Automated Landsat 8 data preprocessing for national forest monitoring system

Sahid Hudjimartsu; Lilik Prasetyo; Yudi Setiawan; Desi Suyamto; Wim Ikbal; Kustiyo

doi:10.1117/12.2326100

6 August 2018 Automated Landsat 8 data preprocessing for national forest monitoring system

Sahid Hudjimartsu, Lilik Prasetyo, Yudi Setiawan, Desi Suyamto, Wim Ikbal, Kustiyo

Proceedings Volume 10773, Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018); 107730R (2018) https://doi.org/10.1117/12.2326100
Event: Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018), 2018, Paphos, Cyprus

Abstract

Precise digital classification for Landsat 8 data of remote sensing images require pre-processing steps. The preprocessing consist of conversion from digital numbers (DN) to top of atmosphere (TOA) reflectance, cloud and cloud shadow masking, topographic correction and image normalization. In general, pre-processing steps were implemented to National scale (Indonesia) excluding topographic correction. The topographic correction algorithm is required to avoid reflectance bias from terrain effects due to shading. The highest mountains in Indonesia were selected as window areas, considering the reflectance bias is produced due to terrain effects. The results showed that algorithm is able to solve overcorrection problems and will be implemented into LAPAN’s system of image pre-processing for National scale. This research is a collaboration between Bogor Agricultural University (IPB) with National Institute of Aeronautics and Space (LAPAN) under Forests2020 Programme, in order to produce Landsat 8 data with the minimal cloud over Indonesia annually and then to automatically digital classification for forest monitoring. The automated system of preprocessing was developed with Perl and Python programming languages.

Citation Download Citation

Sahid Hudjimartsu, Lilik Prasetyo, Yudi Setiawan, Desi Suyamto, Wim Ikbal, and Kustiyo "Automated Landsat 8 data preprocessing for national forest monitoring system", Proc. SPIE 10773, Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018), 107730R (6 August 2018); https://doi.org/10.1117/12.2326100

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