Abstract

In arid lands of Central Kazakhstan, fires commonly occur due to both man-made (e.g. space rocket launches) and natural factors. Remote sensing is the best method to identify burned landscapes and to assess their long-term dynamics. In this paper, we assessed total areas and seasonal features of fires using Landsat 8 OLI data of high spatial and temporal resolution, corrected for atmospheric effects. We defined data requirements, considered various spectral indices, and evaluated their suitability for automated delineation of burned areas. Spectral indices included special burn indices (NBRT, MIRBI, NBR, NBR2, BAI) and vegetation indices (MSAVI, MSAVI2, MTVI2, GEMI3) that allowed us to determine the boundaries and severity of fires indirectly based on the state of vegetation. Our study showed that burned areas were most accurately identified using indices of NBR2 (normalized burn ratio 2) and MSAVI2 (modified soil adjusted vegetation index 2). They were extracted using image segmentation and natural breaks classification methods. We have assessed MSAVI2 and NBR2 segmentation results with machine learning metrics, i.e. for Soyuz drop zone (landing area of rocket stages) precision of NBR segmentation equaled 99.5%, recall − 99.5%, accuracy − 98.5%. For Proton drop zone NBR segmentation precision equaled 99.3%, recall − 99.7%, accuracy − 99.4%. MSAVI2 results are less accurate. The accuracy of segmentation significantly depended on the vegetation state which was predetermined by the frequency and severity of previous fires within certain territory.

摘要

在哈萨克斯坦中部的干旱地区，火灾通常是由于人为（例如太空火箭发射）和自然因素造成的。遥感是识别被烧毁景观并评估其长期动态的最佳方法。在本文中，我们使用高时空分辨率的 Landsat 8 OLI 数据评估了火灾的总面积和季节性特征，修正了大气影响。我们定义了数据要求，考虑了各种光谱指数，并评估了它们对自动描绘燃烧区域的适用性。光谱指数包括特殊燃烧指数（NBRT、MIRBI、NBR、NBR2、BAI）和植被指数（MSAVI、MSAVI2、MTVI2、GEMI3），它们使我们能够根据植被状况间接确定火灾的边界和严重程度。我们的研究表明，使用 NBR2（归一化燃烧比 2）和 MSAVI2（改良土壤调整植被指数 2）的指数最准确地确定了燃烧区域。它们是使用图像分割和自然中断分类方法提取的。我们已经使用机器学习指标评估了 MSAVI2 和 NBR2 分割结果，即 对于联盟号降落区（火箭级着陆区），NBR 分割精度等于 99.5%，召回率 - 99.5%，准确率 - 98.5%。对于质子滴区，NBR 分割精度等于 99.3%，召回率 - 99.7%，准确率 - 99.4%。MSAVI2 结果不太准确。分割的准确性在很大程度上取决于植被状态，该状态由特定领土内先前火灾的频率和严重程度预先确定。