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首页> 外文会议>Remote sensing for agriculture, ecosystems, and hydrology XIII >A multispectral multiplatform based change detection tool for vegetation disturbance on Irish peatlands
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A multispectral multiplatform based change detection tool for vegetation disturbance on Irish peatlands

机译：基于多光谱多平台的爱尔兰泥炭地植被扰动变化检测工具

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摘要

In this study satellite data from five different multispectral sensors were used in a change detection study of vegetation disturbance on an Irish active raised bog. Radiometric normalisation was performed using Temporally Invariant Clusters (TIC) and cross calibration applied using linear regression of radiometrically stable ground-based targets. Erdas Imagine's Spatial Modeller was used to create a change detection model using pixel-to-pixel based subtraction with a Standard Deviation (SD) threshold. The effectiveness of the cross calibration process was shown with the aid of Kolmogorov Smirnov sample tests which showed a reduced D value between master and slave cumulative distribution curves after cross calibration. The spatial accuracy of various SD threshold levels was assessed, with 1.5 SD producing 0.19% error when compared to actual ground truth boundary data of change. An error matrix of change/ no change verified 1.5 SD as the optimum threshold for change detection, with user, producer, overall and kappa values all above 95%. Vegetation disturbance in the study was predominantly attributed to turf cutting on the boundaries of the bog. However in May 2008 a large burn event occurred on the northeastern side of the bog which removed all surface vegetation, equating to an area of 36ha (or 7.85% of total area).
机译：在这项研究中，来自五个不同的多光谱传感器的卫星数据被用于爱尔兰活动凸起沼泽的植被扰动变化检测研究。使用临时不变聚类（TIC）进行辐射归一化，并使用辐射稳定的地面目标的线性回归进行交叉校准。 Erdas Imagine的Spatial Modeller用于创建变化检测模型，该模型使用具有标准偏差（SD）阈值的逐像素减法来创建。借助Kolmogorov Smirnov样本测试显示了交叉校准过程的有效性，该示例测试显示了交叉校准后主，从累积分布曲线之间的D值降低了。评估了各种SD阈值水平的空间精度，其中1.5 SD与实际地面真实边界变化数据相比会产生0.19％的误差。变更/未变更的误差矩阵将1.5 SD验证为变更检测的最佳阈值，用户，生产者，总体和kappa值均高于95％。该研究中的植被干扰主要归因于沼泽边界上的割草。但是，在2008年5月，沼泽的东北侧发生了一次大规模的烧毁事件，清除了所有表面植被，相当于36公顷（占总面积的7.85％）。

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