Vegetation change is one of the most important aspects of environmental change. It also is one of the most important reasons for landscape and ecosystem change. Vegetation change can be quantified by landscape pattern indices (LPIs). The objectives of this study are to compare 3D metrics pattern analysis with 2D common metrics pattern analysis for vegetation changes quantification in mountainous study areas of northwest Yunnan Province, China. In order to achieve this objective, a set of landscape metrics were selected. The calculations of these metrics are based on patch area and perimeter. The results show that at the patch level, except for the metric of fractal dimension (FD), other metrics derived by the 3D approach are significantly larger than those derived by the 2D method. At the class level, the class area (CA) changes quantified by surface geometries are significantly larger than those derived by planimetric area. The changes in surface basic mean patch area metrics both at the class and landscape levels are significantly larger than those of the mean patch areas derived by 2D common flat metrics. However, the results show that there are no significant differences between 3D and 2D shape metrics (SI and FD) for quantifying the patch shape changes over time. Moreover, for richness and evenness metrics there are also no significant differences between the 2D and 3D methods for quantifying the landscape richness and evenness change. The reason could be due to the calculation of shape metrics based on the regression of logP on logA. This regression could reduce the differences between 2D flat area and the surface area, and between flat perimeter and surface perimeter. The calculation of diversity and evenness metrics is based on proportion of CA. The ratio of CA/TA also could reduce the differences between flat area and surface area, and between flat perimeter and surface perimeter. Generally, in steep mountains, the vegetation changes quantified by 2D common metrics can be underestimated dramatically, especially for the change of CA, MPA, and MENN.