Evaluation of Remote Sensing Techniques for Estimation of Forest Variables at Stand Level

Magnusson, M. 2006. Evaluation of remote sensing techniques for estimation of forest variables at stand level. Doctor's dissertation. There is a continuous need for accurate forest description. Forest data at stand level is required in forestry planning, in particular when scheduling treatments within the next few years. Collection of forest data is typically acquired with subjective surveying methods in the field. However, field work is labor intensive and therefore an expensive method. An alternative to surveying methods in the field is to use remotely sensed data acquired by, e.g. optical, radar, and laser sensors. In the present thesis, different remote sensing techniques for estimation of forest variables at stand level have been evaluated. All studies were performed in hemi-boreal coniferous dominated forest at a test site in southern Sweden (lat. 58°30'N, long. 13°40'E), enabling a detailed comparison. Remotely sensed data were related to field data using regression analysis. The root mean square error (RMSE) of the average stem volume using airborne laser scanning data, CARABAS-II radar data, aerial photo-interpretation, and multi-spectral optical satellite data was found to be 13%, 19%, 21-24%, and 24-32%, respectively. The analyses clearly demonstrate that airborne laser scanning is the single remote sensing technique of those investigated that gives the most accurate stem volume and tree height estimates at stand level. The estimates from laser scanning data are better than commonly achieved with subjective surveying methods in the field. The accuracy of forest variable estimation using aerial photo-interpretation of Z/I DMC images was found to be in agreement with the results using conventional film-based panchromatic photos. Hence, the results indicate that photo-interpretation of Z/I DMC images using a digital photogrammetric workstation could replace photo-interpretation of film-based photos using analog or analytical stereoplotters without loss of accuracy. Combining two or more data sources which are complementary gives the possibility of improving estimation accuracies. Here, two particularly successful approaches were found. A combination of multi-spectral optical satellite and tree height data improved the RMSE of stem volume estimation to 11-12%. Using a combination of multi-spectral optical satellite and CARABAS-II radar data improved the RMSE to about 15%. The application of the investigated remote sensing techniques in the forestry sector is restricted to the costs and the availability of remotely sensed data. In Sweden, airborne laser scanning data might be supplied by several companies operating in a pure commercial market. The National Land Survey has the mission of aerial …