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All Journal Journal of Degraded and Mining Lands Management JURNAL ILMIAH GEOMATIKA Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Kustiyo Kustiyo, Kustiyo
LAPAN
Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Mathematics

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METODE CLOUD REMOVAL CITRA SATELIT OPTIK MENGGUNAKAN MAKSIMUM NDVI DAN DATA MULTITEMPORAL Candra, Danang Surya; Kustiyo, Kustiyo
GEOMATIKA Vol 19, No 1 (2013)
Publisher : Badan Informasi Geospasial in Partnership with MAPIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24895/JIG.2013.19-1.167

Abstract

ABSTRAKPermasalahan yang timbul pada citra satelit optik di negara-negara tropis adalah liputan awan. Tingginya liputan awan menyebabkan pemanfaatan data citra satelit optik menjadi kurang optimal. Tujuan dari penelitian ini adalah mengembangkan metode cloud removaluntuk mengatasi permasalahan tersebut. Metode yang dikembangkan pada penelitian ini adalah menggunakan nilai maksimum indek vegetasi dan data multitemporal. Nilai indek vegetasi dari awan dan bayangan awan adalah ekstrim rendah. Sehingga untuk menghilangkan awan dan bayangan, strategi yang digunakan pada penelitian ini adalah memilih nilai maksimum indek vegetasi dari data multitemporal. Hasil cloud removal dari percobaan dengan menggunakan indek vegetasi dan data multitemporal menunjukkan bahwa citra satelit bebas dari awan dan bayangan awan dan penampakan citra meningkat secara visual. Secara kuantitatif, kelebihan dari metode cloud removal dengan menggunakan indek vegetasi dan data multitemporal ini dapat menghilangkan awan secara keseluruhan. Secara teknis, metode ini mempunyai kelebihan yaitu handal, mudah diterapkan dan memperoleh hasil yang optimal.  Kata Kunci: Cloud Removal, SPOT-4, NDVI, Data Multitemporal. ABSTRACTProblem arises in optical satellite imagery in tropical countries is cloud coverage. Utilization of optical satellite image data is not optimum due to the high cloud coverage. The purpose of this research is to develop a cloud-removal method to overcome the problem. This study developed a method using maximum vegetation index and multi-temporal data. Vegetation index values of cloud and cloud shadow is extremely low. Therefore, a strategy used in this study was to select the maximum of vegetation index value from multitemporal data to remove cloud and cloud shadow. The cloud removal resulted from the implementation of vegetation index and multitemporal data shows that the satellite imagery became clear and the visual effect was also enhanced. Quantitatively, the advantage of cloud removal method using vegetation index and multitemporal data is that it can eliminate the cloud as a whole. Technically speaking, this method has several advantages to be reliable, easy to apply and obtain optimum results. Keywords: Cloud Removal, SPOT-4, NDVI, Multitemporal Data. 
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.
PERUBAHAN KERUSAKAN LAHAN PULAU MADURA MENGGUNAKAN DATA PENGINDERAAN JAUH DAN SIG Haryani, Nanik Suryo; Kustiyo, Kustiyo; Khomarudin, Rokhis; Parwali, Parwali
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 3 No. 1 (2006)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v3i1.3184

Abstract

Development activities that do not pay enough attention to the environmental aspect will bring about the effect to the environment, particularly to the event of land damage. The effort to control and recover the land damage calls for the complete and accurate data and information. Along with the remote sensing technology development, it enables to study the land damage effectively and efficiently in the large scale region. The method used in the research is by utilizing the Landsat-TM image and the Geographic Information System (GIS) to determine the stage of land damage. The potential determination of land damage is carried out by weighting the indicator of land damage that all together function as the variable. The output obtained shows that the stage of land damage in Madura Island from 1994 until 2001 is the condition or the stage of land damage that belongs to the rather damaged class decreases to 0.90%, while the stage that belong to the damaged class is 3.90% and the stage of the big damaged is 0.14%.
KAJIAN KOREKSI TERRAIN PADA CITRA LANDSAT THEMATIC MAPPER (TM) Trisakti, Bambang; Kartasasmita, Mahdi; Kustiyo, Kustiyo; Kartika, Tatik
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 6 No. 1 (2009)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v6i1.3239

Abstract

Terrain correction is used to minimize the shadow effect due to variation of earth’s topography. So, the process is very useful to correct the distortion of the pixel value at the mountainous area in the satellite image. The aim of this paper is to study the terrain correction process and its implementation for Landsat TM. The algorithm of the terrain correction was built by determining the pixel normal angle which is defined as an angle between the sun and surface normal directions. The calculation of the terrain correction needs the information of sun zenith angle, sun elevation angle (obtained from header data), pixel slope, and pixel aspect derived from digital elevation model (DEM). The C coefficient from each band was determined by calculating the gradient and the intercept of the correlation between the Cos pixel normal angle and the pixel reflectance in each band. Then, the Landsat TM image was corrected by the algorithm using the pixel normal angle and C coefficient. C Coefficients used in this research were obtained from our calculation and from Indonesia National Carbon Accounting System (INCAS). The result shows that without the C coefficient, pixels value increases very high when the pixel normal angle approximates 90°. The C coefficient prevents that condition, so the implementation of the C coefficient obtained from INCAS in the algorithm can produce the image which has the same topography appearance. Further, each band of the corrected image has a good correlation with the corrected band from the INCAS result. The implementation of the C coefficient from our calculation still needs some evaluation, especially for the method to determine the training sample for calculating the C coefficient.
ANALISIS MISALIGNMENT CITRA MULTISPEKTRAL TERHADAP CITRA PANKROMATIK PADA DATA WORLDVIEW-2 Brahmantara, Randy Prima; Kustiyo, Kustiyo
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 15 No. 1 (2018)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.2018.v15.a2800

Abstract

The standard data of Worldview-2 owned by LAPAN is Ortho-Ready Standard level 2 (OR2A) data consisting of 4 multispectral bands (blue, green, red, NIR) and one panchromatic band each 2 m and 0,5 m spatial resolution. Both images have different metadata and RPC, making it possible to perform geometric corrections separately. This paper discusses the analysis of the inaccuracies of multispectral image positions to panchromatic images compared to those that have been systematically geometric corrected. The method used is fast fourier transform phase matching by taking 500 binding points between the two images. The measurement results prove that the multispectral image of the Worldview-2 data of the OR2A level has a larger shift compared with multispectral image that has been systematically geometric corrected. The multispectral image of the OR2A data shifts are 2,14 m on the X-axis and 0,42 m on the Y-axis. While the multispectral image that has been systematically geometric corrected shifts are 1,72 m on the X-axis and 0,54 m on the Y-axis.
Co-Authors Adhi Harmoko Saputro Bambang Trisakti Brahmantara, Randy Prima Candra, Danang Surya Dimyati, Ratih Dewanti Dony Kushardono Khomarudin, Rokhis LILIK BUDIPRASETYO Mahdi Kartasasmita MASITA DWI MANDINI MANESSA, MASITA DWI MANDINI Mukhoriyah, Mukhoriyah Nanik Suryo Haryani Nurwita Mustika Sari, Nurwita Mustika Parwali, Parwali Rokhmatuloh, Rokhmatuloh Tatik Kartika, Tatik