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Conference paper2023Peer reviewedOpen access

Comparing TanDEM-X InSAR Forest Stand Volume Prediction Models Trained Using Field and ALS Data

Mukhopadhyay, Ritwika; Nilsson, Mats; Ekström, Magnus; Lindberg, Eva; Persson, Henrik

Abstract

Remote sensing (RS) techniques have been used for mapping forest variables, such as stem volume (important for forest management activities associated with timber production), over large areas which can be updated more frequently than with field inventory (FI) data. In this study, wall-to-wall TanDEM-X synthetic aperture radar images were used as auxiliary RS data for model-based prediction of stand-level volumes for two models, trained using volumes computed from FI (A) and airborne laser scanning estimations (B), respectively. The models were validated with harvester data available for independent stands. It was observed that the performance of model B was slightly better compared to model A based on adjusted R-2 and root mean squared error values. Therefore, it can be concluded that a completely RS based approach for prediction and mapping of stand volumes would be as promising as a method based on FI data along with being cost- and labour-efficient.

Keywords

Airborne laser scanning; harvester data; stand volume; synthetic aperture radar; TanDEM-X

Published in

IEEE International Geoscience and Remote Sensing Symposium proceedings
2023, pages: 3253 - 3256
Title: Proceedings of IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
Publisher: IEEE

Conference

IEEE International Geoscience and Remote Sensing Symposium (IGARSS), JUL 16-21, 2023, Pasadena, CA

SLU Authors

UKÄ Subject classification

Remote Sensing

Publication identifier

  • DOI: https://doi.org/10.1109/IGARSS52108.2023.10281911
  • ISBN: 979-8-3503-3174-5
  • eISBN: 979-8-3503-2010-7

Permanent link to this page (URI)

https://res.slu.se/id/publ/126597