As forest is under increasing pressure, the rapid conversion or degradation of forest has attracted strong concern. Obtaining quantitative information of forest change based on satellite imagery becomes necessary and urgent, especially the detailed “from-to” information. In this study, a semi-automatic method called direction-dominated change vector analysis (DCVA) was proposed to detect “from-to” information of forest change. DCVA is composed of three steps: (1) determining candidate changed pixels, (2) determining direction ranges for different forest change types, and (3) determining final changed pixels for each forest change type. Like the classic change vector analysis (CVA), the magnitude and direction of change vector (CV) are used to detect the changed areas and types in DCVA, respectively. However, CVA is “magnitude-dominated” by setting only one magnitude threshold for different change types, while DCVA is “direction-dominated” by determining change types according to change direction at first, followed by setting different magnitude threshold for each change type. In this case, DCVA holds the advantage of accurately detecting changed areas for different change types by considering the specific characteristics of each change type. Experiments are performed with Sentinel-2A satellite images to demonstrate the advantages of DCVA for forest change detection. The changed areas with four types of forest change were successfully extracted by DCVA. The comparison of both geometric and thematic accuracies between DCVA and CVA further indicates the effectiveness of the proposed method for forest change detection.

随着森林面临越来越大的压力，森林的快速转化或退化引起了强烈关注。基于卫星图像获取森林变化的定量信息变得必要和紧迫，尤其是详细的“从到”信息。在这项研究中，提出了一种称为方向主导变化矢量分析（DCVA）的半自动方法来检测森林变化的“从到”信息。DCVA由三个步骤组成：（1）确定候选变化像素，（2）确定不同森林变化类型的方向范围，以及（3）确定每种森林变化类型的最终变化像素。与经典的变化矢量分析（CVA）一样，变化矢量（CV）的大小和方向分别用于检测DCVA中的变化区域和类型。然而，CVA是“量值主导”，对不同的变化类型只设置一个幅度阈值，而DCVA是“方向主导”，首先根据变化方向确定变化类型，然后为每种变化类型设置不同的幅度阈值。在这种情况下，DCVA 具有通过考虑每种变化类型的特定特征来准确检测不同变化类型的变化区域的优势。使用 Sentinel-2A 卫星图像进行实验，以证明 DCVA 在森林变化检测方面的优势。DCVA成功提取了四种森林变化类型的变化区域。DCVA 和 CVA 之间几何和主题精度的比较进一步表明了所提出的森林变化检测方法的有效性。