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All Journal Indonesian Journal of Geography Jurnal Ilmu Lingkungan CIVED (Journal of Civil Engineering and Vocational Education) Tunas Geografi Jurnal Teknosains Majalah Geografi Indonesia Journal of Geoscience, Engineering, Environment, and Technology Journal of Applied Geospatial Information Jurnal Sains & Teknologi Modifikasi Cuaca JURNAL ILMIAH GEOMATIKA MAJALAH ILMIAH GLOBE Jurnal Environmental Science International Journal of Remote Sensing and Earth Sciences (IJReSES) Jurnal Sosial dan Teknologi Buletin GAW Bariri ( BGB) Jurnal Relawan dan Pengabdian Masyarakat REDI Journal of Sciencce Technology and Visual Culture Inovasi Gagasan Abdimas & Kuliah Kerja Nyata International Journal of Oil Palm The International Journal of Remote Sensing and Earth Sciences (IJReSES) Scientific Contributions Oil and Gas BULETIN FISIKA
Retnadi Heru Jatmiko
Fakultas Geografi, Universitas Gadjah Mada, Yogyakarta
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Other

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Identification of Ganoderma boninense Infection Levels on Oil Palm Using Vegetation Index Dhimas Wiratmoko; Agus Eko Prasetyo; Retnadi Heru Jatmiko; Muhammad Arif Yusuf; Suroso Rahutomo
International Journal of Oil Palm Vol. 1 No. 3 (2018): September 2018
Publisher : Indonesian Oil Palm Society /IOPS (Masyarakat Perkelapa-sawitan Indonesia /MAKSI)

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Abstract

Basal stem rot (BSR) is known as a deathly disease in oil palm. It can immediately cause a significant decrease in the population of oil palm per hectare. BSR is associated with infection of Ganoderma boninense. The identification of infected palms at an early stage is the key to control the disease. Manual identification by observing an individual palm in the field is the most common method; however, it is time consuming as well as laborious and expensive. A faster, less laborious, and less expensive method is by analyzing multispectral aerial photograph from unmanned aerial vehicle (UAV). A study to test this method was conducted in an oil palm plantation in Batubara region, North Sumatera. The plantation was acknowledged as an endemic area of G. boninense. The objectives of this study were to identify levels of G. boninense infection in oil palm based on spectral difference by counting the vegetation index from the multispectral image of UAV and mapping the distribution of BSR infection. Four methods were used to transform vegetation index, i.e. simple ratio (SR), normalized different vegetation index (NDVI), enhanced vegetation index (EVI) and atmospherically resistance vegetation index (ARVI). The results show that the index transformation of SR, NDVI, EVI and ARVI was able to identify the infection level of G. boninese.
Pembentukan Kampung Tangguh Bencana Syurdori Distrik Supiori Timur Dina Ruslanjari; Retnadi Heru Jatmiko; Sigit Sulistiyo
Jurnal Relawan dan Pengabdian Masyarakat REDI Vol. 1 No. 3 (2024): Februari
Publisher : Yayasan REDI Tiga Monas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69773/f3x78w05

Abstract

Supiori Regency is one of the areas in Papua Province which is included in the frontier, remote, and underdeveloped region in the Pacific Ocean, which is quite vulnerable to experiencing a series of natural events that have the potential to become a disaster for the local population. The disaster threat index in Supiori Regency includes tsunamis, earthquakes and extreme waves and abrasion (GEA). A total of approximately 20 settlements in Syurdori Village (part of the East Supiori District) were affected by a tsunami with a tsunami height of 7 meters that occurred in 1996 which resulted in several houses being damaged by tsunami waves. The purpose of this article is to examine the process and benefits of establishing a disaster resilient village (Kamtana), the process of increasing community preparedness and the process of creating a participatory evacuation route map in Syurdori Village, East Supiori District, Supiori Regency, Papua Province. The research method used was qualitative, with primary data collection techniques in the form of focus group discussions (FGDs). The sampling technique used in selecting the research location used a purposive sampling method based on the condition of the vulnerability of Syurdori Village to various disasters, especially floods, extreme waves and abrasion, earthquakes and tsunamis. The results of this study are the implementation process of establishing a Disaster Resilient Village, increasing community preparedness and conducting a participatory mapping process related to determining evacuation routes in Syurdori Village, Papua Province.
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.
Improvement of Google Earth Engine-Based Multi-satellite Rainfall Estimation using Rain Gauge Data in South Sulawesi Prayoga Ismail; Retnadi Heru Jatmiko; Nur Mohammad Farda; Muhammad Arif Munandar
BULETIN FISIKA Vol. 26 No. 1 (2025): BULETIN FISIKA
Publisher : Departement of Physics Faculty of Mathematics and Natural Sciences, and Institute of Research and Community Services Udayana University, Kampus Bukit Jimbaran Badung Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/BF.2025.v26.i01.p03

Abstract

Precipitation, particularly rainfall, is vital in understanding weather and climate. In Indonesia, the uneven distribution of in situ rainfall observations poses a challenge to accurately measuring surface rainfall. Remote sensing systems and cloud computing technologies, such as Google Earth Engine (GEE), offer potential solutions. This study evaluates the spatial distribution and performance of four multi-satellite rainfall estimates available in GEE, namely CHIRPS, GSMAP, GPM-IMERG, and PERSIANN-CDR, before and after calibration using BMKG rain gauge data in South Sulawesi during the 2018–2023 period. The original multi-satellite data revealed significant discrepancies, with an annual RMSE of 1534 mm/year, a yearly RSQ value of 0.3, and an annual RBIAS of 27% compared to observational data. Among the datasets, O_CHPS demonstrated the best spatial similarity visually. Calibration using the Geographical Differential Analysis (GDA) method effectively enhanced the accuracy, reducing the annual RMSE to 807 mm/year, increasing the yearly RSQ to 0.5, and lowering the annual bias to 1.6%. Improvements were also noted in monthly and daily rainfall estimates. After calibration, C_PRSN exhibited the most favorable spatial distribution and performance, achieving a 26% reduction in annual RMSE, a 105% increase in annual RSQ, and a 101% decrease in annual bias compared to its initial data. Furthermore, sensitivity to elevation and rainfall intensity was enhanced, with improved detection indicators, particularly for heavy to extreme rainfall events. This included a 43% increase in POD, a 262% increase in CSI, and a 42% reduction in FAR.
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.
Kalibrasi Estimasi Curah Hujan CHIRPS dengan Data Observasi di Semarang Jatmiko, Retnadi Heru; Ismail, Prayoga
Buletin GAW Bariri (BGB) Vol 6 No 2 (2025): BULETIN GAW BARIRI
Publisher : Stasiun Pemantau Atmosfer Global Lore Lindu Bariri - Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/bgb.v6i2.147

Abstract

Precipitation is a crucial component of weather and climate, playing a fundamental role in the Earth's water cycle. However, in situ, rain gauge networks are still limited in their ability to comprehensively monitor precipitation across all regions, including Semarang regency and city. Satellite – based remote sensing and cloud computing technology, such as Google Earth Engine (GEE), offer a solution for generating rainfall estimates with spatial coverage. This study optimizes CHIRPS rainfall estimates through a calibration process using BMKG rain gauge data over the Semarang region for 2021 – 2023. It evaluates the spatial distribution and performance of CHIRPS before and after calibration. Compared to observational data, the original CHIRPS dataset exhibited significant spatial discrepancies, with a daily RMSE of 44 mm/day, a coefficient of determination (RSQ) of 0.02, and a SMAPE of 99%. The collinearity analysis showed that the relationship between CHIRPS and observational data tends to be scattered and less linear on a daily scale, but after calibration, this relationship becomes stronger. Calibration using the Geographical Differential Analysis (GDA) method successfully improved CHIRPS accuracy, as indicated by a reduction in daily RMSE to 25 mm/day, an increase in daily RSQ to 0.62, and a decrease in daily SMAPE to 70%. These improvements were also observed in monthly and annual rainfall estimates. The calibrated CHIRPS data exhibited enhanced spatial distribution and performance, with a 10% reduction in annual RMSE, a 25% increase in annual RSQ, and a 20% decrease in annual SMAPE compared to the original dataset. Furthermore, sensitivity to rainfall intensity improved, particularly for heavy to extreme rainfall events, as evidenced by a 58% reduction in the FAR, a 73% increase in the POD, and a 48% improvement in the CSI.
Optimalisasi Pemanenan Air Hujan untuk Ketahanan Pangan dan Adaptasi Kekeringan di Desa Tepus, Gunungkidul Muhamad Irfan Nurdiansyah; Dina Ruslanjari; Retnadi Heru Jatmiko; Nabilla Auriel Fajarian; Silfani Silfani
Jurnal Igakerta Vol. 2 No. 3 (2025): Jurnal Igakerta
Publisher : IGAKERTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70234/3fgwe262

Abstract

Ketersediaan air bersih di kawasan karst menjadi tantangan utama bagi ketahanan pangan dan adaptasi terhadap kekeringan. Desa Tepus, Kecamatan Tepus, Kabupaten Gunungkidul, menghadapi keterbatasan sumber air bersih akibat rendahnya retensi tanah kapur dan ketergantungan pada distribusi eksternal saat musim kemarau. Program pengabdian masyarakat ini bertujuan mengoptimalkan rainwater harvesting (RWH) berbasis partisipasi komunitas untuk meningkatkan akses air bersih dan mendukung kesejahteraan. Kegiatan dilaksanakan melalui pendekatan partisipatif, meliputi identifikasi kebutuhan, Focus Group Discussion (FGD), pembangunan, pelatihan, dan evaluasi. Hasil program menunjukkan peningkatan ketersediaan air rumah tangga dan dukungan terhadap ketahanan pangan lokal. Program ini menegaskan pentingnya kolaborasi multipihak dalam kerangka pentahelix dan berkontribusi pada pencapaian SDG 6 (Clean Water and Sanitation) serta mitigasi risiko kekeringan. Dengan demikian, RWH berpotensi menjadi model berkelanjutan yang dapat direplikasi di wilayah rawan kekeringan serupa.
Co-Authors Adhitama, Adib Prima Agung Kurniawan Agus Eko Prasetyo Ahmad Kosasih Alpon Sepriando Aminuyati Andi Syahputra Anugerah Ramadhian AP Ardhi Arnanto Arief Yamin Atmojo, Aulia Try Bayu Elwanto Bagus Dewanto Dewanto, Bayu Elwanto Bagus Dhimas Wiratmoko Dina Ruslanjari Dina Ruslanjari Dwi Setyo Aji Dyah Rahmawati Hizbaron Estuning Tyas Wulan Mei Hartono Hartono Hartono Hartono Hartono Hartono Hendry Frananda, Hendry Husain, Rainaldy Ikaf Fajar Maulana Indah Crystiana Ismail, Prayoga Isnaini, Een Lujainatul Junun Sartohadi Laia, Bonifasius Efraim Muhamad Irfan Nurdiansyah Muhammad Arif Munandar Muhammad Arif Munandar Muhammad Arif Yusuf Muhammad Fauzan Ramadhan Muhammad Ulin Nuha Muhammad Usman Zakaria Nabilla Auriel Fajarian Nur Mohammad Farda Nur Mohammad Farda Nur Mohammad Farda Nur Mohammad Farda Nurul Afdal Haris Perdana, Redho Surya Pramudya, Alif Farhan Prawira, Devara Prayoga Ismail Prayoga Ismail Projo Danoedoro R. Suharyadi R. Suharyadi Ramlah Ramlah Sepriando, Alpon Setiaji, Danang Sigit Sulistiyo Silfani Silfani Siti Zahrotunisa Siti Zahrotunisa Sri Retno Murdiyati Sudaryatno Sudaryatno Suroso Rahutomo Taufan Junaedi Trida Ridho Fariz Warsini Handayani Widyatmanti, Wirastuti Wirastuti Widyatmanti Yudha Achmad Perlambang