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遥感技术与应用  2020, Vol. 35 Issue (4): 731-740    DOI: 10.11873/j.issn.1004-0323.2020.4.0731
综述     
竹资源遥感监测研究进展
严欣荣(),官凤英()
国际竹藤中心, 竹藤科学与技术重点实验室, 北京 100020
Overview of Remote Sensing Monitoring of Bamboo Resources
Xinrong Yan(),Fengying Guan()
International Center of Bamboo and Rattan, Key Lab of Bamboo and Rattan Science and Technology, Beijing 100020, China
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摘要:

竹资源广泛分布于热带、亚热带和温带地区,是许多不可再生资源的良好替代品,其生长迅速、分布范围广的特点在减缓气候变化和发展中国家脱贫减困等方面发挥着重要作用。遥感技术广泛应用于资源监测、森林结构定量化反演,具有监测范围广、时空精度高的优点。系统梳理遥感各类数据源在竹资源监测中的应用、竹资源时空动态变化监测及竹资源监测分类方法。重点总结监测制图的数据源与分类方法,并对各类方法的精度进行统计分析。提出竹资源遥感监测要注重使用多源遥感数据和分类方法探索,加强特殊竹种、竹特定生长阶段和竹数量与质量监测为未来研究重点,以期为濒危野生动物保护、贫困地区脱贫、竹产业开发利用、发展中国家民生改善等提供技术支撑。

关键词: 竹资源遥感研究进展    
Abstract:

Bamboo resources are widely distributed in tropical, subtropical and temperate regions. They are good substitutes for many non-renewable resources. Their rapid growth and wide distribution play an important role in mitigating climate change and developing countries to lift poverty and reduce poverty. Remote sensing technology is widely used in resource monitoring and quantitative mapping of forest structures, and has the advantages of wide monitoring range and high precision in space and time. This paper systematically sorts out the application of remote sensing data sources in bamboo resource monitoring, the temporal and spatial dynamic change monitoring of bamboo resources and the bamboo resource monitoring and classification method. Focus on the data sources and classification methods of monitoring and mapping, and statistical analysis of the accuracy of various methods. It is proposed that the remote sensing monitoring of bamboo resources should pay attention to use the multi-source data and classification methods, strengthen the special growth stages of bamboo species and bamboo, and monitor the quantity and quality of bamboo as the future research focus, in order to protect the endangered wild animals, poverty alleviation in poverty-stricken areas, and bamboo industry. Provide technical support for development and utilization, improvement of people's livelihood in developing countries.

Key words: Bamboo resources    Remote sensing    Research progress
收稿日期: 2019-05-06 出版日期: 2020-09-15
ZTFLH:  TP79  
基金资助: “十三五”国家重点研发计划专项“材用竹林高效培育与监测技术”(2018YFD0600103)
通讯作者: 官凤英     E-mail: yanxinrong@foxmail.com;guanfy@icbr.ac.cn
作者简介: 严欣荣(1994-),男,甘肃庆阳人,硕士研究生,主要从事竹资源监测研究。E?mail:yanxinrong@foxmail.com
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引用本文:

严欣荣,官凤英. 竹资源遥感监测研究进展[J]. 遥感技术与应用, 2020, 35(4): 731-740.

Xinrong Yan,Fengying Guan. Overview of Remote Sensing Monitoring of Bamboo Resources. Remote Sensing Technology and Application, 2020, 35(4): 731-740.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.4.0731        http://www.rsta.ac.cn/CN/Y2020/V35/I4/731

竹种地点方法辅助数据数据源精度OA/UA参考
大型丛生竹云南德宏州数字化/QuickBird,GeoEye-1, HyperionEO-1/2012[29,30]
牡竹西高止山脉,印度数字化/Landsat MSS, TM, ETM+, IRS P6 LISS III, Resourcesat-2 LISS III/2016[57]
印度簕竹Wayanad,印度MLC/IRS 1C/1998[36]
毛竹福建永安市MLC地形信息HJ-1A0.75/2014[47]
牡竹喜马偕尔邦,印度MLC/IRS P6 LISS III0.87~0.89/0.74~0.852015[54]
毛竹浙江MLC资源清查数据Landsat TM0.76~0.85/0.91~0.952018[16]
秦岭箭竹和华西箭竹佛坪自然保护区,中国MLC,ANN, 混合神经网络和专家系统的分类系统地面调查数据集ASTER0.52,0.63,0.72/2009[46]
巴山木竹佛坪自然保护区,中国MLC , MDC, ANN/Landsat TM/ETM+0.82,0.81,0.83/2009[58]
/帕尔加那斯,印度MLC ,SVM,RF地面调查数据WorldView 20.58,0.91,0.87/0.4,0.89,0.82,0.42014[42]
毛竹、雷竹浙江MLC、MF/MODIS地表反射率和合成产品 (MOD13Q1)/0.77~0.792017[16]
毛竹顺昌县,中国GLCM,MLC/Landsat TM/ETM0.84/2009[41]
毛竹顺昌县,中国非监督分类,MLC 和子象元分类/TM0.7,0.78,0.83/0.65,0.68,0.842010[59]
下层竹卧龙自然保护区,中国ANN/Landsat TM0.82005[43]
毛竹临安,浙江反向传播BP神经网络Landsat ETM+0.93/0.982009[45]
巴山木竹和拐棍竹卧龙自然保护区,中国物候指数建模海拔信息MODIS反射率,合成产品MOD09Q1/2010[33]
/印度东北部BI/IRS P6 LISS-III0.842010[60]
牡竹Mogi-Gua?u,巴西EMC,IES/ProSpecTIR-V0.64,0.63/0.9,0.762015[31]
巴山木竹和拐棍竹卧龙自然保护区,中国k-NN, gk-NN/WorldView-20.76,0.82 /0.92,0.932016[28]
毛竹漓江流域,中国BEMD融合,基于规则的特征提取/TerraSAR-X, Gaofen-1(多光谱和全色)0.712017[52]
/全球DT调查数据,统计数据和文献数据MODIS地表反射率,合成产品 (MOD13Q1), Landsat8 OLI0.79/0.872018[27]
毛竹福建GLCM\RF地形,植被,气候,土壤Landsat TM0.73~0.93/2018[53]
/东非三国RFMODIS NDVI,地面收集数据Landsat OLI/0.842018[40]
表1  竹资源遥感监测数据源及方法
竹种拉丁名
巴山木竹Bashania fargesii
秦岭箭竹Fargesia qinlingensis
华西箭竹Fargesia nitida
拐棍竹Fargesia robusta
牡竹Dendrocalamus strictus
雷竹Phyllostachys praecox
毛竹Phyllostachys edulis
印度簕竹Bambusa bambos
其他下层竹(文中未具体列出)/
其他大型丛生竹(文中未具体列出)/
表2  文献中监测的竹种
缩写中文名称英文名称
MLC最大似然法分类器Maximum Likelihood Classifier
MDC马氏距离分类器Mahalanobis Distance Classifier
ANN人工神经网络Artificial Neural Networks
SVM支持向量机Support Vector Machine
RF随机森林Random Forest
GLCM灰度共生矩阵Gray-Level Co-Occurrence Matrix
BI竹指数Bamboo Index
k-NNk-最近邻K-Nearest Neighbor
gk-NN地理加权的k-NN分类器Geostatistically-Weighted K-Nn Classifier
DT决策树Decsion Tree
MF匹配滤波Matched Filtering
BEMD二维经验模态分解Bidimensional Empirical Mode Decomposition
IES交互式端元选择Interactive Endmember Selection
EMC端元均方根误差、最小光谱角Endmember Average Root Mean Square Error(EAR),Minimum Average Spectral Angle(MASA),Count-Based(Cob)
表3  文献中竹资源遥感监测方法
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