Mapping Forest Height in Alaska Using GLAS, Landsat Composites, and Airborne LiDAR
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 com...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2014-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/6/12/12409 |
| _version_ | 1818083232075218944 |
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| author | Birgit Peterson Kurtis J. Nelson |
| author_facet | Birgit Peterson Kurtis J. Nelson |
| author_sort | Birgit Peterson |
| collection | DOAJ |
| description | 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 process when developing the LANDFIRE 2010 product. The high latitude of this region enabled dense coverage of discrete GLAS samples, from which forest height was calculated. Different methods for deriving height from the GLAS waveform data were applied, including an attempt to correct for slope. These methods were then evaluated and integrated into the final map according to predefined criteria. The resulting map of forest canopy height includes more height classes than the original map, thereby better depicting the heterogeneity of the landscape, and provides seamless data for fire behavior analysts and other users of LANDFIRE data. |
| first_indexed | 2024-12-10T19:34:43Z |
| format | Article |
| id | doaj.art-fe26c35b8ebb485c9e46ca7dd8dbd911 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-10T19:34:43Z |
| publishDate | 2014-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-fe26c35b8ebb485c9e46ca7dd8dbd9112022-12-22T01:36:10ZengMDPI AGRemote Sensing2072-42922014-12-01612124091242610.3390/rs61212409rs61212409Mapping Forest Height in Alaska Using GLAS, Landsat Composites, and Airborne LiDARBirgit Peterson0Kurtis J. Nelson1ASRC Federal InuTeq, contractor to US Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, 47914 252nd Street, Sioux Falls, SD 57198, USAUSGS EROS, 47914 252nd Street, Sioux Falls, SD 57198, USAVegetation 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 process when developing the LANDFIRE 2010 product. The high latitude of this region enabled dense coverage of discrete GLAS samples, from which forest height was calculated. Different methods for deriving height from the GLAS waveform data were applied, including an attempt to correct for slope. These methods were then evaluated and integrated into the final map according to predefined criteria. The resulting map of forest canopy height includes more height classes than the original map, thereby better depicting the heterogeneity of the landscape, and provides seamless data for fire behavior analysts and other users of LANDFIRE data.http://www.mdpi.com/2072-4292/6/12/12409GLASLiDARAlaskaLANDFIREforest height |
| spellingShingle | Birgit Peterson Kurtis J. Nelson Mapping Forest Height in Alaska Using GLAS, Landsat Composites, and Airborne LiDAR Remote Sensing GLAS LiDAR Alaska LANDFIRE forest height |
| title | Mapping Forest Height in Alaska Using GLAS, Landsat Composites, and Airborne LiDAR |
| title_full | Mapping Forest Height in Alaska Using GLAS, Landsat Composites, and Airborne LiDAR |
| title_fullStr | Mapping Forest Height in Alaska Using GLAS, Landsat Composites, and Airborne LiDAR |
| title_full_unstemmed | Mapping Forest Height in Alaska Using GLAS, Landsat Composites, and Airborne LiDAR |
| title_short | Mapping Forest Height in Alaska Using GLAS, Landsat Composites, and Airborne LiDAR |
| title_sort | mapping forest height in alaska using glas landsat composites and airborne lidar |
| topic | GLAS LiDAR Alaska LANDFIRE forest height |
| url | http://www.mdpi.com/2072-4292/6/12/12409 |
| work_keys_str_mv | AT birgitpeterson mappingforestheightinalaskausingglaslandsatcompositesandairbornelidar AT kurtisjnelson mappingforestheightinalaskausingglaslandsatcompositesandairbornelidar |