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Characteristic of Tidal Currents in the Lombok Strait Using 3D FVCOM Numerical Model

IOP Conference Series: Earth and Environmental Science 2021年第1期925卷

作者： I M D Raharja I M Radjawane I G Hendrawan Earth Science Study Program Faculty of Earth Sciences and Technology Bandung Institute of Technology Indonesia Oceanographic Research Group Bandung Institute of Technology Bandung Indonesia Marine Science Study Program Faculty of Marine Science and Fisheries Udayana University Indonesia Center for Remote Sensing and Ocean Sciences (CReSOS) Udayana University Indonesia

The Lombok Strait located between Bali Island and Lombok Island, Indonesia. Lombok Strait is complex area because influence by Indonesian Throughflow and influence by Tidal Current. For this case want to research about tidal current circulation and simulated using a three-dimensional baroclinic hydrodynamic numerical modelling method by Finite Volume Coastal Ocean Model (FVCOM). The study was simulated during 1-year on 2004, February. The model just simulated by barotropic condition and only influence by elevation tide in open boundary. The verification of ocean current (u and v components) from the model compare with observation data has a high coefficient of determination, i.e., 0.9, respectively. This verification result shows good agreement between model and observation data. For the result model, in the Lombok strait dominant influence by M2 semidiurnal component from Indian Ocean and K1 diurnal component from Pacific Ocean. The current circulation in the near surface dominant movement pattern from southern to northern. On the other hand, for the vertical current in 100 – 600 meter is different with near surface. The current movement from northern to southern. In the sill area have upwelling phenomenon in the north side of the sill and downwelling in south of the sill.

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Designing a neural network for vehicle type classification using extended Haar features

Designing a neural network for vehicle type classification u...

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Applied Mathematics and Engineering (BICAME), Borneo International Conference on

作者： Boby M. Pratama Asep A. Supriyadi Rudy A. G. Gultom Study Program of Sensing Technology Indonesia Defense University Bogor Indonesia

ISBN: (数字)9781538677254

ISBN: (纸本)9781538677261

Vehicle type classification method using artificial neural network (ANN) is proposed in this paper. The presented method relies on ANN to recognize between car, motorcycle and non-vehicle images. Extended Haar feature (EHF) was used to extract unique features from motorcycle and car images. These features were used to train four designed ANNs. The focus of this research is to determine the most optimum ANN for future development, which is good in the term of accuracy and speed. The experimental results show that a more complex ANN has a good accuracy but with a slower processing speed. The most optimum ANN gives 94% of accuracy and 51ms of processing speed to classify a single image.

关键词： Feature extraction Automobiles Motorcycles Neurons Artificial neural networks Real-time systems Mathematics

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Deformation identification using DInSAR multi temporal analysis method in supporting infrastructure development (case study of the area around the nation's new capital)

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IOP Conference Series: Earth and Environmental Science 2021年第1期851卷

作者： A Puspita Y Prihanto S Martha A A Martha A G Gultom Sensing Technology Study Program Faculty of Defense Technology Indonesian Defense University Sentul-Bogor Indonesia Earthquake and Tsunami Data Center Meteorology Climatology and Geophysics Agency (BMKG) Jakarta Indonesia Center of Research Promotion and Cooperation Geospatial Information Agency (BIG) Cibinong-Bogor Indonesia Center of Research and Development Meteorology Climatology and Geophysics Agency (BMKG) Jakarta Indonesia

The relocation of nation's capital is one of the government's flagship programs at this time. The lack of information on soil structures that are prone to deformation can bring an impact on the failure of infrastructure development in the areas of New State Capital (known as Ibu Kota Negara/IKN). Therefore, a significant study is important to find out the deformations that may occur in the region as an initial information in determining the location for safer infrastructure development based on the soil structure conditions in the research area. This research used the Differential Interferometry Synthetic Aperture Radar (DInSAR) method with a multi-temporal approach to identify the deformations in the research area. The data included SAR images of Sentinel 1A type SLC with C band (5.405 GHz) for the period 2015-2019. The results confirmed that deformation had been identified in several areas, both those experiencing subsidence and uplifting. The maximum subsidence was 12.97 cm at Sepaku district on period 2018-2019 and the maximum uplift was 10.01 cm on period 2017-2018. The identified areas with deformation generally take place in areas with a high density of buildings, construction areas, road infrastructure, and river alluvial deposits.

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Application of remote sensing monitoring of limestone mining exploitation in Mountain Kendeng

Application of remote sensing monitoring of limestone mining...

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Applied Mathematics and Engineering (BICAME), Borneo International Conference on

作者： Fifing Rudy A. G. Gultom Boby M. Pratama Garnis Anggraeni Study Program of Sensing Technology Indonesia Defense University Bogor Indonesia Study Program of Economy Defense Indonesia Defense University Bogor Indonesia

ISBN: (数字)9781538677254

ISBN: (纸本)9781538677261

This Rembang and Blora Regencies are one of the areas that store natural resources, especially limestone (karst) in Kendeng Mountain. Assessment the condition of the mining area requires the preparation of systematic image data to determine the change data. Remote sensing technology has the ability to observe earth objects in a certain period of time and can be used as a tool to monitor the environmental conditions of the mining *** research methodology broadly includes data collection, raster data, vector, textual, initial processing, field surveys, and image analysis. With the study of remote sensing technology, it is expected to provide fast and accurate information to the Regional Government regarding the consequences of mining activities and directions for mitigation. Landsat-7 satellite image technology and GIS-based Thematic Maps can provide land cover information on the impact of mining activities quickly, but must be supported by the accuracy and data validation resulting from the field *** on these data it can be seen that the exploitation of limestone mining can affect the area of critical land and cause environmental damage, with illustration in the extent of the negative impact from exploitation that has been done. Using the spatio-temporal (analysis) approach, it can be seen how much the forest changes in limestone mining activities for the period 2012-2017 on Kendeng Mountain, Blora - Rembang Regency in relation to the above mentioned features. Spatio-temporal analysis is a method of combining analysis between spatial and multi-time analysis.

关键词： geographic information systems geophysical image processing geophysical techniques minerals mining remote sensing Limestone Mine geophysical techniques mining remote sensing monitoring geographic information systems remote sensing minerals Remote sensing Technologies exploitation Local Government mountains Debugging Environmental disasters mining areas

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Accuracy assessment of several classification algorithms with and without hue saturation intensity input features on object analyses on benthic habitat mapping in the Pajenekang Island Waters, South Sulawesi

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IOP Conference Series: Earth and Environmental Science 2020年第1期429卷

作者： T Pragunanti B Nababan H Madduppa D Kushardono Marine Technology Study Program Graduate School IPB University Bogor Department of Marine Technology Faculty of Fisheries and Marine Science IPB University Bogor Remote Sensing Center National Aeronautics and Space Agency Jakarta

Object-based image analysis (OBIA) method has been proven to improve the accuracy value on benthic habitat mapping. The purpose of this study was to assess the accuracy of several classification algorithms on benthic habitat mapping based on OBIA method with and without input feature of Hue Saturation Intensity (HSI) in the Pajenekang island waters, South Sulawesi, Indonesia. Sentinel-2A satellite imagery with 10 m2 spatial resolution acquired on 3 September 2018 was used in this study. During OBIA analyses, we segmented the object into 5, 10, and 15 classes and treated each of them with input features of mean+ratio vs mean+ratio+hue saturation intensity (HSI). We later classified the benthic habitat by applying several classification algorithms such as the Bayes, K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Decision Tree (DT). The results showed that the Bayes algorithm produced highest accuracy of 78.35% within 10 segmentation classes and input features of mean+ratio+HSI followed by the KNN of 71.13% with 5 segmentation classes and input features of mean+ration+HSI. The addition of HSI input features into OBIA analyses increased the accuracy of benthic habitat classification mapping of 4.13% with the Bayes classification algorithm.

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Identifying geothermal steam spots based on binary logistic method to a high resolution imagery and field measurement

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IOP Conference Series: Earth and Environmental Science 2020年第1期500卷

作者： F. Z. Gandes A. Saepuloh K. Wikantika Center for Remote Sensing Insitut Teknologi Bandung Remote Sensing and GIS Research Group Faculty of Earth Sciences and Technology Institut Teknologi Bandung Geology Engineering Study Program Faculty of Earth Sciences and Technology Institut Teknologi Bandung 10 Ganesha Street 40135 Bandung Indonesia Indonesian Young Researcher Forum (ForMIND)

The increasing population of a country has an impact on the number of energy needs that must be provided. Therefore, alternative sources of energy need to be developed to meet those needs. Indonesia is known as The Ring of Fire has very high geothermal resource potential. A study of geothermal mapping technology needs to be done to optimize the utilization of this energy. This study aims to recognize the relation between temperature and surface roughness on an up-flow zone of geothermal system and identify the steam spots at surface. We used a high resolution ALOS-2 PALSAR-2 images to obtain surface roughness and ASTER images to obtain a corrected Land Surface Temperature (LST). The study was conducted in an open area Papandayan Crater, Garut, West Java (Indonesia). Data pre-processing including atmospheric and geometric corrections were applied following an image processing to obtain the surface roughness and the corrected LST by estimating non-unique surface emissivity, pixel fitting between measured surface temperature and roughness at field, and identifying the steam spots of the geothermal system using binary logistic regression method. Equally good models of co-polarization and cross-polarization obtained because of the high correlation between co-polarization and cross-polarization surface roughness. There was a positive relationship between land surface temperature and surface roughness in the manifestation area of geothermal resources and surface roughness known more dominant in determining the manifestations of geothermal resources than surface temperature.

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Detecting mangrove deforestation using multi land use land cover change datasets: a comparative analysis in Southeast Asia

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IOP Conference Series: Earth and Environmental Science 2020年第1期500卷

作者： Sonny A. Sudhana Anjar D. Sakti Luri N. Syahid Lilik B. Prasetyo Bambang Irawan Muhammad Kamal Ketut Wikantika Center for Remote Sensing Institut Teknologi Bandung Bandung 40132 Indonesia Remote Sensing and Geographic Information Science Division Faculty of Earth Science and Technology Institut Teknologi Bandung Bandung 40132 Indonesia Department of Natural Resource Conservation and Ecotourism Faculty of Forestry IPB University Bogor 16680 Indonesia Department of Geographic Information Science Faculty of Geography Universitas Gadjah Mada Yogyakarta 55281 Indonesia Biology Study Program Faculty of Science and Technology Airlangga University Surabaya 60286 Indonesia

Mangrove forest grows on tropical coastal areas and has an ecological role for its surrounding environment. Mangrove forest protects the coast from large waves and becomes a habitat for various marine fauna. It stores the highest densities of carbon among any other ecosystem globally. In Southeast Asia, Mangrove forest is highly biodiverse and contributes to the sustainability of the ecosystem. However, based on previous studies, mangrove forests are experiencing deforestation due to high demands of commodities and land use. In this study, we analyzed changes of land cover in Southeast Asia using several global land cover products produced between 2001 and 2012 and their correlation with mangrove deforestation based on Mangrove Forest Watch (CGMFC-21) data. LULC data products applied in this study were ESA CCI LC, MODIS LC, GlobCover. The analysis was carried out by calculating the rate of increase in mangrove deforestation and comparing it with changes in land cover that replaced the mangrove area temporally. The results of this study were land cover classes that replaced mangrove forest areas in the study period. Based on the results it could be concluded that the methods and products used influence the results. There are many sources of data products that might be used for future research, with other methods that are better so that they provide space for future research and development. Our study can be used as a consideration to implement policies that conserve mangrove forest across Southeast Asia.

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A preliminary study on machine learning and google earth engine for mangrove mapping

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IOP Conference Series: Earth and Environmental Science 2020年第1期500卷

作者： Muhammad Kamal Nur Mohammad Farda Ilham Jamaluddin Artha Parela Ketut Wikantika Lilik Budi Prasetyo Bambang Irawan Department of Geographic Information Science Faculty of Geography Universitas Gadjah Mada Sekip Utara Bulaksumur Yogyakarta 55281 Indonesia Remote Sensing and Geographical Information Sciences Research Group Faculty of Earth Science and Technology Bandung Institute of Technology JI. Ganesa 10 Bandung 40132 Indonesia Department of Natural Resource Conservation and Ecotourism Faculty of Forestry IPB University Kampus IPB Darmaga Bogor 16680 Indonesia Biology Study Program Faculty of Science & Technology Airlangga University Jalan Mulyosari Surabaya 60115 Indonesia

The alarming rate of global mangrove forest degradation corroborates the need for providing fast, up-to-date and accurate mangrove maps. Conventional scene by scene image classification approach is inefficient and time consuming. The development of Google Earth Engine (GEE) provides a cloud platform to access and seamlessly process large amount of freely available satellite imagery. The GEE also provides a set of the state-of-the-art classifiers for pixel-based classification that can be used for mangrove mapping. This study is an initial effort which is aimed to combine machine learning and GEE for mapping mangrove extent. We used two Landsat 8 scenes over Agats and Timika Papua area as pilot images for this study; path 102 row 64 (2014/10/19) and path 103 row 63 (2013/05/16). The first image was used to develop local training areas for the machine learning classification, while the second one was used as a test image for GEE on the cloud. A total of 838 points samples were collected representing mangroves (244), non-mangroves (161), water bodies (311), and cloud (122) class. These training areas were used by support vector machine classifier in GEE to classify the first image. The classification result show mangrove objects could be efficiently delineated by this algorithm as confirmed by visual checking. This algorithm was then applied to the second image in GEE to check the consistency of the result. A simultaneous view of both classified images shows a corresponding pattern of mangrove forest, which mean the mangrove object has been consistently delineated by the algorithm.

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Abstracts

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Environmental Management 1977年第1期1卷 67-96页

作者： Frankenfeld, John W. Schulz, Wolfgang McMurty, George J. Petersen, Gary W. May, G. A. Hering, F. S. Schwartz, J. I. Heywood, J. B. Chigier, N. A. Grohse, E. W. Walker, J. D. Colwell, R. R. Petrakis, L. Pergament, H. S. Thorpe, R. D. Schoepf, Richard W. Krzyczkowski, Roman Henneman, Suzanne S. Hudson, Charles L. Putnam, Evelyn S. Thiesen, Donna J. Parks, G. A. McCarty, Perry L. Leckie, J. O. Schrumpf, Barry J. Simonson, G. H. Paine, D. P. Lawrence, R. D. Pyott, W. T. Leh, M. Elders, W. Combs, J. Caplen, T. Harrison, F. L. Wong, K. M. Heft., R. E. Charnell, Robert L. Lehmann, Edward J. Mallon, Lawrence G. Hatfield, Cecile Adams, Gerald H. Johanning, James Talvitie, Antti Noll, Kenneth E. Miller, Terry Smiarowski, Joseph F. Willis, Cleve E. Foster, John H. Schlesinger, Benjamin Daetz, Douglas Lear, Donald U. Smith, Mona F. Hundemann, Audrey S. Crockett, Pernell W. Werner, Kirk G. Carroll, Thomas E. Maase, David L. Genco, Joseph E. Ifeadi, Christopher N. Lowman, F. G. Christensen, S. W. Van Winkle, W. Mattice, J. S. Harrison, Elizabeth A. Barker, James C. Chesness, Jerry L. Smith, Ralph E. Shaheeen, Donald G. Raney, R. Keith Borton, T. Wezernak, C. T. Raney, R. K. Sherwani, Jabbor K. Moreau, David H. Eisenberg, Norman A. Lynch, Cornelius J. Breeding, Roger J. Johnson, J. D. Foster, K. E. Mouat, D. A. Clark, R. Hyden, John William Owen, Wilfred Bayfield, Neil G. Barrow, Graham C. Stolz, Stephanie B. Wienckowski, Louis A. Brown, Betram S. Keyfitz, Nathan Wilson, W. L. Newman, Peter W. G. Bammi, Deepak Bammi, Dalip Goddard, James E. Chisholm, Tony Walsh, Cliff Brennan, Geoffrey Thompson, K. S. Richardson, R. Jensen, Clayton E. Brown, Dail W. Mirabito, John A. Cowing, Thomas G. Binghamton, Suny Siehl, George H. Albrecht, O. W. Alexander, Ariel Barde, Jean -Philippe Darby, William P. McMichael, Francis Clay Dunlap, Robert W. Muckleston, Keith W. Frankenhoff, Charles A. Giulini, Lorenzo T. Wyatt, T. Black, Peter E. Keating, William Thomas Leonard, M. E. Fisher, E. L. Brunelle, M. F. Dickinson, J. E. Pethig, Rudiger Clapham Exxon Research & Engineering Co. Linden Government Research Lab Coast Guard Washington D.C. Department of Transportation Washington D.C. Office For Remote Sensing of Earth Resources. NASA Earth Resources Survey Program Pennsylvania State University Washington D.C. Department of the Navy Washington D.C. Cambridge. Dept. of Mechanical Engineering Massachusetts Inst. of Technology USA Huntsville. School of Graduate Studies and Research Alabama Univ. USA Dept. of Microbiology. Office of Naval Research Maryland Univ. Arlington Research and Development Co. Pittsburgh AeroChem Research Labs. Inc. Princeton Dept. of the Interior Office of Library Services. Bibliographic Series (Final) Washington D.C. Interplan Corp. Santa Barbara Urban Mass Transportation Adm. Washington D.C. Naval Underwater Systems Center Newport Calif. Mercury Project. National Science Found Stanford Univ Washington D.C. Div. of Advanced Environmental Research & Tech. USA Corvallis. NASA Earth Resources Survey Program Oregon State Univ. Washington D.C. Riverside. Inst. of Geophysics and Planetary Physics National Science Foundation California Univ. Washington D.C. Livermore Lawrence Livermore Lab. California Univ. USA Atlantic Oceanographic and Meteorological Labs. National Oceanic and Atmospheric Administration Miami National Technical Information Service Springfield Miami Univ. Coral Gables School of Law National Oceanic and Atmospheric Administration Rockville National Academy of Sciences Office of Sea Grant Washington D. C. School of Law National Oceanic and Atmospheric Administration Office of Sea Grant. Rockville Norman. Dept. of Civil Engineering and Environmental Science. Urban Mass Transportation Administration Oklahoma Univ. Washington D. C. Knoxville.Dept. of Civil Engineering. Federal Highway Administration Tennessee Univ. Washington D. C. Amherst.Water Resources Research Center. Office of Water Research and Technology Massachusetts Univ. Washington D. C. Calif. Dept. of Industria

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