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All Journal Journal of Geoscience, Engineering, Environment, and Technology MAJALAH ILMIAH GLOBE Jurnal Environmental Science The International Journal of Remote Sensing and Earth Sciences (IJReSES) Scientific Contributions Oil and Gas
Retnadi Heru Jatmiko
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Astronomy Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Education Energy Engineering Environmental Science Physics Social Sciences

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Pemanfaatan Citra Optik dan Citra Radar dalam Identifikasi Tambak di Pesisir Kabupaten Pati Provinsi Jawa Tengah Nurul Afdal Haris; Retnadi Heru Jatmiko; Nur Mohammad Farda
Jurnal Environmental Science Vol 4, No 2 (2022): April
Publisher : UNIVERSITAS NEGERI MAKASSAR

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (604.7 KB) | DOI: 10.35580/jes.v4i2.29832

Abstract

Penelitian ini bertujuan untuk membandingkan keakuratan hasil identifikasi penggunaan lahan tambak di daerah pesisir Kabupaten Pati Provinsi Jawa Tengah. Dengan memanfaatkan data citra optik (Sentinel-2) dan citra radar (Sentinel-1). Pemetaan dilakukan dengan menerapkan transformasi indeks air berupa NDWI (Normalized Difference Water Index) untuk citra optik dan SDWI (Sentinel-1 Difference Water Index) untuk citra radar serta pengklasifikasian terbimbing dengan metode Random Forest pada masing-masing citra. Hasil penelitian menunjukkan bahwa penggunaan citra optik dalam pemetaan tambak memberikan akurasi yang lebih baik dibandingkan dengan citra radar berdasarkan hasil uji akurasi dengan metode Confusiion Matrix. Dimana hasil menunjukkan pada citra optik Sentinel-2 menghasilkan akurasi sebesar 94.61% dan untuk citra radar Sentinel-1 sebesar 73.21%. Serta penggunaan transformasi indeks air sangat membantu dalam penentuan sampel klasifikasi. Ditemukan pada penelitan bahwa akurasi pada citra radar cukup rendah dibandingkan dengan citra optik dikarenakan sensitivitas pada citra radar terhadap badan air sangat tinggi. Sehingga memungkinkan kesalahan klasifikasi antara laut dan tambak
ANALISIS PENGARUH SUHU PERMUKAAN LAHAN TERHADAP ELEMEN IKLIM MIKRO DI SURAKARTA MENGGUNAKAN CITRA PENGINDERAAN JAUH MULTITEMPORAL Siti Zahrotunisa; Retnadi Heru Jatmiko; Wirastuti Widyatmanti
Majalah Ilmiah Globe Vol. 22 No. 1 (2020): GLOBE VOL 22 NO 1 TAHUN 2020
Publisher : Badan Informasi Geospasial

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Abstract

Perubahan penutup lahan seperti ekspansi lahan terbangun berpotensi untuk mengalami peningkatan suhu permukaan lahan dan perubahan elemen iklim mikro yang menyebabkan penurunan kenyamanan. Penelitian ini bertujuan untuk mengetahui kemampuan data penginderaan jauh untuk memperoleh paramater penutup lahan dan suhu permukaan lahan serta mengkaji pengaruh suhu permukaan lahan terhadap elemen iklim mikro (suhu udara, kelembapan udara relatif, dan kecepatan angin). Data penginderaan jauh yang digunakan adalah citra Landsat-8 OLI/TIRS, Aqua MODIS perekaman tanggal 19 Juli 2013 dan 23 Juni 2015. Metode yang digunakan dalam penelitian ini adalah klasifikasi Maximum Likelihood, Split Windows Algorithm (SWA), Inverse Distance Weighted (IDW), dan pengukuran di lapangan. Analisis statistik yang digunakan adalah korelasi Pearson Product Moment, regresi, Confusion Matrix, dan RMS Difference. Hasil penelitian menunjukkan bahwa, data penginderaan jauh dapat digunakan untuk memperoleh informasi yang akurat untuk penutup lahan dengan akurasi 92% serta suhu permukaan lahan dengan dengan nilai RMS 5,8°C dan 4,8°C. Suhu permukaan lahan dengan suhu udara dan kelembapan udara tahun 2015 memiliki hubungan yang kuat dan siginifkan, sementara dengan kecepatan angin memiliki hubungan yang rendah dan tidak signifikan. Selain itu, hubungan pada tahun 2013 lebih rendah dibandingkan tahun 2015.
ESTIMATION OF ABOVEGROUND CARBON STOCK USING SAR SENTINEL-1 IMAGERY IN SAMARINDA CITY Bayu Elwanto Bagus Dewanto; Retnadi Heru Jatmiko
International Journal of Remote Sensing and Earth Sciences Vol. 18 No. 1 (2021)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2021.v18.a3609

Abstract

Estimation of aboveground carbon stock on stands vegetation, especially in green open space, has become an urgent issue in the effort to calculate, monitor, manage, and evaluate carbon stocks, especially in a massive urban area such as Samarinda City, Kalimantan Timur Province, Indonesia. The use of Sentinel-1 imagery was maximised to accommodate the weaknesses in its optical imagery, and combined with its ability to produce cloud-free imagery and minimal atmospheric influence. The study aims to test the accuracy of the estimated model of above-ground carbon stocks, to ascertain the total carbon stock, and to map the spatial distribution of carbon stocks on stands vegetation in Samarinda City. The methods used included empirical modelling of carbon stocks and statistical analysis comparing backscatter values and actual carbon stocks in the field using VV and VH polarisation. Model accuracy tests were performed using the standard error of estimate in independent accuracy test samples. The results show that Samarinda Utara subdistrict had the highest carbon stock of 3,765,255.9 tons in the VH exponential model. Total carbon stocks in the exponential VH models were 6,489,478.1 tons, with the highest maximum accuracy of 87.6 %, and an estimated error of 0.57 tons/pixel.
MULTI-POLARIZATION FOR ANALYSIS OF GEOLOGICAL STRUCTURES AS FORMATION OF HYDROCARBON TRAPS CONTROLLER IN EAST JAVA BASIN Indah Crystiana; Hartono Hartono; Retnadi Heru Jatmiko; Taufan Junaedi
Scientific Contributions Oil and Gas Vol 41 No 2 (2018)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/SCOG.41.2.335

Abstract

The decline in oil reserves and the increasing demand for oil and gas energy led to the search for new reserves. The geological structure pattern used to know the pattern of distribution and formation of hydrocarbons traps in the East Java Basin is one of the important information that can be extracted through remote sensing data of multi-polarization system. The multi-polarization system of this study merged the \ Alos Palsar imagery with HH and HV polarization, and Sentinel Image polarized VV and VH. Processing both image data through calibration, multilook, speckle fi ltering, geometric correction and mosaic. Filtered imagery is composite and sharpening. The fi ltering technique use Lee 5x5 kernel fi lter and then continue with 5x5 median fi lter. The results of multi-polarization system image interpretation can be identifi ed by fold, thrust faults, normal faults, strike-slip faults, bedding, and closure structure. In the formation research area the structure lasted two periods, with the main emphasis N-S in the order of 1 and the main direction of the SW-NE direction in the order-2. The hydrocarbon traps and exploration targets can be distinguished in three zones (Zone A, Zone B, and Zone C). Closure in Zone A includes closures 3, 4, 5, 7, 8, 9, 10, 11, 22, 23, 24, 25, 26, 27, 28, 29, 30. Closure in Zone B includes closures 1, 2, 6, 12, 13, 14, 15, 16, 17, 31, 32. Closure on Zone C includes closure18, 19, 20, 21.
Applied One-Dimensional Convolutional Neural Network Image Fusion Sentinel-1 SAR and Sentinel-2 for Classification and Mapping Dynamics of Coastal Wetlands in Segara Anakan, Cilacap Regency, Indonesia Muhammad Usman Zakaria; Wirastuti Widyatmanti; Retnadi Heru Jatmiko
Journal of Geoscience, Engineering, Environment, and Technology Vol. 10 No. 4 (2025): JGEET Vol 10 No 04 : December (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2025.10.4.22909

Abstract

Coastal wetlands have an important function, namely as an economic function and an ecological function, therefore the mapping and classification of wetlands is very important. However, remote sensing has limitations, namely high variability and spectral similarity between kleas. This makes the development of image fusion of SAR and optical images in classification, the combination of SAR and optical can provide better information. Over time, the CNN method of performing image fusion developed, which is a good method used to perform classification. In this study, Sentinel-2 fusion and VV polarization were used to identify the shrub classes that dominate Segara Anakan. The results of the application of CNN1D in the classification of wetlands in Segara Anakan resulted in an overall accuracy of 79.37% and a kappa of 0.76, so that CNN1D is very good at recognizing wetland classes but has limitations in recognizing Nypa which has spectral similarities with other classes. The benefit of using CNN1D that has been trained is that the model can be applied to a variety of other images. In its application, we used the image of Segara Anakan from 2019-2025 so as to gain knowledge, namely that Segara Anakan is controlled by the sedimentation process so that wetland classes increase dynamically. The massive sedimentation process in Segara Anakan was then overgrown by mangrove vegetation, besides that another trend is the change of vegetation from mangroves to nypa vegetation. This is because nypa vegetation is a vegetation that can adapt to medium to low salinity. Despite conducting a multitemporal study with a narrow gap of 6 years, the CNN1D that we have trained can classify wetlands in Segara Anakan well from 2019 to 2025. In addition, CNN1D with a light computing load can be an option if you need deep learning applications in other research.
Co-Authors Aminuyati Bayu Elwanto Bagus Dewanto Hartono Hartono Indah Crystiana Muhammad Usman Zakaria Nur Mohammad Farda Nurul Afdal Haris Siti Zahrotunisa Taufan Junaedi Wirastuti Widyatmanti