Estimation of Available Canopy Fuel of Coppice Oak Stands Using Low-Density Airborne Laser Scanning (LiDAR) Data

  • Farzad Yavari
  • Hormoz SohrabiEmail author
Conference paper
Part of the Advances in Science, Technology & Innovation book series (ASTI)


Predicting fire hazards and simulating fire intensity require knowledge of fuel conditions. Many aspects of wildfire behavior including the rate of spread and intensity are influenced by the amount of vegetation that fuels the fire. Coppice Oak Forests (COF) are strongly influenced by wildfires. In the present study, we examined the ability of airborne LiDAR data to retrieve available canopy fuel (ACF) of coppice Oak forest in Zagros Mountains, Iran. Two different oak-dominated stands were selected based on the stand density including sparse and dense forests. Systematically, 127 plots were established in the field and ACF was calculated using species-specific allometric equations. An outlier filter was used to remove any outlier pulse from the point clouds. Canopy Height Models (CHM) were generated by subtracting DSM and DTM. Different metrics were calculated from CHMs at the plot locations. Linear regression (LR), Artificial Neural Networks (ANN), Boosted Random Forest (BRF), and K-Nearest Neighbor (KNN) were used for modeling. The result showed that there is a strong correlation between ACF and LIDAR-derived metrics (r2 = 0.74 − 0.79). BRF was the best modeling technique. ACF was estimated more accurately in the sparse stand (r2 = 0.79). LIDAR-based predictions can be used to map ACF over coppice oak forests.


Low density LiDAR Stepwise regression Zagros Canopy fuel parameters Canopy height model 


  1. 1.
    Eskandari, S.: A new approach for forest fire risk modeling using fuzzy AHP and GIS in Hyrcanian forests of Iran. Arab. J. Geosci. 10, 190 (2017)CrossRefGoogle Scholar
  2. 2.
    Inan, M., Bilici, E., Akay, A.E.: Using airborne lidar data for assessment of forest fire fuel load potential. In: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, pp. 255–258 (2017)Google Scholar
  3. 3.
    Mutlu, M., Popescu, S.C., Stripling, C., Spencer, T.: Mapping surface fuel models using lidar and multispectral data fusion for fire behavior. Remote Sens. Environ. 112, 274–285 (2008)CrossRefGoogle Scholar
  4. 4.
    García, M., Riaño, D., Chuvieco, E., Salas, J., Danson, F.M.: Multispectral and LiDAR data fusion for fuel type mapping using support vector machine and decision rules. Remote Sens. Environ. 115, 1369–1379 (2011)CrossRefGoogle Scholar
  5. 5.
    Bright, B.C., Hudak, A.T., Meddens, A.J.H., Hawbaker, T.J., Briggs, J.S., Kennedy, R.E.: Prediction of forest canopy and surface fuels from lidar and satellite time series data in a bark beetle-affected forest. Forests 8, 1–22 (2017)CrossRefGoogle Scholar
  6. 6.
    Safari, A., Sohrabi, H., Powell, S., Shataee, S.: A comparative assessment of multi-temporal Landsat 8 and machine learning algorithms for estimating aboveground carbon stock in coppice oak forests. Int. J. Remote Sens. 38, 6407–6432 (2017)CrossRefGoogle Scholar
  7. 7.
    Sohrabi, H.: Estimating mixed broadleaves forest stand volume using DSM extracted from digital aerial images. In: International archives of the photogrammetry, remote sensing and spatial information sciences—ISPRS Archives (2012)Google Scholar
  8. 8.
    Andersen, H.E., McGaughey, R.J., Reutebuch, S.E.: Estimating forest canopy fuel parameters using LIDAR data. Remote Sens. Environ. 94, 441–449 (2005)CrossRefGoogle Scholar
  9. 9.
    Jakubowksi, M.K., Guo, Q., Collins, B., Stephens, S., Kelly, M.: Predicting surface fuel models and fuel metrics using lidar and CIR imagery in a dense, Mountainous Forest. Photogramm. Eng. Remote Sens. 79, 37–49 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Tarbiat Modares UniversityNasrIran

Personalised recommendations