Publikasi

@inproceedings{wijayanto2019characteristic,

title = {The Characteristic of spectral reflectance of LAPAN-IPB (LAPAN-A3) Satellite and Landsat 8 over agricultural area in Probolinggo, East Java},

author = {Arif K Wijayanto and Sri M Yusuf and Wiwid A Pambudi},

url = {https://iopscience.iop.org/article/10.1088/1755-1315/284/1/012004/meta},

doi = {10.1088/1755-1315/284/1/012004},

year  = {2019},

date = {2019-01-01},

booktitle = {IOP Conference Series: Earth and Environmental Science},

volume = {284},

number = {1},

pages = {012004},

organization = {IOP Publishing},

abstract = {LAPAN-IPB Satellite which was developed by the National Agency of Aeronautics and Space (LAPAN) and Landsat 8 have quite equal specification. However, it is important to investigate the difference of characteristic between the two satellites since the Landsat 8 commonly used by Indonesian researcher in the agriculture field for years. The study was done in Probolinggo Regency which is located in East Java, Indonesia – has a large area of agriculture. Satellite data of LAPAN A3/IPB used in the analysis of its spectral characteristic over agricultural area was acquired on September 18, 2018, while the Landsat 8 image data was taken from acquisition date on September 12, 2018. Field data measurement was done by collecting spectral reflectance of some agricultural crops at study area consist of paddy, maize, sugar cane, and onion. Spectral reflectance from the four crops are quietly the same, except for paddy which has the lowest reflectance on peak of green band compared to other crops. Spectral profile of LAPAN-A3/IPB on Blue, Green and Red band are always lower than Landsat 8, while the NIR band is always higher. NDVI from Landsat 8 OLI ranged from -1 to 0.622844, while NDVI from LAPAN-A3/IPB ranged from -1 to 0.461655. NDVI from Landsat is able to differentiate water more clearly than LAPAN-A3/IPB, indicated by low NDVI value. It is concluded that LAPAN-A3/IPB has quite similar spectral characteristic compared to Landsat-8 OLI. Although there is some difference of spectral characteristic from some crops. It is recommended to consider the age or growth stage of each crop.},

keywords = {Landsat, LAPAN, spectral},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{suyamto2017combining,

title = {Combining projective geometry modelling and spectral thresholding for automated cloud shadow masking in Landsat 8 imageries},

author = {Desi Suyamto and Lilik B Prasetyo and Yudi Setiawan and Arif K Wijayanto},

url = {https://ieeexplore.ieee.org/abstract/document/8356785},

doi = {10.1109/EMS.2017.15},

year  = {2017},

date = {2017-01-01},

booktitle = {2017 European Modelling Symposium (EMS)},

pages = {22--27},

organization = {IEEE},

abstract = {The presence of cloud shadows in satellite imageries decreases the reflectance of the objects under the shades to relatively low intensities, leads to identification errors. Thus, cloud shadows detection is crucial in image processing steps. We integrated solar position modelling, projective geometry modelling, and spectral thresholding to detect cloud shadows in Landsat 8 imageries. We evaluated the algorithm using the window area of Mount Halimun-Salak, Bogor, West Java, Indonesia. The best rate accuracies of cloud shadow detection using the algorithm was obtained at producer's accuracy, user's accuracy and κ of 63.79%, 70.58%, and 0.66, respectively. Possibility of improving the algorithm for correcting the reflectance of the objects under the shades instead of removing is discussed.},

keywords = {cloud, Landsat, spectral},

pubstate = {published},

tppubtype = {inproceedings}

}