Study on the spectral characteristics and remote sensing recognition of the <i>Sphagnum</i> community

Pang Yu-Wen; Huang Yu-Xin; Wen Jing-Yi; Xu Jun-Feng

doi:10.11913/PSJ.2095-0837.2019.20125

Plant Science Journal > 2019 > 37(2): 125-135. > DOI: 10.11913/PSJ.2095-0837.2019.20125 CSTR: 32231.14.PSJ.2095-0837.2019.20125

Pang Yu-Wen, Huang Yu-Xin, Wen Jing-Yi, Xu Jun-Feng. Study on the spectral characteristics and remote sensing recognition of the Sphagnum community[J]. Plant Science Journal, 2019, 37(2): 125-135. DOI: 10.11913/PSJ.2095-0837.2019.20125

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Study on the spectral characteristics and remote sensing recognition of the Sphagnum community

1. Research Academy of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou 311121, China;
2. Key Laboratory of Wetland and Regional change in Zhejiang Province, Hangzhou 311121, China

Funds:

This work was supported by grants from the National Natural Science Foundation of China (41571049), Zhejiang Provincial Natural Science Foundation (LY16D010007), and Science and Technology Program of Hangzhou, China (20170533B01).

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Received Date: October 08, 2018
Revised Date: November 29, 2018
Available Online: October 31, 2022
Published Date: April 27, 2019

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Abstract

Abstract

Sphagnum species are among the most important carbon sequestration plants in peatland systems and are closely related to regional and global material energy balance. Here, we analyzed the spectral characteristics of the Sphagnum magellanicum Brid community based on the measured canopy spectrum and remote sensing sensor simulated spectrum. Results showed that the spectral differences between S. magellanicum and northern coniferous forest were obvious, and the best spectral recognition intervals were 740-1140 nm and 1230-1412 nm. In the visible light band, the ‘green peak’ position of S. magellanicum was different from that of Picea engelmannii Parry ex Engelmann and Pinus contorta Douglas ex Loudon. Spectral identification characteristics of Phyllostachys heteroclada Oliver and S. magellanicum were concentrated in the visible-near-infrared bands at 400-550, 560-696, and 1025-1143 nm. There were subtle differences in the characteristic spectral bands between S. magellanicum, northern coniferous forest, and P. heteroclada; for example, S. magellanicum and P. heteroclada showed good separability in the visible light region, thus the characteristic spectral width of the S. magellanicum community in different latitudes was different. The infrared band was the best spectral range for S. magellanicum recognition. The recognition effect of S. magellanicum was better at the multispectral remote sensing level, whereas the recognition performance of the sensor was as follows:MSI > ALI > OLI > ASTER. In the spectral characteristic analysis of the two S. magellanicum communities, the spectral dimension reduction ability of the derivative, logarithm, and continuum removal methods was different, among which the continuum removal method was the best.
- Sphagnum,
- Coniferous forest,
- Phyllostachys heteroclada,
- Spectral analysis,
- Remote sensing

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References (30)

References

[1]	高谦. 中国苔藓志:第1卷[M]. 北京:科学出版社, 1994.
[2]	Yu ZC. Northern peatland carbon stocks and dynamics:a review[J]. Biogeosciences, 2012, 9(10):4071-4085.
[3]	Baird AJ, Belyea LR, Comas X, Reeve AS, Slater LD. Carbon Cycling in Northern Peatlands[DB/OL].[2019-03-05]. https://doi.org/10.1029/GM184.
[4]	Huang XY, Pancost RD, Xue JT, Gu YS, Evershed RP, et al. Response of carbon cycle to drier conditions in the mid-Holocene in central China[J]. Nat Commun, 2018, 9(1):1369.
[5]	Limpens J, Berendse F, Blodau C, Canadell J G, Freeman C, et al. Peatlands and the carbon cycle:from local processes to global implications-a synthesis[J]. Biogeosciences, 2008, 5(6):1475-1491.
[6]	Treat C, Wisser D, Marchenko S, Frolking S. Modelling the effects of climate change and disturbance on permafrost stability in northern organic soils[J]. Mires Peat, 2013, 12(2):1-17.
[7]	Limpens J, Granath G, Gunnarsson U, Aerts R, Bayley S, et al. Climatic modifiers of the response to nitrogen deposition in peat-forming Sphagnum mosses:a meta-analysis[J]. New Phytol, 2011, 191(2):496-507.
[8]	Spahni R, Joos F, Stocker BD, Steinacher M, Yu ZC. Transient simulations of the carbon and nitrogen dynamics in northern peatlands:from the Last Glacial Maximum to the 21st century[J]. Clim Past, 2013, 9(3):1287-1308.
[9]	Vogelmann JE, Moss DM. Spectral reflectance measurements in the genus Sphagnum[J]. Remote Sens Environ, 1993, 45(3):273-279.
[10]	Bubier JL, Moore TR. An ecological perspective on methane emissions from northern wetlands[J]. Trends Ecol Evol, 1994, 9(12):460-464.
[11]	Harris A, Bryant RG, Baird AJ. Detecting near-surface moisture stress in Sphagnum spp.[J]. Remote Sens Environ, 2005, 97(3):371-381.
[12]	Harris A, Bryant RG, Baird AJ. Mapping the effects of water stress on Sphagnum:Preliminary observations using airborne remote sensing[J]. Remote Sens Environ, 2006, 100(3):363-378.
[13]	Harris A. Spectral reflectance and photosynthetic properties of Sphagnum mosses exposed to progressive drought[J]. Ecohydrology, 2010, 1(1):35-42.
[14]	Neta T, Cheng Q, Bello RL, Hu B. Lichens and mosses moisture content assessment through high-spectral resolution remote sensing technology:a case study of the Hudson Bay Lowlands, Canada[J]. Hydrol Process, 2010, 24(18):2617-2628.
[15]	Neta T, Cheng Q, Bello RL, Hu B. Development of new spectral reflectance indices for the detection of lichens and mosses moisture content in the Hudson Bay Lowlands, Canada[J]. Hydrol Process, 2015, 25(6):933-944.
[16]	Meingast KM, Falkowski MJ, Kane ES, Potvin L, Benscoter B, et al. Spectral detection of near-surface moisture content and water-table position in northern peatland ecosystems[J]. Remote Sens Environ, 2014, 152:536-546.
[17]	由佳, 张怀清, 陈永富, 高志海, 刘华. 基于GF-4号卫星影像东洞庭湖湿地植被类型监测能力比较研究[J]. 安徽农业科学, 2018, 46(3):152-156. You J, Zhang HQ, Chen YF, Gao ZH, Liu H. Based on the GF-4 satellite image for the east Dongting Lake wetland vegetation type monitoring ability[J]. Anhui Agricultural Science, 2018, 46(3):152-156.
[18]	柴颖, 阮仁宗, 傅巧妮, 岁秀珍. 面向对象的高光谱影像湿地植被信息提取[J]. 地理空间信息, 2015, 13(4):83-85. Chai Y, Ruan RZ, Fu QN, Sui XZ. Object-oriented information extraction of wetland vegetation using hyperspectral image data[J]. Geospatial Information, 2015, 13(4):83-85.
[19]	张雪薇, 韩震, 刘美君, 丁如一. 长江口南汇湿地植被的光谱吸收特征研究[J]. 海洋学研究, 2018, 36(2):50-54. Zhang XW, Han Z, Liu MJ, Ding RY. Study on spectral absorption characteristics of vegetation in Nanhui Wetland of Yangtze River estuary[J]. Journal of Marine Sciences, 2018, 36(2):50-54.
[20]	黄灵光, 周学林. 南矶湿地国家自然保护区典型植被光谱波段特征分析及建库[J]. 湖北农业科学, 2018, 57(11):103-106. Huang LG, Zhou XL. Characteristic analysis and database construction of typical vegetation spectral band in Nanji Wetland National Nature Reserve[J]. Hubei Agricultural Sciences, 2018, 57(11):103-106.
[21]	凌成星, 刘华, 鞠洪波, 张怀清, 孙华, 等. 基于地面成像光谱数据特征的湿地典型植被类型识别研究:以东洞庭湖核心区湿地为例[J]. 西北林学院学报, 2018, 33(3):208-213. Ling CX, Liu H, Ju HB, Zhang HQ, Sun H, et al. Identifying typical wetland vegetation types based on imaging spectrometer data:a case studying Dongting Lake Wetland area[J]. Journal of Northwest Forestry University, 2018, 33(3):208-213.
[22]	冯家莉, 刘凯, 朱远辉, 李勇, 柳林, 等. 无人机遥感在红树林资源调查中的应用[J]. 热带地理, 2015, 35(1):35-42. Feng JL, Liu K, Zhu YH, LI Yong, Liu L, et al. Application of unmanned aerial vehicles to mangrove resources monitoring[J]. Tropical Geography, 2015, 35(1):35-42.
[23]	Adam E, Mutanga O, Rugege D. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation:a review[J]. Wet Ecol Manag, 2010, 18(3):281-296.
[24]	杨立君, 马明栋, 唐立军. 基于TM影像的崇明东滩湿地植被分类研究[J]. 水土保持研究, 2013, 20(1):126-130. Yang LJ, Ma MD, Tang LJ. Research on wetland vegetation classification of Chongming Easter Tidal Flat based on TM image[J]. Research of Soil and Water Conservation, 2013, 20(1):126-130.
[25]	刘春燕, 张雪红, 陈健. 基于决策树的角度指数方法EO-1 ALI影像的红树林遥感识别[J]. 湿地科学, 2015, 13(4):451-455. Liu CY, Zhang XH, Chen J. Identifying mangrove forest with EO-1 ALI imagery combining decision tree with angle indices[J]. Wetland science, 2015, 13(4):451-455.
[26]	Kokaly RF, Clark RN, Swayze GA, Livo KE, Hoefen TM, et al. USGS spectral library version 7[DB/OL].[2019-03-05]. https://doi.org/10.3133/ds1035.
[27]	王锦地, 张立新, 柳钦火, 等. 中国典型地物波谱知识库[M]. 北京:科学出版社, 2009.
[28]	Liu ZY, Wu HF, Huang JF. Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis[J]. Comput Electron Agr, 2010, 72(2):99-106.
[29]	林海军, 张绘芳, 高亚琪, 李霞, 杨帆, 等. 基于马氏距离法的荒漠树种高光谱识别[J]. 光谱学与光谱分析, 2014, 34(12):3358-3362. Lin HJ, Zhang HF, Gao YQ, Li X, Yang F, et al. Maha-lanobis distance based hyperspectral characteristic discrimination of characteristic discrimination of leaves of different desert tree species[J]. Spectroscopy and spectral analysis, 2014, 34(12):3358-3362.
[30]	丁丽霞, 王志辉, 葛宏立. 基于包络线法的不同树种叶片高光谱特征分析[J].浙江林学院学报, 2010, 27(6):809-814. Ding LX, Wang ZH, Ge HL. Continuum removal based hyperspectral characteristic analysis of leaves of different tree species[J]. Journal of Zhejiang Forestry College, 2010, 27(6):809-814.

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