Forest tree species identification and its response to spatial scale based on multispectral and multi-resolution remotely sensed data.

The effect of spatial scale could not be ignored in identification results of forest types generated by multi-resolution images, and the influence of adding texture information from remote sensing data on the accuracy of forest trees species identification at different spatial resolutions has not been clearly addressed. To clarify this situation, we studied the Wangyedian forest farm in Northeast China, by using quasi-synchronous and geographical coordinate matched multi-resolution satellite observations (six spatial resolution levels: 1, 2, 4, 8, 16 and 30 m) which were supported with GF-1 PMS (pan and multi-spectra sensor), GF-2 PMS, GF-1 WFV (wide field view) and Landsat-8 OLI (operational land imager) and could investigate any possible correlations between spatial resolution and the recognition result, besides the influence of adding texture information. Five dominant tree species were classified and identified using Support Vector Machine (SVM) classifier. We also examined the identification results of the dominant forest trees species obtained by using the up-scaling algorithm. The results showed that overall classification accuracy of tree species was significantly influenced by the spatial resolution of images. The highest accuracy at the 4 m resolution, and the accuracy decreased to a minimum as the resolution reduced to 30 m. The addition of texture information increased classification accuracy using multispectral imagery with resolutions from 1 to 8 m, and the overall accuracy of dominant tree species identification created after adding texture information was 2.0%-3.6% higher than that from results of spectral information alone in the study area. However, the improvement of accuracy did not appear to hold for medium resolution imagery (16 and 30 m spatial resolution). In addition, there was a significant difference between the multi-scale classification results provided by up-scaled images and that obtained from native remote-sensing images for each spatial scale. These results indicated that the real satellite images should be used to ensure the accuracy of results when we examine multi-spatial-scale remote sensing observations or applications.