Measuring fire severity using UAV imagery in semi-arid central Queensland, Australia
Measuring fire severity using UAV imagery in semi-arid central Queensland, Australia
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DOI：
10.1080/01431161.2017.1317942
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发表时间：
2017-01-01
影响因子：
3.4
通讯作者：
Phinn, Stuart
中科院分区：
文献类型：
--
作者：
McKenna, Phill;Erskine, Peter D.;Phinn, Stuart
火灾严重程度制图的遥感方法传统上依赖于由携带诸如大地卫星专题制图仪/增强型专题制图仪或中分辨率成像分光辐射计等被动传感器的卫星平台捕获的多光谱图像。本文介绍了分析的高空间分辨率无人机（UAV）的图像，以评估火灾的严重性，在半干旱的中央昆士兰州，澳大利亚的开放林地环境中进行的煤矿恢复117公顷的实验火灾。三个波段指数，过量绿色指数，过量绿色指数比，和修改后的过量绿色指数，被用来从无人机数据推导出不同的（d）火灾严重程度图。从航空照片判读中获得的火灾严重程度数据集被用来评估采用无人机技术确定火灾严重程度影响的效用。dEGI能够区分高严重度、低严重度和未烧伤区域，总体分类准确度为58%，Kappa统计量为0.37;优于dEGIR（总体准确度55%，Kappa 0.31）和dMEGI（总体准确度38%，Kappa 0.06）。当冠层阴影被掩盖时，所有指数的分类精度都有所提高，dEGI的总体精度提高到68%和0.48 Kappa。McNemar检验表明，dEGI和dEGIR的分类准确性之间没有显著差异（p < 0.05）。该测试还表明，与dEGI和dEGIR相比，dMEGI的准确性显著较低（p < 0.05）。我们量化了每个严重程度等级内的烧伤面积比例，并计算出32%的场地被严重烧伤，34%被严重烧伤，34%的街区由于火灾的不均匀性而未被烧毁。我们讨论了与火灾严重程度制图相关的无人机特定错误，以及无人机协助土地管理者评估火灾的范围和严重程度以及随后在局部尺度（10（4）m（2）-1 km（2））上恢复被烧毁的生态系统的潜力。
Remote-sensing methods for fire severity mapping have traditionally relied on multispectral imagery captured by satellite platforms carrying passive sensors such as Landsat Thematic Mapper /Enhanced Thematic Mapper Plus or Moderate Resolution Imaging Spectroradiometer. This article describes the analysis of high spatial resolution Unmanned Aerial Vehicle (UAV) imagery to assess fire severity on a 117 ha experimental fire conducted on coal mine rehabilitation in an open woodland environment in semi-arid Central Queensland, Australia. Three band indices, Excess Green Index, Excess Green Index Ratio, and Modified Excess Green Index, were used to derive differenced (d) fire severity maps from UAV data. Fire severity data sets derived from aerial photograph interpretation were used to assess the utility of employing UAV technology to determine fire severity impacts. The dEGI was able to separate high severity, low severity, and unburnt areas with an overall classification accuracy of 58% and Kappa statistic of 0.37; outperforming the dEGIR (overall accuracy 55%, Kappa 0.31) and the dMEGI (overall accuracy 38%, Kappa 0.06). Classification accuracy increased for all indices when canopy shadows were masked, with dEGI improving to an overall accuracy of 68% and 0.48 Kappa. The McNemar's test indicated that there was no significant difference between the classification accuracies for dEGI and dEGIR (p < 0.05). The test also demonstrated that dMEGI was significantly lower in accuracy compared to dEGI and dEGIR (p < 0.05). We quantified the proportion of burnt area within each severity class and calculated that 32% of the site was burnt at high severity, 34% was burnt at low severity, and 34% of the block was unburnt due to the patchy nature of the fire. We discuss the UAV-specific errors associated with fire severity mapping, and the potential for UAVs to assist land managers to assess the extent and severity of fire and subsequent recovery of burnt ecosystems at local scales (10(4)m(2)-1 km(2)).