Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks
Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks
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DOI：
10.1016/j.jag.2009.03.003
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发表时间：
2009-08-01
影响因子：
7.5
通讯作者：
Hansen, Matthew C.
中科院分区：
文献类型：
--
作者：
Maeda, Eduardo Eiji;Formaggio, Antonio Roberto;Hansen, Matthew C.
这项工作描述了一种利用人工神经网络(ANN)和MODIS/Terra-Aqua传感器的多时相图像来探测巴西亚马逊森林火灾高风险地区的方法。这项工作的假设是，由于亚马逊森林特有的土地利用和土地覆盖变化动态，可能被烧毁的林区可以与其他土地目标分开。在巴西亚马逊马托格罗索州北部的三个市镇进行了一项研究。采用反向传播算法对具有不同结构的前馈神经网络进行训练，以2005年火灾季节前5个不同时期获取的MODIS图像计算的NDVI值作为输入。选定的样本是从2005年探测到森林火灾的地区以及其他未被烧毁的森林和农业区抽取的。这些样本被用来训练、验证和测试神经网络。测定结果的均方误差为0.07。此外，对整个城市进行了模拟，并将其结果与MODIS传感器在一年中探测到的热点进行了比较。直方图分析表明，火灾危险区域的空间分布与2005年6月至12月观测到的火灾事件一致。神经网络模型为研究区森林火灾事件的预测提供了一种快速、准确的方法。因此，它为支持森林防火政策和协助评估烧毁面积提供了一个极好的替代方案，减少了目前使用的方法所涉及的不确定性。(C)2009爱思唯尔B.V.保留所有权利。
The presented work describes a methodology that employs artificial neural networks (ANN) and multitemporal imagery from the MODIS/Terra-Aqua sensors to detect areas of high risk of forest fire in the Brazilian Amazon. The hypothesis of this work is that due to characteristic land use and land cover change dynamics in the Amazon forest, forest areas likely to be burned can be separated from other land targets. A study case was carried out in three municipalities located in northern Mato Grosso State, Brazilian Amazon. Feedforward ANNs, with different architectures, were trained with a backpropagation algorithm, taking as inputs the NDVI values calculated from MODIS imagery acquired during five different periods preceding the 2005 fire season. Selected samples were extracted from areas where forest fires were detected in 2005 and from other non-burned forest and agricultural areas. These samples were used to train, validate and test the ANN. The results achieved a mean squared error of 0.07. In addition, the model was simulated for an entire municipality and its results were compared with hotspots detected by the MODIS sensor during the year. A histogram analysis showed that the spatial distribution of the areas with fire risk were consistent with the fire events observed from June to December 2005. The ANN model allowed a fast and relatively precise method to predict forest fire events in the studied area. Hence, it offers an excellent alternative for supporting forest fire prevention policies, and in assisting the assessment of burned areas, reducing the uncertainty involved in currently used methods. (C) 2009 Elsevier B.V. All rights reserved.