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《山地学报》[ISSN:1008-2186/CN:51-1516]

卷:

期数:

2017年02期

页码:

246-254

栏目:

山地技术

出版日期:

2017-03-30

文章信息/Info

Title:

Remote Sensing Estimation of Forest Growing Stock Volume Based on Spectral and Texture Information

文章编号:

1008-2786-(2017)2-246-09

作者:

郝 泷¹; 刘 华^1*; 陈永富¹; 吴云华²

1.中国林业科学研究院 资源信息研究所,北京 100091;
2.西藏自治区林业调查规划研究院,拉萨 850000

Author(s):

HAO Shuang¹; LIU Hua^1*; CHEN Yongfu¹; WU Yunhua²

1.Research Institute of Forest Resource Information Techniques,Chinese Academy of Forestry,Beijing,100091,China
2.Forestry Inventory and Planning Institute of Tibet Autonomous Region,Lhasa,Tibet,850000,China

关键词:

森林蓄积量; Landsat8影像; 纹理信息; 回归模型

Keywords:

Forest growing stock volume;  Landsat 8 image;  texture information;  regression model

分类号:

S758.5; TP79

DOI:

10.16089/j.cnki.1008-2786.000218

文献标志码:

A

摘要:

本研究以Landsat8为遥感数据源,以样地调查数据和森林资源二调数据为辅助数据对西藏林芝县的森林蓄积量进行反演研究。研究通过多元回归分析构建了林芝县森林蓄积的估算模型。为验证纹理信息的加入能否提高森林蓄积量遥感反演的精度,研究通过灰度共生矩阵提取了Landsat8的纹理特征。在分析了森林蓄积量与遥感影像各波段、植被指数、纹理特征以及地形因子之间的相关关系后,分别以(1)光谱和地形因子、(2)纹理信息、(3)光谱因子、地形特征和纹理特征结合为自变量构建森林蓄积量的遥感估测回归模型。实验结果表明:传统的森林蓄积量反演方法得到的精度最低,而基于光谱因子、地形特征和纹理特征结合的森林蓄积量估测模型得到结果的精度最高,达到80.24%,均方根误差RMSE为1.018。研究结果证明随着纹理信息的引入,原本仅基于光谱和地形因子的森林蓄积量反演复相关系数从0.5843提高到0.7075,反演精度提高了10.06%,这说明纹理信息对森林蓄积的反演精度有提高的作用。本研究构建的基于光谱因子、地形特征和纹理特征结合的回归模型对研究区内的森林蓄积量反演具有可靠性,对于森林资源的监测和管理具有重要的意义。

Abstract:

In this study,an inversion analysis of the forest growing stock volume in Linzhi County was conducted using a multiple regression method. It collected Landsat 8 remote sensing images,forest inventory data(by field survey organized by forest institutions on a local and national basis),and DEM from Linzhi,Tibet,China. To determine if inclusion of texture feature information into the regression would improve retrieval accuracy for our proposed inversion analysis model,the texture feature information in Landsat 8 was extracted using a Gray Level Co-occurrence Matrix(GLCM). Relationship between the varied bands of remote sensing images,vegetable indices,texture features,topographic factors of the Landsat,and the forest growing stock volume was analyzed and consequently inversion models were established according to the revealed correlation. Three inversion models for determination of forest growing stock volume were introduced by combinations of different independent variables,namely the spectra,topographic and texture feature factors. The first multiple regression equation was organized with two variables,the spectra factor and the topographic factors; the second lied in the texture feature factor only; the last one was complete model with all three included. It found that the accuracy of the traditional regression model which consisted of spectra and topographic factors,was not satisfied with precision,but that of the third model established here was 80.24%,with an RMSE of 1.018,the highest among the three models. This indicated that with the introduction of texture feature information into our suggested inversion model,it increased retrieval accuracy significantly,leading to a percentage of 10.06% improvement,from 0.5843 to 0.7075 for the coefficient of total correlation. It suggested that a regression model containing three variables,the spectra,topographic and texture feature was reliable to determination of the forest growing stock volume,and it would be of great significance in monitoring and management of forest resources.

备注/Memo

备注/Memo:

基金项目(Foundation item):国家科技92基础性工作专项(2013FY111600)[National Science and Technology Basic Work(2013FY111600)]
作者简介(Biography):郝泷(1988-),男,河南安阳人,博士研究生,主要从事森林资源监测研究。[Hao Shuang(1988-), male, born in Anyang, Henan province, Ph.D candidate, engaged in the monitoring of forest resource] E-mail: shinehaosmile@hotmail.com
*通信作者(Corresponding author): 刘华(1971-),女,副研究员,主要从事林业信息技术。[Liu Hua(1971-), female, associate professor, mainly engaged in forestry information technology.]E-mail: liuhua@caf.ac.cn

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更新日期/Last Update: 2017-03-30