欢迎访问林草资源研究

Forest and Grassland Resources Research >

2022 , Vol. 0 >Issue 6: 68 - 75

DOI: https://doi.org/10.13466/j.cnki.lyzygl.2022.06.011

Scientific Research

Two-Stage Sampling Estimation of Above-Ground Biomass of Pinus kesiya var.langbianensis Based on Remote Sensing Factors from Landsat8 OLI

  • Jing NIE ,
  • Chi LU ,
  • Guanglong OU ,
  • Hui XU
Expand
  • 1. Faculty of Forestry,Southwest Forestry University,Kunming 650224,China
    2. Key Laboratory of Southwest Mountain Forest Resources Conservation and Utilization,Ministry of Education,Southwest Forestry University,Kunming 650224,China
    3. Editorial Department of Journal,Southwest Forestry University,Kunming 650224,China

Received date: 2022-06-28

  Revised date: 2022-10-08

  Online published: 2023-01-16

Fold

Abstract

Based on Landsat8 OLI remote sensing imagery and the second class survey data of forest resources,taking Zhenyuan County,Pu'er City,Yunnan Province as the study area,with sampling accuracy (E) of 90% and reliability index (tα) of 95%,the two-stage sampling technique was applied,using AGB per unit area,above-ground accumulation per unit area,seven single bands and five vegetation indices of Simao pine in Zhenyuan County as the sampling markers,so that the overall sampling variance,coefficient of variation,standard error,absolute error,estimation accuracy,AGB estimation value and estimation error of different sampling schemes were analyzed and compared with simple random sampling and systematic sampling to analyze the comprehensive efficiency of different sampling methods applying different sampling marks.The results showed that:1) the comprehensive efficiency of the two-stage sampling was much higher than that of simple sampling and systematic sampling,2) The efficiency of the two-stage sampling based on single band and vegetation indices was generally better than which was based on the 2nd-class survey data,and the best sampling signs of the two-stage sampling efficiency were ARVI and NDVI,and only 154 samples were required for the two vegetation indices,which reduced the sample size by 60% compared with the two-stage sampling based on the 2nd-class survey data,and the precision could reach the highest,which were 97.50% and 97.67%,respectively.The two-stage sampling based on remote sensing factors could significantly improve the sampling efficiency.

Key words： remote sensing factor; 2nd-class survey; two-stage sampling; forest above-ground biomass; Pinus kesiya var. langbianensis

Cite this article

Jing NIE , Chi LU , Guanglong OU , Hui XU . Two-Stage Sampling Estimation of Above-Ground Biomass of Pinus kesiya var.langbianensis Based on Remote Sensing Factors from Landsat8 OLI[J]. Forest and Grassland Resources Research, 2022 , 0(6) : 68 -75 . DOI: 10.13466/j.cnki.lyzygl.2022.06.011

References

[1] Lisboa S N, Guedes B S, Ribeiro N, et al. Biomass allometric equation and expansion factor for a mountain moist evergreen forest in Mozambique[J]. Carbon Balance and Management, 2018, 13(1):23.
[2] Dyderski M K, Pawlik?. Drivers of forest above-ground biomass and its increments in the Tatra Mountains after 15 years[J]. CATENA, 2021, 205:105468.
[3] 谢进金. 基于遥感抽样技术的平南县森林蓄积量估测[D]. 长沙: 中南林业科技大学, 2011.
[4] 张加龙, 胥辉. 基于遥感的森林生物量估测样地调查方法的研究动态[J]. 西南林业大学学报:自然科学, 2019, 39(4):166-173.
[5] 吴恒, 胥辉. 抽样技术在森林生物量调查中的应用综述[J]. 西南林业大学学报:自然科学, 2021, 41(3):183-188.
[6] 李明阳, 菅利荣. 森林资源调查空间抽样与数据分析[M]. 北京: 中国林业出版社, 2013.
[7] 徐延鑫, 李明阳, 郝思宇. 基于GIS的城市森林生物量抽样方法研究[J]. 林业资源管理, 2018(5):123-127.
[8] 孙华, 林辉, 石军南, 等. 湖南省森林资源连续清查遥感抽样技术的对比分析[J]. 中南林业科技大学学报, 2010, 30(11):26-31.
[9] 金瑛. 事后分层抽样技术在森林资源二类调查中的应用[J]. 林业调查规划, 2008, 33(3):19-22.
[10] 宋新民, 李金良. 抽样调查技术[M]. 2版. 北京: 中国林业出版社, 2007.
[11] 苏华, 张明慧, 李静, 等. 基于光学与SAR因子的森林生物量多元回归估算——以福建省为例[J]. 遥感技术与应用, 2019, 34(4):847-856.
[12] 张宗秀, 高天雷, 张文. 双重二阶抽样提高森林资源抽样精度的研究[J]. 四川林业科技, 2013, 34(5):8-12.
[13] 张煜星, 王雪军, 黄国胜, 等. 森林面积多阶遥感监测方法[J]. 林业科学, 2017, 53(7):94-104.
[14] 杨沁雨, 王瑞, 胥辉. 基于森林资源二类调查数据的香格里拉市森林生物量二阶抽样优化[J]. 西南林业大学学报:自然科学, 2021, 41(6):160-167.
[15] 魏东升, 周晓光. 遥感影像变化检测样本自动抽样[J]. 遥感学报, 2019, 23(3):464-475.
[16] 杨娜. 美国国家农业统计体系及其启示[J]. 世界农业, 2012(7):27-31.
[17] 朱磊. 基于高清影像分层抽样技术在森林资源二类调查中的应用探索[J]. 林业资源管理, 2018(3):129-132.
[18] 葛宏立, 周国模, 张国江, 等. 遥感、地面三相抽样及其在森林资源年度监测面积估计中的应用[J]. 林业科学, 2007, 43(6):77-82.
[19] 王雪军, 马炜, 黄国胜, 等. 基于遥感大样地抽样调查的森林面积监测[J]. 北京林业大学学报, 2015, 37(11):1-9.
[20] 云南森林编写委员会. 云南森林[M]. 昆明: 云南科技出版社,1986.
[21] 陈俊, 沈润平, 李博伦, 等. 基于Logistic回归分析的塑料大棚遥感指数构建[J]. 国土资源遥感, 2019, 31(3):43-50.
[22] Huete A R. A soil-adjusted vegetation index(SAVI)[J]. Remote Sensing of Environment, 1988, 25(3):295-309.
[23] Kaufman Y J, Tanre D. Atmospherically resistant vegetation index(ARVI)for EOS-MODIS[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(2):261-270.
[24] 胥辉, 张子翼, 欧光龙. 云南省森林生物量和碳储量估算及分布研究[M]. 昆明: 云南科技出版社, 2019.
[25] 阳帆, 王海宾, 胡晟, 等. 基于群团抽样技术的森林资源监测方案研究[J]. 农业机械学报, 2019, 50(2):212-217.
[26] 陈雪峰, 曾伟生, 熊泽彬, 等. 关于国家森林资源连续清查技术规定的修订[J]. 林业资源管理, 2004(5):40-45.
Options
Outlines

/

〈 〉