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As part of Upper Mississippi River Restoration (UMRR), the U.S. Army Corps of Engineers (USACE) is conducting a study to understand what environmental factors are contributing to the failure of floodplain forests to regenerate. This dataset uses lidar derivatives to identify broken forest canopy along the Mississippi River and Illinois River. A broken forest refers to an area that has a canopy height of greater than or equal to 10 meters. From this layer, forest canopy gaps can be identified by locating areas within the broken forest that have at least a 9.144 meter radius, or a 1-tree gap.
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As part of Upper Mississippi River Restoration (UMRR), the U.S. Army Corps of Engineers (USACE) is conducting a study to understand what environmental factors are contributing to the regeneration of floodplain forest. This dataset uses lidar derivatives to identify forest canopy gaps along select portions of the Mississippi River and Illinois River. USACE will use this dataset to select field sites to collect data in forest canopy gaps. This will also serve as the baseline for long-term forest canopy gap study.
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As part of Upper Mississippi River Restoration (UMRR), the U.S. Army Corps of Engineers (USACE) is conducting a study to understand what environmental factors are contributing to the failure of floodplain forests to regenerate. This dataset uses lidar derivatives to identify broken forest canopy along the Mississippi River and Illinois River. A broken forest refers to an area that has a canopy height of greater than or equal to 10 meters. From this layer, forest canopy gaps can be identified by locating areas within the broken forest that have at least a 9.144 meter radius, or a 1-tree gap.
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This data release supports interpretations of field-observed root distributions within a shallow landslide headscarp (CB1) located below Mettman Ridge within the Oregon Coast Range, approximately 15 km northeast of Coos Bay, Oregon, USA. (Schmidt_2021_CB1_topo_far.png and Schmidt_2021_CB1_topo_close.png). Root species, diameter (greater than or equal to 1 mm), general orientation relative to the slide scarp, and depth below ground surface were characterized immediately following landsliding in response to large-magnitude precipitation in November 1996 which triggered thousands of landslides within the area (Montgomery and others, 2009). The enclosed data includes: (1) tests of root-thread failure as a function of...
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This dataset contains data pertaining to ground surface cover in 30 meter plots around a random selection of points within chaparral from Santa Barbara county south to San Diego County in southern California, USA. These data were obtained from historical aerial imagery from 1943 to 1959 and current imagery from 2016 to 2018 and they were compared to quantify changes in cover type over time. These data support the following publication: Syphard, A.D., Brennan, T.J., Rustigian‐Romsos, H. and Keeley, J.E., 2022. Fire‐driven vegetation type conversion in southern California. Ecological Applications, p.e2626. https://doi.org/10.1002/eap.2626.
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As part of Upper Mississippi River Restoration (UMRR), the U.S. Army Corps of Engineers (USACE) is conducting a study to understand what environmental factors are contributing to the regeneration of floodplain forest. This dataset uses lidar derivatives to identify forest canopy gaps along select portions of the Mississippi River and Illinois River. USACE will use this dataset to select field sites to collect data in forest canopy gaps. This will also serve as the baseline for long-term forest canopy gap study.
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As part of Upper Mississippi River Restoration (UMRR), the U.S. Army Corps of Engineers (USACE) is conducting a study to understand what environmental factors are contributing to the failure of floodplain forests to regenerate. This dataset uses lidar derivatives to identify broken forest canopy along the Mississippi River and Illinois River. A broken forest refers to an area that has a canopy height of greater than or equal to 10 meters. From this layer, forest canopy gaps can be identified by locating areas within the broken forest that have at least a 9.144 meter radius, or a 1-tree gap.
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We developed a LiDAR-based habitat model for the threatened Marbled Murrelet (MAMU) in the Siuslaw National Forest, Oregon, using a two-step approach. First, we tested the applicability of the LiDAR-based model developed for the Coos Bay District of the Bureau of Land Management (BLM) to the Siuslaw N.F. In the second step, we tested alternative habitat models developed with forest structural data and Murrelet survey data from the Siuslaw N.F. We compared the performance of each model to provide forest managers with the best predictive tool to guide habitat management for the Marbled Murrelet. This shapefile contains the probability of Marbled Murrelet occupancy values of each model for vegetation polygons defined...
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As part of Upper Mississippi River Restoration (UMRR), the U.S. Army Corps of Engineers (USACE) is conducting a study to understand what environmental factors are contributing to the failure of floodplain forests to regenerate. This dataset uses lidar derivatives to identify broken forest canopy along the Mississippi River and Illinois River. A broken forest refers to an area that has a canopy height of greater than or equal to 10 meters. From this layer, forest canopy gaps can be identified by locating areas within the broken forest that have at least a 9.144 meter radius, or a 1-tree gap.
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As part of Upper Mississippi River Restoration (UMRR), the U.S. Army Corps of Engineers (USACE) is conducting a study to understand what environmental factors are contributing to the regeneration of floodplain forest. This dataset uses lidar derivatives to identify forest canopy gaps along select portions of the Mississippi River and Illinois River. USACE will use this dataset to select field sites to collect data in forest canopy gaps. This will also serve as the baseline for long-term forest canopy gap study.
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As part of Upper Mississippi River Restoration (UMRR), the U.S. Army Corps of Engineers (USACE) is conducting a study to understand what environmental factors are contributing to the regeneration of floodplain forest. This dataset uses lidar derivatives to identify forest canopy gaps along select portions of the Mississippi River and Illinois River. USACE will use this dataset to select field sites to collect data in forest canopy gaps. This will also serve as the baseline for long-term forest canopy gap study.
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As part of Upper Mississippi River Restoration (UMRR), the U.S. Army Corps of Engineers (USACE) is conducting a study to understand what environmental factors are contributing to the regeneration of floodplain forest. This dataset uses lidar derivatives to identify forest canopy gaps along select portions of the Mississippi River and Illinois River. USACE will use this dataset to select field sites to collect data in forest canopy gaps. This will also serve as the baseline for long-term forest canopy gap study.
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As part of Upper Mississippi River Restoration (UMRR), the U.S. Army Corps of Engineers (USACE) is conducting a study to understand what environmental factors are contributing to the failure of floodplain forests to regenerate. This dataset uses lidar derivatives to identify broken forest canopy along the Mississippi River and Illinois River. A broken forest refers to an area that has a canopy height of greater than or equal to 10 meters. From this layer, forest canopy gaps can be identified by locating areas within the broken forest that have at least a 9.144 meter radius, or a 1-tree gap.
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Most of these data were collected in order to create a database of tree locations for use in calibrating remote sensing tools and products, particularly dead tree detection tools and canopy species maps. Data include tree locations, species identification, and status (live, dead, and, if dead, sometimes includes information on foliage and twig retention). They are a collection of different sampling efforts performed over several years, starting in a period of severe drought mortality. One csv table is included that shows data and validation results for an additional dataset that was used to test the NAIP derived dead tree detection model that is associated with this data release. Locations are not included for that...
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Dataset contains 32 terrestrial lidar scans of conifer forests and associated shapefile of locations in Sequoia and Kings Canyon National parks from the summer of 2022. These scans were co-located within field plots from a larger ongoing U.S. Geological Survey (USGS) project collecting wildfire fuels and forest structure data (informally known as the Fire and Fuels Project). These data can also be found in a USGS Earth Resources Observation and Science (EROS) database named IntELiMon (https://dmsdata.cr.usgs.gov/lidar-monitoring/viewer/).
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We used murrelet occupancy data collected by the Bureau of Land Management Coos Bay District and canopy metrics calculated from discrete return airborne LiDAR data to fit a logistic regression model predicting the probability of occupancy. Our final model for stand-level occupancy included distance to coast and 5 LiDAR-derived variables describing canopy structure. This dataset is a shapefile of forest stands in the Coos Bay district representing the model results.
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As part of Upper Mississippi River Restoration (UMRR), the U.S. Army Corps of Engineers (USACE) is conducting a study to understand what environmental factors are contributing to the regeneration of floodplain forest. This dataset uses lidar derivatives to identify forest canopy gaps along select portions of the Mississippi River and Illinois River. USACE will use this dataset to select field sites to collect data in forest canopy gaps. This will also serve as the baseline for long-term forest canopy gap study.
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As part of Upper Mississippi River Restoration (UMRR), the U.S. Army Corps of Engineers (USACE) is conducting a study to understand what environmental factors are contributing to the regeneration of floodplain forest. This dataset uses lidar derivatives to identify forest canopy gaps along select portions of the Mississippi River and Illinois River. USACE will use this dataset to select field sites to collect data in forest canopy gaps. This will also serve as the baseline for long-term forest canopy gap study.
As part of Upper Mississippi River Restoration (UMRR), the U.S. Army Corps of Engineers (USACE) is conducting a study to understand what environmental factors are contributing to the failure of floodplain forests to regenerate. This dataset uses lidar derivatives to identify broken forest canopy along the Mississippi River and Illinois River. A broken forest refers to an area that has a canopy height of greater than or equal to 10 meters. From this layer, forest canopy gaps can be identified by locating areas within the broken forest that have at least a 9.144 meter radius, or a 1-tree gap.
    map background search result map search result map Estimated Probabilities from Lidar Models for Marbled Murrelet (Brachyramphus marmoratus) Occupancy in Forest Vegetation Stands in the Siuslaw National Forest, Oregon Forest Canopy Gaps Identified by Lidar for the Alton Reach of the Illinois River from the Confluence of the Mississippi River to Kampsville, IL Forest Canopy Gaps Identified by Lidar for Navigational Pool 8 of the Mississippi River Forest Canopy Gaps Identified by Lidar for Navigational Pool 9 of the Mississippi River Forest Canopy Gaps Identified by Lidar for Navigational Pool 13 of the Mississippi River Forest Canopy Gaps Identified by Lidar for Navigational Pool 21 of the Mississippi River Forest Canopy Gaps Identified by Lidar for Navigational Pool 24 of the Mississippi River Forest Canopy Gaps Identified by Lidar for Navigational Pool 26 of the Mississippi River Broken Forest Canopy Identified by Lidar for the Alton Reach of the Illinois River Broken Forest Canopy Identified by Lidar for the Navigational Pool 8 of the Mississippi River Broken Forest Canopy Identified by Lidar for the Navigational Pool 9 of the Mississippi River Broken Forest Canopy Identified by Lidar for the Navigational Pool 13 of the Mississippi River Broken Forest Canopy Identified by Lidar for the Navigational Pool 21 of the Mississippi River Broken Forest Canopy Identified by Lidar for the Navigational Pool 24 of the Mississippi River Root thread strength, landslide headscarp geometry, and observed root characteristics at the monitored CB1 landslide, Oregon, USA Vegetation Type Conversion in Southern California Between 1943 and 2018 Forest stands and LiDAR derived model estimates of marbled murrelet (Brachyramphus marmoratus) occupancy in the Coos Bay BLM District, Southwestern Oregon Terrestrial Lidar Scans of Conifer Forests in Sequoia and Kings Canyon National Parks, 2022 Dead Tree Detection Validation Data from Sequoia and Kings Canyon National Parks Root thread strength, landslide headscarp geometry, and observed root characteristics at the monitored CB1 landslide, Oregon, USA Forest Canopy Gaps Identified by Lidar for Navigational Pool 21 of the Mississippi River Broken Forest Canopy Identified by Lidar for the Navigational Pool 21 of the Mississippi River Forest Canopy Gaps Identified by Lidar for Navigational Pool 8 of the Mississippi River Broken Forest Canopy Identified by Lidar for the Navigational Pool 8 of the Mississippi River Forest Canopy Gaps Identified by Lidar for the Alton Reach of the Illinois River from the Confluence of the Mississippi River to Kampsville, IL Broken Forest Canopy Identified by Lidar for the Alton Reach of the Illinois River Terrestrial Lidar Scans of Conifer Forests in Sequoia and Kings Canyon National Parks, 2022 Forest Canopy Gaps Identified by Lidar for Navigational Pool 24 of the Mississippi River Broken Forest Canopy Identified by Lidar for the Navigational Pool 24 of the Mississippi River Forest Canopy Gaps Identified by Lidar for Navigational Pool 9 of the Mississippi River Dead Tree Detection Validation Data from Sequoia and Kings Canyon National Parks Broken Forest Canopy Identified by Lidar for the Navigational Pool 9 of the Mississippi River Forest Canopy Gaps Identified by Lidar for Navigational Pool 26 of the Mississippi River Forest Canopy Gaps Identified by Lidar for Navigational Pool 13 of the Mississippi River Broken Forest Canopy Identified by Lidar for the Navigational Pool 13 of the Mississippi River Forest stands and LiDAR derived model estimates of marbled murrelet (Brachyramphus marmoratus) occupancy in the Coos Bay BLM District, Southwestern Oregon Estimated Probabilities from Lidar Models for Marbled Murrelet (Brachyramphus marmoratus) Occupancy in Forest Vegetation Stands in the Siuslaw National Forest, Oregon Vegetation Type Conversion in Southern California Between 1943 and 2018