Canopy Height Estimation in Mediterranean Forests of Spain With TanDEM-X Data

Cristina Gómez,J. Lopez-Sanchez,Noelia Romero-Puig,Jianjun Zhu,Haiqiang Fu,Wenjie He,Yanzhou Xie,Qinghua Xie

Published 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

ABSTRACT

Canopy height is an essential feature in forest inventory, and for the assessment of biomass and carbon budgets. Spatially explicit maps of forest height over large areas can be derived from satellite synthetic aperture radar data. We aimed to evaluate the capacity of TanDEM-X (TDX) data to assess canopy height in Mediterranean forests of Spain, which are of relatively short height (typically < 20 m), diverse in species and structure, and adapted to summer drought. Interferogram coherence was retrieved from single-pol image pairs. Forest height estimation was carried out by previously fitting a sinc-type function, with two empirical parameters, to the data measured. Six types of forest were defined to assess the convenience of stratification for model implementation. The influence of terrain slope, forest type, and interferometric baseline on model performance was evaluated, and a strategy for large area mapping was proposed and tested. TDX-derived heights were compared to a contemporaneous LiDAR-derived canopy height model for assessment of quality. Results limited to slopes below 10° provided the best results, reaching R2 = 0.91 and root-mean-square error = 1.24 m in one of the study sites. However, in some areas the results were much worse, especially in regions characterized by rugged terrain with broadleaved species. This work demonstrates the feasibility of deriving a forest height map over the entire area of Spain from TDX data. Stratification per slope interval and selection of long interferometric baselines are recommended.

PUBLICATION RECORD

  • Publication year

    2021

  • Venue

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

  • Publication date

    Unknown publication date

  • Fields of study

    Computer Science, Environmental Science

  • Identifiers
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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