MINING FIRE HOTSPOTS OVER NUSA TENGGARA AND BALI ISLANDS

Abstract

Forest fires are still one of the most common problems in Indonesia. In fact, many of these forest fires origin from human activities, namely fires that are intentionally raised for a purpose such as widening the land to prepare for the planting season in the Nusa Tenggara Island. Forest fire events can be identified by observing hotspot data which are monitored through remote sensing satellites. Hotspot is an area that has a relatively higher surface temperature than the surrounding area based on certain temperature thresholds monitored by remote sensing satellites. The area is represented as a point that has certain coordinates. The actual fires can be monitored by observing the hotspot attribute, namely Confidence, Brightness Temperature and FRP (Fire Radiate Power). To find the similarities of the three mentioned attributes, the clustering process is carried out to make monitoring easier. The objective of this research is to cluster hotspots in the Nusa Tenggara and Bali Islands from year 2013 to 2018 using the K-Means Clustering Method with 28,519 hot spot data. This could be a benefit for the Ministry of Environment and Forestry in Indonesia to identify the priority level of the area to be monitored. By knowing  this result, the ministry can use this data for patrol priority management. This research successfully clustered three types of hotspot classes based on the risk of fire with details as follow; High Risk Class contains 12,212 data with ranges of mean values of confidence in the range of 49.3–100%, brightness in the range of 305.1–421.3o K and FRP in the range of 2.5–714.3; Medium Risk contains 12,250 data mean values of confidence  with a range of 20.3–74.3%, brightness in the range of 301.06–341.86o K and FRP in the range of 3.6–141.4; and Low Risk contains 4,057 data with a range of mean values of confidence in the range of 0–39.8%, brightness in the range of 300–365.86oK and FRP in the range of 3.5–275.6. All of the clusters were obtained by the implementation of K-Means clustering over the hotspot data and its parameter as mentioned, respectively. The cluster performance showed the confidential value of 88.45% accuracy using 100 hotspot data from 2019