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Monitoring Change in Mountainous Dry-heath Vegetation at a Regional Scale Using Multitemporal Landsat TM Data

机译:使用多时态Landsat TM数据监测山区干旱地区的荒地植被变化

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Vegetation cover-change analysis requires selection of an appropriate set of variables for measuring and characterizing change. Satellite sensors like Landsat TM offer the advantages of wide spatial coverage while providing land-cover information. This facilitates the monitoring of surface processes. This study discusses change detection in mountainous dry-heath communities in Jamtland County, Sweden, using satellite data. Landsat-5 TM and Landsat-7 ETM+ data from 1984, 1994 and 2000, respectively, were used. Different change detection methods were compared after the images had been radiometrically normalized, geoeferenced and corrected for topographic effects. For detection of the classes change-no change the NDVI image differencing method was the most accurate with an overall accuracy of 94% (k = 0.87). Additional change information was extracted from an alternative method called NDVI regression analysis and vegetation change in 3 categories within mountainous dry-heath communities were detected. By applying a fuzzy set thresholding technique the overall accuracy was improved from of 65% (k = 0.45) to 74% (k = 0.59). The methods used generate a change product showing the location of changed areas in sensitive mountainous heath communities, and it also indicates the extent of the change (high, moderate and unchanged vegetation cover decrease). A total of 17% of the dry and extremely dry-heath vegetation within the study area has changed between 1984 and 2000. On average 4% of the studied heath communities have been classified as high change, i.e. have experienced "high vegetation cover decrease" during the period. The results show that the low alpine zone of the southern part of the study area shows the highest amount of "high vegetation cover decrease". The results show that the main change occurred between 1994 and 2000.
机译:植被覆盖变化分析需要选择一组适当的变量来测量和表征变化。诸如Landsat TM之类的卫星传感器在提供土地覆盖信息的同时,具有覆盖范围广的优势。这有助于表面处理的监控。这项研究讨论了使用卫星数据在瑞典Jamtland县的山区干旱荒地社区进行变化检测。使用分别来自1984年,1994年和2000年的Landsat-5 TM和Landsat-7 ETM +数据。在对图像进行放射线归一化,地理投影和校正地形影响之后,比较了不同的变化检测方法。为了检测类别不变,NDVI图像差分法是最准确的,总体精度为94%(k = 0.87)。从称为NDVI回归分析的替代方法中提取了其他变化信息,并在山区干旱荒地群落中的3类中检测到植被变化。通过应用模糊集阈值技术,整体准确性从65%(k = 0.45)提高到74%(k = 0.59)。所使用的方法产生变化产物,该变化产物显示了敏感山区荒地社区中变化区域的位置,并且还指示了变化的程度(高,中和不变的植被覆盖减少)。在1984年至2000年之间,研究区域内总共17%的干旱和极度荒地植被发生了变化。平均而言,所研究的荒地群落中有4%被归类为高变化,即经历了“高植被覆盖率下降”在此期间。结果表明,研究区南部的低高山带显示出最高的“高植被覆盖率减少”。结果表明,主要变化发生在1994年至2000年之间。

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