欢迎访问林草资源研究

Spatio-Temporal Error Correction for Accurate Monitoring of Annual Growth Rate of Forest Volume in Zhejiang Province

  • Jixing TAO ,
  • Binglou XIE ,
  • Biyong JI ,
  • Guojiang ZHANG ,
  • Wenwu WANG
Expand
  • Zhejiang Forest Resource Monitoring Center,Hangzhou 310020,China

Received date: 2022-02-21

  Revised date: 2022-04-07

  Online published: 2022-06-13

Abstract

In order to reduce the influence of error value caused by spatio-temporal difference in forest resources monitoring,a spatio-temporal error correction method of annual growth rate of forest volume (AGRFV) was studied in this paper. Both the date of AGRFV from 2004 to 2014 in the Zhejiang Province Forest Resources Survey and the date of AGRFV from 2014 to 2019 in the Zhejiang Province & Eleven Cities Forest Resources Survey were collected and analyzed. The monthly growth rate of forest volume (MGRFV) and the AGRFV of all 12 groups,which are divided into different groups according to the breast high were statistically analyzed and estimated.The results showed that:(1) on the time scale,for all the 4 tree species,the ratio of monthly growth rate to annual growth rate (RMGRAGR) in April or October was 7%~10%,the RMGRAGR in May,June,July,August or September was 10%~17%,and the RMGRAGR between November to next March was lower because of the low growth rate; (2) on the spatial scale,except for Jiaxingin special situation,the ratio of AGRFV in each of other 10 cities to the provincial average value were between 0.95~1.06; (3) for the temporal error,it was better to set the correction base month as June,July or August,and the steps of calibration process were firstly original survey data correction and then statistical summary; (4) the spatial error can be corrected after the temporal error correction of the original survey data,and then the statistical summary data of eachcity can be corrected.

Cite this article

Jixing TAO , Binglou XIE , Biyong JI , Guojiang ZHANG , Wenwu WANG . Spatio-Temporal Error Correction for Accurate Monitoring of Annual Growth Rate of Forest Volume in Zhejiang Province[J]. Forest and Grassland Resources Research, 2022 , 0(2) : 32 -38 . DOI: 10.13466/j.cnki.lyzygl.2022.02.005

References

[1] 邓成, 梁志斌. 国内外森林资源调查对比分析[J]. 林业资源管理, 2012(5):12-17.
[2] 周昌祥. 我国森林资源规划设计调查的回顾与改进意见[J]. 林业资源管理, 2014(4):1-3.
[3] 陶吉兴, 季碧勇. 浙江森林资源一体化监测理论与实践[M]. 北京: 中国林业出版社, 2016.
[4] 郑德祥, 陈清海, 陈平留, 等. 森林资源二类续档误差及原因分析[J]. 林业资源管理, 2004(4):31-34.
[5] 曾伟生, 程志楚, 夏朝宗. 一种衔接森林资源一类清查和二类调查的方法[J]. 中南林业调查规划, 2012, 31(3):1-4.
[6] 李清湖, 余松柏, 薛春泉, 等. 不同森林资源监测体系数据协同性初步分析——以广东省为例[J]. 中南林业调查规划, 2013, 32(4):16-19.
[7] 聂祥永. 森林资源监测成果数据的差异与误差问题研究[J]. 林业资源管理, 2013(1):32-37.
[8] 周昌祥. 对我国森林资源清查体系及年度出数的研究与探讨[J]. 林业资源管理, 2013(2):1-5.
[9] 曾伟生. 全国森林资源年度出数方法探讨[J]. 林业资源管理, 2013,(1):26-31.
[10] 黄国胜, 刘谦, 蒲莹, 等. 大数据时代森林资源监测新模式[J]. 林业资源管理, 2020(6):1-5.
[11] 阎凤文. 测量数据处理方法[M]. 北京: 原子能出版社, 1988.
[12] 於宗俦, 鲁林成. 测量平差基础[M]. 北京: 测绘出版社, 1984.
[13] 杨国宪. 森林抽样调查误差浅谈[J]. 中南林业调查规划, 1984(2):39-41.
[14] 林昌庚. 森林连续清查资源变化抽样误差的控制[J]. 中国林业科学, 1978(3):58-66.
[15] 陶吉兴. 常规立地指数表的误差来源与分析[J]. 浙江林学院学报, 1990, 7(4):391-395.
[16] 陶吉兴, 王文武, 徐达, 等. 基于固定样地连续监测数据的林木蓄积生长率月际分布[J]. 南京林业大学学报:自然科学版, 2017, 41(2):111-116.
[17] 刘安兴. 浙江省森林资源动态监测体系方案[J]. 浙江林学院学报, 2005, 22(4):449-453.
[18] 张国江, 季碧勇, 王文武, 等. 设区市森林资源市县联动监测体系研究[J]. 浙江农林大学学报, 2011, 28(1):46-51.
[19] 陶吉兴, 季碧勇, 张国江, 等. 浙江森林资源省市县三级联动监测体系构建研究[J]. 林业资源管理, 2019(6):12-16.
[20] DB33/T 640—2017,森林资源规划设计调查规程[S].
Outlines

/