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

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.

Cloud removal on satellite imagery using blended model: case study using quick look of high-resolution image of Indonesia

“…Cloud cover information for each scene is needed, especially for compliance with existing regulatory requirements and also for determining the final quality of the image map to be processed. This means that users can not rely on various digitally developed cloud removal models [17], which are only processed after the satellite image order is obtained by the user. Users need cloud cover information in each scene and certainty of compliance with regulatory requirements [3] cloud cover in each scene before the data is ordered for analysis.…”

Evaluation of Lossy Compressed Mosaic for SPOT-6/7 Remote Sensing Data in SPACeMAP

“…Selecting the cleanest/cloud-minimum scene as an input in scene-based mosaics is the key to obtaining a mosaic with minimum clouds. The tile-based research that has been developed is the Tile-Based Mosaic (TBM) for Landsat-8 annual mosaics [10], [11], and 8-steps TBM using haze index for SPOT-6/7 data [12]. Scene-based and Tile-based mosaics are outputs of the LAPAN data processing team produced outside of the work scope of SPACeMAP, but it has been operationalized and crawled/published on SPACeMAP.…”