Accuracy Assessment of Landform Classification Approaches on Different Spatial Scales for the Iranian Loess Plateau
Accuracy Assessment of Landform Classification Approaches on Different Spatial Scales for the Iranian Loess Plateau
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DOI：
10.3390/ijgi6110366
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发表时间：
2017-11
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通讯作者：
Tanja Kramm;D. Hoffmeister;C. Curdt;S. Maleki;F. Khormali;M. Kehl
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作者：
Tanja Kramm;D. Hoffmeister;C. Curdt;S. Maleki;F. Khormali;M. Kehl
伊朗黄土高原景观的准确地貌描述将进一步提高我们的理解，最近和过去的地貌过程中，这强烈解剖景观。因此，四个不同的输入数据集的四种地貌分类方法，以获得最准确的结果相比，地面实况数据的地貌实地调查。5米和10米像素分辨率的输入数据集来自Pleiades立体卫星图像和“航天飞机雷达地形使命”（SRTM），另外还应用了空间分辨率为30米的“高级星载热发射和反射辐射计”（ASTER GDEM）数据集。用这些数据测试的四种分类方法包括Dikau之后的逐步方法、地貌、地形位置指数（TPI）和基于对象的方法。结果表明，具有较高空间分辨率的输入数据集产生大于70%的TPI和地貌的整体精度和大于60%的其他方法。对于较低分辨率的数据集，仅得到约40%的准确度，比来自较高空间分辨率的数据低20-30%。地形位置指数和地貌方法的结果最适合所有选定的输入数据集。
An accurate geomorphometric description of the Iranian loess plateau landscape will further enhance our understanding of recent and past geomorphological processes in this strongly dissected landscape. Therefore, four different input datasets for four landform classification methods were used in order to derive the most accurate results in comparison to ground-truth data from a geomorphological field survey. The input datasets in 5 m and 10 m pixel resolution were derived from Pleiades stereo satellite imagery and the “Shuttle Radar Topography Mission” (SRTM), and “Advanced Spaceborne Thermal Emission and Reflection Radiometer” (ASTER GDEM) datasets with a spatial resolution of 30 m were additionally applied. The four classification approaches tested with this data include the stepwise approach after Dikau, the geomorphons, the topographical position index (TPI) and the object based approach. The results show that input datasets with higher spatial resolutions produced overall accuracies of greater than 70% for the TPI and geomorphons and greater than 60% for the other approaches. For the lower resolution datasets, only accuracies of about 40% were derived, 20–30% lower than for data derived from higher spatial resolutions. The results of the topographic position index and the geomorphons approach worked best for all selected input datasets.