Pub Date : 2018-09-01DOI: 10.1109/AGERS.2018.8554204
Intan Hairani Fitri, A. B. Setyadji, I. Meilano, Susilo
Cimandiri is an active fault located in the region of southern Sukabumi, West Java, that extends from Pelabuhan Ratu to Padalarang. Some researches done with the aim of defining the mechanism of Cimandiri Fault result in various outcomes. INA-CORS GPS observation data around the fault was processed using scientific software, GAMIT 10.6. The processing result in daily coordinates for each GPS site in ITRF2008 that were used to calculate velocity vector and strain around Cimandiri Fault. The results for principal strain around Segment I and II are dominated by both compression and extension, with most of the direction of principal strain axes are around 40°. While for Segment III, principal strain around the fault are much more dominated by compression and the direction is around 20°. According to the principal strain rate and velocity pattern around fault, Segment I and II of Cimandiri Fault have sinistral strike-slip mechanism, while Segment III of Cimandiri Fault has thrust mechanism
{"title":"Analysis of Cimandiri Fault Mechanism Type Based on Strain Pattern from GPS Observations in the Year 2010 – 2017","authors":"Intan Hairani Fitri, A. B. Setyadji, I. Meilano, Susilo","doi":"10.1109/AGERS.2018.8554204","DOIUrl":"https://doi.org/10.1109/AGERS.2018.8554204","url":null,"abstract":"Cimandiri is an active fault located in the region of southern Sukabumi, West Java, that extends from Pelabuhan Ratu to Padalarang. Some researches done with the aim of defining the mechanism of Cimandiri Fault result in various outcomes. INA-CORS GPS observation data around the fault was processed using scientific software, GAMIT 10.6. The processing result in daily coordinates for each GPS site in ITRF2008 that were used to calculate velocity vector and strain around Cimandiri Fault. The results for principal strain around Segment I and II are dominated by both compression and extension, with most of the direction of principal strain axes are around 40°. While for Segment III, principal strain around the fault are much more dominated by compression and the direction is around 20°. According to the principal strain rate and velocity pattern around fault, Segment I and II of Cimandiri Fault have sinistral strike-slip mechanism, while Segment III of Cimandiri Fault has thrust mechanism","PeriodicalId":369244,"journal":{"name":"2018 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"33 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123261495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AGERS.2018.8554208
Dwi R Harman, A. Mugiyantoro, Janitra U. Aprino, M. Maha
Fossil foraminifera is commonly used in the exploration of energy resources, while paleo-environment monitoring studies using foraminifera have not been widely developed. The study was conducted on Wonosari Formation consisting of limestone layered that showed turbidite symptoms. In this study rock samples were taken by stratified sampling method, then analyzed the abundance, richness and diversity of fossil foraminifera bentonic in the sample. Fossil analysis shows the study area is a marine environment with a paleobatimetry Middle shelf - Upper continental slope. Organism’s response to the variability of environmental change is seen by the presence of certain species. This shows fossil foraminifera recording significant changes in samples where abundance, richness and diversity produce a trend that indicates change.
{"title":"Monitoring Environmental Change using Distribution Trends of Foraminiferal Benthic Assemblages At Wonosari","authors":"Dwi R Harman, A. Mugiyantoro, Janitra U. Aprino, M. Maha","doi":"10.1109/AGERS.2018.8554208","DOIUrl":"https://doi.org/10.1109/AGERS.2018.8554208","url":null,"abstract":"Fossil foraminifera is commonly used in the exploration of energy resources, while paleo-environment monitoring studies using foraminifera have not been widely developed. The study was conducted on Wonosari Formation consisting of limestone layered that showed turbidite symptoms. In this study rock samples were taken by stratified sampling method, then analyzed the abundance, richness and diversity of fossil foraminifera bentonic in the sample. Fossil analysis shows the study area is a marine environment with a paleobatimetry Middle shelf - Upper continental slope. Organism’s response to the variability of environmental change is seen by the presence of certain species. This shows fossil foraminifera recording significant changes in samples where abundance, richness and diversity produce a trend that indicates change.","PeriodicalId":369244,"journal":{"name":"2018 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128839418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AGERS.2018.8554200
A. C. Situmorang, D. Gunawan, F. Juwono
C band (3400–4200 MHz) is a highly utilized frequency band used by both mobile services and satellite services worldwide. We analyze C-band utilization by comparing data collected from SIMS (Spectrum Management Information System) database owned by Indonesian Government with data collection from national and foreign satellite transponder tenants. The result shows that C-band use in Indonesia is massive, which support Indonesia’s position to refuse additional candidates for the allocation of IMT bands from satellite bands especially in band 3400–4200 MHz.
{"title":"Indonesia Case: Space and Earth Station Occupancy Accuracy in 3400–4200 MHz band","authors":"A. C. Situmorang, D. Gunawan, F. Juwono","doi":"10.1109/AGERS.2018.8554200","DOIUrl":"https://doi.org/10.1109/AGERS.2018.8554200","url":null,"abstract":"C band (3400–4200 MHz) is a highly utilized frequency band used by both mobile services and satellite services worldwide. We analyze C-band utilization by comparing data collected from SIMS (Spectrum Management Information System) database owned by Indonesian Government with data collection from national and foreign satellite transponder tenants. The result shows that C-band use in Indonesia is massive, which support Indonesia’s position to refuse additional candidates for the allocation of IMT bands from satellite bands especially in band 3400–4200 MHz.","PeriodicalId":369244,"journal":{"name":"2018 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133026782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AGERS.2018.8554097
D. Retnowati, I. Meilano, A. Riqqi
As the part of countries which located in Ring of Fire, Indonesia has 127 active volcanoes or around 13% of all volcanoes over the world. Dense population around volcanoes slope could lead high risk of loss and damage if the volcano erupt. Population growth is one of the aspects that need to be involved in risk assessment because it always changes over the years and effect the land use/cover change. This research tries to estimate the risk of building damage percentage and population affected volcano eruption, also the direct loss caused by building damage. The study area is Mount Guntur, Indonesia, which located in urban area with high population growth. The estimate risk considers land use/cover change for the modelling of residential building as the exposure, beside population. There are four main data used for the estimation, hazard data, exposure data, vulnerability data, and land use/cover change. As the result, up to 114,912 population will be affected, 115,989 damaged building, and loss up to IDR 11,66 trillion in Guntur, Indonesia.
{"title":"Modeling of Volcano Eruption Risk toward Building Damage and Affected Population in Guntur, Indonesia","authors":"D. Retnowati, I. Meilano, A. Riqqi","doi":"10.1109/AGERS.2018.8554097","DOIUrl":"https://doi.org/10.1109/AGERS.2018.8554097","url":null,"abstract":"As the part of countries which located in Ring of Fire, Indonesia has 127 active volcanoes or around 13% of all volcanoes over the world. Dense population around volcanoes slope could lead high risk of loss and damage if the volcano erupt. Population growth is one of the aspects that need to be involved in risk assessment because it always changes over the years and effect the land use/cover change. This research tries to estimate the risk of building damage percentage and population affected volcano eruption, also the direct loss caused by building damage. The study area is Mount Guntur, Indonesia, which located in urban area with high population growth. The estimate risk considers land use/cover change for the modelling of residential building as the exposure, beside population. There are four main data used for the estimation, hazard data, exposure data, vulnerability data, and land use/cover change. As the result, up to 114,912 population will be affected, 115,989 damaged building, and loss up to IDR 11,66 trillion in Guntur, Indonesia.","PeriodicalId":369244,"journal":{"name":"2018 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114671217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AGERS.2018.8554198
I. S. Sitanggang, Sodik Kirono, L. Syaufina
Indonesia’s peatland condition is getting worse because of peatland fire. Peatland fire causes many negative impacts, so early detection is needed. Data mining is one of approach that can be used for finding sequential pattern from hotspot data as one of indicators for peatland fire. This study aims to find sequential patterns on hotspot data in Riau province Indonesia. The Douglas-Peucker algorithm and substring tree structure concept were used for finding the patterns. The experiment results three types of sequential patterns, namely sequences of date, day, and location of hotspot data in 2014. The most interesting frequent pattern of hotspot occurrence is 11 March 2014 -1 13 March 2014 meaning that the hotspot occurrences on 11 March 2014 was followed by the occurrences in the same location on 13 March 2014. This pattern was found in 9 of 12 districts in Riau Province. Another interesting frequent pattern based on day of occurrence is Friday -1 Saturday -1 Sunday meaning that there was hotspot in Friday, Saturday, and Sunday in the same location. The experiment results show that about 22.77% hotspots in 2014 are considered as strong indicator for peatland fires because it occurred in sequence patterns.
{"title":"Temporal Patterns of Hotspot Sequences for Early Detection of Peatland Fire in Riau Province","authors":"I. S. Sitanggang, Sodik Kirono, L. Syaufina","doi":"10.1109/AGERS.2018.8554198","DOIUrl":"https://doi.org/10.1109/AGERS.2018.8554198","url":null,"abstract":"Indonesia’s peatland condition is getting worse because of peatland fire. Peatland fire causes many negative impacts, so early detection is needed. Data mining is one of approach that can be used for finding sequential pattern from hotspot data as one of indicators for peatland fire. This study aims to find sequential patterns on hotspot data in Riau province Indonesia. The Douglas-Peucker algorithm and substring tree structure concept were used for finding the patterns. The experiment results three types of sequential patterns, namely sequences of date, day, and location of hotspot data in 2014. The most interesting frequent pattern of hotspot occurrence is 11 March 2014 -1 13 March 2014 meaning that the hotspot occurrences on 11 March 2014 was followed by the occurrences in the same location on 13 March 2014. This pattern was found in 9 of 12 districts in Riau Province. Another interesting frequent pattern based on day of occurrence is Friday -1 Saturday -1 Sunday meaning that there was hotspot in Friday, Saturday, and Sunday in the same location. The experiment results show that about 22.77% hotspots in 2014 are considered as strong indicator for peatland fires because it occurred in sequence patterns.","PeriodicalId":369244,"journal":{"name":"2018 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128192807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AGERS.2018.8554203
F. Shabrina, W. Windupranata, N. Hanifa, I. Meilano, A. Riqqi
Mentawai Island is adjacent with Indian Ocean that prone to high waves due to the wind in the Australia monsoon periods so that this island affected by coastal erosion. This coastal hazard causes significant risk like physical damage. Accordingly, disaster risk reduction program in coastal areas is indispensable in advance for the next destructive coastal hazard event. This need is answered by conducting disaster risk analysis as the first step in coastal erosion identification in the Mentawai Islands. The risk was obtained using a qualitative approach by combining the multidimensional aspects of hazard as well as vulnerability. Hazards aspect was derived from identification of physical variables to erosion and was represented by index value. Vulnerability aspects consist of physical aspect and social aspect. The physical aspect is the distribution of buildings and the social aspect is population density that obtained by modeling process. The result indicated that the eastern Mentawai coast has a wider area of erosion risk than the west coast due to the influence of the Australian monsoon.
{"title":"Risk Analysis of Coastal Erosion in Mentawai Island, West Sumatra Province, Indonesia","authors":"F. Shabrina, W. Windupranata, N. Hanifa, I. Meilano, A. Riqqi","doi":"10.1109/AGERS.2018.8554203","DOIUrl":"https://doi.org/10.1109/AGERS.2018.8554203","url":null,"abstract":"Mentawai Island is adjacent with Indian Ocean that prone to high waves due to the wind in the Australia monsoon periods so that this island affected by coastal erosion. This coastal hazard causes significant risk like physical damage. Accordingly, disaster risk reduction program in coastal areas is indispensable in advance for the next destructive coastal hazard event. This need is answered by conducting disaster risk analysis as the first step in coastal erosion identification in the Mentawai Islands. The risk was obtained using a qualitative approach by combining the multidimensional aspects of hazard as well as vulnerability. Hazards aspect was derived from identification of physical variables to erosion and was represented by index value. Vulnerability aspects consist of physical aspect and social aspect. The physical aspect is the distribution of buildings and the social aspect is population density that obtained by modeling process. The result indicated that the eastern Mentawai coast has a wider area of erosion risk than the west coast due to the influence of the Australian monsoon.","PeriodicalId":369244,"journal":{"name":"2018 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114990521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AGERS.2018.8554096
A. Saepuloh, E. K. Army, Agustan
The Synthetic Aperture Radar (SAR) data provide great potential for ground surface mapping under canopy vegetation. The SAR observations regardless time and atmospheric condition are superior for mapping under torrid zones such as Indonesia. The ground surface parameters including surface roughness, moisture, and dielectric constant could be derived by SAR backscattering data. However, estimating thermal properties of ground surface based on SAR remote sensing is complicated because of sensor operation in microwave region. Meanwhile, the thermal properties of ground surface are crucial for estimating surface temperature originated from natural or anthropogenic sources such volcanic activity, urban area, forest fire, or steam spots. To identify the thermal signature from the SAR data, we performed laboratory experiments by incorporating thermal property of materials to be derived from SAR parameters. The experiments were performed by heating ground surface materials including altered rocks from geothermal field and peats from dense vegetation field. We measured moisture, electric potential, magnetic susceptibility, and permeability of the samples with variation of temperature. The measured temperature was controlled by thermal camera FLIR C2 and ground thermometer FLUKE 52 up to 250°C. According to the measurement, we identified that the moisture and electric potential of materials are decrease significantly at temperature more than 100°C. An interesting phenomenon could be reported that the magnetic susceptibility and permeability response to the altered rock and peat samples temperature similarly. The increasing temperature leads to decrease magnetic susceptibility and permeability in general. The mineral and organic content of the rocks and peats controlled their magnetic properties. The laboratory measurement results were then compared to the magnetic permeability derived-SAR backscattering data at a steam field of geothermal system.
{"title":"Identifying Thermal Properties of Ground Surface Derived by SAR and Laboratory Measurements","authors":"A. Saepuloh, E. K. Army, Agustan","doi":"10.1109/AGERS.2018.8554096","DOIUrl":"https://doi.org/10.1109/AGERS.2018.8554096","url":null,"abstract":"The Synthetic Aperture Radar (SAR) data provide great potential for ground surface mapping under canopy vegetation. The SAR observations regardless time and atmospheric condition are superior for mapping under torrid zones such as Indonesia. The ground surface parameters including surface roughness, moisture, and dielectric constant could be derived by SAR backscattering data. However, estimating thermal properties of ground surface based on SAR remote sensing is complicated because of sensor operation in microwave region. Meanwhile, the thermal properties of ground surface are crucial for estimating surface temperature originated from natural or anthropogenic sources such volcanic activity, urban area, forest fire, or steam spots. To identify the thermal signature from the SAR data, we performed laboratory experiments by incorporating thermal property of materials to be derived from SAR parameters. The experiments were performed by heating ground surface materials including altered rocks from geothermal field and peats from dense vegetation field. We measured moisture, electric potential, magnetic susceptibility, and permeability of the samples with variation of temperature. The measured temperature was controlled by thermal camera FLIR C2 and ground thermometer FLUKE 52 up to 250°C. According to the measurement, we identified that the moisture and electric potential of materials are decrease significantly at temperature more than 100°C. An interesting phenomenon could be reported that the magnetic susceptibility and permeability response to the altered rock and peat samples temperature similarly. The increasing temperature leads to decrease magnetic susceptibility and permeability in general. The mineral and organic content of the rocks and peats controlled their magnetic properties. The laboratory measurement results were then compared to the magnetic permeability derived-SAR backscattering data at a steam field of geothermal system.","PeriodicalId":369244,"journal":{"name":"2018 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127940686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AGERS.2018.8554092
E. Y. Handoko, H. Hariyadi, A. Wirasatriya
Global mean sea level (GMSL) and El Niño Southern Oscillation (ENSO) has a high correlation. This study aims to investigate the impact of ENSO on the Indonesian seas using satellite altimetry for a period 24 years (1993 – 2016). The correlations between ENSO and sea level variability were performed by ENSO indices. The effect of ENSO to the Indonesian seas is significant which is shown by the correlation index of −0.84, −0.85 and 0.70 for Multivariate ENSO Index (MEI), Oceanic Niño Index (ONI), and Southern Oscillation Index (SOI), respectively. The eastern areas of Indonesian seas have a highest impact of ENSO.
{"title":"The ENSO’s Influence on the Indonesian Sea Level Observed Using Satellite Altimetry, 1993 – 2016","authors":"E. Y. Handoko, H. Hariyadi, A. Wirasatriya","doi":"10.1109/AGERS.2018.8554092","DOIUrl":"https://doi.org/10.1109/AGERS.2018.8554092","url":null,"abstract":"Global mean sea level (GMSL) and El Niño Southern Oscillation (ENSO) has a high correlation. This study aims to investigate the impact of ENSO on the Indonesian seas using satellite altimetry for a period 24 years (1993 – 2016). The correlations between ENSO and sea level variability were performed by ENSO indices. The effect of ENSO to the Indonesian seas is significant which is shown by the correlation index of −0.84, −0.85 and 0.70 for Multivariate ENSO Index (MEI), Oceanic Niño Index (ONI), and Southern Oscillation Index (SOI), respectively. The eastern areas of Indonesian seas have a highest impact of ENSO.","PeriodicalId":369244,"journal":{"name":"2018 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123188930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AGERS.2018.8554199
Duddy Soegiarto, B. Trilaksono, W. Adiprawita, Egi Muhammad Idris, Y. P. Nugraha
In this paper we develop Active SLAM-based on-line planning systems for mobile robot olfaction. This On-line planning system enables mobile robot olfaction to perform mapping tasks autonomously when a disaster occurs involving Chemical, Biological, Radiological or Nuclear (CBRN) materials. In this work the robot will be tasked with mapping gas distribution in contaminated unknown outdoor environments. Global and local planning is a major part of the on-line planning system. Global planning serves to provide global path planning and predict the best location to take measurements. The combination of frontier based exploration and Closest Location-Information Gain (CL-IG) methods is used to build global planning. Local planning controls robot navigation to avoid obstacles and evaluate every robot action when performing area coverage and gas distribution mapping. Local planning was developed using sensor path planning based on Bayesian Adaptive Exploration (BAE) and Vector Field Histograms (VFH) methods for obstacle avoidance. Online planning performance testing for robot navigation when exploring and mapping has been simulated using ROS, Gazebo and Rviz.
{"title":"On-line Planning on Active SLAM-Based Robot Olfaction for Gas Distribution Mapping","authors":"Duddy Soegiarto, B. Trilaksono, W. Adiprawita, Egi Muhammad Idris, Y. P. Nugraha","doi":"10.1109/AGERS.2018.8554199","DOIUrl":"https://doi.org/10.1109/AGERS.2018.8554199","url":null,"abstract":"In this paper we develop Active SLAM-based on-line planning systems for mobile robot olfaction. This On-line planning system enables mobile robot olfaction to perform mapping tasks autonomously when a disaster occurs involving Chemical, Biological, Radiological or Nuclear (CBRN) materials. In this work the robot will be tasked with mapping gas distribution in contaminated unknown outdoor environments. Global and local planning is a major part of the on-line planning system. Global planning serves to provide global path planning and predict the best location to take measurements. The combination of frontier based exploration and Closest Location-Information Gain (CL-IG) methods is used to build global planning. Local planning controls robot navigation to avoid obstacles and evaluate every robot action when performing area coverage and gas distribution mapping. Local planning was developed using sensor path planning based on Bayesian Adaptive Exploration (BAE) and Vector Field Histograms (VFH) methods for obstacle avoidance. Online planning performance testing for robot navigation when exploring and mapping has been simulated using ROS, Gazebo and Rviz.","PeriodicalId":369244,"journal":{"name":"2018 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114180020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/AGERS.2018.8554209
D. B. Sencaki, Dayuf J. Muhammad, L. Sumargana, Laju Gandharum
Peatland is important for global climate as it stores enormous amount of carbon and if degraded could yield devastating effect to atmosphere as it worsen the Earth’s green house level. Given that fact, it is inevitable to start conserving peatland area. The conservation plan requires reliable and clear delineation map to differ between peatland and non peatland. Remote sensing technology is effective tool to solve this task. Its recent products such as Landsat, MODIS and ASTER GDEM are potentially capable of identifying and characterizing peatland. Employing spectral analysis make it possible to identify peatland unique features and discriminate between peat area and non – peat area. Machine Learning (ML) method was used to produce peatland map as it was able to identify class signature data with high dimensionality feature. From early assessment, ML was able to perform classification with accuracy more than 80% using solely testing and training dataset from South Sumatera province. By only using the knowledge from training data in South Sumatera, ML classified Riau, Jambi and South Sumatera itself. The result was quite promising as accuracy attained by Random Forest and Gradient Boosting were 79.95% and 78.60% for 500 meter spacing grid training data, and 73.95% and 78.10% for 750 meter spacing grid training data. The use of machine learning in remote sensing for classification despite not providing perfect result can still be a useful tool to give an insight to solve highly complex classification task.
{"title":"Peatland Delineation Using Remote Sensing Data in Sumatera Island","authors":"D. B. Sencaki, Dayuf J. Muhammad, L. Sumargana, Laju Gandharum","doi":"10.1109/AGERS.2018.8554209","DOIUrl":"https://doi.org/10.1109/AGERS.2018.8554209","url":null,"abstract":"Peatland is important for global climate as it stores enormous amount of carbon and if degraded could yield devastating effect to atmosphere as it worsen the Earth’s green house level. Given that fact, it is inevitable to start conserving peatland area. The conservation plan requires reliable and clear delineation map to differ between peatland and non peatland. Remote sensing technology is effective tool to solve this task. Its recent products such as Landsat, MODIS and ASTER GDEM are potentially capable of identifying and characterizing peatland. Employing spectral analysis make it possible to identify peatland unique features and discriminate between peat area and non – peat area. Machine Learning (ML) method was used to produce peatland map as it was able to identify class signature data with high dimensionality feature. From early assessment, ML was able to perform classification with accuracy more than 80% using solely testing and training dataset from South Sumatera province. By only using the knowledge from training data in South Sumatera, ML classified Riau, Jambi and South Sumatera itself. The result was quite promising as accuracy attained by Random Forest and Gradient Boosting were 79.95% and 78.60% for 500 meter spacing grid training data, and 73.95% and 78.10% for 750 meter spacing grid training data. The use of machine learning in remote sensing for classification despite not providing perfect result can still be a useful tool to give an insight to solve highly complex classification task.","PeriodicalId":369244,"journal":{"name":"2018 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121727652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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