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Benggang as a fragmented landform type, can be characterized by a deep-cut slope with various shapes and depressions on the vast weathered crust slopes in southern China. Normally, the gully heads have been continuously collapsed to form a typical landform, such as chair-like erosion. Benggang usually develops rapidly due to large amount of erosion, and thereby to threaten land resources and ecological environment. The identification of Benggang development become necessary to control erosion, and then clarify the behind mechanism. However, conventional methods have low levels of automation for a large-scale work, particularly on local inquiry, in-situ search, and manual interpretation for high-resolution images from satellite remote sensing. This paper proposes a novel Bag of Visual-Topographic Words (BoV-TW) model combining high resolution Digital Orthophoto Map (DOM) and Digital Surface Model (DSM) local features to represent Benggang areas, according to the classification and recognition methods in remote sensing images. The local features of DOM can be set in Harris-Affine and Maximally Stable Extremal Regions (MSER), with Scale-Invariant Feature Transform (SIFT) descriptors. The local features of DSM were extracted by a 3D Douglas-Peucker algorithm, representing by gradient direction-invariant descriptors developed in this study. A Latent Dirichlet Allocation (LDA) was used to balance latent semantic analysis, thereby to construct low-dimensional high-level semantic representations. Support Vector Machine (SVM) was used as a supervised learning training classifier to achieve high-precision and fast automatic Benggang recognition. Three Benggang areas in Tongcheng County, Hubei Province were selected as the experimental objects. The original data were collected by DJI Phantom 3 Pro micro UAV in September 2016. Photoscan processing was used to obtain the DOM and DSM of three Benggang areas. Uniform grid division can be used to achieve DOM and DSM tiles in 0.15 m resolution of 256 × 256 pixels. A high-resolution 3D Benggang model was used to mark the areas with/without Benggang. Two Benggang areas were taken as the training set, and the rest were taken as the test set, in three comparative experiments. The results show that: 1) With changing numbers of LDA topics, the proposed method can maintain a total accuracy of 95%, while the recall rate and precision of Benggang 80%, indicating the maximums were 97.22% and 94.44%, respectively. The total accuracy, recall and precision have increased by 12%, 11% and 32%, respectively, compared with that of only DOM features. The recognition performance was significantly improved after combining with DSM features. 2) With changing sizes of the BoV-TW vocabulary, the total accuracy was 90%, and the maximum was 96.10%, while the recall rate reached 90%, the maximum recall rate of 100%. Precision gradually increased as the increase of vocabulary size, with the maximum of 85.00%. Compared with that of only BoV-TW, three performance indicators increased by 13%, 12% and 30%, respectively, indicating that LDA for latent semantic analysis can greatly improve the recognition detection performance. 3) The proposed method was better than that on the ResNet50 network using DOM and DOM + DSM as the data source. It infers that Benggang as a landform type cannot be well recognized using only DOM, or the simple combination of DOM and DSM. Meanwhile, it needs to combine feature extraction and fusion strategy for efficient application. This finding can deliver a useful tool for the quantitative analysis on the identification, control and erosion mechanism for Benggang. [ABSTRACT FROM AUTHOR]

The black soil region of Northeast China is one of the most important grain production bases in China. It is also one of the four major water erosion areas in China, in which the erosion gully is the main form of water erosion. In order to analyze the causes of gully erosion and evaluate the basic situation of gully erosion in the northeast black soil area, it is necessary to analyze its morphology and distribution characteristic of gully erosion. In this paper, Nenjiang County was taken as the study area, which is located in the northwest of Heilong province, China. The aerial remote sensing images with ground resolution of 0.2 m and 1:50 000 Digital Elevation Model (DEM) were used to carry out census and statistics on the erosion gully, and the morphological characteristics such as the gully width, gully length and longitudinal slope of the erosion gully were analyzed, as well as the influence of terrain factors such as elevation, slope and aspects. The results of the second land use survey in China were used to obtain the land use situation around the erosion ditch. The results showed that: 1) The number of erosion gullies in Nenjiang County was 38 426, and the whole area of the erosion gullies were 16 277 095 m2. The erosion gully mainly occurred on hilly area, and was dominated by gully erosion, accounting for 74.50% and 51.05% of the total number and area, respectively. The next type was modern incised valley. Erosion gullies were mostly less than 500 m. 2) The width and length of the erosion gullies were positively correlated, and the slope was negatively correlated with the width and length. 3) Compared with slope, aspects generally had a little effect on the distribution of erosion gullies. Rills and ephemeral gullies mainly distributed on slopes of 2~6° and 6~15°, and gullies and modern incised valleys mainly distributed on slopes of 0~2° and 2~6°. More than 90% of rills and ephemeral gullies, and 80% of gullies and modern incised valleys were occurred on cultivated land. 4) Although the topographic threshold model of gulley in Nenjiang County was not fitted well, it could still be used to analyze the process of gully erosion. The model showed that the critical slope of the modern incised valley was small, the parameter which expressed the gully erosion process reflected the occurrence and development of the gullies might be influenced by the underground process. [ABSTRACT FROM AUTHOR]