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Remote Sensing for Tropical Forest Canopy Height Estimation in Lopé National Park, Gabon

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Version 2 2020-07-20, 09:19
Version 1 2020-07-17, 09:37
thesis
posted on 2020-07-20, 09:19 authored by Maryam Pourshamsi
Forest height is one of the most important forest biophysical parameters influencing light competition, stand productivity and carbon sequestration. This thesis considered investigating the existing and new methods for estimating forest height. The specific focus of the research project concerned the improved estimation of forest height (h) over tropical forest from both single- and multi-source remotely sensed datasets. The potential applications of single-baseline (B) PolInSAR, fusion of multi-baseline PolInSAR and LiDAR, and a combined use of polarimetric SAR and LiDAR were assessed. The single-baseline PolInSAR demonstrated the limitation of the model for estimation of forest height over a heterogeneous forest because of the lack of appropriate values of the vertical wavenumber. For the smallest baseline (B = 20m), height is overestimated (~ 5m) for short stand (h < 10m); and for the largest baseline (B = 120m), height is underestimated (~ 30m) for tall stand (20 < h < 60m). As conventional PolInSAR, small baselines are suitable for estimation of larger stand heights, whereas large baselines are for shorter stands. Therefore, for an improved result over a heterogeneous forest with different height ranges (0-60m), a multi-baseline merging approach was introduced based on a fusion of PolInSAR with LiDAR. The results demonstrated improvement (r2 = 0.81, RMSE = 7.1m) in comparison to PolInSAR alone (r2 = 0.67, RMSE = 9.2m). Whilst this fusion indicated improvement, the model performance is limited by the availability of the PolInSAR data. Therefore, a second data fusion approach was introduced using polarimetric SAR and LiDAR. The obtained results are significant (r2 = 0.70, RMSE = 10m) as the technique relies on polarimetric measurements and not interferometric. Overall, the research results demonstrate the capabilities of SAR and LiDAR data fusions in estimation of tropical forest height, adding value to both scientific understanding and management of forest ecosystems.

History

Supervisor(s)

Heiko Balzter; Kirsten Barrett

Date of award

2020-05-07

Author affiliation

Geography

Awarding institution

University of Leicester

Qualification level

  • Doctoral

Qualification name

  • PhD

Language

en

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