利用卫星图像绘制Moguer (Huelva) Las penuelas火灾的影响和植被恢复图。2017年

IF 0.4 Q4 REMOTE SENSING
J. J. Vales, I. Pino, L. Granado, R. Prieto, E. Méndez, M. Rodríguez, F. Giménez de Azcárate, E. Ortega, J. M. Moreira
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引用次数: 2

摘要

深入了解森林火灾后的再生过程是解决其不利环境影响的关键,这在植被中尤为明显。在火灾后的环境背景下,火灾严重程度是影响火灾后植被恢复和水文地貌动态等生态系统响应的关键变量。因此,准确的严重程度评估对于火区管理至关重要,因为它可以确定重点区域,从而有助于实施恢复策略和措施。该地区位于Las Peñuelas (Huelva)的自然地带,于2017年6月24日发生大火,影响了近1万公顷的土地。该方法基于计算RBR (Relativized Burn Ratio)光谱指数来评估火灾的严重程度,NDVI (Normalized Difference Vegetation index)指数来评估植被活力的恢复。在这项工作中,使用了来自Sentinel-2和Pleiades卫星的图像,无人机(UAV)获取的图像和现场采样。结果是一幅显示受影响植被恢复或退化程度的地图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cartografía de la afección y recuperación vegetal del incendio de Las Peñuelas en Moguer (Huelva) con imágenes satelitales. Año 2017
Deep knowledge of the regeneration processes after a forest fire is key to addressing their adverse environmental impacts, these are especially evident in the vegetation. In the post-fire environment context, the fire severity constitutes a critical variable that affects the ecosystem response in terms of vegetation recovery and hydrogeomorphological dynamics after the fire. Therefore, the severity accurate assessment is essential for the burned areas management because of it allows the identification of priority areas and, therefore, it helps to carry out recovery strategies and measures. The area of interest is located in the natural place of Las Peñuelas (Huelva), where a large fire took place on June 24, 2017 that affected almost 10 000 ha. The methodology was based on the calculation of the RBR (Relativized Burn Ratio) spectral index to estimate the severity of the fire, and the NDVI (Normalized Difference Vegetation Index) index to evaluate the recovery of vegetal vigor. For the work, images from the Sentinel-2 and Pleiades satellites, images acquired by UAV (Unmanned Aerial Vehicle) and field samplings were used. The result was a cartography showing the levels of recovery or degradation of the affected vegetation.
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来源期刊
Revista de Teledeteccion
Revista de Teledeteccion REMOTE SENSING-
CiteScore
1.80
自引率
14.30%
发文量
11
审稿时长
10 weeks
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