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.