Net Ecosystem Production NEP

67 precipitation. Year-to-year changes in spatial pattern of NEP were most probably caused by changes in the spatial pattern of precipitation, which can be changed dramatically by the El Nino events Vorosmarty et al., 1996. Ecosystem maintenance involves two tasks: 1 keeping the system‟s carbon and nutrient stocks organized; and 2 minimizing net losses of carbon and nutrients from the system. Both tasks require energy investments in the system. A useful relative index of the magnitude of this investment in maintenance appears to be net ecosystem production NEP. Net ecosystem production refers to the net change in organic matter stocks in the system for some defined period of time. For agricultural systems NEP is the sum of NPP and organic matter inputs associated with maturing minus the sum of heterotrophic respiration plus organic matter outputs associated with harvest and erosion. If NEP is negative, then the system is probably losing nutrients. The optimum rate of NPP in agricultural systems is that where a sufficient fraction of the NPP is invested to maintain NEP equal tu or greater than zero. Agriculturalists should be “caretakers” or “builders” and not “miners”. Refer to the previous research in various areas of tropical forests Potter et al., 2003 showed that the average Rh total respiration of vegetation about 850 g C m -2 yr -1 . Results of this measurement, also have done by Chamber et al., 2004 which measures the risk of canopy cover, tree trunks and forest soil surface, with estimation result about 900 g C m -2 yr -1 , with an annual average of Rh g C 850 m -2 yr -1 , with a range between 846 to 857 g C m -2 yr -1 . Based on results of from previous research, the assumption of estimated annual average of Rh total respiration on island of Sumatra is 850 g C m -2 yr -1 . Estimation of annual Net Ecosystem Productivity NEP obtained from the calculation of total annual NPP subtracted with total respiration. Furthermore, result of estimation average annual carbon fluxes Net Ecosystem Productivity, NEP of Sumatra terrestrial as show in Figure 4.19. 68 Figure 4.19 Annual Net Ecosystem Productions in Sumatra Figure 4.19 shows the pattern of NEP patterns over Sumatra terrestrial during normal climate condition 2005 and during abnormal climate condition in year 2006 El Nino event and 2007 La Nina event. These NEP patterns reflect the complex patterns of precipitation that change among El Nino years. Precipitation has its greatest effect on NPP during the drier part of the year. 69 During normal year or non ENSO year in year 2005, most of the northwest, northeast and western part of Sumatra island acted as a carbon sink positive value meanwhile in the central and southern part of Sumatra island acted as carbon source negative value. During El Nino years much of the Sumatra region releases carbon to the atmosphere, i.e. negative annual NEP. The magnitude and location of this negative annual NEP varied among El Nino events. During El Nino event in year 2006, NEP with positive value indicated as carbon sink in Sumatra Island has occurred in as same as positive value during normal climate event. Increased NEP has occurred in west part of the Island. However, during La Nina event in year 2007, NEP with positive value indicated as carbon sink in Sumatra island has occurred in same area as positive value during normal climate condition or El Nino event. This condition of annual NPP pattern affected with precipitation. Locations with large positive annual NEP are often those that receive a high amount of precipitation. In contrast, locations with negative NEP are often those that receive little precipitation. Understanding the responses of ecosystem processes to climate variability is essential for reducing the uncertainty in the estimates of CO 2 exchange between the biosphere and atmosphere. Plant growth is often limited by sub-optimum climatic conditions such as low temperature, water shortage and light deficiency covered by cloud. Therefore, the distribution of ecosystems and their productivity show predictable relationships to climatic variables. On an annual basis, result analyses have indicated that precipitation, especially the amount falling in the drier part of the year, is the primary factor influencing annual carbon storage in the island. The net exchange of carbon dioxide between tropical land ecosystems and the atmosphere could be influenced by interannual variations in climate that could cause this region to function as a carbon sink in some years and a carbon source in other years. Changes in precipitation combine with changes in temperature to affect soil moisture, a factor 70 we have identified as an important controller of carbon storage in the Amazon Basin. Reduction in soil moisture can lead to a decrease in net ecosystem production through influencing the availability of nitrogen Raich et al., 1991. Braswell et al. 1997 suggested that the terrestrial response to changes in temperature results in either enhanced plant production, reduced heterotrophic respiration, or both, such that global NEP is positive about 2 years after an El Nino event. These observations and analyses indicated that interactions among ecosystem processes are important in controlling the carbon cycle. In addition to the effects of increasing atmospheric CO2 and climate variability, annual carbon storage can also be influenced by nitrogen deposition re-growth on abandoned land, tropical deforestation, and fire Nepstad et al., 1999. Although climate variability mainly determines the signal of terrestrial carbon fluxes on seasonal and interannual time scales, on time scales of decades or centuries, vegetation dynamics following disturbances might play an important role in controlling carbon storage. Managing the global carbon cycle is now firmly on the world‟s environmental agenda. We have a compelling need to understand the components of the global carbon budget and how natural climate variability and human-induced climate changes affect them.

4.5.2. MODIS Land Cover Compare with NEP

MODIS data is available for land cover mapping applications. The enhanced spectral, spatial, radiometric, and geometric quality of MODIS data provides a greatly improved basis for monitoring and mapping global land cover relative to AVHRR data. Further, the algorithms being used with MODIS data have been designed for operational mapping, thereby providing rapid turn-around between data acquisition and map production. The timeliness and quality of land cover maps produced from MODIS should be useful for a wide array of scientific applications that require land cover information at regional to global scales. In this study, MODIS land cover data has used to validate and assessment of NEP. Land cover data type from MODIS satellite is available for download from year 2000 up to 2004. To validate and to see correlation between NEP and 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‟.