[1]杨伟志,赵鹏祥*,薛大庆,等. 基于Landsat-8影像的西宁市南北山森林生物量估测模型研究[J].西北林学院学报,2016,31(2):33-37.[doi:10.3969/j.issn.1001-7461.2016.02.06]
 YANG Wei-zhi,ZHAO Peng-xiang*,XUE Da-qing,et al. Estimation of Forest Biomass Model of North and South Mountains Based on Landsat-8 Remote Sensing Image Data in Xining[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2016,31(2):33-37.[doi:10.3969/j.issn.1001-7461.2016.02.06]
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 基于Landsat-8影像的西宁市南北山森林生物量估测模型研究()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第31卷
期数:
2016年第2期
页码:
33-37
栏目:
出版日期:
2016-03-31

文章信息/Info

Title:
 Estimation of Forest Biomass Model of North and South Mountains Based on Landsat-8 Remote Sensing Image Data in Xining
文章编号:
1001-7461(2016)02-0033-05
作者:
 杨伟志1赵鹏祥1*薛大庆2侯逸晨1张晓莉1王志涛3
 (1.西北农林科技大学 林学院,陕西 杨陵 712100;2.韩城市农林局,陕西 韩城 715400;3.青海省林业科学研究所,青海 西宁 810000)
Author(s):
 YANG Wei-zhi1ZHAO Peng-xiang1*XUE Da-qing2HOU Yi-chen1ZHANG Xiao-li1WANG Zhi-tao3
 (1.College of Forestry,Northwest A&F University,Yangling,Shaanxi 712100,China; 2.Agriculture and Forestry Bureau of Hancheng City,Hancheng,Shaanxi 715400,China; 3.Forestry Research Institute of Qinghai Province,Xining,Qinghai 810000,China)
关键词:
 生物量模型Landsat-8逐步回归分析西宁市
Keywords:
 biomass model Landsat-8 stepwise regression analysis Xining City
分类号:
S757
DOI:
10.3969/j.issn.1001-7461.2016.02.06
文献标志码:
A
摘要:
 基于Landsat-8遥感影像结合同期的野外调查样地数据,建立青海省西宁市南北山森林生物量估测模型。通过对Landsat-8遥感数据6个原始波段的灰度值和4个常用植被指数(DVI、NDVI、EVI、SAVI),开展主成分分析后得到主分量PCA1、缨帽变换得到BRIGHT、GREEN、WET,分析遥感信息与生物量的相关性,运用逐步回归分析法建立研究区森林生物量回归估测模型:B=119.495+3.704E-24eDVI+0.026OLI-52-3.478OLI-5(R2=0.554,p<0.01),经检验模型的平均相对误差为13.509%,反演得到西宁市单位面积森林生物量为5.227 t/hm2,总森林生物量为998 991.768 t。结合西宁市遥感影像,绘制了西宁市森林生物量分布图。
Abstract:
 Based on Landsat-8 remote sensing image data,combined with field survey data during the same period,a forest biomass estimating model of North and South Mountains in Xining,capital of Qinghai Province was established.Different band spectral reflectances of each plot were obtained,and the vegetation indices,bright,green,wet and derived factors by linear or nonlinear transformation were computed by using ENVI software.In order to select appropriate factors as variables for the establishment of forest biomass model,the correlations between those factors and forest biomass were calculated.The multiple regression model was built using stepwise regression analysis.Model B=119.495+3.704E-24eDVI+0.026OLI-52-3.478OLI-5 could be used to estimate forest biomass in Xining,its multiple correlation coefficient was 0.554 (p<0.01).The predicted average relative error was 13.509%.The results showed that the average forest biomass density of Xining was 5.227 t·hm-2,and the total forest biomass was 998 991.768 t.

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备注/Memo

备注/Memo:
 收稿日期:2015-04-18修回日期:2015-05-05
作者简介:杨伟志,男,硕士研究生,研究方向:“3S”技术在资源与环境中的应用。E-mail:zblue_yang@qq.com
*通信作者:赵鹏祥,男,副教授,研究方向:林业遥感。E-mail:zpx666@aliyun.com
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