Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation

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.

A fundamental theorem for eco-environmental surface modelling and its applications

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.

An improved HASM method for dealing with large spatial data sets

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.