Research On DEM Fusion Blending Multi-Source And Multi-scale Elevation Data

Digital elevation models(DEMs)are the digital representation of terrain surface,which have been broadly applied in scientific fields such as ecology,agriculture and hydrological modeling.There are a variety of DEM products available with different sources.Early DEM data were mainly generated by digitizing existing topographic maps.As the development of earth observation technologies,they can now be directly derived using remote sensing and photogrammetric techniques.However,the quality of the DEM data are inevitably affected by the observations and processing methods,which lead to the tradeoff between the spatial resolutions,spatial coverage and vertical accuracy.With the growing demand for the monitoring of Earth surface changes,the absence of a high-quality seamless DEM dataset has been a challenge for the Earth?related research fields.On one hand,modern imaging technologies have been applied to Earth observation,and new DEM products are being generated and released.Nevertheless,the new data generation are not only highly cost and time consuming,but also limitated by the observation imaging mode.Therefore,the analysis and improvement of the currently available datasets through multi-source data fusion also make sense.The current fusion works are inadequate for the use of supplementary DEM information.Moreover,they have difficulty in simultaneously processing multiple problems,including noise,data voids,and resolution enhancement.Given these facts,this paper intended to assess the current DEM datasets,and developed the fusion strategies according to the demand of specific applications.The main contributions of this paper are as followed.(1)Firstly,we proposed a regularized framework for the production of high-resolution(HR)DEM data with extended coverage.To deal with the registration error and the horizontal displacement among multi-scale measurements,robust data fidelity with weighted l1 norm was employed to measure the conformance of the reconstructed HR data to the observed data.Furthermore,a slope-based Markov random field(MRF)regularization was used as the spatial regularization.The proposed method can simultaneously handle complex terrain features,noises and.data voids.Using the proposed method,we can reconstruct a seamless DEM data with the highest resolution among the input data,and an extensive spatial coverage.(2)We proposed a method to generate a seamless DEM dataset blending SRTM-1,ASTER GDEM v2,and ICESat laser altimetry data.The ASTER GDEM v2 data were used as the elevation source for the SRTM void filling.To get a reliable filling source,ICESat GLAS points were incorporated to enhance the accuracy of the ASTER data within the void regions,using an artificial neural network(ANN)model.After correction,the voids in the SRTM-1 data were filled with the corrected ASTER GDEM values.The triangular irregular network based delta surface fill(DSF)method was then employed to eliminate the vertical bias between them.Finally,an adaptive outlier filter was applied to all the data tiles.The final result was a seamless global DEM dataset.(3)The systematic errors of DEM data for glacial mass balance estimation were fully invetigated and corrected.Yulong mountain,which is the southernmost snow mountain in mainland Eurasia where temperate glacier exists,was chosen as the study area.The planimetric misalignment,the systematic elevation-dependent and terrain-dependent errors,and the radar penetration height between the multi-source and multi-temporal DEMs were considered.The topography DEM in 1987 with higher quality was used as the reference,and 2000 SRTM1 DEM and 2009 ASTER DEM were registrated and corrected to the reference.The mass balance can be obtained using the corrected DEMs.Combining the glaicer extent extracted from the Landsat remote sensing images,the comprehensive glacier retreat from the mid-20th century to the early 21st century in Yulong Mountain was analyzed.