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Extrapolation of in situ data from 1-km squares to adjacent squares using remote sensed imagery and airborne lidar data for the assessment of habitat diversity and extent

机译:使用遥感影像和机载激光雷达数据将1公里正方形的原位数据外推到相邻正方形,以评估栖息地的多样性和程度

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Habitat surveillance and subsequent monitoring at a national level is usually carried out by recording data from in situ sample sites located according to predefined strata. This paper describes the application of remote sensing to the extension of such field data recorded in 1-km squares to adjacent squares, in order to increase sample number without further field visits. Habitats were mapped in eight central squares in northeast Estonia in 2010 using a standardized recording procedure. Around one of the squares, a special study site was established which consisted of the central square and eight surrounding squares. A Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image was used for correlation with in situ data. An airborne light detection and ranging (lidar) vegetation height map was also included in the classification. A series of tests were carried out by including the lidar data and contrasting analytical techniques, which are described in detail in the paper. Training accuracy in the central square varied from 75 to 100 %. In the extrapolation procedure to the surrounding squares, accuracy varied from 53.1 to 63.1 %, which improved by 10 % with the inclusion of lidar data. The reasons for this relatively low classification accuracy were mainly inherent variability in the spectral signatures of habitats but also differences between the dates of imagery acquisition and field sampling. Improvements could therefore be made by better synchronization of the field survey and image acquisition as well as by dividing general habitat categories (GHCs) into units which are more likely to have similar spectral signatures. However, the increase in the number of sample kilometre squares compensates for the loss of accuracy in the measurements of individual squares. The methodology can be applied in other studies as the procedures used are readily available.
机译:在国家一级的生境监测和随后的监测通常是通过记录来自根据预定地层定位的原地采样点的数据来进行的。本文介绍了遥感技术在将以1 km的正方形记录的野外数据扩展到相邻的正方形中的应用,以增加样本数量而无需进一步的野外考察。使用标准化记录程序,在2010年爱沙尼亚东北部的八个中央广场绘制了栖息地图。在其中一个广场周围,建立了一个特殊的研究地点,该地点由中央广场和八个周围的广场组成。使用Landsat-7增强主题地图制作工具(ETM +)图像与原位数据进行关联。机载光检测和测距(激光)植被高度图也包括在分类中。通过包括激光雷达数据和对比分析技术进行了一系列测试,在本文中进行了详细描述。中心广场的训练准确性从75%到100%不等。在对周围正方形的外推程序中,精度从53.1%到63.1%不等,在包含激光雷达数据的情况下,精度提高了10%。分类准确率相对较低的原因主要是栖息地光谱特征的固有变异性,还有图像获取日期和野外采样日期之间的差异。因此,可以通过更好地同步野外调查和图像采集,以及将一般栖息地类别(GHC)划分为更可能具有相似光谱特征的单位来进行改进。但是,样品平方千米数量的增加补偿了单个平方的测量精度的损失。该方法可用于其他研究,因为所使用的程序易于获得。

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