III. RESEARCH METHODOLOGY 3.1 Time and Location This research has been conducted in period March until October 2010. Location of research is over Sumatra terrain. Figure 3.1 show the area of interest of the research. Figure 3.1 Location of the study area 3.2 Data Source This research has used remote sensed and spatial data which are TERRA MODIS satellite data and meteorological data (temperature and rainfall). The MODIS satellite data collected by monthly since 2001 up to 2009. The MODIS data were available for free download from the site ftp://e4ftl01u.ecs.nasa.gov/molt/ and also from the site which is; https://wist.echo.nasa.gov/~wist/api/imswelcome/ and the meteorological data were available for free download from the site http://www.esrl.noaa.gov/psd/data/gridded/. 21
3.3 Required Tools Several hardware and software used during this research project such are personal computer and printer, image processing software (ENVI version 4.7, MODIS Tool, Grads version 2.0), geographic information system software (Arc Map version 9.2) and office application software (Microsoft Office 2007) also hand held GPS Garmin. 3.4 Methodology 3.4.1. Determine of Study Area Study area of this research is Sumatera terrestrial. Sumatra (also spelled Sumatera) is an island in western Indonesia. Sumatera is the 5 th highest island in the world's island, and the third highest in the Indonesian archipelago at 473,481 km² with a population of 50,365,538. The longest axis of the island runs approximately 1,790 km (1,110 mil) northwest southeast, crossing the equator near the center. At its widest point the island spans 435 km (270 miles) and administration of Sumatra terrains divided into ten provinces (Wikipedia, 2010). The interior of the island is dominated by two geographical regions: the Barisan Mountains in the west and swampy plains in the east. Most of Sumatra used to be covered by tropical rainforest, but economic development coupled with corruption and illegal logging has severely threatened its existence. Conservation areas have not been spared from destruction. 3.4.2. Data Collecting Remote sensing data of research study are used Terra MODIS satellite imagery data combining with meteorological data. Time series data used acquired data from 2001 up to 2009. The acquisition of MODIS data are downloaded with free of charge from specific website. One requirement specification to make MODIS data available is the availability of high speed internet connection to download the MODIS data. 22
Sumatra terrene has wide areas and cannot cover only by one single image of MODIS data. In this case, the MODIS datasets are provided to user in a tile fashion, each tile covers approximately by 10 latitude and 10 longitudes and to cover the whole of Sumatra terrene its need four tiles of MODIS datasets. The total of MODIS datasets in this study are used 432 of MODIS EVI (enhanced vegetation index) and 1640 of MODIS FPAR. Climatic data derived from monthly meteorological observations (January 2001 December 2009) from NCEP/DOE 2 Reanalysis data was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/. There are two kinds of data were used from this source, which are monthly temperature and monthly precipitation. Monthly minimum and maximum temperature from this source were using a state-of-the-art analysis/forecast system to perform data assimilation data from 1979 to near the present. Temperature data were provided for global gridded with spatial coverage of 1.875-degree latitude x 1.875-degree longitude global grid of monthly anomalies of observed air temperature and combined observed air and marine temperature. Monthly estimates of precipitation were obtained by monthly and global gridded with spatial coverage of 1.875-degree latitude x 1.875-degree longitude precipitation means. This data set consists of monthly averaged precipitation rate values (mm/day). Global monthly precipitation analysis based on gauge observations, satellite estimates, and numerical model outputs. It includes a standard and enhanced version (with NCEP Reanalysis) from 1979 to near the present. Climatic data were provided in the UniData NetCDF (network Common Data Form) format. NetCDF is an interface for array-oriented data access and a library that provides an implementation of the interface. The NetCDF library also defines a machine-independent format for representing scientific data. Together, the interface, library, and format support the creation, access, and sharing of scientific data. 23
3.4.3. Processing of MODIS Data The MODIS website provides a search engine that permits the user to search the MODIS product for particular areas of interest and to select the resolution of the data and specific dates. Note that products earlier than the current year are available from the USGS at the website http://lpdaac.usgs.gov/main.asp. The download options for data stored in the Data Pool can be accessed through an FTP server of the USGS. As stated in literature review, all MODIS land products are reprojected on the Integerized Sinusoidal (IS) 10-degree grid. In this study have been used MODIS EVI (MOD13A3) and MODIS FPAR (MOD15A2) which provided in tile projection characteristic. The tiles are labeled from top to bottom and from left to right starting at 00. The horizontal tiles range from h00 to h36, and the vertical tiles range from v00 to v17. Oftentimes a MODIS HDF-EOS filename will contain.h##v##. This specifies the horizontal and vertical location of the tile. To give an example, the selected product of interest for Sumatera was: Vegetation Indices 500m MOD13A3, for the whole of the year 2004 and the specific tiles we needed were h27v08, h27v09, h28v08 and h28v09. The MODIS data are the key data source for NPP estimation. MODIS system has two types of vegetation indices: normalized difference vegetation index (NDVI), sensitive to chlorophyll and the enhanced vegetation index (EVI), focused on plant structural variation such as physiognomy and leaf type and area. The MODIS enhanced vegetation index (MOD13A3) products will provide consistent, spatial and temporal comparisons of global vegetation conditions which will be used to monitor the Earth's terrestrial photosynthetic vegetation activity. MODIS (EVI) with monthly vegetation index compositing and 1 km resolution. The main reason to use vegetation index compositing is to combine multiple images into a single, gridded, and cloud-free vegetation index map. Other MODIS data which used in this research is MODIS Fraction of Photosynthetically Active Radiation (MOD15A2). MODIS (FPAR) used to defines an important structural property of a plant canopy and FPAR measures the proportion of available radiation in the photosynthetically active wavelengths (0.4 24
to 0.7 mm) that a canopy absorbs. MOD15A2 (FPAR) has 1 km resolution and provided in 8 day basis. MODIS (FPAR) are biophysical variables which describe canopy structure and are related to functional process rates of energy and mass exchange. MODIS (FPAR) have been used extensively as satellite derived parameters for calculation of surface photosynthesis and annual net primary production. These products are essential in calculating terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. For preprocessing the MODIS images in a format compatible with others software it was necessary to obtain software code from the US Geological Survey (USGS) (http://ldpaac.usgs.gov), which are available for different users operating systems. In the case, we used software programs MODIS Reprojection Tool (MRT). The MODIS Reprojection Tool is software designed to help individuals work with MODIS data by reprojecting MODIS images (Level-2G, Level-3, and Level-4 land data products) into more standard map projections. The main function of the MODIS Reprojection Tool is the resampler and mrtmosaic, executable programs that may be run either from the command-line or from the MRT Graphical User Interface (GUI). Resampling is the mathematical technique used to create a new version of the image with a different width and/or height in pixels. Increasing the size of an image is called up-sampling; reducing its size is called down-sampling. Many different resampling schemes are possible. Most techniques work by computing new pixels as a weighted average of the surrounding pixels. The weights depend on the distance between the new pixel location and the neighboring pixels. The simplest methods consider only the immediate neighbors; more advanced methods examine more of the surround pixels to attempt to produce a more accurate result. Following are the most common resampling methods: Nearest neighbor: Each pixel in the output image receives its value from the nearest pixel in the input (reference) image. Bilinear: Each estimated pixel value in the output image is based on a weighted average of the four nearest neighboring pixels in the input image. 25
Cubic convolution: Each estimated pixel value in the output image is based on a weighted average of 16 nearest neighboring pixels in the input image. Cubic convolution is the slowest method, but it yields the smoothest results Mosaicking images involves combining multiple images into a single composite image. The MRT provides a mosaic tool (mrtmosaic) for mosaicking tiles together prior to resampling. The mosaic tool requires that all input files are of the same product type and they must contain the same Scientific Data Set (SDS) names, SDS sizes (number of lines and samples), SDS projection types and projection information, SDS pixel size, etc. If the SDS characteristics for each input tile do not match, then the mosaic tool will exit with an error. The mosaic tool requires an input parameter file which lists the full path and filename of each input file to be mosaicked. The input files can be listed in any order and the mosaic tool will determine how they fit together in the mosaic. The mosaic tool also requires an output filename. The file type of the output file must match that of the input files. Thus, if the input files are HDF-EOS then the output file extension must be.hdf. If the input files are raw binary then the output file extension must be.hdr. 3.4.4. Estimation of Net Primary Production The approach for estimation NPP used NASA Carnegie Ames Stanford Approach (CASA) on the basis of light-use efficiency is conducted using relationship of monthly production of plant biomass is estimated as a product of time-varying surface solar irradiance (S r ) and EVI from the MODIS satellite, plus a constant light utilization efficiency term (e max ) that is modified by time-varying stress scalar terms for temperature (T) and moisture (W) effects. The equitation for estimation annually NPP values is : NPP = Sr EVI e max TW (14) Where : NPP = Net primary production (gc m -2 year -1 ) 26
Sr = Solar irradiance EVI = Enhanced Vegetation Index from MODIS e max T W = Constant Light Utilization Efficiency Term = Optimal temperature for plant production = Monthly water deficit The e max term is set uniformly at 0.39 g C MJ-1 PAR, a value that derives from calibration of predicted annual NPP to previous field estimates (Potter et al., 1993). T is computed with reference to derivation of optimal temperatures (T opt ) for plant production. W is estimated from monthly water deficits, based on a comparison of moisture supply (precipitation and stored soil water) to potential evapotranspiration (PET). T and W value based on the estimation data from climatic data. The equitation for estimation monthly NPP values is : NPP = EVI e max FPAR TW. (15) NPP = Net primary production (gc m -2 year -1 ) e max EVI = Constant Light Utilization Efficiency Term = Enhanced Vegetation Index from MODIS FPAR = Fraction Photosynthetically Active Radiation from MODIS T = Optimal temperature for plant production W = Monthly water deficit 3.4.5. Ground Truth Ground truth refers to information that is collected "on location". In remote sensing, this is especially important in order to relate image data to real features and materials on the ground. The collection of ground-truth data enables calibration of remote-sensing data, and aids in the interpretation and analysis of what is being sensed. More specifically, ground truth may refer to a process in which a pixel on a satellite image is compared to what is there in reality (at the present time) in order to verify the contents of the pixel on the image. 27
Ground truth is usually done on site, performing surface observations and measurements of various properties of the features of the ground resolution cells that are being studied on the remotely sensed digital image. It also involves taking geographic coordinates of the ground resolution cell with GPS technology and comparing those with the coordinates of the pixel being studied provided by the remote sensing software to understand and analyze the location errors and how it may affect a particular study. In this study, ground truth conducted in two province of Sumatra (Aceh and South Sumatra Province). The information that is collected on location (ground truth information) derived from secondary data from other agency or institution. The general flow-chart of research study as show in Figure 3.2. MODIS Data Mosaicking Vector data Climate data Geometric Correction Landcover data Estimation of EVI and FPAR Spatial Distribution of NPP Spatial Distribution of NEP Ground Truth Map Display of Spatial Distribution of NPP Figure 3.2 Flowchart of the research estimation of NPP 28