Garuda - Garba Rujukan Digital

p-Index From 2020 - 2025

1.745

P-Index

This Author published in this journals

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

Unknown Affiliation

Author-ID : 302378

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

Published : 32 Documents Claim Missing Document

Claim Missing Document

Articles

1 2 3 4

Object Segmentation on UAV Photo Data to Support the Provision of Rural Area Spatial Information Sari, Nurwita Mustika; Kushardono, Dony
Forum Geografi Vol 29, No 1 (2015): Forum Geografi
Publisher : Forum Geografi

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

The use of Unmanned Aerial Vehicle (UAV) to take aerial photographs is increasing in recent years. Photo data taken by UAV become one of reliable detailed-scale Â remote sensing data sources. The capability to obtain cloud-free images and the flexibility of time are some of the advantages of UAV photo data compared to satellite images with optical sensor. Displayed area at the data shows the objects clearly. Rural area has certain characteristics in its land cover namely ricefield. To delineate the area correctly there is an object-based image analysis methods (OBIA) that could be applied. In this Â study, proposed a novel method to Â execute the separation of objects that exist in the data with segmentation method. The result shows an effective segmentation method to separate different objects in rural areas recorded on UAV image data. The accuracy obtained is 90.47% after optimization process. This segmentation can be a valid basis to support the provision of spatial information in rural area.

The Relationship between the Mixed Pixel Spectral Value of Landsat 8 OLI Data and LAPAN Surveillance Aircraft (LSA) Aerial-Photo Data Sari, Nurwita Mustika; Chulafak, Galdita Aruba; Zylshal, Zylshal; Kushardono, Dony
Forum Geografi Vol 31, No 1 (2017): July 2017
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v31i1.3500

Medium resolution satellite data such as Landsat is very potential for mixed pixel (mixel) to occur. Indonesian land use diverse especially urban areas makes high potential mixel in the first Landsat pixel size of 30 meters x 30 meters on the actual condition. Aircraft multispectral aerial photo data LAPAN Surveillance Aircraft (LSA) with a spatial resolution reached 58 cm can display objects in more detail in these sizes. The purpose of this research is to study mixel on Landsat data with multispectral data LSA as a complement Landsat data. The method proposed in this study is a visual interpretation with GEOBIA method for classification of land cover, and then test the validity of the sample to be used in research, and the use of such vegetation index NDVI to see the connection between vegetation index data of vegetation index LSA with Landsat data. The results showed that the regression equation obtained by regression between NDVI of Landsat data and NDVI of Â LSA with a significance of less than 0.05 is y = 0.732x - 0102 with a value of R2 = 0.887. Through these results we can conclude that the NDVI values on both the data related to one another.

Object Segmentation on UAV Photo Data to Support the Provision of Rural Area Spatial Information Sari, Nurwita Mustika; Kushardono, Dony
Forum Geografi Vol 29, No 1 (2015): July 2015
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v29i1.792

ANALISIS PERUBAHAN CUACA PADA AREAL PERSAWAHAN DI PULAU JAWA DAN PENGARUHNYA TERAHADAP PRODUKTIVITAS PADI Dony Kushardono; Erna Sri Adiningsih; Agus Hidayat
Agromet Vol. 14 No. 1 & 2 (1999): June 1999
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Abstract is available in the full text (pdf format)

Assessing the Potential of LAPAN-A3 Data for Landuse/landcover Mapping Zylshal Zylshal; Rachmad Wirawan; Dony Kushardono
Indonesian Journal of Geography Vol 50, No 2 (2018): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

LAPAN-A3 / LAPAN-IPB is the third generation of micro-satellite developed by Indonesian National Institute of Aeronautics and Space (LAPAN). The satellite carries a multispectral push-broom sensor that can record the earth's surface at the visible and near-infrared spectrum. Being launched in June 2016, there has no been many publications related to the use of LAPAN-A3 multispectral data for landuse/landcover (LULC) mapping. This paper aims to provide information regarding the use of LAPAN-A3 data for the LULC extraction maximum likelihood algorithm as well as neural network and then evaluate the results. The LAPAN-A3 image was geometrically corrected by using Landsat-8 OLI image as reference data. Three test areas with a size of 1200x945 pixels are then selected for pixel-based classification with the two aforementioned algorithms. For comparison, both LAPAN-A3 and Landsat-8 data were classified for 3 test areas. Accuracy assessment was performed on both datasets using manually interpreted SPOT-6 Pansharpened image as reference data. Preliminary results showed that LAPAN-A3 were able to extract 10 different LULC classes, comprises of built-up area, forest, rivers, fishponds, shrubs, wetland forests, rice fields, sea, agricultural land, and bare soil. The overall accuracy of LAPAN-A3 data is generally lower than Landsat-8, which ranges from 49.76% to 71.74%. These results illustrate the potential of LAPAN-A3 data to derive LULC information. The lack of necessary parameters to perform radiometric correction and blurring effect are several issues that need to be solved to improve the accuracy LULC.

QUALITY ANALYSIS OF SINGLE TREE OBJECT WITH OBIA AND VEGETATION INDEX FROM LAPAN SURVEILLANCE AIRCRAFT MULTISPECTRAL DATA IN URBAN AREA Nurwita Mustika Sari; Dony Kushardono
Geoplanning: Journal of Geomatics and Planning Vol 3, No 2 (2016)
Publisher : Department of Urban and Regional Planning, Diponegoro University

High-resolution remote sensing data as the acquisition result of LAPAN Surveillance Aircraft (LSA) has the potential to analyze urban areas. The purpose of this study was to develop a method of LSA multispectral data utilization with an analysis of the single tree object in urban areas with OBIA and vegetation index. The method proposed in this study is a hierarchical classification to obtain the specific tree object that will be used further to analyze the quality of vegetation. In particular, analysis of the vegetation quality on the tree object was carried out by calculating the value of vegetation index NDVI. As a result, the overall accuracy of the hierarchical classification of objects in urban areas reached 88 %. In conclusion, the analysis of the quality of vegetation NDVI has been able to perceive the condition of trees in the urban area.

Object Segmentation on UAV Photo Data to Support the Provision of Rural Area Spatial Information Nurwita Mustika Sari; Dony Kushardono
Forum Geografi Vol 29, No 1 (2015): July 2015
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v29i1.792

The use of Unmanned Aerial Vehicle (UAV) to take aerial photographs is increasing in recent years. Photo data taken by UAV become one of reliable detailed-scale remote sensing data sources. The capability to obtain cloud-free images and the flexibility of time are some of the advantages of UAV photo data compared to satellite images with optical sensor. Displayed area at the data shows the objects clearly. Rural area has certain characteristics in its land cover namely ricefield. To delineate the area correctly there is an object-based image analysis methods (OBIA) that could be applied. In this study, proposed a novel method to execute the separation of objects that exist in the data with segmentation method. The result shows an effective segmentation method to separate different objects in rural areas recorded on UAV image data. The accuracy obtained is 90.47% after optimization process. This segmentation can be a valid basis to support the provision of spatial information in rural area.

The Relationship between the Mixed Pixel Spectral Value of Landsat 8 OLI Data and LAPAN Surveillance Aircraft (LSA) Aerial-Photo Data Nurwita Mustika Sari; Galdita Aruba Chulafak; Zylshal Zylshal; Dony Kushardono
Forum Geografi Vol 31, No 1 (2017): July 2017
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v31i1.3500

Medium resolution satellite data such as Landsat is very potential for mixed pixel (mixel) to occur. Indonesian land use diverse especially urban areas makes high potential mixel in the first Landsat pixel size of 30 meters x 30 meters on the actual condition. Aircraft multispectral aerial photo data LAPAN Surveillance Aircraft (LSA) with a spatial resolution reached 58 cm can display objects in more detail in these sizes. The purpose of this research is to study mixel on Landsat data with multispectral data LSA as a complement Landsat data. The method proposed in this study is a visual interpretation with GEOBIA method for classification of land cover, and then test the validity of the sample to be used in research, and the use of such vegetation index NDVI to see the connection between vegetation index data of vegetation index LSA with Landsat data. The results showed that the regression equation obtained by regression between NDVI of Landsat data and NDVI of LSA with a significance of less than 0.05 is y = 0.732x - 0102 with a value of R2 = 0.887. Through these results we can conclude that the NDVI values on both the data related to one another.

Identifikasi Kawasan Pertambangan Timah Menggunakan Data Satelit Sentinel – 1 dengan Metode Object Based Image Analysis (OBIA) Udhi C Nugroho; Dony Kushardono; Esthi K Dewi
Jurnal Ilmu Lingkungan Vol 17, No 1 (2019): April 2019
Publisher : School of Postgraduate Studies, Diponegoro Univer

Berdasarkan data Pendapatan Nasional Indonesia 2017, sektor pertambangan dan penggalian mempunyai peran penting bagi Indonesia. Sektor ini menyumbangkan 7,57% pada produk domestik bruto Indonesia di tahun 2017 . Salah satu sektor pertambangan yang potensial di Indonesia adalah pertambangan mineral Timah di Pulau Bangka dan Belitung. Namun kegiatan pertambangan ini banyak menimbulkan dampak negatif dari sisi lingkungan. Salah satu upaya awal untuk menanggulangi dampak negatif terhadap lingkungan adalah melakukan identifikasi kawasan pertambangan timah secara spasial. Teknologi yang dapat membantu untuk hal ini salah satunya adalah teknologi penginderaan jauh radar. Penelitian ini menggunakan data satelit radar sentinel-1 yang diluncurkan oleh European Space Agency (ESA). Tujuan penelitian ini adalah pemanfaatan data radar Sentinel-1 untuk identifikasi kawasan pertambangan menggunakan metode Object-Base Image Analysis (OBIA). Data sentinel-1 disegmentasi menggunakan algorithma multiresolution segmentation kemudian di klasifikasi menggunakan algorithma nearest neighbor. Masukan data yang digunakan untuk proses klasifikasi dibuat menjadi dua variasi, yang pertama adalah data standar deviasi, mean, dan brightness pada masing – masing segmen di tiap band, kemudian variasi kedua adalah penambahan data tekstur berupa nilai grey level coocurance matrix (GLCM). Hasil klasifikasi menunjukan bahwa masukan data yang menggunakan data tekstur GLCM mempunyai akurasi lebih tinggi dibandingkan dengan yang tanpa data tekstur GLCM. Secara statisktik Hasil klasifikasi dengan type satu menunjukan bahwa total akurasi nya adalah sebesar 89,0 %, dengan nilai kappa sebesar 0,48 sedangkan untuk type dua menunjukan bahwa total akurasinya adalah 89,3%, dengan kappa sebesar 0,50. Hasil klasifikasi kawasan pertambangan dapat digunakan sebagai masukan awal dalam rangka identifikasi spasial kerusakan lingkungan akibat aktivitas pertambangan.

OPTIMASI PARAMETER DALAM KLASIFIKASI SPASIAL PENUTUP PENGGUNAAN LAHAN MENGGUNAKAN DATA SENTINEL SAR (PARAMETERS OPTIMIZATION IN SPATIAL LAND USE LAND COVER CLASSIFICATION USING SENTINEL SAR DATA) Galdita Aruba Chulafak; Dony Kushardono; nFN Zylshal
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 14 No. 2 Desember 2017
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

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. AbstrakPada penelitian ini dilakukan kajian mengenai klasifikasi penutup penggunaan lahan menggunakan data Sentinel-1 yang merupakan data Synthetic Aperture Radar (SAR). Informasi tekstur digunakan sebagai masukan dalam pembuatan klasifikasi terbimbing Neural Network dengan menggunakan Dual polarization (VH dan VV). Klasifikasi dilakukan menggunakan informasi tekstur menggunakan Gray Level Co-occurance Matrix (GLCM) dari data Sentinel-1. Tujuan penelitian ini adalah mendapatkan parameter optimum dalam ekstraksi informasi, yaitu ukuran jendela pemrosesan, orientasi hubungan ketetanggaan pada ekstraksi fitur tekstur, serta jenis fitur informasi tekstur yang digunakan dalam klasifikasi. Hasil klasifikasi menunjukkan bahwa pada area yang dikaji, akurasi terbaik adalah pada ukuran jendela 5×5 piksel, sudut orientasi hubungan ketetanggaan 0º, serta penggunaan informasi tekstur entropy sebagai masukan dalam klasifikasi. Serta diketahui bahwa semakin banyak fitur informasi tekstur yang digunakan sebagai masukan klasifikasi dapat meningkatkan akurasi dan pemilihan informasi tekstur yang tepat sebagai masukan klasifikasi akan menghasilkan akurasi terbaik.

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

Title

Found 32 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 32 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 32 Documents
Search