基于K-Means聚类的玛琅森林火灾遥感时空格局分析

Annisa Puspa Kirana, Mungki Astiningrum, Candra Bella Vista, Adhitya Bhawiyuga, Aris Nur Amrozi
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引用次数: 0

摘要

森林和土地火灾严重影响环境的平衡,如雾霾污染、生态系统的破坏、空气中碳的高释放、健康状况的恶化以及其他各个领域的损失。基于这些因素,发展早期预警系统对于防止森林火灾,特别是森林和陆地地区的森林火灾至关重要。可用于监测频繁火灾地区的数据之一是来自NASA MODIS Fire卫星的热点数据。利用数据挖掘技术对热点数据进行处理,得到热点群的分布情况。热点聚类分布的数据用于检测每年容易发生火灾的区域。本研究采用K-Means聚类算法。本研究使用的数据是来自印度尼西亚玛琅地区的热点数据。热点数据范围为2018年1月至2022年6月。我们使用剪影系数测试来获得聚类中的最佳类数,这是本研究最新应用K-means聚类方法来分析时空热点分布。我们使用热点数据在玛琅的森林和土地面积使用热点置信度>80%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatio-Temporal Pattern Analysis of Forest Fire in Malang based on Remote Sensing using K-Means Clustering
Forest and land fire significantly impact the balance of the environment, such as haze pollution, destruction of ecosystems, the high release of carbon in the air, deterioration of health, and losses in various other fields. Based on these factors, developing an early warning system is essential to prevent forest fires, especially in forest and land areas. One of the data that can be used to monitor areas where there are frequent fires is hotspot data taken from the NASA MODIS Fire satellite. Data mining techniques are carried out to process the hotspot data so that the distribution of hotspot swarms is obtained. The data on the distribution of the clustering of hotspots are used to detect areas that are prone to fire from year to year. This study used the K-Means clustering algorithm. The data used in this study is hotspot data from Malang District, Indonesia. The range of hotspot data from January 2018 to June 2022. We use Silhouette coefficient testing to get the best number of classes in the cluster—this study's most recent application of the K-means clustering method to analyze hotspot distribution in a spatial-temporally. We use hotspot data in Malang's forest and land area using hotspot confidence levels >80%.
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