Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand
| Publication Date: |
2018-08-12
|
|---|---|
| Publisher: |
MDPI Publishing
|
| Electronic ISSN: |
2072-4292
|
| Topics: |
Architecture, Civil Engineering, Surveying
Geography
|
| Published by: |
| _version_ | 1836399027092979712 |
|---|---|
| autor | Patrick Shin; Temuulen Sankey; Margaret M. Moore; Andrea E. Thode |
| beschreibung | Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand Remote Sensing doi: 10.3390/rs10081266 Authors: Patrick Shin Temuulen Sankey Margaret M. Moore Andrea E. Thode Forests in the Southwestern United States are becoming increasingly susceptible to large wildfires. As a result, forest managers are conducting forest fuel reduction treatments for which spatial fuels and structure information are necessary. However, this information currently has coarse spatial resolution and variable accuracy. This study tested the feasibility of using unmanned aerial vehicle (UAV) imagery to estimate forest canopy fuels and structure in a southwestern ponderosa pine stand. UAV-based multispectral images and Structure-from-Motion point clouds were used to estimate canopy cover, canopy height, tree density, canopy base height, and canopy bulk density. Estimates were validated with field data from 57 plots and aerial photography from the U.S. Department of Agriculture National Agriculture Imaging Program. Results indicate that UAV imagery can be used to accurately estimate forest canopy cover (correlation coefficient (R2) = 0.82, root mean square error (RMSE) = 8.9%). Tree density estimates correctly detected 74% of field-mapped trees with a 16% commission error rate. Individual tree height estimates were strongly correlated with field measurements (R2 = 0.71, RMSE = 1.83 m), whereas canopy base height estimates had a weaker correlation (R2 = 0.34, RMSE = 2.52 m). Estimates of canopy bulk density were not correlated to field measurements. UAV-derived inputs resulted in drastically different estimates of potential crown fire behavior when compared with coarse resolution LANDFIRE data. Methods from this study provide additional data to supplement, or potentially substitute, traditional estimates of canopy fuel. |
| citation_standardnr | 6318544 |
| datenlieferant | ipn_articles |
| feed_id | 124526 |
| feed_publisher | MDPI Publishing |
| feed_publisher_url | http://www.mdpi.com/ |
| insertion_date | 2018-08-12 |
| journaleissn | 2072-4292 |
| publikationsjahr_anzeige | 2018 |
| publikationsjahr_facette | 2018 |
| publikationsjahr_intervall | 7984:2015-2019 |
| publikationsjahr_sort | 2018 |
| publisher | MDPI Publishing |
| quelle | Remote Sensing |
| relation | http://www.mdpi.com/2072-4292/10/8/1266 |
| search_space | articles |
| shingle_author_1 | Patrick Shin; Temuulen Sankey; Margaret M. Moore; Andrea E. Thode |
| shingle_author_2 | Patrick Shin; Temuulen Sankey; Margaret M. Moore; Andrea E. Thode |
| shingle_author_3 | Patrick Shin; Temuulen Sankey; Margaret M. Moore; Andrea E. Thode |
| shingle_author_4 | Patrick Shin; Temuulen Sankey; Margaret M. Moore; Andrea E. Thode |
| shingle_catch_all_1 | Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand Remote Sensing doi: 10.3390/rs10081266 Authors: Patrick Shin Temuulen Sankey Margaret M. Moore Andrea E. Thode Forests in the Southwestern United States are becoming increasingly susceptible to large wildfires. As a result, forest managers are conducting forest fuel reduction treatments for which spatial fuels and structure information are necessary. However, this information currently has coarse spatial resolution and variable accuracy. This study tested the feasibility of using unmanned aerial vehicle (UAV) imagery to estimate forest canopy fuels and structure in a southwestern ponderosa pine stand. UAV-based multispectral images and Structure-from-Motion point clouds were used to estimate canopy cover, canopy height, tree density, canopy base height, and canopy bulk density. Estimates were validated with field data from 57 plots and aerial photography from the U.S. Department of Agriculture National Agriculture Imaging Program. Results indicate that UAV imagery can be used to accurately estimate forest canopy cover (correlation coefficient (R2) = 0.82, root mean square error (RMSE) = 8.9%). Tree density estimates correctly detected 74% of field-mapped trees with a 16% commission error rate. Individual tree height estimates were strongly correlated with field measurements (R2 = 0.71, RMSE = 1.83 m), whereas canopy base height estimates had a weaker correlation (R2 = 0.34, RMSE = 2.52 m). Estimates of canopy bulk density were not correlated to field measurements. UAV-derived inputs resulted in drastically different estimates of potential crown fire behavior when compared with coarse resolution LANDFIRE data. Methods from this study provide additional data to supplement, or potentially substitute, traditional estimates of canopy fuel. Patrick Shin; Temuulen Sankey; Margaret M. Moore; Andrea E. Thode MDPI Publishing 2072-4292 20724292 |
| shingle_catch_all_2 | Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand Remote Sensing doi: 10.3390/rs10081266 Authors: Patrick Shin Temuulen Sankey Margaret M. Moore Andrea E. Thode Forests in the Southwestern United States are becoming increasingly susceptible to large wildfires. As a result, forest managers are conducting forest fuel reduction treatments for which spatial fuels and structure information are necessary. However, this information currently has coarse spatial resolution and variable accuracy. This study tested the feasibility of using unmanned aerial vehicle (UAV) imagery to estimate forest canopy fuels and structure in a southwestern ponderosa pine stand. UAV-based multispectral images and Structure-from-Motion point clouds were used to estimate canopy cover, canopy height, tree density, canopy base height, and canopy bulk density. Estimates were validated with field data from 57 plots and aerial photography from the U.S. Department of Agriculture National Agriculture Imaging Program. Results indicate that UAV imagery can be used to accurately estimate forest canopy cover (correlation coefficient (R2) = 0.82, root mean square error (RMSE) = 8.9%). Tree density estimates correctly detected 74% of field-mapped trees with a 16% commission error rate. Individual tree height estimates were strongly correlated with field measurements (R2 = 0.71, RMSE = 1.83 m), whereas canopy base height estimates had a weaker correlation (R2 = 0.34, RMSE = 2.52 m). Estimates of canopy bulk density were not correlated to field measurements. UAV-derived inputs resulted in drastically different estimates of potential crown fire behavior when compared with coarse resolution LANDFIRE data. Methods from this study provide additional data to supplement, or potentially substitute, traditional estimates of canopy fuel. Patrick Shin; Temuulen Sankey; Margaret M. Moore; Andrea E. Thode MDPI Publishing 2072-4292 20724292 |
| shingle_catch_all_3 | Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand Remote Sensing doi: 10.3390/rs10081266 Authors: Patrick Shin Temuulen Sankey Margaret M. Moore Andrea E. Thode Forests in the Southwestern United States are becoming increasingly susceptible to large wildfires. As a result, forest managers are conducting forest fuel reduction treatments for which spatial fuels and structure information are necessary. However, this information currently has coarse spatial resolution and variable accuracy. This study tested the feasibility of using unmanned aerial vehicle (UAV) imagery to estimate forest canopy fuels and structure in a southwestern ponderosa pine stand. UAV-based multispectral images and Structure-from-Motion point clouds were used to estimate canopy cover, canopy height, tree density, canopy base height, and canopy bulk density. Estimates were validated with field data from 57 plots and aerial photography from the U.S. Department of Agriculture National Agriculture Imaging Program. Results indicate that UAV imagery can be used to accurately estimate forest canopy cover (correlation coefficient (R2) = 0.82, root mean square error (RMSE) = 8.9%). Tree density estimates correctly detected 74% of field-mapped trees with a 16% commission error rate. Individual tree height estimates were strongly correlated with field measurements (R2 = 0.71, RMSE = 1.83 m), whereas canopy base height estimates had a weaker correlation (R2 = 0.34, RMSE = 2.52 m). Estimates of canopy bulk density were not correlated to field measurements. UAV-derived inputs resulted in drastically different estimates of potential crown fire behavior when compared with coarse resolution LANDFIRE data. Methods from this study provide additional data to supplement, or potentially substitute, traditional estimates of canopy fuel. Patrick Shin; Temuulen Sankey; Margaret M. Moore; Andrea E. Thode MDPI Publishing 2072-4292 20724292 |
| shingle_catch_all_4 | Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand Remote Sensing doi: 10.3390/rs10081266 Authors: Patrick Shin Temuulen Sankey Margaret M. Moore Andrea E. Thode Forests in the Southwestern United States are becoming increasingly susceptible to large wildfires. As a result, forest managers are conducting forest fuel reduction treatments for which spatial fuels and structure information are necessary. However, this information currently has coarse spatial resolution and variable accuracy. This study tested the feasibility of using unmanned aerial vehicle (UAV) imagery to estimate forest canopy fuels and structure in a southwestern ponderosa pine stand. UAV-based multispectral images and Structure-from-Motion point clouds were used to estimate canopy cover, canopy height, tree density, canopy base height, and canopy bulk density. Estimates were validated with field data from 57 plots and aerial photography from the U.S. Department of Agriculture National Agriculture Imaging Program. Results indicate that UAV imagery can be used to accurately estimate forest canopy cover (correlation coefficient (R2) = 0.82, root mean square error (RMSE) = 8.9%). Tree density estimates correctly detected 74% of field-mapped trees with a 16% commission error rate. Individual tree height estimates were strongly correlated with field measurements (R2 = 0.71, RMSE = 1.83 m), whereas canopy base height estimates had a weaker correlation (R2 = 0.34, RMSE = 2.52 m). Estimates of canopy bulk density were not correlated to field measurements. UAV-derived inputs resulted in drastically different estimates of potential crown fire behavior when compared with coarse resolution LANDFIRE data. Methods from this study provide additional data to supplement, or potentially substitute, traditional estimates of canopy fuel. Patrick Shin; Temuulen Sankey; Margaret M. Moore; Andrea E. Thode MDPI Publishing 2072-4292 20724292 |
| shingle_title_1 | Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand |
| shingle_title_2 | Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand |
| shingle_title_3 | Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand |
| shingle_title_4 | Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand |
| timestamp | 2025-06-30T23:36:26.438Z |
| titel | Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand |
| titel_suche | Remote Sensing, Vol. 10, Pages 1266: Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand |
| topic | ZH-ZI R |
| uid | ipn_articles_6318544 |