we proposed a machine-learning based workflow of canopy gap mapping with Gaussian mixture models as the key classifier based on photographic height(DSM), spectral((visible-band difference vegetation index (VDVI)), and textural information(grey level co-occurrence matrix). All the above analyses were programmed in Python 3.6.