The objective of this study is to incorporate a numerical model with GIS to simulate the movement, erosion and deposition of debris flow across the three dimensional complex terrain. In light of the importance of eros...The objective of this study is to incorporate a numerical model with GIS to simulate the movement, erosion and deposition of debris flow across the three dimensional complex terrain. In light of the importance of erosion and deposition processes during debris flow movement, no entrainment assumption is unreasonable. The numerical model considering these processes is used for simulating debris flow. Raster grid networks of a digital elevation model in GIS provide a uniform grid system to describe complex topography. As the raster grid can be used as the finite difference mesh, the numerical model is solved numerically using the Leap-frog finite difference method. Finally, the simulation results can be displayed by GIS easily and used to debris flow evaluation. To illustrate this approach, the proposed methodology is applied to the Yohutagawa debris flow that occurred on 2oth October 2010, in Amami- Oshima area, Japan. The simulation results that reproduced the movement, erosion and deposition are in good agreement with the field investigation. The effectiveness of the dam in this real-ease is also verified by this approach. Comparison with the results were simulated by other models, shows that the present coupled model is more rational and effective.展开更多
Researchers in P.R.China commonly create triangulate irregular networks(TINs) from contours and then convert TINs into digital elevation models(DEMs).However,the DEM produced by this method can not precisely describe ...Researchers in P.R.China commonly create triangulate irregular networks(TINs) from contours and then convert TINs into digital elevation models(DEMs).However,the DEM produced by this method can not precisely describe and simulate key hydrological features such as rivers and drainage borders.Taking a hilly region in southwestern China as a research area and using ArcGISTM software,we analyzed the errors of different interpolations to obtain distributions of the errors and precisions of different algorithms and to provide references for DEM productions.The results show that different interpolation errors satisfy normal distributions,and large error exists near the structure line of the terrain.Furthermore,the results also show that the precision of a DEM interpolated with the Australian National University digital elevation model(ANUDEM) is higher than that interpolated with TIN.The DEM interpolated with TIN is acceptable for generating DEMs in the hilly region of southwestern China.展开更多
In recent years, sedimentation conditions in Dongting Lake have varied greatly because of signifi cant changes in runoff and sediment load in the Changjiang(Yangtze) River following the construction of Three Gorges Da...In recent years, sedimentation conditions in Dongting Lake have varied greatly because of signifi cant changes in runoff and sediment load in the Changjiang(Yangtze) River following the construction of Three Gorges Dam. The topography of the lake bottom has changed rapidly because of the intense exchange of water and sediment between the lake and the Changjiang River. However, time series information on lake-bottom topographic change is lacking. In this study, we introduced a method that combines remote sensing data and in situ water level data to extract a record of Dongting Lake bottom topography from 2003 to 2011. Multi-temporal lake land/water boundaries were extracted from MODIS images using the linear spectral mixture model method. The elevation of water/land boundary points were calculated using water level data and spatial interpolation techniques. Digital elevation models of Dongting Lake bottom topography in different periods were then constructed with the multiple heighted waterlines. The mean root-mean-square error of the linear spectral mixture model was 0.036, and the mean predicted error for elevation interpolation was-0.19 m. Compared with fi eld measurement data and sediment load data, the method has proven to be most applicable. The results show that the topography of the bottom of Dongting Lake has exhibited uneven erosion and deposition in terms of time and space over the last nine years. Moreover, lake-bottom topography has undergone a slight erosion trend within this period, with 58.2% and 41.8% of the lake-bottom area being eroded and deposited, respectively.展开更多
Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accur...Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks(ANNs) were developed to map soil units using digital elevation model(DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base(WRB)classification criteria than the Soil Taxonomy(ST) system, but more soil classes could be predicted when using ST(7 soils in the case of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error(interpolation error) and validation error(extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology as soil-forming factors, should be used as ANN input data.展开更多
Among spatial interpolation techniques,geostatistics is generally preferred because it takes into account the spatial correlation between neighbouring observations in order to predict attribute values at unsampled loc...Among spatial interpolation techniques,geostatistics is generally preferred because it takes into account the spatial correlation between neighbouring observations in order to predict attribute values at unsampled locations.A doline of approximately 15 000 m 2 at 1 900 m above sea level (North Italy) was selected as the study area to estimate a digital elevation model (DEM) using geostatistics,to provide a realistic distribution of the errors and to demonstrate whether using widely available secondary data provided more accurate estimates of soil pH than those obtained by univariate kriging.Elevation was measured at 467 randomly distributed points that were converted into a regular DEM using ordinary kriging.Further,110 pits were located using spatial simulated annealing (SSA) method.The interpolation techniques were multi-linear regression analysis (MLR),ordinary kriging (OK),regression kriging (RK),kriging with external drift (KED) and multi-collocated ordinary cokriging (CKmc).A cross-validation test was used to assess the prediction performances of the different algorithms and then evaluate which methods performed best.RK and KED yielded better results than the more complex CKmc and OK.The choice of the most appropriate interpolation method accounting for redundant auxiliary information was strongly conditioned by site specific situations.展开更多
基金finanicial support from the Global Environment Research Fund of Japan(S-8)from Grants-in-Aid for Scientific Research(Scientific Research(B),22310113,G.Chen)from Japan Society for the Promotion of Science
文摘The objective of this study is to incorporate a numerical model with GIS to simulate the movement, erosion and deposition of debris flow across the three dimensional complex terrain. In light of the importance of erosion and deposition processes during debris flow movement, no entrainment assumption is unreasonable. The numerical model considering these processes is used for simulating debris flow. Raster grid networks of a digital elevation model in GIS provide a uniform grid system to describe complex topography. As the raster grid can be used as the finite difference mesh, the numerical model is solved numerically using the Leap-frog finite difference method. Finally, the simulation results can be displayed by GIS easily and used to debris flow evaluation. To illustrate this approach, the proposed methodology is applied to the Yohutagawa debris flow that occurred on 2oth October 2010, in Amami- Oshima area, Japan. The simulation results that reproduced the movement, erosion and deposition are in good agreement with the field investigation. The effectiveness of the dam in this real-ease is also verified by this approach. Comparison with the results were simulated by other models, shows that the present coupled model is more rational and effective.
基金Funded by the Natural Science Foundation of Chongqing under Grant No. CSTC2006AB1015.
文摘Researchers in P.R.China commonly create triangulate irregular networks(TINs) from contours and then convert TINs into digital elevation models(DEMs).However,the DEM produced by this method can not precisely describe and simulate key hydrological features such as rivers and drainage borders.Taking a hilly region in southwestern China as a research area and using ArcGISTM software,we analyzed the errors of different interpolations to obtain distributions of the errors and precisions of different algorithms and to provide references for DEM productions.The results show that different interpolation errors satisfy normal distributions,and large error exists near the structure line of the terrain.Furthermore,the results also show that the precision of a DEM interpolated with the Australian National University digital elevation model(ANUDEM) is higher than that interpolated with TIN.The DEM interpolated with TIN is acceptable for generating DEMs in the hilly region of southwestern China.
基金Supported by the National Basic Research Program of China(973 Program)(No.2012CB417001)the National Natural Science Foundation of China(No.41271125)
文摘In recent years, sedimentation conditions in Dongting Lake have varied greatly because of signifi cant changes in runoff and sediment load in the Changjiang(Yangtze) River following the construction of Three Gorges Dam. The topography of the lake bottom has changed rapidly because of the intense exchange of water and sediment between the lake and the Changjiang River. However, time series information on lake-bottom topographic change is lacking. In this study, we introduced a method that combines remote sensing data and in situ water level data to extract a record of Dongting Lake bottom topography from 2003 to 2011. Multi-temporal lake land/water boundaries were extracted from MODIS images using the linear spectral mixture model method. The elevation of water/land boundary points were calculated using water level data and spatial interpolation techniques. Digital elevation models of Dongting Lake bottom topography in different periods were then constructed with the multiple heighted waterlines. The mean root-mean-square error of the linear spectral mixture model was 0.036, and the mean predicted error for elevation interpolation was-0.19 m. Compared with fi eld measurement data and sediment load data, the method has proven to be most applicable. The results show that the topography of the bottom of Dongting Lake has exhibited uneven erosion and deposition in terms of time and space over the last nine years. Moreover, lake-bottom topography has undergone a slight erosion trend within this period, with 58.2% and 41.8% of the lake-bottom area being eroded and deposited, respectively.
文摘Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks(ANNs) were developed to map soil units using digital elevation model(DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base(WRB)classification criteria than the Soil Taxonomy(ST) system, but more soil classes could be predicted when using ST(7 soils in the case of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error(interpolation error) and validation error(extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology as soil-forming factors, should be used as ANN input data.
文摘Among spatial interpolation techniques,geostatistics is generally preferred because it takes into account the spatial correlation between neighbouring observations in order to predict attribute values at unsampled locations.A doline of approximately 15 000 m 2 at 1 900 m above sea level (North Italy) was selected as the study area to estimate a digital elevation model (DEM) using geostatistics,to provide a realistic distribution of the errors and to demonstrate whether using widely available secondary data provided more accurate estimates of soil pH than those obtained by univariate kriging.Elevation was measured at 467 randomly distributed points that were converted into a regular DEM using ordinary kriging.Further,110 pits were located using spatial simulated annealing (SSA) method.The interpolation techniques were multi-linear regression analysis (MLR),ordinary kriging (OK),regression kriging (RK),kriging with external drift (KED) and multi-collocated ordinary cokriging (CKmc).A cross-validation test was used to assess the prediction performances of the different algorithms and then evaluate which methods performed best.RK and KED yielded better results than the more complex CKmc and OK.The choice of the most appropriate interpolation method accounting for redundant auxiliary information was strongly conditioned by site specific situations.
