基金项目:国家重点研发计划?021YFE0117700),高分共性产品真实性检验关键技术研究与标准规范编制?1-Y20B01-9001-19/22(/div>
详细信息
丁相元,博士生。主要研究方向:遥感技术与应用。Email9a href="//www.inggristalk.com/j/article/doi/10.12171/mailto:dxy4201@126.com">dxy4201@126.com 地址?00091北京市海淀区东小府1号中国林业科学研究院资源信息研究所
陈尔学,研究员,博士生导师。主要研究方向:雷达应用技术研究、遥感技术与应用。Email9a href="//www.inggristalk.com/j/article/doi/10.12171/mailto:chenerx@ifrit.ac.cn">chenerx@ifrit.ac.cn 地址:同三/span>
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出版历程
- 收稿日期:2022-07-25
- 修回日期:2022-10-31
- 网络出版日期:2023-02-10
- 刊出日期:2023-02-25
Comparison and evaluation of several methods for estimating the average density of total forest volume in forest farm
Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
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、/sec>
方法以内蒙古旺业甸实验林场主要人工林树种为总体,基?019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?种模式,利用适合?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?类共5种估测方法(DB
p、MD
ps、MA
ps、HY
pl以及MD
pls)对总体森林蓄积量密度均值(MSVD)进行估计与对比分析、/sec>
结果?)DB
p、MD
ps、MA
ps、HY
pl、MD
pls5种方法估测的MSVD分别?12.54?02.09?02.38?10.07以及208.96 m
3/hm
2,精度分别为90.44%?1.54%?1.69%?6.35%?6.45%,方差依次减小。(2)其?种方法相对于MD
pls方法的估计效率(RE)均大于1(RE
DBp,MDpls= 5.39,RE
MDps,MDpls= 3.82,RE
MAps,MDpls= 3.69,RE
HYpl,MDpls= 1.07);HY
pl相对于MD
pls的RE略大?,但几倍于其他3种方法(RE
DBp,HYpl= 5.02,RE
MAps,HYpl= 3.43,RE
MDps,HYpl= 3.56)。(3)包含LiDAR数据的HY
pl与MD
pls方法相对于包含Sentinel-2A数据的MD
ps与MA
ps方法均为正效率(RE
MAps,HYpl= 3.43,RE
MDps,HYpl= 3.56,RE
MDps,MDpls= 3.82,RE
MAps,MDpls= 3.69);MD
ps与MA
ps方法之间的RE接近1,但MA
ps的效率微高于MD
ps(RE
MDps,MAps= 1.04)、/sec>
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MD
pls,其次为HY
pl,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?种观测数据协同应用的MD
pls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、/sec>
Abstract:
ObjectiveTaking 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(MSVD)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 multi-source 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.
MethodThe 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 (pl), and sample plot-sampling LiDAR-full coverage satellite (pls) were estimated and compared with five methods, including DB
p, MD
ps, MD
pls, HY
pland MA
ps, 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 DB
p, MD
ps, MA
ps, HY
pl, and MD
plswere 212.54, 202.09, 202.38, 210.07 and 208.96 m
3/ha, respectively. The accuracy (
P) was 90.44%, 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 MD
plswas greater than 1 (RE
DBp,MDpls= 5.39, RE
MDps,MDpls= 3.82, RE
MAps,MDpls= 3.69, RE
HYpl,MDpls= 1.07), and the RE of HY
plcompared with MD
plsmethod was greater than 1, but close to 1. Compared with the other three methods, HY
plwas more efficient (RE
DBp,HYpl= 5.02, RE
MAps,HYpl= 3.43, RE
MDps,HYpl= 3.56). (3) Both HY
pland MD
plsmethods containing LiDAR data had positive efficiency compared with MD
psand MA
psmethods containing Sentinel-2A data (RE
MAps,HYpl= 3.43, RE
MDps,HYpl= 3.56, RE
MDps,MDpls= 3.82, RE
MAps,MDpls= 3.69). The RE of MD
psand MA
pswas close to 1, but the efficiency of MA
pswas slightly higher than that of MD
ps(RE
MDps,MAps= 1.04).
ConclusionCompared 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, MD
plshas the highest estimation accuracy, followed by HY
pl. 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 MD
plsmethod 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.
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