Detecting land-use change from seasonal vegetation dynamics on regional scale with MODIS EVI 250-m time-series imagery
Shared by Yudi Setiawan on May 30, 2013
- Author(s) :
- Yudi Setiawan and Kunihiko Yoshino
Simultaneous analysis of land surface attributes and their seasonal changes provides a broader view of land-use and land-cover change. This study attempted to detect the change in inter-annual temporal vegetation dynamics, which reﬂects a change in land surface attributes. We explored 250-m multi-temporal MODIS EVI 16-day composite data from 2001 to 2007 to characterize a change in vegetation dynamics related to land-use change detection. The MODIS data was ﬁltered in time-frequency space by wavelet function in order to identify and reduce the overall noise so as not to lose useful information from the time series data. The results show that by characterizing temporal vegetation dynamics, it is possible to distinguish actual land-use change based on land-cover dynamics. The result was evaluated using 18,626 reference pixels and showed an overall accuracy of 76.10%. In agricultural land use, such as upland and plantation, the weakest results were caused by mixed pixels from MODIS 250-m grid data as well as by temporal complexity related to the climate-driven change of land cover existing in the study area. On the other hand, for land use types which were not signiﬁcantly affected by climate variability such as paddy rice ﬁelds with sufﬁcient irrigation systems, natural forest and mangrove, the accuracy was satisfactory. Although the mixed pixel issue is quite problematic in this study, the results show that the characterization of temporal vegetation dynamics is an alternative when considering the accuracy of land-use change, which is not necessarily coincident with the temporary change in land cover. The methodology proposed in this research provides sufﬁcient and useful information regarding land-use change, such as location, area, time and pathways of the change; consequently, it should be possible to provide broad scale data on the terrestrial environmental change.
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