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纸质出版日期： 2021-02-07 ，
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黄建文，李增元，陈尔学，赵磊，莫冰萍.2021.高分六号宽幅多光谱数据人工林类型分类.遥感学报，25（2）： 539-548
Huang J W， Li Z Y， Chen E X，Zhao L and Mo B P. 2021. Classification of plantation types based on WFV multispectral imagery of the GF-6 satellite. National Remote Sensing Bulletin， 25（2）：539-548
高分六号（GF-6）卫星于2018年成功发射，2019-03正式投入使用。由于GF-6宽幅相机的WFV（Wide Field of View）影像较GF-1的同类影像新增2个红边波段，将会提高对农业、林业、草原等资源监测能力。为了分析GF-6的WFV影像在人工林分类方面的能力，促进高分数据在林业领域的应用，本文选取广西高峰林场为研究区，以最新的GF-6 WFV影像为数据源，结合地面实测类型数据，进行广西南宁高峰林场的桉树，杉木等人工林类型提取。主要运用随机森林（random forests）的分层分类法：首先计算6种植被指数，并利用随机森林法进行植被指数的特征优选，然后确定4种波段组合数据集的分类方案，4种数据集分别为（1）无红边的前4个波段，（2）有红边的8个波段，（3）8个波段加上未优化的植被指数特征组合，（4）8个波段加上优化的植被指数特征组合。再进行WFV影像4种数据集的随机森林分类，随机森林采用分类回归树（CART）算法来生成分类树，结合了bagging和随机选择特征变量的优点，是一种有效的分类方法。最后比较4个方案的分类结果并进行精度验证。结果表明：方案2比方案1精度提高了4.99%，Kappa系数提高了0.058。说明包含红边的8波段数据比4个波段数据精度有大幅提升。方案4的8波段加上优化植被指数特征组合的分类精度最高，达到了85.38%，比方案2包含红边波段组和方案1无红边波段组的精度分别提高了3.98%，8.97%，Kappa系数分别提高了0.046，0.104。说明WFV影像加入红边波段比无红边波段精度明显增高。由结果可知，红边指数的引入，增强了植被信息，能够较准确地反映人工林类型特征差异，明显提升了人工林的分类精度。本研究方法可以有效改善广西人工林类型信息提取效果，为GF-6影像质量的评价及其在林业应用潜力提供科学参考依据。
The high-spatial-resolution GF-6 satellite has been successfully launched for less than one year
and the application of its imagery has just started. GF-6 is a satellite of a high-resolution earth observation system
which is a major scientific and technological project in China. The Wide Field-of-View (WFV) images of the GF-6 wide-format camera add two red-edged bands in comparison with similar images of the GF-1; thus
the monitoring capacity of agriculture
forestry
and grassland is improved. To analyze the ability of GF-6 WFV imagery in plantation classification and promote the further application of GF-6 data in forestry field
this study provides a hierarchical classification method to extract plantation types
such as eucalyptus and fir
by using the latest WFV images of GF-6 in the Nanning forest farm in Guangxi Province. Furthermore
the classification process was combined with ground measured data.
The random forest classification method was adopted
and the steps are as follows: First
the six vegetation indices were calculated and optimized using the random forest feature selection method
and then the classification scheme of the four datasets was determined. The schemes are as follows: (1) first four bands of WFV image without red edge
(2) eight bands with red edge
(3) eight bands plus unoptimized vegetation index features
and (4) eight bands plus optimized vegetation index features. Then
the random forest classifier was utilized in four datasets. Random forest is an effective classification method that uses the classification regression tree algorithm to generate classification trees and combines the advantages of bagging and the random selection of feature variables. Lastly
the classification results of four schemes were compared; in addition
accuracy assessment was performed in accordance with field survey and forestry inventory data.Results showed that Scheme 2 has an accuracy of 4.99% higher than that of Scheme 1
and the kappa coefficient increased by 0.058. These results indicate that the accuracy of eight-band data with red edges is significantly improved in comparison with four-band data.
The classification accuracy of the eight bands plus optimized vegetation index features was the highest among the schemes
reaching 85.38%. Compared with the bands with and without red edge
the accuracy improved by 3.98% and 8.97%
respectively; moreover
the kappa coefficient increased by 0.046 and 0.104.
WFV images are added with the red-edge index
thereby enhancing vegetation information. This dataset can accurately reflect the differences of plantation type characteristics and significantly improve the classification accuracy of the plantation. Therefore
this study can effectively improve the information extraction effect of plantation types in Guangxi Province and provide a scientific reference for the evaluation of GF-6 image quality and its forestry application potential.
GF-6人工林类型分类随机森林分层分类方法红边植被指数
high spatial resolution GF-6 satelliteplanted forest types classificationrandom forestshierarchical classification methodred-edge vegetation index
Adamczyk J and Osberger A. 2015. Red-edge vegetation indices for detecting and assessing disturbances in Norway spruce dominated mountain forests. International Journal of Applied Earth Observation and Geoinformation 37: 90-99 [DOI: 10.1016/j.jag.2014.10.013http://dx.doi.org/10.1016/j.jag.2014.10.013]
Adelabu S, Mutanga O and Adam E. 2014. Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels. ISPRS Journal of Photogrammetry and Remote Sensing, 95: 34-41 [DOI: 10.1016/j.isprsjprs.2014.05.013http://dx.doi.org/10.1016/j.isprsjprs.2014.05.013]
Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32 [DOI: 10.1023/A:1010933404324http://dx.doi.org/10.1023/A:1010933404324]
Chen X L, Ju Q and Lin K L. 2014. Development status, issues and countermeasures of China’s plantation. World Forestry Research, 27(6): 54-59
陈幸良, 巨茜, 林昆仑. 2014. 中国人工林发展现状、问题与对策. 世界林业研究, 27(6): 54-59 [DOI: 10.13348/j.cnki.sjlyyj.2014.06.008http://dx.doi.org/10.13348/j.cnki.sjlyyj.2014.06.008]
Fang C Y, Wang L and Xu H Q. 2017. A comparative study of different red edge indices for remote sensing detection of urban grassland health status. Journal of Geo-Information Science, 19(10): 1382-1392
方灿莹, 王琳, 徐涵秋. 2017. 不同植被红边指数在城市草地健康判别中的对比研究. 地球信息科学学报, 19(10): 1382-1392 [DOI: 10.3724/SP.J.1047.2017.01382http://dx.doi.org/10.3724/SP.J.1047.2017.01382]
Fourty T, Baret F, Jacquemoud S, Schmuck G and Verdebout J. 1996. Leaf optical properties with explicit description of its biochemical composition: direct and inverse problems. Remote Sensing of Environment, 56(2): 104-117 [DOI: 10.1016/0034-4257(95)00234-0http://dx.doi.org/10.1016/0034-4257(95)00234-0]
Frampton W J, Dash J, Watmough G and Milton E J. 2013. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 82: 83-92. [DOI: 10.1016/j.isprsjprs.2013.04.007http://dx.doi.org/10.1016/j.isprsjprs.2013.04.007]
Gamon J A and Surfus J S. 1999. Assessing leaf pigment content and activity with a reflectometer. New Phytologist, 143(1): 105-117 [DOI: 10.1046/j.1469-8137.1999.00424.xhttp://dx.doi.org/10.1046/j.1469-8137.1999.00424.x]
Genuer R, Poggi J M and Tuleau-Malot C. 2010. Variable selection using random forests. Pattern Recognition Letters, 31(14): 2225-2236 [DOI: 10.1016/j.patrec.2010.03.014http://dx.doi.org/10.1016/j.patrec.2010.03.014]
Gitelson A A, Gritz Y and Merzlyak M N. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3): 271-282 [DOI: 10.1078/0176-1617-00887http://dx.doi.org/10.1078/0176-1617-00887]
National forestry administration.2003.National Technical Regulations for Continuous Forest Inventory 国家林业局.2003.国家森林资源连续清查技术规定［S］
Iverson L R, Prasad A M, Matthews S N and Peters M. 2008. Estimating potential habitat for 134 eastern US tree species under six climate scenarios. Forest Ecology and Management, 254(3): 390-406 [DOI: 10.1016/j.foreco.2007.07.023http://dx.doi.org/10.1016/j.foreco.2007.07.023]
Le Maire G, François C and Dufrêne E. 2004. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment, 89(1): 1-28 [DOI: 10.1016/j.rse.2003.09.004http://dx.doi.org/10.1016/j.rse.2003.09.004]
Li D, Ke Y H, Gong H L, Li X J and Deng Z. 2016. Urban tree species classification with machine learning classifier using WorldView-2 imagery. Geography and Geo-Information Science, 32(1): 84-89
李丹, 柯樱海, 宫辉力, 李小娟, 邓曾. 2016. 基于高分辨率遥感影像的城市典型乔木树种分类研究. 地理与地理信息科学, 32(1): 84-89 [DOI: 10.3969/j.issn.1672-0504.2016.01.016http://dx.doi.org/10.3969/j.issn.1672-0504.2016.01.016]
Liang W H, Liu J K, Zhang W, Li X W and Zhong S Q. 2017. Extracting Eucalyptus information using GF-2 images based on an object-oriented method. Journal of Zhejiang A and F University, 34(4): 721-729
梁文海, 刘吉凯, 张伟, 李新伟, 钟仕全. 2017. 基于面向对象方法的GF-2影像桉树林信息提取. 浙江农林大学学报, 34(4): 721-729 [DOI: 10.11833/j.issn.2095-0756.2017.04.019http://dx.doi.org/10.11833/j.issn.2095-0756.2017.04.019]
Tucker C J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150 [DOI: 10.1016/0034-4257(79)90013-0http://dx.doi.org/10.1016/0034-4257(79)90013-0]
Wu Y D J, Yu L F, Bao J W, Xu H T and Wu L T Y. 2017. Impact of red-edge waveband of RapidEye satellite on recognition ability of main crop. Journal of Northern Agriculture, 45(6): 118-123
乌云德吉, 于利峰, 包珺玮, 许洪滔, 乌兰吐雅. 2017. RapidEye卫星红边波段对主要农作物识别能力的影响研究. 北方农业学报, 45(6): 118-123 [DOI: 10.3969/j.issn.2096-1197.2017.06.22http://dx.doi.org/10.3969/j.issn.2096-1197.2017.06.22]
Zarco-Tejada P J, Hornero A, Hernández-Clemente R and Beck P S A. 2018. Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2A imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 137: 134-148 [DOI: 10.1016/j.isprsjprs.2018.01.017http://dx.doi.org/10.1016/j.isprsjprs.2018.01.017]
Zhang L, Gong Z N, Wang Q W, Jin D D and Wang X. 2019. Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images. Journal of Remote Sensing, 23(2) 313-
张磊, 宫兆宁, 王启为, 金点点, 汪星. 2019. Sentinel-2影像多特征优选的黄河三角洲湿地信息提取. 遥感学报, 23(2): 313-326 [DOI: 10.11834/jrs.20198083http://dx.doi.org/10.11834/jrs.20198083]
Zhou M, Li C G and Dai H B. 2017. Monitoring annual forest change in Eucalyptus plantation based on RGB-NDVI detection of remote sensing imagery. Journal of Nanjing Forestry University (Natural Science Edition), 41(5): 65-71
周梅, 李春干, 代华兵. 2017. 基于RGB-NDVI图像的桉树人工林区森林覆盖变化年度监测. 南京林业大学学报(自然科学版), 41(5): 65-71 [DOI: 10.3969/j.issn.1000-2006.20168031http://dx.doi.org/10.3969/j.issn.1000-2006.20168031]
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