Soil erosion is a very serious ecological problem and remains a highly contentious issue in Nanling National Nature Reserve, China. This paper assessed the spatial relationships between soil erosion risk and the environmental factors affecting soil erosion. Such research is significant for monitoring future land use/cover changes, including agricultural expansion and deterioration of forest resources. First, the soil erosion spatial distribution map was obtained by interpreting consecutive Landsat 8 satellite images, of which the interpreted result was validated via the intensive fieldwork. Then, from the perspective of topography, land cover, soil and rainfall, environmental factors that may influence the soil erosion risk were selected to quantitative test the relationships between soil erosion risk and the environmental factors using a certainty coefficient method. The results indicate that soil erosion is highly correlated to specific slope categories, elevation zones, distance to rivers, land use/cover type, stratum lithology, soil types and annual 24h maximum rainfall. The occurrence probability of soil erosion is high in the area where the slope is larger than 40°. A remarkable variation in soil erosion loss displays in areas above 1300 m of elevation, and areas below 500 m. The probability of soil erosion is the highest in the area within 100 meters of distance to rivers. Cultivated land, grassland and artificial surface land covers have the strongest soil erosion. The probability of soil erosion in the Cambrian and Carboniferous strata is the highest among the lithology categories. The red soil and scrubby-meadow soil have the strongest soil erosion. Soil erosion in the area of annual maximum 24h rainfall between 110 mm and 120 mm is stronger than other area. The reported results provided viable information essential to control soil erosion, reduce soil loss, and achieve sustainable ecological development.
The main purposes of the study were to test the performance of the Revised Universal Soil Loss Equation (RUSLE) and to understand the key factors responsible for generating soil erosion in the Nanling National Nature Reserve (NNNR), South China, where soil erosion has become a very serious ecological and environmental problem. By combining the RUSLE and geographic information system (GIS) data, we first produced a map of soil erosion risk at 30 m-resolution pixel level with predicted factors. We then used consecutive Landsat 8 satellite images to obtain the spatial distribution of four types of soil erosion and carried out ground truth checking of the RUSLE. On this basis, we innovatively developed a probability model to explore the relationship between four types of soil erosion and the key influencing factors, identify high erosion area, and analyze the reason for the differences derived from the RUSLE. The results showed that the overall accuracy of image interpretation was acceptable, which could be used to represent the currently actual spatial distribution of soil erosion. Ground truth checking indicated some differences between the spatial distribution and class of soil erosion derived from the RUSLE and the actual situation. The performance of the RUSLE was unsatisfactory, producing differences and even some errors when used to estimate the ecological risks posed by soil erosion within the NNNR. We finally produced a probability table revealing the degree of influence of each factor on different types of soil erosion and quantitatively elucidated the reason for generating these differences. We suggested that soil erosion type and the key influencing factors should be identified prior to soil erosion risk assessment in a region.
Introduction: In this study, artificial neural network (ANN) model and logistic regression were applied to analyze susceptibility and identify the main controlling factors of landslide in Meijiang River Basin of Southern China. Methods: Methods: Eleven variables such as altitude, slope angle, slope aspect, topographic relief, distance to fault, rock-type, soil-type, land-use type, NDVI, maximum rainfall intensity, distance to river were employed as landslide conditioning factors in landslide susceptibility mapping. Both landsliding and non-landsliding samples were needed as training data for ANN model. 384 landslides and 380 non-landsliding point with no recorded landslides according to field investigation and survey data were chosen as sample data of ANN model. And ROC curve was applied to calculate the prediction accuracy. Results: The validation results showed that prediction accuracy rate of 82.6% exists between the susceptibility map and the location of the initial 384 landsliding samples. However, logistic regression analysis showed that the average correct classification percentage was 75.4%. The prediction results of ANN model in high sensitive zone is more accurate than the logistic regression model. Conclusion: Therefore, the ANN model is valid when assessing the susceptibility. The main controlling factors were identified from the eleven factors by ANN model. The slope, rock and land use type appeared to be the main controlling factors in landslide formation process in Southern China.
Lingding Bay in the Pearl River Estuary, located on the north coast of the South China Sea, experiences frequent storm surges caused by typhoons. The geomorphic features of the Pearl River Estuary have changed tremendously due to natural processes and human activities over the last century, and these changes have led to changes in the hydrodynamic environment, such as a reduced capacity for holding tides in the coastal zone. In this paper, the relation between geomorphic features and the capacity for holding tides is analyzed. In order to ascertain how historical landform change affects this capacity, we study the spatial morphology change of Lingding Bay in the Pearl River Estuary (since 1906) through the analysis of historical topographic maps and nautical charts. The shape index and fractal dimension were introduced as indicators to reflect coastline changes that have affected the tides. The tidal dissipation rate and tidal influx were found to describe a bay’s capacity to hold tides. The results show that, since 1906, the tidal influx and the tidal dissipation rate have decreased by about 14.11% and 23%, respectively, in the study area. We suppose that these changes could be attributed to geomorphic changes, primarily changes brought about by land reclamation projects.
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