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Article

Spectral Diversity Metrics for Detecting Oil Pollution Effects on Biodiversity in the Niger Delta

1
School of Geography, Geology and Environment, University of Leicester, University Road, Leicester LE1 7RH, UK
2
National Centre for Earth Observation, University of Leicester, University Road, Leicester LE1 7RH, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(22), 2662; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11222662
Received: 27 July 2019 / Revised: 11 October 2019 / Accepted: 11 November 2019 / Published: 14 November 2019
Biodiversity monitoring in the Niger delta has become pertinent in view of the incessant spillages from oil production activities and the socio-economic impact of these spillages on the inhabitants who depend on the resources for their livelihood. Conventional methods of post-impact assessments are expensive, time consuming, and cause damage to the environment, as they often require the removal of affected samples/specimens for laboratory analysis. Remote sensing offers the opportunity to track biodiversity changes from space while using the spectral variability hypothesis (SVH). The SVH proposes that the species diversity of a sampled area is linearly correlated with the variability of spectral reflectance of the area. Several authors have tested the SVH on various land cover types and spatial scales; however, the present study evaluated the validity of the SVH against the backdrop of oil pollution impact on biodiversity while using vascular plant species as surrogates. Species richness and diversity indices were computed from vegetation data collected from polluted and non-polluted transects. Spectral metrics that were derived from Sentinel 2 bands and broadband vegetation indices (BVIs) using various algorithms, including averages, spread, dimension reduction, and so on, were assessed for their ability to estimate vascular plants species richness and diversity. The results showed significant differences in vegetation characteristics of polluted and control transects (H = 76.05, p-value = <0.05 for abundance and H = 170.03, p-value < 0.05 for richness). Spectral diversity metrics correlated negatively with species data on polluted transects and positively on control transects. The metrics computed using Sentinel 2A bands and vegetation indices proved to be sensitive to changes in vegetation characteristics following oil pollution. The most robust relationship was observed between the metrics and indices on control transects, whereas the weakest relationships were observed on polluted transects. Index-wise, the Simpson’s diversity index regressed better with spectral metrics (R2 > 0.5), whereas the Chao-1 richness index regressed the least (R2 < 0.5). The strength of the relationship resulted in successfully estimating species richness and diversity values of investigated transects, thereby enhancing biodiversity monitoring over time and space. View Full-Text
Keywords: Spectral Variability Hypothesis (SVH); Sentinel 2; biodiversity; spectral metrics; vegetation indices; diversity indices Spectral Variability Hypothesis (SVH); Sentinel 2; biodiversity; spectral metrics; vegetation indices; diversity indices
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    Description: Figure S1. Spectral profile of vegetation on polluted (red curves) and control (green curves) transects sampled from A. Rumuekpe, B. Umukpoku, C. Omoigwor and D. Alimini locations in Rivers State of Nigeria.Figure S2: Graphical plots of residuals from model validation using test data. Research Data
MDPI and ACS Style

Onyia, N.N.; Balzter, H.; Berrio, J.C. Spectral Diversity Metrics for Detecting Oil Pollution Effects on Biodiversity in the Niger Delta. Remote Sens. 2019, 11, 2662. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11222662

AMA Style

Onyia NN, Balzter H, Berrio JC. Spectral Diversity Metrics for Detecting Oil Pollution Effects on Biodiversity in the Niger Delta. Remote Sensing. 2019; 11(22):2662. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11222662

Chicago/Turabian Style

Onyia, Nkeiruka N., Heiko Balzter, and Juan C. Berrio 2019. "Spectral Diversity Metrics for Detecting Oil Pollution Effects on Biodiversity in the Niger Delta" Remote Sensing 11, no. 22: 2662. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11222662

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