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首页> 外文期刊>Environmental engineering and management journal >DETECTION OF ENVIRONMENTAL CHANGES DUE TO WINDTHROWS USING LANDSAT 7 ETM+ SATELLITE IMAGES
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DETECTION OF ENVIRONMENTAL CHANGES DUE TO WINDTHROWS USING LANDSAT 7 ETM+ SATELLITE IMAGES

机译：使用LANDSAT 7 ETM +卫星图像检测风向造成的环境变化

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The changes caused by windthrows regarding spruce stand lead to significant environmental modifications. In this article aspects regarding the detection of environmental changes caused by windthrows in Sanmartin Forest Division within Miercurea Ciuc Forest District of National Forest Administration are covered using Landsat 7 ETM+ satellite images. Two pre and post-event satellite images, from 2001 when massive windthrows occurred, were used in this study. The techniques used in changes detection were univariate image differencing (UID), selective PCA and change vector analysis (CVA). The indices used in changes detection were NDVI, RVI, SAVI and those obtained from Tasseled Cap transformation (TCW, TCG, TCB). The results show that the most appropriate technique for detecting windthrows regarding the studied area is UID applied to TCW, with a classification accuracy of 82.3%. Poor results were obtained by applying the first component to SAVI, the classification accuracy being of 51.3%. With reference to the images obtained as a result of applying the changes detection techniques suitable threshold values were determined, allowing the detection of changes specific to the studied area and the development of binary maps of vegetation. Thus, it turned out that significant changes are detected using TCW by determining the threshold value to ＜-2s and +＞2s in relation to the average, the pixels from the histogram's tail representing 5-7% out of the total of scene pixels. The use of changes detection techniques together with NDVI, RVI and SAVI vegetation indices allow the detection of changes during the slight interval (average-1s) to moderate (1-2s).
机译：风头引起的云杉林分变化导致环境发生重大变化。在本文中，使用Landsat 7 ETM +卫星图像介绍了有关检测国家森林管理局Miercurea Ciuc森林区的Sanmartin森林分区中因风吹引起的环境变化的各个方面。这项研究使用了2001年事件发生前后的两个卫星图像，当时发生了大风。变化检测中使用的技术是单变量图像差分（UID），选择性PCA和变化矢量分析（CVA）。变化检测中使用的指标为NDVI，RVI，SAVI以及从流苏帽变换获得的指标（TCW，TCG，TCB）。结果表明，对于研究区域而言，最合适的检测风向的技术是将UID应用于TCW，分类精度为82.3％。通过将第一个组件应用于SAVI可获得差的结果，分类精度为51.3％。参照由于应用变化检测技术而获得的图像，确定了合适的阈值，从而允许检测特定于所研究区域的变化以及开发二元植被图。因此，事实证明，通过将阈值确定为相对于平均值的＜-2s和+＞ 2s，使用TCW可以检测到显着变化，直方图尾部的像素占场景像素总数的5-7％。将变化检测技术与NDVI，RVI和SAVI植被指数结合使用，可以检测微小间隔（平均1秒）到中度（1-2秒）内的变化。

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