scholarly journals Spatial Distribution and Implementation of the K-Means Clustering Method at Hotspots in North Sumatra

2021 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
Kartika Dewi Butar-Butar ◽  
Elviawaty Muisa Zamzami ◽  
Nancy Damanik ◽  
Alex Rikki ◽  
Eva Darlina

Hotspots are indicators of forest and land fires. Hotspot monitoring can be carried out with the help of remote sensing tools and geographic information systems. Hotspot data is obtained from the MODIS sensors from the TERRA and AQUA satellites which contain information on latitude and longitude coordinates and the level of confidence divided by three levels, namely low, medium and high confidence levels. Based on the spatial results, the number of hotspots in North Sumatra Regency is in February, March, June, July, and August. Districts that are dominant with hotspots are Karo Regency, Labuhan Batu Regency, Mandailing Natal Regency, Padang Lawas Regency and South Tapanuli Regency. Based on the results, the process of applying the k-means clustering method to the weka application, the data obtained is in the form of a clustered group and the results can be made into indicators in determining hotspots in districts in North Sumatra province per month.

2004 ◽  
Vol 1 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Christopher L. Main ◽  
Darren K. Robinson ◽  
J. Scott McElroy ◽  
Thomas C. Mueller ◽  
John B. Wilkerson

Author(s):  
Nikolaos Stathopoulos ◽  
Kleomenis Kalogeropoulos ◽  
Christos Polykretis ◽  
Panagiotis Skrimizeas ◽  
Panagiota Louka ◽  
...  

Author(s):  
Fadi Abdullah alanazi, Yaser Rashed Alzannan, Faten Hamed Na Fadi Abdullah alanazi, Yaser Rashed Alzannan, Faten Hamed Na

Souda is one of the important regions in Saudi Arabia in terms of spatial and temporal changes in vegetation cover; It includes the National Park, which is a leading tourist destination and one of the most beautiful parks in it. by tracking the spatial and temporal changes of vegetation cover by integrating remote sensing and geographic information systems, through the application of the modified soil vegetation index MSAVI during the period (2014- 2018), it became clear the decrease in the quantity and density of vegetation cover in the area. Thus, the study concluded that this indicator is one of the best indicators that can be used to extract vegetation cover from satellite images.


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