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Benggang, a fragmented landform induced by the overall collapse of soils on slopes, is one of the most serious land erosion problems in Southern China. The reasons for the concentrated development of Benggang and the major influencing factors are still unclear. The key driving factors for Benggang development in Fujian and Jiangxi Provinces were investigated by ArcGIS spatial analysis of the effects of rainfall, lithology, elevation, and geomorphology. It was found that Benggang density increases first and then decreases with annual average rainfall, elevation, and hypsometric integral value (HI) and that the regions with a density of greater than 0.2 Benggang km⁻² are characterized with an average annual rainfall of 1,395–1,645 mm,an elevation of 200–400 m,and an HI of 0.14–0.30. Lithology and weathering crust exhibit an important influence on the development of Benggang, with the formation of 41.94% of Benggangs in 25.88% of the granite area. HI shows a significant positive correlation with elevation in the 1,680 watershed areas (r = .83, p

In vision-based UAV navigation system, it is an important issue to identify the sub-region UAV image in the satellite image of large urban region. In this paper, we propose a method for identifying UAV image using the global feature and local feature. The global feature is obtained by the statistical analysis of lines extracted in remote sensing image. The line parameters (length, orientation, midpoint position) are directly calculated by a new algorithm based on PCA for pixels within the line support region. We also suggest the global feature descriptor based on 3D histogram of the line length and orientation. The reference images of the same size and resolution as UAV image are extracted from large urban satellite image and reference dataset is constructed from feature vectors of them. If several sub-region images are selected by searching for the global feature, the sub-region image would be finally identified by local feature among the selected images. Experiment results show that the proposed method archives a good identification rate and satisfies the robustness for the rotation and illuminate variation.

The effect of prior probabilities in the maximum likelihood classification on individual classes receives little attention, and this is addressed in this paper. Prior probabilities are designed only for overlapping spectral signatures. Accordingly, their effect on an individual class is independent of the classes that are spectrally separable from this class. The theoretical reasoning reveals that an increased prior probability, which shifts the decision boundary away from the class mean, will increase the assignment and boost the producer’s accuracy as compared to the use of equal priors; though the change of the user’s accuracy is not constant, it is expected to decrease in most cases. The tendency is just the opposite when a lower prior probability is used. A case study was conducted using Landsat TM data provided along with ERDAS Imagine ® software. Two other pieces of evidence derived from the published literature are also presented.

Based on data observed at the 12 small watersheds in hilly areas of the Loess Plateau, North China, the relationship between event runoff volume and sediment yield is examined. The results reveal that the runoff-sediment yield relationship at the inter-event timescale is mainly determined by the runoff-sediment concentration relationship at the intra-event timescale. In the study area, the sediment concentration tends to be stable when the flow discharge exceeds a certain critical value. Many factors that are important for determining the characteristics of low-magnitude events, such as flow discharge, particle size of fluvial sediment, and accumulation of loose material on land surfaces prior to a rainstorm, appear to have little importance for high-magnitude events. Consequently, mean sediment concentration tends to be stable for large flood events, suggesting a strong similarity between the two flow-sediment relationships at inter-and intra-event temporal scales. Furthermore, a proportional function is proposed to predict event sediment yield, and the correspondence between the predicted and observed sediment yields is examined. The performance of the model is good for high-magnitude events, especially extreme events. The applicability of the proposed model at the annual timescale is also discussed.

Whether vegetation reduces soil loss by reducing runoff volume or rather by changing runoff-sediment yield relationship has received little attention. Base on the observed data from monitoring stations and the published data from other research, this issue is addressed at different scales in hilly areas of the Loess Plateau, North China. At the plot scale, vegetation helps reduce soil loss not only by reducing runoff volume, but also by changing the runoff-sediment yield relationship, resulting that the sediment-reduction rate is higher than the runoff-reduction rate. At the watershed scale, gully erosion and mass wasting process are dominant. Vegetation measures are insufficient to control local mass movement, implying that sediment availability remains high even after vegetation is established. It is also hard for slope vegetation to change the capacity of the sediment transport system at the watershed scale. Therefore, vegetation cannot change the runoff-sediment yield relationship at the watershed scale. This implies that vegetation reduces sediment yield only by reducing runoff volume and the sediment-reduction rate approximates the runoff-reduction rate at the watershed scale. Other slope measures for soil conservation such as terraces are considered to have the same effect on the runoff-sediment yield relationship as the vegetation. Several case studies involving different spatial scales are presented and confirm this conclusion.

Maximum likelihood classifier is widely used in remote sensing. Many researches have indicated that using prior probabilities call make Maximum likelihood classifier perform better, but it is not true that all individual classes will have higher classification accuracy after prior probabilities are incorporated in MLC. In the present paper, the theoretical analysis about the effect of prior probability on maximum likelihood classifier is given first, including the effect on the discrimination process and classification result. Then a case study is carried out by one approach in which prior probabilities are iteratively modified by using the relative area proportion derived from the last classification as prior probabilities in next classification.