Pub Date : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093550
Aprilia Puspita C., A. Martha, Priyobudi, S. Rohadi, N. Heryandoko, S. Ahadi
The Pasaman earthquake on February 25, 2022, had a magnitude of 6.1 with a depth of 10 km and an epicenter at 0.15 N - 99.98 BT. This earthquake was preceded by a lower magnitude earthquake with a magnitude of 5.2, with an interval of about 4 minutes before the main earthquake. Based on information updates from BMKG until March 7, 2022, there were 279 aftershocks and 10 felt times. Based on information from the Pasaman regency government, the casualties affected as many as 24 people died, 7186 people were displaced and more than 6625 houses were damaged spread across 5 districts, including West Pasaman, Pasaman, Lima Puluh Kota, Agam, and Padang Pariaman districts. This study aims to provide information on the location of deformations caused by this earthquake. Because the impact of the earthquake is quite extensive and destructive, it is very necessary to inform the information that occurs for future mitigation efforts. This research uses the DinSAR method by utilizing data from sentinel 1 type SLC (Single Look Complex) imagery before (11 and 23 February 2022) and after (7 March 2022) the earthquake occurred. In addition, we processed satellite Gravity data from GGMPlus to identify weak structures associated with low anomalies for comparison with the results of the DinSAR Method. The results of the satellite imagery process show that the areas identified as deformation at the time of the earthquake are in zones with low (negative) anomaly residual gravity values.
2022年2月25日发生的巴萨曼地震，震级为6.1级，震源深度10公里，震中为0.15 N - 99.98 BT，地震前有一次较低震级5.2级地震，地震发生在主震发生前4分钟左右。根据BMKG截至2022年3月7日的最新信息，共发生279次余震，10次有震感。据帕沙曼县政府透露，在西帕沙曼、帕沙曼、利马普卢哥打、阿甘、巴东帕沙曼等5个地区，共有24人死亡，7186人流离失所，6625所房屋受损。这项研究的目的是提供这次地震造成的变形的位置信息。由于地震的影响相当广泛和具有破坏性，因此非常有必要将发生的信息告知未来的减灾工作。本研究使用DinSAR方法，利用sentinel 1型SLC (Single Look Complex)图像在地震发生前(2022年2月11日和23日)和之后(2022年3月7日)的数据。此外，我们还处理了来自GGMPlus的卫星重力数据，以识别与低异常相关的弱结构，并与DinSAR方法的结果进行比较。卫星影像处理结果表明，地震时被识别为形变的区域位于低(负)残余重力异常区。
{"title":"Deformation Identification Due to the Pasaman Earthquake On February 25 2022, Using The DinSAR Method","authors":"Aprilia Puspita C., A. Martha, Priyobudi, S. Rohadi, N. Heryandoko, S. Ahadi","doi":"10.1109/AGERS56232.2022.10093550","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093550","url":null,"abstract":"The Pasaman earthquake on February 25, 2022, had a magnitude of 6.1 with a depth of 10 km and an epicenter at 0.15 N - 99.98 BT. This earthquake was preceded by a lower magnitude earthquake with a magnitude of 5.2, with an interval of about 4 minutes before the main earthquake. Based on information updates from BMKG until March 7, 2022, there were 279 aftershocks and 10 felt times. Based on information from the Pasaman regency government, the casualties affected as many as 24 people died, 7186 people were displaced and more than 6625 houses were damaged spread across 5 districts, including West Pasaman, Pasaman, Lima Puluh Kota, Agam, and Padang Pariaman districts. This study aims to provide information on the location of deformations caused by this earthquake. Because the impact of the earthquake is quite extensive and destructive, it is very necessary to inform the information that occurs for future mitigation efforts. This research uses the DinSAR method by utilizing data from sentinel 1 type SLC (Single Look Complex) imagery before (11 and 23 February 2022) and after (7 March 2022) the earthquake occurred. In addition, we processed satellite Gravity data from GGMPlus to identify weak structures associated with low anomalies for comparison with the results of the DinSAR Method. The results of the satellite imagery process show that the areas identified as deformation at the time of the earthquake are in zones with low (negative) anomaly residual gravity values.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"7 Suppl 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":"133407349","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/AGERS56232.2022.10093448
Octo Mario Pasaribu, A. Poniman, Andrian Andaya Lestari, Y. Prihanto, A. Supriyadi, Yahya Darmawan
The availability of spatially and temporally consistent rainfall observation data is needed in various fields. Fields of research related to hydrometeorology are no exception. The limitations of rainfall measuring tools and stations encourage the use of alternative rainfall data derived from estimates based on satellite data. The condition of limited tools and stations to measure rainfall is also experienced in Medan City and Deli Serdang Regency. This study aims to test how far the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite rainfall data can be used as standard rainfall data in Medan City and Deli Serdang Regency. In this study, monthly rainfall forecasts from CHIRPS satellite data are validated by rainfall observations from four station locations. Validation is carried out to determine the level of correlation and the magnitude of the error value from satellite data. The method used is descriptive statistics by calculating the correlation coefficient and error value. The validation results show that the CHIRPS satellite data has a fairly strong correlation greater than 0.6 with observation data from four locations. Therefore, CHIRPS satellite data can be used as an alternative to rainfall data in Medan City and Deli Serdang Regency, especially in areas with the same elevation and topographic conditions as the station location, with the best validation results.
{"title":"Exploration of CHIRPS Satellite Data as Rainfall Estimation Data in Medan City and Deli Serdang Regency","authors":"Octo Mario Pasaribu, A. Poniman, Andrian Andaya Lestari, Y. Prihanto, A. Supriyadi, Yahya Darmawan","doi":"10.1109/AGERS56232.2022.10093448","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093448","url":null,"abstract":"The availability of spatially and temporally consistent rainfall observation data is needed in various fields. Fields of research related to hydrometeorology are no exception. The limitations of rainfall measuring tools and stations encourage the use of alternative rainfall data derived from estimates based on satellite data. The condition of limited tools and stations to measure rainfall is also experienced in Medan City and Deli Serdang Regency. This study aims to test how far the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite rainfall data can be used as standard rainfall data in Medan City and Deli Serdang Regency. In this study, monthly rainfall forecasts from CHIRPS satellite data are validated by rainfall observations from four station locations. Validation is carried out to determine the level of correlation and the magnitude of the error value from satellite data. The method used is descriptive statistics by calculating the correlation coefficient and error value. The validation results show that the CHIRPS satellite data has a fairly strong correlation greater than 0.6 with observation data from four locations. Therefore, CHIRPS satellite data can be used as an alternative to rainfall data in Medan City and Deli Serdang Regency, especially in areas with the same elevation and topographic conditions as the station location, with the best validation results.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"46 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":"134422981","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/agers56232.2022.10093300
{"title":"Welcome Speech from Chair of Indonesia Section IEEE AESS/GRSS Chapter","authors":"","doi":"10.1109/agers56232.2022.10093300","DOIUrl":"https://doi.org/10.1109/agers56232.2022.10093300","url":null,"abstract":"","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"138 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114132187","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/AGERS56232.2022.10093560
D. B. Sencaki, M. N. Putri, H. Sanjaya, Hari Prayogi, N. Anatoly, Afifuddin, P. K. Putra, Tiara Grace F.L, Muhammad Luthfi A.
Agriculture holds an important role in food security management, hence providing the authorities with reliable and updated agriculture field maps from regional to national scale is critical. Unfortunately, conventional digitation on the screen is still dominating the process of mapping production. The recent advancement in remote sensing research has made it possible to optimize the operation of mapping by employing Deep Learning (DL) algorithm to automate the process. This study implemented a novel DL architecture based on multiple blocks of CNN layers which are complemented by a Bi-LSTM and dual FCN layers. Time-series datasets of NDVI were extracted from Landsat 8 OLI (Operational Land Image) ranging from May 2013 to September 2021 as the main features. The validation accuracy score of our DL model during the fitting process was 0.9833. MSAVI replaced NDVI as part of the experiments and our model produced a validation accuracy score of 0.9667. In the latter stage of the experiment, we produced the final comparison using IoU metrics between prediction maps of the agriculture field from our model, ResNet, and ESA WorldCover. Prediction maps from our model topped the chart with highest IoU score amongst others for the NDVI and MSAVI datasets
农业在粮食安全管理中发挥着重要作用，因此向当局提供从区域到国家范围的可靠和最新的农业实地地图至关重要。不幸的是，屏幕上的传统数字化仍然主导着地图制作过程。遥感研究的最新进展使得利用深度学习(DL)算法实现制图过程自动化，从而优化制图操作成为可能。本研究实现了一种基于多个CNN层块的新型深度学习架构，该架构由Bi-LSTM和双FCN层补充。NDVI时序数据集提取自2013年5月至2021年9月的Landsat 8 OLI (Operational Land Image)遥感影像。我们的DL模型在拟合过程中的验证精度得分为0.9833。MSAVI取代NDVI作为实验的一部分，我们的模型产生了0.9667的验证精度分数。在实验的后期，我们使用IoU指标对我们的模型、ResNet和ESA WorldCover中的农业领域预测图进行了最后的比较。在NDVI和MSAVI数据集中，我们模型的预测图以最高的IoU得分位居榜首
{"title":"Time Series Classification using Improved Deep Learning Approach for Agriculture Field Mapping","authors":"D. B. Sencaki, M. N. Putri, H. Sanjaya, Hari Prayogi, N. Anatoly, Afifuddin, P. K. Putra, Tiara Grace F.L, Muhammad Luthfi A.","doi":"10.1109/AGERS56232.2022.10093560","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093560","url":null,"abstract":"Agriculture holds an important role in food security management, hence providing the authorities with reliable and updated agriculture field maps from regional to national scale is critical. Unfortunately, conventional digitation on the screen is still dominating the process of mapping production. The recent advancement in remote sensing research has made it possible to optimize the operation of mapping by employing Deep Learning (DL) algorithm to automate the process. This study implemented a novel DL architecture based on multiple blocks of CNN layers which are complemented by a Bi-LSTM and dual FCN layers. Time-series datasets of NDVI were extracted from Landsat 8 OLI (Operational Land Image) ranging from May 2013 to September 2021 as the main features. The validation accuracy score of our DL model during the fitting process was 0.9833. MSAVI replaced NDVI as part of the experiments and our model produced a validation accuracy score of 0.9667. In the latter stage of the experiment, we produced the final comparison using IoU metrics between prediction maps of the agriculture field from our model, ResNet, and ESA WorldCover. Prediction maps from our model topped the chart with highest IoU score amongst others for the NDVI and MSAVI datasets","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"65 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":"121354929","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/AGERS56232.2022.10093526
A. Anisah, B. Santosa, D. B. Sencaki
Flood Risk Management (FRM) is implemented by the government to cope with floods, with mitigation/prevention, preparedness, response, and recovery phases. The occurrence of urban flooding regularly indicates that the applied FRM has not functioned effectively. This study proposes using systems thinking in flood risk management since the interdependence between flood risk components and the programs in the FRM is complex. For this reason, systems thinking in the form of a causal loop diagram can be used to explore the interdependence between programs within the FRM framework and flood risk components. By identifying the pattern of interdependence between flood risk and FRM, FRM programs can be directed to achieve the final target, namely reducing flood risk in an area. Therefore, life in flood-prone areas can occur sustainably and harmoniously.
{"title":"Conceptual Framework of Systems Thinking based Flood Risk Management: A Preliminary Study","authors":"A. Anisah, B. Santosa, D. B. Sencaki","doi":"10.1109/AGERS56232.2022.10093526","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093526","url":null,"abstract":"Flood Risk Management (FRM) is implemented by the government to cope with floods, with mitigation/prevention, preparedness, response, and recovery phases. The occurrence of urban flooding regularly indicates that the applied FRM has not functioned effectively. This study proposes using systems thinking in flood risk management since the interdependence between flood risk components and the programs in the FRM is complex. For this reason, systems thinking in the form of a causal loop diagram can be used to explore the interdependence between programs within the FRM framework and flood risk components. By identifying the pattern of interdependence between flood risk and FRM, FRM programs can be directed to achieve the final target, namely reducing flood risk in an area. Therefore, life in flood-prone areas can occur sustainably and harmoniously.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"3 12 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":"123696146","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/AGERS56232.2022.10093614
Megivareza Putri Hanansyah, Amalia Putri Rivani, H. Sanjaya, L. M. Jaelani, Nurdiansyah
Kalimantan Island is one of the largest islands in Indonesia, with high natural and mineral resources. Therefore, the mining industry and clearing of forest areas for oil palm plantations cause a decrease in vegetation. However, the existence of vegetation has a positive impact on the sustainability of the ecosystem. For this reason, monitoring the vegetation on the island of Kalimantan regularly using remote sensing data is necessary. This study uses MODIS Nadir BRDF-Adjusted Reflectance Daily 500m (MODIS/006/MCD43A4) satellite imagery consisting of bands 1–7 and 500 meters resolution. Data calculation using the MNDVI algorithm, which can reduce atmospheric effects and, at the same time, adjusts parameters for reflectance data not affected by the atmosphere. Then, data processing was carried out with cloud masking and clipping using the boundaries of Kalimantan, and the results were classified into four classes. The monitoring of vegetation changes will develop into a web-based application. Applications are made using the Streamlit framework and can be accessed by anyone needing data on vegetation changes on the island of Kalimantan from 2000 to 2021.
加里曼丹岛是印度尼西亚最大的岛屿之一，拥有丰富的自然和矿产资源。因此，采矿业和砍伐森林种植油棕造成植被减少。然而，植被的存在对生态系统的可持续性有着积极的影响。因此，利用遥感数据定期监测加里曼丹岛上的植被是必要的。本研究使用MODIS Nadir BRDF-Adjusted Reflectance Daily 500m (MODIS/006/MCD43A4)卫星影像，包括波段1-7和500米分辨率。使用MNDVI算法进行数据计算，该算法可以减少大气影响，同时调整不受大气影响的反射率数据的参数。然后，利用加里曼丹边界对数据进行遮挡和裁剪处理，并将结果划分为4类。对植被变化的监测将发展成为基于网络的应用程序。应用程序是使用Streamlit框架制作的，任何需要加里曼丹岛2000年至2021年植被变化数据的人都可以访问。
{"title":"Development of Vegetation Changes Monitoring Application in Kalimantan Island (2000-2021) with MODIS Satellite Imagery using Streamlit Platform","authors":"Megivareza Putri Hanansyah, Amalia Putri Rivani, H. Sanjaya, L. M. Jaelani, Nurdiansyah","doi":"10.1109/AGERS56232.2022.10093614","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093614","url":null,"abstract":"Kalimantan Island is one of the largest islands in Indonesia, with high natural and mineral resources. Therefore, the mining industry and clearing of forest areas for oil palm plantations cause a decrease in vegetation. However, the existence of vegetation has a positive impact on the sustainability of the ecosystem. For this reason, monitoring the vegetation on the island of Kalimantan regularly using remote sensing data is necessary. This study uses MODIS Nadir BRDF-Adjusted Reflectance Daily 500m (MODIS/006/MCD43A4) satellite imagery consisting of bands 1–7 and 500 meters resolution. Data calculation using the MNDVI algorithm, which can reduce atmospheric effects and, at the same time, adjusts parameters for reflectance data not affected by the atmosphere. Then, data processing was carried out with cloud masking and clipping using the boundaries of Kalimantan, and the results were classified into four classes. The monitoring of vegetation changes will develop into a web-based application. Applications are made using the Streamlit framework and can be accessed by anyone needing data on vegetation changes on the island of Kalimantan from 2000 to 2021.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"3 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":"128005948","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/AGERS56232.2022.10093595
A. Darmawan, N. Setyaningrum, Afifuddin, S. Arfah, Muhammad Iqbal Habibie
Mangroves not only function as carbon sinks but also as food sources, wildlife habitats, and coastal protection. However, behind the enormous benefits, the information and data are still relatively minimal. In the context of the mangrove restoration and rehabilitation program in Indonesia, it is necessary to study the progress that has been achieved so far. One of the indicators assessed is the estimation of mangrove density in an area over a certain period. This study will calculate the density of mangroves in Riau Province, one of 9 priority provinces, using Sentinel 2 satellite data for 2016 and 2021. Estimation of mangrove density is carried out using vegetation indices approach, namely Modified Soil-Adjusted Vegetation Index-2 (MSAVI2), Soil-Adjusted Vegetation Index 2 (SAVI2), and Green Normalized Difference Vegetation Index-2 (GNDVI2). This vegetation index is an empirical mathematical model algorithm of the reflection of electromagnetic, visible, and near-infrared (NIR) waves. From the results of this study, the mangrove restoration and rehabilitation program in Riau Province is going as expected, and it can be seen from the change in the density level. The algorithm shows that the change in mangrove density in 2021 is about 20% for the very dense type compared to 2016.
{"title":"Is the Mangrove Restoration and Rehabilitation Program Successful in Riau Province, Indonesia?","authors":"A. Darmawan, N. Setyaningrum, Afifuddin, S. Arfah, Muhammad Iqbal Habibie","doi":"10.1109/AGERS56232.2022.10093595","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093595","url":null,"abstract":"Mangroves not only function as carbon sinks but also as food sources, wildlife habitats, and coastal protection. However, behind the enormous benefits, the information and data are still relatively minimal. In the context of the mangrove restoration and rehabilitation program in Indonesia, it is necessary to study the progress that has been achieved so far. One of the indicators assessed is the estimation of mangrove density in an area over a certain period. This study will calculate the density of mangroves in Riau Province, one of 9 priority provinces, using Sentinel 2 satellite data for 2016 and 2021. Estimation of mangrove density is carried out using vegetation indices approach, namely Modified Soil-Adjusted Vegetation Index-2 (MSAVI2), Soil-Adjusted Vegetation Index 2 (SAVI2), and Green Normalized Difference Vegetation Index-2 (GNDVI2). This vegetation index is an empirical mathematical model algorithm of the reflection of electromagnetic, visible, and near-infrared (NIR) waves. From the results of this study, the mangrove restoration and rehabilitation program in Riau Province is going as expected, and it can be seen from the change in the density level. The algorithm shows that the change in mangrove density in 2021 is about 20% for the very dense type compared to 2016.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"12 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":"121619414","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/AGERS56232.2022.10093289
A. Agustan, Takeo Ito, E. Kriswati, H. Priyadi, Heri Sadmono, R. Hernawati
Most of the previous geoscience studies in Jakarta found that the area is affected by land subsidence. However, it is difficult to understand the spatial distribution since continuous geodetic observation is not available. The development of SAR satellite-based remote sensing enables ground deformation observation over a long period and in regular time. This study presents the ground deformation time series analysis for the Jakarta Metropolitan area based on the InSAR technique. Using the small baseline subset approach, we construct time series of ground deformation constrained from Sentinel-1 interferograms provided by ASF DAAC Hyp3. Mintpy tool is utilized to obtain the time series analysis. It is found that for 7.6 years observation period since late 2014, land subsidence was only locally spotted in certain areas. The average velocity of ground deformation in the Jakarta Metropolitan area varies from −5.8 cm/year to 1.2 cm/year. It means uplift phenomena are also detected in Jakarta metropolitan area. It is also found that the time series of ground deformation fluctuates in seasonal patterns and may relate to ground water recharge during the rainy season.
{"title":"Time Series InSAR Analysis over Jakarta Metropolitan Area","authors":"A. Agustan, Takeo Ito, E. Kriswati, H. Priyadi, Heri Sadmono, R. Hernawati","doi":"10.1109/AGERS56232.2022.10093289","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093289","url":null,"abstract":"Most of the previous geoscience studies in Jakarta found that the area is affected by land subsidence. However, it is difficult to understand the spatial distribution since continuous geodetic observation is not available. The development of SAR satellite-based remote sensing enables ground deformation observation over a long period and in regular time. This study presents the ground deformation time series analysis for the Jakarta Metropolitan area based on the InSAR technique. Using the small baseline subset approach, we construct time series of ground deformation constrained from Sentinel-1 interferograms provided by ASF DAAC Hyp3. Mintpy tool is utilized to obtain the time series analysis. It is found that for 7.6 years observation period since late 2014, land subsidence was only locally spotted in certain areas. The average velocity of ground deformation in the Jakarta Metropolitan area varies from −5.8 cm/year to 1.2 cm/year. It means uplift phenomena are also detected in Jakarta metropolitan area. It is also found that the time series of ground deformation fluctuates in seasonal patterns and may relate to ground water recharge during the rainy season.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"53 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":"121698618","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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