Pub Date : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452773
E. Irwansyah, Y. Heryadi, Alexander Agung Santoso Gunawan
Detecting building location distribution in an urban area has been a concern of city government in many developing countries as a basis for city planning and development. In recent years, deep learning has gained research attention as the most attractive approach to address classification in the remote sensing field. One application of deep learning is a semantic image segmentation method whose aim is to classify each pixel in the image into a predetermined set of labels. In this experiment, the objective of semantic image segmentation is building detection in urban areas using a deep learning model in which each image pixel is categorized into either building or non-building label. Based on experimentation using aerial photograph imagery of Pasar Minggu Sub-District, South Jakarta City District, DKI. Jakarta Province and UNet model achieved 0.83 average training accuracy and 0,87 testing accuracy
{"title":"Semantic Image Segmentation for Building Detection in Urban Area with Aerial Photograph Image using U-Net Models","authors":"E. Irwansyah, Y. Heryadi, Alexander Agung Santoso Gunawan","doi":"10.1109/AGERS51788.2020.9452773","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452773","url":null,"abstract":"Detecting building location distribution in an urban area has been a concern of city government in many developing countries as a basis for city planning and development. In recent years, deep learning has gained research attention as the most attractive approach to address classification in the remote sensing field. One application of deep learning is a semantic image segmentation method whose aim is to classify each pixel in the image into a predetermined set of labels. In this experiment, the objective of semantic image segmentation is building detection in urban areas using a deep learning model in which each image pixel is categorized into either building or non-building label. Based on experimentation using aerial photograph imagery of Pasar Minggu Sub-District, South Jakarta City District, DKI. Jakarta Province and UNet model achieved 0.83 average training accuracy and 0,87 testing accuracy","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125229839","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 : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452764
B. M. Sukojo, Debyana Nur Savitri
Mangrove forests have important values and roles both ecologically and economically in coastal areas. Over time, the area of mangrove forests in Indonesia decreased due to high rates of deforestation. One of the mangrove forests that has decreased in the area is the mangrove forest on the North Coast of East Java. Therefore, monitoring of changes in the density of the mangrove canopy is needed, especially in Gresik Regency. Mangrove ecosystems can be identified using remote sensing technology. To get the density level of the mangrove canopy, it can be done by calculating the Normalized Difference Vegetation Index (NDVI) algorithm on the Sentinel 2A Level 1C satellite imagery in 2016–2019. Based on the results of the study, it was concluded that the dominant mangroves in Gresik Regency in 2016 to 2019 were mangroves with good density, which amounted to 58.66% in 2016; 53.21% in 2017; 45.22% in 2018, and 46.81% in 2019. Then, the mangrove area in Gresik Regency from 2016 to 2019 increased by 22.09% in 2017, then decreased by 1.74% in 2018 and continued to decrease by 8.28% in 2019. The most influential parameter for the density of mangrove canopy is the water pH with a correlation coefficient of -0.733 which shows that the density of mangroves canopy and the water pH has a strong and significant correlation.
{"title":"Analysis of Changes in Density of Mangrove Using Normalized Difference Vegetation Index Algorithm on Sentinel 2A Level 1C (Case Study: Gresik Regency)","authors":"B. M. Sukojo, Debyana Nur Savitri","doi":"10.1109/AGERS51788.2020.9452764","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452764","url":null,"abstract":"Mangrove forests have important values and roles both ecologically and economically in coastal areas. Over time, the area of mangrove forests in Indonesia decreased due to high rates of deforestation. One of the mangrove forests that has decreased in the area is the mangrove forest on the North Coast of East Java. Therefore, monitoring of changes in the density of the mangrove canopy is needed, especially in Gresik Regency. Mangrove ecosystems can be identified using remote sensing technology. To get the density level of the mangrove canopy, it can be done by calculating the Normalized Difference Vegetation Index (NDVI) algorithm on the Sentinel 2A Level 1C satellite imagery in 2016–2019. Based on the results of the study, it was concluded that the dominant mangroves in Gresik Regency in 2016 to 2019 were mangroves with good density, which amounted to 58.66% in 2016; 53.21% in 2017; 45.22% in 2018, and 46.81% in 2019. Then, the mangrove area in Gresik Regency from 2016 to 2019 increased by 22.09% in 2017, then decreased by 1.74% in 2018 and continued to decrease by 8.28% in 2019. The most influential parameter for the density of mangrove canopy is the water pH with a correlation coefficient of -0.733 which shows that the density of mangroves canopy and the water pH has a strong and significant correlation.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123732219","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 : 2020-12-07DOI: 10.1109/agers51788.2020.9452787
{"title":"Understanding the Interaction of Land, Ocean and Atmosphere: Disaster Mitigation and Regional Resillience [Front cover]","authors":"","doi":"10.1109/agers51788.2020.9452787","DOIUrl":"https://doi.org/10.1109/agers51788.2020.9452787","url":null,"abstract":"","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121525977","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 : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452759
F. Marpaung, D. B. Sencaki, S. Arfah, A. Agustan, O. B. Bintoro, Nugraha Ramadhana
Numerous studies about the transmission of rabies have been reported to not restrict by administrative boundaries and the history of occurrence of the diseases. These conditions were influenced by surrounding environments, economic conditions, and human and animal habits. The environmental factors such as land use, water bodies, forests, and land slopes are considered to be the defining factor for migrating rabid animals to move from place to place. The study of the spread of rabies cases based on environmental elements that can predict the direction of the spread of rabies cases overtime is needed. Yet, information on how environmental conditions affect the dispersal pattern of human rabies or rabid remains unclear. Hence, we analyzed it and they considered it to be the input of the rabies alert system. Environmental factors on human rabies and rabid dogs are explored to define the spatial rating distribution of rabies in North Sulawesi Province, Indonesia. The purpose of this work is to obtain up a spatial model design to help predict rabies spread patterns based on land closure conditions. The result shows that a combination using the land cover, slope/ elevations and location of the cases significantly shows the dispersal pattern of rabid animals. It is dominantly happening in urban areas with a low slope condition and represents about 80% of the total human rabies cases. Still, this study was limited to the movement of rabid animals due to a lack of rabid-animals and animal populations. Thus, future analysis of epidemiology rabies predictions is needed.
{"title":"Environmental Influence on a Rabies Spread Modelling in North Sulawesi, Indonesia","authors":"F. Marpaung, D. B. Sencaki, S. Arfah, A. Agustan, O. B. Bintoro, Nugraha Ramadhana","doi":"10.1109/AGERS51788.2020.9452759","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452759","url":null,"abstract":"Numerous studies about the transmission of rabies have been reported to not restrict by administrative boundaries and the history of occurrence of the diseases. These conditions were influenced by surrounding environments, economic conditions, and human and animal habits. The environmental factors such as land use, water bodies, forests, and land slopes are considered to be the defining factor for migrating rabid animals to move from place to place. The study of the spread of rabies cases based on environmental elements that can predict the direction of the spread of rabies cases overtime is needed. Yet, information on how environmental conditions affect the dispersal pattern of human rabies or rabid remains unclear. Hence, we analyzed it and they considered it to be the input of the rabies alert system. Environmental factors on human rabies and rabid dogs are explored to define the spatial rating distribution of rabies in North Sulawesi Province, Indonesia. The purpose of this work is to obtain up a spatial model design to help predict rabies spread patterns based on land closure conditions. The result shows that a combination using the land cover, slope/ elevations and location of the cases significantly shows the dispersal pattern of rabid animals. It is dominantly happening in urban areas with a low slope condition and represents about 80% of the total human rabies cases. Still, this study was limited to the movement of rabid animals due to a lack of rabid-animals and animal populations. Thus, future analysis of epidemiology rabies predictions is needed.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"101 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126019639","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 : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452779
H. A. Rachman, J. Lumban-Gaol, F. Syamsudin
The South Coast of Java is an area with an intensity of coastal upwelling caused by the Monsoon Winds (Trade Winds). Coastal Upwelling phenomenon will affect oceanographic conditions, especially in areas near Coastal. Also, this region is affected by several regional climate anomalies such as EI Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). This study will analyze how the strength of these two phenomena on the intensity of coastal upwelling. Coastal Upwelling is calculated based on Cross Shore Ekman Transport, Upwelling Index (UI), and Coastal-Offshore SST gradient in South Coast of Java. Based on Partial Correlation Analysis is showing the impact of each of these phenomena on coastal upwelling and oceanographic parameters in both the adjacent coastal and offshore regions. The result show of the analysis using partial correlation shows that the effect of IOD is more significant than ENSO on the intensity of Upwelling and Variability of Oceanographic Parameters in South Coast of Java. The anomaly of UIsst, UIwind, and Chlorophyll-a show that during IOD years is a higher impact on coastal upwelling than during ENSO event
{"title":"Remote Sensing of Coastal Upwelling Dynamics in the Eastern Indian Ocean off Java, Role of ENSO and IOD","authors":"H. A. Rachman, J. Lumban-Gaol, F. Syamsudin","doi":"10.1109/AGERS51788.2020.9452779","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452779","url":null,"abstract":"The South Coast of Java is an area with an intensity of coastal upwelling caused by the Monsoon Winds (Trade Winds). Coastal Upwelling phenomenon will affect oceanographic conditions, especially in areas near Coastal. Also, this region is affected by several regional climate anomalies such as EI Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). This study will analyze how the strength of these two phenomena on the intensity of coastal upwelling. Coastal Upwelling is calculated based on Cross Shore Ekman Transport, Upwelling Index (UI), and Coastal-Offshore SST gradient in South Coast of Java. Based on Partial Correlation Analysis is showing the impact of each of these phenomena on coastal upwelling and oceanographic parameters in both the adjacent coastal and offshore regions. The result show of the analysis using partial correlation shows that the effect of IOD is more significant than ENSO on the intensity of Upwelling and Variability of Oceanographic Parameters in South Coast of Java. The anomaly of UIsst, UIwind, and Chlorophyll-a show that during IOD years is a higher impact on coastal upwelling than during ENSO event","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116969247","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 : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452765
Budi Heru Santosa, R. Koestoer
Urban public green spaces (PGS), with their ecological, health support, and social functions needed by the community, would grow in terms of absolute number and spatial distributed populations. However, PGS's volume and spatial areas did not expand linearly to population growth; even in some cases, there has even been a decrease in volume and area for PGS. This paper examines PGS's planning process and management, which faces problems due to the population growth that requires more settlement areas and other socioeconomic facilities. The methodology applied was a comparative study in planning and managing PGS for two cases, Munich and the Yogyakarta municipal areas, which regionally have similar characteristics. The result shows that both regions tend to have proper governance for PGS. Also, both regions tend to have similar urban spatial structures associated with distributed growth centers of the polycentric system and address similar problems related to population growth. Despite the facts, both have differences in community perception of livable city function, especially for community cultural and social-relational aspects. In conclusion, this paper has highlighted that PGS's comprehensive planning is indispensable to achieve ecological, health support, and social functions to attain a livable city; therefore, a dynamic spatial model that considers variables: housing demand, urban spatial structure, urban growth form, and also community participation, could be a useful detecting tool to measure the level of development.
{"title":"Public Green Space Planning and Management towards Livable City","authors":"Budi Heru Santosa, R. Koestoer","doi":"10.1109/AGERS51788.2020.9452765","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452765","url":null,"abstract":"Urban public green spaces (PGS), with their ecological, health support, and social functions needed by the community, would grow in terms of absolute number and spatial distributed populations. However, PGS's volume and spatial areas did not expand linearly to population growth; even in some cases, there has even been a decrease in volume and area for PGS. This paper examines PGS's planning process and management, which faces problems due to the population growth that requires more settlement areas and other socioeconomic facilities. The methodology applied was a comparative study in planning and managing PGS for two cases, Munich and the Yogyakarta municipal areas, which regionally have similar characteristics. The result shows that both regions tend to have proper governance for PGS. Also, both regions tend to have similar urban spatial structures associated with distributed growth centers of the polycentric system and address similar problems related to population growth. Despite the facts, both have differences in community perception of livable city function, especially for community cultural and social-relational aspects. In conclusion, this paper has highlighted that PGS's comprehensive planning is indispensable to achieve ecological, health support, and social functions to attain a livable city; therefore, a dynamic spatial model that considers variables: housing demand, urban spatial structure, urban growth form, and also community participation, could be a useful detecting tool to measure the level of development.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125965854","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 : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452767
A. G. Suhadha, A. Julzarika
The eastern coast of Lampung Province is located between the sloping subduction transitional zone of the Eurasian Plate and the Indo-Australian Plate along the Sumatra Island and steep subduction along the Java Island. The historical intensity of earthquake events in the Sunda Strait indicates that this region has experienced the earthquake and tsunami effects from the subduction zone. On December 22, 2018, the latest tsunami occurred and was caused by Anak Krakatau's eruption activity. Remote sensing technology has the potential capability to model tsunami inundation and loss estimation when collaborating with geographic information system technology. This research is conducted to model tsunami inundation and estimate the loss caused by the tsunami on the east coast of Lampung Province. Several inundation scenarios were used; there are 1–5 meters, 5–10 meters, and 20 meters wave highs. Based on the model, the 1–5 meters inundation did not affect a wide area. Bandar Lampung, Tanggamus, and Lampung Selatan are located in potential tsunami hazard from the inundation modeling. The most affected areas by tsunami inundation are productive areas include dryland farming and paddy field. The modeling proves that the extent of tsunami inundated areas is directly proportional to the tsunami's wave height.
{"title":"Integration of Remote Sensing and Geographic Information System for Mapping Potential Tsunami Inundation","authors":"A. G. Suhadha, A. Julzarika","doi":"10.1109/AGERS51788.2020.9452767","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452767","url":null,"abstract":"The eastern coast of Lampung Province is located between the sloping subduction transitional zone of the Eurasian Plate and the Indo-Australian Plate along the Sumatra Island and steep subduction along the Java Island. The historical intensity of earthquake events in the Sunda Strait indicates that this region has experienced the earthquake and tsunami effects from the subduction zone. On December 22, 2018, the latest tsunami occurred and was caused by Anak Krakatau's eruption activity. Remote sensing technology has the potential capability to model tsunami inundation and loss estimation when collaborating with geographic information system technology. This research is conducted to model tsunami inundation and estimate the loss caused by the tsunami on the east coast of Lampung Province. Several inundation scenarios were used; there are 1–5 meters, 5–10 meters, and 20 meters wave highs. Based on the model, the 1–5 meters inundation did not affect a wide area. Bandar Lampung, Tanggamus, and Lampung Selatan are located in potential tsunami hazard from the inundation modeling. The most affected areas by tsunami inundation are productive areas include dryland farming and paddy field. The modeling proves that the extent of tsunami inundated areas is directly proportional to the tsunami's wave height.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131195222","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 : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452769
Ghifari Raihan Silam Siregar, Husein Alfarizi, Florence Mila Purnomo, Satria Ginanjar, A. Wirasatriya
Wave information is crucial for maritime activities such as marine transportation, offshore exploration, fisheries, safety management system on ships, coastal development, and coastal disaster mitigation. The Sverdrup, Munk, Bretschneider. SMB is one of the most common methods used for wave forecasting. In the present study, we examine the validation of Easywave, an algorithm that automates wave forecasting with the SMB method. The results are validated using observational data and analyzed using Mean Relative Error (MRE), Root Mean Square Error (RMSE), and a bias value. The level of accuracy of forecasting using the Easywave algorithm is 89.87% for Hs and 78.43% for Ts. The level of precision obtained by on-field observation data is $pm mathbf{0.14 m}$ for Hs and $pmmathbf{0.26 s}$ for Ts.
{"title":"Validation of Wave Forecasting with the Sverdrup, Munk, and Bretschneider (SMB) Method Using Easywave Algorithm","authors":"Ghifari Raihan Silam Siregar, Husein Alfarizi, Florence Mila Purnomo, Satria Ginanjar, A. Wirasatriya","doi":"10.1109/AGERS51788.2020.9452769","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452769","url":null,"abstract":"Wave information is crucial for maritime activities such as marine transportation, offshore exploration, fisheries, safety management system on ships, coastal development, and coastal disaster mitigation. The Sverdrup, Munk, Bretschneider. SMB is one of the most common methods used for wave forecasting. In the present study, we examine the validation of Easywave, an algorithm that automates wave forecasting with the SMB method. The results are validated using observational data and analyzed using Mean Relative Error (MRE), Root Mean Square Error (RMSE), and a bias value. The level of accuracy of forecasting using the Easywave algorithm is 89.87% for Hs and 78.43% for Ts. The level of precision obtained by on-field observation data is $pm mathbf{0.14 m}$ for Hs and $pmmathbf{0.26 s}$ for Ts.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"51 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120836321","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 : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452757
Tri Yoso Astanto, R. Pramono
Indonesia, which is in a tropical monsoon climate that is very sensitive to the E1 Nino Southern Oscillation (ENSO) climate anomaly, causes $pm mathbf{48}$ million hectares or 25% of Indonesia's land area to be affected by the danger of drought. Regional capacity becomes the measure of a region in efforts to reduce losses due to disasters. This study aimed to measure the effect of regional capacity and drought hazard variables on reducing environmental losses due to drought. The analytical method used in this research is multiple linear regression analysis. The results of this study indicate that the r square value of the results of multiple linear regression analysis is 0.533, so that the independent variable affects the dependent variable simultaneously by 53%, while the variables that have a significant effect on environmental losses due to drought include regional capacity $(mathbf{t}=-mathbf{1,981})$, the area of drought hazard $(mathbf{t}= mathbf{2.861})$ and the area of scarcity of regional aquifers $(mathbf{t}=mathbf{6.193})$, the three independent variables have a value of t more than t table so that they have a partial significant effect on environmental losses due to drought.
{"title":"The Effect Of Regional Capacity On Environmental Losses Due To Drought Disaster In Regencies / Cities In Indonesia","authors":"Tri Yoso Astanto, R. Pramono","doi":"10.1109/AGERS51788.2020.9452757","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452757","url":null,"abstract":"Indonesia, which is in a tropical monsoon climate that is very sensitive to the E1 Nino Southern Oscillation (ENSO) climate anomaly, causes $pm mathbf{48}$ million hectares or 25% of Indonesia's land area to be affected by the danger of drought. Regional capacity becomes the measure of a region in efforts to reduce losses due to disasters. This study aimed to measure the effect of regional capacity and drought hazard variables on reducing environmental losses due to drought. The analytical method used in this research is multiple linear regression analysis. The results of this study indicate that the r square value of the results of multiple linear regression analysis is 0.533, so that the independent variable affects the dependent variable simultaneously by 53%, while the variables that have a significant effect on environmental losses due to drought include regional capacity $(mathbf{t}=-mathbf{1,981})$, the area of drought hazard $(mathbf{t}= mathbf{2.861})$ and the area of scarcity of regional aquifers $(mathbf{t}=mathbf{6.193})$, the three independent variables have a value of t more than t table so that they have a partial significant effect on environmental losses due to drought.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"395 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126751052","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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