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刘晓亮,隋立春,白永飞,赵旦,赵玉金,刘艳书,翟秋萍.2020.地基激光雷达灌丛化草原小叶锦鸡儿生物量估算.遥感学报,24(7): 894-903
Liu X L,Sui L C,Bai Y F,Zhao D,Zhao Y J,Liu Y S and Zhai Q P. 2020. Biomass estimation of Caragana microphylla in the shrub-encroached grassland based on terrestrial laser scanning. Journal of Remote Sensing(Chinese)，24(7): 894-903[DOI：10.11834/jrs.20209185]
小叶锦鸡儿是内蒙古灌丛化草原中最具代表性的景观植物，准确估算小叶锦鸡儿灌丛的地上生物量对研究灌丛化草原生态系统、监测草原灌丛化程度具有重要意义。地基激光雷达TLS(Terrestrial Laser Scanning)可通过获取高密度点云数据准确估算灌木体积，被广泛应用于反演灌木生物量，但在灌丛化草原中尚未得到有效应用。本研究首先在中国科学院灌丛化草地植被恢复试验区获取了5个样方(10 m×10 m)共42株灌丛的TLS点云数据及实测生物量信息；然后分别使用整体凸包法、切片凸包法、切片分割法、体积表面差分法、体素法5种方法计算灌丛体积并与实测生物量进行回归分析；最后，通过留一交叉验证对5种方法建立的生物量估算模型精度进行对比分析。结果表明：TLS可在不破坏植被的情况下实现快速、准确地小叶锦鸡儿灌丛生物量反演，是传统野外调查方法的可靠替代技术。研究中采用的5种方法均能较好地估算灌丛生物量，其中：（1）相比于整体凸包法（
R
2
=0.87
p
<
0.001
RMSE=30.50 g），切片凸包法（
R
2
=0.89
p
<
0.001
RMSE=28.01 g）与切片分割法（
R
2
=0.88
p
<
0.001
RMSE=29.03 g）可有效减弱离群点造成的体积高估，生物量估算精度有所提升；（2）格网大小为3 cm、高度统计变量选取标准差时，体积表面差分法计算的体积与实测生物量拟合度最好(
R
2
=0.89
p
<
0.001
RMSE=28.89 g)，表明高度标准差是估算小叶锦鸡儿灌丛生物量的强预测因子；（3）体素法解释了生物量估计值90%的变化(
R
2
=0.90
p
<
0.001
RMSE=26.28 g)，是适合小叶锦鸡儿灌丛生物量反演的最优模型。
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 (
R
2
=0.87
p
<
0.001
RMSE=30.50 g)
the convex hull by slices (
R
2
=0.89
p
<
0.001
RMSE=28.01 g) and the volume calculation by sections (
R
2
=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 (
R
2
=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 (
R
2
=0.90
p
<
0.001
RMSE=26.28 g). Thus
it was the best model for the biomass inversion of
Caragana microphylla
.
遥感地基激光雷达灌丛化小叶锦鸡儿生物量体积
remote sensingterrestrial laser scanningshrub encroachmentCaragana microphyllabiomassvolume
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 [DOI: 10.1016/B978-012631260-7/50011-Xhttp://dx.doi.org/10.1016/B978-012631260-7/50011-X ]
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 [DOI: 10.1145/235815.235821http://dx.doi.org/10.1145/235815.235821 ]
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
毕银丽, 齐礼帅, 陈书琳, 李丽娟, 刘生 . 2013. 基于点云数据的株冠体积测量方法. 科技导报, 31(27): 31-36 [DOI: 10.3981/j.issn.1000-7857.2013.27.004http://dx.doi.org/10.3981/j.issn.1000-7857.2013.27.004 ]
Brown S . 2002. Measuring carbon in forests: current status and future challenges. Environmental Pollution, 116(3): 363-372 [DOI: 10.1016/S0269-7491(01)00212-3http://dx.doi.org/10.1016/S0269-7491(01)00212-3 ]
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
曹林, 徐婷, 申鑫, 佘光辉 . 2016. 集成Landsat OLI和机载LiDAR条带数据的亚热带森林生物量制图. 遥感学报, 20(4): 665-678 [DOI: 10.11834/jrs.20165210http://dx.doi.org/10.11834/jrs.20165210 ]
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 [DOI: 10.1016/j.gecco.2018.e00479http://dx.doi.org/10.1016/j.gecco.2018.e00479 ]
Clarkson K L and Shor P W . 1989. Applications of random sampling in computational geometry, II. Discrete and Computational Geometry, 4(5): 387-421 [DOI: 10.1007/bf02187740http://dx.doi.org/10.1007/bf02187740 ]
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 [DOI: 10.1016/j.rse.2012.04.005http://dx.doi.org/10.1016/j.rse.2012.04.005 ]
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 [DOI: 10.3390/rs9060531http://dx.doi.org/10.3390/rs9060531 ]
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 [DOI: 10.1016/j.fcr.2014.01.008http://dx.doi.org/10.1016/j.fcr.2014.01.008 ]
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 [DOI: 10.1111/j.1461-0248.2011.01630.xhttp://dx.doi.org/10.1111/j.1461-0248.2011.01630.x ]
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 [DOI: 10.1016/j.foreco.2011.07.026http://dx.doi.org/10.1016/j.foreco.2011.07.026 ]
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 [DOI: 10.5589/m06-005http://dx.doi.org/10.5589/m06-005 ]
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
高琼, 刘婷 . 2015. 干旱半干旱区草原灌丛化的原因及影响—争议与进展. 干旱区地理, 38(6): 1202-1212 [DOI: 10.13826/j.cnki.cn65-1103/x.2015.06.013http://dx.doi.org/10.13826/j.cnki.cn65-1103/x.2015.06.013 ]
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 [DOI: 10.5589/m03-023http://dx.doi.org/10.5589/m03-023 ]
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 [DOI: 10.1016/j.jaridenv.2010.11.005http://dx.doi.org/10.1016/j.jaridenv.2010.11.005 ]
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 [DOI: 10.1016/j.rse.2015.02.023http://dx.doi.org/10.1016/j.rse.2015.02.023 ]
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 [DOI: 10.1080/2150704x.2016.1246770http://dx.doi.org/10.1080/2150704x.2016.1246770 ]
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 [DOI: 10.1007/bf00138373http://dx.doi.org/10.1007/bf00138373 ]
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 [DOI: 10.5589/m05-007http://dx.doi.org/10.5589/m05-007 ]
Isenburg M . 2013. LAStools—efficient LiDAR processing software[EB/OL].http://gmv.cast.uark.edu/scanning-2/airborne-laser-scanning/lasthttp://gmv.cast.uark.edu/scanning-2/airborne-laser-scanning/last.
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 [DOI: 10.1111/ele.12322http://dx.doi.org/10.1111/ele.12322 ]
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 [DOI: 10.1109/CSPA.2011.5759902http://dx.doi.org/10.1109/CSPA.2011.5759902 ]
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 [DOI: 10.3390/rs9090903http://dx.doi.org/10.3390/rs9090903 ]
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 [DOI: 10.1016/j.agrformet.2015.06.005http://dx.doi.org/10.1016/j.agrformet.2015.06.005 ]
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
李旺, 牛铮, 王成, 高帅, 冯琦, 陈瀚阅 . 2015. 机载LiDAR数据估算样地和单木尺度森林地上生物量. 遥感学报, 19(4): 669-679 [DOI: 10.11834/jrs.20154116http://dx.doi.org/10.11834/jrs.20154116 ]
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
林燕, 白永飞 . 2010. 内蒙古典型草原小叶锦鸡儿灌丛地上净初级生产力和种群结构对火烧的响应. 草业学报, 19(5): 170-178 [DOI: CNKI:SUN:CYXB.0.2010-05-028http://dx.doi.org/CNKI:SUN:CYXB.0.2010-05-028 ]
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 [DOI: 10.1016/j.foreco.2004.03.048http://dx.doi.org/10.1016/j.foreco.2004.03.048 ]
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 [DOI: 10.1016/j.isprsjprs.2013.12.006http://dx.doi.org/10.1016/j.isprsjprs.2013.12.006 ]
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 [DOI: 10.1016/j.catena.2013.05.008http://dx.doi.org/10.1016/j.catena.2013.05.008 ]
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 [DOI: 10.1007/s40333-018-0017-yhttp://dx.doi.org/10.1007/s40333-018-0017-y ]
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 [DOI: 10.1016/j.agrformet.2010.07.010http://dx.doi.org/10.1016/j.agrformet.2010.07.010 ]
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 [DOI: 10.3390/rs70100229http://dx.doi.org/10.3390/rs70100229 ]
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 [DOI: 10.1111/gfs.12367http://dx.doi.org/10.1111/gfs.12367 ]
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
杨飞, 王卷乐, 陈鹏飞, 姚作芳, 诸云强, 孙九林 . 2012. HJ-1A CCD与TM数据及其估算草地LAI和鲜生物量效果比较分析. 遥感学报, 16(5): 1000-1023 [DOI: 10.11834/jrs.20121159http://dx.doi.org/10.11834/jrs.20121159 ]
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 [DOI: 10.1016/j.rse.2004.08.008http://dx.doi.org/10.1016/j.rse.2004.08.008 ]
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