Biomass estimation of Caragana microphylla in the shrub-encroached grassland based on terrestrial laser scanning

  • role: First author第一作者
  • Affiliation:

    College of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China

  • Email:xlliu@chd.edu.cn
  • Introduction: 1995, ,,E-mail: xlliu@chd.edu.cn
LIU Xiaoliang1,  
  • Affiliation:

    College of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China

SUI Lichun1,  
  • Affiliation:

    State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

BAI Yongfei2,  
  • Affiliation:

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

ZHAO Dan3,  
  • role: Corresponding author通信作者
  • Affiliation:

    State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

  • Email:zhaoyj@ibcas.ac.cn
  • Introduction: 1988, ,,E-mail: zhaoyj@ibcas.ac.cn
ZHAO Yujin2*,  
  • Affiliation:

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

LIU Yanshu4,  
  • Affiliation:

    State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

ZHAI Qiuping2

résumé

Caragana microphylla is the most representative plant in the shrub-encroached grassland of Inner Mongolia. Accurately estimating the aboveground biomass of Caragana microphylla is critical to study the shrub-encroached grassland ecosystem function further and to monitor the degree of shrub encroachment. Terrestrial Laser Scanning (TLS) can accurately estimate shrub volume by acquiring high-density point cloud data. It is widely used to measure shrub biomass. However, this scanning method has not been effectively applied in shrub-encroached grassland. In this study, the field-measured TLS point cloud data and the shrub biomass of 42 shrubs were first obtained in five plots (10 m × 10 m) from the shrub-encroached grassland vegetation restoration experimental area of ​​the Chinese Academy of Sciences. The volume of shrub was then calculated using the method of global convex hull, convex hull by slices, volume calculation by sections, volumetric surface differencing, and voxels. The regression analysis was also carried out to predict biomass. Finally, the accuracy of biomass estimation models established by the five methods was compared and analyzed by leave-one-out cross validation. Results showed that TLS can achieve the rapid and accurate inversion of Caragana microphylla biomass without destroying vegetation, which is a reliable alternative technology for traditional field investigation methods. The five methods used in the study were able to estimate biomass effectively. Compared with the global convex hull (R2=0.87, p<0.001, RMSE=30.50 g), the convex hull by slices (R2=0.89, p<0.001, RMSE=28.01 g) and the volume calculation by sections (R2=0.88, p<0.001, RMSE=29.03 g) could effectively reduce the overestimation of the volume caused by outliers, thus improving the accuracy of biomass estimation. The volumetric surface differencing fitted best with the measured biomass (R2=0.89, p<0.001, RMSE=28.89 g) when the grid size is 3 cm. The standard deviation of height was selected as the optimal height metric of biomass prediction for Caragana microphylla. The method that used voxels explained 90% of the variation in biomass estimates (R2=0.90, p<0.001, RMSE=26.28 g). Thus, it was the best model for the biomass inversion of Caragana microphylla.

mots-clés

remote sensing;terrestrial laser scanning;shrub encroachment;Caragana microphylla;biomass;volume

References

  1. 1.
    Archer S, Boutton T W and Hibbard K A . 2001. Trees in grasslands: biogeochemical consequences of woody plant expansion//Schulze E D, Heimann M, Harrison Sholland E, Lloyd J, Prentice I C, Schimel D, eds. Global Biogeochemical Cycles in the Climate System. Amsterdam: Elsevier, 115-137
  2. 2.
    Barber C B, Dobkin D P and Huhdanpaa H . 1996. The quickhull algorithm for convex hulls. ACM Transactions on Mathematical Software, 22(4): 469-483
  3. 3.
    Bi Y L, Qi L S, Chen S L, Li L J and Liu S . 2013. Canopy volume measurement method based on point cloud data. Science and Technology Review, 31(27): 31-36
  4. 4.
    Brown S . 2002. Measuring carbon in forests: current status and future challenges. Environmental Pollution, 116(3): 363-372
  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.
    Chen W, Zhao J, Cao C X and Tian H J . 2018. Shrub biomass estimation in semi-arid sandland ecosystem based on remote sensing technology. Global Ecology and Conservation, 16: e00479
  7. 7.
    Clarkson K L and Shor P W . 1989. Applications of random sampling in computational geometry, II. Discrete and Computational Geometry, 4(5): 387-421
  8. 8.
    Claverie M, Demarez V, Duchemin B, Hagolle O, Ducrot D, Marais-Sicre C, Dejoux J F, Huc M, Keravec P, Béziat P, Fieuzal R, Ceschia E and Dedieu G . 2012. Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data. Remote Sensing of Environment. 124: 844-857
  9. 9.
    Cooper S D, Roy D P, Schaaf C B and Paynter I . 2017. Examination of the potential of terrestrial laser scanning and structure-from-motion photogrammetry for rapid nondestructive field measurement of grass biomass. Remote Sensing, 9(6): 531
  10. 10.
    Eitel J U H, Magney T S, Vierling L A, Brown T T and Huggins D R . 2014. LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status. Field Crops Research, 159: 21-32
  11. 11.
    Eldridge D J, Bowker M A, Maestre F T, Roger E, Reynolds J F and Whitford W G . 2011. Impacts of shrub encroachment on ecosystem structure and functioning: towards a global synthesis. Ecology Letters, 14(7): 709-722
  12. 12.
    Estornell J, Ruiz L A, Velázquez-Martí B and Fernández-Sarría A . 2011. Estimation of shrub biomass by airborne LiDAR data in small forest stands. Forest Ecology and Management, 262(9): 1697-1703
  13. 13.
    Falkowski M J, Smith A M S, Hudak A T, Gessler P E, Vierling L A and Crookston N L . 2006. Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data. Canadian Journal of Remote Sensing, 32(2): 153-161
  14. 14.
    Gao Q and Liu T . 2015. Causes and consequences of shrub encroachment in arid and semiarid region: a disputable issue. Arid Land Geography, 38(6): 1202-1212
  15. 15.
    Gaveau D L A and Hill R A . 2003. Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data. Canadian Journal of Remote Sensing, 29(5): 650-657
  16. 16.
    Glenn N F, Spaete L P, Sankey T T, Derryberry D R, Hardegree S P and Mitchell J J . 2011. Errors in LiDAR-derived shrub height and crown area on sloped terrain. Journal of Arid Environments, 75(4): 377-382
  17. 17.
    Greaves H E, Vierling L A, Eitel J U H, Boelman N T, Magney T S, Prager C M and Griffin K L . 2015. Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR. Remote Sensing of Environment, 164: 26-35
  18. 18.
    Greaves H E, Vierling L A, Eitel J U H, Boelman N T, Magney T S, Prager C M and Griffin K L . 2017. Applying terrestrial lidar for evaluation and calibration of airborne lidar-derived shrub biomass estimates in Arctic tundra. Remote Sensing Letters, 8(2): 175-184
  19. 19.
    Grover H D and Musick H B . 1990. Shrubland encroachment in southern New Mexico, U.S.A.: An analysis of desertification processes in the American southwest. Climatic Change, 17(2): 305-330
  20. 20.
    Hopkinson C, Chasmer L E, Sass G, Creed I F, Sitar M, Kalbfleisch W and Treitz P . 2005. Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment. Canadian Journal of Remote Sensing, 31(2): 191-206
  21. 21.
    Isenburg M . 2013. LAStools—efficient LiDAR processing software[EB/OL]..
  22. 22.
    Lasky J R, Uriarte M, Boukili V K, Erickson D L, John Kress W and Chazdon R L . 2014. The relationship between tree biodiversity and biomass dynamics changes with tropical forest succession. Ecology Letters, 17(9): 1158-1167
  23. 23.
    Latif Z A, Aman S N A and Ghazali R . 2011. Delineation of tree crown and canopy height using airborne LiDAR and aerial photo//Proceedings of the 2011 IEEE 7th International Colloquium on Signal Processing and its Applications. Penang, Malaysia: IEEE
  24. 24.
    Li A H, Dhakal S, Glenn N F, Spaete L P, Shinneman D J, Pilliod D S, Arkle R S and McIlroy S K . 2017. Lidar aboveground vegetation biomass estimates in shrublands: prediction, uncertainties and application to coarser scales. Remote Sensing, 9(9): 903
  25. 25.
    Li A H, Glenn N F, Olsoy P J, Mitchell J J and Shrestha R . 2015. Aboveground biomass estimates of sagebrush using terrestrial and airborne LiDAR data in a dryland ecosystem. Agricultural and Forest Meteorology, 213: 138-147
  26. 26.
    Li W, Niu Z, Wang C, Gao S, Feng Q and Chen H Y . 2015. Forest above-ground biomass estimation at plot and tree levels using airborne LiDAR data. Journal of Remote Sensing, 19(4): 669-679
  27. 27.
    Lin Y and Bai Y F . 2010. Responses of aboveground net primary production and population structure of Caragana microphylla to prescribed burning in a typical steppe of Inner Mongolia. Acta Prataculturae Sinica, 19(5): 170-178
  28. 28.
    Lu D S, Mausel P, Brondı́zio E and Moran E . 2004. Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management, 198( 1/3): 149-167
  29. 29.
    Olsoy P J, Glenn N F, Clark P E and Derryberry D R . 2014. Aboveground total and green biomass of dryland shrub derived from terrestrial laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 88: 166-173
  30. 30.
    Peng H Y, Li X Y, Li G Y, Zhang Z H, Zhang S Y, Li L, Zhao G Q, Jiang Z Y and Ma Y J . 2013. Shrub encroachment with increasing anthropogenic disturbance in the semiarid Inner Mongolian grasslands of China. CATENA, 109: 39-48
  31. 31.
    Pordel F, Ebrahimi A and Azizi Z . 2018. Canopy cover or remotely sensed vegetation index, explanatory variables of above-ground biomass in an arid rangeland, Iran. Journal of Arid Land, 10(5): 767-780
  32. 32.
    Radtke P J, Boland H T and Scaglia G . 2010. An evaluation of overhead laser scanning to estimate herbage removals in pasture quadrats. Agricultural and Forest Meteorology, 150(12): 1523-1528
  33. 33.
    Sheridan R D, Popescu S C, Gatziolis D, Morgan C L S and Ku N W . 2015. Modeling forest aboveground biomass and volume using airborne lidar metrics and forest inventory and analysis data in the pacific northwest. Remote Sensing, 7(1): 229-255
  34. 34.
    Svinurai W, Hassen A, Tesfamariam E and Ramoelo A . 2018. Performance of ratio-based, soil-adjusted and atmospherically corrected multispectral vegetation indices in predicting herbaceous aboveground biomass in aColophospermum mopane tree–shrub savanna. Grass and Forage Science, 73(3): 727-739
  35. 35.
    Yang F, Wang J L, Chen P F, Yao Z F, Zhu Y Q and Sun J L . 2012. Comparison of HJ-1A CCD and TM data and for estimating grass LAI and fresh biomass. Journal of Remote Sensing, 16(5): 1000-1023
  36. 36.
    Zheng D L, Rademacher J, Chen J Q, Crow T, Bresee M, Le Moine J and Ryu S R . 2004. Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment, 93(3): 402-411

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