Estimation of aboveground biomass of mangrove forest using UAV-LiDAR
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摘要: 红树林作为热带地区碳储量最高的植被类型之一，面积呈现破碎化、减少趋势，地上生物量（AGB）的空间分布及其动态信息对于温室气体通量、碳储量的估算以及政策制定和实施至关重要。但是常用于AGB估算的光学数据或者SAR数据均存在信号饱和现象，且传统估算红树林生物量的算法对数据要求高、估算精度相对较低。针对该问题，本研究使用无人机激光雷达（UAV-LiDAR）数据对比了4种梯度增强决策树算法对于估算入侵红树林物种无瓣海桑AGB的精度，同时探讨了建模过程中的变量重要性。结果表明：（1）XGBR对于评估红树林AGB具有较高的拟合能力，达到R2 = 0.833 8，RMSE = 1.55 Mg/hm2；（2）研究区的无瓣海桑预测AGB的值为73.10～190.00 Mg/hm2，平均值为109.10 Mg/hm2；（3）描述冠层高度特征的激光雷达指标是估计红树林AGB的重要变量。本研究证明了UAV-LiDAR数据与XGBR模型对于估算红树林AGB的可行性，以期为红树林生态系统的蓝碳研究提供数据支撑。Abstract: As one of the vegetation types with the highest carbon storage in tropical regions, the area of mangrove forest shows a trend of fragmentation and reduction. The spatial distribution and dynamic information of mangrove biomass are crucial to the estimation of greenhouse gas flux and carbon storage, as well as policy formulation and implementation. However, both optical data and SAR data commonly used for biomass estimation have signal saturation phenomenon, and traditional estimation algorithms for mangrove biomass estimation have high data requirements and relatively low estimation accuracy. In order to solve this problem, this study compared the accuracy of four gradient enhanced decision tree algorithms for estimating aboveground biomass (AGB) of invasive mangrove species Sonneria apetala used UAV-LiDAR data, and discussed the importance of variables in the modeling process. The results indicate that: (1) XGBR had a high fitting ability for the estimation of mangrove AGB, reaching R² = 0.833 8, RMSE = 1.55 Mg/hm2. (2) The predicted AGB in the study area ranged from 73.10 Mg/hm2 to 190.00 Mg/hm2, with an average of 109.10 Mg/hm2. (3) LiDAR index describing canopy height characteristics is an important variable for estimating mangrove AGB. Conclusion: This study proved the feasibility of UAV-LiDAR data and XGBR model for estimating the AGB of mangrove forests, in order to provide data support for the blue carbon research of mangrove ecosystems.
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图 2 划定样方点（a）、测定坐标（b）、测定胸径（c）、记录参数值（d）
Fig. 2 Delineate quadrat (a), recording coordinates (b), measure the diameter at breast height (c) and recording data (d)
图 4 研究区域红树林的选定变量重要性顺序
Fig. 4 Importance order of selected variables of mangroves in study area
图 5 研究区域红树林的选定变量累计重要性顺序
Fig. 5 The cumulative importance order of selected variables for mangrove forests in the study area
图 6 4种不同ML模型（测试阶段）中实测AGB（X轴）与预测AGB（Y轴）的散点图
Fig. 6 Scatter plots of measured AGB (X axis) and predicted AGB (Y axis) in four different ML models (testing phase)
图 7 4种不同ML模型（训练阶段）中实测AGB（X轴）与预测AGB（Y轴）的散点图
Fig. 7 Scatter plots of measured AGB (X axis) and predicted AGB (Y axis) in four different ML models (training phase)
图 8 XGBR反演红树林地上生物量的空间分布
Fig. 8 Spatial distribution of aboveground biomass in mangroves retrieved by XGBR
表 1 林分地上生物量点云特征统计
Tab. 1 Statistics of point cloud characteristics of aboveground biomass in stands
变量 变量意义 计算公式 elev_p01\int_p01， elev_p05\int_p05， elev_p10\int_p10，…，
elev_p99\int_p99所有点云1%、5%、10%、20%、25%、30%、40%、50%、60%、70%、75%、80%、90%、95%、99%分位数处对应的高度\强度值 elev_cv 样方点云高度的变异系数 ${ {\text{cv} } = \dfrac{ \text{stddev} }{ \text{mean} } }$ elev_stddev 样方点云高度的标准差 ${\text{stddev} = \sqrt {\dfrac{1}{n}{ {\displaystyle\sum\limits_{i = 1}^n {({x_i} - \overline x )^2} }} } }$ elev_iq 样方点云百分位数高度的四分位数间距 iq = p75− p25 elev_kurtosis 样方点云高度分布的平坦度 $ {{\text{kurtosis}} = \dfrac{{\begin{array}{*{20}{c}} {\dfrac{1}{{n - 1}}}\;{\displaystyle\sum\limits_{i = 1}^n {\mathop {(\mathop p\nolimits_i - \bar p)}\nolimits^4 } } \end{array}}}{{\mathop \sigma \nolimits^4 }}} $ elev_skewness 样方点云高度分布的对称程度 $ {{\text{skewness}} = \dfrac{{\begin{array}{*{20}{c}} {\dfrac{1}{{n - 1}}}\;{\displaystyle\sum\limits_{i = 1}^n {\mathop {(\mathop p\nolimits_{\text{i}} - \bar p)}\nolimits^3 } } \end{array}}}{{\mathop \sigma \nolimits^3 }}} $ elev_variance 点云高度方差 elev_max 样方点云高度的最大值 elev_ave 样方点云高度的平均值 elev_mode 样方点云高度的众数 注：cv表示变异系数；stddev表示标准差；iq表示四分位间距；kurtosis表示峰度；skewness表示偏态； p25 为25%高度百分位数； p75 为75%高度百分位数；$ \mathop p\nolimits_i $为每一统计单元内第i个点的高度值；$ \bar p $为每一统计单元内所有点的平均高度；$ \sigma $为统计单元内点云高度分布的标准差。 
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表 2 实测红树林林木参数
Tab. 2 Measured mangrove tree parameters
参数 最大 最小 平均值 胸径/cm 27.27 17.27 22.48 树高/m 16.74 9.40 13.23 AGB/(Mg·hm−2) 205.95 56.75 133.99
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