Segment-based stem volume retrieval in boreal forests using multitemporal ERS-1/2 InSAR data

Abstract

In this study, we demonstrate a method for the combined land cover classification and boreal forest stem volume retrieval using multitemporal interferometric synthetic aperture radar (InSAR) data. The method utilizes the mean InSAR coherence image that is segmented into quasihomogeneous segments, and the land cover classes of the segments are determined based on their multitemporal InSAR signatures. Continuous stem volume estimates for the forest segments are then produced by inverting a semi-empirical backscattering coherence model. A group of forest stands with known stem volumes are required as training areas for determining the values of the model parameters. The performance of the method was studied by estimating the stem volumes of 4176 forest segments using 134 training stands. The results were compared with stem volume estimates produced by ground-based measurements and the satellite-based operational National Forest Inventory (NFI) of Finland. The performance of the method in stem volume estimation for stands larger than 1.5 ha (average stand size 3.2 ha) achieves a correlation coefficient of 0.87 and a root mean square error (RMSE) of 54% in comparison with ground-based reference data. The results are better than the estimates of the operational NFI that saturate at around 200 m3/ha. Markedly higher accuracy is to be expected when applying the method to larger stands in a large-area forest inventory.