文献类型：期刊文献

中文题名：几种林场总体森林蓄积量密度均值估计方法的比较评价

英文题名：Comparison and evaluation of several methods for estimating the average density of total forest volume in forest farm

作者：丁相元[1] 陈尔学[1] 赵磊[1] 刘清旺[1] 范亚雄[1] 赵俊鹏[1] 徐昆鹏[1]

第一作者：丁相元

机构：[1]中国林业科学研究院资源信息研究所,国家林业和草原局林业遥感与信息技术重点实验室,北京100091

年份：2023

卷号：45

期号：2

起止页码：11-23

中文期刊名：北京林业大学学报

外文期刊名：Journal of Beijing Forestry University

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD2023_2024】；

基金：国家重点研发计划(2021YFE0117700);高分共性产品真实性检验关键技术研究与标准规范编制(21-Y20B01-9001-19/22)。

语种：中文

中文关键词：森林资源调查;林场/县;多源数据;总体蓄积量密度均值

外文关键词：forest resource inventory;forest farm/county;multi resource data;mean value of total volume density

分类号：S758.4

摘要：【目的】以林场或县森林资源总体为调查对象,及时、准确地调查监测总体平均每公顷蓄积量,对上级(如市、省)部门开展森林资源宏观管理、生态保护价值评价、森林碳储量计量、领导干部任期绩效考核等工作都有重要支撑作用。将卫星、无人机等多源遥感数据作为辅助数据,采用较少抽样调查样地数据,实现总体参数有效估测的新方法,已成为国内外重要的研究方向,但目前,国内尚无多种现有估计方法的比较评价研究。为了促进新一代遥感技术在森林资源调查业务中的应用,提高我国森林资源天空地一体化调查监测技术水平,亟需对现有林场或县总体参数估测方法进行比较评价研究。【方法】以内蒙古旺业甸实验林场主要人工林树种为总体,基于2019年在林场获取的无人机激光雷达(LiDAR)抽样数据、Sentinel-2A多光谱数据(全覆盖)和少量样地数据,针对样地(p)、样地-卫星(ps)、样地-抽样无人机LiDAR(pl)以及样地-抽样无人机LiDAR-卫星(pls)4种模式,利用适合这4种模式的概率抽样法(DB)、模型辅助法(MA)、模型法(MD)和混合法(HY)4类共5种估测方法(DBp、MDps、MAps、HYpl以及MDpls)对总体森林蓄积量密度均值(MSVD)进行估计与对比分析。【结果】(1)DBp、MDps、MAps、HYpl、MDpls5种方法估测的MSVD分别为212.54、202.09、202.38、210.07以及208.96 m3/hm2,精度分别为90.44%、91.54%、91.69%、96.35%和96.45%,方差依次减小。(2)其他4种方法相对于MDpls方法的估计效率(RE)均大于1(REDBp,MDpls=5.39,REMDps,MDpls=3.82,REMAps,MDpls=3.69,REHYpl,MDpls=1.07);HYpl相对于MDpls的RE略大于1,但几倍于其他3种方法(REDBp,HYpl=5.02,REMAps,HYpl=3.43,REMDps,HYpl=3.56)。(3)包含LiDAR数据的HYpl与MDpls方法相对于包含Sentinel-2A数据的MDps与MAps方法均为正效率(REMAps,HYpl=3.43,REMDps,HYpl=3.56,REMDps,MDpls=3.82,REMAps,MDpls=3.69);MDps与MAps方法之间的RE接近1,但MAps的效率微高于MDps(REMDps,MAps=1.04)。【结论】和只利用样地数据的估计方法相比,将遥感数据作为辅助变量建立估测模型,无论是采用对蓄积量不够敏感的林场全覆盖Sentinel-2A多光谱遥感数据,还是采用对蓄积量很敏感的抽样式获取的LiDAR数据,都可有效提高林场总体MSVD的估测精度。涉及遥感数据应用的4种方法,估计精度最高的为MDpls,其次为HYpl,这2种方法都包含了LiDAR遥感抽样观测数据的应用,都是适应于林场总体MSVD估计的年度监测方法。可实现天空地3种观测数据协同应用的MDpls估测精度和相对效率最高,可作为林场森林蓄积量年度监测的首选方法。
[Objective]Taking the overall forest resources of forest farms or counties as the object of investigation,timely and accurately investigating and monitoring of the mean stock volume density(MS VD)will play an important supporting role in the macro management of forest resources,the evaluation of ecological protection value,the measurement of forest carbon reserves,and the performance evaluation of the tenure of leading cadres by the superior departments(such as cities and provinces).It has become an important research direction at home and abroad using remote sensing data,such as satellites and unmanned aerial vehicles,combining with less sampling plot to effectively estimate the overall parameters.At present,there is no comparative validation of several estimation methods for mean stock volume based on multisource data in domestic.In order to promote the application of remote sensing technology in the forest resource survey,there is an urgent need to compare and evaluate the methods for estimating overall parameters of forest farms or counties.[Method]The main plantation tree species of Wangyedian Forest Farm in Inner Mongolia of northern China were taken as the research object.Based on the sampling UAV LiDAR(herringbone system distribution),Sentinel-2A data(full coverage)and a small amount of sample plot data obtained in 2019,four patterns of sample plot(p),sample plot-satellite(ps),sample plot-sampling LiDAR(p1),and sample plot-sampling LiDAR-full coverage satellite(pls)were estimated and compared with five methods,including DBp,MDps,MDpls,HYpland MAps,which were suitable for these four patterns and belong to design-based method(DB),model-assisted method(MA),model-dependent method(MD)and mixed or hybrid method(HY).[Result](1)The mean stock volume densities of DBp,MDps,MAps,HYpl,and MDplswere 212.54,202.09,202.38,210.07 and 208.96 m~3/ha,respectively.The accuracy(P)was90.44%,91.54%,91.69%,96.35%,and 96.45%,respectively,with variances decreasing in turn.(2)The relative efficiency(RE)of other methods compared with MDplswas greater than 1(REDBp,MDpls=5.39,REMDps,MDpls=3.82,REMAps,MDpls=3.69,REHYpl,MDpls=1.07),and the RE of HYplcompared with MDplsmethod was greater than 1,but close to 1.Compared with the other three methods,HYplwas more efficient(REDBp,HYpl=5.02,REMAps,HYpl=3.43,REMDps,HYpl=3.56).(3)Both HYpland MDpls methods containing LiDAR data had positive efficiency compared with MDps and MAps methods containing Sentinel-2A data(REMAps,HYpl=3.43,REMDps,HYpl=3.56,REMDps,MDpls=3.82,REMAps,MDpls=3.69).The RE of MDpsand MApswas close to 1,but the efficiency of MAps was slightly higher than that of MDps(REMDps,MAps=1.04).[Conclusion]Compared with the estimation method that only used the sample plot data,when the remote sensing data was used as an auxiliary variable to establish the estimation model,it can effectively improve the estimation accuracy of the MSVD of the forest farm,whether the Sentinel-2A multispectral remote sensing data,which is fully covered the forest farm but not sensitive enough to the stock volume,or the sampling LiDAR data which are sensitive to stock volume.Among four methods involving the application of remote sensing data,MDplshas the highest estimation accuracy,followed by HYpl.Both of these two methods including the application of LiDAR remote sensing sampling observation data are suitable for the MSVD estimation of forest farms.The MDplsmethod has the highest estimation accuracy and relative efficiency,which can realize the synergistic application of the Space-Air-Earth multi-source observation data,and can be used as the preferred method for annual monitoring of forest stock in forest farms.