This study evaluates the use of optical multi-spectral satellite data for crop type and land cover identification in Marathwada, India, with a specific focus on disaster management. The region is highly susceptible to various disasters including droughts and other climate-related events that significantly impact agricultural productivity. The study involves analyzing both single-date and multi-temporal satellite imagery to develop composite images using different band combinations, aiming to identify the most accurate combination for crop and land cover identification. A multi-class classification approach based on random forest is employed for feature extraction and the significance of different bands in the imagery is assessed. The results demonstrate that a composite image composed of Red, Green, Blue, Near Infrared and Shortwave Infrared bands yields the highest accuracy with an overall accuracy (OA) of up to 93.69% for all land cover classes and 91.18% for crop classes alone, using six-date multi-temporal imagery. The findings highlight the potential of optical multi-spectral satellite data as an effective tool for crop type and land cover identification in Marathwada, India, particularly in the context of disaster i.e. agricultural draught management. The methodologies and results presented in this study can serve as a valuable reference for similar research endeavors in other agricultural draught prone regions of India and beyond.
{"title":"Evaluation of optical multi-spectral satellite data for crop type and land cover identification in Marathwada, India: a disaster management perspective","authors":"S. Kale, R. S. Holambe, R. H. Chile","doi":"10.25303/1612da042054","DOIUrl":"https://doi.org/10.25303/1612da042054","url":null,"abstract":"This study evaluates the use of optical multi-spectral satellite data for crop type and land cover identification in Marathwada, India, with a specific focus on disaster management. The region is highly susceptible to various disasters including droughts and other climate-related events that significantly impact agricultural productivity. The study involves analyzing both single-date and multi-temporal satellite imagery to develop composite images using different band combinations, aiming to identify the most accurate combination for crop and land cover identification. A multi-class classification approach based on random forest is employed for feature extraction and the significance of different bands in the imagery is assessed. The results demonstrate that a composite image composed of Red, Green, Blue, Near Infrared and Shortwave Infrared bands yields the highest accuracy with an overall accuracy (OA) of up to 93.69% for all land cover classes and 91.18% for crop classes alone, using six-date multi-temporal imagery. The findings highlight the potential of optical multi-spectral satellite data as an effective tool for crop type and land cover identification in Marathwada, India, particularly in the context of disaster i.e. agricultural draught management. The methodologies and results presented in this study can serve as a valuable reference for similar research endeavors in other agricultural draught prone regions of India and beyond.","PeriodicalId":50576,"journal":{"name":"Disaster Advances","volume":"280 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139289240","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}
Thi Thanh Nga Pham, Van Vu Thang, Pham-Thanh Ha, Quang Pham Nam, Van Nguyen Hiep
This study investigated the spatial and temporal characteristics of rapid intensification (RI) in the Vietnam East Sea (VES) and evaluated the predictability of RI using statistical methods. For the purpose of the RI study, this work focused on a dataset of TCs that reach storm level higher, or having a maximum intensity of at least 34 knots (kn) during their existence. The results show that the annual TC activity in the VES is characterized by a dominance of strong TCs (Category 12 and above) and a significant occurrence of RI-TCs accounting for 73.7% and 23% of the total respectively. Remarkably, RI-TCs were consistently observed in 26 out of the 31 years studied, with a tendency to occur during the latter months of the year. Additionally, approximately 20% of these RI-TCs underwent RI near the Vietnam Coastal region. Given the increasing demand for accurate RI forecasts, four probability models namely Linear Discriminant Analysis (LDA), Logistic Regression (LogR), Naïve Bayes Classifier (Bayes) and Ensemble, using predictors from the SHIPS dataset, are developed to evaluate the predictability of the RI forecast. Among the predictors used, thermodynamic factors such as COHC, vertical wind shear (SHRD) and current TC states (PER) play crucial roles in constructing the RI probability models. Verification indices such as POD, FAR, CSI and BSS, indicate significant improvements in RI forecasting over the VES when utilizing the probability models, especially with the ensemble method.
本研究调查了越南东海（VES）快速增强（RI）的时空特征，并利用统计方法评估了 RI 的可预测性。为了进行 RI 研究，本研究重点收集了在其存在期间达到风暴级以上或最大强度至少为 34 节（kn）的热带气旋数据集。研究结果表明，在 VES 中，强热带气旋（12 级及以上）和 RI-TCs 的年度活动分别占总数的 73.7% 和 23%。值得注意的是，在所研究的 31 年中，有 26 年持续观测到区域性热气旋，并倾向于在每年的后几个月出现。此外，这些 RI-TCs 中约有 20% 在越南沿海地区附近发生 RI。鉴于对准确 RI 预报的需求日益增长，我们利用 SHIPS 数据集中的预测因子，开发了四种概率模型，即线性判别分析（LDA）、逻辑回归（LogR）、奈夫贝叶斯分类器（Bayes）和集合（Ensemble），以评估 RI 预报的可预测性。在使用的预测因子中，热动力因子如 COHC、垂直风切变（SHRD）和当前 TC 状态（PER）在构建 RI 概率模型中发挥了关键作用。POD、FAR、CSI 和 BSS 等验证指数表明，利用概率模型，特别是采用集合方法，RI 预报比 VES 预报有显著改进。
{"title":"Predictability of Tropical Cyclone Rapid Intensification based on Statistical Approach","authors":"Thi Thanh Nga Pham, Van Vu Thang, Pham-Thanh Ha, Quang Pham Nam, Van Nguyen Hiep","doi":"10.25303/1612da01011","DOIUrl":"https://doi.org/10.25303/1612da01011","url":null,"abstract":"This study investigated the spatial and temporal characteristics of rapid intensification (RI) in the Vietnam East Sea (VES) and evaluated the predictability of RI using statistical methods. For the purpose of the RI study, this work focused on a dataset of TCs that reach storm level higher, or having a maximum intensity of at least 34 knots (kn) during their existence. The results show that the annual TC activity in the VES is characterized by a dominance of strong TCs (Category 12 and above) and a significant occurrence of RI-TCs accounting for 73.7% and 23% of the total respectively. Remarkably, RI-TCs were consistently observed in 26 out of the 31 years studied, with a tendency to occur during the latter months of the year. Additionally, approximately 20% of these RI-TCs underwent RI near the Vietnam Coastal region. Given the increasing demand for accurate RI forecasts, four probability models namely Linear Discriminant Analysis (LDA), Logistic Regression (LogR), Naïve Bayes Classifier (Bayes) and Ensemble, using predictors from the SHIPS dataset, are developed to evaluate the predictability of the RI forecast. Among the predictors used, thermodynamic factors such as COHC, vertical wind shear (SHRD) and current TC states (PER) play crucial roles in constructing the RI probability models. Verification indices such as POD, FAR, CSI and BSS, indicate significant improvements in RI forecasting over the VES when utilizing the probability models, especially with the ensemble method.","PeriodicalId":50576,"journal":{"name":"Disaster Advances","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139288817","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}
T.F.K. Ngoroyemoto, A.R. Sabao, N. Ndlovu, P. Munemo, V.Y. Katte
High fracturing and faulting at Paari Mine syndicate have resulted in weak ground conditions making it difficult to conduct mining operations safely. The hanging walls and the side walls are covered with numerous faults and intersecting striking joints forming wedges which are extremely unstable. The project aims to solve the mines problem of tunnel subsidence. The work performed included studying the rock formations within the mine and determining their properties and response to excavations. The rock mass was then classified according to the rock mass rating and the rock quality index. Support design was done using guidelines according to rock mass rating tables. Ground support practices that are currently used at the mine and analysing vulnerabilities of the system were then studied and the use of systematic rock bolting, 2 meters long with a diameter of 24 mm fully grouted was proposed. In addition, we recommend that a spacing of 1 m to 1.5 m of the bolds should be used on the crown pillars as well the walls and that wire mesh should also be used as an additional support mechanism.
{"title":"Designing a more stable tunnel at PAARI mining syndicate","authors":"T.F.K. Ngoroyemoto, A.R. Sabao, N. Ndlovu, P. Munemo, V.Y. Katte","doi":"10.25303/1611da053070","DOIUrl":"https://doi.org/10.25303/1611da053070","url":null,"abstract":"High fracturing and faulting at Paari Mine syndicate have resulted in weak ground conditions making it difficult to conduct mining operations safely. The hanging walls and the side walls are covered with numerous faults and intersecting striking joints forming wedges which are extremely unstable. The project aims to solve the mines problem of tunnel subsidence. The work performed included studying the rock formations within the mine and determining their properties and response to excavations. The rock mass was then classified according to the rock mass rating and the rock quality index. Support design was done using guidelines according to rock mass rating tables. Ground support practices that are currently used at the mine and analysing vulnerabilities of the system were then studied and the use of systematic rock bolting, 2 meters long with a diameter of 24 mm fully grouted was proposed. In addition, we recommend that a spacing of 1 m to 1.5 m of the bolds should be used on the crown pillars as well the walls and that wire mesh should also be used as an additional support mechanism.","PeriodicalId":50576,"journal":{"name":"Disaster Advances","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135766197","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}
The river Brahmaputra is a large alluvial river that is prone to frequent bank erosion and channel pattern changes, leading to significant shifts in its course. This study aimed to analyze these changes along a 56-kilometer stretch of the river using a combined approach of remote sensing and GIS techniques. This study utilized USGS and Landsat 8 satellite imagery to map the river's channel configuration from 1985 to 2022, providing valuable insights into the river's morphology and the stability of its banks. Additionally, the analysis provided information on changes in the river's main channel which can help in predicting future behavior and mitigating the impact of these changes. The findings of this study have significant implications for river management, allowing for informed decision-making and improved strategies for protecting communities and infrastructure located along the river's course.
{"title":"Assessment of Changes in Brahmaputra River Course at the Pagladia Confluence Point using Remote Sensing and GIS Techniques","authors":"Mriganka Mazumdar, Saikat Deb","doi":"10.25303/1611da010018","DOIUrl":"https://doi.org/10.25303/1611da010018","url":null,"abstract":"The river Brahmaputra is a large alluvial river that is prone to frequent bank erosion and channel pattern changes, leading to significant shifts in its course. This study aimed to analyze these changes along a 56-kilometer stretch of the river using a combined approach of remote sensing and GIS techniques. This study utilized USGS and Landsat 8 satellite imagery to map the river's channel configuration from 1985 to 2022, providing valuable insights into the river's morphology and the stability of its banks. Additionally, the analysis provided information on changes in the river's main channel which can help in predicting future behavior and mitigating the impact of these changes. The findings of this study have significant implications for river management, allowing for informed decision-making and improved strategies for protecting communities and infrastructure located along the river's course.","PeriodicalId":50576,"journal":{"name":"Disaster Advances","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135766198","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}
Sanjeet Kumar, Madhusudhan M. Reddy, Meena Isukapatla, Mara Suneel Kumar Reddy
Floods are occurrences of natural hazards, frequent during the year in many rivers across the globe. Every year, several rivers in India are vulnerable to flooding, causing loss of property and life. Krishna is one of the major rivers in India which is vulnerable to flooding in every monsoon. In this study, flood analysis was conducted for the year 2019. Rainfall data from AWS was used to estimate discharge levels in dams during the monsoon of 2019. It was observed that moderate rainfall occurred in the months of August and September, corresponding to low rainfall and that extreme flooding occurred in the same month. Compared to the flood inundation map of the satellite, it shows a close relationship with the flood map of 2019 and the affected area. The study shows that proper analysis of rainfall will be helpful in predicting downstream floods. To evaluate the flood control situation with appropriate data management in the Krishna basin, the usage of flood water is strong. Such types of studies would help to provide reliable and prompt flood forecasts and advance warning to redirect the main river flow to small canals, which will help to mitigate, excavate and remediate flooding in any area.
{"title":"Flood frequency and flood forecasting analysis of Krishna basin Andhra Pradesh","authors":"Sanjeet Kumar, Madhusudhan M. Reddy, Meena Isukapatla, Mara Suneel Kumar Reddy","doi":"10.25303/1611da027039","DOIUrl":"https://doi.org/10.25303/1611da027039","url":null,"abstract":"Floods are occurrences of natural hazards, frequent during the year in many rivers across the globe. Every year, several rivers in India are vulnerable to flooding, causing loss of property and life. Krishna is one of the major rivers in India which is vulnerable to flooding in every monsoon. In this study, flood analysis was conducted for the year 2019. Rainfall data from AWS was used to estimate discharge levels in dams during the monsoon of 2019. It was observed that moderate rainfall occurred in the months of August and September, corresponding to low rainfall and that extreme flooding occurred in the same month. Compared to the flood inundation map of the satellite, it shows a close relationship with the flood map of 2019 and the affected area. The study shows that proper analysis of rainfall will be helpful in predicting downstream floods. To evaluate the flood control situation with appropriate data management in the Krishna basin, the usage of flood water is strong. Such types of studies would help to provide reliable and prompt flood forecasts and advance warning to redirect the main river flow to small canals, which will help to mitigate, excavate and remediate flooding in any area.","PeriodicalId":50576,"journal":{"name":"Disaster Advances","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135766204","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}
Khatif Tawaf Mohamed Yusof Mohamed, A Rashid Ahmad Safuan, Mohd Apandi Nazirah, Abdul Khanan Mohd Faisal Bin, Abdul Rahman Muhammad Zulkarnain Bin
Landslide is a part of natural natural disasters that causes fatalities to humans, destroys property and overwhelms the regional economy. Various landslide evaluation attempts have been utilized to determine the landslide susceptibility values. Machine learning (ML) has been used in numerous research areas including geotechnical disciplines to produce an effective model to resolve the geotechnical challenge. The ML model has been adopted to produce a landslide susceptibility map (LSM) in many studies with various types and algorithms. This review paper discusses the ML approach used to develop LSM with specific approaches: Support Vector Machine (SVM). The basic principle of ML in producing the LSM is determined and discussed. The study also provides information on the types of validation and performance of the model in developing LSM. SVM and its hybrid model were found to yield good performance in producing LSM in most of the studies with SVM outperforming most of the other ML approaches. This research contributes to the landslide mapping field by providing a readily available, State-of-the-Art reference for researchers, practitioners and local authorities in producing efficient and reliable LSM based on the SVM principle.
{"title":"A review of the application of support vector machines in landslide susceptibility mapping","authors":"Khatif Tawaf Mohamed Yusof Mohamed, A Rashid Ahmad Safuan, Mohd Apandi Nazirah, Abdul Khanan Mohd Faisal Bin, Abdul Rahman Muhammad Zulkarnain Bin","doi":"10.25303/1611da071083","DOIUrl":"https://doi.org/10.25303/1611da071083","url":null,"abstract":"Landslide is a part of natural natural disasters that causes fatalities to humans, destroys property and overwhelms the regional economy. Various landslide evaluation attempts have been utilized to determine the landslide susceptibility values. Machine learning (ML) has been used in numerous research areas including geotechnical disciplines to produce an effective model to resolve the geotechnical challenge. The ML model has been adopted to produce a landslide susceptibility map (LSM) in many studies with various types and algorithms. This review paper discusses the ML approach used to develop LSM with specific approaches: Support Vector Machine (SVM). The basic principle of ML in producing the LSM is determined and discussed. The study also provides information on the types of validation and performance of the model in developing LSM. SVM and its hybrid model were found to yield good performance in producing LSM in most of the studies with SVM outperforming most of the other ML approaches. This research contributes to the landslide mapping field by providing a readily available, State-of-the-Art reference for researchers, practitioners and local authorities in producing efficient and reliable LSM based on the SVM principle.","PeriodicalId":50576,"journal":{"name":"Disaster Advances","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135766201","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}
Bayu Rizky Prayoga M., Budi Harsoyo, Jon Arifian, Chandra Fadlilah
Weather Modification Technology (WMT) is one of reliable solution that is often used in forest fire mitigation activity in Indonesia. Through the process of physically engineering clouds into rain and rewetting peatlands, it is hoped to help suppress hotspots and prevent forest fires from spreading. In this study, an analysis of forest fire mitigation activities in the area of Sumatra Island, Indonesia, shows that WMT can increase rainfall by up to 30% during its implementation period. WMT activity is also able to assist in suppressing the escalation of hotspots in the targeted areas. By increasing rainfall, WMT also plays a role in maintaining the wetness of peatlands, thus minimizing the potential for fire expansion. This study also explains that the role of the Indonesian Government in implementing WMT for forest fire mitigation continues to experience development.
{"title":"Measuring the Results of Weather Modification Technology for Forest Fire Mitigation in Indonesia","authors":"Bayu Rizky Prayoga M., Budi Harsoyo, Jon Arifian, Chandra Fadlilah","doi":"10.25303/1611da019026","DOIUrl":"https://doi.org/10.25303/1611da019026","url":null,"abstract":"Weather Modification Technology (WMT) is one of reliable solution that is often used in forest fire mitigation activity in Indonesia. Through the process of physically engineering clouds into rain and rewetting peatlands, it is hoped to help suppress hotspots and prevent forest fires from spreading. In this study, an analysis of forest fire mitigation activities in the area of Sumatra Island, Indonesia, shows that WMT can increase rainfall by up to 30% during its implementation period. WMT activity is also able to assist in suppressing the escalation of hotspots in the targeted areas. By increasing rainfall, WMT also plays a role in maintaining the wetness of peatlands, thus minimizing the potential for fire expansion. This study also explains that the role of the Indonesian Government in implementing WMT for forest fire mitigation continues to experience development.","PeriodicalId":50576,"journal":{"name":"Disaster Advances","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135766324","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}
Siddhardha R., Madhusudhan Reddy M., Kiran Rathod, Kalyan Kumar Gonavaram
An updated homogenised earthquake catalogue comprising 753 events from 1800 AD to 2021 AD for Ongole city andhra Pradesh, was compiled for a circular buffer zone of radius 500km with collector office (Lat 15.49880 N and Long 80.04970 E) as the centre. Dependent events were declustered using the Uhrhammer algorithm and the degree of completeness was computed using CUVI and Stepp’s methods. The analysis shows that around 11% of events were foreshocks and aftershocks. The degree of completeness for the magnitude range of 3.0 ≤ Mw < 3.5, 3.5 ≤ Mw < 4.0, 4.0 ≤ Mw < 4.5, 4.5 ≤ Mw < 5.0, 5.0 ≤ Mw < 5.5 and Mw ≥ 5.5 was found to be 23, 56, 56, 63, 105 and 178 years based on CUVI method and 30, 30, 60, 60, 110 and 160 years as per Stepp’s method respectively. Values of the seismicity parameters a and b based on Gutenberg-Ritcher’s recurrence relationship for the study area were 4.02 and 0.83 respectively. Based on the current study, the maximum earthquake magnitude (Mmax) computed for the Ongole city is 7±0.27 on the moment magnitude scale and cumulative seismic energy release is 2.73x1016J. Finally, a seismotectonic map has been developed in ArcGIS 10.5.1 software comprising past seismicity, regional geology and faults details.
{"title":"Seismotectonic mapping and energy release analysis for Ongole city of Andhra Pradesh, India","authors":"Siddhardha R., Madhusudhan Reddy M., Kiran Rathod, Kalyan Kumar Gonavaram","doi":"10.25303/1611da040052","DOIUrl":"https://doi.org/10.25303/1611da040052","url":null,"abstract":"An updated homogenised earthquake catalogue comprising 753 events from 1800 AD to 2021 AD for Ongole city andhra Pradesh, was compiled for a circular buffer zone of radius 500km with collector office (Lat 15.49880 N and Long 80.04970 E) as the centre. Dependent events were declustered using the Uhrhammer algorithm and the degree of completeness was computed using CUVI and Stepp’s methods. The analysis shows that around 11% of events were foreshocks and aftershocks. The degree of completeness for the magnitude range of 3.0 ≤ Mw < 3.5, 3.5 ≤ Mw < 4.0, 4.0 ≤ Mw < 4.5, 4.5 ≤ Mw < 5.0, 5.0 ≤ Mw < 5.5 and Mw ≥ 5.5 was found to be 23, 56, 56, 63, 105 and 178 years based on CUVI method and 30, 30, 60, 60, 110 and 160 years as per Stepp’s method respectively. Values of the seismicity parameters a and b based on Gutenberg-Ritcher’s recurrence relationship for the study area were 4.02 and 0.83 respectively. Based on the current study, the maximum earthquake magnitude (Mmax) computed for the Ongole city is 7±0.27 on the moment magnitude scale and cumulative seismic energy release is 2.73x1016J. Finally, a seismotectonic map has been developed in ArcGIS 10.5.1 software comprising past seismicity, regional geology and faults details.","PeriodicalId":50576,"journal":{"name":"Disaster Advances","volume":"234 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135766200","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}
Structure from motion (SfM) is a photogrammetric method used to reconstruct 3-Dimensional (3D) surfaces from the displacement of the camera in between several 2-dimensional photographs. This method can produce sparse and dense point clouds from which high-resolution surface models such as digital surface models and digital terrain models are obtained. SfM is not restricted to topographic surfaces, it also can be used to model various ground objects at different scales such as trees, buildings, outcrops and any other structure that can be photographed with an ordinary camera. Among this myriad of possibilities, the present contribution focuses on the reconstruction of 3D outcrop models, as they can become digital archives of geological and seismic activity, especially in countries where landscape remodeling activity and mining are important. Outcrop is a vertical exposure of particular rock formation. By knowing the characteristics of the outcrop, we can understand the reservoir characteristics and important stratigraphic information. Also, we can take more attention on microstructural information for both slope instability and seismic characteristics analysis. Thus, although SfM is relatively new in geoscience, this study will provide the usage of the SfM technique to support the outcrop analysis to obtain important micro-structure information of the outcrop. Further, this data was used to support the seismic susceptibility assessment in a very complex geological structure area.
{"title":"Unchartered fault through outcrop study by using simple structure from motion technique in seismicaly active area","authors":"Aditya Saputra, Kuswaji Dwi Priyono, Yuli Priyana, Iqbal Taufiqurrahman Sunariya M., Taryono .","doi":"10.25303/1611da0109","DOIUrl":"https://doi.org/10.25303/1611da0109","url":null,"abstract":"Structure from motion (SfM) is a photogrammetric method used to reconstruct 3-Dimensional (3D) surfaces from the displacement of the camera in between several 2-dimensional photographs. This method can produce sparse and dense point clouds from which high-resolution surface models such as digital surface models and digital terrain models are obtained. SfM is not restricted to topographic surfaces, it also can be used to model various ground objects at different scales such as trees, buildings, outcrops and any other structure that can be photographed with an ordinary camera. Among this myriad of possibilities, the present contribution focuses on the reconstruction of 3D outcrop models, as they can become digital archives of geological and seismic activity, especially in countries where landscape remodeling activity and mining are important. Outcrop is a vertical exposure of particular rock formation. By knowing the characteristics of the outcrop, we can understand the reservoir characteristics and important stratigraphic information. Also, we can take more attention on microstructural information for both slope instability and seismic characteristics analysis. Thus, although SfM is relatively new in geoscience, this study will provide the usage of the SfM technique to support the outcrop analysis to obtain important micro-structure information of the outcrop. Further, this data was used to support the seismic susceptibility assessment in a very complex geological structure area.","PeriodicalId":50576,"journal":{"name":"Disaster Advances","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135766328","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}
Sanjeet Kumar, Madhusudhan M. Reddy, Meena Isukapatla, Kumar A. Vijay
Drought is a natural threat that exists in all climatic zones around the globe. There is a need to categorize drought events and the probability of occurrence for better planning and management of relief and rehabilitation. In this study, drought monitoring indices namely the Standard Precipitation Index (SPI) and Vegetation Condition Index (VCI) were used to analyse the observed variability of monsoon droughts over Andhra Pradesh State. Precipitation data between 1991-2019 was used to evaluate the SPI and to evaluate the VCI from NDVI data collected from 2011 to 2019 using multi-temporal Terra MODIS Vegetation Indices Product (MOD13Q1). In this analysis, more often drought events occurred in 3 and 6 months SPI during monsoon season. In this study, data mining techniques (such as the Association Rules) are used to explain the association between VCI and SPI to predict the probability of occurrence of drought. The association rules formed by the VCI and the 3-month SPI with 77 percentage of confidence and 1.11 of lift indicate the higher accuracy of the rules and the effect on vegetation ford rainfall accumulation. This research incorporated the various software and dataset levels used to predict the probable occurrence and severity of drought using the current situation. The analysis revealed the advantages of NDVI and rainfall for indices of spatial and multitemporal drought to identify and forecast the characteristics of drought.
{"title":"Monitoring and Assessment of Drought using Remote Sensing and association rules","authors":"Sanjeet Kumar, Madhusudhan M. Reddy, Meena Isukapatla, Kumar A. Vijay","doi":"10.25303/1610da030040","DOIUrl":"https://doi.org/10.25303/1610da030040","url":null,"abstract":"Drought is a natural threat that exists in all climatic zones around the globe. There is a need to categorize drought events and the probability of occurrence for better planning and management of relief and rehabilitation. In this study, drought monitoring indices namely the Standard Precipitation Index (SPI) and Vegetation Condition Index (VCI) were used to analyse the observed variability of monsoon droughts over Andhra Pradesh State. Precipitation data between 1991-2019 was used to evaluate the SPI and to evaluate the VCI from NDVI data collected from 2011 to 2019 using multi-temporal Terra MODIS Vegetation Indices Product (MOD13Q1). In this analysis, more often drought events occurred in 3 and 6 months SPI during monsoon season. In this study, data mining techniques (such as the Association Rules) are used to explain the association between VCI and SPI to predict the probability of occurrence of drought. The association rules formed by the VCI and the 3-month SPI with 77 percentage of confidence and 1.11 of lift indicate the higher accuracy of the rules and the effect on vegetation ford rainfall accumulation. This research incorporated the various software and dataset levels used to predict the probable occurrence and severity of drought using the current situation. The analysis revealed the advantages of NDVI and rainfall for indices of spatial and multitemporal drought to identify and forecast the characteristics of drought.","PeriodicalId":50576,"journal":{"name":"Disaster Advances","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135486175","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：