Pub Date : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093651
Laju Gandharum, Heri Sadmono, D. B. Sencaki, A. Eugenie, Hari Prayogi, I. F. Cahyaningtiyas
Hyperspectral remote sensing imaging, like HyMAP, offers extremely precise spectrum data. Therefore, by using a spectral angle mapper (SAM) technique, HyMap was ideal for differentiating tree species in remote places like tropical peat swamp forests in Indonesia. The results showed tree species of Bangka, Gercinia, and Balau were mapped more dominantly than others. At a threshold of 0.2 radians, these three species, in that order, dominated 56.69%, 29.18%, and 4.44% of the study area. The percentage of unclassified pixels was decreased by 3.72% by raising the threshold (from 0.2 to 0.3 radians).
{"title":"Application of Hyperspectral Airborne Data for Discriminating Tree Species in Tropical Peat Swamp Forest, Indonesia","authors":"Laju Gandharum, Heri Sadmono, D. B. Sencaki, A. Eugenie, Hari Prayogi, I. F. Cahyaningtiyas","doi":"10.1109/AGERS56232.2022.10093651","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093651","url":null,"abstract":"Hyperspectral remote sensing imaging, like HyMAP, offers extremely precise spectrum data. Therefore, by using a spectral angle mapper (SAM) technique, HyMap was ideal for differentiating tree species in remote places like tropical peat swamp forests in Indonesia. The results showed tree species of Bangka, Gercinia, and Balau were mapped more dominantly than others. At a threshold of 0.2 radians, these three species, in that order, dominated 56.69%, 29.18%, and 4.44% of the study area. The percentage of unclassified pixels was decreased by 3.72% by raising the threshold (from 0.2 to 0.3 radians).","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128079527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093380
S. Hidayati, Muhammad Izzuddin Al-Islami, D. A. Navastara
Remote sensing offers considerable advantages in detecting and monitoring the physical features of an area. There are remarkable studies in the literature geared towards developing robust machine learning models to automate area change detection based on remote sensing images. However, to date there lacks a detailed investigation into the impact of image enhancement techniques on machine learning models for remote sensing change detection. Remote sensing data is particularly limited to sufficient quality to support area monitoring. This study, therefore, aims to examine how significantly image contrast enhancement, with a focus on histogram matching and median filter techniques, contribute to the remote sensing classification performance. We utilize spatial-temporal attention neural network as the deep neural network-based detector model and conduct experiments on two benchmark datasets. Precision, recall, and F1-score are reported to evaluate the classification performance of the detector model with and without contrast enhancement as the preprocessing step.
{"title":"The Impact of Preprocessing by Contrast Enhancement on Spatial-temporal Attention Neural Network: An Evaluation in Remote Sensing Change Detection","authors":"S. Hidayati, Muhammad Izzuddin Al-Islami, D. A. Navastara","doi":"10.1109/AGERS56232.2022.10093380","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093380","url":null,"abstract":"Remote sensing offers considerable advantages in detecting and monitoring the physical features of an area. There are remarkable studies in the literature geared towards developing robust machine learning models to automate area change detection based on remote sensing images. However, to date there lacks a detailed investigation into the impact of image enhancement techniques on machine learning models for remote sensing change detection. Remote sensing data is particularly limited to sufficient quality to support area monitoring. This study, therefore, aims to examine how significantly image contrast enhancement, with a focus on histogram matching and median filter techniques, contribute to the remote sensing classification performance. We utilize spatial-temporal attention neural network as the deep neural network-based detector model and conduct experiments on two benchmark datasets. Precision, recall, and F1-score are reported to evaluate the classification performance of the detector model with and without contrast enhancement as the preprocessing step.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115794122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093598
I. F. Cahyaningtiyas, M. Djoharin, Tiara Grace, A. Eugenie, Evie Aviantie, R. Amaliyah, E. G. A. Sapan, F. Meliani, R. Sulistyowati, H. Priyadi, S. Lestari, D. Fernando
Flood disasters in Bekasi City almost occur every year, especially during high rainfall with a fairly long duration. Repeated flooding events due to extreme rainfall in Bekasi River Basin can be simulated using a distributed hydrological model. Rainfall-Runoff Inundation (RRI) model is a two-dimensional hydrological model capable of simulating rainfall-runoff and flood inundation simultaneously. The input data used in this study is extreme rainfall data derived from GSMaP satellite rainfall data, topography, and land derived from satellite remote sensing data. In this paper, we analyze the flood simulation in the Kali Bekasi watershed when extreme rainfall occurred on July 14, 15 and 16, 2022. On that date we found flooding in several areas including the Bekasi River Basin. From the results of the flood simulation data processing, it is then calculated how much economic loss due to the flood disaster occurred.
{"title":"Preliminary Study on the Rainfall-Runoff Inundation and Its Economic Lost at Bekasi River Basin, West Jawa","authors":"I. F. Cahyaningtiyas, M. Djoharin, Tiara Grace, A. Eugenie, Evie Aviantie, R. Amaliyah, E. G. A. Sapan, F. Meliani, R. Sulistyowati, H. Priyadi, S. Lestari, D. Fernando","doi":"10.1109/AGERS56232.2022.10093598","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093598","url":null,"abstract":"Flood disasters in Bekasi City almost occur every year, especially during high rainfall with a fairly long duration. Repeated flooding events due to extreme rainfall in Bekasi River Basin can be simulated using a distributed hydrological model. Rainfall-Runoff Inundation (RRI) model is a two-dimensional hydrological model capable of simulating rainfall-runoff and flood inundation simultaneously. The input data used in this study is extreme rainfall data derived from GSMaP satellite rainfall data, topography, and land derived from satellite remote sensing data. In this paper, we analyze the flood simulation in the Kali Bekasi watershed when extreme rainfall occurred on July 14, 15 and 16, 2022. On that date we found flooding in several areas including the Bekasi River Basin. From the results of the flood simulation data processing, it is then calculated how much economic loss due to the flood disaster occurred.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115486072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093267
D. Melati, Astisiasari, Trinugroho
Land use is one of the dynamic features that has an impact on environmental conditions. As the study area, the coastal area in the City of Cilegon, Province of Banten is subjected to land use dynamics for its economic development. Accordingly, this study aimed to provide the land use/land cover (LULC) classification within the study area in the year of 2021. The classification was done using Sentinel-2 images and processed on a free, open-access Google Earth Engine (GEE) environment. In generating the LULC classification, this study applied two approaches, i.e., Object-based Classification (OBC) and Pixel-based Classification (PBC), in order to get a better result in providing the LULC data. The predictor variables integrated several spectral indices and bands from the Sentinel-2. For the OBC, image segmentation was performed with a Simple Non-Iterative Clustering (SNIC). And, the classifier used for the OBC and PBC was Random Forest (RF). As a result, the study area consists of heterogeneous landscape including agricultural area, industrial area, settlement and other vegetated areas. Based on the accuracy assessment, the OBC outperformed the PBC with an overall accuracy at 0.95 and 0.731, respectively.
{"title":"An Assessment of Object-based Classification Compared to Pixel-based Classification in Google Earth Engine Using Random Forest","authors":"D. Melati, Astisiasari, Trinugroho","doi":"10.1109/AGERS56232.2022.10093267","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093267","url":null,"abstract":"Land use is one of the dynamic features that has an impact on environmental conditions. As the study area, the coastal area in the City of Cilegon, Province of Banten is subjected to land use dynamics for its economic development. Accordingly, this study aimed to provide the land use/land cover (LULC) classification within the study area in the year of 2021. The classification was done using Sentinel-2 images and processed on a free, open-access Google Earth Engine (GEE) environment. In generating the LULC classification, this study applied two approaches, i.e., Object-based Classification (OBC) and Pixel-based Classification (PBC), in order to get a better result in providing the LULC data. The predictor variables integrated several spectral indices and bands from the Sentinel-2. For the OBC, image segmentation was performed with a Simple Non-Iterative Clustering (SNIC). And, the classifier used for the OBC and PBC was Random Forest (RF). As a result, the study area consists of heterogeneous landscape including agricultural area, industrial area, settlement and other vegetated areas. Based on the accuracy assessment, the OBC outperformed the PBC with an overall accuracy at 0.95 and 0.731, respectively.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129489206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093257
B. Setiadi, Andria Arisal, Iman Firmansyah, A. Subekti, N. Chasanah, Jefri Abner Hamonangan, F. Kurniawan
The implementation of signal processing algorithms is crucial in the development of Synthetic Aperture Radar (SAR) systems. However, many references do not provide source code-level explanations, making it difficult for researchers and students to understand and implement these algorithms. This paper presents the implementation of the Omega-K algorithm (WKA) for SAR signal processing using Python. By using a freely accessible programming language, we aim to provide a low-cost, simple, and portable way to implement SAR signal processing algorithms compared to using commercial software packages. We describe the implementation details of two WKA variants at the source code level and demonstrate their use in processing Radarsat data. The execution results for various image sizes are tested, and the image results are compared with other reference implementations. Our results indicate that Python has considerable potential for SAR signal processing tasks.
{"title":"Synthetic Aperture Radar Signal Processing Algorithm Implementation in Python","authors":"B. Setiadi, Andria Arisal, Iman Firmansyah, A. Subekti, N. Chasanah, Jefri Abner Hamonangan, F. Kurniawan","doi":"10.1109/AGERS56232.2022.10093257","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093257","url":null,"abstract":"The implementation of signal processing algorithms is crucial in the development of Synthetic Aperture Radar (SAR) systems. However, many references do not provide source code-level explanations, making it difficult for researchers and students to understand and implement these algorithms. This paper presents the implementation of the Omega-K algorithm (WKA) for SAR signal processing using Python. By using a freely accessible programming language, we aim to provide a low-cost, simple, and portable way to implement SAR signal processing algorithms compared to using commercial software packages. We describe the implementation details of two WKA variants at the source code level and demonstrate their use in processing Radarsat data. The execution results for various image sizes are tested, and the image results are compared with other reference implementations. Our results indicate that Python has considerable potential for SAR signal processing tasks.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132670277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093553
R. Virtriana, D. Retnowati, F. Prawiradisastra, Yukni Arifianti, S. Hespiantoro, Yudhi Wahyudi, B. Sugiarto, G. Winarso, E. Kriswati
The Mount Batur volcanic activity provides fertile soil and useful eruption products, but the concentration of the population can also increase the risk factors for being affected by the eruption. Mount Batur in Bangli Regency, Bali Province, attracts sand miners and tourists due to its attractiveness. Its fertility supports the local farming community. The high number of tourists and sand mining activities cause the level of risk in a volcanic eruption scenario to be higher. This research analyzes the social and infrastructures as elements at risk to mitigate the impacts. We acquired high-resolution land cover data obtained from drone mapping. We analyzed social exposure through interviews and filling out questionnaires. We combined the existing hazard zone and exposure analysis. The results of this research are an exposure map and the amount of possible risk of a zone to potential eruption materials. In the volcanic hazard zone of Mount Batur, it is estimated that there are 17,461 people distributed in 15,917 building polygons of the settlement area, or around 16.01% of the total Kintamani District population. For the public facilities identified in this study, there are 8 schools, 1 hospital, and 1 public health center in the volcanic hazard zone of Mount Batur. The types of vegetation that are mostly exposed to the volcanic hazard zone of Mount Batur are fields (±6442.14 hectares) and plantations (±5577.17 hectares).
{"title":"Evaluating population and infrastructure exposure to Mount Batur volcanic risk","authors":"R. Virtriana, D. Retnowati, F. Prawiradisastra, Yukni Arifianti, S. Hespiantoro, Yudhi Wahyudi, B. Sugiarto, G. Winarso, E. Kriswati","doi":"10.1109/AGERS56232.2022.10093553","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093553","url":null,"abstract":"The Mount Batur volcanic activity provides fertile soil and useful eruption products, but the concentration of the population can also increase the risk factors for being affected by the eruption. Mount Batur in Bangli Regency, Bali Province, attracts sand miners and tourists due to its attractiveness. Its fertility supports the local farming community. The high number of tourists and sand mining activities cause the level of risk in a volcanic eruption scenario to be higher. This research analyzes the social and infrastructures as elements at risk to mitigate the impacts. We acquired high-resolution land cover data obtained from drone mapping. We analyzed social exposure through interviews and filling out questionnaires. We combined the existing hazard zone and exposure analysis. The results of this research are an exposure map and the amount of possible risk of a zone to potential eruption materials. In the volcanic hazard zone of Mount Batur, it is estimated that there are 17,461 people distributed in 15,917 building polygons of the settlement area, or around 16.01% of the total Kintamani District population. For the public facilities identified in this study, there are 8 schools, 1 hospital, and 1 public health center in the volcanic hazard zone of Mount Batur. The types of vegetation that are mostly exposed to the volcanic hazard zone of Mount Batur are fields (±6442.14 hectares) and plantations (±5577.17 hectares).","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115864469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093263
H. Sanjaya, Bangun Muljo Sukotjo, L. M. Jaelani, Agustan, Busyairi Latiful Ashar, L. Sumargana, Heri Sadmono, Dewi Nirwati, Ulfah Nuzulullia
Spectroradiometer is a tool to measure both the wavelength and amplitude of the light emitted from a light source. This light measurement identifies wavelengths based on where the light hits the detector array, allowing the entire spectrum to be captured in a single acquisition. How to identify and use a spectrometer correctly so that the acquired wavelength data can form a spectral library. There are many types of spectroradiometers, but it must be ensured that the reflected wavelengths recorded fall within a certain range. This determines which spectroradiometer should be used. In addition, we also need to know which object will be recorded if its reflectivity. With a known wavelength range, the type of spectrometer can be determined. It is also important to consider the measurement step associated with the existence of the observed object. If measurements have been obtained, the results can be used to construct a spectral library representing each agreed end-member. The spectrum library is a very important reference in processing remote sensing data. In this study, a portable spectrometer was used with a wavelength range of 0.450 to 0.800 micrometers. Subjects recorded included leaves of rice plants in healthy plant conditions.
{"title":"Utilizing a Spectroradiometer to Build a Spectral-Library of Rice Leaves","authors":"H. Sanjaya, Bangun Muljo Sukotjo, L. M. Jaelani, Agustan, Busyairi Latiful Ashar, L. Sumargana, Heri Sadmono, Dewi Nirwati, Ulfah Nuzulullia","doi":"10.1109/AGERS56232.2022.10093263","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093263","url":null,"abstract":"Spectroradiometer is a tool to measure both the wavelength and amplitude of the light emitted from a light source. This light measurement identifies wavelengths based on where the light hits the detector array, allowing the entire spectrum to be captured in a single acquisition. How to identify and use a spectrometer correctly so that the acquired wavelength data can form a spectral library. There are many types of spectroradiometers, but it must be ensured that the reflected wavelengths recorded fall within a certain range. This determines which spectroradiometer should be used. In addition, we also need to know which object will be recorded if its reflectivity. With a known wavelength range, the type of spectrometer can be determined. It is also important to consider the measurement step associated with the existence of the observed object. If measurements have been obtained, the results can be used to construct a spectral library representing each agreed end-member. The spectrum library is a very important reference in processing remote sensing data. In this study, a portable spectrometer was used with a wavelength range of 0.450 to 0.800 micrometers. Subjects recorded included leaves of rice plants in healthy plant conditions.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134429112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093456
Muhammad Iqbal Habibie, N. Purwono
Indonesia's urban growth is accelerating due to improvements in infrastructure, utilities, and transportation networks. The activity of individuals over a longer period of time might indicate urbanity activity. The higher the amount of urbanization, the longer the communal activities lasted, even until late at night. Nighttime community activities need the use of an electric light in a public place or settlement. The usage of light at night signified urban community activity. The intercalibration model is used in this research to correct and use the long time series of VIIRS/DNB data. This article estimates the pattern of urban growth based on nighttime light (NTL) illumination from 2014 to 2022. This research combined current geo-referenced population growth rate information, the proportion of poor people, revenue from property and building taxes for the rural sector and urban sector, and it as our measurement to calculate the socio-economic activities and examine city coverage dispersion. Geographic Weighted Regression (GWR) was used to evaluate the role of socio-economic determinants of urban growth in Kendal. The results found that urban activities are related to population growth, the proportion of poor people, and land and building tax revenue. Identification of urban growth in Kendal District can be known by applying remote sensing satellite imagery, using the concept of nighttime lights on the brightness of the lights.
{"title":"Identification of Socio-economic Activities as Urban Growth based on Nighttime Light Data (Study on Kendal District - Indonesia)","authors":"Muhammad Iqbal Habibie, N. Purwono","doi":"10.1109/AGERS56232.2022.10093456","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093456","url":null,"abstract":"Indonesia's urban growth is accelerating due to improvements in infrastructure, utilities, and transportation networks. The activity of individuals over a longer period of time might indicate urbanity activity. The higher the amount of urbanization, the longer the communal activities lasted, even until late at night. Nighttime community activities need the use of an electric light in a public place or settlement. The usage of light at night signified urban community activity. The intercalibration model is used in this research to correct and use the long time series of VIIRS/DNB data. This article estimates the pattern of urban growth based on nighttime light (NTL) illumination from 2014 to 2022. This research combined current geo-referenced population growth rate information, the proportion of poor people, revenue from property and building taxes for the rural sector and urban sector, and it as our measurement to calculate the socio-economic activities and examine city coverage dispersion. Geographic Weighted Regression (GWR) was used to evaluate the role of socio-economic determinants of urban growth in Kendal. The results found that urban activities are related to population growth, the proportion of poor people, and land and building tax revenue. Identification of urban growth in Kendal District can be known by applying remote sensing satellite imagery, using the concept of nighttime lights on the brightness of the lights.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124002182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093500
Rika Prillya Mustafida, Nikita Veronica, Aufa Qoulan Karima, Candida Aulia de Silva Nusantara, W. Windupranata
Batukaras Village is a village located in Cijulang District, in the southern part of Pangandaran Regency, West Java. Batukaras is one of the tourist destinations that have the potential to be visited by the tourist because of its beautiful view and potential economic income from the fishery. Beyond the potential for natural beauty, Batukaras Village also has the potential for disaster. One of them is the tsunami caused by the earthquake, with a magnitude of 7.7 in 2006. The Pangandaran tsunami on July 17 became a memorable disaster for the local community. It reached a height of 21 meters. It has left more than 300 people dead, 301 seriously injured, 551 slightly injured, and 156 missing, accompanied by huge property losses. Batukaras Village community has implemented 12 tsunami ready indicators IOC-UNESCO. Therefore, this study aims to map 12 tsunami ready IOC-UNESCO indicators in Batukaras Village to evaluate which indicators the government and community of Batukaras Village. The field survey and interviews are done to obtain data to identify the 12 tsunami ready indicators in Batukaras Village. Based on the IOC-UNESCO tsunami indicator mapping results, Batukaras Village has fulfilled 11 of the 12 indicators set. Based on the identification result, the village government has not yet fulfilled indicator five. Therefore the ITB Research and Community Service Institute (LPPM) team assisted in making an evacuation route map to fulfill these indicators. However, in the future, the Batukaras Village government, assisted by the entire community, can be committed to maintaining and improving all existing disaster preparedness assets.
{"title":"Identification of the IOC-UNESCO Tsunami Ready Indicator to Improve Coastal Community Preparedness for Tsunami Disaster in Batukaras Village, Pangandaran Regency, Indonesia","authors":"Rika Prillya Mustafida, Nikita Veronica, Aufa Qoulan Karima, Candida Aulia de Silva Nusantara, W. Windupranata","doi":"10.1109/AGERS56232.2022.10093500","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093500","url":null,"abstract":"Batukaras Village is a village located in Cijulang District, in the southern part of Pangandaran Regency, West Java. Batukaras is one of the tourist destinations that have the potential to be visited by the tourist because of its beautiful view and potential economic income from the fishery. Beyond the potential for natural beauty, Batukaras Village also has the potential for disaster. One of them is the tsunami caused by the earthquake, with a magnitude of 7.7 in 2006. The Pangandaran tsunami on July 17 became a memorable disaster for the local community. It reached a height of 21 meters. It has left more than 300 people dead, 301 seriously injured, 551 slightly injured, and 156 missing, accompanied by huge property losses. Batukaras Village community has implemented 12 tsunami ready indicators IOC-UNESCO. Therefore, this study aims to map 12 tsunami ready IOC-UNESCO indicators in Batukaras Village to evaluate which indicators the government and community of Batukaras Village. The field survey and interviews are done to obtain data to identify the 12 tsunami ready indicators in Batukaras Village. Based on the IOC-UNESCO tsunami indicator mapping results, Batukaras Village has fulfilled 11 of the 12 indicators set. Based on the identification result, the village government has not yet fulfilled indicator five. Therefore the ITB Research and Community Service Institute (LPPM) team assisted in making an evacuation route map to fulfill these indicators. However, in the future, the Batukaras Village government, assisted by the entire community, can be committed to maintaining and improving all existing disaster preparedness assets.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126160533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-21DOI: 10.1109/AGERS56232.2022.10093504
Y. Susilowati, Aang Gunawan Sutyawan, W. H. Nur, Y. Kumoro, I. Maryanto, Muklisin, Taufiq Salman Sya'bani, Bridsta Yudha Permana
The research aims to produce a software for mapping and identifying habitat characteristics of flora in East Kalimantan based on remote sensing data. Identification of flora habitat characteristics in East Kalimantan will be very useful for conservation and restoration of flora habitat in the region. Data of flora species obtained from Herbarium Wanariset file which includes textual attribute, coordinates, and specimen photos. Dipterocarpus, Hopea, Macaranga, Shorea, and Vatica were used for the case study. Geological data and soil type were taken from the Indonesian Geospatial website. Slope and altitude were obtained from Digital Elevation Model (DEM) data. Rainfall data was downloaded from the Meteorological, Climatological, and Geophysical Agency (BMKG). Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-Up Index (NDBI) were obtained from Landsat 8 image data after digital processing. The result of this research is a plugin function in QGIS that can be used for flora habitat identification. According to the research findings, only Shorea can grow in all environments. Dipterocarpus, Hopea, Macaranga, and Vatica have the specific characteristic. Dipterocarpus grows in Tanjunggredeb/ Napiku, Sinjin Formation, Telen Formation, and the Maau Formation. Macaranga and Hopea grow in the same place where aluvium deposits. Vatica only grows in the Maau Formation.
{"title":"Spatial Analysis of Flora Habitat Characteristics in East Kalimantan","authors":"Y. Susilowati, Aang Gunawan Sutyawan, W. H. Nur, Y. Kumoro, I. Maryanto, Muklisin, Taufiq Salman Sya'bani, Bridsta Yudha Permana","doi":"10.1109/AGERS56232.2022.10093504","DOIUrl":"https://doi.org/10.1109/AGERS56232.2022.10093504","url":null,"abstract":"The research aims to produce a software for mapping and identifying habitat characteristics of flora in East Kalimantan based on remote sensing data. Identification of flora habitat characteristics in East Kalimantan will be very useful for conservation and restoration of flora habitat in the region. Data of flora species obtained from Herbarium Wanariset file which includes textual attribute, coordinates, and specimen photos. Dipterocarpus, Hopea, Macaranga, Shorea, and Vatica were used for the case study. Geological data and soil type were taken from the Indonesian Geospatial website. Slope and altitude were obtained from Digital Elevation Model (DEM) data. Rainfall data was downloaded from the Meteorological, Climatological, and Geophysical Agency (BMKG). Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-Up Index (NDBI) were obtained from Landsat 8 image data after digital processing. The result of this research is a plugin function in QGIS that can be used for flora habitat identification. According to the research findings, only Shorea can grow in all environments. Dipterocarpus, Hopea, Macaranga, and Vatica have the specific characteristic. Dipterocarpus grows in Tanjunggredeb/ Napiku, Sinjin Formation, Telen Formation, and the Maau Formation. Macaranga and Hopea grow in the same place where aluvium deposits. Vatica only grows in the Maau Formation.","PeriodicalId":370213,"journal":{"name":"2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127558944","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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