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

ZHOU Mei,LI Chungan,DAI Huabing

Journal of Nanjing Forestry University (Natural Sciences Edition） ›› 2017, Vol. 41 ›› Issue (05) : 65-71.

PDF(3807988 KB)
南京林业大学学报（自然科学版）
PDF(3807988 KB)
Journal of Nanjing Forestry University (Natural Sciences Edition） ›› 2017, Vol. 41 ›› Issue (05) : 65-71. DOI: 10.3969/j.issn.1000-2006.20168031

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

  • ZHOU Mei1,LI Chungan2*,DAI Huabing3
Author information +
History +

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(NDVId)was developed based on the two NDVI images. Then, NDVI2014, NDVI2015 and NDVId 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.

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations
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 Sciences Edition）. 2017, 41(05): 65-71 https://doi.org/10.3969/j.issn.1000-2006.20168031

References

[1] 侯元兆. 科学认识我国南方发展桉树速生丰产林问题[J]. 世界林业研究, 2006, 19(3): 71-76. HOU Y Z. Ungerstanding scientifically the issue of developing fast-growing and high-yielding Eucalypt plantation in south China[J]. World Forestry Research, 2006, 19(3): 71-76.
[2] FEARNSIDE P M. Spatial concentration of deforestation in the Brazilian Amazon[J]. Ambio, 1986, 15(2): 74-81.
[3] SADER S A, STONE T A, JOYCE A T. Remote sensing of tropical forests: an overview of research and applications using non-photographic sensors[J]. Photogrammetric Engineering and Remote Sensing, 1990(10): 1343-1351.
[4] SKOLE D, TUCKER C. Tropical deforestation and habitat fragmentation in the Amazon: satellite data from 1978 to 1988[J]. Science, 1993, 260(5116): 1905-1910.
[5] FOODY G M, PALUBINSKA S G, LUCAS R M, et al. Identifying terrestrial carbon sinks: classification of successional stages in regenerating tropical forest from Landsat TM data[J]. Remote Sensing of Environment, 1996, 55(3):205-216. DOI:10.1016/s0034-4257(01)00295-4.
[6] SADER S A, HAYES D J, HEPINSTALL J A, et al. Forest change monitoring of a remote biosphere reserve[J]. International Journal of Remote Sensing, 2001, 22(10): 1937-1950.
[7] BONTEMPS S, LANGNER A, DEFOURNY P. Monitoring forest changes in Borneo on a yearly basis by an object-based change detection algorithm using SPOT-Vegetation time series[J]. International Journal of Remote Sensing, 2012, 33(15): 4673-4699.
[8] DESCLÉE B, BOGAERT P, DEFOURNY P. Forest change detection by statistical object-based method[J]. Remote Sensing of Environment, 2006, 102(1): 1-11.
[9] POTAPOV P V, TURUBANOVA S A, HANSEN M C, et al. Quantifying forest cover loss in Democratic Republic of the Congo, 2000-2010, with Landsat ETM+ data[J]. Remote Sensing of Environment, 2012, 122: 106-116.
[10] 石军南, 李和顺, 刘晓农, 等. 面向对象分类方法在森林采伐遥感监测中的应用[J]. 中南林业科技大学学报, 2010, 30(11): 6-10. SHI J N, LI H S, LIU X N, et al. Application of classification object-oriented to forest cutting monitoring based on remote sensing[J]. Journal of Central South University of Forestry and Technology, 2010, 30(11): 6-10.
[11] 魏安世, 杨志刚.森林资源年度监测小班数据自动更新技术[J]. 南京林业大学学报(自然科学版), 2010, 34(4): 123-128.DOI:10.3969/j.jssn.1000-2006.2010.04.027. WEI A S, YANG Z G. Automatic updating technique of subcompartment data for annual monitoring of forest resource[J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2010, 34(4): 123-128.
[12] 王志慧, 李世明, 张艺伟. 基于C5.0算法的森林资源变化检测方法研究——以山东省徂徕山林区为例[J]. 西北林学院学报, 2011, 26(5): 185-191. WANG Z H, LI S M, ZHANG Y W. Methodsological study on the detection of the variations of forest resources based on C5.0 algorithm:a case of Culai forest in Shandong[J]. Journal of Northwest Forestry University, 2011, 26(5): 185-191.
[13] 王志杰. 基于遥感影像分割单元的土地利用变化快速检测方法[J]. 南京林业大学学报(自然科学版), 2015, 39(3): 1-5. DOI:10.3969/j.issn.1000-2006.2015.03.001. WANG Z J. Rapid detection method for land use change based on remote sensing images segmentation units[J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2015, 39(3): 1-5.
[14] 李春干, 梁文海. 面向对象遥感图像森林变化检测的工程化应用方法[J]. 林业资源管理, 2015(6): 137-143.DOI: 10. 13466 /j. cnki. Lyzygl. 2015. 06. 026. LI C G, LIANG W H. The engineering application of object-based forest change detection using high-resolution remote sensing image[J]. Forest Resources Management, 2015(5): 137-143.
[15] 周启鸣. 多时相遥感影像变化检测综述[J]. 地理信息世界, 2011,9(2): 28-33. ZHOU Q M. Review on change detection using multi-temporal remote sensed imagery[J]. Geomatics World, 2011,9(2): 28-33.
[16] 李德仁. 利用遥感影像进行变化检测[J]. 武汉大学学报·信息科学版, 2003, 28(特刊): 7-12. LI D R. Change detection from remote sensing images[J]. Geomatics and Information Science of Wuhan University, 2003, 28(S1): 7-12.
[17] LU D, MAUSEL P, BRONDÍZIO E, et al. Change detection techniques[J]. International Journal of Remote Sensing, 2004, 25(12): 2365-2401.
[18] 李世明, 王志慧, 韩学文, 等. 森林资源变化遥感监测技术研究进展[J]. 北京林业大学学报, 2011, 33(3): 132-138. LI S M, WANG Z H, HAN X W, et al. Overview of forest resources change detection methods using remote sensing techniques[J]. Journal of Beijing Forestry University, 2011, 33(3): 132-138.
[19] SADER S A, WINNE J C. RGB-NDVI colour composites for visualizing forest change dynamics[J]. International Journal of Remote Sensing, 1992, 13(16): 3055-3067.
[20] HAYER D J, SADER S A. Comparison of change-detection techniques for monitoring tropical forest clearing and vegetation regrowth in a time series[J]. Photogrammetric Engineering & Remote Sensing, 2001, 67(9): 1067-1075.
[21] 黄荣林. 桉树速生丰产林营造技术及效益分析[J]. 广西林业科学, 2006, 35(S1): 27-29, 36. HUANG R L. Superior eucalyptus fast-growing and high yield plantation establishment technique and benefits analysis in Luchuan County, Guangxi[J]. Guangxi Forestry Science, 2006, 35(S1): 27-29,36.
[22] BRUGGEMAN D, MEYFROIDT P, LAMBIN E F. Forest cover changes in Bhutan: revisiting the forest transition[J]. Applied Geography, 2016, 67: 49-66.
[23] PUJIONO E, KWAK D A, LEE W K, et al. RGB-NDVI color composites for monitoring the change in mangrove area at the Maubesi Nature Reserve, Indonesia[J]. Forest Science and Technology,2013,9(4): 171-179.DOI:10.1080/21580103.2012.842327.
PDF(3807988 KB)

Accesses

Citation

Detail
Sections
Recommended
The full text is translated into English by AI, aiming to facilitate reading and comprehension. The core content is subject to the explanation in Chinese.

Author

Reader

Reviewer

www.nldxb.njfu.edu.cn

Edited ＆ Published by Editorial Department of Nanjing Forestry University(Natural Sciences Edition)
Address： No.159 Longpan Road,Nanjing 210037 Jiangsu,P.R.China
E-mail ：xuebao@njfu.edu.cn , xuebao@njfu.com.cn
Telephone +86-25-85428247,85427076
Distributed by China International Book Trading Corporation P.O. Box 399, Beijing ,P.R. China

/

〈 〉