2010 Federal Emergency Management Agency (FEMA) Topographic Lidar: Concord River Watershed, Massachusetts

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GPS based surveys were utilized to support both processing and testing of LiDAR data within FEMA designated Areas of Interest (AOIs). Geographically distinct ground points were surveyed using

GPS technology throughout the AOIs to provide support for three distinct tasks. Task 1 was to provide Vertical Ground Control to support the aerial acquisition and subsequent bare earth model processing.

To accomplish this, survey-grade Trimble R-8 GPS receivers were used to collect a series of control points located on open areas, free of excessive or significant slope, and at least 5 meters away from

any significant terrain break. Most if not all control points were collected at street/road intersections on bare level pavement. Task 2 was to collect Fundamental Vertical Accuracy (FVA) checkpoints

to evaluate the initial quality of the collected point cloud and to ensure that the collected data was satisfactory for further processing to meet FEMA specifications. The FVA points were collected in

identical fashion to the Vertical Ground Control Points, but segregated from the point pool to ensure independent quality testing without prior knowledge of FVA locations by the aerial vendor.

Task 3 was to collect Consolidated Vertical Accuracy CVA) checkpoints to allow vertical testing of the bare-earth processed LiDAR data in different classes of land cover, including: Open (pavement,

open dirt, short grass), High Grass and Crops, Brush and Low Trees, Forest, Urban. CVA points were collected in similar fashion as Control and FVA points with emphasis on establishing point locations

within the predominant land cover classes within each AOI or Functional AOI Group. In order to successfully collect the Forest land cover class, it was necessary to establish a Backsight and Initial Point

with the R8 receiver, and then employ a Nikon Total Station to observe a retroreflective prism stationed under tree canopy. This was necessary due to the reduced GPS performance and degradation of signal

under tree canopy. The R-8 receivers were equipped with cellular modems to receive real-time correction signals from the Keystone Precision Virtual Reference Station (VRS) network encompassing the Region 1 AOIs.

Use of the VRS network allowed rapid collection times (~3 minutes/point) at 2.54 cm (1 inch) initial accuracy. All points collected were below the 8cm specification for testing 24cm, Highest category LiDAR data.

To ensure valid in-field collections, an NGS monument with suitable vertical reporting was measured using the same equipment and procedures used for Control, FVA and CVA points on a daily basis. The measurement

was compared to the NGS published values to ensure that the GPS collection schema was producing valid data and as a physical proof point of quality of collection. Those monument measurements are summarized in the

Accuracy report included in the data delivered to FEMA. In order to meet FEMA budgetary requirements, 20 FVA points are necessary to allow testing to CE95 ? 1 point out of 20 may fail vertical testing and still

allow the entire dataset to meet 95% accuracy requirements. In similar fashion, 20 CVA points are necessary to test to CE95 as discussed above. 15 CVA points were collected with the intention at the outset that 5

of the collected FVAs would perform double ?duty as Open-class CVA points, to total 20 CVAs. The following software packages and utilities were used to control the GPS receiver in the field during data collection,

and then ingest and export the collected GPS data for all points: Trimble Survey Controller, Trimble Pathfinder Office. The following software utilities were used to translate the collected Latitude/Longitude Decimal

Degree HAE GPS data for all points into Latitude/Longitude Degrees/Minutes/Seconds for checking the collected monument data against the published NGS Datasheet Lat/Long DMS values and into UTM NAD83 Northings/Eastings:

U.S. Army Corps of Engineers CorpsCon, National Geodetic Survey Geoid09NAVD88. MSL values were determined using the most recent NGS-approved geoid model to generate geoid separation values for each Lat/Long coordinate pair.

In this fashion, Orthometric heights were determined for each Control, FVA and CVA point by subtracting the generated Geoid Separation value from the Ellipsoidal Height (HAE) for publication and use as MSL NAVD88(09).

Using an Optech Gemini LiDAR system, a total 111 flightlines of highest density (Nominal pulse Spacing of 1.0m) were collected over the Concord area. A total of 405 square miles was collected. A total

of 12 missions were flown between December 2 and December 12, 2010. Two airborne global positioning system (GPS) base stations were used to support the LiDAR data acquisition: BED A-AI5558,and ORH A-AI5600. Coordinates are

available in the Post-Flight Aerial Acquisition Report.

Applanix software was used in the post processing of the airborne GPS and inertial data that is critical to the positioning and orientation of the sensor during all flights. POSPac MMS provides the smoothed best estimate

of trajectory (SBET) that is necessary for Optech's post processor to develop the point cloud from the LiDAR missions. The point cloud is the mathematical three dimensional collection of all returns from all laser pulses

as determined from the aerial mission. Optech?s DASHMap software and Leica?s ALS Post Processor software were used to create the Raw LIDAR Flight Line strips. At this point this data is ready for analysis, classification,

and filtering to generate a bare earth surface model in which the above ground features are removed from the data set. The GeoCue and TerraScan software packages are then used for the automated data classification. Project

specific macros are created to classify the ground and to remove the side overlap between parallel flight lines. LAS Class 2 (Ground) is used to check the surveyed control points against the Triangulated LIDAR surface. Any

bias is then removed using macro functionality within TerraScan. Unclassified Point Cloud tiles are then created using TerraScan macro functionality. These tiles are populated within GeoCue to ensure correct LAS versioning

and LAS Header information. LAS Class 2 is used to check the independent QC points against the Triangulated LiDAR surface. If RMSE is not within guidelines TerraScan software is utilitzed to remove any bias, and the check

is performed again.

Point Cloud data is manually reviewed and any remaining artifacts are removed using functionality provided within the TerraScan and TerraModeler software packages.

Additional project specific macros are created and run within GeoCue/TerraScan to ensure correct LAS classification prior to project delivery.

Final Classified LAS tiles are created within GeoCue to confirm correct LAS versioning and header information.

In-house software is then used to check LAS header information and final LAS classification prior to delivery.

LAS Class 2 is used to check the independent QC points against the Triangulated LiDAR surface.

Created Multipoint feature class from Class 2 LAS files using ArcGIS 3D Analyst converion tool LAS to Multipoint and stored results in APRS_Class_2_Bare_Earth feature dataset

within the Concord_HUC8_Bare_Earth_Data.gdb file geodatabase.

Convert points to raster using the count option...assign one value to all data cells with a conditional statement...fill small nodata areas with expand...reduce the extent with shrink...

vectorize with raster to polygon...clean up and create polygon for LiDAR collection area.

Run point file information tool on classified LAS files...measure tiles and create polyline vector grid that covers collection area...feature to points for the label points of the LAS Information polygon shapefile...

feature to polygon using vector grid as polyline input and LAS Info points as label points...clean up file.

Created ESRI Terrain using class 2 multipoint feature class as mass point input and Concord Collection Area as soft clip.

This data is stored in the APRS_Class_2_Bare_Earth feature dataset within the Concord_HUC8_Bare_Earth_Data.gdb file geodatabase.

Created 5ft floating point raster DEM from ESRI Terrain by converting Terrain to Raster using ArcGIS 3D Analyst. Save results as a ESRI GRID dataset.

Created 10ft floating point raster DEM from ESRI Terrain by converting Terrain to Raster using ArcGIS 3D Analyst. Save results as a ESRI GRID dataset.

Create contours by Extracting by mask from the 5ft DEM using a HUC12 area. Save this raster as HUC12 Name 5ft.

Focal Statistics using Extracted 5ft DEM as input, Intermediate Focal Raster as Output, Neighborhood should be set to weighted kernel, and the statistic should be sum.

Create contours using focal stats raster as input, output polyline should be based on HUC12 name, Contour Interval of 2ft, Set base contour to 0.001.

Check results and store in file geodatabase under the HUC12 name feature dataset.

Create HDEM from the Floating Point 10ft DEM derived from the Concord LiDAR data was constructed to include all drainage areas for the Concord watershed. Used a U.S. Geological Survey (USGS) National

Hydrography Dataset (NHD) polyline featureclass containing the stream channel locations was reviewed and compared with orthophotos to confirm the stream channels were in the correct location.

Using ArcHydro, the DEM and the polyline shapefile were used as inputs to the DEM Reconditioning (AGREE) tool of Arc Hydro. Arc Hydro burned the polylines into the DEM, and thus produced a hydrologically-correct

DEM (HDEM). Sinks are filled, a flow direction and flow accumulation analyses performed, and a final stream GRID developed. The final output of the model is a stream network shapefile. The stream network shapefile

is reviewed and compared to orthophotos and USGS NHD flow lines to confirm the stream channels are in the correct location.

The NOAA Office for Coastal Management (OCM) received the topographic files in LAS V1.2 format. The files contained lidar elevation measurements, classifications,

intensity data, return information, GPS time and scan angle. The data were received in NAD83 UTM Zone 19N (meters) and were vertically referenced to NAVD88

using the Geoid09 model. The vertical units of the data were meters. OCM performed the following processing for data storage and Digital Coast provisioning purposes:

1. All points in Class 11 were changed to Class 12 (Overlap)

2. The topographic las files' global encoding bit was set to '1' to reflect use of the Adjusted Standard GPS Time format.

3. The topographic las files were converted from a Projected Coordinate System (NAD83, UTM Zone 19N) to Geographic coordinates (NAD83).

4. The topographic las files' horizontal units were converted from meters to decimal degrees.

5. The topographic las files were converted from orthometric (NAVD88) heights to ellipsoidal heights using Geoid09.