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All Journal Agromet Jurnal Ilmu dan Teknologi Kelautan Tropis Indonesian Journal of Geography Journal of Marine and Aquatic Sciences Jurnal Ilmu Lingkungan Geoplanning : Journal of Geomatics and Planning Journal of Degraded and Mining Lands Management Forum 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)
Dony Kushardono
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Library & Information Science

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PEMANFAATAN DATA ENHANCED VEGETATION INDEX VIIRS DAN PERBANDINGAN DENGAN MODIS UNTUK PEMANTAUAN PERTUMBUHAN PADI DI PULAU JAWA Anisa Rarasati; Dony Kushardono
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 17 Nr. 2 Desember 2020
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.2020.v17.a3361

Abstract

Beras merupakan salah satu makanan pokok masyarakat Indonesia yang banyak diproduksi di dalam negeri. Karena tingginya tingkat konsumsi beras, pemerintah perlu memprediksi produksi tanaman padi dalam negeri untuk membuat kebijakan. Prediksi produktifitas padi ini dapat dilakukan menggunakan data penginderaan jauh. Di Indonesia telah dibuat pedoman pengolahan prediksi padi oleh Pusat Pemanfaatan Penginderaan Jauh, LAPAN menggunakan enhanced vegetation index (EVI) yang berasal dari sensor Moderate Resolution Imaging Spectroradiometer (MODIS) satelit Terra. Selain itu, data MODIS juga banyak digunakan di bidang pertanian, khususnya padi. Tetapi data MODIS hampir berakhir masa berlakunya sehingga diperlukan data pengganti. Data Visible Infrared Imaging Radiometer Suite (VIIRS) didesain sebagai pengganti MODIS. Untuk itu, penelitian ini dilakukan untuk mengetahui hubungan EVI data dari VIIRS dan MODIS dalam tujuannya menggantikan data MODIS dalam pemantauan padi. Dan hasil yang didapatkan menunjukkan tingkat korelasi tinggi dengan R2 sebesar 0.84 antara kedua EVI tersebut. Oleh karena itu, EVI VIIRS memiliki potensi yang sangat baik untuk menggantikan EVI MODIS.
KLASIFIKASI PENUTUP/PENGGUNAAN LAHAN DENGAN DATA SATELIT PENGINDERAAN JAUH HIPERSPEKTRAL (HYPERION) MENGGUNAKAN METODE NEURAL NETWORK TIRUAN Dony Kushardono
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 13 No. 2 Desember 2016
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1445.91 KB) | DOI: 10.30536/j.pjpdcd.2016.v13.a2516

Abstract

Hyperspectral remote sensing data has numerous spectral information for the land-use/land-cover (LULC) classification, but a large number of hyperspectral band data is becoming a problem in the LULC classification. This research proposes the use of the back propagation neural network for LULC classification with hyperspectral remote sensing data. Neural network used in this study is three layers, in which to test input layer has a number of neurons as many as 242 to process all band data, 163 neurons, and 50 neurons to process the data band has a high average digital number, and data bands at wavelengths of visible to near infrared. The results showed the use of all the data band hyperspectral on classification with the neural network has the highest classification accuracy of up to 98% for 18 LULC class, but it takes a very long time. Selecting a number of bands of precise data for classification with a neural network, in addition to speeding up data processing time, can also provide sufficient accuracy classification results.ABSTRAKData penginderaan jauh hiperspektral memiliki informasi spektral yang sangat banyak untuk klasifikasi penutup/penggunaan lahan (LULC), akan tetapi banyaknya jumlah band data hiperspektral menjadi masalah dalam klasifikasi LULC. Penelitian ini mengusulkan penggunaan back propagation neural network untuk klasifikasi LULC dengan data penginderaan jauh hiperspektral. Neural network yang dipergunakan 3 lapis, dimana untuk uji coba lapis masukan memiliki jumlah neuron sebanyak 242 untuk mengolah seluruh band, 163 neuron, dan 50 neuron untuk mengolah data band yang memiliki nilai digital rataan yang tinggi, dan data band pada panjang gelombang cahaya tampak hingga infra merah dekat. Hasil penelitian menunjukkan penggunaan seluruh band data hiperspektral pada klasifikasi dengan neural network memiliki akurasi hasil klasifikasi tertinggi hingga 98% untuk 18 kelas LULC, akan tetapi waktu yang diperlukan sangat lama. Pemilihan sejumlah band data yang tepat untuk klasifikasi dengan neural network, selain mempercepat waktu pengolahan data, juga bisa memberikan akurasi hasil klasifikasi yang mencukupi.
PRELIMINARY STUDY OF LSU-02 PHOTO DATA APPLICATION TO SUPPORT 3D MODELING OF TSUNAMI DISASTER EVACUATION MAP Linda Yunita; Nurwita Mustika Sari; Dony Kushardono
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 14, No 2 (2017)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (759.97 KB) | DOI: 10.30536/j.ijreses.2017.v14.a2792

Abstract

The southern coast of Pacitan Regency is one of the vulnerable areas to the tsunami. Therefore, the map of the vulnerable and safe area from the tsunami disaster is required. Currently, there are many mapping technologies with UAVs used for spatial analysis. One of the UAV technologies which used in this research is LAPAN Surveillance UAV 02 (LSU-02). This study aims to map the evacuation plan area from LSU-02 aerial imagery. Tsunami evacuation area was identified by processing the aerial photo data into orthomosaic and Digital Elevation Model (DEM). The result shows that there are four points identified as the tsunami evacuation plan area. These points are located higher than the surrounding area and are easily accessible.
LAPAN-A3 SATELLITE DATA ANALYSIS FOR LAND COVER CLASSIFICATION (CASE STUDY: TOBA LAKE AREA, NORTH SUMATRA) Jalu Tejo Nugroho; Zylshal Zylshal; Dony Kushardono
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 15, No 1 (2018)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1109.71 KB) | DOI: 10.30536/j.ijreses.2018.v15.a2782

Abstract

LAPAN-A3 is the 3rdgeneration satellite for remote sensing developed by National Institute of Aeronautics and Space (LAPAN). The camera provides imagery with 15 m spatial resolution and able to view a swath 120 km wide. This research analyzes the performance of LAPAN-A3 satellite data to classify land cover in Toba Lake area, North Sumatera. Data processing starts from the selection of region of interest up to the assessment of accuracy. Supervised classification with maximum likelihood approach and confusion matrix method was applied to classify and evaluate the assessment results. The land cover is classified into five classes; water, bare land, agriculture, forest and secondary forest. The result of accuracy test is 93.71%. It proves that LAPAN-A3 data could classify the land cover accurately. The data is expected to complement the need of the satellite data with medium spatial resolution.
THE USE OF C-BAND SYNTHETIC APERTURE RADAR SATELLITE DATA FOR RICE PLANT GROWTH PHASE IDENTIFICATION Anugrah Indah Lestari; Dony Kushardono
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1553.08 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3171

Abstract

Identification of the rice plant growth phase is an important step in estimating the harvest season and predicting rice production. It is undertaken to support the provision of information on national food availability. Indonesia’s high cloud coverage throughout the year means it is not possible to make optimal use of optical remote sensing satellite systems. However, the Synthetic Aperture Radar (SAR) remote sensing satellite system is a promising alternative technology for identifying the rice plant growth phase since it is not influenced by cloud cover and the weather. This study uses multi-temporal C-Band SAR satellite data for the period May–September 2016. VH and VV polarisation were observed to identify the rice plant growth phase of the Ciherang variety, which is commonly planted by farmers in West Java. Development of the rice plant growth phase model was optimized by obtaining samples spatially from a rice paddy block in PT Sang Hyang Seri, Subang, in order to acquire representative radar backscatter values from the SAR data on the age of certain rice plants. The Normalised Difference Polarisation Index (NDPI) and texture features, namely entropy, homogeneity and the Grey-Level Co-occurrence Matrix (GLCM) mean, were included as the samples. The results show that the radar backscatter value (σ0) of VH polarisation without the texture feature, with the entropy texture feature and GLCM mean texture feature respectively exhibit similar trends and demonstrate potential for use in identifying and monitoring the rice plant growth phase. The rice plant growth phase model without texture feature on VH polarisation is revealed as the most suitable model since it has the smallest average error.
Deteksi Kapal Penangkapan Ikan Menggunakan data Visible Infrared Imaging Radiometer Suite (VIIRS) dan data Vessel Monitoring System (VMS) di Wilayah Pengelolaan Perikanan Negara Republik Indonesia Dominggus Samuel Helberth Lothar Matheus Koreri Awak; Jonson Lumban-Gaol; Dony Kushardono
Journal of Marine and Aquatic Sciences Vol 8 No 1 (2022)
Publisher : Fakultas Kelautan dan Perikanan Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/jmas.2022.v08.i01.p12

Abstract

Perikanan pelagis kecil merupakan salah satu sumber daya ikan yang penting terhadap perekonomian Indonesia. Salah satu permasalahan yang dihadapi saat ini adalah masalah overfishing, seiring dengan kemajuan teknologi penangkapan ikan yang mengakibatkan aktivitas penangkapan yang melebihi tingkat penangkapan ikan lestari serta tidak diimbangi dengan pengelolaan yang baik. Penelitian ini bertujuan menganalisis distribusi kapal penangkapan ikan secara terintegrasi dari data vessel boat detection VIIRS dan data VMS di wilayah pengelolaan perikanan Indonesia. Dalam rangka mendukung upaya pengelolaan maka kajian mengenai distribusi kapal yang aktual beroperasi di perairan Indonesia perlu diketahui secara pasti. Saat ini pemantauan distribusi kapal ikan di perairan Indonesia dilakukan dengan menggunakan vessel monitoring system. Selain dari VMS pendeteksian kapal ikan dapat dilakukan dengan memanfaatkan data penginderaan jauh baik dari sensor aktif maupun pasif yaitu melalui satelit Suomi National Polar-orbiting Partnership yang memiliki visible infrared imaging radimeter suite. Instrumen ini memiliki day/night band yang mampu merekam cahaya lampu di permukaan bumi termasuk kapal-kapal ikan yang menggunakan cahaya lampu sebagai alat bantu tangkap. Metode deteksi kapal ikan menggunakan kombinasi data VBD dari citra satelit VIIRS dan data VMS kapal ikan. Berdasarkan kecocokkan sebesar 26.04% dan berdasarkan kombinasi data VIIRS dan VMS diketahui bahwa daerah dengan distribusi kapal terbanyak di WPP-712
APPLICATION OF LAPAN A3 SATELLITE DATA FOR THE IDENTIFICATION OF PADDY FIELDS USING OBJECT BASED IMAGE ANALYSIS (OBIA) Mukhoriyah, Mukhoriyah; Kushardono, Dony
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.a3378

Abstract

The role of agriculture is directly related to SDG No.2, which is running a programme until 2030 to reduce national poverty, eradicate hunger by increasing food security and improving nutrition and support sustainable agriculture. Problems faced include the reduction in agricultural land, which results in lower rice production, and the limited information on the monitoring of paddy fields using spatial data. The purpose of this study is to identify paddy fields using LAPAN A3 satellite imagery based on OBIA classification. The data used were from LAPAN A3 multispectral imagery dated 19 June 2017, Landsat 8 imagery dated 17 June 2017, DEM SRTM (BIG), and the Administrative Boundary Map (BIG). The analysis method was segmentation by grouping image pixels, and supervised classification by taking several sample areas based on Random Stratified Sampling. The results will be carried using a confusion matrix. The classification results produced four classes; watery paddy fields, vegetation paddy fields, fallow paddy fields, and non-paddy fields, using of the green, red, and NIR bands for the LAPAN A3 data. From the results of the segmentation process, there remain some oversegmented features in the appearance of the same object. Oversegmentation is due to an inaccurate value assignment to each algorithm parameter when the segmentation process is performed. For example, watery paddy fields appear almost the same as open land (fallow paddy fields), the water object is darker purple. The visual classification results (Landsat 8 data) are considered as the reference for the digital classification results (LAPAN A3). Forty-eight samples were taken and divided into four classes, with each class consisting of 12 samples. The results of the accuracy test show that the total accuracy of the object-based digital classification for visual classification is 62.5% with a Kappa accuracy value of 0.5. The conclusion is that LAPAN A3 data can be used to identify paddy fields based on spectral resolution and to complement Landsat 8 data. To improve the accuracy of the classification results, more samples and the correct RGB composition are needed.
FISHING-VESSEL DETECTION USING SYNTHETIC APERTURE RADAR (SAR) SENTINEL-1 (CASE STUDY: JAVA SEA) Fitriani, Sarah Putri; Gaol, Jonson Lumban; Kushardono, Dony
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (809.168 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3235

Abstract

The synthetic aperture radar (SAR) instrument of Sentinel-1 is a remote sensing technology being developed to enable the detection of vessel distribution. The purpose of this research is to study fishing-vessel detection using SAR Sentinel-1 data. In this study, the constant false alarm rate method (CFAR) for Sentinel-1 data is used for the detection of fishing vessels in Indramayu sea waters. The data used to detect ships includes SAR Sentinel-1A images and vessel monitoring system (VMS) data acquired on 8 March and 20 March 2018. SAR Sentinel-1 imagery data is obtained through pre-processing and object identification using Sentinel Application Platform (SNAP) software. Overlay analysis is then used to enable discrimination of immovable and movable objects and validation of ships detected from SAR Sentinel-1 imagery is performed using VMS data. From overlay analysis, 46 ships were detected on 8 March 2018 and 39 ships on 20 March 2018. Of all the ship points detected using SAR Sentinel-1, 7.06% could be detected by VMS data while 92.94% could not. The number of ships detected by SAR Sentinel-1 is greater than those detected by VMS because not all ships use VMS devices. 
Analysis of urban environmental comfort using Landsat-8 multitemporal data and Artificial Neural Network Sari, Nurwita Mustika; Kushardono, Dony; Mukhoriyah, Mukhoriyah; Kustiyo, Kustiyo; Manessa, Masita Dwi Mandini
Journal of Degraded and Mining Lands Management Vol. 12 No. 3 (2025)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2025.123.7591

Abstract

The presence of greenery in urban residential and office areas can improve the comfort of residents who live in these environments. In an urban setting, vegetation serves an ecological purpose by absorbing carbon dioxide, supplying oxygen, lowering the temperature to produce a tolerable microclimate, acting as a water catchment area, and reducing noise. Urbanization and anthropogenic activity-driven growth of urban and            sub-urban regions put stress on the local vegetation and have the potential to lower environmental comfort. To promote the creation of a sustainable urban environment, a thorough analysis of the urban environment is required. Applications for remote sensing in all spectral, geographic, and temporal dimensions have increasingly adopted the usage of deep learning methods with artificial neural networks. This study attempted to predict the application of remote sensing data for analyzing environmental comfort in metropolitan areas based on multitemporal Landsat-8 data. The study area is Greater Jakarta. The approach was based on supervised classification with neural network techniques and land parameters like surface temperature, brightness index, greenness index, and wetness index. According to the study's findings, the proposed method could accurately predict that very uncomfortable classes predominated in Jakarta, Bogor, Depok, Tangerang, Bekasi, and surrounding areas by more than 92%. In addition to being densely populated with communities, urban environments are uncomfortable due to a lack of vegetation cover, which increases surface temperatures. In the future, this research can provide input for similar research, especially in the use of deep learning Artificial Neural Network methods for environmental analysis.
KLASIFIKASI SPASIAL PENUTUP LAHAN DENGAN DATA SAR DUAL-POLARISASI MENGGUNAKAN NORMALIZED DIFFERENCE POLARIZATION INDEX DAN FITUR KERUANGAN DARI MATRIK KOOKURENSI Kushardono, Dony
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 9 No. 1 (2012)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v9i1.3256

Abstract

In this study, the land cover classification method using the spatial information features of co-occurrence matrix and Normalized Difference Polarization Index (NDPI) data from dual polarization SAR Data was proposed. The spatial information features are used as input of supervised classification, and to get the performance of the proposed method, land cover classification was conducted with SAR C-band and L-band satellite data of Envisat ASAR and ALOS PALSAR. The results of the study are, the size of window on the SAR image to get the spatial information features of co-occurrence matrix and the use of additional NDPI data are giving effect to the accuracy of classification results. At the test area in Siak Riau Province which have 7 classes of land use, the optimum window size for co-occurrence matrix is 7 pixel x 7 pixel for ASAR data which has 75m spatial resolution, and more than 9 pixel x 9 pixel for PALSAR data which has 10m spatial resolution. The addition of the co-occurrence matrix information of NDPI data can improve the classification of accuracy up to 2%.
Co-Authors Adhi Harmoko Saputro Agus Hidayat Anisa Rarasati Anugrah Indah Lestari Anugrah Indah Lestari Bisman Nababan Chulafak, Galdita Aruba Chulafak, Galdita Aruba Dede Dirgahayu Dimyati, Ratih Dewanti Dirgahayu Domiri, Dede Dominggus Samuel Helberth Lothar Matheus Koreri Awak Dwi Purwanto, Anang Erna Sri Adiningsih Esthi K Dewi Fitriani, Sarah Putri Galdita Aruba Chulafak Galdita Aruba Chulafak Hawis H Madduppa Ilyas, Turissa Pragunanti Jalu Tejo Nugroho Jalu Tejo Nugroho Jonson Lumban Gaol Jonson Lumban-Gaol Kustiyo Kustiyo, Kustiyo LILIK BUDIPRASETYO Linda Yunita Made Parsa MASITA DWI MANDINI MANESSA, MASITA DWI MANDINI Mukhoriyah Mukhoriyah, Mukhoriyah Nandika, M. Rizki nFN Zylshal Nurwita Mustika Sari Nurwita Mustika Sari Nurwita Mustika Sari Nurwita Mustika Sari Nurwita Mustika Sari, Nurwita Mustika Rachmad Wirawan Rokhmatuloh, Rokhmatuloh Sarah Putri Fitriani Sartono Marpaung, Sartono Sri Harini Teguh Prayogo Udhi C Nugroho Yati, Emi Zylshal Zylshal Zylshal Zylshal Zylshal Zylshal Zylshal Zylshal, Zylshal Zylshal, Zylshal