Modeling forest biomass using Very-High-Resolution data—Combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images

Abstract We used spectral, textural and photogrammetric information from very-high resolution (VHR) stereo satellite data (Pléiades and WorldView-2) to estimate forest biomass across two test sites located in Chile and Germany. We compared Random Forest model performances of different predictor sets (spectral, textural, and photogrammetric), forest inventory designs and filter sizes (texture information). Best model performances were obtained with photogrammetric combined with either textural or spectral information and smaller, but more field plots. Stereo-VHR images showed a great potential for canopy height model (CHM) generation and could be an adequate alternative to LiDAR and InSAR techniques.

• Publication year

  2015

• Venue

  European Journal of Remote Sensing

• Publication date

  2015-01-01

• Fields of study

  Geography, Environmental Science

• Identifiers
  DOI 10.5721/EuJRS20154814
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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