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Transect Data from the 2022 Department of Defense Wildland Fire Science Initi...
To characterize fine-scale variations in vegetation structure, quantify heterogeneity, and capture fuel estimation and consumption, transects were utilized to sample coarse...- CSV
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Vegetation Biomass and Voxel Data from the 2022 Department of Defense Wildlan...
To characterize fine-scale variations in vegetation structure, quantify heterogeneity, and capture fuel estimation and consumption, destructive 3D clip plots were sampled across...- CSV
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Leica BLK360 G1 Terrestrial LiDAR Scans from the 2022 Department of Defense W...
To characterize fine-scale variations in vegetation structure, quantify heterogeneity, and capture fuel estimation and consumption, Leica BLK360 G1 terrestrial LiDAR scans were...- HTML
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Clip Plot, Macroplot, and Hemispherical Photographs Collected at the Winter B...
To characterize fine-scale variations in vegetation structure, quantify heterogeneity, and capture fuel estimation and consumption, clip plot, macroplot, and hemispherical...- HTML
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Clip Plot and Macroplot Photographs Collected at the 2022 Department of Defen...
To characterize fine-scale variations in vegetation structure, quantify heterogeneity, and capture fuel estimation and consumption, photographs of the clip plots and macroplots...- HTML
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Clip Plot and Macroplot Photographs Collected at the 2023 Department of Defen...
To characterize fine-scale variations in vegetation structure, quantify heterogeneity, and capture fuel estimation and consumption, photographs of the clip plots and macroplots...- HTML
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Leica BLK360 G1 Terrestrial LiDAR Scans from the 2023 Department of Defense W...
To characterize fine-scale variations in vegetation structure, quantify heterogeneity, and capture fuel estimation and consumption, Leica BLK360 G1 terrestrial LiDAR scans were...- HTML
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Clip Plot and Macroplot Photographs Collected at the 2024 Department of Defen...
To characterize fine-scale variations in vegetation structure, quantify heterogeneity, and capture fuel estimation and consumption, photographs of the clip plots and macroplots...- HTML
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Leica BLK360 G1 Terrestrial LiDAR Scans from the 2024 Department of Defense W...
To characterize fine-scale variations in vegetation structure, quantify heterogeneity, and capture fuel estimation and consumption, Leica BLK360 G1 terrestrial LiDAR scans were...- HTML
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Vegetation Biomass Data from the 2023 Department of Defense Wildland Fire Sci...
To characterize fine-scale variations in vegetation structure, quantify heterogeneity, and capture fuel estimation and consumption, destructive clip plots were sampled across...- CSV
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Fuel Moisture Content Data from the 2023 Department of Defense Wildland Fire ...
To measure fuel moisture content, vegetation was collected at adjacent burn units with similar land cover, species composition, and time since the last prescribed burn to G-20...- CSV
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Fuel Moisture Content Data from the 2024 Department of Defense Wildland Fire ...
To measure fuel moisture content, vegetation was collected at adjacent burn units with similar land cover, species composition, and time since the last prescribed burn to E16...- CSV
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Vegetation Biomass Data from the 2024 Department of Defense Wildland Fire Sci...
To characterize fine-scale variations in vegetation structure, quantify heterogeneity, and capture fuel estimation and consumption, destructive clip plots were sampled across...- CSV
- HTML
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Transect and Overstory Data from the 2024 Department of Defense Wildland Fire...
To characterize fine-scale variations in vegetation structure, quantify heterogeneity, and capture fuel estimation and consumption, transects were utilized to sample overstory,...- CSV
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Terrestrial laser scanner (TLS) point cloud data gathered in the Macon Study ...
This data product contains point cloud data collected with a terrestrial laser scanner (TLS) at the Macon Study of Dormant Season Prescribed Fire burn units within Osceola...- HTML
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CMS: LiDAR Biomass Improved for High Biomass Forests, Sonoma County, CA, USA,...
This data set provides estimates of above-ground woody biomass and uncertainty at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass...- HTML
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ECHIDNA LIDAR Campaigns: Forest Canopy Imagery and Field Data, U.S.A., 2007-2009
This data set contains forest canopy scan data from the Echidna Validation Instrument (EVI) and field measurements data from three campaigns conducted in the United States: 2007...- HTML
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LiDAR Derived Biomass, Canopy Height and Cover for Tri-State (MD, PA, DE) Reg...
This dataset provides 30-meter gridded estimates of aboveground biomass (AGB), forest canopy height, and canopy coverage for Maryland, Pennsylvania, and Delaware in 2011....- HTML
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Forest Aboveground Biomass and Carbon Sequestration Potential for Maryland, USA
This dataset provides 90-m resolution maps of estimated forest aboveground biomass (Mg/ha) for nominal year 2011 and projections of carbon sequestration potential for the state...- HTML
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CMS: LiDAR-derived Estimates of Aboveground Biomass at Four Forested Sites, USA
These data consist of high-resolution maps of aboveground biomass at four forested sites in the US: Garcia River Tract in California, Anne Arundel and Howard Counties in...- HTML
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