[A-CG06_29PM2] Satellite Earth Environment Observation

[ACG06-33] Assessing the variations of the Alaskan tundra vegetation using MODIS NDVI 250-m imagery

*SETIAWAN Yudi¹、KUSHIDA Keiji¹ (1.College of Bioresource Sci., Nihon Univ. Japan)

キーワード：Vegetation dynamics, Tundra vegetation, Alaska, MODIS

Improving the understanding of Alaskan tundra vegetation using remote sensing data is a challenging task due to a general lack of consistency and coverage from historical and existing platforms. Furthermore, it should be essential for many aspects of global environmental change research.Vegetation dynamics of the land surface is an integrated reflection of the vegetation and physical and chemical factors that shape the environment of a given land area and determinants for overall biological diversity patterns. In this paper, we demonstrate an approach for displaying detailed information of the Alaskan tundra ecosystem from the vegetation dynamics point of view. We assumed that locations displaying similar temporal vegetation patterns are inferred to have a similar vegetation and/or environment characteristics. Differences among land cover types as reflected in temporal profiles of NDVI are caused by differences in vegetation type composition and/or in their densities, and their responses to local environmental conditions, consequently, the use of long time-series NDVI will capture such different patterns in seasonal growth cycles. The clustering method yields sets of clusters, which each cluster represents a significant different NDVI pattern at detailed information in the land cover type. However, the complexity and enormous amount of time-series NDVI datasets may lead to the difficulty of obtaining the actual number of clusters in this study. Therefore, to provide maximum effectiveness of the clustering algorithm, we first consider the number of clusters 15 which correspond to the number of dominant physiognomy of Alaska tundra ecosystem (Raynold et al., 2005). The number of clusters was then evaluated based on a statistical measurement of how separate that pattern is to patterns in its own cluster compared to patterns in other clusters. This separability analysis was applied to discriminate among high detailed significant patterns that were theoretically defined to portray the specific characteristics of each land cover type.