不同健康状况松针反射光谱特征分析
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摘要
为利用高分辨率遥感影像实现大面积的枯死松树监测,本研究通过测量不同程度的枯死和健康松针的光谱曲线,结合GF-2遥感影像的波段范围分析其光谱特征与敏感波段,并借助28种光谱指数探究区分健康松树与枯死松树的适宜光谱指数。结果表明:近红外波段(欧氏距离为118.16)是识别枯死松树的最敏感波段。文中使用的28种光谱指数除R_(gre)-R_(blue)、R_(blue)/R_(nir)+R_(gre)、R_(blue)/R_(nir)+R_(red)、R_(blue)/R_(gre)、(R_(blue)-R_(gre))/(R_(gre)+R_(blue))和R_(blue)/R_(red)+R_(gre)这6种光谱指数外,其余22种光谱指数的J-M距离均超过1.90,表现出很好的光谱可分性。研究结果可为构建适用于GF-2遥感影像进行枯死松树监测的植被指数提供理论基础。
This study aimed to detect large-area dead pine tree monitoring using high-resolution remote sensing images. By measuring the spectral curves of different degrees of dead and healthy pine needles with band range by GF-2 remote sensing image we analysed the spectral characteristics and sensitive bands. Moreover we found the optimum spectral indices to distinguish whether it was healthy or not by using 28 spectral indices. We find the near-infrared band(Euclidean distance is 118.16) is the most sensitive band for identifying dead pine trees. The six spectral indices used in this paper are R_(gre)-R_(blue), R_(blue)/R_(nir)+R_(gre), R_(blue)/R_(nir)+R_(red), R_(blue)/R_(gre),(R_(blue)-R_(gre))/(R_(gre)+R_(blue)) and R_(blue)/R_(red)+R_(gre). In addition, the remaining 22 spectral indices have JM distances exceeding 1.90, showing good spectral separability. The results of the study provide a theoretical basis for the construction of vegetation indices for GF-2 remote sensing images for dead pine monitoring.
This study aimed to detect large-area dead pine tree monitoring using high-resolution remote sensing images. By measuring the spectral curves of different degrees of dead and healthy pine needles with band range by GF-2 remote sensing image we analysed the spectral characteristics and sensitive bands. Moreover we found the optimum spectral indices to distinguish whether it was healthy or not by using 28 spectral indices. We find the near-infrared band(Euclidean distance is 118.16) is the most sensitive band for identifying dead pine trees. The six spectral indices used in this paper are R_(gre)-R_(blue), R_(blue)/R_(nir)+R_(gre), R_(blue)/R_(nir)+R_(red), R_(blue)/R_(gre),(R_(blue)-R_(gre))/(R_(gre)+R_(blue)) and R_(blue)/R_(red)+R_(gre). In addition, the remaining 22 spectral indices have JM distances exceeding 1.90, showing good spectral separability. The results of the study provide a theoretical basis for the construction of vegetation indices for GF-2 remote sensing images for dead pine monitoring.
引文
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[3] 陶欢,李存军,周静平,等.基于高分1号影像的森林植被信息提取[J].自然资源学报,2018,33(6):1068-1079.
[4] 蒋金豹,Michael D Steven,何汝艳,等.水浸胁迫下植被高光谱遥感识别模型对比分析[J].光谱学与光谱分析,2013,33(11):3106-3110.
[5] 黄明祥,龚建华,李顺,等.松材线虫病害高光谱时序与敏感特征研究[J].遥感技术与应用,2012,27(6):954-960.
[6] NIEMANN K O,QUINN G,STEPHEN R,et al. Hyperspectral Remote Sensing of Mountain Pine Beetle with an Emphasis on Previsual Assessment[J]. Canadian Journal of Remote Sensing,2015,41(3): 191-202.
[7] 彭隆赞,金时超,孟凡凡,等.马尾松松树毛虫危害程度的高光谱监测方法[J].湖北林业科技,2017,46(3):1-6.
[8] 李明君,李存军,淮贺举,等.基于模拟光谱数据的冬小麦播期遥感监测最佳时相研究[J].麦类作物学报,2015,35(8):1148-1154.
[9] JORDAN C F. Derivation of Leaf-Area Index from Quality of Light on the Forest Floor[J]. Ecology,1969,50(4): 663-666.
[10] VERRELST J,SCHAEPMAN M E,KOETZ B,et al. Angular Sensitivity Analysis of Vegetation Indices Derived from CHRIS/PROBA Data[J]. Remote Sensing of Environment,2008,112(5):2341-2353.
[11] PERSON R L. Remote Mapping of Standing Crop Biomass for Estimation of the Productivity of the Short-Grass Prairie,Pawnee National Grasslands,Colorado[J].1972,45(2):7-12.
[12] 唐世浩,朱启疆,王锦地,等.三波段梯度差植被指数的理论基础及其应用[J].中国科学:地球科学,2003,33(11):1094-1102.
[13] 王来刚,田永超,李文龙,等.基于地—空遥感耦合的冬小麦叶片氮积累量估算[J].应用生态学报,2012,23(1):73-80.
[14] GITELSON A A,KAUFMAN Y J,MERZLYAK M N. Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS[J]. Remote Sensing of Environment,1996,58(3): 289-298.
[15] ROUSE J W J,HAAS R H,SCHELL J A,et al. Monitoring Vegetation Systems in the Great Plains with ERST[J]. Proceedings 3rd Earth Resources Technology Satellite (ERTS) Symposium,1974(1):309-317.
[16] 张仁华,饶农新,廖国男.植被指数的抗大气影响探讨[J].植物学报(英文版),1996,38(1):53-62.