2012 - 2014 USGS Lidar: Seneca Watershed, NY

The Light Detection and Ranging (LiDAR) dataset is a survey of the Seneca NY Watershed project area. The entire survey area for Seneca encompasses approximately 1638 square miles. The LiDAR point cloud was flown at a nominal post spacing of 1.5 meters for unobscured areas. The LiDAR data and derivative products produced are in compliance with the U.S. Geological Survey National Geospatial Program LIDAR Base Specifications, Version 1.0. The flight lines were acquired by Northrop Grumman Corporation and Digital Aerial Solutions (DAS) and the data was post processed by Northrop GrummanAdvanced GEOINT Solutions Operating Unit (AGSOU), which was flown over twelve missions from May 5,2012-May 6,2012, November 19,2013-November 21,2013, April 17,2014-April 24,2014, June 15,2014. Derivative products from the aerial acquisition include: raw point cloud data in LAS v1.2 format, classified point cloud data in LAS v1.2 format, bare earth surface (raster DEM) tiles in 32-bit floating point raster ERDAS .IMG format, breaklines in ESRI Arc Shape format, control points in ESRI Arc Shape format, project report, and FGDC compliant XML metadata.

The purpose of this project was to produce a high resolution LiDAR data set to assist in change detection mapping of the Seneca NY Watershed located in Central New York

The Light Detection and Ranging (LiDAR) dataset is a survey of the Seneca NY Watershed project area. The entire survey area for Seneca encompasses approximately 1638 square miles. The LiDAR point cloud was flown at a nominal post spacing of 1.5 meters for unobscured areas. The LiDAR data and derivative products produced are in compliance with the U.S. Geological Survey National Geospatial Program LIDAR Base Specifications, Version 1.0. The flight lines were acquired by Northrop Grumman Corporati

Any conclusions drawn from the analysis of this information are not the responsibility of Northrop Grumman, USGS, NOAA, the Office for Coastal Management or its partners.

Data is available online for bulk or custom downloads

None

Users should be aware that temporal changes may have occurred since this data set was collected and some parts of this data may no longer represent actual surface conditions. Users should not use this data for critical applications without a full awareness of its limitations.

Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 3; ESRI ArcCatalog 10.1 Service Pack 1

There is not a systematic method of testing when testing horizontal accuracy in LiDAR. However this is tested during calibration of the sensor and is rechecked during the comparing of parallel and perpendicular flight lines. Additionally the horizontal accuracy is checked by collecting building corners during the survey. Lines are then digitized representing the building outline and the differences are measure from each individual survey point to the corner of the building outline. Stats are calculated to ensure horizontal tolerances are met. These measurements resulted in an RMSEr of 1.86 meters and equals a RMSE accuracy of 3.21 meter horizontal accuracy at the 95 % confidence level. Accuracy defined by NSSDA at the 95 % confidence level would be multiplier by 1.7308 times the RMSE. Quantitative value: 3.21, Test that produced the valued: Value represent horizontal accuracy assessment at the 95% confidence interval.

Tested Fundamental Vertical Accuracy = 0.145 meters at the 95% confidence level (8.57 cm RMSEz).

The accuracy assessment was performed using the NSSDA standard method to compute the root mean square error (RMSE) based on a comparison of ground control points (GCP) and DEM derived from the LiDAR dataset. Testing was performed prior to gridding of the filtered LiDAR data points and construction of the 32-bit ESRI float grid format bare earth tiles. The RMSEz figure was used to compute the vertical National Standard for Spatial Data Accuracy (NSSDA). A spatial proximity analysis was used to select edited LiDAR data points contiguous to the relevant GCPs. A search radius decision rule is applied with consideration of terrain complexity, cumulative error and adequate sample size. Cumulative error results from the errors inherent in the various sources of horizontal measurement. These sources include the airborne GPS, GCPs and the uncertainty of the accuracy of the LiDAR data points. This accuracy is achieved prior to the subsampling that occurs through integration with the inertial measurement unit (IMU) positions that are recorded. It is unclear at this time whether the initial accuracy is maintained. The horizontal accuracy of the GCPs is estimated to be in the range of approximately .03 to .04 meters. Finally, sample size was considered. The specification for the National Standard for Spatial Data Accuracy is a minimum of 20 points to conduct a statistically significant accuracy evaluation which provides a reasonable approximation of a normal distribution. The intent of the NSSDA is to reflect the geographic area of interest and the distribution of error in the data set (Federal Geographic Data Committee, 1998, Geospatial National Standard for Spatial Data Accuracy, Federal Geographic Data Committee Secretariat, Reston, Virginia, p.3-4). Additional steps were taken to ensure the vertical accuracy of the LiDAR data including: Step 1: Precision Bore sighting (Check Edge-matching) Step 2: Compare the LiDAR data to the Field Survey (The vertical accuracy requirements meet or exceed the required RMSEz of 12.5cm and the vertical accuracy of 24.5cm at the 95% confidence level). Data collected under this task order exceeds the required National Standards for Spatial Database Accuracy (NSSDA) accuracy standards. SVA accuracies at the 95th Percentile collected and tested, as target accuracies results as follows:Tall Weeds and Crops=0.196 meters, Forested and Fully Grown= 0.319 meters. Consolidated Vertical Accuracies (CVA) at the 95th Percentile =0.236 meters. Final accuracy statement for this task order is as follows; FVA Tested 0.145 meters vertical accuracy at the 95% confidence level.Quantitative value: 0.145, Test that produced the value: Value represents the Fundamental Vertical Accuracy (FVA) assessment at the 95% confidence interval. This represents the FVA checkpoints compared against the derived DEM at the 95% confidence interval.

Ground Truth data was collected of the three major land cover classes present within the study area. 20 points were collected in each of the three vegetation classes that made up 10% of the vegatation present within the AOI. Those classes are bare earth, tall weeds and crops, and forested and fully grown. A pair of points were surveyed using theNYSNet network once completed the total station is used to collect the taller vegetation classes. A total station was used to collect all the shots collected in the taller vegetation class, due to the limited GPS signal when working in and around vegetation canopy.

The GPS survey was tied into the NYSNet Real Time Network located in New York. The NYSNet network is a network of continuously operating GPS reference stations that provides Real Time Kinematic (RTK) capabilities within a Real Time Network (RTN). This allows corrections to be applied to the points as they are being collected, eliminating the need for an adjustment. As a quality control measure several “check-in” points consisting of NSRS published horizontal and vertical control points were used as checks within the NYSNet network. The survey crew checked into these published points daily to validate the consistency of the network. Also to confirm that the project will meet the 5cm local network accuracy at the 95% confidence level. Survey field work was performed on 3.30.2012-4.12.2012 by Northrop Grumman field crews using Trimble 5700 series Global Positioning System with Zephyr Geodetic antenna. Data analysis was accomplished by comparing ground truth checkpoints with LIDAR points from the derived DEM and reported three ways 1. FVA 2. SVA 3. CVA. Additionally the FVA points were assessed against the TIN derived from the LAS LiDAR point cloud controlled and calibrated swath data to ensure they met the required accuracy of 12.5cm RMSEz and 24.5cm at the 95% confidence interval.

Data is backed up to tape and to cloud storage.