The country of Vietnam has long been recognized as an important region for biodiversity (Sterling et al. 2006). High-profile discoveries in the 1990s of many species new to science including large ones such as the Saola (Pseudoryx nghetinhensis), an 85 kg basal member of the cattle subfamily Bovinae and the first new genus of large land-dwelling mammal described since the okapi (Okapia johnstoni) in 1901, have focused the attention of national and international conservation organizations on Vietnam and surrounding countries in mainland Southeast Asia (Hurley et al. in prep.). Conservation action for these endemic, endangered species relies on a clear understanding of trends in habitat conversion. To track deforestation rates through time in Vietnam, Meyfroidt and Lambin (2008) combined remotely sensed data with landscape metrics such as number of patches, mean patch size, mean proximity index, and total core area index. They tested their analyses across a variety of land cover studies including those using Advanced Very High Resolution Radiometer (AVHRR), Landsat, SPOT, and MODIS data sources. They found that forest cover decreased nationally from the 1980s to the 1990s and then showed an increase between 1990 and 2000, due to plantation forests as well as natural forest regeneration. However, the effects of this forest transition on fragmentation metrics noted above differed across the country. For instance, in some places, such as central Vietnam where forest cover is relatively large and well connected, reforestation led to a decrease in forest fragmentation and secondary forests recovered rapidly. However in others, such as areas in the north where forest fragmentation dates back centuries and forests have therefore long been isolated, reforestation did not seem to have an impact on continued fragmentation and habitat loss. In this chapter we detail the importance of fragmentation and landscape metrics to ecology and conservation, outlining when and where remotely sensed data can help in these analyses. We then discuss a subset of fragmentation metrics and point to some challenges in processing fragmentation data. We provide examples of composition and connectivity metrics illuminated with examples from the remote sensing literature.
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