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Analysis of estimation models of plantation stand heights using UAV LiDAR

  • role： First author第一作者
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

  • Email：limei881107@163.com
  • Introduction：李梅，研究方向为林业遥感 。E-mail： limei881107@163.com
LI Mei12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

  • Email：liuqw@ifrit.ac.cn
  • Introduction：刘清旺，研究方向为林业遥感。E-mail： liuqw@ifrit.ac.cn
LIU Qingwang12*,  
  • Affiliation：

    Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China

FENG Yiming3,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

LI Zengyuan12

реферат

The plantation area of China is the largest in the world. It is very important to precisely monitor plantation structure. The study area is located at Wangyedian forest farm, Chifeng, Inner Mongolia. The dominated tree species include Larix principis-rupprechtii and Pinus tabuliformis. The stand height models of plantation were established using the UAV (Unmanned Aerial Vehicle) LiDAR (Light Detection and Ranging) data and in situ sample plots measurements. The significant independent variables were selected based on the Pearson’s correlations between the six stand heights (Arithmetic mean height, Lorey’s height, Dominated height, Maximum height, Median height and Crown area weighted height) and the statistical metrics of discrete point cloud. The branch-and-bound search of best subset was conducted to fit the estimation models of stand height. The model accuracy was assessed by the cross validation. The results showed that the correlations between the height metrics of LiDAR point cloud and the different stand heights were high. The linear regression obtained the best result for different stand heights. The independent variables of the estimation model were all height metrics. For the six stand heights, the Lorey’s height (R2 = 0.91—0.97, rRMSE = 2.75%—3.96%), dominated height (R2 = 0.86—0.97, rRMSE = 3.72%—3.83%) and Crown area weighted height (R2 =0.86—0.96, rRMSE = 3.81%—4.73%) had the highest accuracy, while arithmetic mean height (R2 =0.85—0.94, rRMSE = 4.52%—6.07%) and median height (R2 =0.80—0.95, rRMSE =5.37%—7.34%) had a lower accuracy, maximum height (R2 = 0.69—0.87, RMSE = 1.30—1.40 m) was the lowest. Considering the forest types, the estimation accuracies of larch plantation stands were better the estimation accuracies of all forest types (ΔR2 = 0—0.05, ΔrRMSE = ‒0.69%—1.97%), which were better than the estimation accuracy of the stand height models of pine stands (ΔR2 = 0.06—0.18, ΔrRMSE = ‒1.90%—1.13%). The UAV LIDAR can be used to estimate the stand height of the northern temperate coniferous forest, and applied for the rapid and accurate investigation of plantation resources.

ключеви́че слова́

remote sensing;UAV;lidar;point clouds;plantation;stand height

References

  1. 1.
    Apostol B, Lorenţ A, Petrila M, Gancz V and Badea O. 2016. Height extraction and stand volume estimation based on fusion airborne LiDAR data and terrestrial measurements for a norway spruce [Picea abies (L.) Karst.] test site in Romania. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 44(1): 313-323
  2. 2.
    Bonan G B. 2008. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science, 320(5882): 1444-1449
  3. 3.
    Bottalico F, Chirici G, Giannini R, Mele S, Mura M, Puxeddu M, Mcroberts R E, Valbuena R and Travaglini D. 2017. Modeling Mediterranean forest structure using airborne laser scanning data. International Journal of Applied Earth Observation and Geoinformation, 57: 145-153
  4. 4.
    Cao L, Dai J S, Xu J X, Xu Z Q and She G H. 2014. Optimized extraction of forest parameters in subtropical forests based on airborne small footprint LiDAR technology. Journal of Beijing Forestry University, 36(5): 13-21
  5. 5.
    Cao L, Xu T, Shen X and She G H. 2016. Mapping biomass by integrating Landsat OLI and airborne LiDAR transect data in subtropical forests. Journal of Remote Sensing, 20(4): 665-678
  6. 6.
    Cord A F, Meentemeyer R K, Leitão P J and Václavík T. 2013. Modelling species distributions with remote sensing data: bridging disciplinary perspectives. Journal of Biogeography, 40(12): 2226-2227
  7. 7.
    Elias A and Daniel H. 2018. The use of three-dimensional convolutional neural networks to interpret LiDAR for forest inventory. Remote Sensing, 10(4): 649
  8. 8.
    Farid A, Goodrich D C and Sorooshian S. 2006. Using airborne LiDAR to discern age classes of cottonwood trees in a riparian area. Western Journal of Applied Forestry, 21(3):149-158
  9. 9.
    Fu T, Pang Y, Huang Q F, Liu Q W and Xu G C. 2011. Prediction of subtropical forest parameters using airborne laser scanner. Journal of Remote Sensing, 15(5): 1092-1104
  10. 10.
    Goodbody T R H, Coops N C, Tompalski P, Crawford P and Day K J K. 2017. Updating residual stem volume estimates using ALS-and UAV-acquired stereo-photogrammetric point clouds. International Journal of Remote Sensing, 38: 2938-2953
  11. 11.
    Guo Q H, Hu T Y, Jiang Y Q, Jin S C, Wang R, Guan H C, Yang Q L, Li Y M, Wu F F, Zhai Q P, Liu J and Su Y J. 2018. Advances in remote sensing application for biodiversity research. Biodiversity Science, 26(8): 789-806
  12. 12.
    Holmgren J. 2004. Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning. Scandinavian Journal of Forest Research, 19(6): 543-553
  13. 13.
    Hu J B and Zhang J. 2018. Unmanned Aerial Vehicle remote sensing in ecology: advances and prospects. Acta Ecologica Sinica, 38(1): 20-30
  14. 14.
    Hudak A T, Crookston N L, Evans J S, Hall D E and Falkowski M J. 2008. Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sensing of Environment, 112(5): 2232-2245
  15. 15.
    Järnstedt J, Pekkarinen A, Tuominen S, Ginzler C, Holopainen M and Viitala R. 2012. Forest variable estimation using a high-resolution digital surface model. ISPRS Journal of Photogrammetry and Remote Sensing, 74: 78-84 .
  16. 16.
    Jensen J L R and Mathews A J. 2016. Assessment of image-based point cloud products to generate a bare earth surface and estimate canopy heights in a woodland ecosystem. Remote Sensing, 8(1): 50
  17. 17.
    Khosravipour A, Skidmore A K, Wang T J, Isenburg M and Khoshelham K. 2015. Effect of slope on treetop detection using a LiDAR canopy height model. ISPRS Journal of Photogrammetry and Remote Sensing, 104: 44-52
  18. 18.
    Lee W J and Lee C W. 2018. Forest canopy height estimation using multiplatform remote sensing dataset. Journal of Sensors, 2018:1-9
  19. 19.
    Li Y C, Li C, Li M Y and Liu Z Z. 2019. Influence of variable selection and forest type on forest aboveground biomass estimation using machine learning algorithms. Forests, 10(12): 1073
  20. 20.
    Li Z Y and Chen E X. 2021. Development course of forestry remote sensing in China. Journal of Remote Sensing，25(1)：292-301
  21. 21.
    Li Z Y, Liu Q W and Pang Y. 2016. Review on forest parameters inversion using LiDAR. Journal of Remote Sensing, 20(5): 1138-1150
  22. 22.
    Lim K S and Treitz P M. 2004. Estimation of aboveground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Scandinavian Journal of Forest Research, 19(6): 558-570
  23. 23.
    Liu H, Zhang Z N and Cao L. 2018. Estimating forest stand characteristics in a coastal plain forest plantation based on vertical structure profile parameters derived from ALS data. Journal of Remote Sensing, 22(5): 872-888
  24. 24.
    Liu K, Shen X, Cao L, Wang G B and Cao F L. 2018. Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations. ISPRS Journal of Photogrammetry and Remote Sensing, 146: 465-482.
  25. 25.
    Lu L, Zhou X C, Yu Z Z, Han S and Wang X Q. 2016. Plot-level forest height inversion using airborne LiDAR data based on the random forest. Journal of Geo-Information Science, 18(8): 1133-1140
  26. 26.
    Means J E, Acker S A, Fitt B J, Renslow M, Emerson L and Hendrix C J. 2000. Predicting forest stand characteristics with airborne scanning LiDAR. Photogrammetric Engineering and Remote Sensing, 66(11): 1367-1371
  27. 27.
    Mu X Y, Zhang Q L, Liu Q W, Pang Y and Hu K L. 2015. Typical forest height mapping in cold temperate zone using airborne LiDAR data. Journal of Beijing Forestry University, 37(7): 58-67
  28. 28.
    Mutwiri F K, Odera P A and Kinyanjui M J. 2017. Estimation of tree height and forest biomass using airborne LiDAR data: a case study of londiani forest block in the Mau Complex, Kenya. Open Journal of Forestry, 7(2): 255-269
  29. 29.
    Næsset E and Bjerknes K O. 2001. Estimating tree heights and number of stems in young forest stands using airborne laser scanner data. Remote Sensing of Environment, 78(3): 328-340
  30. 30.
    Næsset E and Gobakken T. 2005. Estimating forest growth using canopy metrics derived from airborne laser scanner data. Remote Sensing of Environment, 96: 453-465
  31. 31.
    Næsset E. 2005. Assessing sensor effects and effects of leaf-off and leaf-on canopy conditions on biophysical stand properties derived from small-footprint airborne laser data. Remote Sensing of Environment, 98: 356-370
  32. 32.
    Nelson R, Margolis H, Montesano P, Sun G Q, Cook B, Corp L, Andersen H E, DeJong B, Pellat F P, Fickel T, Kauffman J and Prisley S. 2017. Lidar-based estimates of aboveground biomass in the continental US and Mexico using ground, airborne, and satellite observations. Remote Sensing of Environment, 188: 127-140
  33. 33.
    Nijland W, Coops N C, Macdonald S E, Nielsen S E, Bater C W and Stadt J J. 2015. Comparing patterns in forest stand structure following variable harvests using airborne laser scanning data. Forest Ecology and Management, 354: 272-280
  34. 34.
    Nilsson M. 1996. Estimation of tree heights and stand volume using an airborne lidar system. Remote Sensing of Environment, 56(1): 1-7
  35. 35.
    Nilsson M, Nordkvist K, Jonzén J, Lindgren N, Axensten P, Wallerman J, Egberth M, Larsson S, Nilsson L, Eriksson J and Olsson H. 2017. A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory. Remote Sensing of Environment, 194: 447-454
  36. 36.
    Noordermeer L, Bollandsås O M, Gobakken T and Næsset E. 2018. Direct and indirect site index determination for Norway spruce and Scots pine using bitemporal airborne laser scanner data. Forest Ecology and Management, 428: 104-114
  37. 37.
    Noordermeer L, Bollandsås O M., Ørka H O, Næsset E and Gobakken T. 2019. Comparing the accuracies of forest attributes predicted from airborne laser scanning and digital aerial photogrammetry in operational forest inventories. Remote Sensing of Environment, 226: 26-37
  38. 38.
    Pang Y, Zhao F, Li Z Y, Zhou S F, Deng G, Liu Q W and Chen E X. 2008. Forest height inversion using airborne LiDAR technology. Journal of Remote Sensing, 12(1): 152-158
  39. 39.
    Payn T, Carnus J M, Freer-Smith P, Kimberley M, Kollert W, Liu S R, Orazio C, Rodriguez L, Silva L N and Wingfield M J. 2015. Changes in planted forests and future global implications. Forest Ecology and Management, 352(4): 57-67
  40. 40.
    Popescu S C, Wynne R H and Nelson R F. 2002. Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, 37(1-3): 71-95
  41. 41.
    Puliti S, Ørka H O, Gobakken T and Næsset E. 2015. Inventory of small forest areas using an unmanned aerial system. Remote Sensing, 7(8): 9632-9654
  42. 42.
    Saatchi S S, Harris N L, Brown S, Lefsky M, Mitchard E T A, Salas W, Zutta B R, Buermann W, Lewis S L, Hagen S, Petrova S, White L, Silman M and Morel A. 2011. Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences of the United States of America, 108(24): 9899-9904
  43. 43.
    Salamí E, Barrado C and Pastor E. 2014. UAV flight experiments applied to the remote sensing of vegetated areas. Remote Sensing, 6(11): 11051-11081
  44. 44.
    Sibona E, Vitali A, Meloni F, Caffo L, Dotta A, Lingua E, Motta R and Garbarino M. 2017. Direct measurement of tree height provides different results on the assessment of LiDAR Accuracy. Forests, 8(1): 7
  45. 45.
    Szulecka J and Zalazar E M. 2017. Forest plantations in Paraguay: historical developments and a critical diagnosis in a SWOT-AHP framework. Land Use Policy, 60: 384-394
  46. 46.
    Tesfamichael S G, van Aardt J A N and Ahmed F. 2010. Estimating plot-level tree height and volume of Eucalyptus grandis plantations using small-footprint, discrete return lidar data. Progress in Physical Geography, 34(4): 515-540
  47. 47.
    Ullah S, Adler P, Dees M, Datta P, Weinacker H and Koch B. 2017. Comparing image-based point clouds and airborne laser scanning data for estimating forest heights. iForest - Biogeosciences and Forestry, 10: 273-280
  48. 48.
    Wallace L, Lucieer A, Malenovský Z, Turner D and Vopěnka P. 2016. Assessment of forest structure using two UAV techniques: a comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests, 7(3): 62
  49. 49.
    Wang Y H, Chen E X, Guo Y, Li Z Y, Jin Y D, Zhao J P and Zhou Y. 2020. Deep U-net optimization method for forest type classification with high resolution multispectral remote sensing images. Forest Research, 33(1): 11-18
  50. 50.
    White J C, Stepper C, Tompalski P, Coops N C and Wulder M A. 2015. Comparing ALS and image-based point cloud metrics and modelled forest inventory attributes in a complex coastal forest environment. Forests, 6(10): 3704-3732
  51. 51.
    Wulder M A, White J C, Nelson R F, Næsset E, Ørka H O, Coops N C, Hilker T, Bater C W and Gobakken T. 2012. LiDAR sampling for large-area forest characterization: a review. Remote Sensing of Environment, 121: 196-209
  52. 52.
    Wulder M A, White J C, Stinson G, Hilker T, Kurz W A, Coops N C, St-Onge B and Trofymow J A T. 2010. Implications of differing input data sources and approaches upon forest carbon stock estimation. Environmental Monitoring and Assessment, 166: 543-561
  53. 53.
    Xie Y Y, Wang B, Yao Y, Yang L, Gao Y, Zhang Z M and Lin L X. 2020. Quantification of vertical community structure of subtropical evergreen broad-leaved forest community using UAV-Lidar data. Acta Ecologica Sinica, 40(3): 940-951
  54. 54.
    Xu T, Cao L, Shen X and She G H. 2015. Estimates of subtropical forest biomass based on airborne LiDAR and Landsat 8 OLI data. Chinese Journal of Plant Ecology, 39(4): 309-321
  55. 55.
    Xu Z Q, Cao L, Ruan H H, Li W Z and Jiang S. 2015. Inversion of subtropical forest stand characteristics by integrating very high resolution imagery acquired from UAV and LiDAR point-cloud. Chinese Journal of Plant Ecology, 39(7): 694-703
  56. 56.
    Zarco-Tejada P J, Diaz-Varela R, Angileri V and Loudjani P. 2014. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy, 55: 89-99
  57. 57.
    Zhang J, Hu J B, Lian J Y, Fan Z J, Ouyang X J and Ye W H. 2016. Seeing the forest from drones: testing the potential of lightweight drones as a tool for long-term forest monitoring. Biological Conservation, 198: 60-69
  58. 58.
    Zhao K G, Suarez J C, Garcia M, Hu T X, Wang C and Londo A. 2018. Utility of multitemporal lidar for forest and carbon monitoring: tree growth, biomass dynamics, and carbon flux. Remote Sensing of Environment, 204: 883-897
  59. 59.
    Zhen Z, Quackenbush L J and Zhang L J. 2016. Trends in automatic individual tree crown detection and delineation—evolution of LiDAR data. Remote Sensing, 8(4): 333

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