2012 FEMA Risk Map Lidar: Merrimack River Watershed (Massachusetts, New Hampshire)

Create custom data files by choosing data area, product type, map projection, file format, datum, etc.

Simple download of data files. The data is in orthometric heights and not the ellipsoid heights described in this metadata.

Original report from the lidar contractor. It does not reflect any changes made after the original delivery to FEMA.

This graphic shows the lidar coverage for the 2012 FEMA Merrimack River Watershed data set.

Date that the source FGDC record was last modified.

Converted from FGDC Content Standards for Digital Geospatial Metadata (version FGDC-STD-001-1998) using 'fgdc_to_inport_xml.pl' script. Contact Tyler Christensen (NOS) for details.

Partial upload of Positional Accuracy fields only.

Partial upload to move data access links to Distribution Info.

Point Cloud (All Returns) LAS 1.2 point files named according to Merrimack_Tile_Index. | Type of Source Media: DIGITAL

Bare Earth LAS point files named according to the Merrimack_Tile_Index. | Type of Source Media: DIGITAL

1 meter geotif file for Merrimack HUC 8 watershed project area. | Type of Source Media: DIGITAL

2 meter geotif file for Merrimack HUC 8 watershed project area. | Type of Source Media: DIGITAL

1749 1 meter ERDAS Imagine files for Merrimack HUC 8 watershed project area. | Type of Source Media: DIGITAL

Merrimack Watershed 3D polyline breaklines. | Type of Source Media: DIGITAL

TSDN for the acquisition, processing and product development of the LiDAR dataset. | Type of Source Media: DIGITAL

File geodatabase (v9.3) containing 2ft contour polyline feature classes for Merrimack HUC 8 based on HUC 12 boundaries. | Type of Source Media: DIGITAL

File geodatabase (v9.3.1) containing Merrimack HUC 8 ESRI Terrains. Includes bare earth multipoint (mass points),

hydro enforced breaklines (hard breakline), and project area (softclip) feature classes.

| Type of Source Media: DIGITAL

Document contains QC test results for both FVA and CVA blind check point tests against

open area and bare earth surfaces generated from All Returns and Bare Earth (respectively) LAS points.

| Type of Source Media: DIGITAL

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 AOI 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, 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 deliverables. 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, 76 CVA points are necessary to test to CE95 as discussed above. 72 CVA points were collected with the intention at the outset that 4 of the collected FVAs would perform double, duty as Open-class CVA points, to total 76 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 a Leica ALS60 LiDAR system, 268 flight lines of highest density (Nominal Pulse Spacing of 1.0m) were collected over the Merrimack area which encompasses 1302 square miles. Five (5) blocks (block or area is determined by the Base Station control locations, typically airports with ground control monuments available providing coverage within 18 miles of the base as possible) to cover in its entirety.

Area |Flight Lines |Lifts |Dates |System

CON |79 |7 |12/19-12/29 2011 |ALS60

ASH |64 |5 |1/7-1/11 2012 |ALS60

BED |31 |1 |1/11/2012 |ALS60

LCI |34 |2 |11/12-11/13 2011 |ALS60

AFN |48 |4 |11/12-11/13 2011 |ALS60

Cross Flights 12 Flight Lines...Lifts were combined with the acquisition of each area with both sensors

Leica proprietary 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. Pairing the aircraft's raw trajectory data with the stationary GPS base station data, this software yields Leica IPAS TC (Inertial Positioning & Attitude Sensor Tightly Coupled) smoothed best estimate of trajectory (an SBET, in Leica's .sol file format) that is necessary for Leica's ALSPP post processing software to develop the resulting geo-referenced point cloud from the LiDAR missions. The point cloud is the mathematical three dimensional composite of all returns from all laser pulses as determined from the aerial mission. 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 point cloud was created using Leica Post Processor software. GeoCue was used in the creation of some of the files needed in downstream processing, as well as in the tiling of the dataset into more manageable file sizes. The TerraScan and TerraModeler software packages are then used for the automated data classification, manual cleanup, and bare earth generation from this data. Project specific macros were used to classify the ground and to remove the side overlap between parallel flight lines. All data was manually reviewed and any remaining artifacts removed using functionality provided by TerraScan and TerraModeler. QT Modeler was used as a final check of the bare earth dataset. GeoCue was then used to create the deliverable industry-standard LAS tiles for both the All Point Cloud Data and the Bare Earth. In-house software was then used to perform final statistical analysis of the classes in the LAS files.

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.

1-Convert LAS to Multipoint

2-Create Terrain

3-Convert Terrain to 1m Raster

4-Split 1m Raster into 1749 imagine files

5-Contour

Convert LAS to multipoint:

1. Create file geodatabase and create a feature dataset to store terrain information with appropriate projection and spatial domain.

2. Run LAS to multipoint tool in 3D analyst for the classified LAS files and select class 2 and 8.

3. Store results in file geodatabase feature dataset for terrain data.

Create Terrain

1. Create Terrain using multipoint as masspoints, hydro break lines as hard breakline and LiDAR coverage area as soft clip

2. Build Terrain and store in file geodatabase feature dataset for terrain data

Convert Terrain to 1m Raster

1. Run the Terrain to raster tool in 3D analyst

2. Float output data type, Linear as the method, CELLSIZE 1 as sampling distance, and Pyramid Level Resolution 0

3. Save results as a Geotiff dataset.

Split 1m Raster into tiles

1. Load 1m raster into ERDAS Imagine Mosaic pro tool

2. Split raster using LiDAR index

3. Save results as 1m imagine files

Create contours

1. Extract by mask from the 1m DEM using a HUC12 area. Save this raster as HUC12 Name 1m.

2. Focal Statistics using Extracted 1m DEM as input, Intermediate Focal Raster as Output, Neighborhood should be set to weighted kernel, and the statistic should be sum.

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

4. Check results and store in file geodatabase under the Analysis Contours feature dataset.

5. Focal Statistics using Extracted 1m DEM as input, Intermediate Focal Raster as Output, Neighborhood should be set to circle, and the statistic should be mean.

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

7. Check results and store in file geodatabase under the Cartographic Contours feature dataset.

The NOAA Office for Coastal Management received elevation and intensity data las files in LAS v1.2 format. The data were in UTM Zone 19, meters, NAD83 coordinates and were vertically referenced to NAVD88 (Geoid09). The vertical units of the data were meters. OCM performed the following processing for data storage and Digital Coast provisioning purposes:

1. Data were filtered for outliers using the lastools tool, las2las

2. Data points that were withheld points, but were classified as 11, were converted to class 16, using the lastools tool, las2las

3. Data were converted from UTM Zone 19, meters, NAD83 to geographic coordinates.

4. Data were converted from NAVD88 (Geoid09) elevations, to ellipsoid elevations using Geoid09

5. Data were zipped to laz format

The withheld points were returned to class 11. The files were tagged as being in NAD83(2007) based on the date of collection, instead of a generic NAD83. The original report does not specify the NAD83 realization.