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Revista Chapingo serie ciencias forestales y del ambiente

On-line version ISSN 2007-4018Print version ISSN 2007-3828

Abstract

HUERTA-GARCIA, Roberto E.; RAMIREZ-SERRATO, Nelly L.; YEPEZ-RINCON, Fabiola D.  and  LOZANO-GARCIA, Diego F.. Precision of remote sensors to estimate aerial biomass parameters: portable LIDAR and optical sensors. Rev. Chapingo ser. cienc. for. ambient [online]. 2018, vol.24, n.2, pp.219-235. ISSN 2007-4018.  http://dx.doi.org/10.5154/r.rchscfa.2017.09.059.

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.

Keywords : aerial biomass; Quercus; photo-reconstruction; unmanned aerial vehicle; remote sensing.

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