# Postfire Airborne Lidar Point Cloud and Terrain Models for the Bolt Creek Fire, Washington

## Overview
This dataset contains post-fire airborne Lidar point clouds and derived terrain models
for the Bolt Creek Fire burn area in Washington State. Data were collected using an
uncrewed aerial system (UAS) and processed to support post-fire geomorphic, hydrologic,
and hazard analyses, including terrain change assessment relative to a pre-event
reference elevation model.

The resource is organized into folders corresponding to individual Lidar acquisitions.
Within each folder, users will find the original (raw) point cloud, filtered point
cloud products, spatial boundaries, and multiple raster surfaces derived from the Lidar,
including digital surface models (DSM), digital terrain models (DTM), a
USGS reference DEM used for coregistration, and a DEM difference product.

## Data Contents
This directory contains three folders corresponding to Lidar acquisition areas, and an additional folder which contains upsampled (10m) data of the largest survey block. Each folder includes the following data products:

1. Raw Lidar Point Cloud (BoltCreek_ULS_L1_X_YYYYMMDD.laz)
   Unfiltered LAS/LAZ files containing all returns from the UAS Lidar survey, including
   ground, vegetation, structures, and noise. These files represent the closest form of
   the data to the original sensor output and were used as input for subsequent filtering
   and terrain modeling. Note that for the largest survey block, the point cloud was upsampled 
   to 50cm by the producers to improve data manageability. 

2. Filtered Lidar Point Cloud (BoltCreek_ULS_X_YYYYMMDD_filtered.laz)
   LAS/LAZ files derived from the raw point cloud with vegetation and noise returns
   removed. These filtered point clouds were used to generate surface models and reduce
   artifacts associated with non-ground features.

3. Lidar Boundary (BoltCreek_ULS_X_YYYYMMDD_Boundary.zip)
   A polygon dataset defining the spatial extent of the Lidar acquisition. This boundary
   was used to clip raster products and constrain comparisons to areas where Lidar data
   are present.

4. Digital Surface Model (DSM) (BoltCreek_ULS_X_YYYYMMDD_DSM_1m.tif)
   A raster DSM generated from the filtered Lidar point cloud, representing the elevation
   of the uppermost surfaces captured by the Lidar without vegetation and noise filtering.
   The DSM was produced using triangulated interpolation and is provided at high spatial
   resolution.

5. Digital Terrain Model (DTM) (BoltCreek_ULS_X_YYYYMMDD_DTM_1m.tif)
   A raster DTM derived from the filtered Lidar point cloud using ground classification and
   filtering. The DTM represents bare-earth elevations and was used to characterize
   post-fire terrain conditions.

6. USGS Reference DEM (BoltCreek_ULS_X_USGS_Reference_DEM_1m.tif)
   A pre-event digital elevation model obtained from the U.S. Geological Survey and
   reprojected and clipped to match the Lidar boundary. This reference DEM provides a
   baseline for terrain comparison and change analysis.

7. DEM Difference (BoltCreek_ULS_X_YYYYMMDD_DTM_1m_BoltCreek_ULS_X_USGS_Reference_DEM_1m.tif)
   A raster representing the elevation difference between the post-fire Lidar-derived
   DTM and the USGS reference DEM. Positive values indicate areas where the Lidar-derived
   surface is higher than the reference surface, and negative values indicate lower
   elevations. This product is intended to support qualitative and quantitative assessment
   of post-fire surface changes.

All raster products are spatially aligned and clipped to the Lidar acquisition boundary
to ensure consistency across datasets.

## Spatial Reference (GeoTiff/Shapefiles)
Horizontal CRS: NAD83 / UTM Zone 10N  
EPSG code: 26910  
Units: meters  

Vertical datum: NAVD88  
Vertical units: meters  

## Spatial Reference (LAS/LAZ)
Horizontal CRS: NAD83(2011) / Washington North 
EPSG Code: 6596
Units: meters

Vertical datum: NAVD88
EPSG Code: 5703
Units: meters

Geoid: GEOID18
EPSG Code: 6319 (EPOCH 2010)

## LAS/LAZ Coordinate Reference System (CRS) Definition (WKT)
COMPOUNDCRS["Compound CRS NAD83(2011) / Washington North + North American Vertical Datum 1988 + PROJ us_noaa_g2018u0.tif",PROJCRS["NAD83(2011) / Washington North",BASEGEOGCRS["NAD83(2011)",DATUM["NAD83 (National Spatial Reference System 2011)",ELLIPSOID["GRS 1980",6378137,298.257222101,LENGTHUNIT["metre",1]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",6318]],CONVERSION["unnamed",METHOD["Lambert Conic Conformal (2SP)",ID["EPSG",9802]],PARAMETER["Latitude of false origin",47,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8821]],PARAMETER["Longitude of false origin",-120.833333333333,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8822]],PARAMETER["Latitude of 1st standard parallel",48.7333333333333,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8823]],PARAMETER["Latitude of 2nd standard parallel",47.5,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8824]],PARAMETER["Easting at false origin",500000,LENGTHUNIT["metre",1],ID["EPSG",8826]],PARAMETER["Northing at false origin",0,LENGTHUNIT["metre",1],ID["EPSG",8827]]],CS[Cartesian,2],AXIS["easting",east,ORDER[1],LENGTHUNIT["metre",1,ID["EPSG",9001]]],AXIS["northing",north,ORDER[2],LENGTHUNIT["metre",1,ID["EPSG",9001]]]],VERTCRS["North American Vertical Datum 1988 + PROJ us_noaa_g2018u0.tif",VDATUM["North American Vertical Datum 1988",ID["EPSG",5103]],CS[vertical,1],AXIS["gravity-related height",up,LENGTHUNIT["metre",1]]]]

## Temporal Information
BoltCreek_ULS_All_20240624_28 acquisition date(s): 2024-06-24 to 2024-06-28  
BoltCreek_ULS_A_20250904 acquisition date(s): 2025-09-04
BoltCreek_ULS_B_20250904 acquisition date(s): 2025-09-04

## Processing Workflow
Point cloud processing included the following steps:
1. Noise and outlier removal
2. Ground point classification
3. Classification refinement and quality control
4. Rasterization

Final DEM products derived from this point cloud were generated using
TIN-based interpolation following reprojection to a common CRS.

## Point Cloud Characteristics
BoltCreek_ULS_All_20240624_28
Platform: Airborne Lidar
Survey Area: 6.08 km2
Points (Raw): 45,082,512
Points (Filtered): 4,009,878

BoltCreek_ULS_A_20250904
Platform: Airborne Lidar
Survey Area: 0.22 km2
Points (Raw): 61,035,548 
Points (Filtered): 165,266

BoltCreek_ULS_B_20250904
Platform: Airborne Lidar
Survey Area: 0.17 km2
Points (Raw): 72,377,247
Points (Filtered): 215,900

## Data Quality & Limitations
- Dense canopy and steep terrain may result in localized ground gaps.
- Some slope-adjacent triangulation artifacts may occur in derived surfaces.
- Data are intended for research and analysis purposes and are not survey-grade.

## Usage Notes
Users should ensure that vertical datums and projections are consistent
before differencing this dataset with other elevation products.

## Software
Primary processing software:
- PDAL
- CloudCompare
- QGIS

## Software Citations
CloudCompare (version 2.13.1) [GPL software]. (2026). Retrieved from http://www.cloudcompare.org/

PDAL Contributors, 2022. PDAL Point Data Abstraction Library. https://doi.org/10.5281/zenodo.2616780

Shean, D. E., O. Alexandrov, Z. Moratto, B. E. Smith, I. R. Joughin, C. C. Porter, Morin, P. J., An automated, open-source pipeline for mass production of digital elevation models (DEMs) from very high-resolution commercial stereo satellite imagery, ISPRS J. Photogramm. Remote Sens, 116, 101-117, doi: 10.1016/j.isprsjprs.2016.03.012, 2016. 

Zhang W, Qi J, Wan P, Wang H, Xie D, Wang X, Yan G. An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sensing. 2016; 8(6):501.