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

Nkeiruka Onyia,Juan Berrio,Heiko Balzter

doi:10.3390/rs11222662

Nkeiruka Onyia, Juan Berrio + Show 1 more

Open Access

https://doi.org/10.3390/rs11222662

Copy DOI

Abstract

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.

Highlights

The Spectral Variability Hypothesis (SVH) that was proposed by Palmer et al [1] asserts that the spatial heterogeneity of plant species positively correlates with spectral diversity of remotely sensed images
Variations in these properties that are induced by plant response to environmental stress, such as oil pollution, manifest in the spectral signatures acquired by satellite sensors in orbit
A positive linear relationship was expected because of the high species diversity, whereas, on polluted transects, an inverse relationship was expected due to low species diversity, but the results showed that the spectral diversity was high on polluted and control transects with the coefficient of variations for spectral metrics larger on polluted transects

Summary

Introduction

The Spectral Variability Hypothesis (SVH) that was proposed by Palmer et al [1] asserts that the spatial heterogeneity of plant species positively correlates with spectral diversity of remotely sensed images. According to Clevers et al [2], the control of leaf reflectance is by pigments (chlorophyll a and b, β-carotene, and so on) in the visible (VIS); the cellular structure in the near-infrared (NIR) and moisture content in the short-wave infrared (SWIR) regions of the electromagnetic spectrum. Variations in these properties that are induced by plant response to environmental stress, such as oil pollution, manifest in the spectral signatures acquired by satellite sensors in orbit. Environmental stressors that affect plants result in biodiversity loss globally [5,6]

Methods

Results

Discussion

Conclusion