Feasibility of hyperspectral vegetation indices for the detection of chlorophyll concentration in three high Arctic plants: Salix polaris, Bistorta vivipara, and Dryas octopetala

Bogdan Zagajewski, Marlena Kycko, Hans Tømmervik, Zbigniew Bochenek, Bronisław Wojtuń, Jarle W. Bjerke, Andrzej Kłos

Abstract


Remote sensing, which is based on a reflected electromagnetic spectrum, offers a wide range of research methods. It allows for the identification of plant properties, e.g., chlorophyll, but a registered signal not only comes from green parts but also from dry shoots, soil, and other objects located next to the plants. It is, thus, important to identify the most applicable remote-acquired indices for chlorophyll detection in polar regions, which play a primary role in global monitoring systems but consist of areas with high and low accessibility. This study focuses on an analysis of in situ-acquired hyperspectral properties, which was verified by simultaneously measuring the chlorophyll concentration in three representative arctic plant species, i.e., the prostrate deciduous shrub Salix polaris, the herb Bistorta vivipara, and the prostrate semievergreen shrub Dryas octopetala. This study was conducted at the high Arctic archipelago of Svalbard, Norway. Of the 23 analyzed candidate vegetation and chlorophyll indices, the following showed the best statistical correlations with the optical measurements of chlorophyll concentration: Vogelmann red edge index 1, 2, 3 (VOG 1, 2, 3), Zarco-Tejada and Miller index (ZMI), modified normalized difference vegetation index 705 (mNDVI 705), modified normalized difference index (mND), red edge normalized difference vegetation index (NDVI 705), and Gitelson and Merzlyak index 2 (GM 2). An assessment of the results from this analysis indicates that S. polaris and B. vivipara were in good health, while the health status of D. octopetala was reduced. This is consistent with other studies from the same area. There were also differences between study sites, probably as a result of local variation in environmental conditions. All these indices may be extracted from future satellite missions like EnMAP (Environmental Mapping and Analysis Program) and FLEX (Fluorescence Explorer), thus, enabling the efficient monitoring of vegetation condition in vast and inaccessible polar areas.

Keywords


Arctic plants; ASD FieldSpec; remote sensing indices; remote sensing

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References


Swain PH, Davis SM. Remote sensing: the quantitative approach. New York, NY: McGraw-Hill Inc.; 1987.

Roy PS. Spectral reflectance characteristics of vegetation and their use in estimating productive potential. Proceedings: Plant Sciences. 1989;99(1):59–81.

Asner GP. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens Environ. 1998;64(3):234–253. https://doi.org/10.1016/S0034-4257(98)00014-5

Schaepman-Strub G, Schaepman ME, Painter TH, Dangel S, Martonchik JV. Reflectance quantities in optical remote sensing – definitions and case studies. Remote Sens Environ. 2006;103(1):27–42. https://doi.org/10.1016/j.rse.2006.03.002

Gates DM, Keegan HJ, Schleter JC, Weidner VR. Spectral properties of plants. Appl Opt. 1965;4(1):11–20. https://doi.org/10.1364/AO.4.000011

Jensen JR. Biophysical remote sensing – review article. Ann Assoc Am Geogr. 1983;73(1):111–132. https://doi.org/10.1111/j.1467-8306.1983.tb01399.x

Clevers JGPW, Kooistra L, Schaepman ME. Estimating canopy water content using hyperspectral remote sensing data. Int J Appl Earth Obs Geoinf. 2010;12(2):119–125. https://doi.org/10.1016/j.jag.2010.01.007

Zhang M, Ustin SL, Rejmankova E, Sanderson EW. Monitoring pacific coast salt marshes using remote sensing. Ecol Appl. 1997;7(3):1019–1053. https://doi.org/10.1890/1051-0761(1997)007[1039:MPCSMU]2.0.CO;2

Thomas JR, Oerther GF. Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agron J. 1972;64:11–13. https://doi.org/10.2134/agronj1972.00021962006400010004x

Gausman HW, Allen WA, Cardenas R. Reflectance of cotton leaves and their structure. Remote Sens Environ. 1969;1:19–22. https://doi.org/10.1016/S0034-4257(69)90055-8

Gausman HW, Allen WA, Wiegand CL, Escobar DE, Rodrigues RR, Richardson AJ. The leaf mesophyll of twenty crops, their light spectra and optical and geometrical parameters. Weslaco, TX: USDA, Agricultural Research Service, Soil and Water Conservation Research Division, Rio Grande Soil and Water Research Center; 1971. (SWC Research Report; vol 423). https://doi.org/10.5962/bhl.title.149765

Kycko M, Zagajewski B, Lavender S, Romanowska E, Zwijacz-Kozica M. The impact of tourist traffic on the condition and cell structures of alpine swards. Remote Sens. 2018;10(2):220. https://doi.org/10.3390/rs10020220

Richardson AD, Duigan SP, Berlyn GP. An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytol. 2002;153(1):185–194. https://doi.org/10.1046/j.0028-646X.2001.00289.x

Gitelson AA. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J Plant Physiol. 2004;161(2):165–173. https://doi.org/10.1078/0176-1617-01176

Arena C, Vitale L, de Santo AV. Paraheliotropism in Robinia pseudoacacia L.: an efficient strategy to optimise photosynthetic performance under natural environmental conditions. Plant Biol. 2008;10(2):194–201. https://doi.org/10.1111/j.1438-8677.2008.00032.x

Olascoaga B, Juurola E, Lukeš P, Nikinmaa E, Bäck J, Porcar-Castell A, et al. Seasonal variation in the reflectance of photosynthetically active radiation from epicuticular waxes of Scots pine (Pinus sylvestris) needles. Boreal Environ Res. 2014;19(suppl B):132–141. https://doi.org/10.1007/s10534-014-9780-1

Merzlyak MN, Chivkunova OB, Solovchenko AE, Naqvi KR. Light absorption by anthocyanins in juvenile, stressed, and senescing leaves. J Exp Bot. 2008;59(14):3903–3911. https://doi.org/10.1093/jxb/ern230

Porcar-Castell A, Tyystjärvi E, Atherton J, van der Tol C, Flexas J, Pfündel EE, et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J Exp Bot. 2014;65(15):4065–4095. https://doi.org/10.1093/jxb/eru191

Kalaji HM, Bosa K, Kościelniak J, Hossain Z. Chlorophyll a fluorescence – a useful tool for the early detection of temperature stress in spring barley (Hordeum vulgare L.). OMICS: A Journal of Integrative Biology. 2011;15(12):925–934. https://doi.org/10.1089/omi.2011.0070

Porcar-Castell A, Garcia-Plazaola JI, Nichol CJ, Kolari P, Olascoaga B, Kuusinen N, et al. Physiology of the seasonal relationship between the photochemical reflectance index and photosynthetic light use efficiency. Oecologia 2012;170(2):313. https://doi.org/10.1007/s00442-012-2317-9

Zarco-Tejada PJ, González-Dugo V, Berni JAJ. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens Environ. 2012;117:322–337. https://doi.org/10.1016/j.rse.2011.10.007

Gitelson AA, Merzlyak MN. Remote estimation of chlorophyll content in higher plant leaves. Int J Remote Sens. 1997;18(12):2691–2697. https://doi.org/10.1080/014311697217558

Datt B. A new reflectance index for remote sensing of chlorophyll content in higher plants: tests using eucalyptus leaves. J Plant Physiol. 1999;154(1):30–36. https://doi.org/10.1016/S0176-1617(99)80314-9

Zhu XG, Govindjee, Baker NR, DeSturler E, Ort DR, Long SP. Chlorophyll a fluorescence induction kinetics in leaves predicted from a model describing each discrete step of excitation energy and electron transfer associated with photosystem II. Planta. 2005;223(1):114–133. https://doi.org/10.1007/s00425-005-0064-4

Tan CW, Wang DL, Zhou J, Du Y, Luo M, Zhang YL, et al. Assessment of Fv/Fm absorbed by wheat canopies employing in-situ hyperspectral vegetation indexes. Sci Rep. 2018;8:9525. https://doi.org/10.1038/s41598-018-27902-3

Cierniewski J, Kazmierowski C, Krolewicz S, Piekarczyk J, Wrobel M, Zagajewski B. Effects of different illumination and observation techniques of cultivated soils on their hyperspectral bidirectional measurements under field and laboratory conditions. IEEE J Sel Top Appl Earth Obs Remote Sens. 2014;7(6):2525–2530. https://doi.org/10.1109/JSTARS.2014.2298098

Cierniewski J, Ceglarek J, Karnieli A, Królewicz S, Kaźmierowski C, Zagajewski B. Predicting the diurnal blue-sky albedo of soils using their laboratory reflectance spectra and roughness indices. J Quant Spectrosc Radiat Transf. 2017;200:25–31. https://doi.org/10.1016/j.jqsrt.2017.05.033

Rossini M, Fava F, Cogliati S, Meroni M, Marchesi A, Panigada C, et al. Assessing canopy PRI from airborne imagery to map water stress in maize. ISPRS J Photogramm Remote Sens. 2013;86:168–177. https://doi.org/10.1016/j.isprsjprs.2013.10.002

Wieneke S, Ahrends H, Damm A, Pinto F, Stadler A, Rossini M, et al. Airborne based spectroscopy of red and far-red sun-induced chlorophyll fluorescence: implications for improved estimates of gross primary productivity. Remote Sens Environ. 2016;184:654–667. https://doi.org/10.1016/j.rse.2016.07.025

Marcinkowska-Ochtyra A, Zagajewski B, Raczko E, Ochtyra A, Jarocińska A. Classification of high-mountain vegetation communities within a diverse giant mountains ecosystem using airborne APEX hyperspectral imagery. Remote Sens. 2018;10(4):570. https://doi.org/10.3390/rs10040570

Raczko E, Zagajewski B. Tree species classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) using artificial neural networks and APEX hyperspectral images. Remote Sens. 2018;10(7):1111. https://doi.org/10.3390/rs10071111

Hawryło P, Bednarz B, Wężyk P, Szostak M. Estimating defoliation of Scots pine stands using machine learning methods and vegetation indices of Sentinel-2. Eur J Remote Sens. 2018;51(1):194–204. https://doi.org/10.1080/22797254.2017.1417745

Marshall M, Thenkabail P, Bigges T, Post K. Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation). Agric For Meteorol. 2015;218–219:122–134. https://doi.org/10.1016/j.agrformet.2015.12.025

Aspinall R. Use of logistic regression for validation of maps of the spatial distribution of vegetation species derived from high spatial resolution hyperspectral remotely sensed data. Ecol Model. 2002;157:301–312. https://doi.org/10.1016/S0304-3800(02)00201-6

Zagajewski B, Tømmervik H, Bjerke JW, Raczko E, Bochenek Z, Kłos A, et al. Intraspecific differences in spectral reflectance curves as indicators of reduced vitality in high-arctic plants. Remote Sens. 2017;9(12):1289. http://doi.org/10.3390/rs9121289

Darvishzadeh R, Skidmore A, Schlerf M, Atzberger C, Corsi F, Cho M. LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS J Photogramm Remote Sens. 2008;63(4):409–426. https://doi.org/10.1016/j.isprsjprs.2008.01.001

Guanter L, Kaufmann H, Segl K, Foerster S, Rogass C, Chabrillat S, et al. The EnMAP spaceborne imaging spectroscopy mission for Earth observation. Remote Sens. 2015;7:8830–8857. https://doi.org/10.3390/rs70708830

Moreno J, Alonso L, Delegido J, Rivera JP, Ruiz-Verdú A, Sabater N, et al. FLEX (Fluorescence Explorer) mission: observation fluorescence as a new remote sensing technique to study the global terrestrial vegetation state. Revista de Teledetección. 2014;41:111–119. https://doi.org/10.4995/raet.2014.2296

Bjerke JW, Treharne R, Vikhamar-Schuler D, Karlsen SR, Ravolainen V, Bokhorst S, et al. Understanding the drivers of extensive plant damage in boreal and Arctic ecosystems: insights from field surveys in the aftermath of damage. Sci Total Environ. 2017;599–600:1965–1976. https://doi.org/10.1016/j.scitotenv.2017.05.050

Kłos A, Bochenek Z, Bjerke JW, Zagajewski B, Ziółkowski D, Ziembik Z, et al. The use of mosses in biomonitoring of selected areas in Poland and Spitsbergen in the years from 1975 to 2014. Ecological Chemistry and Engineering S. 2015;22(2):201–218. https://doi.org/10.1515/eces-2015-0011

Kłos A, Ziembik Z, Rajfur M, Dolhanczuk-Środka A, Bochenek Z, Bjerke JW, et al. The origin of heavy metals and radionuclides accumulated in the soil and biota samples collected in Svalbard, near Longyearbyen. Ecological Chemistry and Engineering S. 2017;24;223–238. https://doi.org/10.1515/eces-2017-0015

Johansen B, Tømmervik H. The relationship between phytomass, NDVI and vegetation communities on Svalbard. Int J Appl Earth Obs Geoinf. 2014;27(A):20–30. https://doi.org/10.1016/j.jag.2013.07.001

Rønning O. The flora of Svalbard. Oslo: Norwegian Polar Institute; 1996. (Polarhåndbok; vol 10).

Johansen BE, Karlsen SR, Tømmervik H. Vegetation mapping of Svalbard utilising Landsat TM/ETM+ data. Polar Rec. 2012:48:47–63. https://doi.org/10.1017/S0032247411000647

Welker JM, Molau U, Parsons AN, Robinson CH, Wookey PA. Responses of Dryas octopetala to ITEX environmental manipulations: a synthesis with circumpolar comparisons. Glob Chang Biol. 1997;3(S1):61–73. https://doi.org/10.1111/j.1365-2486.1997.gcb143.x

Potůčková M, Červená L, Kupková L, Lhotáková Z, Lukeš P, Hanuš J, et al. Comparison of reflectance measurements acquired with a contact probe and an integration sphere: implications for the spectral properties of vegetation at a leaf level. Sensors. 2016;16:1801. https://doi.org/10.3390/s16111801

Cerovic ZG, Masdoumier G, Ben Ghozlen N, Latouche G. A new optical leaf-clip meter for simultaneous non-destructive assessment of leaf chlorophyll and epidermal flavonoids. Physiol Plant. 2012;146(3):251–260. https://doi.org/10.1111/j.1399-3054.2012.01639.x

Shapiro SS, Wilk MB. An analysis of variance test for normality (complete samples). Biometrika. 1965;52(3–4):591. https://doi.org/10.2307/2333709

Lehmann EL, Romano JP. Testing statistical hypotheses. 3rd ed. New York, NY: Springer; 2005.

Kruskal WH. A nonparametric test for the several sample problem. Annals of Mathematical Statistics. 1952;23:525–540. https://doi.org/10.1214/aoms/1177729332

Spearman Ch. The proof and measurement of association between two things. Am J Psychol. 1904;15:72–101. https://doi.org/10.2307/1412159

Kycko M, Zagajewski B, Zwijacz-Kozica M, Cierniewski J, Romanowska E, Orłowska K, et al. Assessment of hyperspectral remote sensing for analyzing the impact of human trampling on alpine swards. Mt Res Dev. 2017;37(1):66–74. https://doi.org/10.1659/MRD-JOURNAL-D-15-00050.1

Kycko M. Assessment of the dominant alpine sward species condition of the Tatra National Park using hyperspectral remote sensing [PhD thesis]. Warsaw: Faculty of Geography and Regional Studies, University of Warsaw; 2017.

Sims DA, Gamon JA. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ. 2002;81(2–3):337–354. https://doi.org/10.1016/S0034-4257(02)00010-X

Sims D, Luo H, Hastings S, Oechel W, Rahman A, Gamon J. Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem. Remote Sens Environ. 2006;103(3):289–303. https://doi.org/10.1016/j.rse.2005.01.020

Hope AS, Kimball JS, Stow DA. The relationship between tussock tundra spectral reflectance properties and biomass and vegetation composition, Int J Remote Sens. 1993;14(10):1861–1874. https://doi.org/10.1080/01431169308954008

Peng Y, Nguy-Robertson A, Arkebauer T, Gitelson AA. Assessment of canopy chlorophyll content retrieval in maize and soybean: implications of hysteresis on the development of generic algorithms. Remote Sens. 2017;9:226. https://doi.org/10.3390/rs9030226

Shepherd T, Wynne Griffiths D. The effects of stress on plant cuticular waxes. New Phytol. 2006;171(3):469–499. https://doi.org/10.1111/j.1469-8137.2006.01826.x

Gitelson A, Merzlyak MN. Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. J Photochem Photobiol B. 1994;22(3):247–252. https://doi.org/10.1016/1011-1344(93)06963-4

Vogelmann JE, Rock BN, Moss DM. Red edge spectral measurements from sugar maple leaves. Int J Remote Sens. 1993;14(8):1563–1575. https://doi.org/10.1080/01431169308953986

Chappelle EW, Kim MS, McMurtrey JE. Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sens Environ. 1992;39(3):239–247. https://doi.org/10.1016/0034-4257(92)90089-3

Peñuelas J, Baret F, Filella I, Penuelas J, Baret F, Filella I. Semiempirical indexes to assess carotenoids chlorophyll-a ratio from leaf spectral reflectance. Photosynthetica. 1995;31(2):221–230.

Merzlyak MN, Gitelson AA, Chivkunova OB, Rakitin VY. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol Plant. 1999;106(1):135–141. https://doi.org/10.1034/j.1399-3054.1999.106119.x

Gitelson AA, Kaufman YJ, Merzlyak MN. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ. 1996;58(3):289–298. https://doi.org/10.1016/S0034-4257(96)00072-7

Mänd P, Hallik L, Peñuelas J, Nilson T, Duce P, Emmett BA, et al. Responses of the reflectance indices PRI and NDVI to experimental warming and drought in European shrublands along a north–south climatic gradient. Remote Sens Environ. 2010;114:626–636. https://doi.org/10.1016/j.rse.2009.11.003

Zarco-Tejada PJ, Miller JR, Mohammed GH, Noland TL, Sampson PH. Estimation of chlorophyll fluorescence under natural illumination from hyperspectral data. Int J Appl Earth Obs Geoinf. 2001;3(4):321–327. https://doi.org/10.1016/S0303-2434(01)85039-X

Lichtenthaler HK, Lang M, Sowinska M, Heisel F, Miehé JA. Detection of vegetation stress via a new high resolution fluorescence imaging system. J Plant Physiol. 1996;148(5):599–612. https://doi.org/10.1016/S0176-1617(96)80081-2

Haboudane D. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ. 2004;90:337–352. https://doi.org/10.1016/j.rse.2003.12.013

Sripada RP, Heiniger RW, White JG, Meijer AD. Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agron J. 2006;98:968–977. https://doi.org/10.2134/agronj2005.0200

Fuentes DA, Gamon JA, Qiu H, Sims DA, Roberts DA. Mapping Canadian boreal forest vegetation using pigment and water absorption features derived from the AVIRIS sensor. J Geophys Res Atmos. 2001;106(D24):33565–33577. https://doi.org/10.1029/2001JD900110

Zagajewski B. Ocena przydatności sieci neuronowych i danych hiperspektralnych do klasyfikacji roślinności Tatr Wysokich. Warszawa: Klub Teledetekcji Środowiska Polskiego Towarzystwa Geograficznego; 2010. (Teledetekcja Środowiska; vol 43).

Ruban AV, Horton P, Young AJ. Aggregation of higher plant xanthophylls: differences in absorption spectra and in the dependency on solvent polarity. J Photochem Photobiol B. 1993;21(2–3):229–234. https://doi.org/10.1016/1011-1344(93)80188-F

Barton CV, North PR. Remote sensing of canopy light use efficiency using the photochemical reflectance index. Remote Sens Environ. 2001;78(3):264–273. https://doi.org/10.1016/S0034-4257(01)00224-3

Plummer S. Perspectives on combining ecological process models and remotely sensed data. Ecol Modell. 2000;129(2–3):169–186. https://doi.org/10.1016/S0304-3800(00)00233-7

Adams III WW, Demmig-Adams B, Logan BA, Barker DH, Osmond CB. Rapid changes in xanthophyll cycle-dependent energy dissipation and photosystem II efficiency in two vines, Stephania japonica and Smilax australis, growing in the understory of an open eucalyptus forest. Plant Cell Environ. 1999;22(2):125–136. https://doi.org/10.1046/j.1365-3040.1999.00369.x

Lichtenthaler HK, Wellburn RR. Determination of total caretonoids and chlorophyll a and b in the leaf extracts in different solvents. Biochem Soc Trans. 1983;603:591–592. https://doi.org/10.1042/bst0110591

Datt B. Recognition of eucalyptus forest species using hyperspectral reflectance data. In: Stein TI, editor. IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the pulse of the planet: the role of remote sensing in managing the environment. Proceedings (cat. No. 00CH37120); 2000 Jul 24–28; Hilton Hawaiian Village, Honolulu, Hawaii, USA. Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2000. p. 1405–1407. https://doi.org/10.1109/IGARSS.2000.857221

Carter GA. Ratios of leaf reflectance in narrow wavebands as indicators of plant stress. Int J Remote Sens. 1994;15(3):697–703. https://doi.org/10.1080/01431169408954109

Cochrane MA. Spreading like wildfire – tropical forest fires in Latin America and the Caribbean: prevention, assessment and early warning. Mexico: DEWA; 2002. (Early Warning and Assessment Technical Report Series; vol 1).

Cochrane MA. Using vegetation reflectance variability for species level classification of hyperspectral data. Int J Remote Sens. 2000;21(10):2075–2087. https://doi.org/10.1080/01431160050021303

Shaw DT, Malthus TJ, Kupiec JA. High-spectral resolution data for monitoring Scots pine (Pinus sylvestris L.) regeneration. Int J Remote Sens. 1998;19(13):2601–2608. https://doi.org/10.1080/014311698214668

Cochrane MA. Synergistic Interactions between habitat fragmentation and fire in evergreen tropical forests. Conserv Biol. 2001;15(6):1515–1521. https://doi.org/10.1046/j.1523-1739.2001.01091.x




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