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Remote Sens. 2015, 7(11), 14530-14558;

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

Global Environmental Change and Earth Observation Research Group, Geography and Environment, University of Southampton, Southampton SO17 1BJ, UK
Faculty of Science and Technology, Lancaster University, Lancaster LA1 4YR, UK
Faculty of Geosciences, University of Utrecht, Heidelberglaan 2, Utrecht CS 3584, The Netherlands
School of Geography, Archaeology and Palaeoecology, Queen’s University Belfast, Belfast BT7 1NN, Northern Ireland, UK
Academic Unit Sisal, Faculty of Sciences, National Autonomous University of Mexico, Sisal, Yucatan 97355, Mexico
The author contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editors: Chandra Giri, Clement Atzberger and Prasad S. Thenkabail
Received: 29 August 2015 / Revised: 6 October 2015 / Accepted: 26 October 2015 / Published: 4 November 2015
(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)
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There is a need to develop indicators of mangrove condition using remotely sensed data. However, remote estimation of leaf and canopy biochemical properties and vegetation condition remains challenging. In this paper, we (i) tested the performance of selected hyperspectral and broad band indices to predict chlorophyll concentration (CC) on mangrove leaves and (ii) showed the potential of Landsat 8 for estimation of mangrove CC at the landscape level. Relative leaf CC and leaf spectral response were measured at 12 Elementary Sampling Units (ESU) distributed along the northwest coast of the Yucatan Peninsula, Mexico. Linear regression models and coefficients of determination were computed to measure the association between CC and spectral response. At leaf level, the narrow band indices with the largest correlation with CC were Vogelmann indices and the MTCI (R2 > 0.5). Indices with spectral bands around the red edge (705–753 nm) were more sensitive to mangrove leaf CC. At the ESU level Landsat 8 NDVI green, which uses the green band in its formulation explained most of the variation in CC (R2 > 0.8). Accuracy assessment between estimated CC and observed CC using the leave-one-out cross-validation (LOOCV) method yielded a root mean squared error (RMSE) = 15 mg·cm−2, and R2 = 0.703. CC maps showing the spatiotemporal variation of CC at landscape scale were created using the linear model. Our results indicate that Landsat 8 NDVI green can be employed to estimate CC in large mangrove areas where ground networks cannot be applied, and mapping techniques based on satellite data, are necessary. Furthermore, using upcoming technologies that will include two bands around the red edge such as Sentinel 2 will improve mangrove monitoring at higher spatial and temporal resolutions. View Full-Text
Keywords: Landsat 8; mangrove; spatiotemporal; chlorophyll map; vegetation indices Landsat 8; mangrove; spatiotemporal; chlorophyll map; vegetation indices

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Pastor-Guzman, J.; Atkinson, P.M.; Dash, J.; Rioja-Nieto, R. Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8. Remote Sens. 2015, 7, 14530-14558.

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