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| 1 | Sensitivity studies of a high accuracy surface modeling method显示文摘The sensitivities of the initial value and the sampling information to the accuracy of a high accuracy surface modeling(HASM) are investigated and the implementations of this new modeling method are modified and enhanced. Based on the fundamental theorem of surface theory, HASM is developed to correct the error produced in geographical information system and ecological modeling process. However, the earlier version of HASM is theoretically incomplete and its initial value must be produced by other surface modeling methods, such as spline, which limit its promotion. In other words, we must use other interpolators to drive HASM. According to the fundamental theorem of surface theory, we modify HASM, namely HASM.MOD, by adding another important nonlinear equation to make it independent of other methods and, at the same time, have a complete and solid theory foundation. Two mathematic surfaces and monthly mean temperature of 1951–2010 are used to validate the effectiveness of the new method. Experiments show that the modified version of HASM is insensitive to the selection of initial value which is particular important for HASM. We analyze the sensitivities of sampling error and sampling ratio to the simulation accuracy of HASM.MOD. It is found that sampling information plays an important role in the simulation accuracy of HASM.MOD. Another feature of the modified version of HASM is that it is theoretically perfect as it considers the third equation of the surface theory which reflects the local warping of the surface. The modified HASM may be useful with a wide range of spatial interpolation as it would no longer rely on other interpolation methods. | ZHAO Na YUE TianXiang ZHAO MingWei DU ZhengPing FAN ZeMeng CHEN ChuanFa | 2014 | Science China Earth Sciences2014，57，10： | 9 |
| 2 | A fundamental theorem for eco-environmental surface modelling and its applications显示文摘We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling(FTESM). The Beijing-Tianjin-Hebei(BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling(HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-meansquare error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM. | Tianxiang YUE Na ZHAO Yu LIU Yifu WANG Bin ZHANG Zhengping DU Zemeng FAN Wenjiao SHI Chuanfa CHEN Mingwei ZHAO Dunjiang SONG Shihai WANG Yinjun SONG Changqing YAN Qiquan LI Xiaofang SUN Lili ZHANG Yongzhong TIAN Wei WANG Ying’an WANG Shengnan MA Hongsheng HUANG Yimin LU Qing WANG Chenliang WANG Yuzhu WANG Ming LU Wei ZHOU Yi LIU Xiaozhe YIN Zong WANG Zhengyi BAO Miaomiao ZHAO Yapeng ZHAO Yimeng JIAO Ufra NASEER Bin FAN Saibo LI Yang YANG John PWILSON | 2020 | Science China Earth Sciences2020，63，8： | 7 |
| 3 | An improved HASM method for dealing with large spatial data sets显示文摘Surface modeling with very large data sets is challenging. An efficient method for modeling massive data sets using the high accuracy surface modeling method(HASM) is proposed, and HASM_Big is developed to handle very large data sets. A large data set is defined here as a large spatial domain with high resolution leading to a linear equation with matrix dimensions of hundreds of thousands. An augmented system approach is employed to solve the equality-constrained least squares problem(LSE) produced in HASM_Big, and a block row action method is applied to solve the corresponding very large matrix equations.A matrix partitioning method is used to avoid information redundancy among each block and thereby accelerate the model.Experiments including numerical tests and real-world applications are used to compare the performances of HASM_Big with its previous version, HASM. Results show that the memory storage and computing speed of HASM_Big are better than those of HASM. It is found that the computational cost of HASM_Big is linearly scalable, even with massive data sets. In conclusion,HASM_Big provides a powerful tool for surface modeling, especially when there are millions or more computing grid cells. | Na ZHAO Tianxiang YUE Chuanfa CHEN Miaomiao ZHAO Zhengping DU | 2018 | Science China Earth Sciences2018，61，8： | 2 |
| 4 | An Algorithm for Solving High Accuracy Surface Mod- eling显示文摘 | Chen Chuanfa Yue Tianxiang Zhang Zhaojie | | Geomatics and Wuhan University 9 0100，35，3： | 1 |
| 5 | A thin plate spline based feature preserving method for reducing elevat ion points derived from lidar显示文摘 | CHEN CHUANFA LI YANYANoYAN CHANGQING | 2015 | Remote Sensing2015，，： | 1 |
| 6 | A method of DEM construction and related error analysis 显示文摘 | Chen Chuanfa Yue Tianxiang | 2010 | COMPUTERS & GEOSCIENCES2010，36，6： | 1 |
| 7 | A Multiresolution Hierarchical Classification Algorithm for Filtering Airborne Li DAR Data显示文摘 | CHEN CHUANFA LI YANYAN | 2013 | ISPRS Journal of Photogrammetry and Remote Sensing2013，82，： | 1 |
| 8 | A method of DEM construction and related error analysis显示文摘 | Chuanfa Chen Tianxiang Yue | 2010 | Computers and Geosciences2010，，6： | 1 |
| 9 | Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation显示文摘To remove vegetation bias(VB)from the global DEMs(GDEMs),an artificial neural network(ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper.Three study sites with different forest types(evergreen,mixed evergreen-deciduous,and deciduous)are employed to evaluate the performance of the proposed model on three popular 30-m GDEMs,including SRTM1,AW3D30,and COPDEM30.Taking LiDAR DTM as the ground truth,the accuracy of the GDEMs before and after VB correction is assessed,as well as two existing GDEMs including MERIT and FABDEM.Results show that all the original GDEMs significantly overestimate the LiDAR DTM in the three forest types,with the largest biases of 21.5 m for SRTM1,26.3 m for AW3D30,and 27.18 m for COPDEM30.Taking data randomly sampled from the corrected area as the training points,the proposed model reduces the mean errors(root mean square errors)of the three GDEMs by 98.8%-99.9%(55.1%-75.8%)in the three forests.When training data have the same forest type as the corrected GDEM but under different local situations,the proposed model lowers the GDEM errors by at least 76.9%(44.1%).Furthermore,our corrected GDEMs consistently outperform the existing GDEMs for the two cases. | Yanyan Li Linye Li Chuanfa Chen Yan Liu | 2023 | International Journal of Digital Earth2023，16，1： | 0 |
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