基于RGBNDVI图像的桉树人工林区森林覆盖变化年度监测

doi:10.3969/j.issn.1000-2006.20168031

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南京林业大学学报（自然科学版） ›› 2017, Vol. 41 ›› Issue (05): 65-71.doi: 10.3969/j.issn.1000-2006.20168031

基于RGBNDVI图像的桉树人工林区森林覆盖变化年度监测

周梅¹,李春干^2*,代华兵³

1.广西大学计算机与电子信息学院,广西南宁 530004; 2.广西大学林学院,广西南宁 530004; 3.广西林业勘测设计院,广西南宁 530011

出版日期:2017-10-18 发布日期:2017-10-18
基金资助:
基金项目:广西林业科学研究项目(GXLYKJ201423) 第一作者:周梅(zhoumei_gxdx@163.com),实验师。*通信作者:李春干(gxali@126.com),教授。

Monitoring annual forest change in Eucalyptus plantation based on RGB-NDVI detection of remote sensing imagery

ZHOU Mei¹,LI Chungan^2*,DAI Huabing³

1. School of Computer, Electronic and Information in Guangxi University, Nanning 530004, China; 2. Forestry College of Guangxi University, Nanning 530004, China; 3. Guangxi Forest Inventory and Planning Institute, Nanning 530011, China

Online:2017-10-18 Published:2017-10-18

摘要/Abstract

摘要： 【目的】监测桉树(Eucalyptus)人工林区森林覆盖的年度变化。【方法】基于2014年和2015年资源3号卫星遥感图像,分别计算研究区不同时段的归一化植被指数及其差值,得到NDVI₂₀₁₄、NDVI₂₀₁₅和NDVI_d,按R-G-B=NDVI₂₀₁₄-NDVI₂₀₁₅-NDVI_d作假彩色合成,得到RGB-NDVI图像,对该图像分别作基于规则的面向对象分类和非监督分类,提取采伐迹地和更新林地信息,并与NDVI差值法结果进行比较。【结果】RGB-NDVI图像面向对象分类的总体精度为98.4%,Kappa系数为0.906 1,略高于传统的NDVI差值法结果(分别为97.3%和0.817 4),RGB-NDVI图像非监督分类效果(总体精度和Kappa系数分别为96.1%和0.790 4)略低于NDVI差值法。【结论】RGB-NDVI图像可读性强,直观地反映了研究区森林覆盖变化的区域和类型信息,在快速变化林区的森林覆盖年度监测中具有良好的应用前景。

Abstract: 【Objective】ZY-3 remotely sensed images from 2014 and 2015 were used to assess annual forest cover change in Eucalyptus plantations. 【Method】The normalized difference vegetation index(NDVI)was computed for each image(NDVI₂₀₁₄ and NDVI₂₀₁₅), then a difference image(NDVI_d)was developed based on the two NDVI images. Then, NDVI₂₀₁₄, NDVI₂₀₁₅ and NDVI_d were used to generate a color-composite image. Three methods: rule-based object-oriented classification(object RGB-NDVI), unsupervised classification(unsupervised RGB-NDVI)of the RGB-NDVI image, and NDVI image differencing(NDVI-DIFF)were implemented to extract forest cover change information on clear-cut and regrowth areas. 【Result】The object RGB-NDVI method had the highest overall accuracy of 98.4%, followed by the NDVI-DIFF method(97.3%)and the unsupervised RGB-NDVI method(96.1%); their corresponding Kappa coefficients were 0.906 1, 0.817 4 and 0.790 4, respectively. 【Conclusion】The RGB-NDVI image has high readability, represents the magnitude and direction of forest cover change, and can be used to monitor annual forest cover change in rapidly changing forest areas.

中图分类号:

S771.8

周梅,李春干,代华兵. 基于RGBNDVI图像的桉树人工林区森林覆盖变化年度监测[J]. 南京林业大学学报（自然科学版）, 2017, 41(05): 65-71.

ZHOU Mei,LI Chungan,DAI Huabing. Monitoring annual forest change in Eucalyptus plantation based on RGB-NDVI detection of remote sensing imagery[J].Journal of Nanjing Forestry University (Natural Science Edition), 2017, 41(05): 65-71.DOI: 10.3969/j.issn.1000-2006.20168031.

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基于RGBNDVI图像的桉树人工林区森林覆盖变化年度监测

Monitoring annual forest change in Eucalyptus plantation based on RGB-NDVI detection of remote sensing imagery

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