LAI The NetPro Model

6 Chlorophyll a absorbs wavelengths of 0.43 μm and 0.66 μm, and chlorophyll b absorbs wavelengths of 0.45 μm and 0.65 μm or mostly in the blue and red portion of electromagnetic spectrums Jensen, 2000 causing leaves to look green. This kind of interaction and properties between leaves and electromagnetic spectrums are the basis of vegetation indices observation through remote sensing. NDVI is correlated with fraction of PAR absorbed fPAR Coops et al., 1997, and therefore, it is used as an input of NPP estimation. NDVI describes difference of leaf’s reactions to red and infrared energy. A healthy leaf absorbs red energy for photosynthesis, transmits, and reflects infrared energy. The combination between red and near-infrared reflectance measurement is more highly correlated with biomass than either only red or near-infrared measurement Jensen, 2000. The greater the red absorption is, and the least the infrared reflection is, indicating greener leaf. Generally, NDVI is known as “greenness index” of a leaf. Figure 2. Sketch of hypothetical additive reflectance from a two-leaf layer canopy Jensen, 2000. Interaction between near-infrared energy and the leaf is controlled by the spongy mesophyll cells. Heating effect of near-infrared energy can cause irreversible denaturation of its protein. Near-infrared energy is transmitted by the upper leaf layer, and then transmitted again and reflected by the lower layer back to the upper layer. Therefore, the greater the number of leaf layers is, the greater the near-infrared reflectance, and its spectral properties may provide information on plant senescence and stress Jensen, 2000 See Figure 2. Leaf’s treatment to infrared radiation is explained in the following paragraph. For example, the leaf’s transmittance is 50 and its reflectance is 50 of incident radiant flux Φ 1 . Leaf 1 reflects 50 of Φ i back R 1 and transmits it onto Leaf 2 T 1 . Leaf 2 then transmits 50 of T 1 T 2 and reflects it back to Leaf 1 R 2 . Leaf 1 once again transmits 50 of R 2 T 3 and reflects 50 of R 2 R 3 back to Leaf 2. Fifty percents are of R 3 reflected back to Leaf 1 R 4 and another 50 is transmitted through Leaf 2 T 4 . Fifty percents of R 4 are then transmitted by Leaf 1 T 5 and another 50 is then reflected back R 5 . R 1 = ½ Φ i T 1 = ½ Φ i T 2 = ½ R 1 = ¼ Φ i R 2 = ½ R 1 = ¼ Φ i T 3 = ½ R 2 = 8 1 Φ i R 3 = ½ R 2 = 8 1 Φ i T 4 = ½ R 3 = 16 1 Φ i R 4 = ½ R 3 = 16 1 Φ i T 4 = ½ R 4 = 32 1 Φ i R 5 = ½ R 4 = 32 1 Φ i Additive reflectance from Leaf 1 and Leaf 2 is R 1 and T 3 is ½ Φ i + 8 1 Φ i = 8 5 Φ i = 62.5 Φ i based on Jensen, 2000.

2.5. LAI

LAI , the Leaf Area Index, is the total one- sided green leaf area per unit ground-surface area Jensen, 2000. It is an important structural variable influences the energy and mass exchange of plant canopies Amin, 1997. LAI can be determined on site directly by stripping off all leaves, statistical sampling or by remote sensing Short, 2002. model. C3 plant was chosen since 95 of our planet’s vegetation is C3. The model uses mostly mechanistic and semi-mechanistic equations. The model is developed first at a leaf level then is scaled up to become spatial information using remote sensing and GIS. R 3 T 4 T 2 R 5 Leaf 1 R 2 T 1 T 5 T 3 R 1 Φ i Leaf 2 R 4 7 Figure 3 The NetPro v. 1.0 model user’s interface Climate data needed are hourly climate data of maximum and minimum air temperature and global radiation. When hourly data are not available, it can be generated from daily data.

2.6. The NetPro Model

NetPro is a potential NPP estimation model. In this model, water effect is not included yet. The model integrates the use of remote sensing and geographic information system GIS, and written in Visual Basic 6.0 programming and Map Objects 2.1. It uses C3 plant photosynthesis NDVI input is from Landsat image processing then used as an input of LAI and f APAR calculation. f APAR is used for NPP estimation using equation 1. PAR is derived from global radiation data. The model uses radiation use efficiency e based on Ochi and Shibasaki 1999 or June 2004 i.e. 1.5 gC MJ -1 and 1.8 gC MJ -1 respectively. III METHODS 3.1. NDVI The model requires NDVI input as Arc View shp file. NDVI is obtained from Landsat satellite image processing using the ER Mapper software. Cidanau watershed boundary was drawn based on Bakosurtanal maps. Land use map was taken from Rekonvasi Bhumi, an NGO concern about Cidanau watershed. Digitations of watershed boundary were done using Arc View software. Output from this process is used to crop satellite images as the boundary of study area. Before NDVI of Cidanau watershed was calculated, the satellite images were prepared for further process. Output file from ER Mapper was then processed using Arc View software. Result from this step was NDVI classification saved as shp file. NetPro needs average NDVI value per one polygon. The NetPro requires the column of the average NDVI in Arc View given name as “Avgndvi”. Data used in this research included 22 nd May 2002 and 11 th May 2004 Landsat images. A one- year NDVI is represented by the image of the year i.e. 22 nd May 2002 data represents 2002’s NDVI. Landsat satellite image NDVI calculation involves channel 3 red and channel 4 infrared. The equation becomes: 3 4 3 4 Channel Channel Channel Channel NDVI + − = 4 8

3.2. PAR