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Waveform lidar imagery was acquired on September 26, 1999 over the Bartlett Experimental Forest (BEF) in New Hampshire (USA) using NASA's Laser Vegetation Imaging Sensor (LVIS). This flight occurred 20 months after an ice storm damaged millions of hectares of forestland in northeastern North America. Lidar measurements of the amplitude and intensity of ground energy returns appeared to readily detect areas of moderate to severe ice storm damage associated with the worst damage. Southern through eastern aspects on side slopes were particularly susceptible to higher levels of damage, in large part overlapping tracts of forest that had suffered the highest levels of wind damage from the 1938 hurricane and containing...
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U.S Geological Survey (USGS) scientists conducted field data collection efforts during the time periods of September 5 - 14, 2018, November 8 - 13, 2018, June 18 - 27, 2019, July 30 - August 8, 2019, September 13 - 19, 2019, and June 23 - July 1, 2020. These efforts used a combination of technologies to map twenty burned and twelve unburned forest plots at eleven sites in the Black Hills of South Dakota. Twelve burned plots at five sites and nine unburned plots at two sites are located within Custer State Park, five burned plots are located on private land adjacent to Custer State Park at two sites, three unburned plots are located at one site near Hazelrodt Picnic Area in the Black Hills National Forest, and three...
Tags: Black Hills,
Black Hills National Forest,
Burn Severity,
Custer County,
Custer State Park, All tags...
Differenced Normalized Burn Ratio,
EROS,
Earth Resources Observation and Science Center,
Ecology,
Fire Science,
Forest Structure,
Forestry,
Fuels,
GBL,
GNSS,
Geography,
Global Navigational Satellite System,
Ground Based Lidar,
Land Use Change,
Legion Lake,
Legion Lake Fire,
Lidar,
Light Detection and Ranging,
MTBS,
Monitoring Trends in Burn Severity,
NBR,
Normalized Burn Ratio,
Remote Sensing,
State of South Dakota,
TLS,
Terrestrial Laser Scanner,
Total Station,
U.S. Geological Survey,
USGS,
USGS Science Data Catalog (SDC),
Vegetation Structure,
Wildland Fire,
Wind Cave National Park,
biota,
dNBR,
elevation,
geoscientificInformation,
location, Fewer tags
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Vegetation structure, including forest canopy height, is an important input variable to fire behavior modeling systems for simulating wildfire behavior. As such, forest canopy height is one of a nationwide suite of products generated by the LANDFIRE program. In the past, LANDFIRE has relied on a combination of field observations and Landsat imagery to develop existing vegetation structure products. The paucity of field data in the remote Alaskan forests has led to a very simple forest canopy height classification for the original LANDFIRE forest height map. To better meet the needs of data users and refine the map legend, LANDFIRE incorporated ICESat Geoscience Laser Altimeter System (GLAS) data into the updating...
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Accurate information about three-dimensional canopy structure and wildland fuel across the landscape is necessary for fire behaviour modelling system predictions. Remotely sensed data are invaluable for assessing these canopy characteristics over large areas; lidar data, in particular, are uniquely suited for quantifying three-dimensional canopy structure. Although lidar data are increasingly available, they have rarely been applied to wildland fuels mapping efforts, mostly due to two issues. First, the Landscape Fire and Resource Planning Tools (LANDFIRE) program, which has become the default source of large-scale fire behaviour modelling inputs for the US, does not currently incorporate lidar data into the vegetation...
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To make informed decisions, land managers require knowledge about the state of the ecosystems present. Vegetation structure is a key indicator of the state of forested systems; it influences habitat suitability, water quality and runoff, microclimate, and informs wildfire-related characteristics such as fuel loads, burn severity, and post-fire regeneration. Field data used to derive vegetation structure are limited in spatial and temporal extent. Alternatively, forest growth simulation models estimate vegetation structure, but do not capture all factors influencing vegetation growth. Assessment of vegetation structure can be improved by using observations to derive maps which can be used to calibrate modeled forest...
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