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As high spatial resolution remotely sensed image be acquired more easily, there is a great potential for obtaining forest inventory automatically and costefficiently. A method was proposed to detect the lichee’s treetop and delineate treecrown. The method can be divided into three steps. In the first step, a 3×3 mean filter was utilized to smooth image, and then the image was inverted through subtracting image from the maximum of the filtered image. The second step was individual tree detection, namely treetop detection. The inverted image can be viewed as a topographic surface, the flow direction grid was built and then the depressions grid was extracted. The depressions distributed on roads and constructions were deleted according to the predefined threshold. Watersheds were delineated to obtain the contributing area of depressions viewing depressions as the pour point. For solving that the multiple depressions were erroneously identified within the same crown, the depressions were deleted if the distance to the nearest depression was less than threshold and the mean value of depression in the filtered image was not the maximum in multiple depressions, the watersheds of multiple depressions were merged. The remaining depressions were viewed as the detected treetop. The third step was to delineate the treecrown by using region growing method. The remaining depressions were used for seed points, crown regions were expanded from depression to surrounding pixels until the difference between the pixel and mean value of depression exceeded the predefined threshold or to the boundary of depression watershed. A 324 pixel×483 pixel Pléiades image with 0.5 m resolution was employed to test the method. A promising agreement between the detected results and manual delineation results was achieved in counting the number of trees and the area of delineating tree crowns. For individual tree detection, the overall accuracy was 87.75%, user’s accuracy was 80.69%, producer’s accuracy was 96.06%; for individual treecrow delineation, the overall accuracy was 78.69%, user’s accuracy was 71.32%, producer’s accuracy was 87.76%.