Development of narrow band index for accurate mapping of chlorophyll in senescent stage of teak (Tectona grandis L. f.) using Hyperion (EO1) data

Dhaval Vyas

Abstract


Abstract

 

Hyperspectral remote sensing sensors have shown a great promise towards the accurate estimation of chlorophyll content over a large spatial scale.  However, most of the vegetation cover in tropics and subtropics attains maximum chlorophyll content in the monsoon season. The cloud coverage in this period of the year generates a major problem particularly with optical remote sensing data. Therefore, there is an extreme need to develop vegetation   index using space borne reflectance spectra acquired from very low chlorophyll content  samples (of senescent vegetation). In the present study an attempt has been made to develop accurate narrow band index for assessment of chlorophyll in senescent stage of teak (Tectona grandis L. f.) using Hyperion (EO1) data. Pearson’s  correlation coefficient  (PCC)  was calculated to identify the correlation between measured chlorophyll and Hyperion reflectance spectra (spectral subset 436-1346 nm). Wavelength with highest positive correlation and wavelength with highest negative correlation were identified and selected for development of indices. SR 599/1134 gave the  best results for prediction of  chlorophyll in senescent teak vegetation cover with R2 of 0.68  for linear regression model and cross validation R2 0.67 and RMSE 0.15 g m-2.


Keywords


Teak, Hyperspectral remote sensing, Chlorophyll

References


Arnon, DI. 1949. Copper enzymes in isolated chloroplasts. Polyphenoloxidase in Beta

vulgaris. Plant Physiology, 24: 483-485.

Asner GP, Martin RE. 2008. Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels. Remote Sensing of Environment 12: 3958-3970.

Axelsson C, Skidmore AK, Schlerf M, Fauzi A, Verhoef W. 2013. Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression. International Journal of Remote Sensing 34: 1724-1743.

Ball JBD, Pandey S. 1999: Global overview of teak plantations. Presented at Regional

seminar, Chiang Mai, Thailand 26-29 January 1999.

Blackburn GA. 1998. Spectral indices for estimating photosynthetic pigment

concentrations: a test using senescent tree leaves. International Journal of Remote

Sensing 19: 657-675.

Carter GA, Knapp AK. 2001. Leaf optical properties in higher plants: linking spectral

characteristics to stress and chlorophyll concentration. American Journal of

Botany, 88:677-684.

Darvishzadeh, R, Skidmorea A, Schlerf M, Atzberger C, Corsia F, Choa M. 2008. LAI

and chlorophyll estimation for heterogeneous grassland using hyperspectral

measurements. ISPRS Journal of Photogrammetry and Remote Sensing 63: 409-426.

Delegido J, Alonso L, González G, Moreno J. 2010. Estimating chlorophyll content of

crops from hyperspectral data using a normalized area over reflectance curve

(NAOC). International Journal of Applied Earth Observation and Geoinformation, 12:

-174.

Gamon, J. A., Serrano, L., & Surfus, J. S. 1997. The photochemicalreflectance index: an optical indicator of photosynthetic radiation-useefficiency across species, functional types, and nutrient levels. Oecologia,112, 492– 501.

Gamon JA. Qiu H. 1999. Ecological applications of remote sensing at multiple scales. In: Pugnaire, F.I., Valladares, F. (Eds.), Handbook of Functional Plant Ecology. 805: 846.

Gao BC. and Goetz FH. 1990. Column atmospheric water vapor and vegetation liquid

water retrievals from airborne imaging spectrometer data. Journal of Geophysical

Research 95: 3549-3564.

Gao BC, Heidebrecht KB, Goetz AFH. 1993, Derivation of scaled surface reflectance

from AVIRIS data. Remote Sensing of Environment 44: 165-178.

Haboudane D, Tremlay N. Miller JR. Vigneaault P. 2008. Remote estimation of crop

chlorophyll content using spectral indices derived from Hyperspectral data. IEEE

Transactions on Geosciences and Remote sensing 46: 423-437.

Jago RA., Cutler MEJ, Curran PJ. 1999. Estimating canopychlorophyll concentration

from field and airborne spectra. Remote Sensing of Environment 68: 217-224.

Kaul M, Mohren GMJ, Dadhwal VK. 2010,.Carbon storage and sequestration potential

of selected tree species in India. Mitig Adapt Strateg Glob Change 15: 489-510.

le Maire G. Francois C, Dufrene E. 2004. Towards universal broad leaf chlorophyll

indices using PROSPECT simulated database and hyperspectral reflectance

measurements. Remote sensing of environment 89: 1−28.

le Maire G, Francois C, Saoudani K, Berveiller D, Pontailler J, Breda N, Genet H,

Davi H, Dufrene E. 2008. Calobration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf biomass per area and leaf canopy biomass. Remote sensing of Environment 112: 3846-3864.

Delalieux, S., Auwerkerken, A., Verstraeten, W. W., Somers, B., Valcke, R., Lhermitte, S., ... & Coppin, P. 2009. Hyperspectral reflectance and fluorescence imaging to detect scab induced stress in apple leaves. Remote sensing, 1(4), 858-874.

Nelson N, Yocum CF. 2006. Structure of function of photosystem I and II. Annual

Review of Plant Biology 57: 521–565.

Peterson G, Allen CR, Holling CS. 1998. Ecological resilience, biodiversity, and scale. Ecosystems 1: 6-18.

Sampson PH, Zarco-Tejada PJ, Mohammed GH, Miller JR, Noland, TL. 2003.

Hyperspectral remote sensing of forest condition: Estimating chlorophyll content in

tolerant hardwoods. Forest Science 49: 381-391.

Schlerf M, Atzberger C, Hill J, Buddenbaum H, Werner W, Schuler G. 2010. Retrieval of

chlorophyll and nitrogen in Norway spruce (Picea abis L. Karst.) using imaging

spectroscopy. International journal of earth observation and geoinformation 12 : 17-26.

Sims DA, Gamon JA. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment 81: 337−354.

Stagakis S, Markos N, Sykioti O, Kyparissis A. 2010. Monitoring canopy biophysical

and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An

application on a Phlomis fruticosa Mediterrannean ecosystem using multiangular

CHRIS/PROBA observations. Remote Sensing of Environment 114: 977−994.

Thenkabail PS, Smith RB, De Pauw E.2000. Hyperspectral vegetation indices and their

relationships with agricultural crop characteristics. Remote sensing of Environment 71:

-182.

Vyas D, Christian B, Krishnayya NSR. 2013. Canopy level estimations of chorophyll and

LAI for two tropical species (teak and bamboo) from Hyperion (EO1) data. International

Journal of Remote Sensing 34: 1676-1690.

Vyas D, Krishnayya NSR, Manjunath KR, Ray SS, Panigrahy S. 2011. Evaluation of

classifiers for processing Hyperion (EO-1) data of tropical vegetation. International

Journal of Applied Earth Observation and Geoinformation 13: 228-235.

Wang Q, Li P. 2012. Hyperspectral indices for estimating leaf biochemical properties in

temperate deciduous forests: Comparison of simulated and measured reflectance data sets.Ecological Indicators 14: 56-65.

Wu C, Han X, Niu Z, Dong J. 2010, An evaluation of EO-1 hyperspectral Hyperion data

for chlorophyll content and leaf area index estimation. International Journal of Remote

Sensing 31: 1079-1076.

Xue L, Yang L. 2009. Deriving leaf chlorophyll content of green-leafy vegetables from

hyperspectral reflectance. ISPRS Journal of Photogrammetry and Remote Sensing 64: 97- 100

Zarco-Tejada PJ, González-Dugo V, Berni JA. 2012. 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 Sensing of Environment 117:

-337.

Zhang Y, Chen JM, Miller JR, Noland TL. 2008. Leaf chlorophyll content retrieval from

airborne hyperspectral remote sensing imagery. Remote Sensing of Environment 112:

-3247.


Full Text: PDF

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

COPYRIGHT of this Journal vests fully with the National Instional Institute of Ecology. Any commercial use of the content on this site in any form is legally prohibited.