收稿：2017-11-10，
录用：2018-2-8，
纸质出版：2018-09
移动端阅览
中国是世界上人工林面积最大的国家，实时、定量、精确地获取人工林林分特征对于人工林资源监测、管理以及全球碳循环具有重要意义。以北亚热带沿海平原人工林为研究对象，借助机载激光雷达LiDAR(Light Detection And Ranging)点云数据并结合地面实测的55个样地来反演人工林林分特征。首先，构建冠层高度分布剖面CHD(Canopy Height Distribution)和枝叶剖面FP(Foliage Profile)；然后，通过Weibull函数分别对CHD和FP进行拟合并提取Weibull参数作为特征变量(第1组)；同时，还直接基于点云提取了LiDAR高度变量HRM(Height-Related Metrics)和冠层密度变量DRM(Density-Related Metrics) (第2组)；最后，结合地面实测数据和两组特征变量构建了多元回归模型用于预测各林分特征(即林分密度、平均胸径、胸高断面积、Lorey’s树高、蓄积量和地上生物量)。结果表明：(1)与只使用基于点云的特征变量(即第2组)相比，结合点云特征变量(第2组)和冠层垂直结构剖面特征变量(第1组)的各林分特征预测精度均有所提升(ΔAdjusted
R
2
=0—0.13
Δ
r
RMSE=0.08—3.65%)；(2)对各林分特征预测的结果中，Lorey’s树高(Adjusted
R
2
=0.85
r
RMSE=7.66%)和蓄积量(Adjusted
R
2
=0.84
r
RMSE=14.27%)的预测精度最高，地上生物量(Adjusted
R
2
=0.78
r
RMSE=14.15%)、胸高断面积(Adjusted
R
2
=0.73
r
RMSE=14.70%)和平均胸径(Adjusted
R
2
=0.64
r
RMSE=15.05%)次之，林分密度(Adjusted
R
2
=0.58
r
RMSE=26.16%)的预测精度最低；(3)Weibull函数较准确地反映了亚热带人工林垂直冠层结构，可以有效提高林分特征反演精度。
China is a country with the largest plantation area in the world. Timely
quantitatively
and accurately acquiring planted forest stand characteristics is the key to monitoring and managing forest plantation
which contributes to the global carbon cycle. This study was conducted at a north subtropical coastal plain forest plantation to estimate forest stand characteristics using light detection and ranging (LiDAR) point clouds combined with 55 field-measured data. First
Canopy Height Distribution (CHD) and Foliage Profile (FP) were extracted and used to obtain Weibull parameters by fitting a Weibull density function to the profiles (Suite 1). Second
Height-Related Metrics (HRM) and Density-Related Metrics (DRM) (Suite 2) were directly extracted from the LiDAR point clouds. Finally
multiple regression models were constructed to predict the stand characteristics (i.e.
stand density
diameter of breast height (DBH)
basal area
Lorey’s tree height
volume
and aboveground biomass) on the basis of the field-measured data and two suites of metrics. Results showed that (1) the accuracies of the forest stand characteristics that use point cloud (Suite 2) and canopy vertical profile metrics simultaneously (Suite 1) were relatively higher than that only using point cloud metrics (Suite 2) (
Δ
Adjusted
R
2
=0—0.13
Δr
RMSE=0.08%—3.65%). (2) For all the forest stand characteristic estimations
the models of Lorey’s tree height (Adjusted
R
2
=0.85
r
RMSE=7.66%) and volume (Adjusted
R
2
=0.84
r
RMSE=14.27%) demonstrated the highest accuracies
followed by the aboveground biomass (Adjusted
R
2
=0.78
r
RMSE=14.15%)
basal area (Adjusted
R
2
=0.73
r
RMSE=14.70%)
and mean DBH (Adjusted
R
2
=0.64
r
RMSE=15.05%). Stand density exhibited the lowest accuracies (Adjusted
R
2
=0.58
r
RMSE=26.16%). (3) The Weibull function was appropriate for describing the vertical stand canopy structure of planted forests. The Weibull metrics can be favorable in effectively improving the estimation accuracy of the forest stand characteristics. This study provided an effective approach in estimating the forest stand characteristics that use the LiDAR point clouds by combining vertical profile and point cloud metrics. This study also proved that the vertical profile metrics can help in increasing the accuracy of the predictive models and provide the mechanism for interpreting the forest stand vertical structure.
Aber J D. 1979. A method for estimating foliage-height profiles in broad-leaved forests. The Journal of Ecology, 67(1): 35–40
Bengio Y and Grandvalet Y. 2004. No unbiased estimator of the variance of K-fold cross-validation. The Journal of Machine Learning Research, 5: 1089–1105
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
Cao L, Gao S, Li P H, Yun T, Shen X and Ruan H H. 2016. Aboveground biomass estimation of individual trees in a coastal planted forest using full-waveform airborne laser scanning data. Remote Sensing, 8(9): 729
Carle J, Vuorinen P and Del Lungo L. 2002. Status and trends in global forest plantation development. Forest Products Society, 52: 12–23
Coops N C, Hilker T, Wulder M A, St-Onge B, Newnham G, Siggins A and Trofymow J A. 2007. Estimating canopy structure of Douglas-fir forest stands from discrete-return Lidar. Trees, 21(3): 295–310
Coops N C, Smith M L, Jacobsen K L, Martin M and Ollinger S. 2004. Estimation of plant and leaf area index using three techniques in a mature native eucalypt canopy. Austral Ecology, 29(3): 332–341
丁扬. 2008. 苏北杨树人工林生物量与碳贮量的研究. 南京: 南京林业大学
Ding Y. 2008. Biomass and Carbon Storage in Poplar Plantations in the North of Jiangsu Province. Nanjing: Nanjing Forestry University.
Duncanson L I, Niemann K O and Wulder M A. 2010. Integration of GLAS and Landsat TM data for aboveground biomass estimation. Canadian Journal of Remote Sensing, 36(2): 129–141
范换, 王邵军, 阮宏华, 谭艳, 郑阿宝, 徐钰, 许凯, 曹国华. 2014. 苏北沿海不同土地类型土壤动物群落及其对凋落物分解的影响. 南京林业大学学报(自然科学版), 38(3): 1–7
Fan H, Wang S J, Ruan H H, Tan Y, Zheng A B, Xu Y, Xu K and Cao G H. 2014. Effects of soil fauna on litter decomposition and its community structure under different land use patterns in coastal region of northern Jiangsu province. Journal of Nanjing Forestry University (Natural Sciences Edition), 38(3): 1–7 (
Food and Agriculture Organization of the United Nations (FAO). 2015. Global Forest Resources Assessment 2015. Rome, Italy: Food and Agriculture Organization of the United Nations
Frazer G W, Magnussen S, Wulder M A and Niemann K O. 2011. Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of lidar-derived estimates of forest stand biomass. Remote Sensing of Environment, 115(2): 636–649
付甜, 庞勇, 黄庆丰, 刘清旺, 徐光彩. 2011. 亚热带森林参数的机载激光雷达估测. 遥感学报, 15(5): 1092–1104
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 (
Hall S A, Burke I C, Box D O, Kaufmann M R and Stoker J M. 2005. Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. Forest Ecology and Management, 208(1/3): 189–209
何东, 卢振兰, 李德志. 2009. 水杉人工林建植50年后的分化特征. 热带亚热带植物学报, 17(2): 122–130
He D, Lu Z L and Li D Z. 2009. Differentiation characteristics of a 50-year-established Metasequoia glyptostroboides Plantation. Journal of Tropical and Subtropical Botany, 17(2): 122–130 (
Hilker T, van Leeuwen M, Coops N C, Wulder M A, Newnham G J, Juup D L B and Culvenor D S. 2010. Comparing canopy metrics derived from terrestrial and airborne laser scanning in a Douglas-fir dominated forest stand. Trees, 24(5): 819–832
Hurtt G, Xiao X M, Keller M, Palace M, Asner G P, Braswell R, Brondízio E S, Cardoso M, Carvalho C J R, Fearon M G, Guild L, Hagen S, Hetrick S, Moore B, Nobre C, Read J M, Sá T, Schloss A, Vourlitis G and Wickel A J. 2003. IKONOS imagery for the Large Scale Biosphere–Atmosphere Experiment in Amazonia (LBA). Remote Sensing of Environment, 88(1/2): 111–127
Kankare V, Vastaranta M, Holopainen M, Räty M, Yu X W, Hyyppä J, Hyyppä H , Alho P and Viitala R. 2013. Retrieval of forest aboveground biomass and stem volume with airborne scanning Lidar. Remote Sensing, 5(5): 2257–2274
Kerr J T and Ostrovsky M. 2003. From space to species: ecological applications for remote sensing. Trends in Ecology and Evolution, 18(6): 299–305
Kershaw J A Jr and Maguire D A. 1995. Crown structure in western hemlock, Douglas-fir, and grand fir in western Washington: trends in branch-level mass and leaf area. Canadian Journal of Forest Research, 25(12): 1897–1912
Kraus K and Pfeifer N. 1998. Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, 53(4): 193–203
Latham P A, Zuuring H R and Coble D W. 1998. A method for quantifying vertical forest structure. Forest Ecology and Management, 104(1/3): 157–170
Lefsky M A, Cohen W B, Acker S A, Parker G G, Spies T A and Harding D. 1999. Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests. Remote Sensing of Environment, 70(3): 339–361
李增元, 刘清旺, 庞勇. 2016. 激光雷达森林参数反演研究进展. 遥感学报, 20(5): 1138–1150
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 (
Lovell J L, Jupp D L B, Culvenor D S and Coops N C. 2003. Using airborne and ground-based ranging Lidar to measure canopy structure in Australian forests. Canadian Journal of Remote Sensing, 29(5): 607–622
Lu D S, Chen Q, Wang G X, Moran E, Batistella M, Zhang M Z, Laurin G V and Saah D. 2012. Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates. International Journal of Forestry Research, 2012:Article ID 436537 [DOI: 10.1155/2012/436537]
马利群, 李爱农. 2011. 激光雷达在森林垂直结构参数估算中的应用. 世界林业研究, 24(1): 41–45
Ma L Q and Li A N. 2011. Review of application of LiDAR to estimation of forest vertical structure parameters. World Forestry Research, 24(1): 41–45 (
Magnussen S, Eggermont P and Lariccia V N. 1999. Recovering tree heights from airborne laser scanner data. Forest Science, 45(3): 407–422
Maltamo M, Eerikäinen K, Pitkänen J, Hyyppä J and Vehmas M. 2004. Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions. Remote Sensing of Environment, 90(3): 319–330
McElhinny C, Gibbons P, Brack C and Bauhus J. 2005. Forest and woodland stand structural complexity: its definition and measurement. Forest Ecology and Management, 218(1/3): 1–24
Miura N and Jones S D. 2010. Characterizing forest ecological structure using pulse types and heights of airborne laser scanning. Remote Sensing of Environment, 114(5): 1069–1076
Montealegre A L, Lamelas M T, de la Riva J, García-Martín A and Escribano F. 2016. Use of low point density ALS data to estimate stand-level structural variables in Mediterranean Aleppo pine forest. Forestry: An International Journal of Forest Research, 89(4): 373–382
Morsdorf F, Kötz B, Meier E, Itten K I and Allgöwer B. 2006. Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sensing of Environment, 104(1): 50–61
Næsset E. 1997. Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, 52(2): 49–56
Næsset E. 2009. Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data. Remote Sensing of Environment, 113(1): 148–159
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
Næsset E and Gobakken T. 2005. Estimating forest growth using canopy metrics derived from airborne laser scanner data. Remote Sensing of Environment, 96(3/4): 453–465
Næsset E and Gobakken T. 2008. Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sensing of Environment, 112(6): 3079–3090
Næsset E and Økland T. 2002. Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sensing of Environment, 79(1): 105–115
Ni-Meister W, Jupp D L B and Dubayah R. 2001. Modeling Lidar waveforms in heterogeneous and discrete canopies. IEEE Transactions on Geoscience and Remote Sensing, 39(9): 1943–1958
庞勇, 李增元, 陈尔学, 孙国清. 2005. 激光雷达技术及其在林业上的应用. 林业科学, 41(3): 129–136
Pang Y, Li Z Y, Chen E X and Sun G Q. 2005. Lidar remote sensing technology and its application in forestry. Scientia Silvae Sinicae, 41(3): 129–136 (
庞勇, 赵峰, 李增元, 周淑芳, 邓广, 刘清旺, 陈尔学. 2008. 机载激光雷达平均树高提取研究. 遥感学报, 12(1): 152–158
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 (
Pang Y, Li Z Y, Ju H B, Lu H, Jia W, Si L, Guo Y, Liu Q W, Li S M, Liu L X, Xie B B, Tan B X and Dian Y Y. 2016. LiCHy: the CAF's Lidar, CCD and hyperspectral integrated airborne observation system. Remote Sensing, 8(5): 398
Parker G G, Lefsky M A and Harding D J. 2001. Light transmittance in forest canopies determined using airborne laser altimetry and in-canopy quantum measurements. Remote Sensing of Environment, 76(3): 298–309
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: 57–67
Porté A, Trichet P, Bert D and Loustau D. 2002. Allometric relationships for branch and tree woody biomass of Maritime pine ( Pinus pinaster Aït. ). Forest Ecology and Management, 158(1/3): 71–83
Qin H M, Wang C, Xi X H, Tian J L and Zhou G Q. 2017. Simulating the effects of the airborne Lidar scanning angle, flying altitude, and pulse density for forest foliage profile retrieval. Applied Sciences, 7(7): 712
Radtke P J and Bolstad P V. 2001. Laser point-quadrat sampling for estimating foliage-height profiles in broad-leaved forests. Canadian Journal of Forest Research, 31(3): 410–418
Riaño D, Meier E, Allgöwer B, Chuvieco E and Ustin S L. 2003. Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling. Remote Sensing of Environment, 86(2): 177–186
Silva A G P, Görgens E B, Campoe O C, Alvares C A, Stape J L and Rodriguez L C E. 2015. Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations. Scientia Agricola, 72(6): 504–512
宋玉兵, 丁玉平, 沈飞. 2009. JSCORS的建设与最新进展. 测绘通报(2): 73–74
Song Y B, Ding Y P and Shen F. 2009. The development and latest progress of JSCORS. Bulletin of Surveying and Mapping(2): 73–74 (
Spies T A. 1998. Forest structure: a key to the ecosystem. Northwest Science, 72: 34–39
国家林业局. 2014. 第八次全国森林资源清查结果. 林业资源管理(1): 1–2
State Forestry Administration. 2014. The 8th national forestry inventory results. Forest Resources Management(1): 1–2 (
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
Tanabe S I, Toda M J and Vinokurova A V. 2001. Tree shape, forest structure and diversity of drosophilid community: comparison between boreal and temperate birch forests. Ecological Research, 16(3): 369–385
Thomas V, Oliver R D, Lim K and Woods M. 2008. Lidar and Weibull modeling of diameter and basal area. The Forestry Chronicle, 84(6): 866–875
Timilsina N, Cropper W P Jr, Escobedo F J and Lima J M T. 2013. Predicting understory species richness from stand and management characteristics using regression trees. Forests, 4(1): 122–136
王明慧, 王国兵, 阮宏华, 谢涛, 徐长柏, 徐亚明, 赵小龙, 徐涵湄. 2012. 苏北沿海不同土地利用方式土壤水溶性有机碳含量特征. 生态学杂志, 31(5): 1165–1170
Wang M H, Wang G B, Ruan H H, Xie T, Xu C B, Xu Y M, Zhao X L and Xu H M. 2012. Characteristics of soil water-soluble organic carbon under four different land use patterns in coastal area of northern Jiangsu. Chinese Journal of Ecology, 31(5): 1165–1170 (
Wang Y S, Weinacker H and Koch B. 2008. A Lidar point cloud based procedure for vertical canopy structure analysis and 3d single tree modelling in forest. Sensors, 8(6): 3938–3951
Watt M S, Meredith A, Watt P and Gunn A. 2013. Use of Lidar to estimate stand characteristics for thinning operations in young Douglas-fir plantations. New Zealand Journal of Forestry Science, 43(1): 18
Weishampel J F, Drake J B, Cooper A, Blair J B and Hofton M. 2007. Forest canopy recovery from the 1938 hurricane and subsequent salvage damage measured with airborne Lidar. Remote Sensing of Environment, 109(2): 142–153
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
谢涛, 郑阿宝, 王国兵, 阮宏华, 徐亚明, 徐长柏, 葛之葳. 2012. 苏北不同林龄杨树林土壤活性碳的季节变化. 生态学杂志, 31(5): 1171–1178
Xie T, Zheng A B, Wang G B, Ruan H H, Xu Y M, Xu C B and Ge Z W. 2012. Seasonal variation patterns of soil labile organic carbon in poplar plantations with different ages in northern Jiangsu. Chinese Journal of Ecology, 31(5): 1171–1178 (
Xu M G and Harrington T B. 1998. Foliage biomass distribution of loblolly pine as affected by tree dominance, crown size, and stand characteristics. Canadian Journal of Forest Research, 28(6): 887–892
徐亚明, 徐长柏, 曹国华, 冯坤乔, 沈彩芹, 教忠意. 2009. 不同株行距对35杨生长的影响试验初报. 江苏林业科技, 36(1): 31–33
Xu Y M, Xu C B, Cao G H, Feng K Q, Shen C Q and Jiao Z Y. 2009. Preliminary report on the effect of different row spacing on the growth of 35 poplar. Journal of Jiangsu Forestry Science and Technology, 36(1): 31–33 (
杨晓敏. 1987. 盐城市沿海国营林场森林资源永续利用浅析. 江苏林业科技(1): 26–29
Yang X M. 1987. Sustainable utilization of forest resources in the coastal state-run forest farm of Yancheng city. Journal of Jiangsu Forestry Science and Technology(1): 26–29 (
Zhao F, Yang X Y, Schull M A, Román-Colón M O, Yao T, Wang Z S, Zhang Q L, Jupp D L B, Lovell J L, Culvenor D S, Newnham G J, Richardson A D, Ni-Meister W, Schaaf C L, Woodcock C E and Strahler A H. 2011. Measuring effective leaf area index, foliage profile, and stand height in New England forest stands using a full-waveform ground-based Lidar. Remote Sensing of Environment, 115(11): 2954–2964
赵静, 李静, 柳钦火. 2013. 森林垂直结构参数遥感反演综述. 遥感学报, 17(4): 697–716
Zhao J, Li J and Liu Q H. 2013. Review of forest vertical structure parameter inversion based on remote sensing technology. Journal of Remote Sensing, 17(4): 697–716 (
Zhao K G, Popescu S and Nelson R. 2009. Lidar remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers. Remote Sensing of Environment, 113(1): 182–196
郑景明, 张春雨, 周金星, 赵秀海, 余新晓, 秦永胜. 2007. 云蒙山典型森林群落垂直结构研究. 林业科学研究, 20(6): 768–774
Zheng J M, Zhang C Y, Zhou J X, Zhao X H, Yu X X and Qin Y S. 2007. Study on vertical structure of forest communities in Yunmengshan. Forest Research, 20(6): 768–774 (
庄红蕾, Becuwe X, 肖春波, 王月华, 王海, 殷彬, 刘春江. 2012. 上海崇明岛水杉人工林生物量方程构建及固碳潜力研究. 上海交通大学学报(农业科学版), 30(2): 48–55
Zhuang H L, Becuwe X, Xiao C B, Wang Y H, Wang H, Yin B and Liu C J. 2012. Allometric equation-based estimation of biomass carbon sequestration in Metasequoia glyptostroboides plantations in Chongming Island, Shanghai. Journal of Shanghai Jiaotong University(Agricultural Science), 30(2): 48–55 (
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