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Reclassified lidar point cloud data from 2016 LARIAC and 2019 NCALM collections covering part of the Woolsey wildfire near Malibu, California
These lidar data are derived from two airborne lidar surveys: a 2016 Los Angeles Region Imagery Acquisition Consortium (LARIAC) survey, and a 2019 National Center for Airborne Laser Mapping (NCALM) survey. These data were reclassified in order to improve the classification of ground points, and to make the classification of both datasets as consistent as possible. The NCALM data had their position shifted slightly to more closely align with the LARIAC data. The data are organized into two "Child Items": Reclassified lidar point clouds from 2016 LARIAC collection near Malibu, California and Reclassified lidar point clouds from 2019 NCALM collection near Malibu, California. The point clouds are available as ~1 square kilometer tiles with 25 m buffer overlaps to avoid edge effects in further processing. The naming convention includes the name of the original data collection and some reference UTM coordinates.
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Reclassified lidar point cloud data from 2016 LARIAC and 2019 NCALM collections covering part of the Woolsey wildfire near Malibu, California
공공데이터포털
These lidar data are derived from two airborne lidar surveys: a 2016 Los Angeles Region Imagery Acquisition Consortium (LARIAC) survey, and a 2019 National Center for Airborne Laser Mapping (NCALM) survey. These data were reclassified in order to improve the classification of ground points, and to make the classification of both datasets as consistent as possible. The NCALM data had their position shifted slightly to more closely align with the LARIAC data. The data are organized into two "Child Items": Reclassified lidar point clouds from 2016 LARIAC collection near Malibu, California and Reclassified lidar point clouds from 2019 NCALM collection near Malibu, California. The point clouds are available as ~1 square kilometer tiles with 25 m buffer overlaps to avoid edge effects in further processing. The naming convention includes the name of the original data collection and some reference UTM coordinates.
Airborne Lidar Data (2016 and 2021) Capturing Debris Flow Erosion and Deposition after the Grizzly Creek Fire in Glenwood Canyon, Colorado
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This dataset contains lidar digital elevation models (DEMs). The lidar data were collected before (2016) and after (2021) the Grizzly Creek Fire, which occurred in 2020. The 2016 lidar was collected during a series of flights between 10 June and 7 October 2016. The 2021 lidar flight was conducted in full on 24 August 2021. The files are named with the following convention: Vendor_Year_Resolution_merged_Watershed. The vendor is either Merrick (2016 data) or Sanborn (2021), the year is either 2016 or 2021, the resolution is 1 meter in both cases, and the watershed is labeled as HUC1, HUC2, HUC3_N_side, or HUC3_S_side. Additionally, the files from the individual vendors were uploaded to two separate compressed folders: Merrick_2016_1m_merged_HUCx.zip and Sanborn_2021_1m_merged_HUCx.zip.
USFS Moonlight Fire Lidar, CA 2013
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Lidar was collected over the Moonlight Fire region of the Plumas National Forests in California between August 3rd and August 15th 2013 for the USFS Pacific Region. This dataset was collected to aid the USFS in assessing the topographic and geophysical properties of the study area to support planning and development for fire rehabilitation and restoration efforts. This dataset covers over 130,000 acres ( over ~529 km2)
USFS Freds Fire Lidar, CA 2015
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Lidar was collected in the fall of 2014 and spring of 2015 for the USFS Pacific Region 5 sites in California. Data were collected to aid the United States Forest Service in assessing topographic and geophysical properties, as well as supporting ecological restoration planning, and assessing existing conditions of area forests. The Freds Fire project area covers over 150,000 acres (> 600 km 2)
USFS Power Fire Lidar, CA 2015
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Lidar was collected in the fall of 2014 and spring of 2015 for the USFS Pacific Region 5 sites in California. Data were collected to aid United States Forest Service in assessing the topographic and geophysical properties of the study area to support ecological restoration planning and to assess existing conditions of area forests. The Power Fire project area covers over 114,000 acres (> 460 km2)
Point cloud, raster, and GNSS survey data from laser scanning of a debris flow basin near El Portal, California, USA
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This data release is composed of point cloud data from a terrestrial lidar survey, GNSS survey data, and derivative raster data. The data were collected on November 09, 2023. This survey was used to characterize a debris flow basin near El Portal, California for the National Park Service.
Las Lomas Hillside Lidar
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This data release contains point clouds obtained from three terrestrial laser scanner (TLS) surveys of a hillslope (NAD 83/11 N/ 412828E/ 3780128N) burned by the 2016 Fish Fire in the San Gabriel Mountains, CA, USA. The TLS surveys were completed with a Leica ScanStation C10. The first survey was made on 19 November 2016 prior to the first post-wildfire rainstorm. The second survey was performed on 5 January 2017. Two runoff-generating rainstorms occurred between the first and second surveys. The two rainstorms had peak fifteen-minute average rainfall intensities of 27 mm/h and 10 mm/h, respectively. The third survey was performed on 22 February 2017, following five additional runoff-generating post-wildfire rainstorms. Peak fifteen-minute average rainfall intensities for the five rainstorms were 8 mm/h, 11 mm/h, 16 mm/h, 25 mm/h, and 38 mm/h, respectively. Maps of hillslope erosion derived from the TLS data can be used to document hillslope erosion resulting from these two sets of rainstorms, including the initiation and growth of a substantial rill network. Additional details and a description of the study site can be found in the journal article: Hui T, McGuire LA, Rengers FR, Kean JW, Staley DM, Smith JB. Evolution of debris flow initiation mechanisms and sediment sources during a sequence of post-wildfire rainstorms. Journal of Geophysical Research. 2018.