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首页> 外文会议>Pixels, Objects, Intelligence: GEOgraphic Object Based Image Analysis for the 21st Century >VEGETATION FIRE FUELS MAPPING IN THE SAN DIEGO CITY CANYONS - A METHOD COMPARISON
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VEGETATION FIRE FUELS MAPPING IN THE SAN DIEGO CITY CANYONS - A METHOD COMPARISON

机译：植被消防燃料在圣地亚哥市峡谷映射 - 一种方法比较

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Fire risk is a major threat to life, property and natural resources in southern California. Recent fire disasters occurred in autumn 2003 and 2007. Fire risk management deals with these hazards, input data are collected, analyzed and evaluated. One of the most important input data is the vegetation density in the endangered areas. Here we describe methods to map vegetation density forming five hazard classes. The main objective of this study is to explore the benefits of using remote sensed data for the accurate classification of vegetation in San Diego city canyons. Three very high resolution remote sensing data sets (< 1 m) were used in comparison: scanned color infrared film (CIR) airborne, digital multi-spectral airborne (ADS40) and digital multi-spectral satellite imagery (QuickBird). Different classification approaches (e.g. pixel-based, segment-based and knowledge-based) were tested and analyzed to separate the vegetation into five hazard classes. Accuracy assessment indicated low overall accuracies of 58percent on average. With regard to an optimized classification result in particular unsupervised and segment-based classification can be recommended. The overall accuracy for these two methods reached around 62percent. The use of specially selected reference areas for validation helped to increase the accuracies up to 81percent. Also a separating between three instead of five different hazard classes resulted in accuracies around 80percent. Furthermore it could be shown that all three data sets can be used for successful classification procedures. The resulting fire risk maps can help to reduce or prevent fire hazards. The maps are a basis for the brush management of the Fire Department to manage sites of high risk next to residential areas (e.g. establish a 30 m fire break around properties concerned).
机译：火灾风险是南加州南部生活，财产和自然资源的重大威胁。最近的火灾灾害发生在2003年和2007年秋季。火灾风险管理涉及这些危害，分析和评估了输入数据。最重要的输入数据之一是濒危区域的植被密度。在这里，我们描述了形成五个危险类的植被密度的方法。本研究的主要目的是探讨使用遥感数据进行准确分类圣地亚哥市峡谷的益处。相比之下，使用三个非常高分辨率的遥感数据集（<1米）：扫描彩色红外薄膜（CIR）空气传播，数字多光谱机载（ADS40）和数字多光谱卫星图像（Quickbird）。测试并分析了不同分类方法（例如基于像素的，基于段和知识的），将植被分成五个危险类。精度评估表明平均总体精度为58％。关于优化的分类，可以建议使用特定的无监督和基于分段的分类。这两种方法的总体精度达到62％。使用特殊选定的参考区域进行验证有助于增加高达81的准确性。此外，三个而不是五种不同的危险类别之间的分离导致了80％的精度。此外，可以说明所有三种数据集可用于成功的分类程序。由此产生的火灾风险地图可以帮助减少或防止火灾危害。地图是消防部门的刷子管理的基础，以管理住宅区旁边的高风险网站（例如，建立30米的灭火周围有关房产）。

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