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Implementation DBSCAN algorithm to clustering satellite surface temperature data in Indonesia

Register: Jurnal Ilmiah Teknologi Sistem Informasi

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Title Implementation DBSCAN algorithm to clustering satellite surface temperature data in Indonesia
 
Creator Kristianto, Ariel
Sediyono, Eko
Hartomo, Kristoko Dwi
 
Subject cluster; DBSCAN; forest fire; Landsat-8; silhouette
 
Description Forest and land fires are national and international problems. The frequency of fires in one of Indonesia's provinces, Riau, is a significant problem. Knowing where to repair the burn is essential to prevent more massive fires. Fires occur because of a fire triangle, namely fuel, oxygen, and heat. The third factor can be seen through remote sensing. Using the Landsat-8 satellite, named the Enhanced Vegetation Index (EVI) variable, Normalized Burn Area (NBR), Normal Difference Humidity Index (NDMI), Normal Difference Difference Vegetation Index (NDVI), Soil Adapted Vegetation Index (SAVI), and Soil Surface Temperature (LST). DBSCAN, as a grouping algorithm that can group the data into several groups based on data density. This is used because of the density of existing fire data, according to the character of this algorithm. The selected cluster is the best cluster that uses Silhouette Coefficients, eps, and minutes value extracted from each variable, so there is no noise in the resulting cluster. The result is more than 0, and the highest is the best cluster result. There are 5 clusters formed by clustering, each of which has its members. This cluster is formed enough to represent the real conditions, cluster which has a high LST value or has an NBR value. A high  LST value indicates an increase in the area's temperature; a high NBR value indicates a fire has occurred in the area. The combination of LST and NBR values indicates the area has experienced forest and land fires. This study shows that DBSCAN clustered fire and surface temperature data following data from the Central Statistics Agency of Riau Province. Proven DBSCAN can cluster satellite imagery data in Riau province into several clusters that have a high incidence of land fires.
 
Publisher Information Systems - Universitas Pesantren Tinggi Darul Ulum
 
Contributor —
 
Date 2020-07-02
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://journal.unipdu.ac.id/index.php/register/article/view/1913
10.26594/register.v6i2.1913
 
Source Register: Jurnal Ilmiah Teknologi Sistem Informasi; Vol 6, No 2 (2020): July; 109-118
Register: Jurnal Ilmiah Teknologi Sistem Informasi; Vol 6, No 2 (2020): July; 109-118
2502-3357
2503-0477
10.26594/register.v6i2
 
Language eng
 
Relation https://journal.unipdu.ac.id/index.php/register/article/view/1913/pdf
 
Rights Copyright (c) 2020 Kristianto et al.