Précédent|Prochain

Extraction of digital surface model based on TerraSAR-X radargrammetry over a typical forest region in northeast China

  • role： First author第一作者
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    Shandong University of Science and Technology, Geomatics College, Qingdao 266590, China

  • Email：yuhuina1017@163.com
  • Introduction：于慧娜(1992—　)，女，硕士研究生，研究方向为森林遥感、资源环境遥感。E-mail：yuhuina1017@163.com
Huina YU12,  
  • role： Corresponding author通信作者
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：niwj@radi.ac.cn
  • Introduction：倪文俭(1982—　)，男，研究员，研究方向为森林遥感正向机理模型发展和基于多源遥感数据的森林植被碳储量及其动态变化监测。E-mail：niwj@radi.ac.cn
Wenjian NI1*,  
  • Affiliation：

    Shandong University of Science and Technology, Geomatics College, Qingdao 266590, China

Yulin CAI2,  
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Zhiyu ZHANG1,  
  • Affiliation：

    The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Guoqing SUN1,  
  • Affiliation：

    Beijing Normal University, College of Information Science and Technology, Beijing 100875, China

Haoyang YU3

résumé

The information on vertical forest structure is important to accurately estimate aboveground biomass. Stereoscopic radar has the capability to detect the vertical structure of forest stands. However, the radar image resolution is low in the early stages. The emergence of high-resolution radar data such as TerraSAR-X provides an opportunity to detect vertical forest structures using stereoscopic radar data. The goal of the current study is to examine the influence of critical matching parameters on DSM extraction using stereoscopic radar data. We evaluated the DSMs extracted using the TerraSAR-X stereoscopic radar data of stripmap mode over the Daxing’anling forest area and Changbai Mountain nature reserve. The critical parameters examined included the number of pyramid layers, matching window size, and search range. Result: (1) In the Daxing’anling study area where the parallax was small, the DSM accuracy improved as the number of pyramid layers decreased. We used five, six, and seven pyramid layers in the matching process. DSM errors within ± 10 m accounted for 81.8%, 77.7%, and 77.1% of the total number of pixels. Over the area where the parallax was large, such as in the Changbai Mountain study area, increasing the number of pyramids proved useful in correcting matching errors. The DSM obtained by the seven pyramid layers could reflect the correct terrain. (2) A large matching window improved the correlation of the image pair and reduced the noise caused by the lack of texture information in the forest area. The average correlation of the two study areas increased from 0.43 and 0.40 to 0.49 and 0.45, respectively, when a large window was applied over the two test areas. (3) Compared with that of the reference data, the RMSEs of the extracted DSM were 6.682 and 10.384 m over the Daxing’anling area and Changbai Mountain, respectively. Large errors occurred where the slope changed significantly. Significant slope changes resulted in large geometric distortions such as foreshortening and overlap. In these cases, corresponding matching points could not be found with our matching algorithm, resulting in the failure of matching results. The influences of critical matching parameters on DSM extraction were obvious. The proper settings of the number of pyramid layers, matching window size, and search ranges should be carefully evaluated.

mots-clés

TerraSAR-X;stereoscopic SAR;forested area;DSM;matching

References

  1. 1.
    Capaldo P, Crespi M, Fratarcangeli F, Nascetti A and Pieralice F. 2011. High-resolution SAR radargrammetry: a first application with COSMO-SkyMed spotlight imagery. IEEE Geoscience and Remote Sensing Letters, 8(6): 1100–1104
  2. 2.
    Capaldo P, Nascetti A, Porfiri M, Pieralice F, Fratarcangeli F, Crespi M and Toutin T. 2015. Evaluation and comparison of different radargrammetric approaches for digital surface models generation from COSMO-SkyMed, TerraSAR-X, RADARSAT-2 imagery: analysis of Beauport (Canada) test site. ISPRS Journal of Photogrammetry and Remote Sensing, 100: 60–70
  3. 3.
    d’Ozouville N, Deffontaines B, Benveniste J, Wegmüller U, Violette S and de Marsily G. 2008. DEM generation using ASAR (ENVISAT) for addressing the lack of freshwater ecosystems management, Santa Cruz Island, Galapagos. Remote Sensing of Environment, 112(11): 4131–4147
  4. 4.
    He X Y, Zhang L, Balz T and Liao M S. 2013. Topographic mapping in mountainous areas using StereoSAR assisted by external DEM. Acta Geodaetica et Cartographica Sinica, 42(3): 425–432 (
  5. 5.
    Hou G L, Zhang H Y, Guo D and Guo X Y. 2012. Spatial-temporal variation of NDVI in the growing season and its sensitivity to climatic factors in Changbai mountains. Progress in Geography, 31(3): 285–292 (
  6. 6.
    Huang S Q and Liu D Z. 2007. Analysis of some uncertain factors in spaceborne SAR imaging and SAR image. Acta Geodaetica et Cartographica Sinica, 36(2): 152–157 (
  7. 7.
    La Prade G. 1963. An analytical and experimental study of stereo for radar. Photogrammetric Engineering, 29(2): 294–300
  8. 8.
    Lee J S, Wen J H, Ainsworth T L, Chen K S and Chen A J. 2009. Improved sigma filter for speckle filtering of SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 47(1): 202–213
  9. 9.
    Li Z L, Liu G X and Ding X L. 2006. Exploring the generation of digital elevation models from same-side ERS SAR images: topographic and temporal effects. The Photogrammetric Record, 21(114): 124–140
  10. 10.
    Liu Q, Yang L, Liu Q H and Li J. 2015. Review of forest above ground biomass inversion methods based on remote sensing technology. Journal of Remote Sensing, 19(1): 62–74 (
  11. 11.
    Nonaka T, Hayakawa T, Griffiths S and Mercer B. 2009. DEM production utilizing stereo technology of TerraSAR-X data//Proceedings of IEEE International, IGARSS 2009 Geoscience and Remote Sensing Symposium. Cape Town: IEEE: IV-157-IV-160 [DOI: 10.1109/IGARSS.2009.5417350]
  12. 12.
    Palm S, Oriot H M and Cantalloube H M. 2012. Radargrammetric DEM extraction over urban area using circular SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 50(11): 4720–4725
  13. 13.
    Perko R, Raggam H, Deutscher J, Gutjahr K and Schardt M. 2011. Forest assessment using high resolution SAR data in X-band. Remote Sensing, 3(4): 792–815
  14. 14.
    Persson H and Fransson J E S. 2014. Forest variable estimation using radargrammetric processing of Terrasar-X images in boreal forests. Remote Sensing, 6(3): 2084–2107
  15. 15.
    Raggam H, Gutjahr K, Perko R and Schardt M. 2010. Assessment of the stereo-radargrammetric mapping potential of TerraSAR-X multibeam spotlight data. IEEE Transactions on Geoscience and Remote Sensing, 48(2): 971–977
  16. 16.
    Salvini R, Carmignani L, Francioni M and Casazza P. 2015. Elevation modelling and palaeo-environmental interpretation in the Siwa area (Egypt): application of SAR interferometry and radargrammetry to COSMO-SkyMed imagery. CATENA, 129: 46–62
  17. 17.
    Solberg S, Riegler G and Nonin P. 2015. Estimating forest biomass from TerraSAR-X stripmap radargrammetry. IEEE Transactions on Geoscience and Remote Sensing, 53(1): 154–161
  18. 18.
    Toutin T. 2000. Evaluation of radargrammetric DEM from RADARSAT images in high relief areas. IEEE Transactions on Geoscience and Remote Sensing, 38(2): 782–789
  19. 19.
    Wei Y W, Zhou W M, Zhou L, Yu D P, Zhou Y B, Qin S J and Dai L M. 2015. Carbon storage and its distribution pattern in the natural Larix gmelinii forests on Daxing’an mountains. Acta Ecologica Sinica, 35(1): 189–195 (
  20. 20.
    Woodwell G M, Whittaker R H, Reiners W A, Likens G E, Delwiche C C and Botkin D B. 1978. The biota and the world carbon budget. Science, 199(4325): 141–146
  21. 21.
    Yu J H, Ge L L and Li X J. 2014. Radargrammetry for digital elevation model generation using envisat reprocessed image and simulation image. IEEE Geoscience and Remote Sensing Letters, 11(9): 1589–1593
  22. 22.
    Zhang Y H, Zhang J X, Liu M J and Lin Z J. 2007. Imaging model for space-borne SAR and its application in orbit refinement. Journal of Remote Sensing, 11(6): 771–777 (
  23. 23.
    Zhou Y Q, Zheng Z B, Li D R and Shu N. 1998. Stereopair positioning algorithm for SAR images and its accuracy analysis model. Journal of Remote Sensing, 2(4): 245–250 (

Lire l'article complet

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website