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首页> 外文期刊>Turkish Journal of Agriculture & Forestry >A remote sensing approach for detecting agricultural encroachment on the eastern Mediterranean coastal dunes of Turkey
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A remote sensing approach for detecting agricultural encroachment on the eastern Mediterranean coastal dunes of Turkey

机译：一种检测土耳其东部地中海沿海沙丘上农业侵占的遥感方法

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The aim of this study was to develop an effective procedure for detecting land use/land cover (LU/LC) changes resulting from agricultural encroachment on eastern Mediterranean coastal dunes by using remote-sensing techniques. Historic LU/LC information was extracted from aerial photos taker, in 1976 and IKONOS imagery was acquired in 2002 to determine the current LU/LC pattern. The remotely sensed aerial and satellite data were classified by integrating spectral information with measures of texture, in the form of statistics derived from the variance, co-occurrence matrix and variograrn. The performance of these classification approaches was evaluated in terms of error matrices. The accuracy of the classification was greater when using the variogram texture measure and spectral data together than when using spectral data alone or incorporating with co-occurrence matrix texture for both image classifications. Co-occurrence texture measures did not result in a significant increase in accuracy withthe maximum likelihood classifiers in the classification of IKONOS imagery. The addition of variogram texture information in particular, with variogram lags of 1, 2 and 3 pixels, increased the overall classification accuracy by 11.3 percent. The principal conclusion of this paper is that the accuracy of agricultural land cover and semi-natural vegetation classification can be maximised by incorporating textural information with remotely sensed spectral data in the Mediterranean coastal environment.
机译：这项研究的目的是开发一种有效的程序，通过遥感技术检测地中海东部沿海沙丘上农业侵占引起的土地利用/土地覆被（LU / LC）变化。 1976年从航拍者中提取了历史LU / LC信息，并于2002年获取了IKONOS图像以确定当前的LU / LC模式。通过将光谱信息与纹理量度集成在一起，对遥感的航空和卫星数据进行分类，其形式为从方差，共现矩阵和可变粒度得出的统计量。这些分类方法的性能是根据误差矩阵进行评估的。当将变异函数纹理量度和光谱数据一起使用时，分类的准确性要比单独使用光谱数据或结合使用共现矩阵纹理进行两种图像分类的分类准确性更高。在IKONOS影像的分类中，使用最大似然分类器，共现纹理量度并未导致准确性的显着提高。特别是增加了变异图纹理信息（变异图滞后为1、2和3个像素），总体分类精度提高了11.3％。本文的主要结论是，通过在地中海沿岸环境中将纹理信息与遥感光谱数据结合起来，可以最大程度地提高农业用地覆盖和半自然植被分类的准确性。

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