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Accuracy evaluation of multi-source DEM data based on the analysis of vegetation-induced penetration rate in the forest area

  • role： First author第一作者
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China

  • Email：1565585625@qq.com
  • Introduction：蔡士雪，研究方向为多源高程数据精度评价及树高去除。E-mail： 1565585625@qq.com
CAI Shixue1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China

  • Email：yuelw@cug.edu.cn
  • Introduction：岳林蔚，研究方向为空间数据质量改善、多源遥感数据融合、图像超分辨率重建、水环境协同分析等。E-mail： yuelw@cug.edu.cn
YUE Linwei1*,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China

YIN Chao1,  
  • Affiliation：

    School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

QIU Zhonghang2

Resümee

With the development of modern remote sensing observation technology, there are several versions of public elevation datasets. However, due to the shielding effect or absorption of electromagnetic waves by dense vegetation in forest areas, the elevation obtained by remote sensing techniques is inevitably influenced by the systematic deviation. In this paper, the radar-derived DEM products (i.e., SRTM1 and TanDEM-X 90) and optical-sensor based AW3D30 dataset were chosen to analyze the characteristics of elevation deviations of multi-source DEM products in forest areas. The Maryland and Sonoma County of the United States were selected as the study regions. Using the 3DEP as the reference of bare-ground elevation, the vegetation-induced penetration rates of the three elevation products in the areas with different terrain conditions and covered by various forest types were obtained. The results showed that the overall penetration rate of SRTM1 obtained through C-band radar detection is the largest among the three datasets, followed by TanDEM-X 90 and AW3D 30 with comparable penetration rates. In terms of forest types, the penetration rates in coniferous forests are greater than that in broad-leaved forest. Moreover, the forest penetration rate decreases with the increase of canopy coverage, especially for TanDEM-X 90 and AW3D 30. In addition to the influence of detection wavelength, the main reason is that both TanDEM-X 90 and AW3D 30 are sampled from high-resolution data, and the high-resolution raw data before resampling make it easier to detect the open area between trees. In addition, the complex terrain environment will decrease the vertical accuracy of DEM datasets, and result in relatively lower penetration rates. Based on the results, the vegetation-induced penetration rate was synthetically affected by multiple factors, including sensor’s working waveband, forest type, canopy coverage, and terrain slope. The canopy height should be removed for the elevation datasets considering different penetration power of vegetation to obtain more accurate surface information. This research is helpful in improving the accuracy of canopy height removal for elevation products, and it provides a scientific reference basis for users when selecting the appropriate elevation products in the applications.

Schlüsselwort

SRTM1;TanDEM-X 90;AW3D 30;forest area;penetration rate;accuracy evaluation

References

  1. 1.
    Altunel A O. 2019. Evaluation of TanDEM-X 90 m digital elevation model. International Journal of Remote Sensing, 40(7): 2841-2854
  2. 2.
    Askne J I H, Persson H J and Ulander L M H. 2019. On the sensitivity of Tandem-X-Observations to boreal forest structure. Remote Sensing, 11(14):1644-1666
  3. 3.
    Bhang K J, Schwartz F W and Braun A. 2007. Verification of the vertical error in C-band SRTM DEM using ICESat and Landsat-7, Otter Tail County, MN. IEEE Transactions on Geoscience and Remote Sensing, 45(1): 36-44
  4. 4.
    Bourgine B and Baghdadi N. 2005. Assessment of C-band SRTM DEM in a dense equatorial forest zone. Comptes Rendus Geoscience, 337(14): 1225-1234
  5. 5.
    Carabajal C C and Harding D J. 2005. ICESat validation of SRTM C-band digital elevation models. Geophysical Research Letters, 32(22): L22S01
  6. 6.
    Carabajal C C and Harding D J. 2006. SRTM C-band and ICESat laser altimetry elevation comparisons as a function of tree cover and relief. Photogrammetric Engineering and Remote Sensing, 72(3): 287-298
  7. 7.
    Chen C F, Yang S, Li Y Y. 2020. Accuracy Assessment and Correction of SRTM DEM Using ICESat/GLAS Data under Data Coregistration. Remote Sensing, 12(20): 3435.
  8. 8.
    Chen H N, Xu S F, Huang Y, Wang S J,Shu S, Yu B L and Wu J P. 2020. Vertical accuracy correction and analysis of ASTER GDEM V2 over Antarctic Glacier. Journal of Remote Sensing, 24(8): 1010-1022
  9. 9.
    Dubayah R O, Swatantran A, Huang W, Duncanson L, Tang H, Johnson K, Dunne J O and Hurtt G C. 2017. CMS: LiDAR-Derived Biomass, Canopy Height and Cover, Sonoma County, California, 2013. Oak Ridge, Tennessee, USA: ORNL DAAC
  10. 10.
    Farr T G, Rosen P A, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodriguez E, Roth L, Seal D, Shaffer S, Shimada J, Umland J, Werner M, Oskin M, Burbank D and Alsdorf D. 2007. The shuttle radar topography mission. Reviews of Geophysics, 45(2): 361-403
  11. 11.
    Friedl M A, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A and Huang X M. 2010. MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1): 168-182
  12. 12.
    Goud G P S, Bhardwaj A. 2021. Estimation of Building Heights and DEM Accuracy Assessment Using ICESat-2 Data Products. Engineering Proceedings,10(1):37
  13. 13.
    Grohmann C H. 2018. Evaluation of TanDEM-X DEMs on selected Brazilian sites: comparison with SRTM, ASTER GDEM and ALOS AW3D30. Remote Sensing of Environment, 212: 121-133
  14. 14.
    Hawker L, Neal J and Bates P. 2019. Accuracy assessment of the TanDEM-X 90 Digital Elevation Model for selected floodplain sites. Remote Sensing of Environment, 232(C): 361-375
  15. 15.
    Huang C Q, Yang L M, Wylie B K and Homer C G. 2001. A strategy for estimating tree canopy density using Landsat 7 ETM+ and high resolution images over large areas//Proceedings of the Third International Conference on Geospatial Information in Agriculture and Forestry. Denver, Colorado: Earth Resources Observation and Science (EROS) Center
  16. 16.
    Jin B F. 2012. Influencing factors and significance of the skewness coefficient in grain size analysis. Marine Sciences, 36(2): 129-135
  17. 17.
    Kenyi L, Dubayah R, Hofton M, Blair J B and Schardt M. 2007. Comparison of SRTM-NED data to LIDAR derived canopy metrics//2007 IEEE International Geoscience and Remote Sensing Symposium. Barcelona: IEEE: 2825-2829
  18. 18.
    Li P, Li Z H, Shi C and Liu J N. 2016. Quality evaluation of 1 arc second version SRTM DEM in China. Bulletin of Surveying and Mapping, (9): 24-28
  19. 19.
    Miliaresis G and Delikaraoglou D. 2009. Effects of percent tree canopy density and DEM misregistration on SRTM/NED vegetation height estimates. Remote Sensing, 1(2): 36-49
  20. 20.
    Ni W J, Sun G Q, Zhang Z Y, Guo Z F and He Y T. 2014. Co-registration of two DEMs: impacts on forest height estimation from SRTM and NED at mountainous areas. IEEE Geoscience and Remote Sensing Letters, 11(1): 273-277
  21. 21.
    NOAA 2018. Vdatum[EB/OL]. National Oceanic and Atmospheric Administration.
  22. 22.
    Rodríguez E, Morris C S and Belz J E. 2006. A global assessment of the SRTM performance. Photogrammetric Engineering and Remote Sensing, 72(3): 249-260
  23. 23.
    Shortridge A and Messina J. 2011. Spatial structure and landscape associations of SRTM error. Remote Sensing of Environment, 115(6): 1576-1587
  24. 24.
    Tachikawa T, Kaku M, Iwasaki A, Gesch D B, Oimoen M J, Zhang Z, Danielson J J, Krieger T, Curtis B, Haase J, Abrams M and Carabajal C. 2011. ASTER Global Digital Elevation Model Version 2 – Summary of Validation Results. Earth Resources Observation and Science (EROS) Center
  25. 25.
    Tang X M, Li S J, Li T, Gao Y D, Zhang S B, Chen Q F and Zhang X. 2021. Review on global digital elevation products. National Remote Sensing Bulletin， 25（1）：167-181
  26. 26.
    Toutin T. 2002. Impact of terrain slope and aspect on radargrammetric DEM accuracy. ISPRS Journal of Photogrammetry and Remote Sensing, 57(3): 228-240
  27. 27.
    USGS 2019. 3DEP[EB/OL]. U.S. Geological Survey.
  28. 28.
    Van N T G, McVicar T R, Li L T, Gallant J C and Yang Q K. 2008. The impact of misregistration on SRTM and DEM image differences. Remote Sensing of Environment, 112(5): 2430-2442
  29. 29.
    Wessel B, Huber M, Wohlfart C, Marschalk U, Kosmann D and Roth A. 2018. Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS data. ISPRS Journal of Photogrammetry and Remote Sensing, 139: 171-182
  30. 30.
    Yuan X Q, Li G Y, Gao X M, Zhang W J and Lu J. 2018. Evaluation of AW3D 30 m DSM data elevation quality and precision validation of typical region. Geomatics & Spatial Information Technology, 41(4): 98-101
  31. 31.
    Yue L W. 2017. Research on DEM Fusion Blending Multi-Source and Multi-Scale Elevation Data. Wuhan: Wuhan University
  32. 32.
    Zhan L, Tang G A and Yang X. 2010. Evaluation of SRTM DEMs' elevation accuracy: a case study in Shaanxi Province. Geography and Geo-information Science, 26(1): 34-36
  33. 33.
    Zhang J, Wang M Z and Hu Y F. 2021. Skewness and kurtosis analysis of cavitation noise for underwater vehicles. Technical Acoustics, 40(6):757-762
  34. 34.
    Zhao X Q, Su Y J, Hu T Y, Chen L H, Gao S, Wang R, Jin S C and Guo Q H. 2018. A global corrected SRTM DEM product for vegetated areas. Remote Sensing Letters, 9(4): 393-402
  35. 35.
    Zhou Q M and Liu X J. 2004. Analysis of errors of derived slope and aspect related to DEM data properties. Computers and Geosciences, 30(4): 369-378
  36. 36.
    Zhu H C. 2003. A Research on Spatial Data Mining of Digital Elevation Models: A Case Study in the Loess Plateau of North Shaanxi Province. Xi’an: Northwest University

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