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A NORMALISED DIFFERENCE ALPHA-ANGLE APPROACH TO BURN SCAR EXTRACTION ON MULTIPLE-POLARISATION SAR DATA

机译:一种归一化差异α角方法,以在多极化SAR数据上燃烧瘢痕提取

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Wild fires are often disastrous events, damaging infrastructure and the environment. However, in fire-prone ecosystems, periodic fires are vital for ecosystem functioning. Fire managers in these regions seek to promote the optimal fire regime by managing fire season and fire frequency. For this purpose, detailed information on the extent and date of previous burns over large areas are required. This paper proposes a Normalised Difference α-Angle approach to burn scar mapping using α-angles derived from a pre-burn scene and a post-burn RADARSAT-2 scene. A threshold-based segmentation approach was used to extract the burnt-area extent while minimizing speckle effects. The results of the burn scar extraction were compared to the extent of a burn scar derived from Sentinel-2 data. The results suggest that, in general, a good agreement between the two approaches was obtained with an overall accuracy of 0.98 achieved. High errors of omission was associated with areas of high local incidence angle. In general, the result suggests that SAR data, analysed using the proposed approach, can be used in areas where persistent cloud cover or smoke from active fires prevents the use of conventional approaches.
机译:狂野的火灾往往是灾难性的事件,破坏性基础设施和环境。然而,在火灾易发的生态系统中,定期火灾对生态系统运行至关重要。这些地区的消防管理人员通过管理火季和火频来寻求推广最佳的消防制度。为此,需要有关在大面积上以前燃烧的程度和日期的详细信息。本文提出了一种归一化差异α角方法,以使用从预燃烧场景和后烧纳雷达拉特-2场景的α角燃烧瘢痕映射。基于阈值的分割方法用于提取烧坏区域范围,同时最小化散斑效果。将烧伤瘢痕提取的结果与来自哨子-2数据的烧伤瘢痕进行了比较。结果表明,一般而言,获得两种方法之间的良好一致性,以0.98达到0.98。遗漏的高误差与高局部入射角的区域有关。通常,结果表明,使用所提出的方法分析的SAR数据可以用于持续云覆盖或来自主动火灾的烟雾的区域中防止使用传统方法。

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