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were used to validate the integrated developed system to ensure that there is no mistake both in processing flow and output data for each processing steps. Second, Suomi NPP remote sensing satellite “real” data in rawdata level that were acquired in direct broadcast mode by LAPAN remote sensing ground station. These data were used to validate conformity of the developed system not only for sample data but also for real data.
2.3 Evaluation Method
All of the hardwares that were used in the developed system have fullfiled the minimum specifications that are required as stated in operating system and each of the integrated softwares installation technical document. But, the main consideration must be taken to the storage system. The capacity of the storage system is limited so that it can contribute to one of the key system quality attributes, maintainability (Pahl et al., 2009). Maintainability can determine whether a system is useful or not.
Hence, it is important to know whether the existing storage system can accommodate Suomi NPP remote sensing satellite data in rawdata level and their associated resulted SDR level and vegetation indices data.
The storage requirement can be determined by using one of the three kinds of evaluation methods or techniques that are existed, as follows: empirical method, simulation technique, and analysis modeling (Lilja, 2000). Empirical method is done through metric calculation or measurement activities. Simulation technique is done by using artificial behaviour of a program execution. Analysis modeling is done via explanation in mathematical way about the running program. From the three methods or techniques, the empirical method gives the most accurate result because it directly connects with the actual data and do not use asumptions as many as the other method or technique.
Therefore, the storage system requirement was evaluated by using empirical method.
Empirical method itself can be done by using 2 (two) kinds of approach, namely sampling and event tracing. Sampling approach is done by taking some processing results or output data after the system runs several times. One of the benefits of this approach is evaluation implementation can be done without stopping the running system first. In the other hand, event tracing approach is done by inserting tracing code into the programs that formed the developed system. Before the evaluation can be implemented, the running system must be stopped first, the tracing code must be inserted, and then the stopping system is run back again. Evaluation implementation by using event tracing approach will require more system resource such as memory be compared with sampling approach (Lilja, 2000). Hence, the evaluation implementation of storage system requirement was evaluated by using empirical method with sampling approach.
Sample data that were used in evaluation implementation were 688 data in rawdata level and their corresponding SDR level data as well as vegetation indices data. The acquisition times were from June 27th, 2014 to February 25th, 2015. Actually there were more than 688 data in rawdata level and their corresponding SDR level data. But, there were only 688 vegetation indices data that have been produced in the period of question. It was because not all rawdata level data were successfully processed into SDR level data or not all SDR level data were successfully processed into vegetation indices data. Several factors can influence this, such as too little scanned area or incomplete SDR level data. Evaluation was implemented by measuring smallest, biggest, and average size of resulted vegetation indices data.
Besides size of resulted data, evaluation was also implemented to processing speed so that it can be known whether the developed system has potential to be developed further in the future by implementing automation method in order to make the system as the near real time system.
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renamed Suomi NPP in January 24th, 2012 (AMS, 2012), but the last revised file-naming conventions was determined in December 8th, 2009 (GSFC, 2012). The third field (dYYYYMMDD) is named Data Start Date and it is dynamically coded identified by a “d” letter followed by year, month, and day based on acquisition date of the earliest granule in the data. The fourth field (tHHMMSSS) is named Data Start Time and it is dynamically coded identified by a “t” letter followed by hour, minute, sec, and tenths of seconds (in UTC) based on acquisition start time of the earliest granule in the data. The fifth field (eHHMMSSS) is named Data Stop Time and it is dynamically coded identified by a “e” letter followed by hour, minute, second, and tenths of seconds (in UTC) based on acquisition end time of the lattest granule in the data. The sixth field (bNNNNN) is named Orbit Number and it is dynamically coded based on the orbit number when the time of acquisition occured. The seventh field (cYYYYMMDDHHMMSSSSSSSS) is named Creation Date and it is dynamically coded identified by a
“c” letter followed by year, month, day, hour, minute, second, and microseconds (in UTC) based on creation time of VI data. The eighth field (cspp) is named Origin that indicates this data is produced by using CSPP software (in this case, CSPP EDR). The ninth or last field (dev) is named Domain Description. Actually, this field is recognized only for domain values of JPSS in the future.
Field 1 Field 3 Field 5 Field 7 Field 9 ↓ ↓ ↓ ↓ ↓ VIVIO_npp_dYYYYMMDD_tHHMMSSS_eHHMMSSS_bNNNNN_cYYYYMMDDHHMMSSSSSSSS_cspp_dev.h5
↑ ↑ ↑ ↑ Field 2 Field 4 Field 6 Field 8 note: = dynamically coded based on data acquisition start date, start time, stop time, and VI
creation date.
= field separator (underline and dot).
= Hierarchical Data Format version 5 (HDF5) extension.
Figure 3 Graphical illustration of file-naming convention for VI data that are produced by the developed Suomi NPP remote sensing satellite data processing system.
The “h5” suffix in the end of the file-naming convention indicates the file is in Hierarchical Data Format (HDF) version 5 (HDF5) that is compatible with the JPSS Common Data Format Control Book (CDFCBs) (GSFC, 2012). The HDF5 data format is a standardized data format that is used to store Suomi NPP remote sensing satellite data and its derived data both in rawdata, RDR, SDR, IP, ARP, and EDR level (including VI data). This data format is development of HDF data format version 4 that has been used as standard data format to store Terra and Aqua remote sensing satellite data and its derived data. (The HDF Group, 2013).
Data with HDF5 data format can be read by using various softwares, but the most native softwares that can be used to examine the contents of an HDF5 file are h5dump and HDFView that are available freely. Both of them are maintained and developed by the official developer of HDF data format, The HDF Group (http://www.hdfgroup.org), who is responsible to ensure the sustainable development of HDF technologies and the ongoing accessibility of HDF-stored data. h5dump can be accessed through command line that usually is used to dump the HDF5 file contents to an ASCII file.
Whereas, HDFView can be accessed through a Graphical User Interface (GUI) in a visual manner for browsing and editing the HDF5 files.
One example of the reading vegetation indices data by using HDFView software is illustrated in Figure 4. From the file name that is shown by the red box, it is known that this is a vegetation indices file that is acquired in March 23th, 2015 from 05:24:23 UTC to 05:37:11 UTC (in 13 minutes and 48 seconds acquisition time) with an orbit number 17618. The vegetation indices file was created in the same day at 09:25:41 UTC by using CSPP EDR software. One vegetation indices data contains several sub data. The key sub data is shown by the blue box in Figure 4. They are QF1_VIIRSVIEDR, QF2_VIIRSVIEDR, QF3_VIIRSVIEDR, TOA_NDVI, TOA_NDVI_Factors, TOC_EVI, and TOC_EVI_Factors.
TOA_NDVI sub data contains values (unitless) of Normalized Difference Vegetation Index – Top of Atmosphere. This values are most directly related to absorption of photosynthetically active radiation, but is often correlated with biomass or primary productivity. TOC_EVI sub data contains values (also unitless) of Enhanced Vegetation Index – Top of Canopy. Unlike TOA_NDVI, this values adjust for soil moisture background and minimize feedback and errors from soil and atmospheric effects.
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QF1_VIIRSVIEDR sub data contains values (unitless) of overall NDVI quality, overall EVI quality, availability of related SDR data, and information whether the EVI values is valid or out of range (< -1.0 or > 4.0). QF2_VIIRSVIEDR sub data contains values (unitless) of pixel category (land or water), cloud confidence, sun glint availability in pixel, and thin cirrus detection in pixel. QF3_VIIRSVIEDR sub data contains values (unitless) of degradation condition, pixel exclusion, snow or ice availability in pixel, adjacency to cloud pixels, aerosol quantity, and cloud shadows. The others sub data just contain additional information that were generated during data processing from SDR level data to vegetation indices data. Sample of detailed sub data contents are illustrated in Figure 5.
Explanations about each of the values in the tables can be found in GSFC (2014). Samples of TOA_NDVI and TOC_EVI sub data visualization in tabular and image formats is illustrated in Figure 6 and 7, respectively.
Figure 4 Reading vegetation indices data by using HDFView software.
Figure 5 Sample of detailed sub data contents of a vegetation indices data that is produced by the developed system.
In the previous version of CSPP EDR software, there was a cross granule requirement when three contigous SDR granules only produces one VI granule. For example, if the software processed seven
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granules of SDR, it would produce only five VI granules, while the earliest and the latest granules were took out from the account. Currently, the requirement has been diminished. Thus, the software will produce number of VI granules as many as number of SDR granules that are used as the input.
Figure 6 Sample of TOA_NDVI sub data visualization in tabular and image formats by using HDFView software.
Figure 7 Sample of TOC_EVI sub data visualization in tabular and image formats by using HDFView software.
One granule of VI data is estimated to have a size of 65.6 MB (GSFC, 2014). It contains 1536 column x 6400 row of pixels with spatial resolution of 375 m in nadir. This size does not include geolocation and metadata attributes as well as additional size added by HDF5 packaging. Hence, in order to know the precise size of VI data, evaluation was taken by using empirical method with sampling approach that was described in the previous section. From the 688 samples data in level rawdata that were successfully processed into VI data, the smallest size of VI data is 68.853.832 Bytes (65.66 MB) and the largest size of VI data is 757.070.240 Bytes (721.99 MB). It means that the smallest VI data contains 1 (one) granule and the largest VI data contains 11 (eleven) granules in one acquisition of Suomi NPP remote sensing satellite data. Details of the evaluation implementation are
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illustrated in Table 2. The average size of VI data are 446.769.230 Bytes (426.07 MB) or contains approximately 6 granules.
Table 2 Evaluation implementation results by using empirical method with sampling approach for VI data that were produced from Suomi NPP remote sensing satellite VIIRS instrument rawdata level data with acquisition date from June 27th, 2014 to February 25th, 2015.
Number(s) of granule in 1
(one) VI data Numbers of samples
Size of corresponding VI data (in MB)
1 30 65.66
2 28 131.29
3 71 196.92
4 82 262.56
5 74 328.19
6 52 393.82
7 69 459.46
8 67 525.09
9 76 590.72
10 75 656.36
11 64 721.99
LAPAN remote sensing ground station can acquire maximum 4 (four) Suomi NPP remote sensing satellite data in one day. It has been assumed that the satellite’s mission lifetime is about 5 years or 1825 days (1 year ≈ 365 days), hence, taken the largest size of VI data as consideration for “the worst case scenario”, the storage system that will be required to accommodate all the VI data is about 1825 x 4 x 721.99 MB = 5.02 TB.
From the previous activities, the requirement of the storage system in the same period of question to accommodate data in rawdata level is 3.45 TB, RDR level is 1.89 TB (Gustiandi et al., 2013), and SDR level is 22.29 TB (Gustiandi and Indradjad, 2013). Therefore, the total storage system requirement to accommodate Suomi NPP remote sensing satellite data from rawdata level to VI data until the satellite mission over is 32.65 TB. The existing storage system with a capacity of 20 TB must be upgraded to fullfil this requirement.
Besides performance evaluation implementation was taken to know the required storage system, evaluation implementation was also taken to know how long it takes to process Suomi NPP remote sensing satellite data from rawdata level to produce VI data by using the developed system. The fastest processing time was 4 minutes and 22 seconds for Suomi NPP remote sensing satellite data with 1 (one) granule. The longest processing time was 1 hour, 51 minutes, and 10 seconds for Suomi NPP remote sensing satellite data with 11 (eleven) granules. The average processing time was 39 minutes and 51 seconds. The fastest interval time between 2 (two) consecutive Suomi NPP remote sensing satellite data acquisition is 1 hour and 35 minutes. Although the longest processing time to produce VI data from rawdata level data was still longer than the fastest interval time, but from the 688 samples that were measured, only 3 (three) samples that have processing time that exceed the fastest interval acquisition time. Therefore, Suomi NPP remote sensing satellite data processing system that has been developed can be enhanced further to become a near real time system by implementing automation technique. But, the system must be monitored intensively to anticipate the availability of processing time that may still be longer than the fastest interval acquisition time.