| Summary: | Primorsky Krai is a unique area where a very rich and important ecosystem provides vital life to many endangered flora and fauna. This northern temperate forest is located near the Pacific Ocean providing a moderating climate for a unique blend of taiga and broadleaved tree species. Forest management of this region has changed over the past 35 years, during the timescale of this analysis, as a result of the Soviet Union break up and new timber demand from nearby China. The Primorsky Krai region is specifically valuable due to its unique mammal species, notably the Siberian Tiger, one of the only locations on earth where they are still found. It is very important to preserve this ecosystem. To analyze how this forested region has changed, time-series Landsat data were analyzed for a representative path/row (path x, row x) footprint of 185km x 185k. Image data from 1976, 1989, 1998/1999, and 2009 were classified using a hybrid classification method. Resulting land cover maps indicate represent four important times during Russian history: during the Soviet Union (1976), near the time of transition (1989), in a post-Soviet transitioning economy (1998/1999), and during a more recent time of new management practices and new global forest demands (2009). Accuracy of the automated classification was assessed using a set of pixels for each class that had been selected based visual interpretation Landsat imagery and in comparison with very high spatial resolution data from Google Earth and other ancillary data. Maps were then compared to analyze land-cover change over the three periods 1976-1989, 1989-1999, and 1999-2009. Change direction from one land cover to another were analyzed further and checked for illogical changes that might have resulted from classification error. Multiple dates were compared with one another using a combination of logic related to land use and forest succession logic and a more general process of elimination. Classification results were improved based on accuracy assessment and change ...
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