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Journal of Applied Remote Sensing • Open Access

Spectral response to varying levels of leaf pigments collected from a degraded mangrove forest
Author(s): Chunhua Zhang; Ke Chen; Yali Liu; John M. Kovacs; Francisco Flores-Verdugo; Francisco J. Flores de Santiago

Paper Abstract

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.

Paper Details

Date Published: 12 March 2012
PDF: 15 pages
J. Appl. Remote Sens. 6(1) 063501 doi: 10.1117/1.JRS.6.063501
Published in: Journal of Applied Remote Sensing Volume 6, Issue 1
Show Author Affiliations
Chunhua Zhang, East Tennessee State Univ. (United States)
Ke Chen, East Tennessee State Univ. (United States)
Yali Liu, East Tennessee State Univ. (United States)
John M. Kovacs, Nipissing Univ. (Canada)
Francisco Flores-Verdugo, Univ. Nacional Autónoma de México (Mexico)
Francisco J. Flores de Santiago, The Univ. of Western Ontario (Canada)


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