Abstract

Site index and stand age are important variables in forestry. Site index describes the growing potential at a given location, expressed as the height that trees can attain at a given age under favorable growing conditions. It is traditionally used to classify forests in terms of future timber yield potential. Stand age is used for the planning of management activities such as thinning and harvest. SI has previously been predicted using remote sensing, but usually relying on either very short time series or repeated ALS acquisitions. In this study, site index and forest stand age were predicted from time series of interferometric TanDEM-X data spanning seven growth seasons in a hemi-boreal forest in Remningstorp, a test site located in southern Sweden. The goal of the study was to see how satellite-based radar time series could be used to estimate site index and stand age. Compared to previous studies, we used a longer time series and applied a penetration depth correction to the phase heights, thereby avoiding the need for calibration using ancillary field or ALS data. The time series consisted of 30 TanDEM-X strip map scenes acquired between 2011 and 2018. Established height development curves were fitted to the time series of TanDEM-X-based top heights. This enabled simultaneous estimation of both age and site index on 91 field plots with a 10 m radius. The RMSE of predicted SI and age were 6.9 m and 38 years for untreated plots when both SI and age were predicted. When predicting SI and the age was known, the RMSE of the predicted SI was 4.0 m. No significant prediction bias was observed for untreated plots, while underestimation of SI and overestimation of age increased with the intensity of treatment.

Keywords

site index; time series; InSAR; height development curves; growth measurement; forestry; TanDEM-X

Published in

Remote Sensing
2023, Volume: 15, number: 17, article number: 4195

SLU Authors

• Huuva, Ivan

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Wallerman, Jörgen

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Persson, Henrik

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Remote Sensing
Forest Science


Publication identifier

DOI: https://doi.org/10.3390/rs15174195

Permanent link to this page (URI)

https://res.slu.se/id/publ/123709