Pub Date : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093406
Y. Heryadi, E. Irwansyah, Eka Miranda, Haryono Soeparno, Herlawati, Kiyota Hashimoto
Food sustainability is still one of the main priorities for many countries as it contributes to the economy and stability of the nation. For government in many countries whose peoples consumes rice as its staple food, food self-sufficiency initiatives highly depend on accurate prediction of paddy field map. Mapping paddy field task is a challenging problem which cannot be handled manually especially when the paddy fields are spread out in very wide geographical areas such as those in Indonesia. Fortunately, wide availability of satellite imagery and the advent of deep learning technology in the past ten years have made it possible to improve efficiency of most parts of those manual works involving image semantic segmentation tasks. However, satellite image-based semantic segmentation is a challenging task. High object complexity, cloud partial occlusion, larger image size than a computer memory can stored can hinder accuracy of the image segmentation results. This paper presents a method for paddy field map generating using semantic image segmentation approach in which Pyramid Scene Parsing Net model is used for segmenting satellite imagery. The generated paddy map can be used as a basis for decision-making, especially in the agricultural sector. Analysis of local land use/land cover dynamics. The results of his experiments using SPOT 6 satellite imagery from the Pahung region of Central Kalimantan achieved average training accuracy, best training accuracy and test accuracy of 0.85, 0.86 and 0.89 respectively. These results indicated that the semantic segmentation model is suitable for addressing the same task in different crops.
{"title":"Pyramid Scene Parsing Net Model for Automated Paddy Field Map using SPOT 6 Satellite Images","authors":"Y. Heryadi, E. Irwansyah, Eka Miranda, Haryono Soeparno, Herlawati, Kiyota Hashimoto","doi":"10.1109/AGERS56232.2022.10093406","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093406","url":null,"abstract":"Food sustainability is still one of the main priorities for many countries as it contributes to the economy and stability of the nation. For government in many countries whose peoples consumes rice as its staple food, food self-sufficiency initiatives highly depend on accurate prediction of paddy field map. Mapping paddy field task is a challenging problem which cannot be handled manually especially when the paddy fields are spread out in very wide geographical areas such as those in Indonesia. Fortunately, wide availability of satellite imagery and the advent of deep learning technology in the past ten years have made it possible to improve efficiency of most parts of those manual works involving image semantic segmentation tasks. However, satellite image-based semantic segmentation is a challenging task. High object complexity, cloud partial occlusion, larger image size than a computer memory can stored can hinder accuracy of the image segmentation results. This paper presents a method for paddy field map generating using semantic image segmentation approach in which Pyramid Scene Parsing Net model is used for segmenting satellite imagery. The generated paddy map can be used as a basis for decision-making, especially in the agricultural sector. Analysis of local land use/land cover dynamics. The results of his experiments using SPOT 6 satellite imagery from the Pahung region of Central Kalimantan achieved average training accuracy, best training accuracy and test accuracy of 0.85, 0.86 and 0.89 respectively. These results indicated that the semantic segmentation model is suitable for addressing the same task in different crops.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128987081","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 : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093530
A. A. Ruhaima, Dunya Mohee Hayder, Jamal Kamil Al-Rudaini
The human body is such a complicated structure full of fine details, small and big details, some diseases affect the small parts of the body, so a doctor has to use every tool to diagnose the disease like Lab testing and imaging (Imaging means sending the patient to do X-Ray, MRI, CT scan, etc.). So, receiving a clear image with no noise is important to reach a precise diagnosis rather than a different one. Thus, finding a program to find the lost data due to noise is the dream of every physician. A nonlinear two-dimensional image restoration filter structure is introduced in this work. A nonlinear prediction structure is proposed using nonlinear elements depending on the eye's visual phenomena of noise detection. Filter stability is demanded in this structure. Impulse noise recovery is guaranteed in this filter. An advantage of the filter is in preserving textures and keeping fine details. Median-based filters are proposed for noise recovery. The filter structure shows a superior method for noise detection and precise location determination.
{"title":"Human Visual System Algorithm for Medical Images Recovery","authors":"A. A. Ruhaima, Dunya Mohee Hayder, Jamal Kamil Al-Rudaini","doi":"10.1109/AGERS56232.2022.10093530","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093530","url":null,"abstract":"The human body is such a complicated structure full of fine details, small and big details, some diseases affect the small parts of the body, so a doctor has to use every tool to diagnose the disease like Lab testing and imaging (Imaging means sending the patient to do X-Ray, MRI, CT scan, etc.). So, receiving a clear image with no noise is important to reach a precise diagnosis rather than a different one. Thus, finding a program to find the lost data due to noise is the dream of every physician. A nonlinear two-dimensional image restoration filter structure is introduced in this work. A nonlinear prediction structure is proposed using nonlinear elements depending on the eye's visual phenomena of noise detection. Filter stability is demanded in this structure. Impulse noise recovery is guaranteed in this filter. An advantage of the filter is in preserving textures and keeping fine details. Median-based filters are proposed for noise recovery. The filter structure shows a superior method for noise detection and precise location determination.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129785064","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 : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093664
Adifa Anafiatun Nisa, B. M. Sukojo, N. Nurwatik
Rain with light heavy intensity hit Central Kalimantan Province starting on Thursday (19/8/2021), and submerged 13 sub-districts in Katingan Regency, Central Kalimantan. The study analyzed the weather during heavy rains using reanalyzed data from Copernicus ECMWF, namely Convective Available Potential Energy (CAPE) which was then processed into a map and supported by data from the Himawari-8 satellite to analyze Streamline, Time series, 24-Hours Microphysics RGB starting on the 18th. −24 August 2021. On 18 August 2021 Cumulonimbus clouds were detected in the Katingan Regency area, with the highest cloud top temperature reaching −67°C whereas when the temperature was above −60 °C, this indicates that these clouds are Cumulonimbus convective clouds with a high peak, and the highest CAPE value is 1001–1500 J/kg which means it is categorized as moderate energy. On August 19, 2021, Cumulonimbus clouds were detected, the highest cloud top temperature reached −70°C, and the CAPE value was 1501-2100 J/kg which means it is categorized as moderately strong energy. On August 20, 2021, Cumulonimbus clouds were detected, the highest cloud top temperature reached −78°C, and the CAPE value was 2101-3545.6 J/kg which means it is categorized as strong energy. On August 21, there were thin Cumulonimbus clouds that did not spread evenly, the highest cloud top temperature reached −38°C where the temperature was already below the freezing level and already contained cloud crystals, and the CAPE value in most areas of Katingan Regency was 200–400 J/kg which means it is categorized as weak energy. On August 22, 2021, Cumulonimbus clouds were detected, the highest cloud top temperature reached −77°C, and the CAPE value was 701–1400 J/kg which means it is categorized as moderately weak energy. On August 23, there were no Cumulonimbus clouds in the Katingan Regency area, the highest cloud top temperature reached −37°C, and the CAPE value in most areas of the Katingan Regency was 0-600 J/kg which means it is categorized as weak energy. On August 24, 2021, Cumulonimbus clouds were detected in the Katingan Regency area, the highest cloud top temperature reached −78°C, and the CAPE value in the Katingan Regency area was 2102-3474.5 J/kg which means it is categorized as strong energy. Streamline analysis dated 18,19,20,22,24 in the 850 mb layer above the Katingan Regency area, there is a Shearline. Meanwhile, on 21 and 23 August 2021, the wind over Katingan Regency looked straight and there were no very significant turns so on 21 and 23 August 2021 there was no formation or formation of very thin convective clouds.
{"title":"Analysis of Weather Change Using Himawari-8 Satellite Image with 24-Hours Microphysics RGB and Convective Available Potential Energy Method (Study Case: Flood Central Kalimantan 18-24 August 2021)","authors":"Adifa Anafiatun Nisa, B. M. Sukojo, N. Nurwatik","doi":"10.1109/AGERS56232.2022.10093664","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093664","url":null,"abstract":"Rain with light heavy intensity hit Central Kalimantan Province starting on Thursday (19/8/2021), and submerged 13 sub-districts in Katingan Regency, Central Kalimantan. The study analyzed the weather during heavy rains using reanalyzed data from Copernicus ECMWF, namely Convective Available Potential Energy (CAPE) which was then processed into a map and supported by data from the Himawari-8 satellite to analyze Streamline, Time series, 24-Hours Microphysics RGB starting on the 18th. −24 August 2021. On 18 August 2021 Cumulonimbus clouds were detected in the Katingan Regency area, with the highest cloud top temperature reaching −67°C whereas when the temperature was above −60 °C, this indicates that these clouds are Cumulonimbus convective clouds with a high peak, and the highest CAPE value is 1001–1500 J/kg which means it is categorized as moderate energy. On August 19, 2021, Cumulonimbus clouds were detected, the highest cloud top temperature reached −70°C, and the CAPE value was 1501-2100 J/kg which means it is categorized as moderately strong energy. On August 20, 2021, Cumulonimbus clouds were detected, the highest cloud top temperature reached −78°C, and the CAPE value was 2101-3545.6 J/kg which means it is categorized as strong energy. On August 21, there were thin Cumulonimbus clouds that did not spread evenly, the highest cloud top temperature reached −38°C where the temperature was already below the freezing level and already contained cloud crystals, and the CAPE value in most areas of Katingan Regency was 200–400 J/kg which means it is categorized as weak energy. On August 22, 2021, Cumulonimbus clouds were detected, the highest cloud top temperature reached −77°C, and the CAPE value was 701–1400 J/kg which means it is categorized as moderately weak energy. On August 23, there were no Cumulonimbus clouds in the Katingan Regency area, the highest cloud top temperature reached −37°C, and the CAPE value in most areas of the Katingan Regency was 0-600 J/kg which means it is categorized as weak energy. On August 24, 2021, Cumulonimbus clouds were detected in the Katingan Regency area, the highest cloud top temperature reached −78°C, and the CAPE value in the Katingan Regency area was 2102-3474.5 J/kg which means it is categorized as strong energy. Streamline analysis dated 18,19,20,22,24 in the 850 mb layer above the Katingan Regency area, there is a Shearline. Meanwhile, on 21 and 23 August 2021, the wind over Katingan Regency looked straight and there were no very significant turns so on 21 and 23 August 2021 there was no formation or formation of very thin convective clouds.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127665225","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 : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093313
M. Devy, H. Sanjaya, L. Y. Irawan, I. Astina, Heri Sadmono, Ariani Andayani
Mangroves have the capabilities to both mitigate and adapt to climate change impact. However, it varies between species. Therefore, it is substantial to upscale mangrove explorations and studies to the species level. This study aims to perform a spectral-library-based linear spectral unmixing (LSU) analysis technique on Landsat 9 OLI-2 imagery as an alternative to the conventional mangrove species mapping methods. We used the center wavelength of Landsat 9 OLI-2's B2, B3, B4, and B5 bands to define the spectra of Sonneratia alba, Rhizophora apiculata, and Avicennia marina. We performed the LSU analysis on the Muaragembong mangrove forest area, Bekasi, West Java, Indonesia as the area of interest. The result showed that the mangrove species has a unique spectral signature. The reflectance is slightly higher at around 500–600 nm and lower at 750–770 nm than the typical vegetation spectral signature. Most of Muaragembong is covered with R. apiculata and A. marina. However, there is a distinctive spatial distribution pattern for each species. Based on the RMSE result, the model can produce a ±0.3% error in each pixel. Empirical evidence from the ground truthing helped to validate the distribution pattern. It is associated with environmental factors, such as supporting substrate and water access. This paper concludes that it is possible to perform the LSU analysis using multispectral satellite data for a large-extent mangrove species mapping. However, it is mandatory to validate the result on a ground-truthing process.
{"title":"Large-Extent Mangrove Species Mapping Using Landsat 9 OLI-2: A Subpixel Analysis","authors":"M. Devy, H. Sanjaya, L. Y. Irawan, I. Astina, Heri Sadmono, Ariani Andayani","doi":"10.1109/AGERS56232.2022.10093313","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093313","url":null,"abstract":"Mangroves have the capabilities to both mitigate and adapt to climate change impact. However, it varies between species. Therefore, it is substantial to upscale mangrove explorations and studies to the species level. This study aims to perform a spectral-library-based linear spectral unmixing (LSU) analysis technique on Landsat 9 OLI-2 imagery as an alternative to the conventional mangrove species mapping methods. We used the center wavelength of Landsat 9 OLI-2's B2, B3, B4, and B5 bands to define the spectra of Sonneratia alba, Rhizophora apiculata, and Avicennia marina. We performed the LSU analysis on the Muaragembong mangrove forest area, Bekasi, West Java, Indonesia as the area of interest. The result showed that the mangrove species has a unique spectral signature. The reflectance is slightly higher at around 500–600 nm and lower at 750–770 nm than the typical vegetation spectral signature. Most of Muaragembong is covered with R. apiculata and A. marina. However, there is a distinctive spatial distribution pattern for each species. Based on the RMSE result, the model can produce a ±0.3% error in each pixel. Empirical evidence from the ground truthing helped to validate the distribution pattern. It is associated with environmental factors, such as supporting substrate and water access. This paper concludes that it is possible to perform the LSU analysis using multispectral satellite data for a large-extent mangrove species mapping. However, it is mandatory to validate the result on a ground-truthing process.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121182839","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 : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093465
Nur Aini Qolbi Fadhilah, Nurya Ramadhania, H. Sanjaya, B. M. Sukojo, Meuthia Djoharin Poespo
Based on data from MAGMA PVMBG, the Geological Agency of the Ministry of Energy and Mineral Resources, in 2019, about 10 mountains have erupted and some have erupted more than twice. Therefore, a spatiotemporal analysis was carried out on SO2 concentrations due to volcanic eruptions in Indonesia in the 2019-2022 period through Sentinel-5P image data with the help of a cloud-based application, Earth Engine. Analysis was carried out at pre-eruption, during an eruption, and post-eruption to determine the difference. This analysis is performed over a weekly time frame. From the results obtained, the concentration of SO2 in the eruption area increased during the eruption and tended to be high compared to the concentration during pre and post-disaster. The distribution of sulfur dioxide is influenced by wind direction and speed, so the sulfur concentration is not always high near the area around the eruption. The value of SO2 concentration in the volcanic eruption area ranges from 0.00-0.007 mol/m2. Based on the correlation test with BMKG environmental data, the surface temperature parameter is known to be positively correlated with the SO2 value and has a negative correlation with humidity. The data that has been validated is then displayed on the website with a simple and easy-to-understand interface for users.
{"title":"Spatio-Temporal Analysis of SO2 Concentrations Due to Volcanic Eruptions in Indonesia Using Sentinel-5P with Earth Engine Platform","authors":"Nur Aini Qolbi Fadhilah, Nurya Ramadhania, H. Sanjaya, B. M. Sukojo, Meuthia Djoharin Poespo","doi":"10.1109/AGERS56232.2022.10093465","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093465","url":null,"abstract":"Based on data from MAGMA PVMBG, the Geological Agency of the Ministry of Energy and Mineral Resources, in 2019, about 10 mountains have erupted and some have erupted more than twice. Therefore, a spatiotemporal analysis was carried out on SO2 concentrations due to volcanic eruptions in Indonesia in the 2019-2022 period through Sentinel-5P image data with the help of a cloud-based application, Earth Engine. Analysis was carried out at pre-eruption, during an eruption, and post-eruption to determine the difference. This analysis is performed over a weekly time frame. From the results obtained, the concentration of SO2 in the eruption area increased during the eruption and tended to be high compared to the concentration during pre and post-disaster. The distribution of sulfur dioxide is influenced by wind direction and speed, so the sulfur concentration is not always high near the area around the eruption. The value of SO2 concentration in the volcanic eruption area ranges from 0.00-0.007 mol/m2. Based on the correlation test with BMKG environmental data, the surface temperature parameter is known to be positively correlated with the SO2 value and has a negative correlation with humidity. The data that has been validated is then displayed on the website with a simple and easy-to-understand interface for users.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124764339","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 : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093315
Ja'far Shadiq Alatas, K. Priandana, Medria Kusuma Dewi Hardhienata, Wulandari
Smart agriculture 4.0 has recently been implemented in Indonesia to enhance agricultural productivity through the use of advance technology. Unmanned Autonomous Vehicle (UAVs) is one of the technologies that have been utilized in the agricultural sector to improve production quality and quantity. Although some advanced technology has been used, currently there are some challenges that remain to be solved to implement multi-UAV in the real environment. Some of these challenges include battery limitations in UAV and the long duration to queue at the charging station. To address this issue, a previous study has proposed Cloud Based Drone Navigation (CBDN) algorithm that can be employed to optimize multi-UAV coordination by selecting the best flight path for the UAV to reach a charging station. Such an approach has resulted in reducing the waiting time of UAVs to be charged. However, the algorithm has not considered swarm robot parameters. This study aims to analyze the use of CBDN algorithm with parameters derived from swarm robots. The performance of the CBDN algorithm will then be evaluated and compared to the Shortest Flight Time (SFT) and Individual Reservation Navigation System (IRN) algorithms as two benchmark algorithms, in terms of the total travel time. By considering real swarm robot parameters, the CBDN algorithm has resulted in an average total travel time of 17.44% less than the average total travel time of SFT and 17.25% less than the average total travel time of IRN.
{"title":"Implementation of Cloud-Based Drone Navigation for Swarm Robot Coordination","authors":"Ja'far Shadiq Alatas, K. Priandana, Medria Kusuma Dewi Hardhienata, Wulandari","doi":"10.1109/AGERS56232.2022.10093315","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093315","url":null,"abstract":"Smart agriculture 4.0 has recently been implemented in Indonesia to enhance agricultural productivity through the use of advance technology. Unmanned Autonomous Vehicle (UAVs) is one of the technologies that have been utilized in the agricultural sector to improve production quality and quantity. Although some advanced technology has been used, currently there are some challenges that remain to be solved to implement multi-UAV in the real environment. Some of these challenges include battery limitations in UAV and the long duration to queue at the charging station. To address this issue, a previous study has proposed Cloud Based Drone Navigation (CBDN) algorithm that can be employed to optimize multi-UAV coordination by selecting the best flight path for the UAV to reach a charging station. Such an approach has resulted in reducing the waiting time of UAVs to be charged. However, the algorithm has not considered swarm robot parameters. This study aims to analyze the use of CBDN algorithm with parameters derived from swarm robots. The performance of the CBDN algorithm will then be evaluated and compared to the Shortest Flight Time (SFT) and Individual Reservation Navigation System (IRN) algorithms as two benchmark algorithms, in terms of the total travel time. By considering real swarm robot parameters, the CBDN algorithm has resulted in an average total travel time of 17.44% less than the average total travel time of SFT and 17.25% less than the average total travel time of IRN.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115452695","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 : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093495
E. Kriswati, Oktory Prambada, D. Syahbana, B. Sugiarto, G. Winarso, Yukni Arifianti, I. A. Kurniawan, R. Virtriana, W. Banggur, Agustan, Firman Prawidisastra, Aditya Pratama
The volcanic eruption provides valuable products and resources, but can also endanger people's lives, property, and activities. This research models the potential hazards from Mount Batur's eruption to mitigate the impacts in the future. Our research focused on numerical models of volcanic hazards with input parameters derived from geology, deformation, seismic, and geochemical data. We acquired high-resolution DEM data obtained from DEMNAS. We compare modelling results with the existing hazard map of Mount Batur.
{"title":"Modeling of Mount Batur lava flows and ejecta as new approaches in Indonesian short-term volcanic hazard assessment","authors":"E. Kriswati, Oktory Prambada, D. Syahbana, B. Sugiarto, G. Winarso, Yukni Arifianti, I. A. Kurniawan, R. Virtriana, W. Banggur, Agustan, Firman Prawidisastra, Aditya Pratama","doi":"10.1109/AGERS56232.2022.10093495","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093495","url":null,"abstract":"The volcanic eruption provides valuable products and resources, but can also endanger people's lives, property, and activities. This research models the potential hazards from Mount Batur's eruption to mitigate the impacts in the future. Our research focused on numerical models of volcanic hazards with input parameters derived from geology, deformation, seismic, and geochemical data. We acquired high-resolution DEM data obtained from DEMNAS. We compare modelling results with the existing hazard map of Mount Batur.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134224094","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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