Use of GIS to Discover the Existence of Terrorism

Threats to national defense can be military or non-military. The greatest unresolved threat and challenge facing the Indonesian state is terrorism. The Indonesian government has dealt with terrorism, but catching terrorists remains difficult. The purpose of this research is to provide an alternative that uses Geospatial Intelligence (GEOINT) to find out where terrorists are hiding. The limitation of this research is the mountainous region in Central Sulawesi Province. The method used in this study is to use the GEOINT approach which is a combination of remote sensing, geographic information systems (GIS) and cartography, to extract information and analyze the results. The analysis was performed using a weighted linear combination method. The quantification process is carried out on all spatial data used for each parameter related to the presence of terrorists in the mountains. Quantification is done by changing each sub-parameter class to a value between 1-5. Each value is then weighted as a coefficient to arrive at the final score. From the results of the analysis and discussion it can be concluded that the GEOINT analysis can be used as an initial research on terrorist hideouts

大気遅延勾配と地殻上下変動を利用した稠密GNSS網の気象学的応用 : 平成30年7月豪雨の事例研究

Heavy rain from late June to early July 2018 brought disastrous flood in Southwest (SW) Japan, especially in Kyushu. By using a dense array of Global Navigation Satellite System (GNSS) receivers in Japan GEONET, I study this episode with two different space geodetic approaches, i.e., measurements of atmospheric water vapor and crustal deformation due to surface water load. The first approach is the recovery of precipitable water vapor (PWV) using the zenith wet delays (ZWD). Because atmospheric water vapor concentrates in relatively low altitudes, 2-D distribution of ZWDs often represent that of elevation of the observing stations rather than the relative humidity of the air column above the stations. To overcome the difficulty, I reconstructed ZWDs converted to sea-level values by spatially integrating the tropospheric delay gradient (azimuthal asymmetry of water vapor) vectors from coastal GNSS stations. I also calculated convergence of such delay gradients, equivalent to water vapor convergence (WVC) index proposed by Shoji (2013). I found that extreme rainfall occurs in the region and time, where both the sea-level ZWD and the WVC index are high. I confirmed this was the case also for similar disastrous heavy rain episodes in SW Japan in 2017 July and 2019 August. Next, I studied vertical crustal movements associated with surface water loads brought by heavy rainfall, using the official F3 solution of the GEONET station coordinates. Rainwater would act as the surface load and depress the ground to a detectable level. I removed common mode errors by adjusting ~100 reference stations to the median positions over a 1-month period using the Helmert transformation. I confirm land subsided by up to ~ 2 cm in some areas where major floods occurred. Land subsidence was observed to recover with a time constant of 1-2 days, which reflects the rapid drainage of rainwater into the sea due to the large topographic slope of the Japanese Islands and the proximity of the flooded areas to the sea. Then, I estimated the distribution of surface water load over the entire SW Japan using the GNSS station subsidence as the input. The estimated distribution of surface water resembled to the rainfall distribution from the AMEDAS rain gauge data from Japan Meteorological Agency (JMA). Then, I compared the amount of water of the 2018 heavy rain episode using the three ways, i.e. (1) spatially integrated PWV, (2) cumulative rainfall from AMEDAS rain gage, and (3) surface water distribution estimated from crustal subsidence. Cumulative rain was larger than atmospheric PWV, which is reasonable considering that the atmospheric water vapor only represents the capacity of the “bucket” to 29 carry seawater to land. Regarding the comparison of the rain gauge data and the surface water estimated from crustal subsidence, the latter largely exceeded the former. One may point out that the AMEDAS stations tend to be built in low-altitude valleys and may not represent true amount of rainfall over the whole land. I compared cumulative rain from the AMeDAS rainfall data and radar rain-gauge analyzed precipitation and confirmed that AMeDAS rain gauge data do not seriously underestimate real precipitation. The problem may come from the GEONET station distributions. They tend to be located in low-elevation densely populated area, and stormwater may concentrate on their vicinity. Thick sedimentary layers beneath the GEONET stations may also locally reduce the crustal rigidity. I performed similar studies using GNSS data taken at stations in Indonesia. I processed the raw GNSS data to estimate ZTD and PWV values using open-source software packages such as goGPS. I validated the derived tropospheric parameters by comparing them with those from other research centers, such as University of Nevada Reno (UNR). Next, I applied the methods to the disastrous heavy rain events that caused floods in Jakarta in early January 2020 and studied the time series of PWV and vertical coordinates. I confirmed the enhancement of PWV prior to the heavy rainfall onset and significant subsidence of GNSS stations located in the flooded area

Crustal response to heavy rains in Southwest Japan 2017-2020

Weather fronts stationary above Southwest Japan often bring disastrous heavy rains in early summer. Here we study four such episodes in each of four summers between 2017 and 2020, and investigate transient lithospheric subsidence caused by rainwater loads using the daily coordinates of a dense network of continuous GNSS stations. After applying a network filter to remove common mode errors, we isolated subsidence signals of 1-2 centimeters in flooded regions. Such subsidence recovered mostly in a day as rainwater drained rapidly to nearby ocean promoted by large topographic slopes. Spatiotemporal correlation between subsidence and precipitation was weak due possibly to rapid post-precipitation migration of rainwater. However, a strong correlation was found between subsidence and rain spatially integrated over the entire Southwest Japan, i.e., bulk subsidence of ∼0.1 km3 (equivalent to the uniform subsidence ∼0.6 mm) occurred for every 1 Gt rainwater per day. This linearity breaks down for rains exceeding ∼10 Gt/day as rainwater possibly exceeds the water-holding capacity of forest catchments

Estimation Precipitable Water Vapor Using (PWV) The Permanent Single GPS Station in Makasar and Bitung, Indonesia

Meteorological investigations using global positioning systems (GPS) are based on permanent networks that are expensive to develop globally on Earth. In this study, it was confirmed that a single station GPS meteorology was feasible where there was no possibility for the development of a sophisticated, reliable GPS network. In Sulawesi, there are several GPS stations since 2009 GPS stations have been installed in Makassar and Bitung by the Indonesian Geospatial Information Agency, in which meteorological sensors are also installed in the station. GPS data is processed to estimate the total zenith delay (ZTD) of GPS signals in the troposphere. The ZTD estimate is then automatically converted to stored water vapor (PWV) using goGPS software. Two types of validation were applied to the PWV estimation. All of them proved the validity of the GPS results: (1) PWV was measured using radiosondes in Makassar and Bitung with almost the same climate regime, each showing a correlation of 96.5 and 83.0% with GPS PWV time series.  (2) a global reanalysis dataset was showing correlations of 60.1 and 75.3%, respectively, with GPS results. This validation shows that a permanent GPS network can be an alternative to get temporally more detailed and accurate meteorological data and lower costs and time-saving operations

GNSS Meteorology for Disastrous Rainfalls in 2017-2019 Summer in SW Japan: A New Approach Utilizing Atmospheric Delay Gradients

We studied disastrous heavy rainfall episodes in 2017-2019 summer in SW Japan, especially in the Kyushu region using tropospheric delay data from the Japanese dense global navigation satellite system (GNSS) network GEONET (GNSS Earth Observation Network). This region often suffers from extremely heavy rains associated with stationary fronts during summer. In this study, we first analyze behaviors of water vapor on July 6, 2018, using tropospheric parameters obtained from the database at the University of Nevada, Reno. The data set includes tropospheric delay gradient vectors (G), as well as zenith tropospheric delays (ZTD), estimated every 5 min. At first, we interpolatedGto obtain those at grid points and calculated their convergence, similar to the quantity proposed byShoji (2013)as water vapor concentration (WVC) index. We obtained zenith wet delay (ZWD) from ZTD by removing zenith hydrostatic delay. The raw ZWD values do not really reflect the wetness of the atmosphere above the GNSS station because they largely depend on the station altitudes. To study the dynamics of water vapor before heavy rains, we estimated ZWD converted to the values at sea level. In the inversion scheme, we usedGat all GEONET stations and ZWD data at low-altitude (50 mm/h) episodes occurred, that is, July 5, 2017, July 6, 2018, and August 27, 2019. Next, we performed high time resolution analysis (every 5 min) on the days of heavy rain. The results suggest that both WVC and sea-level ZWD go up prior to the onset of the rain, and ZWD decreases rapidly once the heavy rain started. It is a future issue, however, how far these two quantities contribute to forecast heavy rains

Topographic Amplification of Crustal Subsidence by the Rainwater Load of the 2019 Typhoon Hagibis in Japan

The super typhoon Hagibis traveled northeastward through eastern Honshu, Japan, causing disastrous heavy rainfalls along its path on October 11 and 12, 2019. We performed a comprehensive space geodetic study of water brought by this typhoon using a dense network of Global Navigation Satellite System (GNSS) receivers in Japan. First, we studied the time evolution of altitude-corrected precipitable water vapor field and compare the movement of water vapor centroid with the rain distribution from radar rain gauge analyzed precipitation. The total amount of water vapor derived by spatially integrating precipitable water vapor on land remained steady at similar to 20 Gt. The total precipitation by this typhoon was similar to 92, and similar to 33 Gt of it fell onto the land area of eastern Honshu. Next, we studied crustal subsidence caused by the typhoon rainwater as surface load. The GNSS stations located under the typhoon path temporarily subsided 1-2 cm on the landfall day and the subsidence mostly recovered on the next day. Using the vertical crustal movement data, we estimated the distribution of surface water in eastern Honshu assuming the layered spherical earth. The amount of the surface load on October 12 was similar to 71 Gt, which significantly exceeds the cumulative rainfall on land. We consider that the excess subsidence largely originates from the selective deployment of GNSS stations in the concave topography, for example, along valleys and within basins, in the mountainous Japanese Islands