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A Minimum Cloud Cover Mosaic Image Model of the Operational Land Imager Landsat-8 Multitemporal Data using Tile based Dewanti Dimyati, Ratih; Danoedoro, Projo; Hartono, Hartono; Kustiyo, Kustiyo
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 1: February 2018
Publisher : Institute of Advanced Engineering and Science

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Abstract

The need for remote sensing minimum cloud cover or cloud free mosaic images is now increasing in line with the increased of national development activities based on one map policy. However, the continuity and availability of cloud and haze free remote sensing data for the purpose of monitoring the natural resources are still low. This paper presents a model of medium resolution remote sensing data processing of Landsat-8 uses a new approach called mosaic tile based model (MTB), which is developed from the mosaic pixel based model (MPB) algorithm, to obtain an annual multitemporal mosaic image with minimum cloud cover mosaic imageries. The MTB model is an approach constructed from a set of pixels (called tiles) considering the image quality that is extracted from cloud and haze free areas, vegetation coverage, and open land coverage of multitemporal imageries. The data used in the model are from Landsat-8 Operational Land Imager (OLI) covering 10 scenes area, with 2.5 years recording period from June 2015 to June 2017; covered Riau, West Sumatra and North Sumatra Provinces. The MTB model is examined with tile size of 0.1 degrees (11x11 km2), 0.05 degrees (5.5x5.5 km2), and 0.02 degrees (2.2x2.2 km2). The result of the analysis shows that the smallest tile size 0.02 gives the best result in terms of minimum cloud cover and haze (or named clear area). The comparison of clear area values to cloud cover and haze for three years (2015, 2016 and 2017) for the three mosaic images of MTB are 68.2%, 78.8%, and 86.4%, respectively.
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 Juni 2018
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

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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.ABSTRAKData standar Worldview-2 yang dimiliki oleh LAPAN merupakan data Ortho-Ready Standard level 2 (OR2A) yang terdiri dari 4 kanal multispektral (biru, hijau, merah, NIR) dan satu kanal pankromatik masing-masing memiliki resolusi spasial 2 meter dan 0,5 meter. Kedua kanal tersebut memiliki metadata dan RPC yang berbeda, sehingga memungkinkan untuk melakukan koreksi geometrik secara terpisah. Tulisan ini membahas tentang analisis misalignment citra multispektral terhadap citra pankromatik dibandingkan dengan yang telah terkoreksi geometrik sistematik. Metode yang digunakan adalah fast fourier transform phase matching dengan mengambil 500 titik ikat antara kedua citra tersebut. Hasil pengukuran membuktikan bahwa citra multispektral data Worldview-2 level OR2A memiliki pergeseran yang lebih besar dibandingkan dengan citra multispektral yang terkoreksi geometrik sistematik. Citra multispektral data OR2A bergeser 2,14 meter pada sumbu X dan 0,42 meter pada sumbu Y. Sedangkan citra multispektral data terkoreksi geometrik sistematik bergeser 1,72 meter pada sumbu X dan 0,54 meter pada sumbu Y.
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

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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. 
Digital Interpretability of Annual Tile-based Mosaic of Landsat-8 OLI for Time-series Land Cover Analysis in the Central Part of Sumatra Dimyati, Ratih Dewanti; Danoedoro, Projo; Hartono, Hartono; Kustiyo, Kustiyo; Dimyati, Muhammad
Indonesian Journal of Geography Vol 50, No 2 (2018): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

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Abstract

This paper presents an interoperability of annual tile-based mosaic (MTB) images, as well as a verification of the validity of the model for the time series land cover analysis purposes. The primary data used are MTB image of Landsat-8 of the central part of Sumatra, acquired from January 2015 to June 2017. The method used for the interoperability validation is the digital analysis of three-years time series land cover. The classification was performed with four band spectral groups. Training samples are taken from the image of 2016. The results are then reclassified to improve the overall accuracy score based on Jefferies Matusita (JM) distance. The interoperability can be measured by the average of overall accuracy (AOA) score, namely Good (scores > 80%), Fair (70.0% -79.9%), and Bad (< 70%). The results show that the use of the groups Bands 6-5-4-3-2 performs the consistent accuracy level of Good with an AOA score of 86% for six classes object. Whereas the use of the groups Bands 6-5-4-3-2, Bands 6-5-4, and Bands 6-5 shows the consistent accuracy level of Good up to four classes object with an AOA score of 89%, 82%, and 81%, respectively. It means that the annual mosaic image of MTB model is accepted for the image interoperability with an AOA score of > 80% for six and four classes object. Thus the most efficient for interoperability is the use of Bands 6-5 to analyze four class object of land cover. 
Interpretability Evaluation of Annual Mosaic Image of MTB Model for Land Cover Changes Analysis Dimyati, Muhammad; Dimyati, Ratih Dewanti; Kustiyo, Kustiyo; Danoedoro, Projo; Hartono, Hartono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

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To verify whether the annual mosaic image of MTB model is acceptable for further digital analysis, it is necessary to evaluate the visual interpretability. The MTB model is an effort to integrate multi-scene and multi-temporal data, to obtain a minimum cloud cover mosaic image in locations that are often covered by clouds and haze. This study is to evaluate the interpretability of the annual mosaic image for analysis of the land cover changes. The data used are the images of 2015, 2016, and 2017 covers a part of central Sumatra. Visual interpretations with a series of steps are used, starting with identification of the objects using interpretation keys, followed by spectral band correlations, scattergram analysis, and ended by consistency assessment. The consistency assessment step is performed to determine the level of clearness and easiness of the object recognition in the annual mosaic images. The results showed that the most optimal spectral bands used for RGB combinations for visual interpretation were Band SWIR-1, Band NIR, and Band Red. Based on the evaluation results, the annual mosaic image of MTB model performed the consistent results of the clearness objects and the easiness of the object recognition. Thus the annual mosaic image of MTB model of 0.02x0.02 degree tile is acceptable for further digital processing as well as digital land cover analysis.
Paddy field classification with MODIS-terra multi-temporal image transformation using phenological approach in Java Island Dimyati, Muhammad; Kustiyo, Kustiyo; Dimyati, Ratih Dewanti
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 2: April 2019
Publisher : Institute of Advanced Engineering and Science

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This paper presents the paddy field classification model using the approach based on periodic plant life cycle events and how these elevations in climate as well as habitat factors, such as elevation. The data used are MODIS-Terra two tiles of H28v09 and H29v09 of 2016, consist of 46 series of 8-daily data, with 500 meter resolution in Java region. The paddy field classification method based on the phenological model is done by Maximum Likelihood on the transformed annual multi-temporal image of the reflectance data, index data, and the combination of reflectance and index data. The results of the study showed that, with the reference of the Paddy Field Map from the Ministry of Agriculture (MoA), the overall accuracies of the paddy field classification results using the combination of reflectance and index data provide the highest (85.4%) among the reflectance data (83.5%) and index data (81.7%). The accuracy levels were varied; these depend on the slope and the types of paddy fields. Paddy fields on the slopes of 0-2% could be well identified by MODIS-Terra data, whereas it was difficult to identify the paddy fields on the slope &gt;2%. Rain-fed lowland paddy field type has a lower user accuracy than irrigated paddy fields. This study also performed correlation (r2) between the analysis results and the statistical data based on district and provincial boundaries were &gt;0.85 and &gt;0.99 respectively. These correlations were much higher than the previous study results, which reached 0.49-0.65 (hilly-flat areas of county-level), and 0.80-0.88 (hilly-flat areas of provincial level) for China, and reached 0.44 for Indonesia.