【Objective】Traditional forest resources surveys take sub-compartments as a unit, and use sampling or a census to obtain tree information. High resolution remote sensing images can be used to improve forest resource surveys from stand accuracy to the individual tree level, as well as extracting individual tree information such as the individual tree position, structural parameters and tree species. An individual tree database can then be established to implement intensive management for individual trees. This is of considerable importance in achieving precision forestry, and especially the sustainable management of urban trees. In recent years, research on individual tree locations based on remote sensing images has increased. However, the use of traditional methods can lead to missing and misjudging individual crown and the overlapping crown area.【Method】This paper attempts to apply a method based on a Chan-Vase (CV) model for individual tree localization. The forest area is first extracted based on the greenness segmentation, so that the individual tree localization algorithm only works on the tree area. The Gaussian filtering method is then used to reduce the noise generated during the process of image formation and to enhance the difference between the canopy and non-canopy. The local maximum region is extracted by combining the morphological features of the tree crown and its image spectral features, and the connected region is searched and marked. The CV model is then used to construct the level set function, and the initial contour line is iterated to obtain the individual tree crown contour. Finally, the individual tree location information is calculated. Seven high-resolution satellite images of different forest types including coniferous forest, broad-leaved forest, economic forest, and non-forest stands were selected successively for this paper. This was based on visual interpretation data as a reference, and compared with the traditional individual tree positioning method for experimental analysis.【Result】Compared with traditional methods such as the gradient watershed method, the marker watershed method, and the local maximum method, the individual tree location method based on the CV model has a higher matching rate with an average improvement of nearly 23%.【Conclusion】By automatically setting the initial contour, this research method solves the problem that CV model often use interactive or manual settings for the initial contour position. This is inefficient and leads to considerable differences in the results. It can also better deal with the joint and overlap conditions of the tree crowns and has a better positioning effect with strong application potential.