以激光雷达(LiDAR)地面点云数据为数据源,将北亚热带天然次生林下的丘陵地形作为研究对象,分析了6种常用局部插值方法生成DEM的全局误差及其与地形因子、地面插值点密度和地表植被状况的相互关系,并借助随机森林方法进行插值误差不确定性制图。研究表明:各插值表面的预测值总体偏低,其最佳输出空间分辨率为2 m; 其中以自然邻近法插值生成的数字地形精度最高且可视化效果最好,而张力样条法的精度最低; 全局误差随坡度增大而逐渐提升,随地面插值点密度提高逐渐降低; 幼龄和中龄天然次生林所在区域地形插值的误差较大而成熟林的误差最大,灌木区全局误差不高但误差变异较大。同时,以LiDAR提取的植被参数与地形插值误差表现了较好的相关性,而归一化植被指数(NDVI)与误差之间的相关不明显,这表明以LiDAR数据提取植被参数在NDVI易饱和地区也可以较好地反映地形插值精度。
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.
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[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.
基金
收稿日期:2013-12-26 修回日期:2014-03-03
基金项目:江苏高校优势学科建设工程资助项目(PAPD); 江苏省科技支撑计划(农业部分)(BE2013443); 南京林业大学实验教学专项课题(2013-2015)
第一作者:曹林,讲师,博士生。*通信作者:佘光辉,教授。E-mail: ghshe@njfu.edu.cn。
引文格式:曹林,朱兴洲,代劲松,等. 基于机载小光斑LiDAR数据插值的亚热带森林丘陵地形的误差分析[J]. 南京林业大学学报:自然科学版,2014,38(4):7-13.
PDF(3902794 KB)
