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Assessing the Impact of Soil Hydrocarbon Properties on Plant Functional Types Using Hyperspectral Data in the Niger Delta

Journal of Marine Science and Engineering · 12 May 2026 · 10.3390/jmse14100892

Abstract

The presence of soil hydrocarbon parameters (SHPs), including total petroleum hydrocarbons (TPHs), total organic carbon (TOC; %), and soil toxicity (EC50; mg L−1), can affect vegetation in several ways. This study assessed the impact of SHPs on vegetation in the Niger Delta using field-measured, leaf-scale hyperspectral data acquired across the region. Red-edge position (REP) and four hyperspectral vegetation indices (HVIs)—mND705, photochemical reflectance index (PRI), Normalised Difference Vegetation Vigour Index (NDVVI844,447; a vegetation vigour index), and modified DATT (MDATT; a chlorophyll-sensitive red-edge index)—were used to quantify chlorophyll content in the vegetation types of Awolowo grass, elephant grass, mango trees, oil palm trees, and mangrove vegetation and to explore their variation with SHPs. The results show that mangrove vegetation was the most impacted by TPHs (R = −0.683), while mango vegetation was the most impacted by TOC (R = −0.725), based on Pearson correlation coefficients derived from the mND705 index. Similarly, mango and mangrove vegetation showed the strongest responses to soil toxicity (EC50; mg L−1), based on Spearman correlation coefficients (rs = 0.657 and rs = 0.870, respectively) using the MDATT index. These findings highlight species-specific physiological responses to soil hydrocarbon contamination and demonstrate the applicability of red-edge-based hyperspectral techniques for assessing vegetation stress in complex coastal ecosystems such as the Niger Delta.

Plant phenotyping relevance

葉面ハイパースペクトルデータとレッドエッジ指標により植物のクロロフィル量・生理的ストレスを定量化する手法を中心的に適用しており、植物状態の測定方法として実質的です。

abstractfield-measured, leaf-scale hyperspectral data acquired across the region
abstractwere used to quantify chlorophyll content in the vegetation types
abstractdemonstrate the applicability of red-edge-based hyperspectral techniques for assessing vegetation stress

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