Until now, few studies have used the mainstreaming models to simulate the land use changes in the cities of rapid urbanizing regions. Therefore, we aimed to develop a methodology to simulate the land use changes in rapid urbanizing regions that could reveal the land use change trend in the cities of the regions. Taking the urban areas of Wuhan, a typical rapid urbanizing region in China, as the study area, this study built a Markov chain–artificial neural network (ANN)–cellular automaton (CA) coupled model. The model used land use classification spatial data with a spatial resolution of 5 m in 2010 and 2020, obtained by remote sensing image interpretation, and data on natural and socio-economic driving forces for land use change simulation. Using the coupled model, the land use patterns of Wuhan urban areas in 2020 were simulated, which were validated in comparison with the actual land use data in 2020. Finally, the model was used to simulate the land uses in the study area in 2030. The model validation indicates that the land use change simulation has a high accuracy of 90.7% and a high kappa coefficient of 0.87. The simulated land uses of the urban areas of Wuhan show that artificial surfaces will continue to expand, with an area increase of approximately 7% from 2020 to 2030. Moreover, the area of urban green spaces will also increase by approximately 7%, while that of water bodies, grassland, cropland, and forests will decrease by 12.6%, 13.6%, 34.9%, and 1.3%, respectively, from 2020 to 2030. This study provides a method of simulating the land use changes in the cities of rapid urbanizing regions and helps to reveal the patterns and driving mechanisms of land use change in Wuhan urban areas.
<p>How to use a suitable method to accurately measure gully morphology is very important in the study of gully erosion monitoring and development, and the development of Unmanned Aerial Vehicle (UAV) has made it easy to apply UAV photogrammetry techniques to gully erosion studies. The aim of this study is to evaluate the accuracy of data and the efficiency of data processing by analyzing the errors of different schemes, and to provide suitable plan design ideas for the study of gully by UAV. Gully is the object of study and different flight schemes and Ground Control Point (GCP) placement schemes are used to acquire and process the data, and finally the errors are analyzed by Digital Surface Model (DSM) and orthophoto. Among all the schemes, the one with a flight altitude of 30m, 80%/70% photo overlap and 11 GCPs had the highest accuracy (Mean absolute error of 0.0353m and root mean square error of 0.0525m), but this scheme took more data collection and processing time and was less efficient. The number of GCPs and the placement location also have a significant impact on the accuracy&#65292;the position closer to the GCPs has a smaller error&#65292;and this study proves that the number of GCPs should not be more than 9 and should be evenly distributed in different parts of the gully.. When the flight altitude is 70m, the overlap is not less than 50%/40%, and the number of control points is 6, both accuracy and measurement efficiency can be taken into account at the same time. In addition, the sources of errors and the distribution locations of checkpoints with high errors were analyzed in four aspects: shadow, slope gradient, slope direction and vegetation. The use of UAVs in gully erosion studies is very convenient to get the later products with centimeter-level accuracy, and based on the results of the study we suggest that the flight altitude and photo overlap can be appropriately reduced when designing the scheme, and the number of GCP can be increased in the areas that need to be focused on and the areas with large elevation changes. At the same time, flight safety, UAV battery power, data collection efficiency and processing efficiency should be considered comprehensively.</p>
<p>Gully erosion was one of the key processes of soil erosion in Hengduan mountain region, which belonged to the eastern part of Qinghai-Tibet Plateau. This dramatic changes in both horizontal and vertical direction has led to a diversity soil groups within the region. The aims of this study were to investigate the gully distribution and density in different soil zones, and find out the key factors that influenced the susceptibility and intensity of gully erosion of Hengduan mountain area. Totally 2300 investigation quadrats were randomly set with the size of 1 km &#215; 1 km to check whether the occurrence and the density by Google Earth images. The ratio of gully occurrence (GR) was 25.5%, and the average gully density (GD) and gully number (GN) was 2.22 km km<sup>-2</sup> and 20.4 of Hengduan mountain area. The annual temperature, vegetation and slope were the key factors that influences the occurrences of gullies in the alpine (>3700 m a.s.l), middle mountain (2000-3700 m a.s.l) and low mountain (<2000 m a.s.l) soil zones, respectively. The intensity of gully erosion showed exponential decreasing relationships with soil property including soil organic matters and silt content, and the average GD in different soil zones showed the same relationships with the R2 higher than 0.7. These results indicating that the distribution of gully erosion were more related to the external environmental factors, and the intensity of gully erosion were determined by soil properties at the regional scales.</p>
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