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Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning

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journal contribution
posted on 05.02.2021, 16:02 by M Pourshamsi, J Xia, N Yokoya, M Garcia, M Lavalle, E Pottier, H Balzter
Forest height is an important forest biophysical parameter which is used to derive important information about forest ecosystems, such as forest above ground biomass. In this paper, the potential of combining Polarimetric Synthetic Aperture Radar (PolSAR) variables with LiDAR measurements for forest height estimation is investigated. This will be conducted using different machine learning algorithms including Random Forest (RFs), Rotation Forest (RoFs), Canonical Correlation Forest (CCFs) and Support Vector Machine (SVMs). Various PolSAR parameters are required as input variables to ensure a successful height retrieval across different forest heights ranges. The algorithms are trained with 5000 LiDAR samples (less than 1% of the full scene) and different polarimetric variables. To examine the dependency of the algorithm on input training samples, three different subsets are identified which each includes different features: subset 1 is quiet diverse and includes non-vegetated region, short/sparse vegetation (0–20 m), vegetation with mid-range height (20–40 m) to tall/dense ones (40–60 m); subset 2 covers mostly the dense vegetated area with height ranges 40–60 m; and subset 3 mostly covers the non-vegetated to short/sparse vegetation (0–20 m). The trained algorithms were used to estimate the height for the areas outside the identified subset. The results were validated with independent samples of LiDAR-derived height showing high accuracy (with the average R2 = 0.70 and RMSE = 10 m between all the algorithms and different training samples). The results confirm that it is possible to estimate forest canopy height using PolSAR parameters together with a small coverage of LiDAR height as training data.

Funding

Engineering and Physical Science Research Council (EPSRC), grant reference: EP/M508081/1.

History

Citation

ISPRS Journal of Photogrammetry and Remote Sensing Volume 172, February 2021, Pages 79-94

Author affiliation

School of Geography, Geology and Environment, University of Leicester

Version

VoR (Version of Record)

Published in

ISPRS Journal of Photogrammetry and Remote Sensing

Volume

172

Pagination

79 - 94

Publisher

Elsevier BV

issn

0924-2716

Acceptance date

15/11/2020

Copyright date

2020

Available date

19/12/2020

Language

en