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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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KLASIFIKASI PENUTUP LAHAN BERBASIS OBJEK PADA DATA FOTO UAV UNTUK MENDUKUNG PENYEDIAAN INFORMASI PENGINDERAAN JAUH SKALA RINCI Sari, Nurwita Mustika; Kushardono, Dony
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 11 No. 2 (2014)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v11i2.3304

Abstract

The need of spatial information from detailed-scale remote sensing is increasing. Unmanned Aerial Vehicle or UAV become one of vehicles that is expected to obtain such information. Production of land cover spatial information using UAV photo data requires appropriate method for classification. This study proposes an object-based classification method for land cover based on Haralick texture information namely homogeneity, contrast, dissimilarity, entropy, angular second moment, mean, standard deviation, and correlation. As a comparison method, a conventional land cover-object-based classification is implemented using the same information features, there are brightness, compactness, and density. The result shows that method using texture feature in object-based classification has reached 95.22% accuracy or 17.5% difference that is much better than conventional method that reaches 77.71%.
KLASIFIKASI PENUTUP/PENGGUNAAN LAHAN DENGAN DATA SATELIT PENGINDERAAN JAUH HIPERSPEKTRAL (HYPERION) MENGGUNAKAN METODE NEURAL NETWORK TIRUAN Kushardono, Dony
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 13 No. 2 (2016)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.2016.v13.a2516

Abstract

Hyperspectral remote sensing data has numerous spectral information for the land-use/landcover (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.
OPTIMASI PARAMETER DALAM KLASIFIKASI SPASIAL PENUTUP PENGGUNAAN LAHAN MENGGUNAKAN DATA SENTINEL SAR Chulafak, Galdita Aruba; Kushardono, Dony; Zylshal, Zylshal
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 14 No. 2 (2017)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.1017.v14.a2746

Abstract

In this study, application of Sentinel-1 Synthetic Aperture Radar (SAR) data for the land use cover classification was investigated. The classification was implemented with supervised Neural Network classifier for Dual polarization (VH and VV) Sentinel-1 data using texture information of gray level co-occurance matrix (GLCM). The purpose of this study was to obtain the optimum parameters in the extraction of texture information of pixel window size, the orientation of neighboring relationships on the texture feature extraction, and the type of texture information feature used for the classification. The classification results showed that in the study area, the best accuracy obtained is 5 × 5 pixel window size, 00 orientation angle, and the use of entropy texture information as classification input. It was also found that more features texture information used as classification input can improve the accuracy, and with careful selection of appropriate texture information as classification input will give the best accuracy.
PEMETAAN EKOSISTEM LAMUN DENGAN DAN TANPA KOREKSI KOLOM AIR DI PERAIRAN PULAU PAJENEKANG, SULAWESI SELATAN Ilyas, Turissa Pragunanti; Bisman Nababan; Hawis Madduppa; Dony Kushardono
Jurnal Ilmu dan Teknologi Kelautan Tropis Vol. 12 No. 1 (2020): Jurnal Ilmu dan Teknologi Kelautan Tropis
Publisher : Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1024.995 KB) | DOI: 10.29244/jitkt.v12i1.26598

Abstract

Koreksi kolom air dalam pemetaan habitat bentik menggunakan data satelit dapat meningkatkan nilai akurasi informasi yang dihasilkan, seperti yang telah dilakukan oleh peneliti sebelumnya. Penelitian ini bertujuan untuk melihat distribusi lamun dengan perlakuan dengan dan tanpa koreksi kolom air menggunakan klasifikasi berbasis objek (OBIA) di Pulau Pajanekang. Data sebaran padang lamun dan non lamun sebanyak 347 titik diambil pada Juli-Agustus 2018 dengan transek 1x1 m2. Data satelit yang digunakan adalah citra satelit SPOT-7 akuisisi pada 27 Maret 2017 dengan resolusi 6x6 m2. Pada penelitian ini metode klasifikasi OBIA menggunakan beberapa algoritma klasifikasi seperti Support Vector Machine (SVM), Bayes, K-Nearest Neighbour (KNN), dan Decision Tree (DT) untuk memetakan habitat bentik dan lamun. Hasil penelitian menunjukkan bahwa penerapan perlakuan dengan koreksi kolom air dan tanpa koreksi kolom air pada pemetaan ekosistem habitat bentik dan lamun dengan menggunakan beberapa algoritma klasifikasi menunjukkan hasil akurasi yang tidak berbeda nyata. Namun demikian, dari empat algoritma yang digunakan, algoritma Bayes tanpa koreksi kolom air memberikan nilai akurasi tertinggi untuk pemetaan habitat bentik sebesar 70,36% dan habitat lamun sebesar 66,47%. Hal tersebut menunjukkan bahwa koreksi kolom air tidak selamanya memberikan hasil yang lebih baik dalam klasifikasi habitat bentik dan lamun dari citra satelit digital.
ANALISIS KARAKTERISTIK NET PRIMARY PRODUCTIVITY DAN KLOROFIL-A DI LAUT BANDA DAN SEKITARNYA Prayogo, Teguh; Yati, Emi; Dwi Purwanto, Anang; Nandika, M. Rizki; Dirgahayu Domiri, Dede; Kushardono, Dony; Marpaung, Sartono
Jurnal Ilmu dan Teknologi Kelautan Tropis Vol. 14 No. 1 (2022): Jurnal Ilmu dan Teknologi Kelautan Tropis
Publisher : Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jitkt.v14i1.36757

Abstract

Net primary productivity (NPP) and chlorophyll-a (Chl-a) are indicators of water productivity. In this study, an analysis of NPP and Chl-a characteristics in the Banda Sea was carried out using the Hovmöller diagram and Pearson’s correlation. The NPP data used comes from VGPM and Chl-a from Aqua MODIS satellite. The results of data analysis from January 2003-December 2020, NPP and Chl-a reached highest concentrations in dry season and lowest in wet season. For monthly data, the highest concentrations occurred in August and the lowest in April and December. The waters of the Banda Sea include mesotrophic waters with monthly average of NPP 429 mg C/m2/day and Chl-a 0.24 mg/m3. During La Niña and El Niño, there was a change (decrease/increase) the concentration of NPP and Chl-a in dry season and transition period II. NPP and Chl-a have a high correlation and a strong linear relationship. NPP and Chl-a have almost the same pattern/tendency temporally. The change of NPP concentration temporally corresponded to change of Chl-a concentration. Seasonal factors, La Niña and El Niño have a strong influence in influencing the variability of NPP and Chl-a concentrations. High productivity based on NPP and Chl-a didn’t affect for skipjack and tuna seasons (big pelagic), that occurs in wet season and transition period II. High productivity affects to flying fish season (small pelagic) that occurs in dry season.
APPLICATION OF LAPAN A3 SATELLITE DATA FOR THE IDENTIFICATION OF PADDY FIELDS USING OBJECT BASED IMAGE ANALYSIS (OBIA) Mukhoriyah; Dony Kushardono
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.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) Sarah Putri Fitriani; Jonson Lumban Gaol; Dony Kushardono
International Journal of Remote Sensing and Earth Sciences Vol. 16 No. 2 (2019)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | 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.Â
OPTIMIZATION OF RICE FIELD CLASSIFICATION MODEL BASED ON THRESHOLD INDEX OF MULTITEMPORAL LANDSAT IMAGES Made Parsa; Dede Dirgahayu; Sri Harini; Dony Kushardono
International Journal of Remote Sensing and Earth Sciences Vol. 17 No. 1 (2020)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2020.v17.a3333

Abstract

The development of rice land classification models in 2018 has shown that the phenology-based threshold of rice crops from the multi-temporal Landsat image index can be used to classify rice fields relatively well. The weakness of the models was the limitations of the research area, which was confined to the Subang region, West Java, so it is was deemed necessary to conduct further research in other areas. The objective of this study is to obtain optimal parameters of classification model of rice and land based on multi-temporal Landsat image indexes. The study was conducted in several districts of rice production centers in South Sulawesi and West Java (besides Subang). The threshold method was employed for the Landsat Image Enhanced Vegetation Index (EVI). Classification accuracy was calculated in two stages, the first using detailed scale reference information on rice field base, and the second using field data (from a survey). Based on the results of the analysis conducted on several models, the highest accuracy is generated by the three index parameter models (EVI_min, EVI_max, and EVI_range) and adjustable threshold with 94.8% overall accuracy. Therefore this model was acceptable for used for nationally rice fields mapping.
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 Vol. 16 No. 1 (2019)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/ijreses.v16i1.13836

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.
LAPAN-A3 SATELLITE DATA ANALYSIS FOR LAND COVER CLASSIFICATION (CASE STUDY: TOBA LAKE AREA, NORTH SUMATRA) Jalu Tejo Nugroho; Dony Kushardono; Zylshal
International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 1 (2018)
Publisher : BRIN

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