Pub Date : 2024-03-18DOI: 10.15625/2615-9783/20366
Trang Thanh Pham, Thuan Chu, Bao Quang Tran
Forest fires present a significant threat to the tropical forest ecosystem in the northwestern region of Vietnam. Our study aimed to assess the impacts of environmental factors on forest fire occurrence and to map forest fire probability for the whole region. The forest fire occurrence data over the period 2003 – 2016, environmental factors (climate, fuel condition, topography, and human activity), and the MaxEnt approach were used for this study. The MaxEnt model performed better than the random model (AUC>0.88). Climatic factors (especially climatic seasonality: annual temperature range (bio_07), isothermality (bio_03), and precipitation of warmest quarter (bio_18)) had the highest contribution to the model, followed by population density and elevation. In contrast, fuel condition (Land cover type) had a small contribution to the model. While medium, high, and very high probabilities of forest fire occurred at medium to high elevations (e.g., Dien Bien, Son La, and Lai Chau provinces) throughout southern to northern and western areas, very low and low probability concentrated southeastern areas at lower elevations (mainly in Hoa Bình province). Our results may be helpful references for fire managers and policymakers to establish more effective fire management strategies for the region's forest.
{"title":"Modelling spatial patterns of forest fire occurrence in the Northwestern region of Vietnam","authors":"Trang Thanh Pham, Thuan Chu, Bao Quang Tran","doi":"10.15625/2615-9783/20366","DOIUrl":"https://doi.org/10.15625/2615-9783/20366","url":null,"abstract":"Forest fires present a significant threat to the tropical forest ecosystem in the northwestern region of Vietnam. Our study aimed to assess the impacts of environmental factors on forest fire occurrence and to map forest fire probability for the whole region. The forest fire occurrence data over the period 2003 – 2016, environmental factors (climate, fuel condition, topography, and human activity), and the MaxEnt approach were used for this study. The MaxEnt model performed better than the random model (AUC>0.88). Climatic factors (especially climatic seasonality: annual temperature range (bio_07), isothermality (bio_03), and precipitation of warmest quarter (bio_18)) had the highest contribution to the model, followed by population density and elevation. In contrast, fuel condition (Land cover type) had a small contribution to the model. While medium, high, and very high probabilities of forest fire occurred at medium to high elevations (e.g., Dien Bien, Son La, and Lai Chau provinces) throughout southern to northern and western areas, very low and low probability concentrated southeastern areas at lower elevations (mainly in Hoa Bình province). Our results may be helpful references for fire managers and policymakers to establish more effective fire management strategies for the region's forest.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140233079","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 : 2024-03-11DOI: 10.15625/2615-9783/20316
Bien Tran Xuan, Trinh Pham The, Duong Luu Thuy, Phong Tran Van, Nhat Vuong Hong, Hiep Van Le, Dam Duc Nguyen, Indra Prakash, Tam Pham Thanh, Binh Binh Thai
In this work, the main aim is to map the potential zones of groundwater in Central Highlands (Vietnam) using a novel ensemble machine learning model, namely CG-LMT, which is a combination of two advanced techniques, namely Cascade Generalization (CG) and Logistics Model Trees (LMT). For this, a total of 501 wells data and a set of twelve affecting factors were gathered and selected to generate training and testing datasets used for building and validating the model. Validation of the models was implemented utilizing various quantitative indices, including ROC curve. Results of the present study indicated that the novel ensemble model performed well for groundwater potential mapping and modeling (AUC = 0.742), and its predictive capability is even better than a single LMT model (AUC = 0.727). Thus, the CG-LMT is a promising tool for accurately predicting potential groundwater areas. In addition, the potential map of groundwater generated from the CG-LMT model is a helpful tool for better-studying water resource management in the area.
{"title":"Groundwater potential zoning using Logistics Model Trees based novel ensemble machine learning model","authors":"Bien Tran Xuan, Trinh Pham The, Duong Luu Thuy, Phong Tran Van, Nhat Vuong Hong, Hiep Van Le, Dam Duc Nguyen, Indra Prakash, Tam Pham Thanh, Binh Binh Thai","doi":"10.15625/2615-9783/20316","DOIUrl":"https://doi.org/10.15625/2615-9783/20316","url":null,"abstract":"In this work, the main aim is to map the potential zones of groundwater in Central Highlands (Vietnam) using a novel ensemble machine learning model, namely CG-LMT, which is a combination of two advanced techniques, namely Cascade Generalization (CG) and Logistics Model Trees (LMT). For this, a total of 501 wells data and a set of twelve affecting factors were gathered and selected to generate training and testing datasets used for building and validating the model. Validation of the models was implemented utilizing various quantitative indices, including ROC curve. Results of the present study indicated that the novel ensemble model performed well for groundwater potential mapping and modeling (AUC = 0.742), and its predictive capability is even better than a single LMT model (AUC = 0.727). Thus, the CG-LMT is a promising tool for accurately predicting potential groundwater areas. In addition, the potential map of groundwater generated from the CG-LMT model is a helpful tool for better-studying water resource management in the area.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140253875","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 : 2024-03-04DOI: 10.15625/2615-9783/20261
Thang Vu Van, Tuan Bui Minh, Thuc Tran Duy, Thanh Cong
Tropical cyclones (TCs) frequently occurs and result in substantial socio-economic consequences for the countries around the East Sea. In this study, the Joint Typhoon Warning Center (JTWC) best tracks and the NCEP/NCAR reanalysis data are employed to investigate the effect of jetstream on the seasonal variations of the TC tracks. The results reveal four primary directions of the tracks: northeastward, northwestward, westward and southwestward. A low-level anomalous cyclone moving from the Western North Pacific (WNP) to the Indochina Peninsula (IP) plays a significant role in guiding the movement of the TCs. This anomalous cyclone is strongly modulated by the development of a wavetrain along the subtropical jetstream. In May, the wavetrain induces strong anomalous divergence to the east of China, leading to the northeastward expansion of the low-level anomalous cyclones, thereby directing the TCs to the northeast. From June to August, the jetstream shifts to higher latitudes, reducing its impact on the TC tracks. From September to December, the jetstream moves back to the south; however, its effect on the TC tracks is opposite to that in May. During this time, the wavetrain accelerates an anomalous anticyclone in Southeast China and the Western North Pacific, which in turn pushes the anomalous cyclone to the south and promotes westward and southwestward movement of the TCs in the East Sea.
{"title":"Effects of jetstream on seasonal variations of tropical cyclone tracks in the East Sea","authors":"Thang Vu Van, Tuan Bui Minh, Thuc Tran Duy, Thanh Cong","doi":"10.15625/2615-9783/20261","DOIUrl":"https://doi.org/10.15625/2615-9783/20261","url":null,"abstract":"Tropical cyclones (TCs) frequently occurs and result in substantial socio-economic consequences for the countries around the East Sea. In this study, the Joint Typhoon Warning Center (JTWC) best tracks and the NCEP/NCAR reanalysis data are employed to investigate the effect of jetstream on the seasonal variations of the TC tracks. The results reveal four primary directions of the tracks: northeastward, northwestward, westward and southwestward. A low-level anomalous cyclone moving from the Western North Pacific (WNP) to the Indochina Peninsula (IP) plays a significant role in guiding the movement of the TCs. This anomalous cyclone is strongly modulated by the development of a wavetrain along the subtropical jetstream. In May, the wavetrain induces strong anomalous divergence to the east of China, leading to the northeastward expansion of the low-level anomalous cyclones, thereby directing the TCs to the northeast. From June to August, the jetstream shifts to higher latitudes, reducing its impact on the TC tracks. From September to December, the jetstream moves back to the south; however, its effect on the TC tracks is opposite to that in May. During this time, the wavetrain accelerates an anomalous anticyclone in Southeast China and the Western North Pacific, which in turn pushes the anomalous cyclone to the south and promotes westward and southwestward movement of the TCs in the East Sea.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140080465","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 : 2024-02-06DOI: 10.15625/2615-9783/20094
Co Nguyen Thanh, Thuy Hoang Luu Thu, Manh Dinh Van, Thuy Trinh Thi Thu
The activity of El Niño - Southern Oscillation (ENSO) has been found to alter the characteristics of tropical cyclones (TCs) worldwide. This paper examines how ENSO influences the active time, occurence frequencies, and intensities of TCs over the coastal zones of Vietnam. The study uses Best Track Data in the Western North Pacific region from 1961 to 2020. The wind speed fields of all TCs over the coastal zones are calculated using a semi-empirical model with a square grid of 0.05° resolution. The results show that the frequencies of TCs over the coastal zones decrease during El Niño years, whilethe TC intensities increase significantly compared to those in Neutral years. Meanwhile, TC intensities over all coastal zones during La Niña years, and some coastal zones during El Niño years, decrease significantly compared to those in Neutral years. The magnitudes of both the increase and decrease in TC frequencies and intensities over the coastal zones vary significantly. Moreover, the active time of TCs during La Niña years in some coastal zones is shorter and occurs later than during the Neutral years.
{"title":"Impacts of EL NIÑO-Southern oscillation on tropical cyclone activity over the coastal zones of Vietnam","authors":"Co Nguyen Thanh, Thuy Hoang Luu Thu, Manh Dinh Van, Thuy Trinh Thi Thu","doi":"10.15625/2615-9783/20094","DOIUrl":"https://doi.org/10.15625/2615-9783/20094","url":null,"abstract":"The activity of El Niño - Southern Oscillation (ENSO) has been found to alter the characteristics of tropical cyclones (TCs) worldwide. This paper examines how ENSO influences the active time, occurence frequencies, and intensities of TCs over the coastal zones of Vietnam. The study uses Best Track Data in the Western North Pacific region from 1961 to 2020. The wind speed fields of all TCs over the coastal zones are calculated using a semi-empirical model with a square grid of 0.05° resolution. The results show that the frequencies of TCs over the coastal zones decrease during El Niño years, whilethe TC intensities increase significantly compared to those in Neutral years. Meanwhile, TC intensities over all coastal zones during La Niña years, and some coastal zones during El Niño years, decrease significantly compared to those in Neutral years. The magnitudes of both the increase and decrease in TC frequencies and intensities over the coastal zones vary significantly. Moreover, the active time of TCs during La Niña years in some coastal zones is shorter and occurs later than during the Neutral years. ","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139799450","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 : 2024-02-06DOI: 10.15625/2615-9783/20094
Co Nguyen Thanh, Thuy Hoang Luu Thu, Manh Dinh Van, Thuy Trinh Thi Thu
The activity of El Niño - Southern Oscillation (ENSO) has been found to alter the characteristics of tropical cyclones (TCs) worldwide. This paper examines how ENSO influences the active time, occurence frequencies, and intensities of TCs over the coastal zones of Vietnam. The study uses Best Track Data in the Western North Pacific region from 1961 to 2020. The wind speed fields of all TCs over the coastal zones are calculated using a semi-empirical model with a square grid of 0.05° resolution. The results show that the frequencies of TCs over the coastal zones decrease during El Niño years, whilethe TC intensities increase significantly compared to those in Neutral years. Meanwhile, TC intensities over all coastal zones during La Niña years, and some coastal zones during El Niño years, decrease significantly compared to those in Neutral years. The magnitudes of both the increase and decrease in TC frequencies and intensities over the coastal zones vary significantly. Moreover, the active time of TCs during La Niña years in some coastal zones is shorter and occurs later than during the Neutral years.
{"title":"Impacts of EL NIÑO-Southern oscillation on tropical cyclone activity over the coastal zones of Vietnam","authors":"Co Nguyen Thanh, Thuy Hoang Luu Thu, Manh Dinh Van, Thuy Trinh Thi Thu","doi":"10.15625/2615-9783/20094","DOIUrl":"https://doi.org/10.15625/2615-9783/20094","url":null,"abstract":"The activity of El Niño - Southern Oscillation (ENSO) has been found to alter the characteristics of tropical cyclones (TCs) worldwide. This paper examines how ENSO influences the active time, occurence frequencies, and intensities of TCs over the coastal zones of Vietnam. The study uses Best Track Data in the Western North Pacific region from 1961 to 2020. The wind speed fields of all TCs over the coastal zones are calculated using a semi-empirical model with a square grid of 0.05° resolution. The results show that the frequencies of TCs over the coastal zones decrease during El Niño years, whilethe TC intensities increase significantly compared to those in Neutral years. Meanwhile, TC intensities over all coastal zones during La Niña years, and some coastal zones during El Niño years, decrease significantly compared to those in Neutral years. The magnitudes of both the increase and decrease in TC frequencies and intensities over the coastal zones vary significantly. Moreover, the active time of TCs during La Niña years in some coastal zones is shorter and occurs later than during the Neutral years. ","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139859094","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 : 2024-02-01DOI: 10.15625/2615-9783/20065
Viet Long Doan, Ba-Quang-Vinh Nguyen, Chi Cong Nguyen, Cuong Tien Nguyen
Rainfall is a triggering factor that causes landslides, especially in the regions where landslides often occur after consecutive days of heavy rainfall. Most previous studies only used a specific rainfall map for landslide susceptibility assessment. However, this approach was unreasonable because rainfall is a time-variant data. This study uses the time series data of 1-day, 3-day, 5-day, and 7-day maximum precipitation from 2016 to 2020 in the mountainous area of Quang Ngai province for landslide susceptibility assessment. These data and other influencing factors were used to develop landslide spatial prediction models using the Extreme Gradient Boosting method. The prediction model's performance was assessed using the statistical index and receiver operating characteristic curve methods. The testing results of 4 cases using consecutive days of maximum rainfall data demonstrated excellent performance. Of these, the model with a 3-day maximum rainfall with ACC = 0.813, kappa = 0.625, SST = 0.872, SPF = 0.754, and AUC = 0.895 had the best performance. In addition, these results were compared to the previous approach that used average annual rainfall. The validation result indicates that the cases using a time series of maximum precipitation (with AUC of approximately 0.9) outperform the cases with average annual rainfall (AUC=0.838). Finally, the model using 3-day maximum rainfall is then used for landslide spatial prediction mapping. These maps provide spatial prediction and assess landslide susceptibility corresponding to rainfall frequencies.
{"title":"Effect of time-variant rainfall on landslide susceptibility: A case study in Quang Ngai Province, Vietnam","authors":"Viet Long Doan, Ba-Quang-Vinh Nguyen, Chi Cong Nguyen, Cuong Tien Nguyen","doi":"10.15625/2615-9783/20065","DOIUrl":"https://doi.org/10.15625/2615-9783/20065","url":null,"abstract":"Rainfall is a triggering factor that causes landslides, especially in the regions where landslides often occur after consecutive days of heavy rainfall. Most previous studies only used a specific rainfall map for landslide susceptibility assessment. However, this approach was unreasonable because rainfall is a time-variant data. This study uses the time series data of 1-day, 3-day, 5-day, and 7-day maximum precipitation from 2016 to 2020 in the mountainous area of Quang Ngai province for landslide susceptibility assessment. These data and other influencing factors were used to develop landslide spatial prediction models using the Extreme Gradient Boosting method. The prediction model's performance was assessed using the statistical index and receiver operating characteristic curve methods. The testing results of 4 cases using consecutive days of maximum rainfall data demonstrated excellent performance. Of these, the model with a 3-day maximum rainfall with ACC = 0.813, kappa = 0.625, SST = 0.872, SPF = 0.754, and AUC = 0.895 had the best performance. In addition, these results were compared to the previous approach that used average annual rainfall. The validation result indicates that the cases using a time series of maximum precipitation (with AUC of approximately 0.9) outperform the cases with average annual rainfall (AUC=0.838). Finally, the model using 3-day maximum rainfall is then used for landslide spatial prediction mapping. These maps provide spatial prediction and assess landslide susceptibility corresponding to rainfall frequencies.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139830465","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 : 2024-02-01DOI: 10.15625/2615-9783/20065
Viet Long Doan, Ba-Quang-Vinh Nguyen, Chi Cong Nguyen, Cuong Tien Nguyen
Rainfall is a triggering factor that causes landslides, especially in the regions where landslides often occur after consecutive days of heavy rainfall. Most previous studies only used a specific rainfall map for landslide susceptibility assessment. However, this approach was unreasonable because rainfall is a time-variant data. This study uses the time series data of 1-day, 3-day, 5-day, and 7-day maximum precipitation from 2016 to 2020 in the mountainous area of Quang Ngai province for landslide susceptibility assessment. These data and other influencing factors were used to develop landslide spatial prediction models using the Extreme Gradient Boosting method. The prediction model's performance was assessed using the statistical index and receiver operating characteristic curve methods. The testing results of 4 cases using consecutive days of maximum rainfall data demonstrated excellent performance. Of these, the model with a 3-day maximum rainfall with ACC = 0.813, kappa = 0.625, SST = 0.872, SPF = 0.754, and AUC = 0.895 had the best performance. In addition, these results were compared to the previous approach that used average annual rainfall. The validation result indicates that the cases using a time series of maximum precipitation (with AUC of approximately 0.9) outperform the cases with average annual rainfall (AUC=0.838). Finally, the model using 3-day maximum rainfall is then used for landslide spatial prediction mapping. These maps provide spatial prediction and assess landslide susceptibility corresponding to rainfall frequencies.
{"title":"Effect of time-variant rainfall on landslide susceptibility: A case study in Quang Ngai Province, Vietnam","authors":"Viet Long Doan, Ba-Quang-Vinh Nguyen, Chi Cong Nguyen, Cuong Tien Nguyen","doi":"10.15625/2615-9783/20065","DOIUrl":"https://doi.org/10.15625/2615-9783/20065","url":null,"abstract":"Rainfall is a triggering factor that causes landslides, especially in the regions where landslides often occur after consecutive days of heavy rainfall. Most previous studies only used a specific rainfall map for landslide susceptibility assessment. However, this approach was unreasonable because rainfall is a time-variant data. This study uses the time series data of 1-day, 3-day, 5-day, and 7-day maximum precipitation from 2016 to 2020 in the mountainous area of Quang Ngai province for landslide susceptibility assessment. These data and other influencing factors were used to develop landslide spatial prediction models using the Extreme Gradient Boosting method. The prediction model's performance was assessed using the statistical index and receiver operating characteristic curve methods. The testing results of 4 cases using consecutive days of maximum rainfall data demonstrated excellent performance. Of these, the model with a 3-day maximum rainfall with ACC = 0.813, kappa = 0.625, SST = 0.872, SPF = 0.754, and AUC = 0.895 had the best performance. In addition, these results were compared to the previous approach that used average annual rainfall. The validation result indicates that the cases using a time series of maximum precipitation (with AUC of approximately 0.9) outperform the cases with average annual rainfall (AUC=0.838). Finally, the model using 3-day maximum rainfall is then used for landslide spatial prediction mapping. These maps provide spatial prediction and assess landslide susceptibility corresponding to rainfall frequencies.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139890088","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 : 2024-01-25DOI: 10.15625/2615-9783/20010
P. U. Aprina, Djoko Santoso, S. Alawiyah, Nugroho Prasetyo, Khalil Ibrahim
Halmahera Island is the result of the interaction between the Indo-Australian Plate, the Molucca Sea Plate, and the Philippine Plate, which gave rise to many active geological structures that are vulnerable to seismic activity around the island. The complexity of this geological structure makes Halmahera Island very interesting to study. This study aims to identify geological structures by integrating remote sensing and gravity satellite data. Surface lineament analysis using the remote sensing method was carried out based on Sentinel-1A imagery data. The gravity method uses GGMPlus satellite data to clarify the continuity of geological structures that cannot be clearly mapped on the surface. Lineament analysis on gravity data uses techniques such as fast sigmoid edge detection (FSED) and Euler deconvolution. The results of lineament interpretation based on integrating remote sensing and gravity satellite data show that the NE-SW structure controls the northern and northeastern arms of Halmahera. In contrast, the southern arm is dominated by the NW-SE structure. The Euler depth estimation shows that the Halmahera area contributes to having geological structures at various depths. Deep structures reach 4 km, while shallow structures are found at depths of up to 2 km. Earthquake hypocenter data strengthen the interpretation of this geological structure. This comprehensive study yields an excellent correlation between gravity and remote sensing techniques in describing the general structural framework of the area. The new finding is an NE-SW trending geological structure on the northern Halmahera arm, which may be caused by two different tectonics first, the subduction of the Molucca Sea Plate with the Halmahera Plate in the west. Second, the strike-slip movement is trending NE-SW, which cuts the northern and northeastern arms due to the rotational movement of the thrust fault with the Philippine Plate to the west.
{"title":"Delineating geological structure utilizing integration of remote sensing and gravity data: a study from Halmahera, North Molucca, Indonesia","authors":"P. U. Aprina, Djoko Santoso, S. Alawiyah, Nugroho Prasetyo, Khalil Ibrahim","doi":"10.15625/2615-9783/20010","DOIUrl":"https://doi.org/10.15625/2615-9783/20010","url":null,"abstract":"Halmahera Island is the result of the interaction between the Indo-Australian Plate, the Molucca Sea Plate, and the Philippine Plate, which gave rise to many active geological structures that are vulnerable to seismic activity around the island. The complexity of this geological structure makes Halmahera Island very interesting to study. This study aims to identify geological structures by integrating remote sensing and gravity satellite data. Surface lineament analysis using the remote sensing method was carried out based on Sentinel-1A imagery data. The gravity method uses GGMPlus satellite data to clarify the continuity of geological structures that cannot be clearly mapped on the surface. Lineament analysis on gravity data uses techniques such as fast sigmoid edge detection (FSED) and Euler deconvolution. The results of lineament interpretation based on integrating remote sensing and gravity satellite data show that the NE-SW structure controls the northern and northeastern arms of Halmahera. \u0000In contrast, the southern arm is dominated by the NW-SE structure. The Euler depth estimation shows that the Halmahera area contributes to having geological structures at various depths. Deep structures reach 4 km, while shallow structures are found at depths of up to 2 km. Earthquake hypocenter data strengthen the interpretation of this geological structure. This comprehensive study yields an excellent correlation between gravity and remote sensing techniques in describing the general structural framework of the area. The new finding is an NE-SW trending geological structure on the northern Halmahera arm, which may be caused by two different tectonics first, the subduction of the Molucca Sea Plate with the Halmahera Plate in the west. Second, the strike-slip movement is trending NE-SW, which cuts the northern and northeastern arms due to the rotational movement of the thrust fault with the Philippine Plate to the west.","PeriodicalId":23639,"journal":{"name":"VIETNAM JOURNAL OF EARTH SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139597603","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 : 2024-01-25DOI: 10.15625/2615-9783/20009
Thunyapat Sattraburut
Kanchanaburi Province in western Thailand is recognized as an exceptional natural tourist destination, offering many historical attractions and recreational activities. The Sai Yok District, located within Kanchanaburi Province, is characterized by distinctive geological and geomorphological features, hosting numerous remarkable geosites and geomorphosites, including waterfalls, caves, lapiés, and scenic karst topography. These features make it an exceptional location for geotourism. Inventory and quantitative assessments were conducted on seven Permian limestone geosites, namely Mueang Sing Historical Park, Tham Krasae, Tham Lawa, Tham Dao Wadung, Nam Tok Sai Yok Noi, Nam Tok Sai Yok Yai, and Hellfire Pass. The quantitative assessment process involved evaluating the scientific value and determining the level of deterioration of the geosites. Overall, these geosites were classified as having medium scientific value, with Mueang Sing Historical Park having the highest total score, while Tham Krasae had the lowest. Six of the seven geosites are classified as having a medium risk of deterioration, except Tham Dao Wadung, which has a low risk. The assessment of the total geosite value reveals that Mueang Sing Historical Park and Tham Dao Wadung possess a positive overall geosite value. At the same time, the other five have a negative value. It is important to note that because six of these seven geosites are classified as having a medium risk of deterioration, there is a need for increased attention and protection.
泰国西部的尖竹汶府（Kanchanaburi）是公认的天然旅游胜地，拥有众多历史景点和休闲活动。位于尖竹汶府的赛育区具有独特的地质和地貌特征，拥有众多引人注目的地质遗迹和地貌景观，包括瀑布、洞穴、青石板和风景优美的喀斯特地貌。这些特征使其成为地质旅游的绝佳地点。对七个二叠纪石灰岩地貌进行了清查和定量评估，即 Mueang Sing 历史公园、Tham Krasae、Tham Lawa、Tham Dao Wadung、Nam Tok Sai Yok Noi、Nam Tok Sai Yok Yai 和 Hellfire Pass。定量评估过程包括评估科学价值和确定地质遗迹的退化程度。总体而言，这些地质遗迹被归类为具有中等科学价值，其中 Mueang Sing 历史公园的总分最高，而 Tham Krasae 的总分最低。除 Tham Dao Wadung 的退化风险较低外，其他 7 个地质遗迹中有 6 个被列为具有中等退化风险。对地质遗迹总价值的评估显示，Mueang Sing 历史公园和 Tham Dao Wadung 的地质遗迹总价值为正值。与此同时，其他五个地质遗迹的价值为负值。值得注意的是，由于这七处地质遗迹中有六处被归类为具有中度退化风险，因此有必要加强关注和保护。
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Pub Date : 2024-01-15DOI: 10.15625/2615-9783/19926
Ahmad Zul Amal Zaini, M. Vonnisa, M. Marzuki
The El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) are widely recognized as the leading modes of climate variability in the tropics. This paper investigates the impact of different ENSO positions and IOD events on Indonesian rainfall during the period 1950–2021. The ENSO position is determined by the largest value of four Niño indices: Niño 1+2, Niño 3, Niño 3.4, and Niño 4. These ENSO positions are hereafter referred to as El-Niño/La-Niña 1+2, El-Niño/La-Niña 3, El-Niño/La-Niña 3.4, and El-Niño/La-Niña 4, respectively. The Dipole Mode Index (DMI) was used to observe IOD events. Different ENSO positions and IOD events result in different responses to Indonesian rainfall, obtained from the European Center for Medium-Range Weather Forecasts (ECMWF) ERA-5 data. The most significant decrease in rainfall occurs during the June-to-Septempber (JJAS) season of El-Niño 3. Conversely, during El-Niño 3.4, rainfall increases in the Sumatra and part of Kalimantan regions. The most significant increase in rainfall occurs during La-Niña 3.4, followed by La-Niña 4, La-Niña 3, and La-Niña 1+2. During a positive IOD phase, the southern part of western Indonesia experiences a decrease in precipitation of more than 30%. A more significant decrease in rainfall (>40%) occurs when a positive IOD co-occurs with El-Niño. During a negative IOD phase, Indonesia's rainfall patterns become more spatially variable. An increase in rainfall is more pronounced when a negative IOD co-occurs with La-Niña. The difference in Indonesian rainfall during different ENSO positions and IOD phases is related to differences in atmosphere-ocean interaction during each condition.
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