This study presents the information on the dynamics of changes in land use/land cover (LULC) spatially and temporally related to the causes of flooding in the study area. The dynamics of LULC changes have been derived based on the classification of Landsat imagery for the period between 1990 and 2016. Terrain surface classification (TSC) was proposed as a micro-landform classification approach in this study to create flood hazard assessment and mapping that was produced based on the integration of TSC with a probability map for flood inundation, and flood depth information derived from field observation. TSC as the micro-landform classification approach was derived from SRTM30 DEM data. Multi-temporal Sentinel-1 data were used to construct a pattern of historical inundation or past flooding in the study area and also to produce the flood probability map. The results of the study indicate that the proposed flood hazard mapping (FHM) from the TSC as a micro-landform classification approach has the same pattern with the results of the integration of historical inundation or previous floods, as well as field investigations in the study area. This research will remain an important benchmark for planners, policymakers and researchers regarding spatial planning in the study area. In addition, the results can provide important input for sustainable land use plans and strategies for mitigating flood hazards.

Abstract.One of the steps on mastery the remote sensing technology (inderaja) for satellite was the development of aerial camera prototype that could be an alternative for LAPAN light cargo aircraft mission (LAPAN Surveillance Aircraft, LSA-01). This system was expected could be operated to fulfill the emptiness or change the remote sensing data of optical satellite as the observer of vegetation covered by cloud. On this research, it was developed a prototype of pushbroom airborne camera 4-channels spectrum with very high resolution that worked on wavelength range seem near infra-red/ NIR used simple components that were available in the commercial market (commercial off-the-shelf/ COTS components). This research also developed georeference imagery software module used method of direct georeference rigorous model that had been applied on SPOT satellite. For this one, it was installed supported sensory for GPS and IMU as the writer of location coordinate and camera behavior while doing the imagery exposure or acquisition. The testing result gave confirmation that COTS components, such as industry camera LQ-200CL, and lower class GPS and IMU could be integrated became a cheaper remote sensing system, which its imagery product could be corrected systematically. The corrected data product showed images with GSD 0.4m still had positioning mistakes on average 157m (400 pixel) from the original position on GoogleEarth. On spectroradiomatic aspect, the used camera had much higher sensitivity of NIR channel than the lookedchannel so it caused bored faster. On the future, this system needed to be fixed by increasing the rate of GPS/ IMU data updates, and increased enough time resolution system so that the synchronization process and the availability supported data for completing more accurate georeference process.Besides, the sensitivity of NIR channel needed to be lower down to make it balance to the lookedchannel.

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.

Geometric correction is necessary in photogrammetry and remote sensing to avoid geometric distortions and establish relationship between image coordinate and its corresponding ground coordinate. Rigorous sensor model is one of the methods which considered the most precise and accurate for geometric correction. However, as rigorous sensor model contains many equations that depend on the actual physical properties of the sensor, the model is specialized for each sensor. In this paper, we modified a rigorous sensor model of geometric correction for pushbroom imager into geometric correction model for dual sensor pushbroom imager. The result shows that a model has been successfully obtained and can be used to geometrically correct coordinates of dual sensor pushbroom imagery.

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