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All Journal International Journal of Remote Sensing and Earth Sciences (IJReSES) Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Wismu Sunarmodo, Wismu
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering

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CLOUD IDENTIFICATION FROM MULTITEMPORAL LANDSAT-8 USING K-MEANS CLUSTERING Sunarmodo, Wismu; Hayati, Anis Kamilah
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (593.999 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3285

Abstract

In the processing and analysis of remote-sensing data, cloud that interferes with earth-surface data is still a challenge. Many methods have already been developed to identify cloud, and these can be classified into two categories: single-date and multi-date identification. Most of these methods also utilize the thresholding method which itself can be divided into two categories: local thresholding and global thresholding. Local thresholding works locally and is different for each pixel, while global thresholding works similarly for every pixel. To determine the global threshold, two approaches are commonly used: fixed value as threshold and adapted threshold. In this paper, we propose a cloud-identification method with an adapted threshold using K-means clustering. Each related multitemporal pixel is processed using K-means clustering to find the threshold. The threshold is then used to distinguish clouds from non-clouds. By using the L8 Biome cloud-cover assessment as a reference, the proposed method results in Kappa coefficient of above 0.9. Furthermore, the proposed method has lower levels of false negatives and omission errors than the FMask method.
Pengembangan Tiling database untuk Penyimpanan Data Penginderaan Jauh pada Pembangunan LAPAN Engine Widipaminto, Ayom; Safitri, Yuvita Dian; Sunarmodo, Wismu; Rokhmatullah, Rokhmatullah
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 1 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v16i1.3350

Abstract

Remote sensing image data is included in the unstructured data category which is characterized by large volumes of data and is regularly updated. Special techniques are needed in large capacity data storage and supported by high-capacity data processing machines. This study aims to find a design representation of remote sensing image data that is more efficient in storage and processing than conventional methods. The design proposed is with the concept of tiling databases, namely the method of breaking down image data into small size pieces with certain identities and then entering them into a database. The test results compared to the conventional method found that the storage volume can be reduced by up to 25%, the speed of reading the data also increases by about 21%. This system can support the development of LAPAN Engine because it offers a storage strategy that is more effective in terms of volume, and efficient in terms of the speed of reading data even though the tiling process into the database takes pretty long time.
ANALISIS TINGKAT AKURASI TITIK HOTSPOT DARI S-NPP VIIRS DAN TERRA/AQUA MODIS TERHADAP KEJADIAN KEBAKARAN Indradjad, Andy; Purwanto, Judin; Sunarmodo, Wismu
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 1 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v16i1.3351

Abstract

Accuracy analysis of the forest fire detection by using remote sensing data hotspots from SNPP and TERRA/AQUA has been carried out. The sensors used were MODIS sensors for TERRA/AQUA satellites and VIIRS sensors for S-NPP satellites. The detection of hotspots from remote sensing satellite data can be used as an early warning of forest fires. Hotspot can be derived from 2 sensors, namely MODIS and VIIRS sensors using algorithms that have been developed by science team from satellite developer. This hotspot information need to be accurately analysis by ground thruth of the fire events. This aims to analize the accuracy of hotspot information detection for forest fires. By comparing fire event data in 2018 and hotspot information data on hotspot databases owned by LAPAN. The results show that MODIS sensors are 39% and for VIIRS sensors are 20%. That result using 2 km of buffer radius which is the most significant result comparing others. It is clearly indicates that improvements are needed to improve the accuracy of hotspot derived from VIIRS data.
IDENTIFIKASI AWAN PADA DATA TIME SERIES MULTITEMPORAL MENGGUNAKAN PERBANDINGAN DATA SEKUENSIAL Hayati, Anis Kamilah; Sunarmodo, Wismu
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 18 No. 1 (2021)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v18i1.3363

Abstract

Cloud identification is an important pre-processing step of remote sensing data.Generally, cloud identifications could be classified into single-date and multi-date methods. Furthermore, the single-date method could be divided into physical-rules-based and machine-learning-based. Physical-rules-based method generally need data with sufficient spectral resolution while machine-learning-based method depend on training dataset. While the multi-date method usually using clear data as a reference. The clear data itself could be a whole scene or built from many scenes. Processing cloud-free data is a challenge in areas with high cloud coverage such as Indonesia. In this paper, a cloud identification method using multi-date time series scenes is proposed. This method only uses RGB channels which are common in remote sensing visual data. In addition, this method does not require or process cloud-free data mosaics in advance. A pixel value from an examined scene is compared to other pixel values from other scenes in the same position. The other scenes are the scenes that were acquired before and after the examined scene. The value differences between the examined pixel and it's before and after then evaluated using some thresholds to determine whether the pixel is a cloud or not. Assessment is done by using L8 Biome as a reference. The result shows that using some thresholds in our proposed method has a Kappa coefficient higher than 0.9.
Co-Authors Andy Indradjad, Andy Anis Kamilah Hayati, Anis Kamilah Ayom Widipaminto, Ayom Judin Purwanto, Judin Rokhmatullah, Rokhmatullah Safitri, Yuvita Dian