2018 USGS Lidar: Southern California Wildfires

Product: Processed, classified lidar point cloud data tiles in LAS 1.4 format.

Geographic Extent:

QL2 Area - Approximately 8,146 square miles in portions of Los Angeles, Orange, Riverside, San Bernardino, Kern, San Luis Obispo, Santa Barbara, Ventura Counties

QL1 Area - Approximately 1,343 square miles in portion of Santa Barbara County.

Dataset Description:

QL2 Area: The CA SoCal Wildfires 2018 D18 lidar project called for the planning, acquisition, processing, and production of derivative products of QL2 lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters .Project specifications were based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.3. The data was developed based on a horizontal datum/projection of NAD83 (2011) Albers Equal Area, meters, and a vertical datum of NAVD88 (GEOID12B) meters. Lidar data was delivered as processed LAS 1.4 files formatted to 24,518 individual 1,000-meter x 1,000-meter tiles.

QL1 Area: The CA SoCal Wildfires 2018 D18 lidar project called for the planning, acquisition, processing, and production of derivative products of QL1 lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications were based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.3. The data was developed based on a horizontal datum/projection of NAD83 (2011) Albers Equal Area, meters, and a vertical datum of NAVD88 (GEOID12B) meters. Lidar data was delivered as processed LAS 1.4 files formatted to 3,479 individual 1,000-meter x 1,000-meter tiles.

Ground Conditions:

QL2 Area: Lidar was collected from May 27, 2018 through October 12, 2018 while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Woolpert established 275 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 351 independent accuracy checkpoints (205 NVA points and 146 VVA points), were collected and used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.

QL1 Area: Lidar was collected from June 13, 2018 through August 6, 2018 while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Woolpert established 275 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 351 independent accuracy checkpoints (205 NVA points and 146 VVA points), were collected and used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.

The NOAA Office for Coastal Management (OCM) downloaded 5 data sets from this USGS site:

ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/

The data sets downloaded were:

USGS_LPC_CA_SoCAL_Wildfires_TL_2018_LAS_2019/ Number of files: 108

USGS_LPC_CA_SoCal_Wildfires_B1_2018_LAS_2019/ Number of files: 7868

USGS_LPC_CA_SoCal_Wildfires_B2_2018_LAS_2019/ Number of files: 5807

USGS_LPC_CA_SoCal_Wildfires_B3_2018_LAS_2019/ Number of files: 7261

USGS_LPC_CA_SoCal_Wildfires_B4_2018_LAS_2019/ Number of files: 3474

These files were processed to the Data Access Viewer (DAV) and https. The total number of files downloaded and processed was 24,518.

Hydro breaklines are also available. These data are available for download at the link provided in the URL section of this metadata record. Please note that these products have not been reviewed by the NOAA Office for Coastal Management (OCM) and any conclusions drawn from the analysis of this information are not the responsibility of NOAA or OCM.

Lidar data are used in predicting wildfire risk, and in post-wildfire hazards analyses. For example, lidar data provide 3D information about forest canopy and vegetation structure that is used to map wildfire fuel, a key risk factor for fire occurrence and intensity. Lidar maps the local topographic conditions (elevation, slope, and aspect), which are critical components to understanding and predicting fire behavior. After wildfires occur, lidar data are used to develop predictive models of flood and mudflow hazards that can result from steep terrain that has been stripped of vegetation. Conducting these analyses and understanding the vulnerability of California communities, farms, and infrastructure to future wildfires relies on the availability of high resolution, accurate lidar data. Wildfire applications for high resolution elevation data include: assessment of wildfire hazards, estimates of wildfire fuel loads, flood and mudflow hazard models, development of plans and site engineering documents to support reconstruction activities, 3D data to support ecological assessments and other long-term science requirements.

USGS Contract No. G16PC00022, Task Order No. 140G0218F0246. Contractor: Woolpert.

The following are the USGS lidar fields in JSON:

{

"ldrinfo" : {

"ldrspec" : "USGS NGP Base Specifications v1.3",

"ldrsens" : "Leica ALS80",

"ldrmaxnr" : "infinite",

"ldrnps" : "0.7",

"ldrdens" : "2",

"ldranps" : "0.7",

"ldradens" : "2",

"ldrfltht" : "1981",

"ldrfltsp" : "150",

"ldrscana" : "40",

"ldrscanr" : "42.9",

"ldrpulsr" : "272",

"ldrpulsd" : "5",

"ldrpulsw" : "2.5",

"ldrwavel" : "1064",

"ldrmpia" : "1",

"ldrbmdiv" : "0.20",

"ldrswatw" : "1443",

"ldrswato" : "25",

"ldrgeoid" : "GEOID12B"

},

"ldrinfo" : {

"ldrspec" : "USGS NGP Base Specifications v1.3",

"ldrsens" : "Leica ALS80",

"ldrmaxnr" : "infinite",

"ldrnps" : "0.7",

"ldrdens" : "2",

"ldranps" : "0.7",

"ldradens" : "2",

"ldrfltht" : "2377",

"ldrfltsp" : "150",

"ldrscana" : "40",

"ldrscanr" : "35.5",

"ldrpulsr" : "346",

"ldrpulsd" : "5",

"ldrpulsw" : "2.5",

"ldrwavel" : "1064",

"ldrmpia" : "1",

"ldrbmdiv" : "0.20",

"ldrswatw" : "1731",

"ldrswato" : "25",

"ldrgeoid" : "GEOID12B"

},

"ldraccur" : {

"ldrchacc" : "0",

"rawnva" : "0.104",

"rawnvan" : "205",

"clsnva" : "0.112",

"clsnvan" : "205",

"clsvva" : "0.211",

"clsvvan" : "146"

},

"lasinfo" : {

"lasver" : "1.4",

"lasprf" : "6",

"laswheld" : "Geometrically unreliable points were identified using a combination of automated and manual techniques and subsequently classified as high or low noise and given the standard Withheld bit.",

"lasolap" : "Swath "overage" points were identified in these files using the standard LAS overlap bit.",

"lasintr" : "16",

"lasclass" : {

"clascode" : "1",

"clasitem" : "Processed but not classified"

},

"lasclass" : {

"clascode" : "2",

"clasitem" : "Bare earth ground"

},

"lasclass" : {

"clascode" : "7",

"clasitem" : "Noise"

},

"lasclass" : {

"clascode" : "9",

"clasitem" : "Water"

},

"lasclass" : {

"clascode" : "17",

"clasitem" : "Bridge Decks"

},

"lasclass" : {

"clascode" : "18",

"clasitem" : "High Noise"

},

"lasclass" : {

"clascode" : "20",

"clasitem" : "Ignored ground"

}

}}

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

Data is available online for bulk and 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 10 Version 1607 (Build 14393.2189); ArcMap 10.3; ArcCatalog 10.3; TerraScan ver. 17; TerraModeler ver. 17; Geocue 2017.2.20.0.

The project specifications require that only Non-Vegetated Vertical Accuracy (NVA) be computed for raw lidar point cloud swath files. The required accuracy (ACCz) is: 19.6 cm at a 95% confidence level, derived according to NSSDA, i.e., based on RMSE of 10 cm in non-vegetated land cover classes.

The NVA was tested with 205 checkpoints located in bare earth and urban (non-vegetated) areas. These check points were not used in the calibration or post processing of the lidar point cloud data. The checkpoints were distributed throughout the project area and were surveyed using GPS techniques. See survey report for additional survey methodologies. Elevations from the unclassified lidar surface were measured for the x,y location of each check point. Elevations interpolated from the lidar surface were then compared to the elevation values of the surveyed control points. AccuracyZ has been tested to meet 19.6 cm or better Non-Vegetated Vertical Accuracy at 95% confidence level using RMSE(z) x 1.9600 as defined by the National Standard for Spatial Data Accuracy (NSSDA); assessed and reported using National Digital Elevation Program (NDEP)/ASRPS Guidelines.

Tested 0.104 meters NVA at a 95% confidence level using RMSE(z) 0.053 x 1.9600 as defined by the National Standard for Spatial Data Accuracy (NSSDA). The NVA of the raw lidar point cloud swath files was calculated against TINs derived from the final calibrated and controlled swath data using 205 independent checkpoints located in non-vegetated land cover categories.

These LAS data files include all data points collected. No points have been removed or excluded. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist.

Data covers the entire area specified for this project.

Data is backed up to tape and to cloud storage.