Mortality Assessment of Redwood and Mixed Conifer Forest Types in Santa Cruz County Following Wildfire

Auten, Steve

doi:10.15368/theses.2012.200

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Field survey crews were trained in proper procedures, and mortality was assessed three separate times over three years [17]. However, it is possible that there are errors in the field data used.…”

Section: Discussionmentioning

confidence: 99%

“…Tree status was assessed each year following the fire, through 2012, with the most recent status (2012) being used for this analysis. The diameters of trees in plots measured in 2003 was projected to 2008 using a growth rate of 2% [17]. The plot mortality used is the percent mortality in trees at least 25.4 cm (10′′) DBH, to avoid the confounding effect of the presence or absence, before the fire, of a large number of small trees in a plot; the 47 plots used are the subset of a total of 83 plots in the study area which have at least 15 trees which are at least 25.4 cm DBH.…”

Section: Tree Measurementsmentioning

confidence: 99%

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Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR

et al. 2014

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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 OPEN ACCESS Remote Sens. 2014, 6 1955 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.

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Section: Discussionmentioning

confidence: 99%

Section: Tree Measurementsmentioning

confidence: 99%

Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR

et al. 2014

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“…Overall, initial assessment was 87.9% accurate, including smaller diameter classes which had lower accuracy (Auten, 2012). It can reasonably be assumed that a few trees which were removed during salvage operations may have actually survived the fire, meaning that the percent mortality for a few plots may be slightly inaccurate.…”

Section: Errors In Mortality Datamentioning

confidence: 95%

Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and Lidar

Bishop

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Mortality Assessment of Redwood and Mixed Conifer Forest Types in Santa Cruz County Following Wildfire

Cited by 2 publications

References 9 publications

Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR

Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR

Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and Lidar

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