Karst rocky desertification (KRD) has become one of the most important ecological and environmental problems in China, and the control of rocky desertification has been listed as a goal of both social development and national environmental managment projects. However, patterns of plant succession in the process of KRD reversal activities are still unclear. Understanding plant dynamics is important for both the theory and practice for successful ecosystem restoration. We used multi-temporal, remotely sensed images and a Geo-informatic Tupu method to investigate the succession patterns of vegetation restoration at Zhongliang Mountain in Chongqing, Southwest China. This region is a typical KRD vegetation restoration area, with a rich diversity of regional vegetation types. In this study, remotely sensed images for four different time periods (1996, 2001, 2007, and 2013), representing four different stages in vegetation succession, were selected and analyzed using back-propagation (BP) neural network models for interpretation and classification. This resulted in maps of vegetation restoration based spatial structure rather than time series images, captured before and after the Grain for Green project, and thus, established information about principles for vegetation restoration succession in the region. Thereafter, the maps were analyzed using Geo-informatic Tupu to identify the dynamic patterns of vegetation restoration succession in the region. Our results indicate the following. (1) The BP neural network model provides an efficient vegetation classification method in the Zhongliang Mountain region. The overall accuracy of the (BP) neural network classification was 87.42%, which was 5.57% higher than traditional supervised methods. (2) Since 2002, a series of ecological restoration projects, including the Grain for Green project (the conversion of cropland into forest or pasture), have been implemented in this region, leading to a reduction in the area of farmland and an increase in the area of natural vegetation. The positive trends observed in the study site are interpreted as being the result of human-induced restoration. Comparing vegetation change in the different sub-regions of the study site, the most significant vegetation changes occurred on farmland that was located in the valley and foothills of Zhongliang Mountain. In contrast, regions with moderate change included the acid and the alkaline soil areas at higher elevations of Zhongliang Mountain. Here, the Masson pine community and kashiwagi community, respectively, are dominant. These communities have effective energy and nutrient conversion capabilities and form stable ecosystems that moderate changes in the natural succession of vegetation. (3) The Zhongliang Mountain communities were classified into two different stages, forward succession and reverse succession, during the period 2001-2013. In 18.63% of the region, reverse succession occurred, resulting in complex patterns of vegetation change. (4) Analysis of changes in vegetation structure was performed based on the centroid shifting method. From 1996 to 2013, the centroid of the Masson pine community and deciduous broad-leaved forest communities changed only slightly, while changes observed in other vegetation types were more marked. The Tupu method quantified the spatial pattern, distribution, and change processes of vegetation in different successional stages. It appears that the Tupu method can identify the state of each image element in each sampling time and achieve an integration of Space-Property-Process, providing a strong scientific basis and technical means for vegetation restoration in karst rocky desertification regions.