冲沟侵蚀是大堡礁（GBR）世界遗产地区沉积物和颗粒物的主要来源。我们选择了Bowen集水区,Burdekin盆地的支流,作为我们的研究领域；该地区与高密度的沟渠网络有关。我们的目标是结合多源和多尺度遥感和地面数据，使用半自动的基于对象的沟渠网络检测过程。通过将基于地理对象的图像分析（GEOBIA）与当前的机器学习（ML）模型集成，采用了一种先进的方法。这些包括人工神经网络(ANN)，支持向量机(SVM)，和随机森林(RF)，和堆叠的集成ML模型来处理沟渠网络检测中的空间缩放问题。归一化植被指数(NDVI)和地形条件因子等光谱指数,比如海拔，斜坡,方面,地形湿度指数(TWI),斜坡长度(SL),和曲率，是从Sentinel2A图像和ALOS12-m数字高程模型(DEM)生成的，分别。对于图像分割，ESP2工具用于获得三个最佳比例因子。在使用对象纯度指数(OPI)时，对象匹配索引(OMI)，和对象适应度指数(OFI)，评价图像分割中各尺度的准确性。规模参数为45，OFI为0.94，是OPI和OMI指数的组合，证明是图像分割的最佳尺度参数。此外，根据比例45分割的对象覆盖70%和30%的准备好的沟渠库存图，以选择ML模型的训练和测试对象，分别。精密度的定量精度评定方法,回想一下，并使用F1度量来评估模型的性能。使用45的比例将GEOBIA与堆叠模型集成在一起，从而可以检测到F1度量值为0.89的沟渠网络。这里,我们得出的结论是，在GEOBIA中采用最佳尺度对象定义并应用ML模型的集成堆叠可以提高检测沟渠网络的准确性。
Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models\' training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model\'s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.
PDF(Sci-hub)