Better resolution of remotely sensed satellite images will make images clearer and interpretation easier but will increase the total volume of data that has to be managed. In order to reduce data volume for easier satellite communication transmission and reduce the total volume of data needed to be stores, the images should be compressed. Image compression in wavelet domain can be used for both lossy or lossless compression. Four major compression methods are available using the wavelet domain, i.e. CCSDS, Wavelet, Bandelet, and JPEG 2000. Some optical satellite images, were used as input data in simulation software which analyzed and compared the four compression methods in the wavelet domain. The result showed that the CCSDS method yielded the fastest compression and decompression time, but the Bandelet method retained better image quality when reconstructing original images or approximations of them compared to CCSDS. The JPEG 2000 method delivered better quality images than CCSDS for low bit rate. In summary at a rate of 0.25 bpp, CCSDS is 15 times faster than Bandelet and 3 times faster than JPEG2000. However, CCSDS quality is lower by up to 8.77% compared with Bandelet and up to 13.64% compared with JPEG2000.

Hotspot monitoring system using remote sensing satellite data such as Terra/Aqua Modis and SNPP VIIRS. In order to manage national natural disaster (such as forest/land fire disaster), accurate-real time-accessible data and information are required. In this case the use of remote sensing satellites data is use for the monitoring of natural disasters, especially forest/land fires that occur very massive need monitoring in real time and up to date. Information systems for monitoring of forest/land fires, can be built using remote sensing data of Terra/Aqua Modis and SNPP satellites that continuously monitor the condition of the land/forest by photographing the Indonesian territory respectively four times a day. The hotspot data requirement is 30 minute after the reception in ground station. The built system consists of the reception, processing, cataloguing and dissemination data, in order to fit the requirement the system need to be automation. The data receiving process is done at Parepare and Rumpin ground stations, followed by sending data to Pekayon in real time. Data processing is done in Pekayon using automated software open source module. Furthermore, data cataloguing is built using spatial and numerical based databases. The appearance system is built interactively with web-based online and mobile, user can do searching hotspot information based on location, degree of trust and time of incident. The monitoring system of forest / land fires that have been built have been published and utilized nationally, especially the BNPB in the context of the prevention and mitigation of forest / land fire disasters in Indonesia. Users can access through the website http://modis-catalog.lapan.go.id or can download android-based mobile app “Hotspot LAPAN”. It is expected that with online and mobile web based online and mobile fire monitoring system, it can be used for the prevention and mitigation of forest/land fire disaster in Indonesia.

Geolocation processing to produce spatial greenhouse gases data products consisting of CH4, CO2 and N20 gases has been carried out systematically. The greenhouse gases data are derived from Enviomental Data Record (EDR) Suomi NPP Satellite CrIS and ATMS Sensor products. During this process, there is an obstacle while performing the information data of greenhouse gases concentrations, due to the result of systematic processing files from EDR are still in netcdf format, so that it could not be distributed to users as they expected. The unique of  unlimited netcdf format is that, it displays only numeric values with irregular resolution, unregistered and incompatible with commonly processing data software. This research aims to produce geolocation processing module in order to provide information of greenhouse gases data spatially by using coordinate pixel registration method into image data, convert Digital Number (DN) value with scale corresponding to Indonesian region and interpolation value between pixels with Radial Basis Function (RBF) method using linear function. The result from the geolocation processing module of greenhouse gases data product are concentration information from some altitude level. The product is in geotiff format with 50 km spasial resolusion. AbstrakPengolahan geolokasi untuk menghasilkan produk data gas rumah kaca (GRK) spasial yang terdiri dari gas CH4,CO2 dan N20 telah dilakukan secara sistematis. Data gas rumah kaca tersebut dihasilkan dari produk Enviomental Data Record (EDR) Satelit Suomi NPP Sensor CrIS dan ATMS. Hingga saat ini terdapat permasalahan dalam penyajian data informasi konsentrasi gas rumah kaca, yaitu file hasil pengolahan sistematis masih dalam format netcdf sehingga belum dapat didistribusikan untuk melayani kebutuhan pengguna. Format netcdf terbatas hanya menampilkan nilai berupa angka, resolusi yang tidak seragam, belum teregistrasi dan tidak compatible dengan aplikasi pengolahan data yang umumnya digunakan. Penelitian ini bertujuan untuk menghasilkan modul pengolahan geolokasi  yang dapat menyajikan informasi data gas rumah kaca secara spasial. Metode yang digunakan dalam penelitian ini adalah registrasi piksel koordinat ke dalam data citra, konversi nilai Digital Number (DN). Interpolasi nilai antar piksel menggunakan metode Radial Basis Function (RBF) dengan fungsi linier. Hasil dari penelitian ini adalah modul pengolahan geolokasi produk data yang dapat menyajikan informasi konsentrasi gas rumah kaca pada beberapa level ketinggian. Produk yang dihasilkan dalam format geotiff dengan resolusi spasial 50 km.

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