Normalized Difference Vegetation Index (NDVI) is a very useful feature for differentiating vegetation and non-vegetationin remote sensed imagery. In the light of the function of NDVI and the spatial patterns of the vegetation landscapes, weproposed the lacunarity texture derived from NDVI to characterize the spatial patterns of vegetation landscapes concerningthe “gappiness” or “emptiness” characteristics. The NDVI-based lacunarity texture was incorporated into object-orientedclassification for improving the identification of vegetation categories, especially Torreya which was the targeted treespecies in the present research. A three-level hierarchical network of image objects was defined and the proposed texturewas integrated as potential sources of information in the rules base. A knowledge base of rules created by classifierC5.0 indicated that the texture could potentially be applied in object-oriented classification. It was found that the additionof such texture improved the identification of every vegetation category. The results demonstrated that the texture couldcharacterize the spatial patterns of vegetation structures, which could be a promising approach for vegetation identification.