height extraction system and interpolation boundaries obtained from the building detection system. Other fusion techniques and the potential improvements due to these are also discussed. Quantitative analysis was performed for each algorithm presented in this thesis and the results support the newly

  3. Inlining 3d Reconstruction, Multi-Source Texture Mapping and Semantic Analysis Using Oblique Aerial Imagery

    Science.gov (United States)

    Frommholz, D.; Linkiewicz, M.; Poznanska, A. M.

    2016-06-01

    This paper proposes an in-line method for the simplified reconstruction of city buildings from nadir and oblique aerial images that at the same time are being used for multi-source texture mapping with minimal resampling. Further, the resulting unrectified texture atlases are analyzed for façade elements like windows to be reintegrated into the original 3D models. Tests on real-world data of Heligoland/ Germany comprising more than 800 buildings exposed a median positional deviation of 0.31 m at the façades compared to the cadastral map, a correctness of 67% for the detected windows and good visual quality when being rendered with GPU-based perspective correction. As part of the process building reconstruction takes the oriented input images and transforms them into dense point clouds by semi-global matching (SGM). The point sets undergo local RANSAC-based regression and topology analysis to detect adjacent planar surfaces and determine their semantics. Based on this information the roof, wall and ground surfaces found get intersected and limited in their extension to form a closed 3D building hull. For texture mapping the hull polygons are projected into each possible input bitmap to find suitable color sources regarding the coverage and resolution. Occlusions are detected by ray-casting a full-scale digital surface model (DSM) of the scene and stored in pixel-precise visibility maps. These maps are used to derive overlap statistics and radiometric adjustment coefficients to be applied when the visible image parts for each building polygon are being copied into a compact texture atlas without resampling whenever possible. The atlas bitmap is passed to a commercial object-based image analysis (OBIA) tool running a custom rule set to identify windows on the contained façade patches. Following multi-resolution segmentation and classification based on brightness and contrast differences potential window objects are evaluated against geometric constraints and

  4. Error Detection, Factorization and Correction for Multi-View Scene Reconstruction from Aerial Imagery

    Energy Technology Data Exchange (ETDEWEB)

    Hess-Flores, Mauricio [Univ. of California, Davis, CA (United States)

    2011-11-10

    reconstruction pre-processing, where an algorithm detects and discards frames that would lead to inaccurate feature matching, camera pose estimation degeneracies or mathematical instability in structure computation based on a residual error comparison between two different match motion models. The presented algorithms were designed for aerial video but have been proven to work across different scene types and camera motions, and for both real and synthetic scenes.

  5. Terrestrial and unmanned aerial system imagery for deriving photogrammetric three-dimensional point clouds and volume models of mass wasting sites

    Science.gov (United States)

    Hämmerle, Martin; Schütt, Fabian; Höfle, Bernhard

    2016-04-01

    Three-dimensional (3-D) geodata of mass wasting sites are important to model surfaces, volumes, and their changes over time. With a photogrammetric approach commonly known as structure from motion, 3-D point clouds can be derived from image collections in a straightforward way. The quality of point clouds covering a quarry dump derived from terrestrial and aerial imagery is compared and assessed. A comprehensive set of quality indicators is calculated and compared to surveyed reference data and to a terrestrial LiDAR point cloud. The examined indicators are completeness of coverage, point density, vertical accuracy, multiscale point cloud distance, scaling accuracy, and dump volume. It is found that the photogrammetric datasets generally represent the examined dump well with, for example, an area coverage of up to 90% and 100% in case of terrestrial and aerial imagery, respectively, a maximum scaling difference of 0.62%, and volume estimations reaching up to 100% of the LiDAR reference. Combining the advantages of 3-D geodata derived from terrestrial (high detail, accurate volume calculation even with a small number of input images) and aerial images (high coverage) can be a promising method to further improve the quality of 3-D geodata derived with low-cost approaches.

  6. Tree Crown Delineation on Vhr Aerial Imagery with Svm Classification Technique Optimized by Taguchi Method: a Case Study in Zagros Woodlands

    Science.gov (United States)

    Erfanifard, Y.; Behnia, N.; Moosavi, V.

    2013-09-01

    The Support Vector Machine (SVM) is a theoretically superior machine learning methodology with great results in classification of remotely sensed datasets. Determination of optimal parameters applied in SVM is still vague to some scientists. In this research, it is suggested to use the Taguchi method to optimize these parameters. The objective of this study was to detect tree crowns on very high resolution (VHR) aerial imagery in Zagros woodlands by SVM optimized by Taguchi method. A 30 ha plot of Persian oak (Quercus persica) coppice trees was selected in Zagros woodlands, Iran. The VHR aerial imagery of the plot with 0.06 m spatial resolution was obtained from National Geographic Organization (NGO), Iran, to extract the crowns of Persian oak trees in this study. The SVM parameters were optimized by Taguchi method and thereafter, the imagery was classified by the SVM with optimal parameters. The results showed that the Taguchi method is a very useful approach to optimize the combination of parameters of SVM. It was also concluded that the SVM method could detect the tree crowns with a KHAT coefficient of 0.961 which showed a great agreement with the observed samples and overall accuracy of 97.7% that showed the accuracy of the final map. Finally, the authors suggest applying this method to optimize the parameters of classification techniques like SVM.

  7. National Agriculture Imagery Program

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP...

  8. Detecting new Buffel grass infestations in Australian arid lands: evaluation of methods using high-resolution multispectral imagery and aerial photography.

    Science.gov (United States)

    Marshall, V M; Lewis, M M; Ostendorf, B

    2014-03-01

    We assess the feasibility of using airborne imagery for Buffel grass detection in Australian arid lands and evaluate four commonly used image classification techniques (visual estimate, manual digitisation, unsupervised classification and normalised difference vegetation index (NDVI) thresholding) for their suitability to this purpose. Colour digital aerial photography captured at approximately 5 cm of ground sample distance (GSD) and four-band (visible–near-infrared) multispectral imagery (25 cm GSD) were acquired (14 February 2012) across overlapping subsets of our study site. In the field, Buffel grass projected cover estimates were collected for quadrates (10 m diameter), which were subsequently used to evaluate the four image classification techniques. Buffel grass was found to be widespread throughout our study site; it was particularly prevalent in riparian land systems and alluvial plains. On hill slopes, Buffel grass was often present in depressions, valleys and crevices of rock outcrops, but the spread appeared to be dependent on soil type and vegetation communities. Visual cover estimates performed best (r 2 0.39), and pixel-based classifiers (unsupervised classification and NDVI thresholding) performed worst (r 2 0.21). Manual digitising consistently underrepresented Buffel grass cover compared with field- and image-based visual cover estimates; we did not find the labours of digitising rewarding. Our recommendation for regional documentation of new infestation of Buffel grass is to acquire ultra-high-resolution aerial photography and have a trained observer score cover against visual standards and use the scored sites to interpolate density across the region.

  9. Automatic Object-Oriented, Spectral-Spatial Feature Extraction Driven by Tobler’s First Law of Geography for Very High Resolution Aerial Imagery Classification

    Directory of Open Access Journals (Sweden)

    Zhiyong Lv

    2017-03-01

    Full Text Available Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.

  10. An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV Imagery, Based on Structure from Motion (SfM Point Clouds

    Directory of Open Access Journals (Sweden)

    Christopher Watson

    2012-05-01

    Full Text Available Unmanned Aerial Vehicles (UAVs are an exciting new remote sensing tool capable of acquiring high resolution spatial data. Remote sensing with UAVs has the potential to provide imagery at an unprecedented spatial and temporal resolution. The small footprint of UAV imagery, however, makes it necessary to develop automated techniques to geometrically rectify and mosaic the imagery such that larger areas can be monitored. In this paper, we present a technique for geometric correction and mosaicking of UAV photography using feature matching and Structure from Motion (SfM photogrammetric techniques. Images are processed to create three dimensional point clouds, initially in an arbitrary model space. The point clouds are transformed into a real-world coordinate system using either a direct georeferencing technique that uses estimated camera positions or via a Ground Control Point (GCP technique that uses automatically identified GCPs within the point cloud. The point cloud is then used to generate a Digital Terrain Model (DTM required for rectification of the images. Subsequent georeferenced images are then joined together to form a mosaic of the study area. The absolute spatial accuracy of the direct technique was found to be 65–120 cm whilst the GCP technique achieves an accuracy of approximately 10–15 cm.

  11. Hurricane Rita Aerial Photography: High-Resolution Imagery of the Texas and Louisiana Gulf Coast After Landfall

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is of the Texas and Louisiana Gulf Coast after Hurricane Rita made landfall. The regions photographed range from San Luis Pass, Texas...

  12. Hurricane Dennis Aerial Photography: High-Resolution Imagery of the Florida Panhandle and Surrounding Regions After Landfall

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is of the Florida panhandle and surrounding regions after Hurricane Dennis made landfall. The regions photographed range from...

  13. Hurricane Katrina Aerial Photography: High-Resolution Imagery of the Gulf Coast of Louisiana, Mississippi and Alabama After Landfall

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is of the Gulf Coast of Louisiana, Mississippi and Alabama after Hurricane Katrina made landfall. The regions photographed range from...

  14. Hurricane Ivan Aerial Photography: High-Resolution Imagery of the Florida Panhandle and Surrounding Regions After Landfall

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is of the Florida panhandle and surrounding regions after Hurricane Ivan made landfall. The regions photographed range from Gulf...

  15. Aerial Photography and Imagery, Uncorrected, Original photography for the 1951-1952 consisted of 812 black and white 9"X9" photopositive prints at a scale of 1 inch = 1667 feet. 10/20/1951 - 11/22/1951 & 05/16/1952 - 06/13/1952, Published in 2002, 1:24000 (1in=2000ft) scale, State of Rhode Island and Providence Plantations.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Uncorrected dataset, published at 1:24000 (1in=2000ft) scale, was produced all or in part from Uncorrected Imagery information...

  16. An Accuracy Assessment of Georeferenced Point Clouds Produced via Multi-View Stereo Techniques Applied to Imagery Acquired via Unmanned Aerial Vehicle

    Science.gov (United States)

    Harwin, S.; Lucieer, A.

    2012-08-01

    Low-cost Unmanned Aerial Vehicles (UAVs) are becoming viable environmental remote sensing tools. Sensor and battery technology is expanding the data capture opportunities. The UAV, as a close range remote sensing platform, can capture high resolution photography on-demand. This imagery can be used to produce dense point clouds using multi-view stereopsis techniques (MVS) combining computer vision and photogrammetry. This study examines point clouds produced using MVS techniques applied to UAV and terrestrial photography. A multi-rotor micro UAV acquired aerial imagery from a altitude of approximately 30-40 m. The point clouds produced are extremely dense (study area, a 70 m section of sheltered coastline in southeast Tasmania. Areas with little surface texture were not well captured, similarly, areas with complex geometry such as grass tussocks and woody scrub were not well mapped. The process fails to penetrate vegetation, but extracts very detailed terrain in unvegetated areas. Initially the point clouds are in an arbitrary coordinate system and need to be georeferenced. A Helmert transformation is applied based on matching ground control points (GCPs) identified in the point clouds to GCPs surveying with differential GPS. These point clouds can be used, alongside laser scanning and more traditional techniques, to provide very detailed and precise representations of a range of landscapes at key moments. There are many potential applications for the UAV-MVS technique, including coastal erosion and accretion monitoring, mine surveying and other environmental monitoring applications. For the generated point clouds to be used in spatial applications they need to be converted to surface models that reduce dataset size without loosing too much detail. Triangulated meshes are one option, another is Poisson Surface Reconstruction. This latter option makes use of point normal data and produces a surface representation at greater detail than previously obtainable. This

  17. Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

    Directory of Open Access Journals (Sweden)

    Mahdi Javanmardi

    2017-09-01

    Full Text Available Various applications have utilized a mobile mapping system (MMS as the main 3D urban remote sensing platform. However, the accuracy and precision of the three-dimensional data acquired by an MMS is highly dependent on the performance of the vehicle’s self-localization, which is generally performed by high-end global navigation satellite system (GNSS/inertial measurement unit (IMU integration. However, GNSS/IMU positioning quality degrades significantly in dense urban areas with high-rise buildings, which block and reflect the satellite signals. Traditional landmark updating methods, which improve MMS accuracy by measuring ground control points (GCPs and manually identifying those points in the data, are both labor-intensive and time-consuming. In this paper, we propose a novel and comprehensive framework for automatically georeferencing MMS data by capitalizing on road features extracted from high-resolution aerial surveillance data. The proposed framework has three key steps: (1 extracting road features from the MMS and aerial data; (2 obtaining Gaussian mixture models from the extracted aerial road features; and (3 performing registration of the MMS data to the aerial map using a dynamic sliding window and the normal distribution transform (NDT. The accuracy of the proposed framework is verified using field data, demonstrating that it is a reliable solution for high-precision urban mapping.

  18. Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle.

    Science.gov (United States)

    Diaz-Varela, R A; Zarco-Tejada, P J; Angileri, V; Loudjani, P

    2014-02-15

    Agricultural terraces are features that provide a number of ecosystem services. As a result, their maintenance is supported by measures established by the European Common Agricultural Policy (CAP). In the framework of CAP implementation and monitoring, there is a current and future need for the development of robust, repeatable and cost-effective methodologies for the automatic identification and monitoring of these features at farm scale. This is a complex task, particularly when terraces are associated to complex vegetation cover patterns, as happens with permanent crops (e.g. olive trees). In this study we present a novel methodology for automatic and cost-efficient identification of terraces using only imagery from commercial off-the-shelf (COTS) cameras on board unmanned aerial vehicles (UAVs). Using state-of-the-art computer vision techniques, we generated orthoimagery and digital surface models (DSMs) at 11 cm spatial resolution with low user intervention. In a second stage, these data were used to identify terraces using a multi-scale object-oriented classification method. Results show the potential of this method even in highly complex agricultural areas, both regarding DSM reconstruction and image classification. The UAV-derived DSM had a root mean square error (RMSE) lower than 0.5 m when the height of the terraces was assessed against field GPS data. The subsequent automated terrace classification yielded an overall accuracy of 90% based exclusively on spectral and elevation data derived from the UAV imagery.

  19. [Retrieval of crown closure of moso bamboo forest using unmanned aerial vehicle (UAV) remotely sensed imagery based on geometric-optical model].

    Science.gov (United States)

    Wang, Cong; Du, Hua-qiang; Zhou, Guo-mo; Xu, Xiao-jun; Sun, Shao-bo; Gao, Guo-long

    2015-05-01

    This research focused on the application of remotely sensed imagery from unmanned aerial vehicle (UAV) with high spatial resolution for the estimation of crown closure of moso bamboo forest based on the geometric-optical model, and analyzed the influence of unconstrained and fully constrained linear spectral mixture analysis (SMA) on the accuracy of the estimated results. The results demonstrated that the combination of UAV remotely sensed imagery and geometric-optical model could, to some degrees, achieve the estimation of crown closure. However, the different SMA methods led to significant differentiation in the estimation accuracy. Compared with unconstrained SMA, the fully constrained linear SMA method resulted in higher accuracy of the estimated values, with the coefficient of determination (R2) of 0.63 at 0.01 level, against the measured values acquired during the field survey. Root mean square error (RMSE) of approximate 0.04 was low, indicating that the usage of fully constrained linear SMA could bring about better results in crown closure estimation, which was closer to the actual condition in moso bamboo forest.

  20. A New Approach for Realistic 3D Reconstruction of Planar Surfaces from Laser Scanning Data and Imagery Collected Onboard Modern Low-Cost Aerial Mapping Systems

    Directory of Open Access Journals (Sweden)

    Zahra Lari

    2017-02-01

    Full Text Available Over the past few years, accurate 3D surface reconstruction using remotely-sensed data has been recognized as a prerequisite for different mapping, modelling, and monitoring applications. To fulfill the needs of these applications, necessary data are generally collected using various digital imaging systems. Among them, laser scanners have been acknowledged as a fast, accurate, and flexible technology for the acquisition of high density 3D spatial data. Despite their quick accessibility, the acquired 3D data using these systems does not provide semantic information about the nature of scanned surfaces. Hence, reliable processing techniques are employed to extract the required information for 3D surface reconstruction. Moreover, the extracted information from laser scanning data cannot be effectively utilized due to the lack of descriptive details. In order to provide a more realistic and accurate perception of the scanned scenes using laser scanning systems, a new approach for 3D reconstruction of planar surfaces is introduced in this paper. This approach aims to improve the interpretability of the extracted planar surfaces from laser scanning data using spectral information from overlapping imagery collected onboard modern low-cost aerial mapping systems, which are widely adopted nowadays. In this approach, the scanned planar surfaces using laser scanning systems are initially extracted through a novel segmentation procedure, and then textured using the acquired overlapping imagery. The implemented texturing technique, which intends to overcome the computational inefficiency of the previously-developed 3D reconstruction techniques, is performed in three steps. In the first step, the visibility of the extracted planar surfaces from laser scanning data within the collected images is investigated and a list of appropriate images for texturing each surface is established. Successively, an occlusion detection procedure is carried out to identify the

  1. Aerial Photography and Imagery, Ortho-Corrected, 6 inch PIXEL Resolution Color Orhophotography taken in the spring of 2003 for all of the MCCOG MPA area., Published in 2003, 1:600 (1in=50ft) scale, Madison County Council of Governments.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  2. Aerial Photography and Imagery, Ortho-Corrected, 2007 Fly Over - Urbanized Area = 6" Pixel Resolution, Un-Urbanized Area = 1' Pixel Resolution - Natural Color - Tiff w/ World File - Nad83, Nevada State Plane, West Zone, US Foot - Broken up by Township, Range, and Sections, Published in 2007, City of Carson City.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, was produced all or in part from Other information as of 2007. It is described as '2007 Fly Over -...

  3. Aerial Photography and Imagery, Ortho-Corrected, 2006 Fly Over - Urbanized Area = 6" Pixel Resolution, Un-Urbanized Area = 1' Pixel Resolution - Natural Color - Tiff w/ World File - Nad83, Nevada State Plane, West Zone, US Foot - Broken up by Township, Range, and Sections, Published in 2006, City of Carson City.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, was produced all or in part from Other information as of 2006. It is described as '2006 Fly Over -...

  4. Aerial Photography and Imagery, Ortho-Corrected, The extensive tile libraries of the 2007/2008 Maryland Statewide Orthophotography Collection (100 scale - 6"pixels) have been compressed into a single ECW mosaic for each individual county., Published in 2010, 1:1200 (1in=100ft) scale, Eastern Shore Regional GIS Cooperative.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  5. Aerial Photography and Imagery, Ortho-Corrected, 2006 NBNERR - Save the Bay - URI 1:12,000 Digital True Color Orthophotography; Narragansett Bay orthophotography, prints, and transparancies were taken during late summer of 2006, Published in 2009, 1:12000 (1in=1000ft) scale, State of Rhode Island and Providence Plantations.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:12000 (1in=1000ft) scale, was produced all or in part from Orthoimagery information as...

  6. Aerial Photography and Imagery, Ortho-Corrected, Digital orthophotography was last flown for our county in March 2007 and is available by request in Mr. SID format. Plans are in place for receiving data from the state by early 2012 that was flown in 2010., Published in 2007, 1:600 (1in=50ft) scale, Harford County Government.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:600 (1in=50ft) scale, was produced all or in part from Orthoimagery information as of...

  7. Aerial Photography and Imagery, Ortho-Corrected, Sedgwick County, excluding outside the Wichita metro area, Year 2006, Sectionwide 1:2400 Digital Color Orthophotography, Tiff and MrSID., Published in 2006, 1:2400 (1in=200ft) scale, Sedgwick County, Kansas.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Other information as of 2006....

  8. Aerial Photography and Imagery, Ortho-Corrected, To improve the understanding of coastal uplands and wetlands, and their linkages with the distribution, abundance, and health of living marine resources., Published in 2006, 1:24000 (1in=2000ft) scale, Louisiana State University.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:24000 (1in=2000ft) scale as of 2006. It is described as 'To improve the understanding of...

  9. Aerial Photography and Imagery, Ortho-Corrected, April 2012, color and b/w and NIR, tiff and MrSID, section tiles or countywide mosaic- plan to refly in 2017 at same resolution (6" pixel), Published in 2012, 1:1200 (1in=100ft) scale, Dodge County, Wisconsin.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:1200 (1in=100ft) scale, was produced all or in part from Orthoimagery information as of...

  10. Comparison of satellite imagery and infrared aerial photography as vegetation mapping methods in an arctic study area: Jameson Land, East Greenland

    DEFF Research Database (Denmark)

    Hansen, Birger Ulf; Mosbech, Anders

    1994-01-01

    Remote Sensing, vegetation mapping, SPOT, Landsat TM, aerial photography, Jameson Land, East Greenland......Remote Sensing, vegetation mapping, SPOT, Landsat TM, aerial photography, Jameson Land, East Greenland...

  11. Biomass Estimation of Xerophytic Forests Using Visible Aerial Imagery: Contrasting Single-Tree and Area-Based Approaches

    Directory of Open Access Journals (Sweden)

    Luca Bernasconi

    2017-03-01

    Full Text Available A large part of arid areas in tropical and sub-tropical regions are dominated by sparse xerophytic vegetation, which are essential for providing products and services for local populations. While a large number of researches already exist for the derivation of wall-to-wall estimations of above ground biomass (AGB with remotely sensed data, only a few of them are based on the direct use of non-photogrammetric aerial photography. In this contribution we present an experiment carried out in a study area located in the Santiago Island in the Cape Verde archipelago where a National Forest Inventory (NFI was recently carried out together with a new acquisition of a visible high-resolution aerial orthophotography. We contrasted two approaches: single-tree, based on the automatic delineation of tree canopies; and area-based, on the basis of an automatic image classification. Using 184 field plots collected for the NFI we created parametric models to predict AGB on the basis of the crown projection area (CPA estimated from the two approaches. Both the methods produced similar root mean square errors (RMSE at pixel level 45% for the single-tree and 42% for the area-based. However, the latest was able to better predict the AGB along all the variable range, limiting the saturation problem which is evident when the CPA tends to reach the full coverage of the field plots. These findings demonstrate that in regions dominated by sparse vegetation, a simple aerial orthophoto can be used to successfully create AGB wall-to-wall predictions. The level of these estimations’ uncertainty permits the derivation of small area estimations useful for supporting a more correct implementation of sustainable management practices of wood resources.

  12. Characterization of Shrubland-Atmosphere Interactions through Use of the Eddy Covariance Method, Distributed Footprint Sampling, and Imagery from Unmanned Aerial Vehicles

    Science.gov (United States)

    Anderson, C.; Vivoni, E. R.; Pierini, N.; Robles-Morua, A.; Rango, A.; Laliberte, A.; Saripalli, S.

    2012-12-01

    Ecohydrological dynamics can be evaluated from field observations of land-atmosphere states and fluxes, including water, carbon, and energy exchanges measured through the eddy covariance method. In heterogeneous landscapes, the representativeness of these measurements is not well understood due to the variable nature of the sampling footprint and the mixture of underlying herbaceous, shrub, and soil patches. In this study, we integrate new field techniques to understand how ecosystem surface states are related to turbulent fluxes in two different semiarid shrubland settings in the Jornada (New Mexico) and Santa Rita (Arizona) Experimental Ranges. The two sites are characteristic of Chihuahuan (NM) and Sonoran (AZ) Desert mixed-shrub communities resulting from woody plant encroachment into grassland areas. In each study site, we deployed continuous soil moisture and soil temperature profile observations at twenty sites around an eddy covariance tower after local footprint estimation revealed the optimal sensor network design. We then characterized the tower footprint through terrain and vegetation analyses derived at high resolution (<1 m) from imagery obtained from a fixed-wing and rotary-wing Unmanned Aerial Vehicles (UAV). Our analysis focuses on the summertime land-atmosphere states and fluxes during which each ecosystem responded differentially to the North American monsoon. We found that vegetation heterogeneity induces spatial differences in soil moisture and temperature that are important to capture when relating these states to the eddy covariance flux measurements. Spatial distributions of surface states at different depths reveal intricate patterns linked to vegetation cover that vary between the two sites. Furthermore, single site measurements at the tower are insufficient to capture the footprint conditions and their influence on turbulent fluxes. We also discuss techniques for aggregating the surface states based upon the vegetation and soil

  13. Vector shorelines and associated shoreline change rates derived from Lidar and aerial imagery for Dauphin Island, Alabama: 1940-2015

    Science.gov (United States)

    Henderson, Rachel; Nelson, Paul R.; Long, Joseph W.; Smith, Christopher G.

    2017-01-01

    In support of studies and assessments of barrier island evolution in the Gulf of Mexico, rates of shoreline change for Dauphin Island, Alabama, were calculated using two different shoreline proxy datasets with a total temporal span of 75 years.  Mean High Water line (MHW) shorelines were generated from 14 lidar datasets from 1998 to 2014, and Wet Dry Line (WDL) shorelines were digitized from ten sets of georeferenced aerial images from 1940 to 2015. Rates of change for the open-ocean (south-facing) and back-barrier (north-facing) coast were calculated for three groups of shorelines:  MHW (lidar), WDL (aerial) and MHW and WDL shorelines combined. Calculations were performed using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey (Thieler and others, 2009).  Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, Ayhan, 2009, Digital Shoreline Analysis System (DSAS) version 4.0—An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2008-1278, https://woodshole.er.usgs.gov/project-pages/DSAS/version4/.

  14. Low aerial imagery – an assessment of georeferencing errors and the potential for use in environmental inventory

    Directory of Open Access Journals (Sweden)

    Smaczyński Maciej

    2017-06-01

    Full Text Available Unmanned aerial vehicles are increasingly being used in close range photogrammetry. Real-time observation of the Earth’s surface and the photogrammetric images obtained are used as material for surveying and environmental inventory. The following study was conducted on a small area (approximately 1 ha. In such cases, the classical method of topographic mapping is not accurate enough. The geodetic method of topographic surveying, on the other hand, is an overly precise measurement technique for the purpose of inventorying the natural environment components. The author of the following study has proposed using the unmanned aerial vehicle technology and tying in the obtained images to the control point network established with the aid of GNSS technology. Georeferencing the acquired images and using them to create a photogrammetric model of the studied area enabled the researcher to perform calculations, which yielded a total root mean square error below 9 cm. The performed comparison of the real lengths of the vectors connecting the control points and their lengths calculated on the basis of the photogrammetric model made it possible to fully confirm the RMSE calculated and prove the usefulness of the UAV technology in observing terrain components for the purpose of environmental inventory. Such environmental components include, among others, elements of road infrastructure, green areas, but also changes in the location of moving pedestrians and vehicles, as well as other changes in the natural environment that are not registered on classical base maps or topographic maps.

  15. A Multi-View Dense Image Matching Method for High-Resolution Aerial Imagery Based on a Graph Network

    Directory of Open Access Journals (Sweden)

    Li Yan

    2016-09-01

    Full Text Available Multi-view dense matching is a crucial process in automatic 3D reconstruction and mapping applications. In this paper, we present a robust and effective multi-view dense matching algorithm for high-resolution aerial images based on a graph network. The overlap ratio and intersection angle between image pairs are used to find candidate stereo pairs and build the graph network. A Coarse-to-Fine strategy based on an improved Semi-Global Matching algorithm is applied for disparity computation across stereo pairs. Based on the constructed graph, point clouds of base views are generated by triangulating all connected image nodes, followed by a fusion process with the average reprojection error as a priority measure. The proposed method was successfully applied in experiments on aerial image test dataset provided by the ISPRS of Vaihingen, Germany and an oblique nadir image block of Zürich, Switzerland, using three kinds of matching configurations. The proposed method was compared to other state-of-art methods, SURE and PhotoScan. The results demonstrate that the proposed method delivers matches at higher completeness, efficiency, and accuracy than the other methods tested; the RMS for average reprojection error reached the sub pixel level and the actual positioning deviation was better than 1.5 GSD.

  16. 1946 Penasco DDQ Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  17. 1943 AAF 394 Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  18. 1954 Lea County DHO Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  19. 1936 Curry County AG Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  20. 1955 Lea County DHO Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  1. 1947 Sierra County DEZ Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  2. 1944 AAF 661 Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  3. 1947 Bernalillo County DFC Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  4. 1949 Roosevelt County CIK Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  5. 1944 AAF 649 Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  6. 1936 Harding County AG Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  7. 1950 Pecos River CIII Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  8. 1947 Sandoval County DFD Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  9. Aerial Photos - Photo Reference Mosaics -CS

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — USGS and Non USGS Agencies Aerial Photo Reference Mosaics inventory contains indexes to aerial photographs. The inventory contains imagery from various government...

  10. 1946 Macho Border DDO Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  11. Building block extraction and classification by means of Markov random fields using aerial imagery and LiDAR data

    Science.gov (United States)

    Bratsolis, E.; Sigelle, M.; Charou, E.

    2016-10-01

    Building detection has been a prominent area in the area of image classification. Most of the research effort is adapted to the specific application requirements and available datasets. Our dataset includes aerial orthophotos (with spatial resolution 20cm), a DSM generated from LiDAR (with spatial resolution 1m and elevation resolution 20 cm) and DTM (spatial resolution 2m) from an area of Athens, Greece. Our aim is to classify these data by means of Markov Random Fields (MRFs) in a Bayesian framework for building block extraction and perform a comparative analysis with other supervised classification techniques namely Feed Forward Neural Net (FFNN), Cascade-Correlation Neural Network (CCNN), Learning Vector Quantization (LVQ) and Support Vector Machines (SVM). We evaluated the performance of each method using a subset of the test area. We present the classified images, and statistical measures (confusion matrix, kappa coefficient and overall accuracy). Our results demonstrate that the MRFs and FFNN perform better than the other methods.

  12. Low-altitude aerial imagery and related field observations associated with unmanned aerial systems (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016

    Science.gov (United States)

    Sherwood, Christopher R.

    2016-01-01

    launch site; they have horizontal and vertical uncertainties of approximately +/ 0.03 m. The locations of the ground control points can be used to constrain photogrammetric reconstructions based on the aerial imagery. The locations of the 144 transect points can be used for independent evaluation of the photogrammetric products.This data release includes the four sets of original aerial images; tables listing the image file names and locations; locations of the 140 transect points; and locations of the ground control points with photographs of the four in-place features and images showing the location of the two a posteriori points at two zoom levels.Collection of these data were supported by the USGS Coastal and Marine Geology Program and the USGS Innovation Center and were conducted under USGS field activity number 2016-007-FA and National Park Service Scientific Research and Collecting Permit, study number CACO-00285, permit number CACO-2016-SCI-003. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

  13. From meadow to shallow lake: Monitoring secondary succession in a coastal fen after rewetting by flooding based on aerial imagery and plot data

    Directory of Open Access Journals (Sweden)

    M. Koch

    2017-05-01

    Full Text Available Year-round flooding can be a cost-effective measure for rewetting highly degraded fens, and is gaining popularity for lowland fen restoration in Europe. We investigated the short-term effects of such permanent inundation on species composition and spatial distribution of the vegetation of a formerly drained coastal fen, and addressed the question of whether re-establishment of peat-forming reed vegetation is foreseeable. For vegetation mapping and monitoring we combined permanent plot data acquired during four years following shallow flooding, high-resolution aerial imagery and an elevation model. Five vegetation types were distinguished, and we analysed their spatial distribution and succession patterns throughout the years. Pre-existing vegetation, its spatial arrangement and the water level played major roles in secondary succession. Existing patches of Phragmites australis showed high stability, but their growth was not consistent through the years and at all inundation depths. Existing stands of Bolboschoenus maritimus were outcompeted by Schoenoplectus tabernaemontani or vanished and formed relatively stable ponds of open water with hydrophytic species. We concluded that the expansion of reed as peat-forming vegetation is likely to proceed slowly, but fluctuations in water level and edge effects will probably maintain a persistent mosaic of vegetation and open water in the near future.

  14. Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery

    Science.gov (United States)

    Ma, Lei; Cheng, Liang; Li, Manchun; Liu, Yongxue; Ma, Xiaoxue

    2015-04-01

    Unmanned Aerial Vehicle (UAV) has been used increasingly for natural resource applications in recent years due to their greater availability and the miniaturization of sensors. In addition, Geographic Object-Based Image Analysis (GEOBIA) has received more attention as a novel paradigm for remote sensing earth observation data. However, GEOBIA generates some new problems compared with pixel-based methods. In this study, we developed a strategy for the semi-automatic optimization of object-based classification, which involves an area-based accuracy assessment that analyzes the relationship between scale and the training set size. We found that the Overall Accuracy (OA) increased as the training set ratio (proportion of the segmented objects used for training) increased when the Segmentation Scale Parameter (SSP) was fixed. The OA increased more slowly as the training set ratio became larger and a similar rule was obtained according to the pixel-based image analysis. The OA decreased as the SSP increased when the training set ratio was fixed. Consequently, the SSP should not be too large during classification using a small training set ratio. By contrast, a large training set ratio is required if classification is performed using a high SSP. In addition, we suggest that the optimal SSP for each class has a high positive correlation with the mean area obtained by manual interpretation, which can be summarized by a linear correlation equation. We expect that these results will be applicable to UAV imagery classification to determine the optimal SSP for each class.

  15. Use of ultra-high spatial resolution aerial imagery in the estimation of chaparral wildfire fuel loads.

    Science.gov (United States)

    Schmidt, Ian T; O'Leary, John F; Stow, Douglas A; Uyeda, Kellie A; Riggan, Phillip J

    2016-12-01

    Development of methods that more accurately estimate spatial distributions of fuel loads in shrublands allows for improved understanding of ecological processes such as wildfire behavior and postburn recovery. The goal of this study is to develop and test remote sensing methods to upscale field estimates of shrubland fuel to broader-scale biomass estimates using ultra-high spatial resolution imagery captured by a light-sport aircraft. The study is conducted on chaparral shrublands located in eastern San Diego County, CA, USA. We measured the fuel load in the field using a regression relationship between basal area and aboveground biomass of shrubs and estimated ground areal coverage of individual shrub species by using ultra-high spatial resolution imagery and image processing routines. Study results show a strong relationship between image-derived shrub coverage and field-measured fuel loads in three even-age stands that have regrown approximately 7, 28, and 68 years since last wildfire. We conducted ordinary least square analysis using ground coverage as the independent variable regressed against biomass. The analysis yielded R (2) values ranging from 0.80 to 0.96 in the older stands for the live shrub species, while R (2) values for species in the younger stands ranged from 0.32 to 0.89. Pooling species-based data into larger sample sizes consisting of a functional group and all-shrub classes while obtaining suitable linear regression models supports the potential for these methods to be used for upscaling fuel estimates to broader areal extents, without having to classify and map shrubland vegetation at the species level.

  16. NOAA Emergency Response Imagery

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The imagery posted on this site is in response to natural disasters. The aerial photography missions were conducted by the NOAA Remote Sensing Division. The majority...

  17. An Effective Method for Detecting Potential Woodland Vernal Pools Using High-Resolution LiDAR Data and Aerial Imagery

    Directory of Open Access Journals (Sweden)

    Qiusheng Wu

    2014-11-01

    Full Text Available Effective conservation of woodland vernal pools—important components of regional amphibian diversity and ecosystem services—depends on locating and mapping these pools accurately. Current methods for identifying potential vernal pools are primarily based on visual interpretation and digitization of aerial photographs, with variable accuracy and low repeatability. In this paper, we present an effective and efficient method for detecting and mapping potential vernal pools using stochastic depression analysis with additional geospatial analysis. Our method was designed to take advantage of high-resolution light detection and ranging (LiDAR data, which are becoming increasingly available, though not yet frequently employed in vernal pool studies. We successfully detected more than 2000 potential vernal pools in a ~150 km2 study area in eastern Massachusetts. The accuracy assessment in our study indicated that the commission rates ranged from 2.5% to 6.0%, while the proxy omission rate was 8.2%, rates that are much lower than reported errors of previous vernal pool studies conducted in the northeastern United States. One significant advantage of our semi-automated approach for vernal pool identification is that it may reduce inconsistencies and alleviate repeatability concerns associated with manual photointerpretation methods. Another strength of our strategy is that, in addition to detecting the point-based vernal pool locations for the inventory, the boundaries of vernal pools can be extracted as polygon features to characterize their geometric properties, which are not available in the current statewide vernal pool databases in Massachusetts.

  18. Novel Object-Based Filter for Improving Land-Cover Classification of Aerial Imagery with Very High Spatial Resolution

    Directory of Open Access Journals (Sweden)

    Zhiyong Lv

    2016-12-01

    Full Text Available Land cover classification using very high spatial resolution (VHSR imaging plays a very important role in remote sensing applications. However, image noise usually reduces the classification accuracy of VHSR images. Image spatial filters have been recently adopted to improve VHSR image land cover classification. In this study, a new object-based image filter using topology and feature constraints is proposed, where an object is considered as a central object and has irregular shapes and various numbers of neighbors depending on the nature of the surroundings. First, multi-scale segmentation is used to generate a homogeneous image object and extract the corresponding vectors. Then, topology and feature constraints are proposed to select the adjacent objects, which present similar materials to the central object. Third, the feature of the central object is smoothed by the average of the selected objects’ feature. This proposed approach is validated on three VHSR images, ranging from a fixed-wing aerial image to UAV images. The performance of the proposed approach is compared to a standard object-based approach (OO, object correlative index (OCI spatial feature based method, a recursive filter (RF, and a rolling guided filter (RGF, and has shown a 6%–18% improvement in overall accuracy.

  19. Semi-Automated Approach for Mapping Urban Trees from Integrated Aerial LiDAR Point Cloud and Digital Imagery Datasets

    Science.gov (United States)

    Dogon-Yaro, M. A.; Kumar, P.; Rahman, A. Abdul; Buyuksalih, G.

    2016-09-01

    Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.

  20. Analysis of Biophysical Mechanisms of Gilgai Microrelief Formation in Dryland Swelling Soils Using Ultra-High Resolution Aerial Imagery

    Science.gov (United States)

    Krell, N.; DeCarlo, K. F.; Caylor, K. K.

    2015-12-01

    Microrelief formations ("gilgai"), which form due to successive wetting-drying cycles typical of swelling soils, provide ecological hotspots for local fauna and flora, including higher and more robust vegetative growth. The distribution of these gilgai suggests a remarkable degree of regularity. However, it is unclear to what extent the mechanisms that drive gilgai formation are physical, such as desiccation-induced fracturing, or biological in nature, namely antecedent vegetative clustering. We investigated gilgai genesis and pattern formation in a 100 x 100 meter study area with swelling soils in a semiarid grassland at the Mpala Research Center in central Kenya. Our ongoing experiment is composed of three 9m2 treatments: we removed gilgai and limited vegetative growth by herbicide application in one plot, allowed for unrestricted seed dispersal in another, and left gilgai unobstructed in a control plot. To estimate the spatial frequencies of the repeating patterns of gilgai, we obtained ultra-high resolution (0.01-0.03m/pixel) images with an unmanned aerial vehicle (UAV) from which digital elevation models were also generated. Geostatistical analyses using wavelet and fourier methods in 1- and 2-dimensions were employed to characterize gilgai size and distribution. Preliminary results support regular spatial patterning across the gilgaied landscape and heterogeneities may be related to local soil properties and biophysical influences. Local data on gilgai and fracture characteristics suggest that gilgai form at characteristic heights and spacing based on fracture morphology: deep, wide cracks result in large, highly vegetated mounds whereas shallow cracks, induced by animal trails, are less correlated with gilgai size and shape. Our experiments will help elucidate the links between shrink-swell processes and gilgai-vegetation patterning in high activity clay soils and advance our understanding of the mechanisms of gilgai formation in drylands.

  1. SEMI-AUTOMATED APPROACH FOR MAPPING URBAN TREES FROM INTEGRATED AERIAL LiDAR POINT CLOUD AND DIGITAL IMAGERY DATASETS

    Directory of Open Access Journals (Sweden)

    M. A. Dogon-Yaro

    2016-09-01

    Full Text Available Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.

  2. OrthoImagery Submission for Isabella county, MI

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — This data set contains 1-meter resolution imagery derived from the 2005 National Agriculture Imagery Program (NAIP) statewide aerial imagery acquisition. Data have...

  3. Regional albedo of Arctic first-year drift ice in advanced stages of melt from the combination of in situ measurements and aerial imagery

    Directory of Open Access Journals (Sweden)

    D. V. Divine

    2014-07-01

    Full Text Available The paper presents a case study of the regional (≈ 150 km broadband albedo of first year Arctic sea ice in advanced stages of melt, estimated from a combination of in situ albedo measurements and aerial imagery. The data were collected during the eight day ICE12 drift experiment carried out by the Norwegian Polar Institute in the Arctic north of Svalbard at 82.3° N from 26 July to 3 August 2012. The study uses in situ albedo measurements representative of the four main surface types: bare ice, dark melt ponds, bright melt ponds and open water. Images acquired by a helicopter borne camera system during ice survey flights covered about 28 km2. A subset of > 8000 images from the area of homogeneous melt with open water fraction of ≈ 0.11 and melt pond coverage of ≈ 0.25 used in the upscaling yielded a regional albedo estimate of 0.40 (0.38; 0.42. The 95% confidence interval on the estimate was derived using the moving block bootstrap approach applied to sequences of classified sea ice images and albedo of the four surface types treated as random variables. Uncertainty in the mean estimates of surface type albedo from in situ measurements contributed some 95% of the variance of the estimated regional albedo, with the remaining variance resulting from the spatial inhomogeneity of sea ice cover. The results of the study are of relevance for the modeling of sea ice processes in climate simulations. It particularly concerns the period of summer melt, when the optical properties of sea ice undergo substantial changes, which existing sea ice models have significant diffuculty accurately reproducing.

  4. Use of Aerial high resolution visible imagery to produce large river bathymetry: a multi temporal and spatial study over the by-passed Upper Rhine

    Science.gov (United States)

    Béal, D.; Piégay, H.; Arnaud, F.; Rollet, A.; Schmitt, L.

    2011-12-01

    Aerial high resolution visible imagery allows producing large river bathymetry assuming that water depth is related to water colour (Beer-Bouguer-Lambert law). In this paper we aim at monitoring Rhine River geometry changes for a diachronic study as well as sediment transport after an artificial injection (25.000 m3 restoration operation). For that a consequent data base of ground measurements of river depth is used, built on 3 different sources: (i) differential GPS acquisitions, (ii) sounder data and (iii) lateral profiles realized by experts. Water depth is estimated using a multi linear regression over neo channels built on a principal component analysis over red, green and blue bands and previously cited depth data. The study site is a 12 km long reach of the by-passed section of the Rhine River that draws French and German border. This section has been heavily impacted by engineering works during the last two centuries: channelization since 1842 for navigation purposes and the construction of a 45 km long lateral canal and 4 consecutive hydroelectric power plants of since 1932. Several bathymetric models are produced based on 3 different spatial resolutions (6, 13 and 20 cm) and 5 acquisitions (January, March, April, August and October) since 2008. Objectives are to find the optimal spatial resolution and to characterize seasonal effects. Best performances according to the 13 cm resolution show a 18 cm accuracy when suspended matters impacted less water transparency. Discussions are oriented to the monitoring of the artificial reload after 2 flood events during winter 2010-2011. Bathymetric models produced are also useful to build 2D hydraulic model's mesh.

  5. A new technique for the detection of large scale landslides in glacio-lacustrine deposits using image correlation based upon aerial imagery: A case study from the French Alps

    Science.gov (United States)

    Fernandez, Paz; Whitworth, Malcolm

    2016-10-01

    Landslide monitoring has benefited from recent advances in the use of image correlation of high resolution optical imagery. However, this approach has typically involved satellite imagery that may not be available for all landslides depending on their time of movement and location. This study has investigated the application of image correlation techniques applied to a sequence of aerial imagery to an active landslide in the French Alps. We apply an indirect landslide monitoring technique (COSI-Corr) based upon the cross-correlation between aerial photographs, to obtain horizontal displacement rates. Results for the 2001-2003 time interval are presented, providing a spatial model of landslide activity and motion across the landslide, which is consistent with previous studies. The study has identified areas of new landslide activity in addition to known areas and through image decorrelation has identified and mapped two new lateral landslides within the main landslide complex. This new approach for landslide monitoring is likely to be of wide applicability to other areas characterised by complex ground displacements.

  6. Block aerial triangulation based on two flight levels ADS40 imagery data%利用双高度层ADS40影像进行区域网空中三角测量

    Institute of Scientific and Technical Information of China (English)

    丁启伟; 戴晨光; 刘亚璠; 万欢

    2012-01-01

    Aerial triangulation, which is the crucial part in the photogrammetry process, is a method to calculate the plane coordinates and elevation of the tie points and the exterior orientation parameters of the photo based on some amount control points. With the development of navigating and positioning technique, highly accurate POS system is equipped on the aerial aircraft to record the position and attitude of the camera at the shooting moment, which is used to support the aerial triangulation. In this paper, it used the two flight levels ADS40 imagery data, POS data and some ground control data to perform aerial triangulation based on ORIMA software. And finally the accuracy of the aerial triangulation with two flight levels imagery data was compared with that of the solo flight level imagery data.%空中三角测量是利用航摄像片与被摄目标之间的空间几何位置关系,根据少量的野外控制点,计算节点的平面坐标与高程以及航摄像片的外方位元素的方法,是摄影测量过程中的重要环节.随着导航定位技术的发展,高精度POS装置被广泛安装到航拍飞机上,用于记录航拍时刻相机的位置与姿态,从而进行辅助空中三角测量.本文研究利用双高度层的ADS40航线影像,结合POS数据,利用一定数量的控制点,基于ORIMA软件进行空中三角测量,并对其解算精度与同一区域单高度层航线影像的空三精度进行比较.

  7. Aerial Photography and Imagery, Ortho-Corrected, Digital orthophotography for all 100 counties in North Carolina. Full FGDC compliant metadata are available for each county at the NC OneMap Geospatial Portal. The metadata link below is to the OGC WMS metadata record, which is a partial record., Published in 2010, 1:2400 (1in=200ft) scale, NC OITS / Center for Geographic Information & Analysis.

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — This Aerial Photography and Imagery, Ortho-Corrected dataset, published at 1:2400 (1in=200ft) scale, was produced all or in part from Orthoimagery information as of...

  8. Characterisation of recently retrieved aerial photographs of Ethiopia (1935-1941) and their fusion with current remotely sensed imagery for retrospective geomorphological analysis

    Science.gov (United States)

    Nyssen, Jan; Gebremeskel, Gezahegne; Mohamed, Sultan; Petrie, Gordon; Seghers, Valérie; Meles Hadgu, Kiros; De Maeyer, Philippe; Haile, Mitiku; Frankl, Amaury

    2013-04-01

    8281 assemblages of aerial photographs (APs) acquired by the 7a Sezione Topocartografica during the Italian occupation of Ethiopia (1935-1941) have recently been discovered, scanned and organised. The oldest APs of the country that are known so far were taken in the period 1958-1964. The APs of the 1930s were analysed for their technical characteristics, scale, flight lines, coverage, use in topographic mapping, and potential future uses. The APs over Ethiopia in 1935-1941 are presented as assemblages on approx. 50 cm x 20 cm cardboard tiles, each holding a label, one nadir-pointing photograph flanked by two low-oblique photographs and one high-oblique photograph. The four APs were exposed simultaneously and were taken across the flight line; the high-oblique photograph is presented alternatively at left and at right; there is approx. 60% overlap between subsequent sets of APs. One of Santoni's glass plate multi-cameras was used, with focal length of 178 mm, flight height at 4000-4500 m a.s.l., which results in an approximate scale of 1:11 500 for the central photograph and 1:16 000 to 1:18 000 for the low-oblique APs. The surveyors oriented themselves with maps of Ethiopia at 1:400 000 scale, compiled in 1934. The flights present a dense AP coverage of Northern Ethiopia, where they were acquired in the context of upcoming battles with the Ethiopian army. Several flights preceded the later advance of the Italian army southwards towards the capital Addis Ababa. Further flights took place in central Ethiopia for civilian purposes. As of 1936, the APs were used to prepare highly detailed topographic maps at 1:100 000 scale. These APs (1935-1941) together with APs of 1958-1964, 1994 and recent high-resolution satellite imagery are currently being used in spatially explicit change studies of land cover, land management and (hydro)geomorphology in Ethiopia over a time span of almost 80 years, the first results of which will be presented.

  9. Near infrared-red models for the remote estimation of chlorophyll- a concentration in optically complex turbid productive waters: From in situ measurements to aerial imagery

    Science.gov (United States)

    Gurlin, Daniela

    Today the water quality of many inland and coastal waters is compromised by cultural eutrophication in consequence of increased human agricultural and industrial activities and remote sensing is widely applied to monitor the trophic state of these waters. This study explores near infrared-red models for the remote estimation of chlorophyll-a concentration in turbid productive waters and compares several near infrared-red models developed within the last 35 years. Three of these near infrared-red models were calibrated for a dataset with chlorophyll-a concentrations from 2.3 to 81.2 mg m -3 and validated for independent and statistically significantly different datasets with chlorophyll-a concentrations from 4.0 to 95.5 mg m-3 and 4.0 to 24.2 mg m-3 for the spectral bands of the MEdium Resolution Imaging Spectrometer (MERIS) and Moderate-resolution Imaging Spectroradiometer (MODIS). The developed MERIS two-band algorithm estimated chlorophyll-a concentrations from 4.0 to 24.2 mg m-3, which are typical for many inland and coastal waters, very accurately with a mean absolute error 1.2 mg m-3. These results indicate a high potential of the simple MERIS two-band algorithm for the reliable estimation of chlorophyll-a concentration without any reduction in accuracy compared to more complex algorithms, even though more research seems required to analyze the sensitivity of this algorithm to differences in the chlorophyll-a specific absorption coefficient of phytoplankton. Three near infrared-red models were calibrated and validated for a smaller dataset of atmospherically corrected multi-temporal aerial imagery collected by the hyperspectral airborne imaging spectrometer for applications (AisaEAGLE). The developed algorithms successfully captured the spatial and temporal variability of the chlorophyll-a concentrations and estimated chlorophyll- a concentrations from 2.3 to 81.2 mg m-3 with mean absolute errors from 4.4 mg m-3 for the AISA two band algorithm to 5.2 mg m-3

  10. Auto-registration of aerial imagery and airborne LiDAR data based on structure feature%基于结构特征的机载LiDAR数据与航空影像自动配准

    Institute of Scientific and Technical Information of China (English)

    徐景中; 寇媛; 袁芳; 张伟

    2013-01-01

    Current algorithms of registration of aerial imagery with airborne LiDAR data has the major issue of str ong dependency upon the matching feature, so these methods are impressionable to the texture feature of image and the density of LiDAR point cloud. A new method of auto-registration of aerial imagery with airborne LiDAR data based on structure feature was proposed. The first step was the automated extraction of structure feature from LiDAR range image and aerial imagery. After that the LiDAR structure features were projected onto aerial imagery and corresponding features were determined using geometry constraints. The second step was the wrong matches eliminating by two points geometric constraint after calculating the DLT parameters as the initial value, and iteration strategy was adopted to obtain optimal results. The last step was the pose parameters calculated by the optimal matching results using quaternion-based solution of space resection. Experimental studies have demonstrated that this algorithm is effective in auto-registration of aerial imagery with airborne LiDAR data and little influenced by noise.%针对现有机载LiDAR数据与航空影像配准方法对匹配特征具有较强的依赖性,易受数据等影响的问题,提出了一种基于结构特征的自动配准方法。该方法首先提取LiDAR距离图像与对应影像的结构特征,利用初始姿态参数将LiDAR结构特征投影至影像坐标系下,根据结构特征的几何约束条件获取初始匹配点集,完成粗匹配;接着利用粗匹配结果计算直接变换模型(DLT)参数,并以此为初值引入双点几何约束,采用循环迭代的匹配策略,不断剔除错误匹配,获得一组新的匹配点集,完成精匹配;最后根据精匹配结果,采用基于单位四元数的空间后方交会方法解算航空影像的姿态参数,实现机载LiDAR数据与航空影像的自动配准。实验证明,该方法受噪声影响小,能

  11. Low-altitude aerial imagery obtained with unmanned aerial systems (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (JPEG images)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This dataset contains images obtained from unmanned aerial systems (UAS) flown in the Cape Cod National Seashore. The objective of the field work was to evaluate the...

  12. Low-altitude aerial imagery obtained with unmanned aerial systems (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (JPEG images)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This dataset contains images obtained from unmanned aerial systems (UAS) flown in the Cape Cod National Seashore. The objective of the field work was to evaluate the...

  13. 1949-50 DIO USFS Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  14. 1946-49 Northeast New Mexico DCE Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  15. 1935 15' Quad #227 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  16. 1935 15' Quad #298 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  17. 1935 15' Quad #132 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  18. 1935 15' Quad #082 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  19. 1935 15' Quad #373 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  20. 1935 15' Quad #364 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  1. 1935 15' Quad #273 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  2. 1935 15' Quad #203 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  3. 1935 15' Quad #315 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  4. 1935 15' Quad #100 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  5. 1935 15' Quad #414 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  6. 1935 15' Quad #080 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  7. 1935 15' Quad #413 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  8. 1935 15' Quad #341 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  9. 1935 15' Quad #243 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  10. 1935 15' Quad #242 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  11. 1935 15' Quad #251 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  12. 1935 15' Quad #126 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  13. 1935 15' Quad #171 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  14. 1935 15' Quad #178 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  15. 1935 15' Quad #049 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  16. 1935 15' Quad #346 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  17. 1935 15' Quad #223 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  18. 1935 15' Quad #035 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  19. 1935 15' Quad #318 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  20. 1935 15' Quad #342 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  1. 1935 15' Quad #267 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  2. 1935 15' Quad #386 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  3. 1935 15' Quad #322 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  4. 1935 15' Quad #274 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  5. 1935 15' Quad #226 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  6. 1935 15' Quad #152 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  7. 1935 15' Quad #125 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  8. 1935 15' Quad #121 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  9. 1935 15' Quad #388 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  10. 1935 15' Quad #145 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  11. 1935 15' Quad #389 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  12. 1935 15' Quad #490 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  13. 1935 15' Quad #442 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  14. 1946 Whitewater-Animas DDR Aerial Photo Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth...

  15. 1935 15' Quad #174 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  16. 1935 15' Quad #457 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  17. 1935 15' Quad #225 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  18. 1935 15' Quad #060 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  19. 1935 15' Quad #087 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  20. 1935 15' Quad #104 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  1. 1935 15' Quad #299 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  2. 1935 15' Quad #081 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  3. 1935 15' Quad #177 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  4. 1935 15' Quad #349 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  5. 1935 15' Quad #004 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  6. 1935 15' Quad #350 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  7. 1935 15' Quad #219 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  8. 1935 15' Quad #013 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  9. 1935 15' Quad #037 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  10. 1935 15' Quad #196 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  11. 1935 15' Quad #250 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  12. 1935 15' Quad #150 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  13. 1935 15' Quad #218 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  14. 1935 15' Quad #014 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  15. 1935 15' Quad #127 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  16. 1935 15' Quad #195 Aerial Photo Mosaic Index - NM

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  17. 1935 15' Quad #149 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  18. 1935 15' Quad #057 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  19. 1935 15' Quad #058 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...

  20. 1935 15' Quad #197 Aerial Photo Mosaic Index

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Aerial Photo Reference Mosaics contain aerial photographs that are retrievable on a frame by frame basis. The inventory contains imagery from various sources that...