A standing tree disparity image is the basis of tree factor measurement and 3D reconstruction. However, one challenge is its difficulty in obtaining high-quality standing tree disparity image due to the complex structure of standing tree images and large illumination interference in the natural environment. Combined with the characteristics of standing tree images, in this paper, we propose a method for generating a standing tree disparity image using improved semi-global matching (SGM) algorithm. To solve the problem of poor disparity image generated using the SGM algorithm when the image texture and illuminations are weak and unstable, respectively, we employ an improved Census transform to replace the Census center pixel value with the median of the surrounding pixels to improve the reliability of the initial cost. Furthermore, the mean shift algorithm is used for image segmentation in the process of cost aggregation to enhance the robustness of the algorithm and effectively reduce the false matching rate for repeated and weak texture regions. Finally, we adopt the adaptive window to fill in invalid values and apply a median filter to eliminate unreliable parallax values, so that the area with discontinuous disparity can also obtain accurate disparity value. The proposed method was verified on the Middlebury public dataset. The results show that the average mismatch rate of the proposed method is approximately 5.23%, compared with the traditional semi-global block matching (SGBM), Boyer-Moore (BM), and SGM algorithms with improvements of 9.47 percentage points, 9.345 percentage points, and 8.96 percentage points, respectively. In the natural environment, the proposed SGM algorithm can be used to generate a standing tree disparity image with higher accuracy.