Estimating Forest Age and Site Productivity using Time Series of 3D Remote Sensing Data
Paper in proceedings, 2015

Three-dimensional (3D) data about forest captured by airborne laser scanning (ALS) have revolutionized forest management planning. Accurate, updated large-scale maps of forest variables produced with low costs today support greatly improved decisions about silvicultural treatments compared to the past practice based on field surveyed data only. These maps usually lack important information about forest age and site productivity, as this cannot be accurately assessed from the available ALS data. In Sweden, ALS has recently been performed nation-wide, except the mountainous area, to produce a new and accurate digital terrain model (DTM). This DTM enables extremely costefficient extraction of 3D data about the forest from other sources than ALS, such as automatic stereo-matching of aerial images as well as from single-pass spaceborne interferometric synthetic aperture radar (InSAR). In contrast to ALS, these data sources can provide low-cost time-series of 3D data. Aerial images of Sweden are often available in archives back to approximately 1960, and the TanDEM-X SAR system has the potential to provide new data every second week over large areas. These data have a potentially high value for forest management planning, since they may provide missing and highly important information -forest site productivity, Site Index (SI) and forest age. This pilot study explores a least-squares minimization approach to estimate forest age and SI from time series of 3D data produced by 1) image matching of DMC aerial images, and 2) TanDEM-X SAR data.

aerial photography

3D data

Forestry

radar

site index

Author

J. Wallerman

Swedish University of Agricultural Sciences (SLU)

K. Nystrom

Swedish University of Agricultural Sciences (SLU)

Jonas Bohlin

Swedish University of Agricultural Sciences (SLU)

Henrik Persson

Swedish University of Agricultural Sciences (SLU)

Maciej Soja

Chalmers, Earth and Space Sciences, Radar Remote Sensing

Johan Fransson

Swedish University of Agricultural Sciences (SLU)

International Geoscience and Remote Sensing Symposium (IGARSS)

3321-3324 7326529
978-1-4799-7929-5 (ISBN)

Driving Forces

Sustainable development

Subject Categories

Remote Sensing

Roots

Basic sciences

DOI

10.1109/IGARSS.2015.7326529

ISBN

978-1-4799-7929-5

More information

Latest update

4/11/2018