The traditional analysis methods of mangrove spatial dynamic are generally top-down approaches that only summarize the overall dynamic trends and their underlining mechanism. Those methods can help reveal how much area of mangrove has been lost or gained and what possible drivers are but don't deal with the change procedures and the patch number dynamic. The fact is that a contiguous region of mangrove can be composed with hundreds or thousands of interconnected patches and its overall dynamics is a summation of changes in those individual patches. Therefore, it is important to study patch dynamics, including changes in patch boundary and area, in order to accurately understand and assess mangrove's overall dynamic processes and mechanisms at a landscape scale. In this paper we introduce a patch-based method for analyzing and monitoring temporal changes in spatial distribution of mangrove. We used two-time, high-resolution remote sensing imagery to quantify spatial distributions of mangrove and their changes over time. We used geographic information systems (GIS) to obtain patch-level spatial properties, including spatial position, shape, and area, at each time when the remote sensing data were acquired. We then compare patch-level mangrove between the two times to group mangrove patches into six categories: stable, expanded, shrank, fragmented, disappeared, and new patches. These six categories reflect the dynamic procedures for mangrove. Following similar remote sensing data interpretation, we identified five causes or drivers of mangrove dynamics, include nature process, inning, marineculture and saltern, construction, and plantation. We assigned each mangrove patch into one of the six dynamic procedures and one of the five drivers. By summing up mangrove patches of the same categories, we built a driver-procedure matrix for patch-numbrer and area, respectively. Thus we were able to calculate a series of indices, including the gross driving amount, gross driving rate, net driving amount, net driving rate, predicted driving rate, gross flowing amount, gross flowing rate, net flowing amount, net flowing rate, predicted flowing rate, and acting force, to assess mangrove patch dynamics in patch number and area. This analysis method is helpful to explicitly reveal changes in mangrove patches, in patch number or area, and what drivers trigger the changes and how the changes take place. The information obtained through such analysis provides important clues about the mechanisms involved in the changes in the mangrove landscape. This method means a site-specific, quantified, and precise analysis on mangrove's spatial distributions and has scientific values for the protection and restoration of mangrove.