Object-based gully system prediction from medium resolution imagery using Random Forests
Object-based gully system prediction from medium resolution imagery using Random Forests
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DOI：
10.1016/j.geomorph.2014.04.006
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发表时间：
2014-07
期刊：
影响因子：
3.9
通讯作者：
R. Shruthi;N. Kerle;V. Jetten;A. Stein
中科院分区：
文献类型：
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作者：
R. Shruthi;N. Kerle;V. Jetten;A. Stein
侵蚀，特别是沟蚀，是一个普遍存在的问题。其测绘对于侵蚀监测和退化地区的补救至关重要。此外，绘制未来极有可能发生沟蚀的地区的地图可用于协助预防战略。与从实地收集的地形变量的良好关系适合于确定易受冲沟影响的地区。事实证明，对高分辨率遥感图像进行图像分析并结合实地核查是一种很好的方法，尽管这依赖于昂贵的图像。自动和半自动方法，如面向对象分析（OOA），是快速和可重复的。然而，并不总是能够获得人力资源指数数据。因此，我们试图识别沟壑系统使用统计建模的图像特征，从中等分辨率的图像，这里ASTER。这些数据用于确定区域内沟系统边界（GSB）使用基于OOA的半自动方法。我们评估，如果选择有用的对象功能可以在一个客观的和可转让的方式，使用随机森林（RF）在区域尺度上的沟道系统的预测，在这里的Sehoul地区，附近的拉巴特，摩洛哥。使用半自动基于对象的RF模型实现了中等成功（袋外误差为18.8%）。除了弥补沟壑和非沟壑类之间的不平衡，在这项研究中遵循的程序，使我们能够平衡的分类错误率。用户和生产者的准确性的数据与一组平衡的类表现出更高的精度的空间估计的沟道系统相比，不平衡的类的数据。该模型高估了GSB内的区域（13-27%），但其总体表现表明，中等分辨率的卫星图像包含足够的信息来识别沟系统，因此可以用相对较少的努力和可接受的精度绘制大面积的地图。
Erosion, in particular gully erosion, is a widespread problem. Its mapping is crucial for erosion monitoring and remediation of degraded areas. In addition, mapping of areas with high potential for future gully erosion can be used to assist prevention strategies. Good relations with topographic variables collected from the field are appropriate for determining areas susceptible to gullying. Image analysis of high resolution remotely sensed imagery (HRI) in combination with field verification has proven to be a good approach, although dependent on expensive imagery. Automatic and semi-automatic methods, such as object-oriented analysis (OOA), are rapid and reproducible. However, HRI data are not always available. We therefore attempted to identify gully systems using statistical modeling of image features from medium resolution imagery, here ASTER. These data were used for determining areas within gully system boundaries (GSB) using a semi-automatic method based on OOA. We assess if the selection of useful object features can be done in an objective and transferable way, using Random Forests (RF) for prediction of gully systems at regional scale, here in the Sehoul region, near Rabat, Morocco. Moderate success was achieved using a semi-automatic object-based RF model (out-of-bag error of 18.8%). Besides compensating for the imbalance between gully and non-gully classes, the procedure followed in this study enabled us to balance the classification error rates. The user's and producer's accuracy of the data with a balanced set of class showed an improved accuracy of the spatial estimates of gully systems, when compared to the data with imbalanced class. The model over-predicted the area within the GSB (13–27%), but its overall performance demonstrated that medium resolution satellite images contain sufficient information to identify gully systems, so that large areas can be mapped with relatively little effort and acceptable accuracy.