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All Journal Indonesian Journal of Geography TELKOMNIKA (Telecommunication Computing Electronics and Control) Geoplanning : Journal of Geomatics and Planning MAJALAH ILMIAH GLOBE Jurnal Geografi Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital International Journal of Remote Sensing and Earth Sciences (IJReSES) Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital The International Journal of Remote Sensing and Earth Sciences (IJReSES) Biodiversitas Journal of Biological Diversity
Rokhmatuloh, Rokhmatuloh
Department Of Geography, Faculty Of Mathematics And Natural Science, Indonesia University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Environmental Science Social Sciences Other

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Roof materials identification based on pleiades spectral responses using supervised classification Ayom Widipaminto; Yohanes Fridolin Hestrio; Yuvita Dian Safitri; Donna Monica; Dedi Irawadi; Rokhmatuloh Rokhmatuloh; Djoko Triyono; Erna Sri Adiningsih
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.18155

Abstract

The current urban environment is very dynamic and always changes both physically and socio-economically very quickly. Monitoring urban areas is one of the most relevant issues related to evaluating human impacts on environmental change. Nowadays remote sensing technology is increasingly being used in a variety of applications including mapping and modeling of urban areas. The purpose of this paper is to classify the Pleiades data for the identification of roof materials. This classification is based on data from satellite image spectroscopy results with very high resolution. Spectroscopy is a technique for obtaining spectrum or wavelengths at each position from various spatial data so that images can be recognized based on their respective spectral wavelengths. The outcome of this study is that high-resolution remote sensing data can be used to identify roof material and can map further in the context of monitoring urban areas. The overall value of accuracy and Kappa Coefficient on the method that we use is equal to 92.92% and 0.9069.
Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta Province Ardiansyah Ardiansyah; Revi Hernina; Weling Suseno; Faris Zulkarnain; Ramadhani Yanidar; Rokhmatuloh Rokhmatuloh
Indonesian Journal of Geography Vol 50, No 2 (2018): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3151.982 KB) | DOI: 10.22146/ijg.36113

Abstract

This study developed a model to identify the percent of building density (PBD) of DKI Jakarta Province in each pixel of Landsat 8 imageries through a multi-index approach. DKI Jakarta province was selected as the location of the study because of its urban environment characteristics.  The model was constructed using several predictor variables i.e.  Normalized Difference Built-up Index (NDBI), Soil-adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and surface temperature from thermal infrared sensor (TIRS). The calculation of training sample data was generated from high-resolution imagery and was correlated to the predictor variables using multiple linear regression (MLR) analysis. The R values of predictor variables are significantly correlated. The result of MLR analysis shows that the predictor variables simultaneously have correlation and similar pattern to the PBD based on high-resolution imageries. The Adjusted R Square value is 0,734, indicates that all four variables influences predicting the PBD by 73%.
A Preliminary Study of the Physico-Chemical Parameters and Potential Pollutant Sources in Urban Lake Rawa Besar, Depok, Indonesia Mangapul Parlindungan Tambunan; Kuswantoro Marko; Ratna Saraswati; Rokhmatuloh Rokhmatuloh; Revi Hernina
Indonesian Journal of Geography Vol 53, No 2 (2021): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.60420

Abstract

Lake Rawa Besar is an urban lake surrounded by dense settlements and commercial areas that are currently experiencing physical and ecological pressures due to uncontrolled land-use change around the lake. Therefore, this preliminary study aimed to investigate the sustainable management of the lake in order to create a recreational destination area. It was carried out by ascertaining the lake water quality status through the analysis of the physical and chemical parameters and identifying the potential pollutant sources due to land use and human activities. The physical parameters include TDS, TSS, Turbidity, while the chemical parameters include Nitrate-N, Total Phosphate-P, and BOD. Furthermore, field surveys on 30 water samples were conducted once at noon and statistical analysis was used to ascertain the correlation between the physical and chemical parameters. Finally, Geographic Information System (GIS) tools were used to investigate the spatial distribution of the Physico-chemical parameters and the potential pollutant sources. The results showed that based on the six parameters of the water quality status, the lake was lightly polluted. It also showed that three parameters such as Turbidity, BOD, and TSS exceed the permissible limit with 93.3, 66.7, 43.7% of the total samples, respectively. Additionally, a strong correlation existed between BOD and Turbidity with r=0.95, while a medium correlation existed between Nitrate-N and Phosphate-P with r=0.40. The spatial distribution of the concentration of the physico-chemical parameters generally had a varied pattern,  however, Turbidity and BOD had a similar pattern, especially in the bank areas. Finally, domestic and organic wastes were indicated as pollutant sources, which increased eutrophication in the lake.
Monitoring Dynamics of Vegetation Cover with the Integration of OBIA and Random Forest Classifier Using Sentinel-2 Multitemporal Satellite Imagery Nurwita Mustika Sari; R. Rokhmatuloh; Masita Dwi Mandini Manessa
Geoplanning: Journal of Geomatics and Planning Vol 8, No 2 (2021)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/geoplanning.8.2.75-84

Abstract

The existence of vegetation in an area has an important role to maintain the carrying capacity of the environment and create a comfortable environment as a place to live. In an effort to create a sustainable environment, there are various pressures on vegetation that cause a decrease in vegetation area. Economic activity, population growth and other anthropogenic activities trigger the dynamics of vegetation cover in an area that causes land cover changes from vegetation to non-vegetation. Majalengka Regency as one of the areas with intensive regional physical development in line with the operation of BIJB Kertajati and the Cipali toll road became the study area in this research. This study aims to monitor the dynamics of vegetation cover with the proposed method namely the integration of the OBIA and Random Forest classifier using multi temporal Sentinel-2 satellite imagery. The results show that there is a decrease in the area of vegetation in the research area as much as 4,329.6 hectares to non-vegetation areas in the period 2016-2020. The vegetation area in 2020 is 84,716.07 hectares and non-vegetation area is 35,708 hectares. Thus, there has been a decrease in the percentage of vegetation area from 73.94% in 2016 to 70.35% in 2020, meanwhile for non-vegetation areas there has been an increase from 26.06% in 2016 to 29.65% in 2020.
The Spatial Model of Paddy Productivity Based on Environmental Vulnerability in Each Phase of Paddy Planting Rahmatia Susanti; S. Supriatna; R. Rokhmatuloh; Masita Dwi Mandini Manessa; Aris Poniman; Yoniar Hufan Ramadhani
Geoplanning: Journal of Geomatics and Planning Vol 8, No 2 (2021)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/geoplanning.8.2.127-136

Abstract

The national primary always growth and increase in line with the increase in population, such as the rise of rice consumption in Indonesia.  Paddy productivity influenced by the physical condition of the land and the declining of those factors can detected from the environmental vulnerability parameters. Purpose of this study was to compile a spatial model of paddy productivity based on environmental vulnerability in each planting phase using the remote sensing and GIS technology approaches. This spatial model is compiled based on the results of the application of two models, namely spatial model of paddy planting phase and paddy productivity. The spatial model of paddy planting phase obtained from the analysis of vegetation index from Sentinel-2A imagery using the random forest classification model. The variables for building the spatial model of the paddy planting phase are a combination of NDVI vegetation index, EVI, SAVI, NDWI, and time variables. The overall accuracy of the paddy planting phase model is 0.92 which divides the paddy planting phase into the initial phase of planting, vegetative phase, generative phase, and fallow phase. The paddy productivity model obtained from environmental vulnerability analysis with GIS using the linear regression method. The variables used are environmental vulnerability variables which consist of hazards from floods, droughts, landslides, and rainfall. Estimation of paddy productivity based on the influence of environmental vulnerability has the best accuracy done at the vegetative phase of 0.63 and the generative phase of 0.61 while in the initial phase of planting cannot be used because it has a weak relationship with an accuracy of 0.35.
ANALISIS METODE KOMPRESI BERDOMAIN WAVELET PADA CITRA SATELIT RESOLUSI SANGAT TINGGI Ayom Widipaminto; Andy Indradjad; Donna Monica; Rokhmatuloh Rokhmatuloh
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 1 Juni 2019
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1188.623 KB) | DOI: 10.30536/j.pjpdcd.2019.v16.a3058

Abstract

Masalah yang kerap terjadi pada citra satelit penginderaan jauh, terutama citra resolusi sangat tinggi, salah satunya adalah besarnya media penyimpanan dan bandwidth yang dibutuhkan saat data ditransmisi ke tempat lain. Pada pengolahan citra satelit, kompresi data perlu dilakukan pada data citra satelit yang ada demi memudahkan transmisi dan penyimpanan citra. Makalah ini melakukan komparasi pada metode-metode kompresi domain wavelet yaitu metode wavelet, bandelet, dan CCSDS agar ditemukan metode terbaik untuk mengompresi data citra satelit resolusi sangat tinggi Pleiades. Hasil percobaan menunjukkan bahwa metode wavelet dan bandelet lebih baik dalam hal mempertahankan kualitas citra dengan PSNR di kisaran 50 dB, sementara metode CCSDS lebih baik dalam hal mereduksi ukuran citra menjadi seperdelapan citra asli.
PENGEMBANGAN MODEL IDENTIFIKASI DAERAH BEKAS KEBAKARAN HUTAN DAN LAHAN (BURNED AREA) MENGGUNAKAN CITRA MODIS DI KALIMANTAN (MODEL DEVELOPMENT OF BURNED AREA IDENTIFICATION USING MODIS IMAGERY IN KALIMANTAN) - Suwarsono; - Rokhmatuloh; Tarsoen Waryono
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 10 No.2 Desember 2013
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1362.495 KB)

Abstract

Kebakaran hutan dan lahan telah menjadi ancaman cukup serius bagi masyarakat secara global pada dua dekade terakhir, terutama terkait dengan degradasi aspek-aspek lingkungan dan sumberdaya alam. Kalimantan merupakan daerah di Indonesia yang paling rawan terhadap bencana kebakaran hutan dan lahan. Penelitian ini bertujuan untuk mengembangkan model-model algoritma untuk mengidentifikasi area terbakar yang paling sesuai diaplikasikan di Kalimantan menggunakan citra MODIS. Metode penelitian dilakukan dengan menggunakan variabel indeks vegetasi (NDVI), indeks kebakaran (NBR), dan reflektansi dari citra MODIS untuk mengidentifikasi area terbakar. Identifikasi area terbakar dilakukan dengan metode pengambangan (thresholding), yaitu perhitungan nilai ambang batas dari perubahan nilai-nilai variabel NDVI, NBR, dan reflektansi untuk piksel-piksel yang dinyatakan sebagai area terbakar. Kemudian dilakukan perhitungan tingkat separabilitas dan akurasi untuk menguji validitas tiap-tiap model. Hasil penelitian ini menunjukkan bahwa pada dasarnya semua model algoritma baik perubahan NDVI, NBR dan reflektansi memiliki kemampuan yang baik dalam mendeteksi area terbakar di Kalimantan. Namun demikian, dari semua model algoritma tersebut, hanya model algoritma perubahan NBR yang memberikan tingkat akurasi paling tinggi, yaitu sebesar 0,635 atau 63,5%. Dengan demikian, model algoritma identifikasi area terbakar yang paling sesuai diaplikasikan untuk daerah Kalimantan dengan menggunakan citra MODIS adalah model algoritma perubahan NBR.Kata kunci: Identifikasi, Area terbakar, NBR, MODIS, Kalimantan
SPATIO-TEMPORAL ANOMALIES IN SURFACE BRIGHTNESS TEMPERATURE PRECEDING VOLCANO ERUPTIONS DETECTED BY THE LANDSAT-8 THERMAL INFRARED SENSOR (CASE STUDY: KARANGETANG VOLCANO) Suwarsono, Suwarsono; Triyono, Djoko; Khomarudin, Muhammad Rokhis; Rokhmatuloh, Rokhmatuloh
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 18, No 1 (2021)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2021.v18.a3465

Abstract

Indonesia's geological as part of the “ring of fire” includes the consequence that community life could be affected by volcanic activity. The catastrophic incidence of volcanic eruptions in the last ten years has had a disastrous impact on human life. To overcome this problem, it is necessary to conduct research on the strengthening of the early warning system for volcanic eruptions utilising remote sensing technology.  This study analyses spatial and temporal anomalies of surface brightness temperature in the peak area of Karangetang volcano during the 2018-2019 eruption. Karangetang volcano is an active volcano located in North Sulawesi, with a magmatic eruption type that releases lava flow. We analyse the anomalies in the brightness temperature from channel-10 of the Landsat-8 TIRS (Thermal Infrared Scanner) time series during the period in question. The results of the research demonstrate that in the case of Karangetang Volcano the eruptions of 2018-2019 indicate increases in the surface brightness temperature of the crater region. As this volcano has many craters, the method is also very useful to establish in which crater the center of the eruption occurred.
LAND USE AND LAND COVER (LULC) CLASSIFICATION WITH MACHINE LEARNING APPROACH USING ORTHOPHOTO DATA Mochamad Irwan Hariyono; Rokhmatuloh; Ratna Sari Dewi
Majalah Ilmiah Globe Vol. 25 No. 1 (2023): GLOBE VOL 25 NO 1 TAHUN 2023
Publisher : Badan Informasi Geospasial

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penggunaan teknologi penginderaan jauh semakin berkembang, salah satu aplikasinya adalah analisis perubahan penggunaan dan tutupan lahan (LULC). Informasi LULC dibutuhkan untuk berbagai analisis terkait permukaan bumi. Berbagai jenis data digunakan dalam analisis permukaan bumi dengan memanfaatkan data penginderaan jauh. Tujuan dari penelitian ini adalah untuk mengklasifikasikan LULC dengan pendekatan machine learning menggunakan data orthophoto. Lokasi penelitian adalah Desa Tanjung Karang, Mataram, Nusa Tenggara Barat. Metode yang digunakan untuk proses klasifikasi adalah algoritma machine learning yaitu Support Vector Machine (SVM). Dilakukan proses pemisahan band (band slicing) pada data orthophoto yaitu Red, Green, Blue, dan Near Infra Red (NIR). Band Normalized Difference Water Index (NDWI) digunakan untuk analisis badan air yang merupakan refleksi dari band Red dan NIR. Skema klasifikasi klasifikasi yang diterapkan dalam penelitian ini adalah membandingkan klasifikasi antara satu band dan kombinasi band untuk mendapatkan hasil klasifikasi terbaik. Hasil penelitian ini menunjukkan bahwa klasifikasi dengan kombinasi band memiliki akurasi yang lebih baik. Klasifikasi dengan satu band memiliki akurasi rata-rata di bawah 55%, sedangkan kombinasi band memiliki akurasi rata-rata di atas 60%. Hasil klasifikasi dengan nilai akurasi tertinggi adalah kombinasi band R-B-NDWI dengan nilai 71,81%.
PENGEMBANGAN MODEL IDENTIFIKASI DAERAH BEKAS KEBAKARAN HUTAN DAN LAHAN (BURNED AREA) MENGGUNAKAN CITA MODIS DI KALIMANTAN Suwarsono, Suwarsono; Rokhmatuloh, Rokhmatuloh; Waryono, Tarsoen
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 10 No. 2 (2013)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v10i2.3278

Abstract

Co-Authors - Suwarsono Adhi Harmoko Saputro AHMAD DWI SETYAWAN AHMAD DWI SETYAWAN AHMAD DWI SETYAWAN Andy Indradjad Anggrahita, Hayuning Ardiansyah Ardiansyah Aris Poniman Ayom Widipaminto Ayom Widipaminto D. DARNAEDI Dedi Irawadi DEDY DARNAEDI DEDY DARNAEDI Dimyati, Ratih Dewanti Donna Monica Donna Monica Dony Kushardono Erna Sri Adiningsih Faris Zulkarnain ILYAS NURSAMSI J. Supriatna Jatna Supriatna Khomarudin, Muhammad Rokhis Kustiyo Kustiyo, Kustiyo LILIK BUDIPRASETYO Marko, Kuswantoro Masita Dwi Mandini Manessa Mochamad Irwan Hariyono Muhammad Dimyati Muhammad Rokhis Khomarudin Nurwita Mustika Sari PRAKASH PRADAN Rahmatia Susanti Ramadhani Yanidar Ratna Saraswati Ratna Sari Dewi Revi Hernina Saipiatuddin SUGIYARTO SUGIYARTO SUGIYARTO SUGIYARTO SUGIYARTO SUGIYARTO SUNARTO SUNARTO SUTARNO SUTARNO SUTARNO SUTARNO SUTARNO SUTARNO Suwarsono Suwarsono Suwarsono Tambunan, Mangapul Parlindungan Tarsoen Waryono Tarsoen Waryono, Tarsoen Triyono, Djoko Weling Suseno WULAN RATNA KOMALA Yohanes Fridolin Hestrio Yoniar Hufan Ramadhani YULITA KUSUMADEWI Yuvita Dian Safitri