Forest Applications
Book chapter, 2021

The application of polarimetric Synthetic Aperture Radar (SAR) to forest observation for mapping, classification and parameter estimation (especially biomass) has a relatively long history. The radar penetration through forest volume, and hence the multi-layer nature of scattering models, make fully polarimetric data the observation space enabling a robust and full inversion of such models. A critical advance came with the introduction of polarimetric SAR interferometry, where polarimetry provides the parameter diversity, while the interferometric baseline proves a user-defined entropy control as well as spatial separation of scattering components, together with their location in the third dimension (height). Finally, the availability of multiple baselines leads to the full 3-D imaging of forest volumes through TomoSAR, the quality of which is again greatly enhanced by the inclusion of polarimetry. The objective of this Chapter is to review applications of SAR polarimetry, polarimetric interferometry and tomography to forest mapping and classification, height estimation, 3-D structure characterization and biomass estimation. This review includes not only models and algorithms, but it also contains a large number of experimental results in different test sites and forest types, and from airborne and space borne SAR data at different frequencies.

Author

Kostas Papathanassiou

German Aerospace Center (DLR)

S. R. Cloude

AEL Consultants

M. Pardini

German Aerospace Center (DLR)

M. J. Quiñones

SarVision

D. Hoekman

Wageningen University and Research

L. Ferro-Famil

University of Rennes 1

D. Goodenough

University of Victoria

H. Chen

Canadian Forest Service

S. Tebaldini

Polytechnic University of Milan

M. Neumann

Google Inc.

Lars Ulander

Electric, Computer, IT and Industrial Engineering

Maciej Soja

University of Tasmania

Horizon Geoscience Consulting

Remote Sensing and Digital Image Processing

59-117

Subject Categories

Remote Sensing

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1007/978-3-030-56504-6_2

More information

Latest update

7/2/2021 7