Skip to main content
SpringerLink
Log in

The influence of seasonality in estimating mangrove leaf chlorophyll-a content from hyperspectral data

  • Original Paper
  • Published:
Wetlands Ecology and Management Aims and scope Submit manuscript

Abstract

Mangrove photosynthetic activity and, consequently, physiological stress can be monitored indirectly using leaf chlorophyll-a (Chla) measurements. Recent studies have demonstrated the feasibility of mangrove leaf Chla content estimation from in situ hyperspectral vegetation indices (VI) but no such research has been conducted using data collected from contrasting seasons (i.e. dry and rainy). In this study, mangrove leaves were collected in a sub-tropical forest of the Mexican Pacific for Chla content determination and in situ hyperspectral measurements (450–1,000 nm). Specifically, we tested 35 VI to estimate Chla content based on a leaf sample of 360 collected from the same trees during both the dry and rainy seasons. The forest examined contained three species of mangrove (Rhizophora mangle, Avicennia germinans and Laguncularia racemosa) exhibiting various conditions of health (dwarf condition, tall and healthy). A principal component analysis, followed by linear regression analyses, were conducted in order to identify those VI that best predict mangrove leaf Chla content during the two seasons. The results indicate that VI derived from hyperspectral measurements collected during the dry season are better at estimating leaf Chla content than those collected during the rainy season. Among the 35 VI, the Vog1 (R740/R720) index was found to be the best predictor of mangrove leaf Chla content, resulting in R 2 values of 0.80 and 0.68 for the dry and rainy season respectively. These results would suggest that for identifying variation in mangrove forest stress (i.e. health) in sub-tropical regions, hyperspectral measurements should be carried out during the dry season.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Biber PD (2007) Evaluating a chlorophyll content meter on three coastal wetland plant species. Agric Food Environ Sci 1(2):1–11

    Google Scholar 

  • Blackburn GA (1998) Quantifying chlorophylls and carotenoids at leaf and canopy sales: an evaluation of some hyperspectral approaches. Remote Sens Environ 66:273–285. doi:10.1016/S0034-4257(98)00059-5

    Article  Google Scholar 

  • Blackburn GA (1999) Relationships between spectral reflectance and pigment concentrations in stacks of deciduous broad leaves. Remote Sens Environ 70:224–237. doi:10.1016/S0034-4257(99)00048-6

    Article  Google Scholar 

  • Blasco F, Saenger P, Janodet E (1996) Mangroves as indicators of coastal change. Catena 27:167–178. doi:10.1016/0341-8162(96)00013-6

    Article  Google Scholar 

  • Blasco F, Gauquelin T, Rasolofoharinoro M, Denis J, Aizpuru M, Caldairou V (1998) Recent advances in mangrove studies using remote sensing data. Mar Freshw Res 49:287–296. doi:10.1071/MF97153

    Article  CAS  Google Scholar 

  • Broge NH, Leblanc E (2001) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens Environ 76:156–172. doi:10.1016/S0034-4257(00)00197-8

    Article  Google Scholar 

  • Buschmann C, Nagel E (1993) In vivo spectroscopy and internal optics of leaves as a basis for remote sensing of vegetation. Int J Remote Sens 14:711–722. doi:10.1080/01431169308904370

    Article  Google Scholar 

  • Cannicci S, Burrows D, Fratini S, Smith TJ III, Offenberg J, Dahdouh-Guebas F (2008) Faunal impact on vegetation structure and ecosystem function in mangrove forests: a review. Aquat Bot 89:186–220. doi:10.1016/j.aquabot.2008.01.009

    Article  Google Scholar 

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

    Google Scholar 

  • Cater GA, Miller RL (1994) Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands. Remote Sens Environ 50:295–302. doi:10.1016/0034-4257(94)90079-5

    Article  Google Scholar 

  • Carter GA (1998) Reflectance wavebands and indices for remote estimation of photosynthesis and stomatal conductance in pine canopies. Remote Sens Environ 63:61–72. doi:10.1016/S0034-4257(97)00110-7

    Article  Google Scholar 

  • Chapelle EW, Kim MS, McMurtrey JE III (1992) 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 39:239–247. doi:10.1016/0034-4257(92)90089-3

    Article  Google Scholar 

  • Cho MA, Sobhan IM, Skidmore AK (2006) Estimating fresh grass/herb biomass from HYMAP data using the red-edge position. Proc SPIE 6298:629805–629809. doi:10.1117/12.681640

    Google Scholar 

  • Cliff N (1988) The eigenvalues-greater-than-one rule and the reliability of components. Psychol Bull 103:276–279. doi:10.1037/0033-2909.103.2.276

    Article  Google Scholar 

  • Daughtry CST, Walthall CL, Kim MS, De Colstoun EB, McMurtrey JE (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens Environ 74:229–239. doi:10.1016/S0034-4257(00)00113-9

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Duke NC, Meynecke JO, Dittman S, Ellison AM, Anger K, Berger U, Cannicci S, Diele K, Ewel KC, Field CD, Koedam N, Lee SY, Marchand C, Nordhaus I, Dahdouh-Guebas F (2007) A world without mangroves? Science 317:41–42. doi:10.1126/science.317.5834.41b

    Article  PubMed  CAS  Google Scholar 

  • Feller IC, Whigham DF, O’Neill JP, McKee KL (1999) Effects of nutrient enrichment on within-stand cycling in a mangrove forest. Ecology 80:2193–2205. doi:10.1890/0012-9658(1999)080[2193:EONEOW]2.0.CO;2

    Google Scholar 

  • Filella I, Peñuelas J (1994) The red-edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Int J Remote Sens 15:1459–1470. doi:10.1080/01431169408954177

    Article  Google Scholar 

  • Flores-de-Santiago F, Kovacs JM, Flores-Verdugo F (2012) Assessing seasonal changes in leaf Chlorophyll-a content and leaf morphology in a sub-tropical mangrove forest of the Mexican Pacific. Mar Ecol Prog Ser 444:57–68. doi:10.3354/meps09474

    Article  CAS  Google Scholar 

  • Flores-de-Santiago F, Kovacs JM, Flores-Verdugo F (2013) Assessing the utility of a portable pocket instrument for estimating seasonal mangrove leaf chlorophyll contents. Bull Mar Sci. doi:10.5343/bms.2012.1032

    Google Scholar 

  • Flores-Verdugo F, Day JW, Briseño-Dueñas R (1987) Structure, litter fall, decomposition, and detritus dynamics of mangroves in a Mexican coastal lagoon with an ephemeral inlet. Mar Ecol Prog Ser 35:83–90. doi:10.3354/meps035083

    Article  Google Scholar 

  • Gamon JA, Peñuelas J, Field CB (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens Environ 41:35–44. doi:10.1016/0034-4257(92)90059-S

    Article  Google Scholar 

  • Gilman EL, Ellison J, Duke NC, Field C (2008) Threats to mangrove from climate change and adaptation options: a review. Aquat Bot 89:237–250. doi:10.1016/j.aquabot.2007.12.009

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Gitelson AA, Merzlyak MN (1996) Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. J Plant Physiol 148:494–500. doi:10.1016/S0176-1617(96)80284-7

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  • Haboudane D, Miller JR, Tremblay N, Zarco-Tejada PJ, Dextrazel L (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens Environ 81:416–426. doi:10.1016/S0034-4257(02)00018-4

    Article  Google Scholar 

  • Haboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB (2004) 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 90:337–352. doi:10.1016/j.rse.2003.12.013

    Article  Google Scholar 

  • Horler DNH, Dockray M, Barber J (1983) The red-edge of plant leaf reflectance. Int J Remote Sens 4:273–288. doi:10.1080/01431168308948546

    Article  Google Scholar 

  • Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295–309. doi:10.1016/0034-4257(88)90106-X

    Article  Google Scholar 

  • Jordan CF (1969) Derivation of leaf-area index from quality of light on the forest floor. Ecology 50:663–666. http://www.jstor.org/stable/1936256

    Google Scholar 

  • Kamaruzaman J, Kasawani I (2007) Imaging spectrometry on mangrove species identification and mapping in Malaysia. WSEAS Trans Biol Biomed 8:118–126

    Google Scholar 

  • Komiyama A, Eong OJ, Poungparn S (2008) Allometry, biomass, and productivity of mangrove forest: a review. Aquat Bot 89:128–137. doi:10.1016/j.aquabot.2007.12.006

    Article  Google Scholar 

  • Kovacs JM, Wang J, Flores-Verdugo F (2005) Mapping mangrove leaf area index at the species level using IKONOS and LAI-2000 sensors for the Agua Brava Lagoon, Mexican Pacific. Estuar Coast Shelf Sci 62:377–384. doi:10.1016/j.ecss.2004.09.027

    Article  Google Scholar 

  • Kovacs JM, King JML, Flores-de-Santiago F, Flores-Verdugo F (2009) Evaluating the condition of a mangrove forest of the Mexican Pacific based on an estimated leaf area index mapping approach. Environ Monit Assess 157:137–149. doi:10.1007/s10661-008-0523-z

    Article  PubMed  CAS  Google Scholar 

  • Kovacs JM, Flores-de-Santiago F, Bastien J, Lafrance P (2010) An assessment of mangroves in Guinea, West Africa, using a field and remote sensing based approach. Wetlands 30:773–782. doi:10.1007/s13157-010-0065-3

    Article  Google Scholar 

  • Kovacs JM, Liu Y, Zhang C, Flores-Verdugo F, Flores-de-Santiago J (2011) A field based statistical approach for validating a remotely sensed mangrove forest classification scheme. Wetl Ecol Manag 19:409–421. doi:10.1007/s11273-011-9225-3

    Article  Google Scholar 

  • Kristensen E, Bouillonn S, Dittmar T, Marchand C (2008) Organic carbon dynamics in mangrove ecosystems: a review. Aquat Bot 89:201–219. doi:10.1016/j.aquabot.2007.12.005

    Article  CAS  Google Scholar 

  • Lichtenthaler HK, Wellburn AR (1983) Determinations of total carotenoids and chlorophylls a and b in leaf extracts in different solvents. Biochem Soc Trans 11:591–592

    CAS  Google Scholar 

  • Lichtenthaler HK, Gitelson A, Lang M (1996) Non-destructive determination of chlorophyll content of leaves of a green and an aurea mutant of tobacco by reflectance measurements. J Plant Physiol 148:483–493. doi:10.1016/S0176-1617(96)80283-5

    Article  CAS  Google Scholar 

  • Lichtenthaler HK (1998) The stress concept in plants: an introduction. Ann NY Acad Sci 851:187–198. doi:10.1111/j.1749-6632.1998.tb08993.x

    Article  PubMed  CAS  Google Scholar 

  • McGarigal K, Cushman S, Stafford S (2000) Multivariate statistics for wildlife and ecology research. Springer, New York

    Book  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Moroyoqui-Rojo M (2005) Análisis de la eficiencia en la remoción de nutrients en un sistema experimental silvo pesquero (manglar-ictiofauna) con recirculación de agua. Dissertation, CIDIR Instituto Politécnico Nacional de México

  • Müller K, Böttcher U, Meyer-Schatz F, Kage H (2008) Analysis of vegetation indices derived from hyperspectral reflection measurements for estimating crop canopy parameters of oilseed rape (Brassica napus L.). Biosyst Eng 101:172–182. doi:10.1016/j.biosystemseng.2008.07.004

    Article  Google Scholar 

  • Nagelkerken I, Blaber SJM, Bouillon S, Green P, Haywood M, Kirton LG, Meynecke JO, Pawlik J, Penrose HM, Sasekumar A, Somerfield PJ (2008) The habitat function of mangrove for terrestrial and marine fauna: a review. Aquat Bot 89:155–185. doi:10.1016/j.aquabot.2007.12.007

    Article  Google Scholar 

  • Peñuelas J, Gamon JA, Fredeen AL, Merino J, Field CB (1994) Reflectance indices associated with physiological changes in nitrogen-and water-limited sunflower leaves. Remote Sens Environ 48:135–146. doi:10.1016/0034-4257(94)90136-8

    Article  Google Scholar 

  • Peñuelas J, Inque Y (1999) Reflectance indices indicative of changes in water and pigment contents of peanut and wheat leaves. Photosynthetica 36:355–360. doi:10.1023/A:1007033503276

    Article  Google Scholar 

  • Polidoro BA, Carpenter KE, Collins L, Duke NC, Ellison AM et al (2010) The loss of species: mangrove extinction risk and Geographic areas of global concern. PLoS One 5:e10095. doi:10.1371/journal.pone.0010095

    Article  PubMed  Google Scholar 

  • Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S (1994) A modified soil adjusted vegetation index. Remote Sens Environ 48:119–126. doi:10.1016/0034-4257(94)90134-1

    Article  Google Scholar 

  • Raven PH, Evert RF, Eichhorn SE (1992) Biology of plants. Worth Publishers, New York

    Google Scholar 

  • Rock BN, Hoshizaki T, Miller JR (1988) Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline. Remote Sens Environ 24:109–127. doi:10.1016/0034-4257(88)90008-9

  • Rondeaux G, Steven M, Baret F (1996) Optimization of soil-adjusted vegetation indices. Remote Sens Environ 55:95–107. doi:10.1016/0034-4257(95)00186-7

    Article  Google Scholar 

  • Rougean JL, Breon FM (1995) Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens Environ 51:375–384. doi:10.1016/0034-4257(94)00114-3

    Article  Google Scholar 

  • Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the great plains with ERTS. In: N SP-351, 3rd edn. ERTS symposium. NASA, Washington, DC, pp 309–317

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

    Article  Google Scholar 

  • Sokal RR, Rohlf FJ (1994) Biometry the principles and practice of statistics in biological research. WH Freeman and Company, San Francisco

    Google Scholar 

  • Uraibi HS, Midi H, Talib BA, Yousif JB (2009) Linear regression model selection based on robust bootstrapping technique. Am J Appl Sci 6:1191–1198. doi:10.3844/ajassp.2009.1191.1198

    Article  Google Scholar 

  • Valiela I, Bowen JL, York JK (2001) Mangrove forest: one of the worlds threatened major tropical environments. BioSci 51:807–815. doi:10.1641/0006-3568(2001)051[0807:MFOOTW]2.0.CO;2

    Google Scholar 

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

    Article  Google Scholar 

  • Wang L, Sousa WP (2009) Distinguishing mangrove species with laboratory measurements of hyperspectral leaf reflectance. International Journal of Remote Sensing 30:1267–1281. doi:10.1080/01431160802474014

    Google Scholar 

  • Walters BB, Rönnbäck P, Kovacs JM, Crona B, Hussain A, Badola R, Dahdouh-Guebas F, Barbier E (2008) Ethnobiology, socio-economics and management of mangrove forests: a review. Aquat Bot 89:220–236. doi:10.1016/j.aquabot.2008.02.009

    Article  Google Scholar 

  • Wu C, Niu Z, Tang Q, Huang W (2008) Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agric For Meteorol 148:1230–1241. doi:10.1016/j.agrformet.2008.03.005

    Article  Google Scholar 

  • Zarco-Tejada PJ, Miller JR, Noland TL, Mohammed GH, Sampson PH (2001) Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Trans Geosci Remote Sens 39:1491–1506. doi:10.1109/36.934080

    Article  Google Scholar 

  • Zhang C, Yali L, Kovacs JM, Flores-Verdugo F, Flores-de-Santiago F, Chen K (2012) Spectral response to varying levels of leaf pigments collected from a degraded mangrove forest. J Appl Remote Sens 6:063501. doi:10.1117/1.JRS.6.063501

    Article  Google Scholar 

Download references

Acknowledgments

FFdeS acknowledges financial support through a Grant (No. 198885) provided by the Consejo Nacional de Ciencia y Tecnología of México (CONACYT). Financial support for this study was also provided to JMK through a Natural Sciences and Engineering Research Council of Canada Grant (No. 249496). The costs associated with the field campaign were covered by FFV through the Instituto de Ciencias del Mar y Limnología (Universidad Nacional Autónoma de México), Estación Mazatlán, México.

Author information

Authors and Affiliations

  1. Department of Geography, The University of Western Ontario, London, ON, N6A 5C2, Canada

    Francisco Flores-de-Santiago

  2. Department of Geography, Nipissing University, North Bay, ON, P1B 8L7, Canada

    John M. Kovacs

  3. Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, 82000, Mazatlán, SIN, Mexico

    Francisco Flores-Verdugo

Authors
  1. Francisco Flores-de-Santiago
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John M. Kovacs
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco Flores-Verdugo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francisco Flores-de-Santiago.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Flores-de-Santiago, F., Kovacs, J.M. & Flores-Verdugo, F. The influence of seasonality in estimating mangrove leaf chlorophyll-a content from hyperspectral data. Wetlands Ecol Manage 21, 193–207 (2013). https://doi.org/10.1007/s11273-013-9290-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11273-013-9290-x

Keywords

Search

Navigation