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All Journal REKA GEOMATIKA Geoplanning : Journal of Geomatics and Planning Journal of Engineering and Technological Sciences ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Jurnal Tekno Insentif Jurnal Kelautan Nasional Jurnal Rekayasa Hijau International Journal of Remote Sensing and Earth Sciences (IJReSES) Jurnal Tekno Global Indonesian Journal for Physical Education and Sport The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Soni Darmawan
Jurusan Teknik Geodesi, FTSP Institut Teknologi Nasional Bandung
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Health Professions

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Kajian Kerentanan Pesisir Terhadap Kenaikan Muka Air Laut di Kabupaten Subang-Jawa Barat Dian Noor Handiani; Soni Darmawan; Aida Heriati; Yohanes D. Aditya
Jurnal Kelautan Nasional Vol 14, No 3 (2019): DESEMBER
Publisher : Pusat Riset Kelautan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (403.662 KB) | DOI: 10.15578/jkn.v14i3.7583

Abstract

Akresi dan erosi di sepanjang garis pantai merupakan salah satu masalah di pesisir Kabupaten Subang-Jawa Barat. Kondisi tersebut digabungkan dengan kenaikan muka air laut akibat perubahan iklim global mengakibatkan wilayah pesisir tersebut rentan mengalami bencana. Penelitian ini bertujuan menentukan indeks kerentanan wilayah pesisir Kabupaten Subang berdasarkan parameter fisik pesisir, yaitu geomorfologi, rentang pasang surut, rata-rata ketinggian gelombang dan permukaan air laut, jenis batuan geologi, serta perubahan garis pantai (akresi dan erosi). Data-data spasial pesisir diklasifikasikan berdasarkan tingkat kerentanan, dan nilai kerentanan total dihitung dengan rumusan coastal vulnerability index. Hasilnya menunjukkan parameter fisik oseanografi (pasut dan tinggi gelombang) memiliki tingkat kerentanan sangat rendah. Sedangkan, ketinggian permukaan air laut dan jenis batuan geologi di sekitar pantai menunjukkan kerentanan tinggi dan sangat tinggi. Adapun klasifikasi perubahan garis pantai di sepanjang kecamatan bervariasi dan kerentanannya berkorealsi dengan tingkat akresi dan erosinya. Hasil perhitungan indeks kerentanan total CVI di semua kecamatan pesisir di Kabupaten Subang dikategorikan sangat rendah, akan tetapi kajian indeks secara lokal menunjukkan Kecamatan Sukasari dan Blanakan memiliki kerentanan sangat tinggi. Variasi indeks ini menunjukkan perubahan lokal di pesisir Kabupaten Subang berkorelasi dengan perubahan global yang terjadi, dimana kenaikan permukaan air laut lokal merupakan dampak perubahan laut dan iklim secara global.
Prediksi Perubahan Kawasan Hutan Mangrove Menggunakan Model Cellular Automata Markov pada Citra Penginderaan Jauh Landsat (Studi Kasus: Kawasan Resort Bama, Taman Nasional Baluran, Kabupaten Situbondo, Jawa Timur) Soni Darmawan; Aprilia Claudia; Anggun Tridawati
Rekayasa Hijau : Jurnal Teknologi Ramah Lingkungan Vol 6, No 1 (2022)
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/jrh.v6i1.57-72

Abstract

ABSTRACTTaman Nasional Baluran merupakan taman konservasi yang mengalami degradasi mangrove. Upaya restorasi mangrove perlu dilakukan untuk mendukung Peraturan Daerah pada Kabupaten Situbondo No 6 tahun 2014. Penelitian ini bertujuan untuk menghitung luasan perubahan kawasan hutan mangrove setiap tahun dan pada tahun prediksi. Penelitian ini menggunakan model terintegrasi Markov Chain danCellular Automata untuk menyimulasikan perubahan penggunaan lahan periode 2000 dan 2020 dan memprediksi penggunaan lahan mangrove periode 2030. Teknologi penginderaan jauh digunakan untuk menganalis penggunaan lahan melalui citra satelit Landsat (tahun 2000, 2010, dan 2020). Hasil penelitian menunjukkan bahwa penutupan lahan mangrove mengalami penurunan sebesar 0,5% pada tahun 2000 – 2010 dan mengalami peningkatan sebesar 3,5% pada tahun 2010-2020. Luasan mangrove terus mengalami peningkatan pada tahun 2020 – 2030 yaitu sebesar 9,3% atau 122 Ha. Penerapan model CA-Markov dalam memprediksi penutupan lahan menunjukan nilai kstandard 0,8 yang dapat diartikan bahwa pemodelan dapat diterima secara ilmiah. ABSTRAKTaman Nasional Baluran is a conservation park that is experiencing mangrove degradation. Mangrove restoration efforts need to be carried out to support the Regional Regulation of Situbondo Regency No. 6 of 2014. This study aims to calculate the extent of changes in mangrove forest areas every year and in the predicted year. This study used an integrated Markov Chain and Cellular Automata model to simulate land use change for the period 2000 and 2020 and predict mangrove land use for the period 2030. Remote sensing technology was used to analyze land use through Landsat satellite imagery (2000, 2010, and 2020). The results showed that mangrove land cover decreased by 0.5% in 2000 – 2010 and increased by 3.5% in 2010 – 2020. Mangrove area continues to increase in 2020 – 2030, which is 9.3% or 122 Ha. The application of the CA-Markov model to predict land cover shows a standard value of 0.8 which means that the modeling is scientifically accepted.
Potensi Objek Wisata di Kabupaten Semarang Darmawan, Soni; Setiawan, Ipang
Indonesian Journal for Physical Education and Sport Vol 4 No 2 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/inapes.v4i2.52317

Abstract

purpose of the research is to describe and assess the potential tourist attraction, mapping the potential of attractions, and obstacles in developing tourist objects in the City Semarang. The method in this study is a qualitative descriptive triangulation of data collection techniques in the form of observation, interviews and documentation. Analysis is done by data reduction, data presentation, drawing conclusions, and scoring method to determine the potential of attractions.The result of the research shows that the tourism object based on artificial tourism has an advantage compared to nature tourism and cultural tourism. Constraints of synergy between the government, stake holders and Public. The tourism potential that currently exists has not been evenly distributed by the government's role in places tourist attractions as a whole and the community lacks a sense of tourism awareness.The conclusion of the research is the potential of Semarang Regency tourism object natural tourism base, cultural tourism, and artificial tourism. Where artificial base tourist attractions have higher potential, which will later become the leading tourist base in Semarang Regency. High potential tourism objects in Semarang Regency include Bandungan Subdistrict (Candi Gedong Songo), Bawen District (Hortimart Agro Center).Constraints have not been completely involve or not involved the role of the government as a whole and the community lack of awareness of travel. Suggestions should attractions in the city of Semarang be develop, tourism awareness education needs to be held in the community.
Kecerdasan Buatan berbasis Geospasial (GeoAI) menggunakan Google Earth Engine untuk Monitoring Fenomena Urban Heat Island di Indonesia DARMAWAN, SONI; NURULHAKIM, NADA NAFISYAH; HERNAWATI, RIKA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 12, No 2: Published April 2024
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v12i2.303

Abstract

ABSTRAKFenomena Urban Heat Island (UHI) sangat penting untuk dimonitor agar terjaga kualitas lingkungan perkotaan. Dewasa ini teknologi kecerdasan buatan berbasis geospasial (GeoAI) merupakan teknologi yang menjanjikan untuk mengidentifikasi dan monitoring secara cepat dan efisien suatu kawasan yang luas. Walaupun Kecerdasan buatan sudah banyak diteliti namun GeoAI untuk identifikasi dan monitoring fenomena UHI di Indonesia masih terbatas. Penelitian ini bertujuan untuk membangun sistem GeoAI menggunakan google earth engine untuk monitoring fenomena UHI di Indonesia. Metodologi pada penelitian ini dimulai dari perancangan sistem, penghimpunan data dan komputasi, pembuatan dashboard, pengujian, hingga visualisasi UHI di Indonesia. Hasil penelitian ini berupa sistem aplikasi untuk monitoring fenomena UHI di Indonesia yang divisualisasikan dalam sebuah dashboard menggunakan Earth Engine Apps yang dapat diakses pada tautan https://bit.ly/UHIGDItenas.Kata kunci: Kecerdasan buatan, Penginderaan jauh dan Geospasial ABSTRACTUrban Heat Island (UHI) phenomenon is very important to monitor for managing the quality of the urban environment. Recently geospatial-based artificial intelligence (GeoAI) technology is a promising technology for quickly and efficiently identifying and monitoring on the large area. Even though artificial intelligence has been widely researched, GeoAI for identifying and monitoring the UHI phenomenon in Indonesia is still limited. This research aims to build a GeoAI system using the Google Earth engine for monitoring the UHI phenomenon in Indonesia. The methodology in this research starts from system design, data collection and computing, dashboard creation, testing and visualization of UHI in Indonesia. The results of this research are an application system for monitoring the UHI phenomenon in Indonesia which is visualized in a dashboard using Earth Engine Apps which can be accessed on https://bit.ly/UHIGDItenas.Keywords: Artificial Intelligence, Remote sensing, and Geospatial
Distribusi Spasial Kesehatan Mangrove Pada Citra Satelit Multitemporal Sebagai Upaya Mitigasi Perubahan Iklim tridawati, anggun; Armijon, Armijon; Sari, Atika; Darmawan, Soni
Jurnal Tekno Global Vol. 14 No. 01
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jtg.v14i01.5300

Abstract

ABSTRACT Mangrove forests are coastal ecosystems that play a strategic role in climate change mitigation through shoreline protection, blue carbon storage, and the provision of ecosystem services. Periodic monitoring of mangrove health is essential to support sustainable management and ecosystem restoration. This study aims to map the spatial distribution and analyze the dynamics of mangrove health in Labuhan Maringgai District, East Lampung Regency, Lampung Province, using Sentinel-2A imagery from 2015, 2016, 2017, 2018, 2020, and 2023. Mangrove health was assessed using the Mangrove Health Index (MHI), developed from four vegetation indices: Normalized Burn Ratio (NBR), Green Chlorophyll Index (GCI), Structure Insensitive Pigment Index (SIPI), and Atmospherically Resistant Vegetation Index (ARVI). Validation of annual mangrove spatial distribution using a confusion matrix resulted in an overall accuracy (OA) ranging from 90% to 94%. The results show a significant increase in the area of mangroves in the good health category from 1.73 ha (2015) to 540.04 ha (2023), accompanied by a decrease in the poor health category from 138.64 ha to 21.34 ha. Fluctuations in the moderate health category reflect natural growth dynamics and the influence of anthropogenic activities. These findings highlight the role of adaptive, multi-temporal spatial data-based management in maintaining mangrove health and prioritizing restoration in areas classified as poor. Keywords : mangrove health index, mangrove, vegetation index   ABSTRAK Hutan mangrove merupakan ekosistem pesisir yang memiliki peran strategis dalam mitigasi perubahan iklim melalui perlindungan garis pantai, penyimpanan karbon biru, dan penyediaan jasa ekosistem. Pemantauan kesehatan mangrove secara periodik sangat penting untuk mendukung pengelolaan berkelanjutan dan restorasi ekosistem. Penelitian ini bertujuan memetakan distribusi spasial dan menganalisis dinamika kesehatan mangrove di Kecamatan Labuhan Maringgai, Lampung Timur, Provinsi Lampung, menggunakan citra Sentinel-2A tahun 2015, 2016, 2017, 2018, 2020, dan 2023. Kesehatan mangrove dihitung menggunakan Mangrove Health Index (MHI) yang dibangun dari empat indeks vegetasi, yaitu Normalized Burn Ratio (NBR), Green Chlorophyll Index (GCI), Structure Insensitive Pigment Index (SIPI), dan Atmospherically Resistant Vegetation Index (ARVI). Validasi distribusi spasial mangrove setiap tahun menggunakan confusion matrix menghasilkan tingkat akurasi keseluruhan (OA) antara 90–94%. Hasil penelitian menunjukkan tren peningkatan signifikan luas mangrove kategori kesehatan baik dari 1,73 ha (2015) menjadi 540,04 ha (2023), diikuti penurunan luas kategori buruk dari 138,64 ha menjadi 21,34 ha. Fluktuasi pada kategori sedang menunjukkan dinamika pertumbuhan alami dan pengaruh aktivitas antropogenik. Temuan ini menegaskan peran pengelolaan adaptif berbasis data spasial multi-temporal untuk mempertahankan kesehatan mangrove serta memprioritaskan restorasi pada area berkategori buruk. Kata Kunci : mangrove health index, mangrove, indeks vegetasi
MANGROVE ABOVE GROUND BIOMASS ESTIMATION USING COMBINATION OF LANDSAT 8 AND ALOS PALSAR DATA Gathot Winarso; Yenni Vetrita; Anang D Purwanto; Nanin Anggraini; Soni Darmawan; Doddy M. Yuwono
International Journal of Remote Sensing and Earth Sciences Vol. 12 No. 2 (2015)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2015.v12.a2687

Abstract

Mangrove ecosystem is important coastal ecosystem, both ecologically and economically. Mangrove provides rich-carbon stock, most carbon-rich forest among ecosystems of tropical forest. It is very important for the country to have a large mangrove area in the context of global community of climate change policy related to emission trading in the Kyoto Protocol. Estimation of mangrove carbon-stock using remote sensing data plays an important role in emission trading in the future. Estimation models of above ground mangrove biomass are still limited and based on common forest biomass estimation models that already have been developed. Vegetation indices are commonly used in the biomass estimation models, but they have low correlation results according to several studies. Synthetic Aperture Radar (SAR) data with capability in detecting volume scattering has potential applications for biomass estimation with better correlation. This paper describes a new model which was developed using a combination of optical and SAR data. Biomass is volume dimension related to canopy and height of the trees. Vegetation indices could provide two dimensional information on biomass by recording the vegetation canopy density and could be well estimated using optical remote sensing data. One more dimension to be 3 dimensional feature is height of three which could be provided from SAR data. Vegetation Indices used in this research was NDVI extracted from Landsat 8 data and height of tree estimated from ALOS PALSAR data. Calculation of field biomass data was done using non-decstructive allometric based on biomass estimation at 2 different locations that are Segara Anakan Cilacap and Alas Purwo Banyuwangi, Indonesia. Correlation between vegetation indices and field biomass with ALOS PALSAR-based biomass estimation was low. However, multiplication of NDVI and tree height with field biomass correlation resulted R2 0.815 at Alas Purwo and R2 0.081 at Segara Anakan. Low correlation at Segara anakan was due to failed estimation of tree height. It seems that ALOS PALSAR height was not accurate for determination of areas dominated by relative short trees as we found at Segara Anakan Cilacap, but the result was quite good for areas dominated by high trees. To improve the accuracy of tree height estimation, this method still needs validation using more data.
AN EFFECTIVE INFORMATION SYSTEM OF DROUGHT IMPACT ON RICE PRODUCTION BASED ON REMOTE SENSING Rizatus Shofiyati; Wataru Takeuchi; Soni Darmawan; Parwati Sofan
International Journal of Remote Sensing and Earth Sciences Vol. 11 No. 2 (2014)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2014.v11.a2613

Abstract

Long droughts experienced in the past are identified as one of the main factors in the failure of rice production. In this regard, special attention to monitor the condition is encouraged to reduce the damage. Currently, various satellite data and approaches can withdraw valuable information for monitoring and anticipating drought hazards. MODIS, MTSAT, AMSR-E, TRMM and GSMaP have been used in this activity. Meteorological drought index (SPI) of the daily and monthly rainfall data from TRMM and GSMaP have analyzed for last 10-year period. While, agronomic drought index has been studied by observing the character of some indices (EVI, VCI, VHI, LST, and NDVI) of sixteen-day and monthly MODIS, MTSAT, and AMSR-E data at a period of 4 years. Network for satellite data transfer has been built between LAPAN (data provider), ICALRD (implementer), IAARD Cloud Computing, University of Tokyo (technical supporter), and NASA. Two information system have been developed: 1) agricultural drought using the model developed by LAPAN, and 2) meteorological drought developed by Takeuchi (University of Tokyo).The accuracy study using quantitative method for LAPAN model uses VHI is 60% (Kappa 0,44), while that of for University of Tokyo model uses qualitative model with KBDI value 500-600 shows an early indication of drought for paddy field. This will help the government or field officers in rapid management actions for the indicated drought area.This paper describes the implementation and dissemination of drought impact monitoring model on the area of rice production center using an integrated information system satellite based model. The two developed information systems are effective for spatially dissemination of drought information.
Co-Authors Aditya, Yohanes D Agung Budi Harto Aida Heriati Anang D Purwanto Anang D. Purwanto Anggun Tridawati Anggun Tridawati Aprilia Claudia Armijon . Asmi M. Napitu Asmi M. Napitu Atika Sari Dhiky Hartono Dian N Handiani Doddy M. Yuwono Doddy M. Yuwono Dudung M Hakim Dudung Muhally Hakim Dudung Muhally Hakim Elysabeth Nindy Nasing Gathot Winarso Gathot Winarso Handiani, Dian Noor Hartono, Dhiky Hernawati, Rika Ipang Setiawan Irland Fardani Ketut Wikantika Muhammad F Suryahadi Nanin Anggraini Nanin Anggraini Nengah Widiadnyana Nengah Widiadnyana NURULHAKIM, NADA NAFISYAH Parwati Sofan Rizatus Shofiyati Suryahadi, Muhammad F Tridawati, Anggun Wataru Takeuchi Yenni Vetrita Yenni Vetrita Yohanes D Aditya Yohanes D. Aditya