LAUSR

Picea schrenkiana var. tianshanica (PSVT) is an endemic tree species in Xinjiang, and serves as windbreak and soil consolidation, to ensure the stability of ecological environment. To efficiently and quickly grasp the ecological status of PSVT and the stability of forest ecosystem, we used images of different resolutions (GF-2 (1 m), GF-1 (8 m), GF-1 (16 m), Landsat 8 (30 m)) combined with field survey data, and performed multi-resolution segmentation to select the best segmentation scales. Based on the spectrum, texture and terrain factors, the canopy closure inversion of PSVT was performed to select the characteristic factors suitable. Then, we applied three object-oriented methods (i.e. support vector machine (SVM), classification and regression tree (CART), and nearest neighbor classification (NNC)) to classify the forest land. The result shows that the near-infrared (NIR) band is highly independent and makes an important contribution to the optimum index factor (OIF), in which the realtime adjustments of segmentation results are made to achieve better effect. There is a significant relationship between textural features of each band. The canopy closure estimation model performs better with a combination of spectral, terrain, and texture factors. Compared to CART and NNC models, SVM classification achieved better accuracy.