Budhi Gustiandi
Remote Sensing Technology and Data Center – National Institute of Aeronautics and Space (LAPAN) Jl. LAPAN No. 70, Pekayon, Pasar Rebo, Jakarta Timur 13710, Indonesia
[email protected], (+62) 21 8710786
Abstract.
A Suomi National Polar-orbiting Partnership (NPP) satellite data processing system has been built to produce vegetation indices. The system integrated open source based operating system (Linux CentOS) and application softwares (Real-Time Software Telemetry Processing System / RT-STPS, Community Satellite Processing Package Science Data Record / CSPP SDR, and CSPP Environmental Data Record / CSPP EDR) into a single server computer. Integration was done by using bash shell scripting language. The vegetation indices that are produced by the system are described in detail. Empirical method of evaluation technique with sampling approach is utilized to assess the system performance. Two main considerations are size of data and processing speed.
The evaluation results show that the server computer needs a minimum storage capacity of 32.65 TB to accommodate Suomi NPP satellite data from rawdata level to vegetation indices storage requirement until the satellite’s predicted mission life will be over. The system also has potential to be developed as a near real time system in the future, but it has to be monitored intensively because there were processing times that were longer than the fastest interval time between two consecutive satellite’s data acquisition times (1 hour 35 minutes).
Keywords: CSPP, empirical method, processing system, remote sensing, RT-STPS, satellite, sampling approach, suomi NPP, vegetation indices.
1 Introduction
South East Asia has long shorelines, its population and economic activities mainly concentrate in coastal areas, and highly dependent to agriculture and forestry, so that it becomes as one of the world regions which is very vulnerable to the climate change. Increasing frequency and intensity of extreme weather events, such as heat wave, drought, flood, and tropical cyclone in the last few decades shows that climate change has afflicted this region. World ocean surface is predicted to increase of about 70 centimeters in the year of 2100. Average annual temperature in three countries (Indonesia, Thailand, and Vietnam) is predicted to increase in the mean of 4.8 oC in the same period compared with their average temperature in the 1990s and is projected to experience drier weather in the two to three decades in the future. This climate change will result disadvantages that have to be beared by the South East Asia with estimation of two-fold of global average loss (ADB, 2009).
Land use, land-use change, and forestry (LULUCF) activities which are mostly (up to three quarter) consist of forest deforestation and degradation that do not well managed, are main source as well as active contributor to greenhouse gases or carbon emission that related to the climate change and global warming. Deforestation related to decreasing in forest area, and degradation related to decreasing in forest quality. Although the existence of LULUCF can not be avoided in a country (Mather, 1992), but currently LULUCF contibutes total carbon emission in the world that is higher than the emission from fossil fuel usage in global transportation sector (Stern, 2006). As an illustration, approximately 1.6 billion tons of carbon are emitted every year and 13 million hectares of forest are dissapeared because of the LULUCF (Denman et al., 2007).
The forest capability to absorb carbon is reducing caused by decreasing area and quality of the forest itself. CO2 absoprtion by each trees makes forest hold the key position in climate change and global warming mitigation efforts. Forest resources have limited capability so that they must be controlled wisely, well planned, and sustainable. The world can not ignore the forest deforestation and degradation impacts to the climate change and global warming (Angelsen & Atmadja, 2010).
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Indonesia is one of the countries that have largest tropical forest in the world so that it become as an important country that can reduce emission through carbon absorption by forest. Indonesia has positioned itself in international level emission reduction negotiation effort with its strongly commitment to actively role in emission reduction of 26 – 41 % in 2020. Increasingly widespread forest area conversion to become oil palm plantations, especially in Sumatera and Kalimantan islands (Purwadhi and Haryani, 2006), require a tool that can quickly produce spatial information to monitor and inventarise forest condition in Indonesia that is measurable, reportable, and verifiable (MRV).
Three of the ninety nine developing countries in the tropical region have had very good capability in implementing system that meet the MRV criteria. Karousakis (2007) studied two of them (Costa Rica and Mexico) in depth. Most of the other countries only monitor forest areas that have commercial purpose (De Fries et al., 2006). Truthfully, national policy formulation and development that is more appropriate and related with the problems in the real world will be succeed with availability of well structured MRV (Barr, 2001; Spek, 2006).
One of the key point in implementing efective and eficient measurement and monitoring is by utilizing remote sensing data which is supported by in situ measurement, particularly in countries who have very large forest area (DeFries et al., 2007). Remote sensing is more suggested by using satellite platform than aerial platform because it has advantage in speed and easier availability as well as larger area scope (Jaya, 1997). Several remote sensing satellite applications that have been implemented to support forest resources monitoring are biomass measurement (Sutanto, 1986;
Januardi, 1998), land cover determination (Sumaryono, 1999; Dewanti, Agus, and Susanna, 1999;
Kurniawan, 2000; Gantini et al., 2010), stands density level determination (Harsanugraha et al., 1999), land cover change monitoring (Purwadhi & Haryani, 2006; Hansen et al., 2008), and forest plant phenology monitoring (Kross et al., 2011).
Remote sensing data that are mostly utilized as main component in forest monitoring activities are vegetation indices (VI). Vegetation indices have not only qualitative applications but also quantitative applications (GSFC, 2014). Qualitatively, they provide a means of separating vegetation from other surface types, and they also give a general indication of the “greenness” which is a combined measure of the type, density, and health of vegetation present within a given region. Quantitatively, the changes in vegetation indices can be analyzed both seasonally and in the longer term by adjusting for bidirectional reflectance and atmospheric effects if their retrievals are made consistently.
Real world quantities such as Leaf Area Index (LAI) and chlorophyll absorption also can be estimated from regression products of vegetation indices.
VI have been used with other statistical data to support rubber tree area growth in the mainland of South East Asia (Li & Fox, 2012). Vegetation change assessment in urban area also has been done regarding to VI (Schumacher et al., 2009; Fousenni et al., 2011). Moreover, VI have been utilized to map land cover in very large ecological area in regional scale (including some countries in Southern America) (Clark et al., 2010). Furthermore, VI have been used to monitor land cover in global scale (Fritz et al., 2012).
Back to more than a decade ago, National Aeronautics and Space Administration (NASA) has launched a group of satellites that present an unmatched appearance of Earth from space (Salomonson et al., 2006). That group is as known as NASA’s Earth Observing System (EOS), has supplied extraordinary recent awareness into many dynamics of Earth, including its clouds, oceans, vegetation, ice, and atmosphere. On the other hand, a next generation of Earth-observing satellite has been prepared to substitute as the EOS satellites lifetime will be over.
A critical first step in building this next-generation satellite system is represented by National Polar- orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) (Murphy, 2006). This is a first satellite of the next-generation satellite system that is used to observe more aspects of Earth’s dynamic. It is designed mainly to retrieve important data that are required to develop understanding, monitoring, and predicting the trend of long-term climate change and short- term weather fluctuations.
To respect of a former University of Wisconsin’s meteorologist who is known widely as “the father of meteorology satellite”, Verner E. Suomi, NPP satellite has been renamed by NASA. The
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announcement was decraled at the annual conference of the American Meteorologal Society in New Orleans in Januari 24th, 2012 (AMS, 2012). It has been renamed as Suomi National Polar-orbiting Partnership or Suomi NPP.
Suomi NPP satellite collects and distributes land, ocean, and atmosphere remote sensing data to its users as responsibility of measurements continuity that are required to bridge current NASA EOS (Terra, Aqua, and Aura satellites) missions and future low-Earth orbiting weather and enviromental observation satellite systems (Joint Polar Satellite System / JPSS – formerly named NPOESS, a joint program between National Oceanic and Atmospheric Administration / NOAA and NASA Goddard Space Flight Center / GSFC, which its first launch is slated in 2017) (Xiong et al., 2012). The satellite provides atmosphere and sea surface temperature, humidity, land and ocean biology productivity, including cloud and aerosol properties data.
Suomi NPP satellite orbits the Earth about 14 times a day and monitors the planet’s surface nearly as a whole. The satellite was launched toward orbit on October 28th, 2011 at 5:48 a.m. Eastern Daylight Time by using United Launch Alliance Delta II rocket from Vandenberg Air Force Base in California. It was predicted to have a 7-year design life with 5-year mission life (GSFC, 2015a, Spacecraft and Instruments section).
Suomi NPP satellite brings a diverse payload of scientific instruments for Earth surface observation purpose. It has weight about 4,600 pound (2,100 kilograms), overpass the equator each afternoon at around 1:30 p.m. local time. The key instruments are as follows:
Visible Infrared Imager Radiometer Suite (VIIRS) – a 22-band radiometer builded by Raytheon Space and Airborne System that is very much like with the Moderate Resolution Imaging Spectroradiometer (MODIS) heritage instruments on Terra and Aqua satellites (Schueler &
Barnes, 1998; Ardanuy et al., 2001; Murphy et al., 2001; Schueler et al., 2001; Murphy, 2006).
VIIRS obtains Earth’s dynamic surface processes, such as land changes, wildfires, and ice movement through its infrared and visible appearances. Oceanic and atmospheric profiles, including sea surface temperature and clouds, also be quantified (Murphy et al., 2006);
Advanced Technology Microwave Sounder (ATMS) – a 22-band passive microwave radiometer that is used to develop global models of humidity and temperature properties which are required by meteorologists to model weather prediction (Goldberg & Weng, 2006);
Cross-track Infrared Sounder (CrIS) – an interferometer that is used to monitor atmosphere properties, such as pressure and humidity to develop enhancements in both long- and short-term weather prediction (Susskind, 2006);
Ozone Mapping and Profiler Suite (OMPS) – an innovative nadir-looking and a very advanced limb-looking pairs hyperspectral imaging spectrometer sensor developed by Ball Aerospace. The instrument is used to measure Earth’s ozone layers, particularly around the poles where the layers very fluctuative (Flynn et al., 2006); and
Clouds and the Earth’s Radiant Energy System (CERES) – a 3-band radiometer that is used to quantify returned solar radiation, diffused radiation from the Earth, and complete radiation in order to observe human activities and natural effects on the Earth’s complete thermal radiation measurement (GSFC, 2011).
All of the instruments above were adopting NASA’s EOS, NOAA’s Polar Operational Environmental Satellite (POES), and Department of Defense’s (DoD) Defense Meteorological Satellite Program (DMSP) instruments.
It has been more than a decade Indonesian National Institute of Aeronautics and Space (LAPAN) also consistently monitoring vegetation indices using remote sensing satellite data in Indonesia. In the national level, most of the vegetation inidices data are acquired by processing Terra and Aqua satellites MODIS instruments data (Gustiandi et al., 2012). As Suomi NPP satellite has been launched, Indonesia also makes several efforts to ensure data availability from this satellite to complement the existing data from Terra and Aqua satellites.
Indonesian National Institute of Aeronautics and Space (LAPAN) remote sensing ground station in Parepare, South Sulawesi, has received Suomi NPP satellite data in Direct Broadcast (DB) mode since May 2012. Signals from the satellites are captured by the antenna and converted by the acquisition server computer into rawdata level data. The data then are sent to the Remote Sensing Data Center (RSDC) in Pekayon, East Jakarta through a Virtual Private Network (VPN) with bandwidth of 30 Mbps. They have to be processed into higher levels data, such as Raw Data Record
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(RDR), Sensor Data Record (SDR), Intermediate Product (IP), Application Related Product (ARP), Environmental Data Record (EDR), and Climate Data Record (CDR) so that the data are ready to be utilized by the end users. VI themselves are one of the EDR level data that are processed from VIIRS instrument data.
The rawdata level data to RDR level data processing system has been built in the previous activity (Gustiandi, Indradjad, & Bagdja, 2013). Then, the system was developed so that it can produce SDR level data from RDR level data (Gustiandi and Indradjad, 2013). Although the system is physically located remote from the ground station, but it behaves as an integrated system with the ground station.
However, the processing system that can produce VI from their corresponding SDR level data was not available yet int LAPAN’s remote sensing ground station. Hence, we developed the system further so that it can produce VI products by adopting previous system and integrating it to the ground station seamlessly.