Error analysis of DEM interpolation based on small-footprint airborne LiDAR in subtropical hilly forests

doi:10.3969/j.issn.1000-2006.2014.04.002

CAO Lin, ZHU Xingzhou, DAI Jinsong, XU Ziqian, SHE Guanghui

Journal of Nanjing Forestry University (Natural Sciences Edition） ›› 2014, Vol. 38 ›› Issue (04) : 7-13.

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Journal of Nanjing Forestry University (Natural Sciences Edition） ›› 2014, Vol. 38 ›› Issue (04) : 7-13. DOI: 10.3969/j.issn.1000-2006.2014.04.002

Error analysis of DEM interpolation based on small-footprint airborne LiDAR in subtropical hilly forests

CAO Lin, ZHU Xingzhou, DAI Jinsong, XU Ziqian, SHE Guanghui^*

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Abstract

In this research, ground LiDAR point cloud was used as a data source; hilly terrain under the northern subtropical secondary forest was set as a research object. Six commonly used zonal interpolation methods were applied to create DEMs, global error and the relationship between the errors and levels of the terrain factor, ground interpolation point density and surface vegetation cover were analyzed. Then the uncertainty map of interpolation error was created by the random forest method. The research results demonstrated that: The predicted values on the interpolation surface were generally underestimated and the best output resolution was 2 m; the natural neighbor interpolation method showed the highest accuracy and the best visual quality, but the tension spline method had the poorest accuracy; the global error increased corresponding to the increment of slope but decreased by the increment of ground interpolation point density; the terrain interpolation errors were relatively high under the young and middle age natural secondary forest but received the highest error under mature forest. The global errors under shrubs were not high but the variations of errors were high. Meanwhile, there existed a relative high correlation between LiDAR-derived forest parameters and the errors of terrain interpolation, while no significant relationship was found between NDVI and the global errors; this again proved that LiDAR-derived forest variables could better reflect the terrain interpolation accuracy in the NDVI saturation regions.

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CAO Lin, ZHU Xingzhou, DAI Jinsong, XU Ziqian, SHE Guanghui. Error analysis of DEM interpolation based on small-footprint airborne LiDAR in subtropical hilly forests[J]. Journal of Nanjing Forestry University (Natural Sciences Edition）. 2014, 38(04): 7-13 https://doi.org/10.3969/j.issn.1000-2006.2014.04.002

References

[1] Ferster C J, Coops N C, Trofymow J A. Aboveground large tree mass estimation in a coastal forest in British Columbia using plot-level metrics and individual tree detection from lidar[J]. Canadian Journal of Remote Sensing, 2009,35(3): 270-275.
[2] Lefsky M A, Cohen W B, Acker S A, et al. Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests[J]. Remote Sensing of Environment, 1999,70(3): 339-361.
[3] Lim K, Treitz P, Baldwin K, et al. Lidar remote sensing of biophysical properties of tolerant northern hardwood forests[J]. Canadian Journal of Remote Sensing, 2003, 29(5): 658-678.
[4] Naesset E. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data[J]. Remote Sensing of Environment, 2002, 80(1): 88-99.
[5] Drake J B, Dubayah R O, Clark D B, et al, Estimation of tropical forest structural characteristics using large-footprint LiDAR[J]. Remote Sensing of Environment, 2002,79:305-319.
[6] Lefsky M A, Cohen W B, Harding D, et al. Remote sensing of aboveground biomass in three biomes:International archives of the Photogrammetry[J]. Remote Sensing and Spatial Information Sciences, 2001, 34:155-160.
[7] Adams J C, Chandler J H. Evaluation of lidar and medium scale photogrammetry for detection soft-cliff coastal change[J]. Photogrammetric Record,2002, 17(99): 405-418.
[8] Hodgson M E, Jensen J, Raber G, et al. An evaluation of lidar-derived elevation and terrain slope in leaf-off conditions[J]. Photogrammetric Engineering & Remote Sensing, 2005,71(7): 817-823.
[9] Yanalak M. Effect of gridding method on digital terrain model profile data based on scattered data[J]. Journal of Computing in Civil Engineering,2003,17(1): 58-67.
[10] Abramov O, McEwan A. An evaluation of interpolation methods for Mars Orbiter Laser Altimeter(MOLA)data[J]. International Journal of Remote Sensing, 2004,25(3): 669-676.
[11] Lloyd C D, Atkinson P M. Deriving DSMs from lidar data with kriging[J]. International Journal of Remote Sensing, 2002, 23: 2519-2524.
[12] 庞勇,李增元.基于机载激光雷达的小兴安岭温带森林组分生物量反演[J].植物生态学报,2012,36(10):1095-1105.Pang Y, Li Z Y. Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing’an Mountains, Northeastern of China[J]. Chinese Journal of Plant Ecology, 2012, 36(10): 1095 1105.
[13] 潘百红,张正明,曹铁如.江苏虞山国家森林公园的天然植被[J].中南林业科技大学学报,2007,27(4):123-128.Pan B H, Zhang Z M, Cao T R. Natural vegetation of Yusan National Forest Park in Jiangsu province, China[J]. Journal of Central South University of Forestry & Technology, 2007, 27(4): 123-128.
[14] Philip G M, Watson D F. A precise method for determining contoured surfaces[J]. Australian Petroleum Exploration Association Journal, 1982, 22: 205-212.
[15] Watson D F, Philip G M. A refinement of inverse distance weighted interpolation[J]. Geoprocessing, 1985, 2:315-327.
[16] Sibson R. A Brief Description of Natural Neighbor Interpolation [M]. New York: John Wiley & Sons,1981.
[17] Watson D. Contouring: A Guide to the Analysis and Display of Spatial Data [M]. London: Pergamon Press, 1992.
[18] Heritagea G, Milanb D, Largec A. Influence of survey strategy and interpolation model on DEM quality[J]. Geomorphology,2009,112(3):334-344.
[19] Yue T, Du Z, Song D, et al. A new method of surface modeling and its application to DEM construction[J]. Geomorphology. 2007,91(1):161-172.
[20] 周淑芳, 李增元, 范文义,等,基于机载激光雷达数据的DEM 获取及应用[J]. 遥感技术与应用, 2007, 22(3):356-360.
[21] Tucker C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing of Environment,1979,8:127-150.
[22] Breiman L. Random forests[J].Machine Learning, 2001, 45(1):5-32.
[23] Rodríguez V F,Abarca F,Ghimire B. Incorporating spatial variability measures in land-cover classification using random forest[J]. Procedia Environmental Sciences, 2011(3): 44-49.
[24] Pall O G,Jon A B,Johannes R S. Random forests for land cover classification[J].Pattern Recognition Letters, 2006, 27(4):294-300.
[25] Verikas A,Gelzin A,Bacauskiene M. Mining data with random forests: a survey and results of new tests[J].Pattern Recognition,2011,44(2):330-349.

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