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

doi:10.12928/telkomnika.v16i3.9331

Cited by 5 publications

(8 citation statements)

References 17 publications

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“…Three of tile sizes of 0.1 degrees (11x11 km2), 0.05 degrees (5.5x5.5 km2), and 0.02 degrees (2.2x2.2 km2) were used to examine the reliability and simultaneously the level of visual interpretability as well as digital interpretability of the produced images. Of the three tiles, a 0.02x0.02 degree tile had the most optimum accuracy (Dimyati RD. et al, 2018).…”

Section: The Methodsmentioning

confidence: 99%

“…The selected study area covered the central part of Sumatra, including parts of Riau, North Sumatra, and West Sumatra Provinces (Figure 1). The main reasons for the selection of this study area are, among others, that as a part of Indonesia which is often covered by clouds and haze disturbance (Gastellu-Etchegorry 1998;Roswintiarti et al, 2014), and the Landsat-8 OLI images of the TBM model for 2015, 2016, and 2017 are ready for the region (Dimyati, RD. et al, 2018), and has been shown to have high interpretability for visual land cover analysis (Dimyati, M. et al, 2018).…”

Section: Study Areamentioning

confidence: 99%

“…The primary data used for this study were annual mosaic images of Landsat-8 OLI of 2015, 2016. Those data have been geometrically corrected at Level-1T (precision and terrain correction level) and radiometrically corrected of ToA (Top of Atmosphere) and BRDF (Bi-directional Reflectance Distribution Function), covering parts of Riau, West Sumatra, and North Sumatra Provinces (Dimyati, RD. et al, 2018, Dimyati, M. et al, 2018.…”

Section: Datamentioning

confidence: 99%

“…The characteristics of spectral bands of Landsat-8 OLI are shown in Table 1. Therefore only 5 (five) spectral bands among 9 (nine) available spectral bands of OLI had been selected for this research (Dimyati, RD. et al 2018).…”

Section: Datamentioning

confidence: 99%

“…The proposed TBM model was applied to Landsat-8 OLI data in the central part of Sumatra to obtain the minimum cloud cover of the annual TBM image (hereinafter called annual mosaic image). The algorithm of the model was (Dimyati, RD. et al, 2018): The purpose of this study was to examine whether the digital interpretability of the annual mosaic image results was acceptable for the digital analysis of time-series land cover.…”

mentioning

confidence: 99%

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Digital Interpretability of Annual Tile-based Mosaic of Landsat-8 OLI for Time-series Land Cover Analysis in the Central Part of Sumatra

Dimyati

Danoedoro

HARTONO

et al. 2018

IJG

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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.

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Section: The Methodsmentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

mentioning

confidence: 99%

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Digital Interpretability of Annual Tile-based Mosaic of Landsat-8 OLI for Time-series Land Cover Analysis in the Central Part of Sumatra

Dimyati

Danoedoro

HARTONO

et al. 2018

IJG

Self Cite

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Developing automatic tiled SAR image mosaic process for faster online access

Dyatmika,

Setiyoko,

Indriasari

2023

AIP Conference Proceedings

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Design of automate SAR processing system in LAPAN to support easier and fast data services

Budiono

Dyatmika

Indriasari

et al. 2020

IOP Conf. Ser.: Earth Environ. Sci.

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One of the Indonesia’s National Institute of Aeronautics and Space (LAPAN) task is to supply and distribute remote sensing data based on Indonesia national needs as mandated by space law no 21 year 2013. One of the satellite image acquired and distributed by LAPAN including TerraSAR-X/TanDEM-X, a twin satellite from Germany launched in 2007. Acquisition of the TerraSAR-X/TanDEM-X satellite began in 2017 with aim for coverage of whole Indonesian area. With respect to the data size and data availability, an automate data processing method is needed to process all of the available TerraSAR-X/TanDEM-X data. This paper will discuss a system design that automate the LAPAN’s TerraSAR-X/TanDEM-X data processing and method of presenting the data to the users as tiled data.

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Interpretability Evaluation of Annual Mosaic Image of MTB Model for Land Cover Changes Analysis

Cited by 5 publications

References 17 publications

Digital Interpretability of Annual Tile-based Mosaic of Landsat-8 OLI for Time-series Land Cover Analysis in the Central Part of Sumatra

Digital Interpretability of Annual Tile-based Mosaic of Landsat-8 OLI for Time-series Land Cover Analysis in the Central Part of Sumatra

Developing automatic tiled SAR image mosaic process for faster online access

Design of automate SAR processing system in LAPAN to support easier and fast data services

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