Revista Chapingo Serie Ciencias Forestales y del Ambiente
Precision of remote sensors to estimate aerial biomass parameters: portable LIDAR and optical sensors
ISSNe: 2007-4018   |   ISSN: 2007-3828
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Keywords

aerial biomass
Quercus
photo-reconstruction
unmanned aerial vehicle
remote sensing

How to Cite

Huerta-García, R. E., Ramírez-Serrato, N. L., Yépez-Rincón, F. D., & Lozano-García, D. F. (2018). Precision of remote sensors to estimate aerial biomass parameters: portable LIDAR and optical sensors. Revista Chapingo Serie Ciencias Forestales Y Del Ambiente, 24(2), 219–235. https://doi.org/10.5154/r.rchscfa.2017.09.059

##article.highlights##

  • Forest parameters of 26 Quercus trees were obtained using a portable LIDAR and optical sensors.
  • Aerial biomass estimated with sensors was compared with that obtained by means of the traditional forest method (TFM).
  • Aerial biomass estimation using LIDAR was more precise (R2 = 0.94) than that with photo-reconstruction (PR).
  • Data collection with sensors was faster (> 80 %) with respect to TFM

Abstract

Introduction: Aerial biomass estimation using the traditional forestry method is laborious, expensive and time consuming. An alternative to solve this problem is the use of remote sensing.
Objective: To evaluate the precision of portable LIDAR technology and photogrammetry (photo-reconstruction) in the generation of point clouds to estimate aerial biomass.
Materials and methods: A total of 26 Quercus L. trees were analyzed from an urban forest in the south of Monterrey, Mexico. Diameter at breast height (DBH), total height and crown diameter were obtained with six methods: 1) traditional forest, 2) portable LIDAR at ground level, 3) normal color photo-reconstruction (PR) at ground level, 4) infrared color PR at ground level, 5) PR of normal color aerial image and 6) PR of infrared aerial image. Aerial biomass was estimated and the precision of each method was evaluated taking as reference the traditional forest method.
Results and discussion: Portable LIDAR offers more accurate information to estimate the aerial biomass (R2 = 0.945), followed by normal color PR at ground level (R2 = 0.824), when compared with that obtained by the traditional forest method. PR of normal color aerial images showed the poorest results (R2 = 0.653), due to the impossibility to measure the DBH. Data collection with sensors was faster (>80 %) with respect to the TFM.
Conclusion: Remote sensing techniques have the potential to obtain forest parameters in large-scale projects.

https://doi.org/10.5154/r.rchscfa.2017.09.059
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