Please use this identifier to cite or link to this item: http://paper.sci.ui.ac.id/jspui/handle/2808.28/415
Title: Interpretability Evaluation of Annual Mosaic Image of MTB Model for Land Cover Changes Analysis
Authors: Dimyati, Muhammad
Dimyati, Ratih Dewanti
Kustiyo, Kustiyo
Danoedoro, Projo
Hartono, Hartono
Keywords: interpretability evaluation
annual mosaic image
visual interpretations
scattergram analysis
consistency of the results
the level of clearness
the level of easiness
standardization of data processing
Issue Date: Jun-2018
Publisher: Institute of Advanced Engineering and Science (IAES)
Series/Report no.: Volume 16;No. 3
Abstract: 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 o f 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.
URI: http://paper.sci.ui.ac.id/jspui/handle/2808.28/415
ISSN: 1693-6930
Appears in Collections:Journal Collection



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