Department of Geography and Environmental Studies, University of Haifa, Mount Carmel, 3498838, Israel.
J Environ Manage. 2019 May 15;238:224-234. doi: 10.1016/j.jenvman.2019.02.091. Epub 2019 Mar 7.
Wildfires occurring near and within cities are a potential threat to the population's life and health and can cause significant economic damage by destroying infrastructure and private property. Due to the relatively small area of these wildlands, the accuracy of fire risk-assessment plays a significant role in fire management. Introducing the experience of real events can improve accuracy. But this approach is limited by a lack of knowledge of pre-fire conditions, mainly vegetation characteristics as related to their definition as a fuel parameter because of their high temporal variation. To solve this problem, an Artificial Neural Network (ANN) was designed to reconstruct the spectral characteristics of the vegetation just before the fire with spatial resolution 0.5-2 m from the Landsat image. To test the effectiveness of the proposed methods, the approach has been examined on urban vegetation sites and applied to restore spectral information of the actual vegetation patch before it was burned in 2016 in Haifa, Israel. The results show that the reconstructed RGB image allows for mapping the location of green vegetation with high spatial accuracy. However, spectral data in the visible range have some limitations when it comes to identifying differences between soil and dry plants. The reconstructed image was used to sharp the original data from Landsat. Normalized Difference Vegetation Index maps were produced from the resulting high-resolution multispectral image. The output maps allow to determine the location of vegetation and estimate the level of its dryness on the urban wildland landscape. The proposed method aims to estimate vegetation dryness and, as a result, identify the fuel characteristics at the time of the fire. It has the potential of using for evaluation and improve the weights of the input parameters for the fire-risk assessment and fire-behavior modeling on a specific area.
城市及其周边地区发生的野火对人口的生命和健康构成潜在威胁，并可能通过摧毁基础设施和私人财产造成重大经济损失。由于这些荒地的面积相对较小，火灾风险评估的准确性在火灾管理中起着重要作用。引入真实事件的经验可以提高准确性。但是，这种方法受到缺乏火灾前条件知识的限制，主要是与植被特征有关的燃料参数定义，因为它们具有很高的时间变化。为了解决这个问题，设计了一个人工神经网络（ANN），以从 Landsat 图像以 0.5-2 m 的空间分辨率重建火灾前植被的光谱特征。为了测试所提出方法的有效性，该方法已经在城市植被站点进行了检验，并应用于 2016 年在以色列海法实际植被斑块燃烧前恢复其光谱信息。结果表明，重建的 RGB 图像允许以高精度绘制绿色植被的位置。然而，当涉及到识别土壤和干植物之间的差异时，可见光范围内的光谱数据具有一些局限性。重建的图像用于锐化 Landsat 的原始数据。从生成的高分辨率多光谱图像制作归一化差异植被指数图。输出地图允许确定植被的位置并估计其在城市荒地景观上的干燥程度。所提出的方法旨在估计植被的干燥度，并因此识别火灾时的燃料特征。它有可能用于评估和改进特定区域火灾风险评估和火灾行为建模的输入参数的权重。
