不同地面样地下无人机高光谱森林地上碳储量估测差异  

Differences in estimation of above-ground carbon stocks in forests by UAV hyperspectral underground sample plots

作　　者：倪辰 黄庆丰[1,2] 孔令瑗 葛春雨 唐雪海[1,2] NI Chen;HUANG Qingfeng;KONG Lingyuan;GE Chunyu;TANG Xuehai(College of Forestry and Landscape Architecture,Anhui Agricultural University,Hefei 230036,China;Anhui Provincial Key Laboratory of Forest Resources and Silviculture,Anhui Agricultural University,Hefei 230036,China)

机构地区：[1]安徽农业大学林学与园林学院,安徽合肥230036 [2]安徽农业大学安徽省林木资源培育重点实验室,安徽合肥230036

出　　处：《江西农业大学学报》2025年第2期451-464,共14页Acta Agriculturae Universitatis Jiangxiensis

基　　金：安徽省2023年林业科研创新研究项目(20221229)。

摘　　要：【目的】地面样地布设是遥感森林参数反演的必要手段,地面样地的形状和面积直接影响遥感森林参数反演的准确性和可靠性,以及森林资源调查监测的效率。选择合适的样地形状和面积是确保遥感数据准确反映地面实际情况,提高监测效率的关键步骤之一。【方法】以北亚热带针叶林、阔叶林为研究对象,通过典型抽样选取样地(25.82 m×25.82 m),进行每木检查(起测直径5.0 cm),利用已有树种的立木生物量模型计算得到单木生物量,累加求出样地林分生物量,根据碳计量参数最终获得样地碳储量;同时,利用无人机机载高光谱获取地面样地的高光谱数据,依据样地林木RTK位置信息绘制出林木平面位置图,继而划分不同形状(圆形或方形)和不同面积的小样地;在此基础上利用多元散射校正(MSC)、标准正态变量变换(SNV)、多项式平滑(Savitzky-Golay平滑)与导数计算、离散小波变换(DWT)进行高光谱数据预处理,提取植被指数、纹理特征等特征因子;根据随机森林重要性排序筛选出不同森林类型不同样地特征的最优特征子集变量;利用随机森林(RF)和极端梯度提升(XGBoost)2种机器学习算法,构建针叶林、阔叶林及全部森林(针叶林和阔叶林)的森林地上碳储量估测模型,确定各森林类型最优样地布设方式,研究样地特征对森林地上碳储量建模的影响。【结果】经Savitzky-Golay平滑-导数、DWT和SNV变换后的植被指数因子更适用于碳储量建模;与标准方形样地相比,圆形样地在针叶林、阔叶林的碳储量模型构建中表现突出,半径为12.91 m的样地碳储量估测模型精度明显优于其他尺寸模型精度。最优的针叶林、阔叶林碳储量模型测试集评价结果为R^(2)_(test)=0.78,RMSE_(test)=10.15 t/hm^(2),rRMSE_(test)=18.6%;R^(2)_(test)=0.77,RMSE_(test)=5.77 t/hm^(2),rRMSE_(test)=10.95%。【结论】半径为12.91 m的圆形样地和XGBoost算法结合能[Objective]The deployment of ground sample plots represents a fundamental aspect of remote sensing-based forest parameter inversion.The configuration and dimensions of these plots play a pivotal role in determining the precision and dependability of forest parameter retrievals derived from remote sensing data.Moreover,they substantially affect the effectiveness of forest resource assessments and monitoring initiatives.Consequently,choosing an optimal plot shape and size becomes crucial,as it ensures that remote sensing data effectively mirror actual forest conditions while simultaneously improving the efficiency of monitoring operations.[Method]Taking the north subtropical coniferous forests and broad-leaved forests as the research objects,sample plots(25.82 m×25.82 m)are selected through typical sampling.Within these designated plots,all trees with a diameter of at least 5.0 cm were comprehensively surveyed.The biomass of individual trees was determined through the standing tree biomass models specific to the existing tree species.Subsequently,the total biomass of the sample plots was calculated,and the carbon stock of the plot was derived based on the established carbon measurement parameters.Simultaneously,unmanned aerial vehicle(UAV)based hyperspectral data of the ground sample plots were acquired to obtain high-resolution spectral information.With the aid of RTK position data,a detailed planimetric map of the trees in each plot was developed.To explore the effect of plot structure,the sample plots were further subdivided into smaller units of various shapes(e.g.,circular and square)and sizes.A series of preprocessing techniques were applied,including multiple scattering correction(MSC),standard normal variate transformation(SNV),polynomial smoothing(Savitzky-Golay smoothing),derivative computation,and discrete wavelet transform(DWT)to process the hyperspectral data.A comprehensive range of feature factors,including vegetation indices and texture features,was extracted.Subsequently,the optimal subset of feat

关 键 词：无人机高光谱 样地特征 光谱特征变换 机器学习 碳储量估测 

分 类 号：S757.2[农业科学—森林经理学]