Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR
Aerial and satellite imagery are widely used to assess the severity and impact of wildfires. Light detection and ranging (LiDAR) is a newer remote sensing technology that has demonstrated utility in measuring vegetation structure. Combined use of imagery and LiDAR may improve the assessment of wildf...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/6/3/1954 |
| _version_ | 1819002405556060160 |
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| author | Brian D. Bishop Brian C. Dietterick Russell A. White Tom B. Mastin |
| author_facet | Brian D. Bishop Brian C. Dietterick Russell A. White Tom B. Mastin |
| author_sort | Brian D. Bishop |
| collection | DOAJ |
| description | Aerial and satellite imagery are widely used to assess the severity and impact of wildfires. Light detection and ranging (LiDAR) is a newer remote sensing technology that has demonstrated utility in measuring vegetation structure. Combined use of imagery and LiDAR may improve the assessment of wildfire impacts compared to imagery alone. Estimation of tree mortality at the plot scale could serve for more rapid, broad-scale, and lower cost post-fire assessments than feasible through field assessment. We assessed the accuracy of classifying color-infrared imagery in combination with post-fire LiDAR, and with differenced (pre- and post-fire) LiDAR, in estimating plot percent mortality in a second-growth coast redwood forest near Santa Cruz, CA. Percent mortality of trees greater than 25.4 cm DBH in 47 permanent 0.08 ha plots was categorized as low (<25%), moderate (25%–50%), or high (>50%). The model using Normalized Difference Vegetation Index (NDVI) from National Agricultural Imagery Program (NAIP) was 74% accurate; the model using NDVI and post-fire LiDAR was 85% accurate, while the model using NDVI and differenced LiDAR was 83% accurate. The addition of post-fire LiDAR data provided a modest increase in accuracy compared to imagery alone, which may not justify the substantial cost of data acquisition. The method demonstrated could be applied to rapidly estimate tree mortality resulting from wildfires at fine to moderate scale. |
| first_indexed | 2024-12-20T23:04:35Z |
| format | Article |
| id | doaj.art-75cb3f42380a423dbe48e63671f36493 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T23:04:35Z |
| publishDate | 2014-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-75cb3f42380a423dbe48e63671f364932022-12-21T19:23:55ZengMDPI AGRemote Sensing2072-42922014-03-01631954197210.3390/rs6031954rs6031954Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDARBrian D. Bishop0Brian C. Dietterick1Russell A. White2Tom B. Mastin3Natural Resources and Environmental Science Department, California Polytechnic State University, San Luis Obispo, CA 93407, USASwanton Pacific Ranch, California Polytechnic State University, 125 Swanton rd., Davenport, CA 95017, USARobert E. Kennedy Library, California Polytechnic State University, San Luis Obispo, CA 93407, USABioResource and Agricultural Engineering Department, California Polytechnic State University, San Luis Obispo, CA 93407, USAAerial and satellite imagery are widely used to assess the severity and impact of wildfires. Light detection and ranging (LiDAR) is a newer remote sensing technology that has demonstrated utility in measuring vegetation structure. Combined use of imagery and LiDAR may improve the assessment of wildfire impacts compared to imagery alone. Estimation of tree mortality at the plot scale could serve for more rapid, broad-scale, and lower cost post-fire assessments than feasible through field assessment. We assessed the accuracy of classifying color-infrared imagery in combination with post-fire LiDAR, and with differenced (pre- and post-fire) LiDAR, in estimating plot percent mortality in a second-growth coast redwood forest near Santa Cruz, CA. Percent mortality of trees greater than 25.4 cm DBH in 47 permanent 0.08 ha plots was categorized as low (<25%), moderate (25%–50%), or high (>50%). The model using Normalized Difference Vegetation Index (NDVI) from National Agricultural Imagery Program (NAIP) was 74% accurate; the model using NDVI and post-fire LiDAR was 85% accurate, while the model using NDVI and differenced LiDAR was 83% accurate. The addition of post-fire LiDAR data provided a modest increase in accuracy compared to imagery alone, which may not justify the substantial cost of data acquisition. The method demonstrated could be applied to rapidly estimate tree mortality resulting from wildfires at fine to moderate scale.http://www.mdpi.com/2072-4292/6/3/1954LiDARforestrywildfireremote sensingnormalized difference vegetation indexcoast redwood |
| spellingShingle | Brian D. Bishop Brian C. Dietterick Russell A. White Tom B. Mastin Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR Remote Sensing LiDAR forestry wildfire remote sensing normalized difference vegetation index coast redwood |
| title | Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR |
| title_full | Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR |
| title_fullStr | Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR |
| title_full_unstemmed | Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR |
| title_short | Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR |
| title_sort | classification of plot level fire caused tree mortality in a redwood forest using digital orthophotography and lidar |
| topic | LiDAR forestry wildfire remote sensing normalized difference vegetation index coast redwood |
| url | http://www.mdpi.com/2072-4292/6/3/1954 |
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