Spectral response to varying levels of leaf pigments collected from a degraded mangrove forest

Mangrove forests are being removed or degraded at an alarming rate, even though they play a vital role in the sustainability of tropical coastal communities. Many of these forests are identified as degraded based on observable changes in their leaves (e.g., density, size, color, etc.). Of these, color can be considered one of the most important indicators of degradation because changes in the spectral response may be indicative of changes in the leaf pigment content. In this investigation, hyperspectral laboratory techniques were applied to examine potential relationships between the mangrove leaf spectral response and three leaf pigments: chlorophyll a, chlorophyll b, and total carotenoid content. Using an ASD spectroradiometer, the spectral reflectance of leaf samples were collected from poor condition, dwarf and healthy black (Avicennia germinans) and from healthy and poor condition red (Rhizophora mangle) mangroves located in a degraded mangrove system of the Mexican Pacific. A subset of 150 representational leaves was then used for pigment content analysis. The results indicate significant relationships between the spectral response and the levels of chlorophyll a, b, and total carotenoid content contained in the leaves. In particular, wavebands at the red edge position were shown to be the best predictors of the pigment contents. The results also indicate that vegetation indices do not necessarily improve the ability to predict these constituents. Finally, the red edge position was found to be significantly different between the healthy and poor condition mangroves ( P = 0 ), with the healthy mangroves having longer wavelengths associated with the red edge position.

[1]  Shiv O. Prasher,et al.  ESTIMATION OF CROP BIOPHYSICAL PARAMETERS THROUGH AIRBORNE AND FIELD HYPERSPECTRAL REMOTE SENSING , 2003 .

[2]  Armando Apan,et al.  Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery , 2004 .

[3]  B. Turner,et al.  Estimating foliage nitrogen concentration from HYMAP data using continuum, removal analysis , 2004 .

[4]  Le Wang,et al.  Distinguishing mangrove species with laboratory measurements of hyperspectral leaf reflectance , 2009 .

[5]  A. Gitelson,et al.  Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700 nm , 1996 .

[6]  C. Vaiphasa Remote sensing techniques for mangrove mapping , 2006 .

[7]  C. Elvidge Visible and near infrared reflectance characteristics of dry plant materials , 1990 .

[8]  D. Horler,et al.  The red edge of plant leaf reflectance , 1983 .

[9]  B. Datt Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll a+b, and Total Carotenoid Content in Eucalyptus Leaves , 1998 .

[10]  G. A. Blackburn,et al.  Hyperspectral remote sensing of plant pigments. , 2006, Journal of experimental botany.

[11]  Chaoyang Wu,et al.  Estimating chlorophyll content from hyperspectral vegetation indices : Modeling and validation , 2008 .

[12]  Jinfei Wang,et al.  The Use of Multipolarized Spaceborne SAR Backscatter for Monitoring the Health of a Degraded Mangrove Forest , 2008 .

[13]  G. Carter,et al.  Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands , 1994 .

[14]  J. Schjoerring,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[15]  John R. Miller,et al.  Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy , 2005 .

[16]  George Alan Blackburn,et al.  Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation , 2007 .

[17]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[18]  J. Kovacs,et al.  Evaluating the condition of a mangrove forest of the Mexican Pacific based on an estimated leaf area index mapping approach , 2009, Environmental monitoring and assessment.

[19]  E. Barbier,et al.  Ethnobiology, socio-economics and management of mangrove forests: A review , 2008 .

[20]  P. Curran Remote sensing of foliar chemistry , 1989 .

[21]  D. M. Moss,et al.  Red edge spectral measurements from sugar maple leaves , 1993 .

[22]  Huang Wenjiang,et al.  Using hyperspectral indices to estimate foliar chlorophyll a concentrations of winter wheat under yellow rust stress , 2007 .

[23]  G. Naidoo Factors contributing to dwarfing in the mangrove Avicennia marina. , 2006, Annals of botany.

[24]  Moon S. Kim,et al.  Ratio Analysis Of Reflectance Spectra , 1990, 10th Annual International Symposium on Geoscience and Remote Sensing.

[25]  G. A. Blackburn,et al.  Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .

[26]  A. Skidmore,et al.  Tropical mangrove species discrimination using hyperspectral data: A laboratory study , 2005 .

[27]  G. Bonham-Carter Numerical procedures and computer program for fitting an inverted Gaussian model to vegetation reflectance data , 1988 .

[28]  H. Lichtenthaler CHLOROPHYLL AND CAROTENOIDS: PIGMENTS OF PHOTOSYNTHETIC BIOMEMBRANES , 1987 .

[29]  Jinfei Wang,et al.  Mapping mangrove leaf area index at the species level using IKONOS and LAI-2000 sensors for the Agua Brava Lagoon, Mexican Pacific , 2005 .

[30]  Ariel E. Lugo,et al.  Ecophysiology of a Mangrove Forest in Jobos Bay, Puerto Rico , 2007 .

[31]  Raymond I Carruthers,et al.  Canopy assessment of biochemical features by ground-based hyperspectral data for an invasive species, giant reed (Arundo donax) , 2008, Environmental monitoring and assessment.

[32]  Teferi D. Tsegaye,et al.  Relationship Between Hyperspectral Reflectance, Soil Nitrate-Nitrogen, Cotton Leaf Chlorophyll, and Cotton Yield: A Step Toward Precision Agriculture , 2003 .

[33]  Siza D. Tumbo,et al.  HYPERSPECTRAL CHARACTERISTICS OF CORN PLANTS UNDER DIFFERENT CHLOROPHYLL LEVELS , 2000 .

[34]  Mary E. Martin,et al.  Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectance : a comparison of statistical methods , 1996 .

[35]  George Alan Blackburn,et al.  Relationships between Spectral Reflectance and Pigment Concentrations in Stacks of Deciduous Broadleaves , 1999 .

[36]  P. Curran,et al.  Technical Note Grass chlorophyll and the reflectance red edge , 1996 .

[37]  Fumin Wang,et al.  Comparison between back propagation neural network and regression models for the estimation of pigment content in rice leaves and panicles using hyperspectral data , 2007 .

[38]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[39]  O. Mutanga,et al.  Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review , 2010, Wetlands Ecology and Management.