Chin J Plan Ecolo ›› 2015, Vol. 39 ›› Issue (4): 309-321.doi: 10.17521/cjpe.2015.0030

• Orginal Article •     Next Articles

Estimates of subtropical forest biomass based on airborne LiDAR and Landsat 8 OLI data

XU Ting, CAO Lin, SHEN Xin, SHE Guang-Hui*()   

  1. Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
  • Received:2014-10-11 Accepted:2015-02-15 Online:2015-04-21 Published:2015-04-01
  • Contact: Guang-Hui SHE E-mail:ghshe@njfu.com.cn
  • About author:

    # Co-first authors

Abstract: <i>Aims</i>

Estimating forest biomass at regional scale with high accuracy is among the pressing challenges in evaluating ecosystem functions and characteristics (e.g., carbon storage).

<i>Methods</i>

This study is based on airborne small-footprint discrete-return LiDAR data, Landsat 8 OLI multispectral data, and in situ measurements from 55 forest plots in Yushan, Changshu, Jiangsu Province. A total of 87 independent variables (53 from OLI metrics and 34 from LiDAR metrics) were used in the Pearson correlation analysis for estimating aboveground (WA) and belowground (WB) biomass by identifying the significant independent variables. Three independent models by using OLI, LiDAR and their combinations (i.e., the combo model) were established through stepwise regression analysis.

<i>Important findings</i>

The correlation coefficients of determination (R2) for WA and WB models are greater than 0.4. The R2 seemed much higher when the estimations were type-specific (e.g., coniferous, broad-leaf and mixed forest), with R2 of >0.67. The Combo model by forest type yielded an R2 of 0.88 for WA and 0.92 for WB, while the OLI-based model had R2 of 0.73 and 0.81 for WA and WB, respectively. The LiDAR-based model has R2 of 0.86 and 0.83 for WA and WB, respectively.

Key words: estimate of forest biomass, subtropical forest, OLI multispectral data, air borne small-footprint LiDAR data, stepwise regression

Fig. 1

Methodological flowchart for estimating forest biomass using small-footprint discrete-return LiDAR data and OLI multispectral data. DTM, digital terrain model; WA, above-ground biomass; WB, below-ground biomass."

Fig. 2

Aerial photo of the study site and distribution of 55 sampling plots."

Table 1

Summary of the forests from field sampling plots"

森林参数
Forest metrics
针叶林 Coniferous forest (n = 13) 阔叶林 Broad-leaf forest (n = 16) 混交林 Mixed forest (n = 26)
数值范围
Range
平均值
Mean
标准偏差
SD
数值范围
Range
平均值
Mean
标准偏差
SD
数值范围
Range
平均值
Mean
标准偏差
SD
地上生物量 WA (t·hm-2) 47.66-102.76 76.52 18.44 32.03-151.45 91.02 31.74 49.65-192.22 87.28 28.22
地下生物量 WB (t·hm-2) 14.42-32.59 22.81 5.34 10.31-37.39 26.27 6.08 15.69-61.89 25.82 8.41

Appendix 1

Summary of metrics computed from OLI multispectral data"

特征变量 Metrics 变量描述 Description
原始单波段
Initial bands
B2-B7 OLI第2-7波段(经过大气校正和几何精校正)
Second to seventh band from OLI (after atmospheric correction and geometric exact correction)
波段组合
Band combination
Albedo Albedo = B2 + B3 + B4 + B5 + B6 + B7
B4/Albedo B4/Albedo = B4 / (B2 + B3 + B4 + B5 + B6 + B7)
B24 B24 = B2 / B4
B74 B74 = B7 / B4
B76 B76 = B7 / B6
B547 B547 = B5 · B4 / B7
B65 B65 = B6 / B5
B345 B345 = B3 · B4 / B5
B53 B53 = B5 / B3
VIS234 VIS234 = B2 + B3 + B4
信息增强组
Information enhance
绿度
Greenness (TCG)
提取缨帽变换绿度波段
Extract greenness from tasseled cap transform
亮度
Brightness (TCB)
提取缨帽变换亮度波段
Extract brightness from tasseled cap transform
湿度
Wetness (TCW)
提取缨帽变换湿度波段
Extract wetness from tasseled cap transform
第一主成分
First principal component (PC1)
提取主成分分析第一波段
Extract first band from principal component analysis
第二主成分
Second principal component (PC2)
提取主成分分析第二波段
Extract second band from principal component analysis
第三主成分
Third principal component (PC3)
提取主成分分析第三波段
Extract third band from principal component analysis
最小噪声分离变换第一波段
First band of minimum noise fraction rotation (MNF1)
提取MNF变换第一波段
Extract first band from minimum noise fraction rotation
最小噪声分离变换第二波段
Second band of minimum noise fraction rotation (MNF2)
提取MNF变换第二波段
Extract second band from minimum noise fraction rotation
最小噪声分离变换第三波段
Third band of minimum noise fraction rotation (MNF3)
提取MNF变换第三波段
Extract third band from minimum noise fraction rotation
最小噪声分离变换第四波段
Forth band of minimum noise fraction rotation (MNF4)
提取MNF变换第四波段
Extract forth band from minimum noise fraction rotation
植被指数
Vegetation index
增强型植被指数
Enhanced vegetation index (EVI)
归一化植被指数
Normalized difference vegetation index (NDVI)
土壤调整植被指数
Soil-adjusted vegetation index (SAVI)
有效叶面积指数
Specific leaf area vegetation index (SLAVI)
简单比值植被指数
Ratio vegetation index (RVI)
中红外植被指数
Mid-infrared vegetation index (VI3)
垂直植被指数
Perpendicular vegetation index (PVI)
土壤调整比值植被指
Soil-adjusted ratio vegetation index (SARVI)
差值植被指数
Difference vegetation index (DVI)
转换型植被指数
Transformed normalized difference vegetation index (TNDVI)
大气阻抗植被指数
Atmospherically resistant vegetation index (ARVI)
修正型土壤调整植被指数
Modified soil-adjusted vegetation index (MSAVI)
修正型简单比值植被指数
Modified simple ratio vegetation index (MSR)
非线性指数
Nonlinear index (NLI)
重归一化植被指数
Renormalized difference vegetation index (RDVI)
归一化植被指数
Normalized difference vegetation index (ND43)
归一化植被指数
Normalized difference vegetation index (ND67)
归一化植被指数
Normalized difference vegetation index (ND563)
纹理信息
Texture information
相关度
Correlation (CR)
对比度
Contrast (CO)
相异性
Dissimilarity (DI)
信息熵
Entropy (EN)
均匀度
Homogeneity (HO)
均值
Mean (ME)
二阶矩
Second moment (SM)
偏斜度
Skewness (SK)
方差
Variance (VA)

Appendix 2

Summary of metrics computed from LiDAR"

特征变量 Metrics 变量描述 Description
高度百分位数 Percentile height
h10, h20, h25, h30, h40, h50, h60, h70, h75, h80, h90, h95, h99 第一回波返回点的冠层高度分布百分位数(10th, 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th, 80th, 90th, 95th, 99th)
The percentiles of the canopy height distributions (10th, 20th,…99th) of first returns
高度变量 Height metrics
hmin 激光雷达树高的最小值 Minimum height above ground of all first returns
hskew 激光雷达树高的偏斜度 Skewness of heights of all first returns
hkur 激光雷达树高的峰度 Kurtosis of heights of all first returns
hiq 激光雷达树高的75%和25%分位数的差值 Difference between 75% and 25% of canopy height of first returns
hmean 激光雷达树高的平均值 Mean height above ground of all first returns
hmax 激光雷达树高的最大值 Maximum height above ground of all first returns
hcv 激光雷达树高的高度变异系数 Coefficient of variation of heights of all first returns
hmode 激光雷达树高的众数 Mode of heights of all first returns
hvar 激光雷达树高的方差 Variance of heights of all first returns
hstd 激光雷达树高的标准差 Standard deviation of heights of all first returns
冠层密度 Canopy density
d0, d1, d2, d3, d4, d5, d6, d7, d8, d9 高于一定范围相对高度的冠层返回密度, 即第一回波中高于(0、10、20、…90)分位数的点占第一回波所有点的百分比(0-100%)
The canopy return density over a range of relative heights, i.e., percentage (0%-100%) of first returns above the quantiles (0, 10, 20…90) to total number of first returns
覆盖度 Cover
c 取高于2 m的冠层返回点在所有激光返回点中所占的比例 Percentages of first returns above 2 m

Fig. 3

Pearson correlation coefficient of determination (R2) between the metrics and above-ground biomass (A), below-ground biomass (B) in study area. The meaning and calculation of the metrics see appendixes."

Table 2

Assessment accuracy of three empirical models by forest type"

OLI模型 OLI model LiDAR模型 LiDAR model 综合模型 Combo model
R2 RMSE rRMSE (%) R2 RMSE rRMSE (%) R2 RMSE rRMSE (%)
地上生物量
Above-ground
biomass
所有样地 All plots 0.41 21.89 26 0.65 16.96 20 0.65 16.95 20
针叶林 Coniferous forest 0.67 12.89 17 0.82 9.61 13 0.86 8.59 11
阔叶林 Broad-leaf forest 0.74 19.02 20 0.93 9.52 10 0.93 9.52 10
混交林 Mixed forest 0.77 14.70 17 0.80 13.74 16 0.83 12.84 15
地下生物量
Below-ground
biomass
所有样地 All plots 0.57 4.90 19 0.64 4.47 17 0.69 4.17 16
针叶林 Coniferous forest 0.70 3.59 16 0.75 3.24 14 0.91 2.00 9
阔叶林 Broad-leaf forest 0.80 3.11 12 0.86 2.69 10 0.92 2.03 8
混交林 Mixed forest 0.84 3.71 14 0.83 3.74 14 0.92 2.55 10

Fig. 4

Scatter plots and 1:1 line of between observed and estimated biomass using three different models by forest type. A, OLI model for WA. B, OLI model for WB. C, LiDAR model for WA. D, LiDAR model for WB. E, Combo model WA. F, Combo model for WB. WA, above-ground biomass; WB, below-ground biomass."

Fig. 5

Difference between Observed and predicted biomass by using the general model and type-specific model. A, General model for WA. B, General model for WB. C, Type-specific model for WA. D, type-specific model for WB. WA, above-ground biomass; WB, below-ground biomass."

Table 3

Selected independent variables, R2, and confidence interval values for the OLI and LiDAR model by forest type"


所有样地
All plots
针叶林
Coniferous forest
阔叶林
Broad-leaf forest
混交林
Mixed forest
WA WB WA WB WA WB WA WB
截距 Intercept 2 409.14 864.48 1.04E+09 2.85E+08 187.60 36.70 2446.32 914.45 OLI模型
OLI
model
B3 0.37
B547 -0.035 -0.007 -0.15 -0.06 -0.009
VIS234 0.01 0.0065
PC3 -0.04
MNF4 -96.44 -36.26
MSR 4.10
CO 218.01 80.05 1.04E+08 28 480 530 219.33 84.60
DI -1 335.77 -491.58 -6.3E+08 -1.7E+08 -1 338.25 -520.93
HO -2 273.01 -829.24 -1E+09 -2.8E+08 -2 271.07 -875.005
VA 273.39 58.36
置信区间
Confidence interval (t·hm-2)
[47.50-
192.26]
[11.56-
61.86]
[55.48-
96.23]
[16.03-
29.58]
[39.10-
135.24]
[12.32-
35.68]
[52.64-
192.38]
[19.05-
61.92]
截距 Intercept -32.81 -2.17 -21.61 13.50 -115.94 9.48 -60.81 -9.77 LiDAR模型
LiDAR
model
hmode 13.94 3.49 -5.87 2.73
hmax -7.31
h30 -120.56 138.36
h40 235.50 -218.91 -87.73
h50 325.85 21.61
h60 -284.52
h70 -12.92 -41.51
h75 -228.71
h80 -93.72 -27.86 16.68
h90 123.51
h95 81.22 23.80 162.76 19.67 55.78 20.99
h99 -73.61 -8.65
d7 0.89 0.36 0.31
d9 -13.79
置信区间
Confidence interval (t·hm-2)
[36.84-
162.70]
[13.74-
47.48]
[50.83-
100.67]
[15.71-
32.93]
[34.84-
144.47]
[10.90-
34.27]
[50.67-
173.34]
[15.24-
56.67]

Table 4

Selected independent variables and associated statistics in our combo model by forest type"

所有样地
All plots
针叶林
Coniferous forest
阔叶林
Broad-leaf forest
混交林
Mixed forest
WA WB WA WB WA WB WA WB
截距 Intercept -34.93 785.68 1.32E+09 -9.94 -115.94 65.97 78.07 748.99
B3 0.02
B547 -0.05
CO 10.31 76.24 1.32E+08 35.53 75.10
DI -462.14 -7.9E+08 -31.68 -448.39
HO -776.42 -1.3E+09 -743.58
VA 17.21 55.73
hmode 10.53
hmean 8.57
hvar 7.42
hstd 8.76
h30 138.36
h40 -218.91 2.27
h50 1.58
h70 112.63
h80 -49.47 -114.51
h95 45.75 162.76
h99 -73.61
d7 -1.80 -0.25
置信区间
Confidence interval (t·hm-2)
[37.59-
169.87]
[16.16-
61.90]
[51.46-
99.03]
[13.44-
32.78]
[34.84-
144.47]
[12.17-
34.96]
[46.16-
184.05]
[19.59-
61.90]
[1] Cao L, Coops NC, Innes J, Dai JS, She GH (2014a). Mapping above- and below-ground biomass components in subtropical forests using small-footprint LiDAR.Forests, 5, 1356-1373.
[2] Cao L, Coops NC, Hermosilla T, Innes J, Dai JS, She GH (2014b). Using small-footprint discrete and full-waveform airborne LiDAR metrics to estimate total biomass and biomass components in subtropical forests.Remote Sensing, 6, 7110-7135.
[3] Cao L, Dai JS, Xu JX, Xu ZQ, She GH (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.(in Chinese with English abstract)
[曹林, 代劲松, 徐建新, 许子乾, 佘光辉 (2014). 基于机载小光斑LiDAR技术的亚热带森林参数信息优化提取. 北京林业大学学报, 36(5), 13-21.]
[4] Dixon RK, Solomon AM, Brown S, Houghton RA, Trexier MC, Wisniewski J (1994). Carbon pools and flux of global forest ecosystems.Science, 263, 185-190.
[5] Dong LX (2008). Estimation of Forest Canopy Height and Biomass in Three Gorges Reservoir Area Based on Multi-source Remote Sensing Data. PhD dissertation, Graduate University of Chinese Academy of Sciences, Beijing.(in Chinese)
[董立新 (2008). 基于多源遥感数据的三峡库区森林冠层高度与生物量估算方法研究. 博士学位论文, 中国科学院研究生院, 北京.]
[6] Dubayah RO, Drake JB (2000). Lidar remote sensing for forestry.Journal of Forestry, 98(6), 44-46.
[7] Fang JY, Chen AP, Zhao SQ, Ci LJ (2002). Estimating biomass carbon of China’s forests: Supplementary notes on report published in Science (291: 2320-2322) by FANG et al. (2001).Acta Phytoecologica Sinica, 26, 243-249.(in Chinese with English abstract)
[方精云, 陈安平, 赵淑清, 慈龙俊 (2002). 中国森林生物量的估算: 对Fang等Science一文(Science, 2001, 291: 2320-2322)的若干说明. 植物生态学报, 26, 243-249.]
[8] Fang JY, Wang W (2007). Soil respiration as a key below ground process: issues and perspectives. Journal of Plant Ecology (Chinese Version), 31, 345-347.(in Chinese with English abstract)
[方精云, 王娓 (2007). 作为地下过程的土壤呼吸: 我们理解了多少? 植物生态学报, 31, 345-347.]
[9] Feng ZW, Wang XK, Wu G (1999). The Forest Ecosystem Biomass and Productivity in China. Science Press, Beijing. 99-187.(in Chinese)
[冯宗炜, 王效科, 吴刚 (1999). 中国森林生态系统的生物量和生产力. 科学出版社, 北京. 99-187.]
[10] Fu T, Pang Y, Huang QF, Liu QW, Xu GC (2011). Prediction of subtropical forest parameters using airborne laser scanner.Journal of Remote Sensing, 15, 1092-1104.(in Chinese with English abstract)
[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, 1092-1104.]
[11] Guo ZF, Chi H, Sun GQ (2010). Estimating forest aboveground biomass using HJ-1 satellite CCD and ICESat GLAS waveform data.Science China Earth Sciences, 53, 16-25.
[12] Jiang GZ, Han B, Gao YB, Yang CJ (2013). Review of 40-year earth observation with landsat series and prospects of LDCM.Journal of Remote Sensing, 17, 1033-1048.(in Chinese with English abstract)
[姜高珍, 韩冰, 高应波, 杨崇俊 (2013). Landsat系列卫星对地观测40年回顾及LDCM前瞻. 遥感学报, 17, 1033-1048.]
[13] Kraus K, Pfeifer N (1998). Determination of terrain models in wooded areas with airborne laser scanner data.ISPRS Journal of Photogrammetry and Remote Sensing, 53, 193-203.
[14] Lefsky MA, Cohen WB, Parker GG, Harding DJ (2002). Lidar remote sensing for ecosystem studies.Bioscience, 52, 19-30.
[15] Li MS, Tan Y, Pan J, Peng SK (2006). Modeling forest aboveground biomass by combining the spectrum, textures with topographic features.Remote Sensing Information, (6), 6-9.(in Chinese with English abstract)
[李明诗, 谭莹, 潘洁, 彭世揆 (2006). 结合光谱、纹理及地形特征的森林生物量建模研究. 遥感信息, (6), 6-9.]
[16] Liu LJ (2011). Forest Parameters Inversion Using Airborne LiDAR and Hyperspectral Data Fusion. PhD dissertation, Northeast Forestry University, Harbin.(in Chinese)
[刘丽娟 (2011). 基于机载LIDAR和高光谱融合的森林参数反演研究. 博士学位论文, 东北林业大学, 哈尔滨.]
[17] Lu DS, Mausel P, Brondzio E, Moran E (2003). Estimation of forest stand parameters using Landsat TM images in the brazilian amazon basin. International Symposium on Remote Sensing of Environment: Information for Risk Management and Sustainable Development, 423-426.
[18] Ma LQ, Li AN (2011). Review of application of LiDAR to estimation of forest vertical structure parameters.World Forestry Research, 24(1), 41-45.(in Chinese with English abstract)
[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.]
[19] Næsset E, 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, 3079-3090.
[20] Pang Y, Li ZY (2012). Inversion of biomass components of the temperate forest using airborne lidar technology in Xiaoxing’an mountains, northeastern of China.Chinese Journal of Plant Ecology, 36, 1095-1105.(in Chinese with English abstract)
[庞勇, 李增元 (2012). 基于机载激光雷达的小兴安岭温带森林组分生物量反演. 植物生态学报, 36, 1095-1105.]
[21] Popescu SC, Wynne RH, Scrivani JA (2004). Fusion of small footprint LiDAR and multispectral data to estimate plot-level volume and biomass in deciduous and pine forests in Virginia, USA.Forest Sciences, 50, 551-565.
[22] Post WM, Emanuel WR, Zinke PJ, Stangenberger AG (1982). Soil carbon pools and world life zones.Nature, 298, 156-159.
[23] Xu HQ, Tang F (2013). Analysis of new characteristics of the first Landsat 8 image and their eco-environmental significance.Acta Ecologica Sinica, 33, 3249-3257.(in Chinese with English abstract)
[徐涵秋, 唐菲(2013). 新一代Landsat系列卫星: Landsat 8遥感影像新增特征及其生态环境意义. 生态学报, 33, 3249-3257.]
[24] Zhang Z, Tian X, Chen EX, He QS (2011). Review of methods on estimating forest above ground biomass.Journal of Beijing Forestry University, 33(5), 144-150.(in Chinese with English abstract)
[张志, 田昕, 陈尔学, 何祺胜 (2011). 森林地上生物量估测方法研究综述. 北京林业大学学报, 33(5), 144-150.]
[25] Zhu GL, Wei WS, Zhang SM, Wu DX (2008). An overview of methods of measuring underground-biomass and introduction of new technique.Chinese Journal of Grassland, 30(3), 94-99.(in Chinese with English abstract)
[朱桂林, 韦文珊, 张淑敏, 吴冬秀 (2008). 植物地下生物量测定方法概述及新技术介绍. 中国草地学报, 30(3), 94-99.]
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