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Spatiotemporal variation in mangrove chlorophyll concentration using Landsat 8

Spatiotemporal variation in mangrove chlorophyll concentration using Landsat 8
Spatiotemporal variation in mangrove chlorophyll concentration using Landsat 8
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.
2072-4292
14530-14558
Pastor-guzman, Julio
2f7c88eb-3af8-4cb5-93e6-2f93cf63ae0b
Atkinson, Peter
96e96579-56fe-424d-a21c-17b6eed13b0b
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
Rioja-nieto, Rodolfo
6e98f638-46d9-4752-ac88-3973b04b970c
Pastor-guzman, Julio
2f7c88eb-3af8-4cb5-93e6-2f93cf63ae0b
Atkinson, Peter
96e96579-56fe-424d-a21c-17b6eed13b0b
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
Rioja-nieto, Rodolfo
6e98f638-46d9-4752-ac88-3973b04b970c

Pastor-guzman, Julio, Atkinson, Peter, Dash, Jadunandan and Rioja-nieto, Rodolfo (2015) Spatiotemporal variation in mangrove chlorophyll concentration using Landsat 8. Remote Sensing, 7 (11), 14530-14558. (doi:10.3390/rs71114530).

Record type: Article

Abstract

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.

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Accepted/In Press date: 26 October 2015
Published date: 4 November 2015

Identifiers

Local EPrints ID: 441863
URI: http://eprints.soton.ac.uk/id/eprint/441863
ISSN: 2072-4292
PURE UUID: eb3c2e76-4721-409e-a4eb-a27fa3de71dd
ORCID for Peter Atkinson: ORCID iD orcid.org/0000-0002-5489-6880
ORCID for Jadunandan Dash: ORCID iD orcid.org/0000-0002-5444-2109

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Date deposited: 30 Jun 2020 16:37
Last modified: 18 Feb 2021 17:01

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