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

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 Mei,LI Chungan,DAI Huabing

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2017, Vol. 41 ›› Issue (05) : 65-71.

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JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 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 Chungan^2*,DAI Huabing³

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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.

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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. 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.

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