MODIS Land Cover Compare with NEP

71 land cover type 1, it has chosen land cover data are used land cover year 2004 and NEP estimation during normal climate event year 2005. Figure 4.20 show land cover type-1 derived from MODIS data and estimation result of Net Ecosystem Production NEP. The MODIS land cover product is designed to support scientific investigations that require information related to the current state and seasonal-to- decadal scale dynamics in global land cover properties. The product consists of two suites of science datasets. MODIS Land Cover Type includes five main layers in which land cover is mapped using different classification systems. Figure 4.20 Comparison of land cover data type-1 from MODIS and NEP MODIS land cover data type I used to validate and assessment of NEP has shown with general result. There are 16 classes of land cover data over Sumatra Island but the result is not give clearly result. For example, when overlay estimation of NEP result with positive value as indicated carbon sink into land cover data, the result has shown with the same classes which is as class of „evergreen need-leaf forest‟. However, when overlay estimation of NPP result with negative value as indicated as carbon source into land cover data, the result has shown with the same classes which is as class of „urban areas‟. 72 Figure 4.20 also has shown that urban areas in land cover data have same location with estimation result of NEP. Urban areas indicated as carbon source and in NEP values is show with negative value. As the MODIS land cover and other products have demonstrated, robust, repeatable, and semi-automated mapping of global land cover with wide areas. However, there is some limitation of information resulted from MODIS land cover with general information. Moving forward, significant challenges exist in merging high frequency moderate resolution observations from sensors like MODIS with lower frequency but higher spatial resolution sensors such as Landsat or other satellite data. 73 V. CONCLUSION AND RECOMENDATION

5.1 Conclusion

1. The average values of EVI is relatively constant between 0.4 – 0.6. The pattern of EVI fluctuation started increase in April and decreased in November. However, the effects of ENSO to the temporal changes of EVI are small. 2. Estimation of NPP using NASA CASA model has shown that high value of Net primary productivity NPP has occurred in April and low NPP has occurred during September to October. Furthermore, NPP over Sumatera Island ranges from 0 to 1600 g C m -2 yr -1 . 3. NPP flux within the forest region is ranging from 150 to 400 g C m 2 month -1 and monthly average of NPP flux is 50 to 180 g C m -2 month -1 . 4. Effect of inter-annual variation of El Nino is not clearly seen. However NPP has decreased 106 g C m -2 yr -1 during El Nino event. On the contrary, The NPP is increasing of 283 g C m -2 yr -1 during La Nina event. 5. Variations in NPP across years are tightly to variations in climate, particularly precipitation. NPP is highly correlated with the Indian Ocean Dipole IOD. The NPP is the highest in April during the monsoonal transitional period, and decreases to the lowest in September to October during the peak of Australian Monsoon. 6. Net Ecosystem Production NEP has shown complex patterns carbon flux. Positive and negative values as indication of carbon sink and carbon source have occurred in the same areas during normal or abnormal climate condition. 7. Effect of climate variability is not clearly seen for carbon sink although there is increased NPP during La Nina event. 8. Locations with large positive annual NEP are often those receive a high amount of precipitation. In contrast, locations with negative NEP are often those that receive little precipitation. 74

5.2 Recommendation

1. Although the results in this study represent our current understanding of how climate change affects terrestrial NPP, they do not consider the redistribution of vegetation that may result from climate change. It is recommended to do evaluate the sensitivity of NPP to vegetation redistribution in addition to changes in climate. 2. To improve the prediction of net primary production, it is recommended to apply more parameter data such as land cover data from high resolution satellite data. 3. It is important to conduct field experiments and observations for advancing our understanding of the interactions between the carbon and nitrogen cycles in the tropics.