Nowadays, spatial simulation on land use patterns is one of the key contents of LUCC. Modeling is an important tool for simulating land use patterns due to its ability to integrate measurements of changes in land cove...Nowadays, spatial simulation on land use patterns is one of the key contents of LUCC. Modeling is an important tool for simulating land use patterns due to its ability to integrate measurements of changes in land cover and the associated drivers. The conventional regression model can only analyze the correlation between land use types and driving factors but cannot depict the spatial autocorrelation characteristics. Land uses in Yongding County, which is located in the typical karst mountain areas in northwestern Hunan province, were investigated by means of modeling the spatial autocorrelation of land use types with the purpose of deriving better spatial land use patterns on the basis of terrain characteristics and infrastructural conditions. Through incorporating components describing the spatial autocorrelation into a conventional logistic model, we constructed a regression model （Autologistic model）, and used this model to simulate and analyze the spatial land use patterns in Yongding County. According to the comparison with the conventional logistic model without considering the spatial autocorrelation, this model showed better goodness and higher accuracy of fitting. The distribution of arable land, wood land, built-up land and unused land yielded areas under the ROC curves （AUC） was improved to 0.893, 0.940, 0.907 and 0.863 respectively with the autologistic model. It is argued that the improved model based on autologistic method was reasonable to a certain extent. Meanwhile, these analysis results could provide valuable information for modeling future land use change scenarios with actual conditions of local and regional land use, and the probability maps of land use types obtained from this study could also support government decision-making on land use management for Yongding County and other similar areas.展开更多
The visible and infrared bands of Landsat Thematic Mapper (TM) can be used for inland water studies. A method of retrieving water-leaving radiance from TM image over Taihu Lake in Jiangsu Province of China was investi...The visible and infrared bands of Landsat Thematic Mapper (TM) can be used for inland water studies. A method of retrieving water-leaving radiance from TM image over Taihu Lake in Jiangsu Province of China was investigated in this article. To estimate water-leaving radiance, atmospheric correction was performed in three visible bands of 485nm, 560nm and 660nm. Rayleigh scattering was computed precisely, and the aerosol contribution was estimated by adopting the clear-water-pixels approach. The clear waters were identified by using the Landsat TM middle-infrared band (2.1μm), and the water-leaving radiance of clear water pixels in the green band was estimated by using field data. Aerosol scattering at green band was derived for six points, and interpolated to match the TM image. Assuming the atmospheric correction coefficient was 1.0, the aerosol scattering image at blue and red bands were derived. Based on a simplified atmospheric radiation transfer model, the water-leaving radiance for three visible bands was retrieved. The water-leaving radiance was normalized to make it comparable with that estimated from other remotely sensed data acquired at different times, and under different atmospheric conditions. Additionally, remotely sensed reflectance of water was computed. To evaluate the atmospheric correction method presented in this article, the correlation was analyzed between the corrected remotely sensed data and the measured water parameters based on the retrieval model. The results show that the atmospheric correction method based on the image itself is more effective for the retrieval of water parameters from Landsat TM data than 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) code based on standard atmospheric and aerosol models.展开更多
TOPMODEL,a semi-distributed hydrological model,has been widely used.In the process of simulation of the model,Digital Elevation Model(DEM) is used to provide the input data,such as topographic index and distance to th...TOPMODEL,a semi-distributed hydrological model,has been widely used.In the process of simulation of the model,Digital Elevation Model(DEM) is used to provide the input data,such as topographic index and distance to the drainage outlet;thus DEM plays an important role in TOPMODEL.This study aims at examining the impacts of DEM uncertainty on the simulation results of TOPMODEL.In this paper,the effects were evaluated mainly from quantitative and qualitative aspects.Firstly,DEM uncertainty was simulated by using the Monte Carlo method,and for every DEM realization,the topographic index and distance to the drainage outlet were extracted.Secondly,the obtained topographic index and the distance to the drainage outlet were input to the TOPMODEL to simulate seven rain-storm-flood events,and four evaluation indices,such as Nash and Sutcliffe efficiency criterion(EFF),sum of squared residuals over all time steps(SSE),sum of squared log residuals over all time steps(SLE) and sum of absolute errors over all time steps(SAE) were recorded.Thirdly,these four evaluation indices were analyzed in statistical manner(minimum,maximum,range,standard deviation and mean value),and effect of DEM uncertainty on TOPMODEL was quantitatively analyzed.Finally,the simulated hydrographs from TOPMODEL using the original DEM and realizations of DEM were qualitatively evaluated under each flood cases.Results show that the effect of DEM uncertainty on TOPMODEL is inconsiderable and could be ignored in the model’s application.This can be explained by:1) TOPMODEL is not sensitive to the distribution of topographic index and distance to the drainage outlet;2) the distri-bution of topographic index and distance to the drainage outlet are slightly affected by DEM uncertainty.展开更多
基金National High Technology Research and Development Program of China, No.2008AA12Z106 National Natural Science Foundation of China, No.40801166 No.40771198
文摘Nowadays, spatial simulation on land use patterns is one of the key contents of LUCC. Modeling is an important tool for simulating land use patterns due to its ability to integrate measurements of changes in land cover and the associated drivers. The conventional regression model can only analyze the correlation between land use types and driving factors but cannot depict the spatial autocorrelation characteristics. Land uses in Yongding County, which is located in the typical karst mountain areas in northwestern Hunan province, were investigated by means of modeling the spatial autocorrelation of land use types with the purpose of deriving better spatial land use patterns on the basis of terrain characteristics and infrastructural conditions. Through incorporating components describing the spatial autocorrelation into a conventional logistic model, we constructed a regression model （Autologistic model）, and used this model to simulate and analyze the spatial land use patterns in Yongding County. According to the comparison with the conventional logistic model without considering the spatial autocorrelation, this model showed better goodness and higher accuracy of fitting. The distribution of arable land, wood land, built-up land and unused land yielded areas under the ROC curves （AUC） was improved to 0.893, 0.940, 0.907 and 0.863 respectively with the autologistic model. It is argued that the improved model based on autologistic method was reasonable to a certain extent. Meanwhile, these analysis results could provide valuable information for modeling future land use change scenarios with actual conditions of local and regional land use, and the probability maps of land use types obtained from this study could also support government decision-making on land use management for Yongding County and other similar areas.
基金Under the auspices of National Natural Science Foundation of China (No. 40671138)
文摘The visible and infrared bands of Landsat Thematic Mapper (TM) can be used for inland water studies. A method of retrieving water-leaving radiance from TM image over Taihu Lake in Jiangsu Province of China was investigated in this article. To estimate water-leaving radiance, atmospheric correction was performed in three visible bands of 485nm, 560nm and 660nm. Rayleigh scattering was computed precisely, and the aerosol contribution was estimated by adopting the clear-water-pixels approach. The clear waters were identified by using the Landsat TM middle-infrared band (2.1μm), and the water-leaving radiance of clear water pixels in the green band was estimated by using field data. Aerosol scattering at green band was derived for six points, and interpolated to match the TM image. Assuming the atmospheric correction coefficient was 1.0, the aerosol scattering image at blue and red bands were derived. Based on a simplified atmospheric radiation transfer model, the water-leaving radiance for three visible bands was retrieved. The water-leaving radiance was normalized to make it comparable with that estimated from other remotely sensed data acquired at different times, and under different atmospheric conditions. Additionally, remotely sensed reflectance of water was computed. To evaluate the atmospheric correction method presented in this article, the correlation was analyzed between the corrected remotely sensed data and the measured water parameters based on the retrieval model. The results show that the atmospheric correction method based on the image itself is more effective for the retrieval of water parameters from Landsat TM data than 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) code based on standard atmospheric and aerosol models.
基金Under the auspices of the National Natural Science Foundation of China (No. 40171015)
文摘TOPMODEL,a semi-distributed hydrological model,has been widely used.In the process of simulation of the model,Digital Elevation Model(DEM) is used to provide the input data,such as topographic index and distance to the drainage outlet;thus DEM plays an important role in TOPMODEL.This study aims at examining the impacts of DEM uncertainty on the simulation results of TOPMODEL.In this paper,the effects were evaluated mainly from quantitative and qualitative aspects.Firstly,DEM uncertainty was simulated by using the Monte Carlo method,and for every DEM realization,the topographic index and distance to the drainage outlet were extracted.Secondly,the obtained topographic index and the distance to the drainage outlet were input to the TOPMODEL to simulate seven rain-storm-flood events,and four evaluation indices,such as Nash and Sutcliffe efficiency criterion(EFF),sum of squared residuals over all time steps(SSE),sum of squared log residuals over all time steps(SLE) and sum of absolute errors over all time steps(SAE) were recorded.Thirdly,these four evaluation indices were analyzed in statistical manner(minimum,maximum,range,standard deviation and mean value),and effect of DEM uncertainty on TOPMODEL was quantitatively analyzed.Finally,the simulated hydrographs from TOPMODEL using the original DEM and realizations of DEM were qualitatively evaluated under each flood cases.Results show that the effect of DEM uncertainty on TOPMODEL is inconsiderable and could be ignored in the model’s application.This can be explained by:1) TOPMODEL is not sensitive to the distribution of topographic index and distance to the drainage outlet;2) the distri-bution of topographic index and distance to the drainage outlet are slightly affected by DEM uncertainty.
