Evaluation of Time-series of Modis Data for Transitional Land Mapping in Support of Bioenergy Policy Development

Demanding for information on spatial distribution of biomass as feedstock supply and on land resources that could potentially be used for renewable bioenergy production is rising as a result of increasing government investment for bioenergy and bioeconomy development, and as a way of adaptation to climate warning. Lands transitioned over the past between the types of forest, grassland, forage land, and cropland are considered as the most promising for the production of dedicated bioenergy crops as a primary source of biomass feedstock for the development of the second generation biofuels, without compromising regular agriculture production. Aimed at the transitional land mapping at a region scale, Earth Observation data with medium spatial resolution are considered as one of the most effective data sources. Time series of 10 days cloud-free composite MODIS images and its derivation, NDVI and vegetation phenology in the vegetation-growing season, are then used to derive the required information. With these datasets, three groups of data combinations are explored for the identification of the best combinations for land cover identification, then for transitional land mapping, using a data mining tool. Results showed that longer time series of Earth Observation data could lead to more accurate land cover identification than that of shorter time series of data; Bands (1-7) only and NDVI or phenology with other bands (3-7) could yield almost the same highest accurate information. Results also showed that land cover identification accuracy depends on the degree of homogeneity of the landscape of the region under the study.