Reflectance Spectroscopic Study of Hydrocarbon-Induced Vegetation Stress Assessment in Chalinger-Garangan and Surrounding Oil Fields, Zagros Fold-Thrust Belt, Iran

Reflectance Spectroscopic Study of Hydrocarbon-Induced Vegetation Stress Assessment in Chalinger-Garangan and Surrounding Oil Fields, Zagros Fold-Thrust Belt, Iran | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Reflectance Spectroscopic Study of Hydrocarbon-Induced Vegetation Stress Assessment in Chalinger-Garangan and Surrounding Oil Fields, Zagros Fold-Thrust Belt, Iran Yasmin Elhaei, Kazem Rangzan, Mostafa Kabolizadeh, Saeid Asadzadeh, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8756547/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Vegetation stress is a common phenomenon in sensitive ecosystems affected by petroleum-related activities and natural seepage, making its early detection essential for both environmental management and exploration purposes. Reflectance spectroscopy provides an effective tool for monitoring subtle physiological changes in vegetation at different scales, outperforming traditional approaches. This study applies a statistical framework based on spectral indices derived from point spectroscopy to identify hydrocarbon-induced stress in vegetation samples from the South Dezful Embayment, Iran—an area within the Zagros Fold-Thrust Belt that has long experienced continuous seepage due to intensive oil production and exploration activities. Leaf-level spectral measurements were collected from 68 vegetation samples using an ASD FieldSpec Pro spectroradiometer. Fourteen vegetation indices, including the CTR, PSSRa, RENDVI, NDCI, LIC, VOG, starch index, NDVI, SRI, OSAVI, ARVI, GNDVI, GNIR, and phenolic index, were assessed for this purpose. In addition, principal component analysis (PCA) was used to identify stress-responsive indices, and K-Means clustering was employed to objectively classify vegetation into high, moderate, and low stress levels. The results show that the CTR2 and NDCI exhibit pronounced sensitivity to stress-induced spectral variations. K-Means clustering effectively separated the samples into three stress categories, with highly stressed samples corresponding to productive oil fields. There were significant differences (p < 0.05) in the CTR and NDCI values for highly stressed samples, which was consistent with the reduction in chlorophyll content under hydrocarbon exposure. This was corroborated by visual inspection of spectral plots from stressed plants, which revealed increased visible-region reflectance, a blueshifted red edge, and decreased near-infrared reflectance. Overall, the findings demonstrate the ability of reflectance spectroscopy—supported by robust statistical validation—to detect vegetation stress induced by hydrocarbon seepage/leakage in arid and semiarid regions. This approach is recommended as a complementary tool for early environmental assessments and long-term ecological monitoring in hydrocarbon-rich provinces. Petroleum contamination Plant reflectance characteristics Remote sensing Spectral indices Environmental monitoring Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The leakage of hydrocarbons from natural and anthropogenic sources can have a substantial impact on the environment (Khan & Jacobson, 2008 ; Schumacher, 1996 ). Such leakages may occur as natural and low-intensity hydrocarbon microseepage, characterized by the continuous upward migration of light hydrocarbons from subsurface accumulations, or as acute and large-scale releases associated with human activities during petroleum extraction, transportation, and utilization. While acute spills typically cause sudden and severe ecological damage, hydrocarbon microseepage results in long-term and chronic exposure of surface ecosystems, particularly vegetation. Natural seepage may occur at the surface through anomalous hydrocarbon concentrations in soils, mineralogical alterations, and physiological stress in vegetation growing above petroleum-prone environments (Asadzadeh & de Souza Filho, 2017 ; Schumacher, 1996 ). Continuous hydrocarbon leakage can alter soil chemistry, reduce oxygen availability, and create reducing environments that disrupt plant physiological processes, leading to chlorosis, reduced photosynthesis, and biomass decline (Duke et al., 2007 ). The impact of hydrocarbons on vegetation arises not only from direct toxicity but also from soil chemical and physical alterations, reduced nutrient and oxygen availability, and the creation of reducing conditions, all of which contribute to stress and impaired plant performance. Consequently, vegetation monitoring serves as a crucial ecological indicator for detecting hydrocarbon leakage, providing a dual-purpose tool that can guide exploration by identifying hydrocarbon-prone areas and simultaneously assessing the environmental impacts and effectiveness of postspill mitigation measures(Arellano et al., 2015 ; Lassalle et al., 2020 ). Spectral remote sensing is a proven technology for the efficient detection and monitoring of hydrocarbon-induced vegetation stress, addressing the spatial limitations of traditional sample-based geobotanical, geochemical, and geophysical methods (Jacquemoud & Ustin, 2001 ; Lassalle et al., 2023 ). Spectral information acquired in the visible–near-infrared (VNIR) and shortwave-infrared (SWIR) regions enables the identification of changes in the spectral behavior of vegetation under stress (Cloutis, 1989 ; Lammoglia & Souza Filho, 2011 ). Vegetation indices (VIs), such as the NDVI and RENDVI, quantify foliar biochemical and structural properties and have demonstrated utility in early stress detection (Berger et al., 2022 ). A decrease in chlorophyll-sensitive indices reflects chlorophyll degradation and reduced photosynthetic efficiency caused by hydrocarbon-induced physiological stress. This biochemical alteration leads to a displacement of the red-edge position toward shorter wavelengths (blueshift), a well-established spectral response to decreasing chlorophyll content. Simultaneously, hydrocarbon-related stress disrupts the internal structure and mesophyll integrity of leaves, resulting in reduced near-infrared (NIR) reflectance. Together, these spectral features provide a mechanistic basis for interpreting hydrocarbon-induced vegetation stress(Arellano et al., 2015 ; Duke, 2016 ; Grant et al., 1993 ). Numerous studies have applied multispectral and hyperspectral data—using derivative analysis, band ratios, and spectral mixture analysis—to detect hydrocarbon seepage and associated vegetation anomalies (Asadzadeh et al., 2022 ; Kokaly, 2013 ; Lassalle, 2021). Recent studies, including weighted vegetation index models (Kashyap, 2022 ) and detailed assessments of acute versus chronic vegetation stress, have confirmed the sensitivity of spectral responses to petroleum contamination (Duke et al., 2007 ; Rodrigues et al., 2024 ) and the sensitivity of spectral responses to petroleum contamination. Long-term ecological effects have also been demonstrated, with persistent reductions in plant vitality and biomass decades after oil spills (Lassalle et al., 2023 ). Spectroscopic techniques have also proven successful in detecting HC-specific absorption features directly over oil sands, oil spills, and oil pollution (Asadzadeh & de Souza Filho, 2016; Correa Pabón et al., 2019 ; Speta et al., 2015 ). Collectively, these studies highlight the ability of spectral techniques to link laboratory, field, and airborne observations across multiple environmental settings while also emphasizing the importance of scale and environmental context (Lassalle et al., 2023 ; Rodrigues et al., 2024 ). These authors demonstrated that oil spills and chronic hydrocarbon exposure induce persistent biochemical and spectral changes in vegetation, reducing forest biomass and generating diagnostic spectral anomalies observable remotely in the VNIR and SWIR wavelength ranges. Despite these successful case studies, applying remote sensing techniques in arid and semiarid but petroleum-rich regions remains challenging. This is because, on the one hand, vegetation cover is sparse in these regions, and on the other hand, natural stressors such as drought, soil salinity, heat stress, and nutrient deficiency may produce spectral anomalies that resemble hydrocarbon-induced stress, complicating accurate diagnosis via visual or spectral methods and making accurate assessment via traditional visual observations or standard spectral analyses more difficult (Das et al., 2016 ; Osorio et al., 2017 ). Although field spectroscopy provides precise spectral measurements under controlled conditions, its application in arid and semiarid regions offers a practical approach for detecting hydrocarbon-induced vegetation stress at the local scale without relying on large-scale imaging spectroscopy datasets. Given these gaps, the South Dezful Embayment in southwestern Iran, a region characterized by arid climatic conditions, ecological sensitivity, and the presence of multiple active and prospective oil fields, was selected to evaluate the spectral responses of vegetation to long-term hydrocarbon microseepage. Leaf-level field spectroscopy was employed to analyze a range of narrow- and broad-band vegetation indices, aiming to identify those most sensitive to hydrocarbon-induced stress and to assess their potential as robust surface indicators for both hydrocarbon exploration and environmental monitoring. This approach allows a clear link between observed vegetation stress and specific spectral indicators, providing a practical methodology for both scientific analysis and environmental assessment. The present study, therefore, aimed to determine whether hydrocarbon microseepage induces detectable and persistent changes in vegetation spectral properties and whether vegetation indices differ meaningfully between samples from affected and unaffected areas. Moreover, this knowledge, while valuable for petroleum exploration, can help establish a baseline of vegetation stress and provide additional insights for environmental assessment in arid and semiarid regions. Materials and methods Study area The study area is part of the South Dezful Embayment, which is situated in southwestern Iran within the Zagros Fold-Thrust Belt (Fig. 1 ). Geographically, it spans between 48°00′–50°30′ E and 30°00′–32°30′ N, stretching in three provinces. The region's climate is arid to semiarid, characterized by extreme summer temperatures often exceeding 50°C and mild winters ranging from 10–20°C. Annual precipitation, which primarily occurs in winter and spring, averages 200–400 mm (Alavi, 2004 ). The air humidity is generally low (i.e., < 20% in summer), increasing only in proximity to rivers and marshlands. The vegetation in the embayment is sparse, adapting to arid conditions, and is predominantly composed of drought-resistant shrubs and grasses. Higher elevations support oak and wild pistachio species, with reeds and hydrophilic plants thriving near rivers and marshlands (Zohdi et al., 2013 ). Geologically, the embayment is composed of Cretaceous to Miocene carbonate and clastic rocks. Key reservoir formations include the Asmari Formation (Oligo-Miocene limestones) and Bangestan Formation, which are composed of Cretaceous sandstones that are effectively sealed by evaporites of the Gachsaran Formation (Bordenave & Hegre, 2005 ). The study area encompasses six major oil fields delineated by prominent anticlinal and fold structures. The Chilingar, Garangan, and Chahar Bishah oil fields are currently active, producing hydrocarbons from the Alban–Cenomanian limestones and dolomites of the Khami Group's carbonate reservoirs. Conversely, Bidkarz, Khairabad, and Sarburi represent undeveloped prospects. These latter fields target the Oligo-Miocene limestones of the Asmari Formation, which have substantial hydrocarbon potential because of their inherent porosity and structural traps (Derikvand et al., 2018 ; Sherkati & Letouzey, 2004 ). The presence of these giant oil fields is assumed to induce stress on the overlying local vegetation, particularly the prevalent shrubs and grasses, which may manifest as detectable spectral anomalies. This makes the region an ideal setting for investigating vegetation responses to hydrocarbon influence through spectral analysis. Field Sampling and Sites Field sampling was conducted in the South Dezful Embayment during the peak growing season to capture optimal vegetation conditions. A total of 68 vegetation samples were collected from 13 strategically selected sites within the oilfields (Fig. 1 ; see also Table 1 ). Sites were chosen based on their proximity to known hydrocarbon seepage zones and related infrastructure, reflecting the hydrocarbon potential and characteristics of the oilfields (Fig. 2 ). Sampling took place over two consecutive days, from March 14 to 15, 2022. The collected samples included various plant components, such as leaves, flowers, and stems. Both mature and herbaceous individuals of the dominant species were sampled. Within each site, sampling was performed randomly to capture representative spectral variability.All samples were processed promptly for vegetation index analysis aimed at assessing hydrocarbon-induced stress. They were stored in appropriate containers until spectroscopic measurements could be performed. Table 1 Overview of Study Sites, Sampling Series, and Classification Types Site Name Sampling Date (Series) Type 01 (Chilingar7) March 15, 2022 (1) Oilfeild 02 (Chilingar4) March 15, 2022 (1) Oilfeild 03 (East Chilingar5) March 15, 2022 (1) Oilfeild 04 (Bid Karz ring) March 14, 2022 Prospect 05 (Topside of Bid Karz ring) March 14, 2022 Prospect 06 (South beginning of Bid Karz ring) March 14, 2022 Prospect 07 (South of Chilingar ring) March 14, 2022 Oilfeild 08 (Pazanan 13) March 15, 2022 (1) Oilfeild 09 (Khairabad) March 15, 2022 (1) Prospect 10 (Garangan5) March 15, 2022 (1) Oilfeild 11 (North of Garangan1) March 15, 2022 (1) Oilfeild 12 (Southern ridge of Garangan) March 15, 2022 (1) Oilfeild 13 (Gachsaran2) March 15, 2022 (2) Oilfeild Note: The numbers in parentheses, (1) and (2), indicate the sampling series on March 15, 2022. Series 1 and 2 were two separate groups of samples collected on the same day. Characteristics of Plant Specimens The Dezful depression, a semi-arid landscape in southwestern Iran, showcases notable taxonomic diversity, providing insights into ecological adaptability in water-limited environments. The Poaceae family was the most abundant, consistent with other arid and semi-arid regions in Iran, where species like Bromus and Stipellula show strong adaptability to water scarcity (Eghdami et al., 2019 ; Hayati et al., 2024 ). The Dezful depression is characterized by hot summers (mean temperature ≈ 40°C) and low annual precipitation (200–300 mm), conditions typical of semi-arid bioclimates in southwestern Iran (Hayati et al., 2024 ). The Fabaceae family—particularly genera such as Astragalus—was also prevalent (Fig. 3 ). These results support previous findings showing that Fabaceae species can tolerate nutrient-poor soils and high evapotranspiration in Iranian drylands (Eghdami et al., 2019 ). Brassicaceae members, such as Hirschfeldia and Lepidium, were found mainly at disturbed sites, likely due to their short life cycles and rapid colonization strategies under anthropogenic or soil-degraded conditions (Ramzi et al., 2024 ). Each sample was identified by family, genus, and species, using the IranVeg classification system and recent botanical surveys (see Table 2 ; (Ramzi et al., 2024 ). Table 2 Summary of plant taxa identified in southern Dezful Depression Family Genus Species Amaranthaceae Soda Soda inermis Fourr. Apiaceae A System: ethum Anethum graveolens L. Asteraceae Achillea Achillea tomentosa L. Artemisia Artemisia ludoviciana Nutt. Bombycilaena Bombycilaena erecta (L.) Smoljan. Santolina Santolina rosmarinifolia L. Boraginaceae Ehretia Ehretia microphylla Lam. Brassicaceae Berteroa Berteroa incana (L.) DC. Cardamine Cardamine pratensis L. Hirschfeldia Hirschfeldia incana (L.) Lagr.-Foss. Lepidium Lepidium draba L. Matthiola Matthiola longipetala (Vent.) DC. Moricandia Moricandia arvensis (L.) DC. Schouwia Schouwia purpurea (Forssk.) Schweinf. Caryophyllaceae Dianthus Dianthus chinensis L. Fabaceae Astragalus Astragalus sempervirens Lam., Astragalus armatus Willd., Astragalus fasciculifolius Boiss. Genista Genista scorpius (L.) DC. Lotus Lotus dorycnium L. Lamiaceae Hyssopus Hyssopus officinalis L. Satureja Satureja hortensis L. Thymbra Thymbra capitata (L.) Cav. Linaceae Linum Linum usitatissimum L. Nitrariaceae Peganum Peganum harmala L. Plantaginaceae Plantago Plantago ovata Forssk. Poaceae Bromus Bromus hordeaceus L., Bromus sterilis L., Bromus racemosus L. Cynosurus Cynosurus echinatus L. Festuca Festuca bromoides L., Festuca myuros L. Hordeum Hordeum murinum L. Nassella Nassella tenuissima (Trin.) Barkworth Stipellula Stipellula capensis (Thunb.) Röser & Hamasha Ranunculaceae Ranunculus Ranunculus asiaticus L., Ranunculus trichophyllus Chaix Rhamnaceae Rhamnus, Rhamnus saxatilis Jacq. Rhamnaceae Ziziphus Ziziphus lotus (L.) Lam. Laboratory Spectroscopy The plant samples were measured spectrally via a FieldSpec® 3 spectroradiometer. This instrument measures reflectance across the 350–2500 nm range, offering spectral resolutions of 3 nm at 700 nm and 10 nm at 2100 nm. Measurements were performed via a contact probe with integrated artificial illumination, averaging 50 spectral readings per sample to minimize instrumental noise. A Spectralon® white reference panel was used to convert the measurements to reflectances. The resulting spectral library was smoothed via a Savitzky‒Golay filter (Savitzky & Golay, 1964 ) over the 400–2400 nm wavelength range. This dataset served as the basis for calculating the vegetation indices (Fig. 4 ). Vegetation indices calculation We calculated 14 different vegetation indices (to assess stress in vegetation induced by hydrocarbon contamination) (Table 3 ). These indices were chosen for their proven sensitivity and ability to detect variations in chlorophyll content, carbohydrate metabolism, plant structure, and defensive compounds (Arellano & Stratoulias, 2020 ; Lassalle et al., 2023 ; Rodrigues et al., 2024 ). These indices can be categorized into four groups: Chlorophyll-sensitive indices such as NDCI, CTR, PSSRa, LIC, RENDVI, and VOG were used to detect chlorophyll reduction due to hydrocarbon stress (Arellano et al., 2017 ; Rodrigues et al., 2024 ). Metabolic indices such as the starch index are used to identify disruptions in carbohydrate metabolism (Rodrigues et al., 2024 ). Structural indices , including the NDVI, SRI, OSAVI, ARVI, GNDVI, and GNIR, are used to monitor changes in biomass and vegetation structure (Arellano & Stratoulias, 2020 ; Zhang et al., 2009 ). Defensive indices such as the phenolic index are used to detect abnormal phenolic compound responses in hydrocarbon-stressed plants (Rodrigues et al., 2024 ). The complete list of indices and rationales for their selection are provided in Table 3 . Data processing was performed in Python via the NumPy and Pandas libraries. Statistical parameters, including the minimum, maximum, and mean, were calculated for each index to quantify variability and assess their sensitivity to vegetation stress (Adamu et al., 2018 ). Each of the 14 indices was assigned a weight on the basis of its normalized range value. The range value represents the extent of variation in a given vegetation index; a higher range value indicates greater sensitivity to variations within an area. Consequently, incides with greater variation (larger range values) receive higher weights. The normalized range values for each index were determined via the formula (max − min)/(max + min) of the range values, establishing an unbiased and more accurate weighting technique (Kashyap, 2022 ). The VIs were then ranked from 14 to 1, with 14 assigned to the index showing the maximum normalized range value and 1 assigned to the index showing the minimum range value. Table 3 Summary of VIs, their Formulas, and applications in detecting hydrocarbon stress Category Subcategory Index Formula Application and Reference Chlorophyll- Sensitive Leaf-Level Chlorophyll Detection CTR \(R695/R760\) Identifies hydrocarbon-induced stress in Amazon forests, sensitive to chlorophyll degradation (Arellano et al., 2015 ) PSSRa \(R800/R680\) Estimates chlorophyll in oil-affected vegetation, effective at leaf level (Arellano et al., 2015 ) Canopy-Level Chlorophyll Detection RENDVI \((R750-R705)/(R750+R705)\) Detects canopy stress from hydrocarbons, sensitive to foliage changes (Arellano et al., 2015 ) NDCI \((R708-R665)/(R708+R665)\) Quantifies chlorophyll reduction due to hydrocarbon stress, sensitive in low-vegetation environments (Rodrigues et al., 2024 ) Fluorescence and Water Content LIC \((R800-R680)/(R800+R680)\) Detects hydrocarbon stress, sensitive to chlorophyll fluorescence changes (Arellano & Stratoulias, 2020 ) VOG \(R740/R720\) Measures chlorophyll and water content in hydrocarbon-stressed vegetation (Arellano & Stratoulias, 2020 ) Metabolic Carbohydrate Metabolism Starch Index \(R930/R720\) Identifies disruptions in carbohydrate metabolism due to hydrocarbon stress(Rodrigues et al., 2024 ) Structural Biomass and Vigor NDVI (NIR - Red)/ (NIR + Red) Identifies vegetation stress in hydrocarbon microseepage zones; cancels out noise from sun angles, topography, clouds, and atmosphere(Kashyap, 2022 ) SRI \(NIR/Red\) Assesses leaf area index (LAI) and biomass in high-biomass vegetation like forests; reduces effects of topography and atmosphere (Kashyap, 2022 ) OSAVI \((1+0.16)\times\left(\right(R800-R670)/(R800+R670+0.16\left)\right)\) Highlights vigor in hydrocarbon-polluted areas, minimizes soil effects (Arellano & Stratoulias, 2020 ) Atmospheric and Soil Noise Reduction ARVI \(0.18+1.7\times\left(\right(NIR-Red)/(NIR+Red\left)\right)\) Detects hydrocarbon pollution in tropical forests, robust against atmospheric noise(Arellano & Stratoulias, 2020 ) GNDVI \((NIR-Green)/(NIR+Green)\) Monitors chlorophyll in oil-affected canopies, effective for dense vegetation (Arellano et al., 2015 ) Stress Pattern Mapping GNIR \((Green-NIR)/(Green+NIR)\) Maps hydrocarbon-impacted vegetation, highlights stress patterns(Arellano et al., 2015 ) Defensive Phenolic Compounds Phenolic Index R800/R550 Detects abnormal phenolic compound responses in hydrocarbon-stressed plants (Rodrigues et al., 2024 ) Principal component analysis (PCA) PCA was applied to reduce dataset dimensionality, summarize variability across the indices, and extract the most informative components. For this purpose, the plant indices were standardized via Standard Scaler (scikit-learn, Python v.3.x) to prevent variables with larger numerical ranges from dominating the analysis. PCA is also widely used in hyperspectral vegetation studies to highlight spectral dimensions linked to plant physiological status and stress (Abdelbaki & Udelhoven, 2022 ; Verrelst et al., 2019 ). Clustering K-Means clustering was applied to cluster the samples on the basis of their spectral similarity to distinguish different levels of vegetation stress. Following preliminary statistical analyses, clustering was applied to three of the most informative indices (i.e., CTR2, NDCI, and the starch index). Before clustering, all selected indices were normalized via the min–max scaler to ensure that variables with larger numerical ranges did not disproportionately influence the clustering process(Arellano et al., 2017 ; Athar et al., 2016 ; Rodrigues et al., 2024 ). The K-Means algorithm has been widely used in remote sensing and vegetation studies to differentiate plant stress levels on the basis of spectral similarity patterns. Statistical analysis To quantify vegetation responses to potential hydrocarbon microseepage, a multistep statistical workflow was applied on the basis of established methodologies in vegetation spectroscopy and environmental stress analysis (Kashyap, 2022 ; Lassalle et al., 2020 ; Thenkabail & Lyon, 2016 ). Descriptive Statistics and Weighting of Vegetation Indices The sensitivity of the selected vegetation indices to stress was evaluated. To allow comparisons across indices with different numerical ranges, all indices were normalized, and indices with higher normalized ranges were assigned greater weights, reflecting their sensitivity to vegetation stress(Arellano & Stratoulias, 2020 ; Gao, 2000 ); see Appendix A for details). Computation of the stress score Three stress-responsive indices (CTR2, NDCI, and the starch index) were selected on the basis of their physiological relevance to chlorophyll content, pigment variation, and biochemical stress markers (Gitelson et al., 2006 ; Rodrigues et al., 2024 ). The stress score was calculated as follows: Higher stress scores indicate stronger deviations from healthy vegetation reflectance, which is consistent with the stress-detection frameworks in remote sensing (Zhang & Lu, 2019 ). T Test for Group Comparison A To evaluate whether high-stress samples differed significantly from other samples, Welch’s independent t test (unequal variance) was performed for the three selected indices (CTR2, NDCI, and starch index), following common practices in environmental and ecological statistics (Ruxton, 2006 ): Significance was assessed at p < 0.05, enabling the detection of spectral differences associated with hydrocarbon-related stress. Stress Distribution Visualization (Pie Charts) A Stress categories (low, medium, high) were assigned on the basis of stress score thresholds. For each site type (active oilfields vs. prospect sites), category proportions were calculated: $${\text{Proportion}}_{c}=\frac{{N}_{c}}{{N}_{\text{total}}}$$ These proportions were visualized as pie charts to illustrate spatial differences in stress occurrence, which is consistent with standard ecological visualization approaches (Campbell & Wynne, 2011 ; Jensen, 2007 ). Results Statistical analysis and key index selection A summary of the statistical analysis and indices applied to the dataset is presented in Table 4 . The results show notable differences in their normalized ranges, reflecting varying sensitivities to vegetation stress. Among all indices, CTR2 presented the highest normalized range (2.09), indicating strong variability between stressed and nonstressed vegetation. The NDCI showed a moderately high normalized range (1.38), suggesting sensitivity to changes in chlorophyll-related stress. In contrast, the starch index displayed a lower normalized range (0.17), whereas the phenolic index showed moderate variability (0.84). These findings indicate that CTR2, NDCI, and the starch index were the most responsive indicators of vegetation stress within the dataset. The relatively higher ranges and clearer separation between sample conditions justified their selection as the key indices for subsequent analyses. Table 4 Statistical Summary of Vegetation Indices Used to Detect Vegetation Stress Potentially Induced by Hydrocarbon Seepage VIs Min Max Mean Range Normalized Range Weight ARVI 2 0.4211 1.5952 1.2342 1.1741 0.9513 7 CTR 2 0.1391 0.857 0.344 0.7179 2.0866 14 GNDVI 0.2674 0.6617 0.4584 0.3943 0.8602 6 GNIR -0.6617 -0.2674 -0.4584 0.3943 -0.8602 1 LIC 1 (PSNDa) 0.134 0.8242 0.6128 0.6902 1.1263 9 NDCI 0.0556 0.5119 0.3318 0.4563 1.3752 10 NDVI 0.1418 0.8324 0.6201 0.6906 1.1137 8 OSAVI 1.0541 1.1342 1.1118 0.0801 0.072 2 Phenolic Index 0.3754 0.8617 0.5807 0.4863 0.8374 5 PSSRa 1.3094 10.3738 4.8864 9.0644 1.855 12 RENDVI 0.0534 0.5769 0.3513 0.5235 1.4903 11 SRI 1.3305 10.9363 5.0576 9.6058 1.8993 13 Starch Index 0.8415 1.0043 0.9703 0.1627 0.1677 3 VOG 1 1.0475 1.6845 1.3462 0.637 0.4732 4 Note: The range column shows the variation in each index, and the normalized range column normalizes these values to allow for a direct comparison of their sensitivity to vegetation stress. Results of PCA The PC analysis revealed that the first three components can collectively explain 91.19% of the total variance, with PC1 to 3 contributing 75.81%, 8.24%, and 7.14%, respectively. A clear “elbow point” was observed after the third component in the scree plot (Fig. 5 ). The cumulative explained variance curve in Table 4 also supported the selection of three principal components. The spatial distribution of the PCs in 3D (Fig. 5 ) provided further insight into sample clustering patterns. The component loadings (Table 5 ) highlight the contribution of each vegetation index to the extracted components. PC1 is characterized by strong negative loadings from CTR2 and strong positive loadings from the NDVI and SRI indices, indicating its strong association with overall vegetation vigor and biomass. PC2 was influenced mainly by the chlorophyll-sensitive NDCI index, reflecting variations related to chlorophyll content. PC3 showed a dominant loading from the starch index, suggesting its role in explaining biochemical variations within the dataset. On the basis of the strength of their loadings and their distinct contributions to the PCs, CTR2, NDCI, and the starch index were selected as the key indices for further analysis. Table 5 Component Loadings of Selected Spectral Indices on the First Three Principal Components Spectral Index PC1 (Loadings) PC2 (Loadings) PC3 (Loadings) ARVI 2 0.294688 0.235478 0.059372 CTR 2 -0.292561 -0.114097 0.090251 GNDVI 0.252503 -0.458875 -0.056967 GNIR -0.252503 0.458875 0.056967 LIC 1 (PSNDa) 0.293583 0.246209 -0.06167 NDCI 0.253271 0.460048 0.03097 NDVI 0.294688 0.235478 -0.059372 OSAVI 0.259442 0.069761 0.023126 Phenolic Index -0.245452 0.140785 -0.36365 PSSRa 0.290598 0.069268 -0.051297 RENDVI 0.287624 -0.177997 -0.115315 SRI 0.289503 0.045958 -0.048295 Starch Index 0.117624 -0.030881 0.895496 VOG 1 0.265507 -0.344169 -0.143527 Note: the highlighted number indicates the highest load, which represents the largest contribution to the variance and the most important variable in interpreting each principal component. K-Means Clustering and Stress Classification K-Means clustering following the elbow method (Fig. 6 ) indicated that three clusters (k = 3) were optimal, facilitating the classification of samples into three distinct stress levels: "High Stress," "Medium Stress," and "Low Stress." Among the 68 analyzed samples, the majority (88%) were classified into low- and medium-stress clusters. Specifically, 29 samples (43%) were assigned to the "Low Stress" cluster, 31 samples (46%) were assigned to the "Medium Stress" cluster, and 8 samples (12%) exhibited "High Stress." Detailed information, including the sample ID, source (location), plant species, and stress score, is provided in Table 6 . These samples had the highest observed stress scores, notably, Sample 17 (2.53), Sample 11 (2.00), Sample 7 (1.98), Sample 2 (1.94), and Sample 10 (1.75). These high values are particularly indicative of significant levels of hydrocarbon impact on specific vegetation in areas overlapping with active oilfields. Spatial and visual analysis of stress The observed spatial concentration of high-stress samples within the "High Stress Cluster" was found near active oilfields. Specifically, high-stress samples (e.g., Samples 17, 11, 7, 2, and 10 in Table 6 ) and medium-stress samples were predominantly collected from areas directly overlying the active oilfields of Gachsaran, Chelingar, and Garangan. Conversely, low-stress samples were obtained primarily from prospective fields such as Bidkarz, Kheirabad, and Sarbari. More detailed analyses of the stress distributions across different sites, clearly illustrated in the pie charts of Fig. 7 , reveal significant differences. In active oilfields, the distribution of stress levels was as follows: 43.40% low stress, 43.40% medium stress, and 13.21% high stress. In contrast, 56.25% of the samples presented a low stress, 43.75% a medium stress, and none presented a high stress. Table 6 Top-stressed samples with vegetation indices and botanical data Spectral Sample Site Location Family Probable Species CTR 2 NDCI Starch Index Stress_Level Stress_Score 017 13 (Gachsaran2) Poaceae Stipellula capensis 0.85 0.05 0.91 High Stress 2.53 011 13 (Gachsaran2) Fabaceae Astragalus fasciculifolius 0.51 0.1 0.9 High Stress 2 007 13 (Gachsaran2) Brassicaceae Moricandia arvensis 0.82 0.05 1 High Stress 1.97 002 12 (Southern Ridge of Garangan) Fabaceae Astragalus armatus 0.6 0.13 0.93 High Stress 1.94 010 11 (North of Garangan1) Poaceae Hordeum murinum 0.62 0.14 0.95 High Stress 1.75 025 13 (Gachsaran2) Fabaceae Lotus dorycnium L. 0.57 0.08 0.98 High Stress 1.65 006 12 (Southern Ridge of Garangan) Poaceae Bromus sterilis 0.57 0.1 0.98 High Stress 1.6 024 13 (Gachsaran2) Brassicaceae Hirschfeldia incana (L.) Lagr.-Foss. 0.32 0.18 0.84 High Stress 1.97 The spectral plots corresponding to these quantitative findings are presented in Fig. 8 , which compares the spectral reflectance curves of high-stress samples (from the "High Stress Cluster") with those of their respective healthier counterparts. In the visible spectrum (400–700 nm), stressed vegetation typically exhibits increased reflectance in the blue (~ 450 nm) and red (~ 670 nm) regions, accompanied by a less pronounced green peak (~ 550 nm). The position of the red edge typically undergoes a blueshift and shows a shallower slope in stressed samples than in healthy controls. In the near-infrared (NIR) region (750–1300 nm), stressed plants consistently presented relatively low reflectance values. T test Statistical differences between high-stress samples and other samples were assessed via Welch’s independent t test on the selected spectral indices (CTR2, NDCI, and Starch Index; see Methods for details). The t test results for the CTR 2 index revealed a statistically significant difference between the two groups (t statistic = 2.5592, P value = 0.0172) (Table 7 ). The mean CTR 2 values in the high-stress sample group (mean = 0.4228) were significantly greater than the mean CTR 2 value in the other sample groups (mean = 0.3096). Similarly, for the NDCI index, the t test also revealed a statistically significant difference between the two groups (t statistic = -2.4840, P value = 0.0191). The mean NDCI of the high-stress sample group (mean = 0.2711) was significantly lower than the mean for the other sample groups (mean = 0.3583). In contrast to the previous two indices, the T test for the starch index did not reveal a statistically significant difference between the high-stress sample group and the other samples (T statistic = -1.5302, P value = 0.1387). Table 7 T test results for spectral index comparisons between high-stress samples and other samples Spectral Index Mean of High-Stress Group Mean of Other Samples Group T-Statistic P Value Significant Difference (p < 0.05) CTR 2 0.42 0.31 2.56 0.017 Yes ✔ NDCI 0.27 0.36 -2.48 0.019 Yes ✔ Starch Index 0.96 0.97 -1.53 0.139 No ✖ Discussion Physiological Interpretation of Key Spectral Indices The three selected indices—CTR₂, NDCI, and the starch index—capture complementary dimensions of plant stress physiology, providing an integrated picture of biochemical and structural alterations across the study area. The pronounced increase in CTR₂ within the high-stress cluster (mean ≈ 0.83 vs. 0.47 in the low-stress group; Table 1 ) reflects reduced absorption and enhanced reflectance in the red domain, indicative of chlorophyll degradation and partial loss of photosynthetically active pigments. This observation aligns with that of Rodrigues et al. ( 2024 ), who demonstrated that mangroves exposed to both acute and chronic oil contamination experience substantial alterations in foliar pigment pools, particularly chlorophyll and carotenoids, which are detectable as changes in reflectance between 400–700 nm and in the red-edge region. Moreover, Arellano et al. ( 2015 , 2017 ) and Das et al. ( 2016 ) reported similar decreases in chlorophyll content and photosynthetic efficiency in terrestrial plants under hydrocarbon stress, showing the generalizability of red-edge sensitive indices across different plant systems. The marked decline in NDCI values in our high-stress samples (mean ≈ 0.05 vs. 0.24 in low-stress samples; Table 6 ) further reflects impaired photosynthetic capacity and reduced chlorophyll-a. Mostafa et al.( 2021). similarly found that crude oil exposure in arid and semiarid regions caused significant structural and functional alterations in Azolla pinnata, including decreased chlorophyll and carotenoid contents, changes in frond tissue, and impaired growth, suggesting that the NDCI is a sensitive indicator of sublethal hydrocarbon stress even in water-limited environments. In the study by Rodrigues et al. ( 2024 ), linear discriminant analysis confirmed that chlorophyll and phenolic compounds were the most informative foliar traits for differentiating oil exposure from biotic stressors, with overall accuracies above 76% and kappa values ≥ 0.70. These findings reinforce the novel insight that chlorophyll-sensitive indices, such as the NDCI, can specifically detect early physiological responses to hydrocarbon stress, distinguishing them from general drought effects. The reflectance curves presented in Fig. 8 provide additional physiological insights. High-stress samples consistently display (i) increased reflectance in the blue (~ 450 nm) and red (~ 670 nm) regions, (ii) a diminished green peak, (iii) a blueshifted red edge with a shallower slope, and (iv) reduced NIR reflectance relative to their lower-stress counterparts. These spectral patterns reflect structural disruptions in mesophyll tissues and pigment degradation, in agreement with the findings of Zhang et al. ( 2009 ) in heavily oil-impacted regions, Arellano and Stratoulias ( 2020 ) in controlled hydrocarbon experiments, and Gürtler et al. (2022), who identified distinguishable spectral signatures of both leaves and soil in oil-contaminated Brazilian environments. Lassalle et al. ( 2021 , 2023 ) further emphasized that chronic sublethal oil stress leads to gradual deterioration in canopy structure and photosynthetic efficiency in mangrove forests, demonstrating that spectral anomalies persist even after vegetation recolonization. These collective insights underscore the utility of hyperspectral indices in capturing both immediate and cumulative biochemical alterations induced by hydrocarbon stress.Although the starch index did not differ significantly between the high-stress samples and the other samples (p = 0.138; Table 7 ), its trend towards lower mean values in the high-stress cluster (approximately 0.91) is physiologically meaningful. Rodrigues et al. ( 2024 ) reported that starch reserves were depleted more rapidly in acutely exposed mangroves than in chronically exposed ones, reflecting altered carbon allocation and impaired energy storage. This pattern is echoed by Mostafa et al. (2021), who reported that crude oil stress under arid conditions induced subtle but measurable reductions in the carbohydrate content and structural integrity of fronds. Additionally, Athar et al. ( 2016 ) demonstrated that even low-dose hydrocarbon exposure can disrupt photosynthetic efficiency and energy storage, pointing out the sensitivity of starch as an early stress indicator. In arid and semiarid ecosystems, where water deficit and nutrient limitation further influence carbohydrate dynamics, these subtle changes may reflect intricate physiological interactions that are not fully captured by spectral indicators alone.Taken together, the convergence of CTR₂ and NDCI shifts, the spectral signatures in Fig. 8 , and the subtle variations in the starch index provide a coherent physiological narrative. Leaf-level hyperspectral observations indicate that the vegetation in the high-stress cluster is experiencing both general environmental stress and specific pigment and structural alterations linked to hydrocarbon exposure. Integrating insights from Rodrigues et al. ( 2024 ), Mostafa et al. (2021), Arellano et al. ( 2015 , 2017 ), Das et al. ( 2016 ), and Lassalle et al. (2022, 2023 ) reinforces the interpretation that these indices serve as sensitive indicators of sublethal and chronic stress, even in arid environments such as the South Dezful Embayment. This perspective emphasizes the dual relevance of these indices for both early detection of hydrocarbon impact and ongoing environmental monitoring, providing a bridge between plant physiological responses and applied ecological management. Geospatial Patterns of Plant Stress The spatial distribution of vegetation stress in the South Dezful Embayment provides important insight into geographic patterns, complementing the leaf-level physiological patterns described earlier. Clustering analysis (Fig. 7 ) revealed that approximately 12% of all samples were classified as high-stress; however, these samples were almost exclusively located in or near active oilfields, including Gachsaran, Chelingar, and Garangan. In contrast, no high-stress samples were recorded at prospective (non-producing) sites, where 56.25% of the vegetation was classified as low-stress and the remainder as medium-stress. The distribution is asymmetric. This shows that unsupervised clustering and spatial analysis are useful. They help identify areas influenced by hydrocarbons. They also highlight regions that may be affected by seepage. These spatial patterns are consistent with observations by Mostafa et al. (2021) in arid and semiarid regions, where hydrocarbon contamination caused reductions in vegetation cover, structural alterations in leaves, and tissue damage, even at relatively low hydrocarbon concentrations. Similarly, Osorio et al. ( 2017 ) and Veldkornet et al. (2020) demonstrated that spatial analysis and unsupervised clustering effectively detect localized vegetation stress patterns induced by both natural and anthropogenic hydrocarbon seepage. These studies underscore the potential of spectral and spatial approaches as reliable proxies for monitoring environmental perturbations in petroleum-impacted landscapes. Furthermore, the spatial patterns observed in this study align with long-term mangrove investigations by Lassalle et al. ( 2023 ) in Brazil, where a 40-year-old oil spill resulted in (i) an initial dieback phase, (ii) an eight-year recolonization period, and (iii) a persistent reduction in canopy cover of approximately 20–30% relative to pre-spill conditions, accompanied by measurable decreases in forest biomass and productivity. These findings illustrate that hydrocarbon-induced stress can remain spatially concentrated over decades as sublethal yet detectable signals in vegetation structure and function. Our leaf-level observations in an arid petroleum province resonate with this broader pattern: localized high-stress clusters near producing fields may represent early-stage or small-scale analogs of long-term, spatially coherent degradation documented at the landscape level in mangrove ecosystems. Key spectral indices, including CTR₂ and NDCI, consistently capture these physiological responses, reflecting pigment loss and alterations in chlorophyll-a activity. These results are supported by studies such as those of Duke ( 2016 ), Lassalle et al. (2022), and Rodrigues et al. ( 2024 ), which demonstrated chronic and sublethal hydrocarbon effects and the capacity of foliar traits and hyperspectral measurements to distinguish hydrocarbon-related stress from other biotic stressors. This approach, through the integration of spectral and spatial analyses, enables the delineation of oilfields and identification of potentially impacted areas, providing a scientifically robust framework for environmental monitoring and petroleum field management. By combining spectral and spatial analyses with soil chemistry, gas flux measurements, and additional physiological indices, a comprehensive tool is created. This approach helps distinguish the effects of hydrocarbons from concurrent natural stressors. It also supports more informed decisions in environmental protection and resource management. Conclusion The results of this study demonstrate that vegetation stress associated with hydrocarbon microseepage and long-term leakage can be systematically captured through the spectral responses of plant species in arid and semi-arid environments. In the South Dezful Embayment, active oil fields exist alongside prospective hydrocarbon areas. This setting allowed us to examine how underground hydrocarbon activity affects vegetation physiological responses. Our analysis of spectral indices showed that CTR2 and NDCI were responsive to hydrocarbon-related stress. These indices reflected reductions in chlorophyll content and alterations in leaf structure. The stress influenced plant reflectance in several ways: it increased reflectance in the visible spectrum, caused a blue shift of the red edge, and decreased near-infrared reflectance. These patterns were confirmed through statistical analysis. The spatial stress pattern derived from K-Means clustering indicates that the highest stress levels are predominantly associated with active producing oil fields, whereas lower stress levels are more commonly observed in prospect areas. This contrast suggests that vegetation spectral responses are capable of reflecting not only the presence of hydrocarbons but also the relative intensity and effectiveness of seepage processes. These patterns serve as a useful basis for prioritizing areas susceptible to microseepage. They also help reduce uncertainty during early-stage hydrocarbon exploration and support reconnaissance-scale assessments in petroleum-rich basins. At the same time, the observed vegetation responses provide important insight into environmental pressure exerted by chronic hydrocarbon exposure, enabling the identification of areas where ecosystem health may gradually deteriorate.This is particularly important in dry areas, where plants are highly sensitive to persistent contamination and conducting extensive field-based monitoring can often be difficult. Consequently, the framework presented in this study is transferable to other hydrocarbon-bearing sedimentary basins with comparable climatic conditions and can be applied as a complementary approach alongside geological and geochemical investigations for both exploration-oriented and environmental monitoring purposes. List of Acronyms and Abbreviations Abbreviation Full Term VI Vegetation Index HCs Hydrocarbons RS Remote Sensing VNIR Visible-Near Infrared SWIR Short-Wave Infrared NDVI Normalized Difference Vegetation Index PCA Principal Component Analysis CTR Chlorophyll Transmittance Ratio NDCI Normalized Difference Chlorophyll Index SSE Sum of Squared Errors ASD Analytical Spectral Devices NIR Near-Infrared PSSRa Plant Stress Spectral Ratio a RENDVI Red-Edge Normalized Difference Vegetation Index LIC Leaf-Level Chlorophyll Index VOG Vegetation Optical Greenness SRI Simple Ratio Index OSAVI Optimized Soil-Adjusted Vegetation Index ARVI Atmospherically Resistant Vegetation Index GNDVI Green Normalized Difference Vegetation Index GNIR Green-Near Infrared Ratio IPNI International Plant Names Index LAI Leaf Area Index TBC Total Bitumen Content Declarations Author Contribution Y.E.: Conceptualization, investigation, methodology, writing—original draft, data analysis (spectral ,statistical and spatial), figure preparation, review & editing. K.R.: Sample collection, spectroscopy measurements, conceptualization, supervision, review & editing. M.K.: Review & editing. S.A.: Data analysis support, conceptualization of results, review & editing. A.A.: Review & editing, support in data analysis. M.S.: Review & editing. All authors have read and approved the final manuscript. The authors declare that they have no known financial interests, commercial affiliations, or other competing interests that could have appeared to influence the work reported in this paper. AI-assisted copy editing was used solely to improve readability and language quality. Code, Data, and Materials Availability The data that support the findings of this study are not publicly available. They can be requested from the corresponding author at [email protected] Acknowledgments The authors gratefully acknowledge the plant sampling team at the Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, for their valuable assistance in collecting the plant samples. We sincerely thank Prof. Dr. Sabine Chabrillat at the Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, for her insightful guidance and support during Yasmin Elhaei’s visit as a PhD researcher. Additionally, an AI-based language tool was used to improve the clarity and grammar of the manuscript. Funding Not applicable. References Abdelbaki, A., Udelhoven, T., 2022. A Review of Hybrid Approaches for Quantitative Assessment of Crop Traits Using Optical Remote Sensing: Research Trends and Future Directions. Remote Sensing 14, 3515. https://doi.org/10.3390/rs14153515 Adamu, B., Tansey, K., Ogutu, B., 2018. Remote sensing for detection and monitoring of vegetation affected by oil spills. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8756547","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593610232,"identity":"d63aa3b7-5cf0-41bc-b323-14dc4b55e89b","order_by":0,"name":"Yasmin Elhaei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYNACAwYGPgnmA0CWhAzxWtgk2BJAWniIt4hNgscARBPWYnD8+MNPNwruyLFJ93x+daPGgoeB/fDRDXi1nMkxls4xeGbMJnN2m3XOMaDDeNLSbuDVciCHAajlcGKbRO424xyg84DeMcOv5fzzx7+BWurbJHKeGef8I0bLjQQzkC0JbBI5zI9z24jQInnjjZk1UIthm0SaGXNunwQPGyG/8J1Pf3w7589heX6J5Mefc77VyfGzHz6GV4vCAQSbTQJM4lMOAvINCDbzB0KqR8EoGAWjYGQCAO1gRmFO3qoQAAAAAElFTkSuQmCC","orcid":"","institution":"Shahid Chamran University of Ahvaz","correspondingAuthor":true,"prefix":"","firstName":"Yasmin","middleName":"","lastName":"Elhaei","suffix":""},{"id":593610233,"identity":"bb1780dd-29b1-4246-91e6-2d683b12790f","order_by":1,"name":"Kazem Rangzan","email":"","orcid":"","institution":"Shahid Chamran University of Ahvaz","correspondingAuthor":false,"prefix":"","firstName":"Kazem","middleName":"","lastName":"Rangzan","suffix":""},{"id":593610234,"identity":"ba42e9f2-fac1-4089-80b1-2cb20ebc6346","order_by":2,"name":"Mostafa Kabolizadeh","email":"","orcid":"","institution":"Shahid Chamran University of Ahvaz","correspondingAuthor":false,"prefix":"","firstName":"Mostafa","middleName":"","lastName":"Kabolizadeh","suffix":""},{"id":593610235,"identity":"785b88c4-8914-45ff-ac76-da38ac532fcb","order_by":3,"name":"Saeid Asadzadeh","email":"","orcid":"","institution":"Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Section of Remote Sensing and Geoinformatics","correspondingAuthor":false,"prefix":"","firstName":"Saeid","middleName":"","lastName":"Asadzadeh","suffix":""},{"id":593610236,"identity":"dc0f1967-a759-428d-9369-f5d25cd783d8","order_by":4,"name":"Asmaa Abdelbaki","email":"","orcid":"","institution":"Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Section of Remote Sensing and Geoinformatics","correspondingAuthor":false,"prefix":"","firstName":"Asmaa","middleName":"","lastName":"Abdelbaki","suffix":""},{"id":593610237,"identity":"125559fc-e5df-4a47-b39c-1c47952b56ec","order_by":5,"name":"Mohammad Seraj","email":"","orcid":"","institution":"National Iranian South Oilfields Company (NISOC)","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Seraj","suffix":""}],"badges":[],"createdAt":"2026-02-01 13:38:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8756547/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8756547/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105480013,"identity":"ff8212bf-2915-4fc9-bf40-18f60cabbc45","added_by":"auto","created_at":"2026-03-26 13:28:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":413808,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the study area in the South Dezful Embayment, southwestern Iran. Panel (a) shows the inset map illustrating the position of the South Dezful Embayment within Khuzestan, Kohgiluyeh and Boyer-Ahmad, and Fars Provinces along the northeastern end of the Persian Gulf. Panel (b) presents the outline of the study area over the satellite imagery, locations of the vegetation sampling sites (green icons; n =13) and borders of active oil fields.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8756547/v1/9edffd84b6be6dc989982d5a.jpg"},{"id":105479996,"identity":"ed972c60-9b5e-4a77-8a22-429da41671bf","added_by":"auto","created_at":"2026-03-26 13:27:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":227062,"visible":true,"origin":"","legend":"\u003cp\u003ePlant samples collected for spectroscopic analysis in the laboratory. (a) and (b): Plant samples collected from site 011 (north of Garangan). (c) and (d): Plant samples collected from site 04 (Bid Karz ring).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8756547/v1/109ddd1c256891b0c733e115.jpg"},{"id":105480016,"identity":"853f7c7f-2544-4832-a81b-5c9bd9b47d4b","added_by":"auto","created_at":"2026-03-26 13:28:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55412,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies Frequency by Family for the samples collected in this study\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8756547/v1/73254d6eb284696f8582fcf1.jpg"},{"id":105479999,"identity":"3e2fb90a-b193-4d1a-9834-9a5f77617344","added_by":"auto","created_at":"2026-03-26 13:27:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":200812,"visible":true,"origin":"","legend":"\u003cp\u003eSpectral reflectance curves of healthy (green) and stressed (blue) vegetation sampled from oilfield environments in the South Dezful Embayment. The figures onthe right side represent species from Poaceae and Brassicaceae.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8756547/v1/c6fcc7cf1a834e0e94f38e2c.jpg"},{"id":105480010,"identity":"fd5fd526-ab60-4f6a-8ae1-869ccf3244c7","added_by":"auto","created_at":"2026-03-26 13:28:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":177447,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) results of the vegetation index dataset(a) PCA results showing the explained variance and cumulative variance for all the components. The elbow after PC3 indicates that the first three components capture the essential structure of the vegetation index dataset. The PC1–PC2 biplot illustrates the sample distribution together with loading vectors, revealing the relative contributions of the vegetation indices to the first two components. (b) Heatmap of component loadings for PC1–PC3 and the 3D PCA score plot (PC1–PC3). CTR2 (negative) and the NDVI/SRI (positive) dominate PC1, the NDCI contributes most strongly to PC2, and the starch index is the main driver of PC3. (c) The 3D plot highlights sample clustering patterns based on the three principal components.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8756547/v1/5ba7a1cce9a26c41d593c61d.jpg"},{"id":105480023,"identity":"c6ef1891-8afc-41f0-a8e4-d0e62fb131a0","added_by":"auto","created_at":"2026-03-26 13:28:06","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":158068,"visible":true,"origin":"","legend":"\u003cp\u003eClustering analysis and statistical relationships among selected vegetation indices (a) Elbow plot for determining the optimal number of clusters (k=3). (b) Pair plotof vegetation indices (CTR 2, NDCI, and starch index) based on stress levels (high, medium, low). (c) Box plotsshowing the distribution of each index across different stress levels. (d) Correlation matrix of the three vegetation indices.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8756547/v1/73f11357745d66ad7189b3ac.jpg"},{"id":105480046,"identity":"ce563869-2942-41aa-af36-f5820961f753","added_by":"auto","created_at":"2026-03-26 13:28:20","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":51724,"visible":true,"origin":"","legend":"\u003cp\u003ePie charts of stress levels in active oilfields and prospective sites. The stress levels in both charts are categorized as low, medium, and high. In oilfields, low and medium stress levels each account for the largest share at 43.4%. At prospective sites, low stress accounts for the majority of the total stress, at 56.2%.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8756547/v1/00241cb45ba0c9872be58f0a.jpg"},{"id":105479983,"identity":"d903d4cf-917e-4be5-bc28-409aae5f7b97","added_by":"auto","created_at":"2026-03-26 13:27:44","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":507015,"visible":true,"origin":"","legend":"\u003cp\u003eSpectral reflectance curves of stressed (blue) and healthy (green) vegetation samples from the South Dezful Embayment. Each subplot presents a high-stress sample identified in this study compared with its healthy counterpart, with comparisons grouped by plant family. Stressed samples consistently exhibit higher visible reflectance, a blueshifted red edge, and lower near-infrared (NIR) reflectance, indicative of chlorophyll degradation and structural damage.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8756547/v1/5679014b90d5637638a5ae5d.jpg"},{"id":105480051,"identity":"cff3e492-7e39-4a0a-a1fd-99503fe1a5be","added_by":"auto","created_at":"2026-03-26 13:28:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3228506,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8756547/v1/e1a472bd-628e-4821-aa8e-3038b6924f14.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reflectance Spectroscopic Study of Hydrocarbon-Induced Vegetation Stress Assessment in Chalinger-Garangan and Surrounding Oil Fields, Zagros Fold-Thrust Belt, Iran","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe leakage of hydrocarbons from natural and anthropogenic sources can have a substantial impact on the environment (Khan \u0026amp; Jacobson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Schumacher, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Such leakages may occur as natural and low-intensity hydrocarbon microseepage, characterized by the continuous upward migration of light hydrocarbons from subsurface accumulations, or as acute and large-scale releases associated with human activities during petroleum extraction, transportation, and utilization. While acute spills typically cause sudden and severe ecological damage, hydrocarbon microseepage results in long-term and chronic exposure of surface ecosystems, particularly vegetation. Natural seepage may occur at the surface through anomalous hydrocarbon concentrations in soils, mineralogical alterations, and physiological stress in vegetation growing above petroleum-prone environments (Asadzadeh \u0026amp; de Souza Filho, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schumacher, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Continuous hydrocarbon leakage can alter soil chemistry, reduce oxygen availability, and create reducing environments that disrupt plant physiological processes, leading to chlorosis, reduced photosynthesis, and biomass decline (Duke et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The impact of hydrocarbons on vegetation arises not only from direct toxicity but also from soil chemical and physical alterations, reduced nutrient and oxygen availability, and the creation of reducing conditions, all of which contribute to stress and impaired plant performance. Consequently, vegetation monitoring serves as a crucial ecological indicator for detecting hydrocarbon leakage, providing a dual-purpose tool that can guide exploration by identifying hydrocarbon-prone areas and simultaneously assessing the environmental impacts and effectiveness of postspill mitigation measures(Arellano et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lassalle et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpectral remote sensing is a proven technology for the efficient detection and monitoring of hydrocarbon-induced vegetation stress, addressing the spatial limitations of traditional sample-based geobotanical, geochemical, and geophysical methods (Jacquemoud \u0026amp; Ustin, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Lassalle et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Spectral information acquired in the visible\u0026ndash;near-infrared (VNIR) and shortwave-infrared (SWIR) regions enables the identification of changes in the spectral behavior of vegetation under stress (Cloutis, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Lammoglia \u0026amp; Souza Filho, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Vegetation indices (VIs), such as the NDVI and RENDVI, quantify foliar biochemical and structural properties and have demonstrated utility in early stress detection (Berger et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A decrease in chlorophyll-sensitive indices reflects chlorophyll degradation and reduced photosynthetic efficiency caused by hydrocarbon-induced physiological stress. This biochemical alteration leads to a displacement of the red-edge position toward shorter wavelengths (blueshift), a well-established spectral response to decreasing chlorophyll content. Simultaneously, hydrocarbon-related stress disrupts the internal structure and mesophyll integrity of leaves, resulting in reduced near-infrared (NIR) reflectance. Together, these spectral features provide a mechanistic basis for interpreting hydrocarbon-induced vegetation stress(Arellano et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Duke, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Grant et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Numerous studies have applied multispectral and hyperspectral data\u0026mdash;using derivative analysis, band ratios, and spectral mixture analysis\u0026mdash;to detect hydrocarbon seepage and associated vegetation anomalies (Asadzadeh et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kokaly, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lassalle, 2021).\u003c/p\u003e \u003cp\u003eRecent studies, including weighted vegetation index models (Kashyap, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and detailed assessments of acute versus chronic vegetation stress, have confirmed the sensitivity of spectral responses to petroleum contamination (Duke et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Rodrigues et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and the sensitivity of spectral responses to petroleum contamination. Long-term ecological effects have also been demonstrated, with persistent reductions in plant vitality and biomass decades after oil spills (Lassalle et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Spectroscopic techniques have also proven successful in detecting HC-specific absorption features directly over oil sands, oil spills, and oil pollution (Asadzadeh \u0026amp; de Souza Filho, 2016; Correa Pab\u0026oacute;n et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Speta et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Collectively, these studies highlight the ability of spectral techniques to link laboratory, field, and airborne observations across multiple environmental settings while also emphasizing the importance of scale and environmental context (Lassalle et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rodrigues et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These authors demonstrated that oil spills and chronic hydrocarbon exposure induce persistent biochemical and spectral changes in vegetation, reducing forest biomass and generating diagnostic spectral anomalies observable remotely in the VNIR and SWIR wavelength ranges.\u003c/p\u003e \u003cp\u003eDespite these successful case studies, applying remote sensing techniques in arid and semiarid but petroleum-rich regions remains challenging. This is because, on the one hand, vegetation cover is sparse in these regions, and on the other hand, natural stressors such as drought, soil salinity, heat stress, and nutrient deficiency may produce spectral anomalies that resemble hydrocarbon-induced stress, complicating accurate diagnosis via visual or spectral methods and making accurate assessment via traditional visual observations or standard spectral analyses more difficult (Das et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Osorio et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Although field spectroscopy provides precise spectral measurements under controlled conditions, its application in arid and semiarid regions offers a practical approach for detecting hydrocarbon-induced vegetation stress at the local scale without relying on large-scale imaging spectroscopy datasets.\u003c/p\u003e \u003cp\u003eGiven these gaps, the South Dezful Embayment in southwestern Iran, a region characterized by arid climatic conditions, ecological sensitivity, and the presence of multiple active and prospective oil fields, was selected to evaluate the spectral responses of vegetation to long-term hydrocarbon microseepage. Leaf-level field spectroscopy was employed to analyze a range of narrow- and broad-band vegetation indices, aiming to identify those most sensitive to hydrocarbon-induced stress and to assess their potential as robust surface indicators for both hydrocarbon exploration and environmental monitoring. This approach allows a clear link between observed vegetation stress and specific spectral indicators, providing a practical methodology for both scientific analysis and environmental assessment. The present study, therefore, aimed to determine whether hydrocarbon microseepage induces detectable and persistent changes in vegetation spectral properties and whether vegetation indices differ meaningfully between samples from affected and unaffected areas. Moreover, this knowledge, while valuable for petroleum exploration, can help establish a baseline of vegetation stress and provide additional insights for environmental assessment in arid and semiarid regions.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe study area is part of the South Dezful Embayment, which is situated in southwestern Iran within the Zagros Fold-Thrust Belt (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Geographically, it spans between 48\u0026deg;00\u0026prime;\u0026ndash;50\u0026deg;30\u0026prime; E and 30\u0026deg;00\u0026prime;\u0026ndash;32\u0026deg;30\u0026prime; N, stretching in three provinces. The region's climate is arid to semiarid, characterized by extreme summer temperatures often exceeding 50\u0026deg;C and mild winters ranging from 10\u0026ndash;20\u0026deg;C. Annual precipitation, which primarily occurs in winter and spring, averages 200\u0026ndash;400 mm (Alavi, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The air humidity is generally low (i.e., \u0026lt;\u0026thinsp;20% in summer), increasing only in proximity to rivers and marshlands. The vegetation in the embayment is sparse, adapting to arid conditions, and is predominantly composed of drought-resistant shrubs and grasses. Higher elevations support oak and wild pistachio species, with reeds and hydrophilic plants thriving near rivers and marshlands (Zohdi et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGeologically, the embayment is composed of Cretaceous to Miocene carbonate and clastic rocks. Key reservoir formations include the Asmari Formation (Oligo-Miocene limestones) and Bangestan Formation, which are composed of Cretaceous sandstones that are effectively sealed by evaporites of the Gachsaran Formation (Bordenave \u0026amp; Hegre, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The study area encompasses six major oil fields delineated by prominent anticlinal and fold structures. The Chilingar, Garangan, and Chahar Bishah oil fields are currently active, producing hydrocarbons from the Alban\u0026ndash;Cenomanian limestones and dolomites of the Khami Group's carbonate reservoirs. Conversely, Bidkarz, Khairabad, and Sarburi represent undeveloped prospects. These latter fields target the Oligo-Miocene limestones of the Asmari Formation, which have substantial hydrocarbon potential because of their inherent porosity and structural traps (Derikvand et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sherkati \u0026amp; Letouzey, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The presence of these giant oil fields is assumed to induce stress on the overlying local vegetation, particularly the prevalent shrubs and grasses, which may manifest as detectable spectral anomalies. This makes the region an ideal setting for investigating vegetation responses to hydrocarbon influence through spectral analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eField Sampling and Sites\u003c/h3\u003e\n\u003cp\u003eField sampling was conducted in the South Dezful Embayment during the peak growing season to capture optimal vegetation conditions. A total of 68 vegetation samples were collected from 13 strategically selected sites within the oilfields (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; see also Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Sites were chosen based on their proximity to known hydrocarbon seepage zones and related infrastructure, reflecting the hydrocarbon potential and characteristics of the oilfields (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Sampling took place over two consecutive days, from March 14 to 15, 2022. The collected samples included various plant components, such as leaves, flowers, and stems. Both mature and herbaceous individuals of the dominant species were sampled. Within each site, sampling was performed randomly to capture representative spectral variability.All samples were processed promptly for vegetation index analysis aimed at assessing hydrocarbon-induced stress. They were stored in appropriate containers until spectroscopic measurements could be performed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of Study Sites, Sampling Series, and Classification Types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSampling Date (Series)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e01 (Chilingar7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarch 15, 2022 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOilfeild\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e02 (Chilingar4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarch 15, 2022 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOilfeild\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e03 (East Chilingar5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarch 15, 2022 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOilfeild\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e04 (Bid Karz ring)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarch 14, 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProspect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e05 (Topside of Bid Karz ring)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarch 14, 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProspect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e06 (South beginning of Bid Karz ring)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarch 14, 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProspect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e07 (South of Chilingar ring)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarch 14, 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOilfeild\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e08 (Pazanan 13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarch 15, 2022 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOilfeild\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e09 (Khairabad)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarch 15, 2022 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProspect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e10 (Garangan5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarch 15, 2022 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOilfeild\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e11 (North of Garangan1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarch 15, 2022 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOilfeild\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e12 (Southern ridge of Garangan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarch 15, 2022 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOilfeild\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e13 (Gachsaran2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarch 15, 2022 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOilfeild\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: The numbers in parentheses, (1) and (2), indicate the sampling series on March 15, 2022. Series 1 and 2 were two separate groups of samples collected on the same day.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCharacteristics of Plant Specimens\u003c/h3\u003e\n\u003cp\u003eThe Dezful depression, a semi-arid landscape in southwestern Iran, showcases notable taxonomic diversity, providing insights into ecological adaptability in water-limited environments. The Poaceae family was the most abundant, consistent with other arid and semi-arid regions in Iran, where species like Bromus and Stipellula show strong adaptability to water scarcity (Eghdami et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hayati et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Dezful depression is characterized by hot summers (mean temperature\u0026thinsp;\u0026asymp;\u0026thinsp;40\u0026deg;C) and low annual precipitation (200\u0026ndash;300 mm), conditions typical of semi-arid bioclimates in southwestern Iran (Hayati et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Fabaceae family\u0026mdash;particularly genera such as Astragalus\u0026mdash;was also prevalent (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These results support previous findings showing that Fabaceae species can tolerate nutrient-poor soils and high evapotranspiration in Iranian drylands (Eghdami et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Brassicaceae members, such as Hirschfeldia and Lepidium, were found mainly at disturbed sites, likely due to their short life cycles and rapid colonization strategies under anthropogenic or soil-degraded conditions (Ramzi et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Each sample was identified by family, genus, and species, using the IranVeg classification system and recent botanical surveys (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; (Ramzi et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of plant taxa identified in southern Dezful Depression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmaranthaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSoda inermis Fourr.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApiaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA System: ethum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAnethum graveolens L.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAsteraceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAchillea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAchillea tomentosa L.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArtemisia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003eArtemisia ludoviciana\u003c/em\u003e Nutt.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBombycilaena\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eBombycilaena erecta (L.) Smoljan.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSantolina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSantolina rosmarinifolia L.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoraginaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEhretia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eEhretia microphylla Lam.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eBrassicaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBerteroa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eBerteroa incana (L.) DC.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCardamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCardamine pratensis L.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHirschfeldia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHirschfeldia incana (L.) Lagr.-Foss.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLepidium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eLepidium draba L.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatthiola\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMatthiola longipetala (Vent.) DC.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoricandia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMoricandia arvensis (L.) DC.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchouwia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSchouwia purpurea (Forssk.) Schweinf.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaryophyllaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDianthus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDianthus chinensis L.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFabaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAstragalus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAstragalus sempervirens Lam., Astragalus armatus Willd., Astragalus fasciculifolius Boiss.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenista\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGenista scorpius (L.) DC.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLotus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eLotus dorycnium L.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLamiaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHyssopus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyssopus officinalis L.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSatureja\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSatureja hortensis L.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eThymbra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThymbra capitata (L.) Cav.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLinum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLinum usitatissimum L.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrariaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePeganum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeganum harmala L.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlantaginaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePlantago\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlantago ovata Forssk.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePoaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBromus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBromus hordeaceus L., Bromus sterilis L., Bromus racemosus L.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCynosurus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCynosurus echinatus L.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFestuca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFestuca bromoides L., Festuca myuros L.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHordeum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHordeum murinum L.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNassella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNassella tenuissima (Trin.) Barkworth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eStipellula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStipellula capensis (Thunb.) R\u0026ouml;ser \u0026amp; Hamasha\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRanunculaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRanunculus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRanunculus asiaticus L., Ranunculus trichophyllus Chaix\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRhamnaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRhamnus, Rhamnus saxatilis Jacq.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRhamnaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eZiziphus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZiziphus lotus (L.) Lam.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eLaboratory Spectroscopy\u003c/h3\u003e\n\u003cp\u003eThe plant samples were measured spectrally via a FieldSpec\u0026reg; 3 spectroradiometer. This instrument measures reflectance across the 350\u0026ndash;2500 nm range, offering spectral resolutions of 3 nm at 700 nm and 10 nm at 2100 nm. Measurements were performed via a contact probe with integrated artificial illumination, averaging 50 spectral readings per sample to minimize instrumental noise. A Spectralon\u0026reg; white reference panel was used to convert the measurements to reflectances. The resulting spectral library was smoothed via a Savitzky‒Golay filter (Savitzky \u0026amp; Golay, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1964\u003c/span\u003e) over the 400\u0026ndash;2400 nm wavelength range. This dataset served as the basis for calculating the vegetation indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eVegetation indices calculation\u003c/h3\u003e\n\u003cp\u003eWe calculated 14 different vegetation indices (to assess stress in vegetation induced by hydrocarbon contamination) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These indices were chosen for their proven sensitivity and ability to detect variations in chlorophyll content, carbohydrate metabolism, plant structure, and defensive compounds (Arellano \u0026amp; Stratoulias, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lassalle et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rodrigues et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These indices can be categorized into four groups:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eChlorophyll-sensitive indices\u003c/b\u003e such as NDCI, CTR, PSSRa, LIC, RENDVI, and VOG were used to detect chlorophyll reduction due to hydrocarbon stress (Arellano et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rodrigues et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMetabolic indices\u003c/b\u003e such as the starch index are used to identify disruptions in carbohydrate metabolism (Rodrigues et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStructural indices\u003c/b\u003e, including the NDVI, SRI, OSAVI, ARVI, GNDVI, and GNIR, are used to monitor changes in biomass and vegetation structure (Arellano \u0026amp; Stratoulias, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDefensive indices\u003c/b\u003e such as the phenolic index are used to detect abnormal phenolic compound responses in hydrocarbon-stressed plants (Rodrigues et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe complete list of indices and rationales for their selection are provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eData processing was performed in Python via the NumPy and Pandas libraries. Statistical parameters, including the minimum, maximum, and mean, were calculated for each index to quantify variability and assess their sensitivity to vegetation stress (Adamu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Each of the 14 indices was assigned a weight on the basis of its normalized range value. The range value represents the extent of variation in a given vegetation index; a higher range value indicates greater sensitivity to variations within an area. Consequently, incides with greater variation (larger range values) receive higher weights. The normalized range values for each index were determined via the formula (max\u0026thinsp;\u0026minus;\u0026thinsp;min)/(max\u0026thinsp;+\u0026thinsp;min) of the range values, establishing an unbiased and more accurate weighting technique (Kashyap, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The VIs were then ranked from 14 to 1, with 14 assigned to the index showing the maximum normalized range value and 1 assigned to the index showing the minimum range value.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of VIs, their Formulas, and applications in detecting hydrocarbon stress\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubcategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eApplication and Reference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eChlorophyll-\u003c/p\u003e \u003cp\u003eSensitive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLeaf-Level Chlorophyll Detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(R695/R760\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIdentifies hydrocarbon-induced stress in Amazon forests, sensitive to chlorophyll degradation\u003c/p\u003e \u003cp\u003e(Arellano et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePSSRa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(R800/R680\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimates chlorophyll in oil-affected vegetation, effective at leaf level (Arellano et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCanopy-Level Chlorophyll Detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRENDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((R750-R705)/(R750+R705)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetects canopy stress from hydrocarbons, sensitive to foliage changes (Arellano et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((R708-R665)/(R708+R665)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQuantifies chlorophyll reduction due to hydrocarbon stress, sensitive in low-vegetation environments (Rodrigues et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFluorescence and Water Content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((R800-R680)/(R800+R680)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetects hydrocarbon stress, sensitive to chlorophyll fluorescence changes (Arellano \u0026amp; Stratoulias, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVOG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(R740/R720\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeasures chlorophyll and water content in hydrocarbon-stressed vegetation (Arellano \u0026amp; Stratoulias, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbohydrate Metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStarch Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(R930/R720\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIdentifies disruptions in carbohydrate metabolism due to hydrocarbon stress(Rodrigues et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eStructural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBiomass and Vigor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(NIR - Red)/\u003c/p\u003e \u003cp\u003e(NIR\u0026thinsp;+\u0026thinsp;Red)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIdentifies vegetation stress in hydrocarbon microseepage zones; cancels out noise from sun angles, topography, clouds, and atmosphere(Kashyap, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(NIR/Red\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAssesses leaf area index (LAI) and biomass in high-biomass vegetation like forests; reduces effects of topography and atmosphere (Kashyap, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOSAVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((1+0.16)\\times\\left(\\right(R800-R670)/(R800+R670+0.16\\left)\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHighlights vigor in hydrocarbon-polluted areas, minimizes soil effects (Arellano \u0026amp; Stratoulias, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAtmospheric and Soil Noise Reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eARVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(0.18+1.7\\times\\left(\\right(NIR-Red)/(NIR+Red\\left)\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetects hydrocarbon pollution in tropical forests, robust against atmospheric noise(Arellano \u0026amp; Stratoulias, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((NIR-Green)/(NIR+Green)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMonitors chlorophyll in oil-affected canopies, effective for dense vegetation (Arellano et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress Pattern Mapping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGNIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((Green-NIR)/(Green+NIR)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaps hydrocarbon-impacted vegetation, highlights stress patterns(Arellano et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhenolic Compounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhenolic Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR800/R550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetects abnormal phenolic compound responses in hydrocarbon-stressed plants (Rodrigues et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal component analysis (PCA)\u003c/h2\u003e \u003cp\u003ePCA was applied to reduce dataset dimensionality, summarize variability across the indices, and extract the most informative components. For this purpose, the plant indices were standardized via Standard Scaler (scikit-learn, Python v.3.x) to prevent variables with larger numerical ranges from dominating the analysis. PCA is also widely used in hyperspectral vegetation studies to highlight spectral dimensions linked to plant physiological status and stress (Abdelbaki \u0026amp; Udelhoven, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Verrelst et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClustering\u003c/h3\u003e\n\u003cp\u003eK-Means clustering was applied to cluster the samples on the basis of their spectral similarity to distinguish different levels of vegetation stress. Following preliminary statistical analyses, clustering was applied to three of the most informative indices (i.e., CTR2, NDCI, and the starch index). Before clustering, all selected indices were normalized via the min\u0026ndash;max scaler to ensure that variables with larger numerical ranges did not disproportionately influence the clustering process(Arellano et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Athar et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rodrigues et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The K-Means algorithm has been widely used in remote sensing and vegetation studies to differentiate plant stress levels on the basis of spectral similarity patterns.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo quantify vegetation responses to potential hydrocarbon microseepage, a multistep statistical workflow was applied on the basis of established methodologies in vegetation spectroscopy and environmental stress analysis (Kashyap, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lassalle et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Thenkabail \u0026amp; Lyon, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics and Weighting of Vegetation Indices\u003c/h2\u003e \u003cp\u003eThe sensitivity of the selected vegetation indices to stress was evaluated. To allow comparisons across indices with different numerical ranges, all indices were normalized, and indices with higher normalized ranges were assigned greater weights, reflecting their sensitivity to vegetation stress(Arellano \u0026amp; Stratoulias, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gao, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e); see Appendix A for details).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComputation of the stress score\u003c/h2\u003e \u003cp\u003eThree stress-responsive indices (CTR2, NDCI, and the starch index) were selected on the basis of their physiological relevance to chlorophyll content, pigment variation, and biochemical stress markers (Gitelson et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rodrigues et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 424px; height: 52.531px;\" width=\"424\" height=\"52.531\"\u003e\u003c/p\u003e\n\u003cp\u003eThe stress score was calculated as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 482px; height: 29.2348px;\" width=\"482\" height=\"29.2348\"\u003e\u003c/p\u003e\n\u003cp\u003eHigher stress scores indicate stronger deviations from healthy vegetation reflectance, which is consistent with the stress-detection frameworks in remote sensing (Zhang \u0026amp; Lu, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eT Test for Group Comparison\u003c/h2\u003e \u003cp\u003eA To evaluate whether high-stress samples differed significantly from other samples, Welch\u0026rsquo;s independent t test (unequal variance) was performed for the three selected indices (CTR2, NDCI, and starch index), following common practices in environmental and ecological statistics (Ruxton, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 376px; height: 74.2197px;\" width=\"376\" height=\"74.2197\"\u003e\u003c/p\u003e\u003cp\u003eSignificance was assessed at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, enabling the detection of spectral differences associated with hydrocarbon-related stress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStress Distribution Visualization (Pie Charts)\u003c/h2\u003e \u003cp\u003eA Stress categories (low, medium, high) were assigned on the basis of stress score thresholds. For each site type (active oilfields vs. prospect sites), category proportions were calculated:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$${\\text{Proportion}}_{c}=\\frac{{N}_{c}}{{N}_{\\text{total}}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese proportions were visualized as pie charts to illustrate spatial differences in stress occurrence, which is consistent with standard ecological visualization approaches (Campbell \u0026amp; Wynne, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Jensen, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis and key index selection\u003c/h2\u003e \u003cp\u003eA summary of the statistical analysis and indices applied to the dataset is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The results show notable differences in their normalized ranges, reflecting varying sensitivities to vegetation stress. Among all indices, CTR2 presented the highest normalized range (2.09), indicating strong variability between stressed and nonstressed vegetation. The NDCI showed a moderately high normalized range (1.38), suggesting sensitivity to changes in chlorophyll-related stress. In contrast, the starch index displayed a lower normalized range (0.17), whereas the phenolic index showed moderate variability (0.84). These findings indicate that CTR2, NDCI, and the starch index were the most responsive indicators of vegetation stress within the dataset. The relatively higher ranges and clearer separation between sample conditions justified their selection as the key indices for subsequent analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical Summary of Vegetation Indices Used to Detect Vegetation Stress Potentially Induced by Hydrocarbon Seepage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNormalized Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARVI 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTR 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.0866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.6617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.8602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIC 1 (PSNDa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.1263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.1137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSAVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenolic Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSSRa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.0644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRENDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.6058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.8993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStarch Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVOG 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: The range column shows the variation in each index, and the normalized range column normalizes these values to allow for a direct comparison of their sensitivity to vegetation stress.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eResults of PCA\u003c/h2\u003e \u003cp\u003eThe PC analysis revealed that the first three components can collectively explain 91.19% of the total variance, with PC1 to 3 contributing 75.81%, 8.24%, and 7.14%, respectively. A clear \u0026ldquo;elbow point\u0026rdquo; was observed after the third component in the scree plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The cumulative explained variance curve in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e also supported the selection of three principal components. The spatial distribution of the PCs in 3D (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) provided further insight into sample clustering patterns. The component loadings (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) highlight the contribution of each vegetation index to the extracted components. PC1 is characterized by strong negative loadings from CTR2 and strong positive loadings from the NDVI and SRI indices, indicating its strong association with overall vegetation vigor and biomass. PC2 was influenced mainly by the chlorophyll-sensitive NDCI index, reflecting variations related to chlorophyll content. PC3 showed a dominant loading from the starch index, suggesting its role in explaining biochemical variations within the dataset. On the basis of the strength of their loadings and their distinct contributions to the PCs, CTR2, NDCI, and the starch index were selected as the key indices for further analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComponent Loadings of Selected Spectral Indices on the First Three Principal Components\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1 (Loadings)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC2 (Loadings)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC3 (Loadings)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARVI 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.294688\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.235478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.059372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTR 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.292561\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.114097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.090251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.252503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.458875\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.056967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.252503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.458875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.056967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIC 1 (PSNDa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.293583\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.246209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.06167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.253271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.460048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.294688\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.235478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.059372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSAVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.259442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.069761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenolic Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.245452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.140785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.36365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSSRa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.290598\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.069268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.051297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRENDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.287624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.177997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.115315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.289503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.045958\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.048295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStarch Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.117624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.030881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.895496\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVOG 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.265507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.344169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.143527\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: the highlighted number indicates the highest load, which represents the largest contribution to the variance and the most important variable in interpreting each principal component.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eK-Means Clustering and Stress Classification\u003c/h2\u003e \u003cp\u003eK-Means clustering following the elbow method (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) indicated that three clusters (k\u0026thinsp;=\u0026thinsp;3) were optimal, facilitating the classification of samples into three distinct stress levels: \"High Stress,\" \"Medium Stress,\" and \"Low Stress.\" Among the 68 analyzed samples, the majority (88%) were classified into low- and medium-stress clusters. Specifically, 29 samples (43%) were assigned to the \"Low Stress\" cluster, 31 samples (46%) were assigned to the \"Medium Stress\" cluster, and 8 samples (12%) exhibited \"High Stress.\" Detailed information, including the sample ID, source (location), plant species, and stress score, is provided in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. These samples had the highest observed stress scores, notably, Sample 17 (2.53), Sample 11 (2.00), Sample 7 (1.98), Sample 2 (1.94), and Sample 10 (1.75). These high values are particularly indicative of significant levels of hydrocarbon impact on specific vegetation in areas overlapping with active oilfields.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSpatial and visual analysis of stress\u003c/h2\u003e \u003cp\u003eThe observed spatial concentration of high-stress samples within the \"High Stress Cluster\" was found near active oilfields. Specifically, high-stress samples (e.g., Samples 17, 11, 7, 2, and 10 in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and medium-stress samples were predominantly collected from areas directly overlying the active oilfields of Gachsaran, Chelingar, and Garangan. Conversely, low-stress samples were obtained primarily from prospective fields such as Bidkarz, Kheirabad, and Sarbari. More detailed analyses of the stress distributions across different sites, clearly illustrated in the pie charts of Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, reveal significant differences. In active oilfields, the distribution of stress levels was as follows: 43.40% low stress, 43.40% medium stress, and 13.21% high stress. In contrast, 56.25% of the samples presented a low stress, 43.75% a medium stress, and none presented a high stress.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop-stressed samples with vegetation indices and botanical data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral Sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite Location\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProbable Species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCTR 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNDCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStarch Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStress_Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStress_Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (Gachsaran2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStipellula capensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (Gachsaran2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFabaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAstragalus fasciculifolius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (Gachsaran2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrassicaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMoricandia arvensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (Southern Ridge of Garangan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFabaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAstragalus armatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (North of Garangan1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHordeum murinum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (Gachsaran2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFabaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLotus dorycnium L.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (Southern Ridge of Garangan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBromus sterilis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (Gachsaran2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrassicaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHirschfeldia incana (L.) Lagr.-Foss.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe spectral plots corresponding to these quantitative findings are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, which compares the spectral reflectance curves of high-stress samples (from the \"High Stress Cluster\") with those of their respective healthier counterparts. In the visible spectrum (400\u0026ndash;700 nm), stressed vegetation typically exhibits increased reflectance in the blue (~\u0026thinsp;450 nm) and red (~\u0026thinsp;670 nm) regions, accompanied by a less pronounced green peak (~\u0026thinsp;550 nm). The position of the red edge typically undergoes a blueshift and shows a shallower slope in stressed samples than in healthy controls. In the near-infrared (NIR) region (750\u0026ndash;1300 nm), stressed plants consistently presented relatively low reflectance values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eT test\u003c/h2\u003e \u003cp\u003eStatistical differences between high-stress samples and other samples were assessed via Welch\u0026rsquo;s independent t test on the selected spectral indices (CTR2, NDCI, and Starch Index; see Methods for details). The t test results for the CTR 2 index revealed a statistically significant difference between the two groups (t statistic\u0026thinsp;=\u0026thinsp;2.5592, P value\u0026thinsp;=\u0026thinsp;0.0172) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The mean CTR 2 values in the high-stress sample group (mean\u0026thinsp;=\u0026thinsp;0.4228) were significantly greater than the mean CTR 2 value in the other sample groups (mean\u0026thinsp;=\u0026thinsp;0.3096). Similarly, for the NDCI index, the t test also revealed a statistically significant difference between the two groups (t statistic = -2.4840, P value\u0026thinsp;=\u0026thinsp;0.0191). The mean NDCI of the high-stress sample group (mean\u0026thinsp;=\u0026thinsp;0.2711) was significantly lower than the mean for the other sample groups (mean\u0026thinsp;=\u0026thinsp;0.3583). In contrast to the previous two indices, the T test for the starch index did not reveal a statistically significant difference between the high-stress sample group and the other samples (T statistic = -1.5302, P value\u0026thinsp;=\u0026thinsp;0.1387).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eT test results for spectral index comparisons between high-stress samples and other samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean of High-Stress Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean of Other Samples Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificant Difference\u003c/p\u003e \u003cp\u003e(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTR 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes ✔\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.48\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes ✔\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStarch Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-1.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.139\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eNo ✖\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePhysiological Interpretation of Key Spectral Indices\u003c/p\u003e \u003cp\u003eThe three selected indices\u0026mdash;CTR₂, NDCI, and the starch index\u0026mdash;capture complementary dimensions of plant stress physiology, providing an integrated picture of biochemical and structural alterations across the study area. The pronounced increase in CTR₂ within the high-stress cluster (mean\u0026thinsp;\u0026asymp;\u0026thinsp;0.83 vs. 0.47 in the low-stress group; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) reflects reduced absorption and enhanced reflectance in the red domain, indicative of chlorophyll degradation and partial loss of photosynthetically active pigments. This observation aligns with that of Rodrigues et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who demonstrated that mangroves exposed to both acute and chronic oil contamination experience substantial alterations in foliar pigment pools, particularly chlorophyll and carotenoids, which are detectable as changes in reflectance between 400\u0026ndash;700 nm and in the red-edge region. Moreover, Arellano et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Das et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported similar decreases in chlorophyll content and photosynthetic efficiency in terrestrial plants under hydrocarbon stress, showing the generalizability of red-edge sensitive indices across different plant systems.\u003c/p\u003e \u003cp\u003eThe marked decline in NDCI values in our high-stress samples (mean\u0026thinsp;\u0026asymp;\u0026thinsp;0.05 vs. 0.24 in low-stress samples; Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) further reflects impaired photosynthetic capacity and reduced chlorophyll-a. Mostafa et al.( 2021). similarly found that crude oil exposure in arid and semiarid regions caused significant structural and functional alterations in Azolla pinnata, including decreased chlorophyll and carotenoid contents, changes in frond tissue, and impaired growth, suggesting that the NDCI is a sensitive indicator of sublethal hydrocarbon stress even in water-limited environments. In the study by Rodrigues et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), linear discriminant analysis confirmed that chlorophyll and phenolic compounds were the most informative foliar traits for differentiating oil exposure from biotic stressors, with overall accuracies above 76% and kappa values\u0026thinsp;\u0026ge;\u0026thinsp;0.70. These findings reinforce the novel insight that chlorophyll-sensitive indices, such as the NDCI, can specifically detect early physiological responses to hydrocarbon stress, distinguishing them from general drought effects. The reflectance curves presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e provide additional physiological insights. High-stress samples consistently display (i) increased reflectance in the blue (~\u0026thinsp;450 nm) and red (~\u0026thinsp;670 nm) regions, (ii) a diminished green peak, (iii) a blueshifted red edge with a shallower slope, and (iv) reduced NIR reflectance relative to their lower-stress counterparts. These spectral patterns reflect structural disruptions in mesophyll tissues and pigment degradation, in agreement with the findings of Zhang et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) in heavily oil-impacted regions, Arellano and Stratoulias (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in controlled hydrocarbon experiments, and G\u0026uuml;rtler et al. (2022), who identified distinguishable spectral signatures of both leaves and soil in oil-contaminated Brazilian environments. Lassalle et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) further emphasized that chronic sublethal oil stress leads to gradual deterioration in canopy structure and photosynthetic efficiency in mangrove forests, demonstrating that spectral anomalies persist even after vegetation recolonization. These collective insights underscore the utility of hyperspectral indices in capturing both immediate and cumulative biochemical alterations induced by hydrocarbon stress.Although the starch index did not differ significantly between the high-stress samples and the other samples (p\u0026thinsp;=\u0026thinsp;0.138; Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), its trend towards lower mean values in the high-stress cluster (approximately 0.91) is physiologically meaningful. Rodrigues et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported that starch reserves were depleted more rapidly in acutely exposed mangroves than in chronically exposed ones, reflecting altered carbon allocation and impaired energy storage. This pattern is echoed by Mostafa et al. (2021), who reported that crude oil stress under arid conditions induced subtle but measurable reductions in the carbohydrate content and structural integrity of fronds. Additionally, Athar et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) demonstrated that even low-dose hydrocarbon exposure can disrupt photosynthetic efficiency and energy storage, pointing out the sensitivity of starch as an early stress indicator. In arid and semiarid ecosystems, where water deficit and nutrient limitation further influence carbohydrate dynamics, these subtle changes may reflect intricate physiological interactions that are not fully captured by spectral indicators alone.Taken together, the convergence of CTR₂ and NDCI shifts, the spectral signatures in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, and the subtle variations in the starch index provide a coherent physiological narrative. Leaf-level hyperspectral observations indicate that the vegetation in the high-stress cluster is experiencing both general environmental stress and specific pigment and structural alterations linked to hydrocarbon exposure. Integrating insights from Rodrigues et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Mostafa et al. (2021), Arellano et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Das et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and Lassalle et al. (2022, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reinforces the interpretation that these indices serve as sensitive indicators of sublethal and chronic stress, even in arid environments such as the South Dezful Embayment. This perspective emphasizes the dual relevance of these indices for both early detection of hydrocarbon impact and ongoing environmental monitoring, providing a bridge between plant physiological responses and applied ecological management.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eGeospatial Patterns of Plant Stress\u003c/h2\u003e \u003cp\u003eThe spatial distribution of vegetation stress in the South Dezful Embayment provides important insight into geographic patterns, complementing the leaf-level physiological patterns described earlier. Clustering analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) revealed that approximately 12% of all samples were classified as high-stress; however, these samples were almost exclusively located in or near active oilfields, including Gachsaran, Chelingar, and Garangan. In contrast, no high-stress samples were recorded at prospective (non-producing) sites, where 56.25% of the vegetation was classified as low-stress and the remainder as medium-stress. The distribution is asymmetric. This shows that unsupervised clustering and spatial analysis are useful. They help identify areas influenced by hydrocarbons. They also highlight regions that may be affected by seepage. These spatial patterns are consistent with observations by Mostafa et al. (2021) in arid and semiarid regions, where hydrocarbon contamination caused reductions in vegetation cover, structural alterations in leaves, and tissue damage, even at relatively low hydrocarbon concentrations. Similarly, Osorio et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Veldkornet et al. (2020) demonstrated that spatial analysis and unsupervised clustering effectively detect localized vegetation stress patterns induced by both natural and anthropogenic hydrocarbon seepage. These studies underscore the potential of spectral and spatial approaches as reliable proxies for monitoring environmental perturbations in petroleum-impacted landscapes. Furthermore, the spatial patterns observed in this study align with long-term mangrove investigations by Lassalle et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in Brazil, where a 40-year-old oil spill resulted in (i) an initial dieback phase, (ii) an eight-year recolonization period, and (iii) a persistent reduction in canopy cover of approximately 20\u0026ndash;30% relative to pre-spill conditions, accompanied by measurable decreases in forest biomass and productivity. These findings illustrate that hydrocarbon-induced stress can remain spatially concentrated over decades as sublethal yet detectable signals in vegetation structure and function. Our leaf-level observations in an arid petroleum province resonate with this broader pattern: localized high-stress clusters near producing fields may represent early-stage or small-scale analogs of long-term, spatially coherent degradation documented at the landscape level in mangrove ecosystems. Key spectral indices, including CTR₂ and NDCI, consistently capture these physiological responses, reflecting pigment loss and alterations in chlorophyll-a activity. These results are supported by studies such as those of Duke (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Lassalle et al. (2022), and Rodrigues et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which demonstrated chronic and sublethal hydrocarbon effects and the capacity of foliar traits and hyperspectral measurements to distinguish hydrocarbon-related stress from other biotic stressors.\u003c/p\u003e \u003cp\u003eThis approach, through the integration of spectral and spatial analyses, enables the delineation of oilfields and identification of potentially impacted areas, providing a scientifically robust framework for environmental monitoring and petroleum field management. By combining spectral and spatial analyses with soil chemistry, gas flux measurements, and additional physiological indices, a comprehensive tool is created. This approach helps distinguish the effects of hydrocarbons from concurrent natural stressors. It also supports more informed decisions in environmental protection and resource management.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThe results of this study demonstrate that vegetation stress associated with hydrocarbon microseepage and long-term leakage can be systematically captured through the spectral responses of plant species in arid and semi-arid environments. In the South Dezful Embayment, active oil fields exist alongside prospective hydrocarbon areas. This setting allowed us to examine how underground hydrocarbon activity affects vegetation physiological responses. Our analysis of spectral indices showed that CTR2 and NDCI were responsive to hydrocarbon-related stress. These indices reflected reductions in chlorophyll content and alterations in leaf structure. The stress influenced plant reflectance in several ways: it increased reflectance in the visible spectrum, caused a blue shift of the red edge, and decreased near-infrared reflectance. These patterns were confirmed through statistical analysis. The spatial stress pattern derived from K-Means clustering indicates that the highest stress levels are predominantly associated with active producing oil fields, whereas lower stress levels are more commonly observed in prospect areas. This contrast suggests that vegetation spectral responses are capable of reflecting not only the presence of hydrocarbons but also the relative intensity and effectiveness of seepage processes. These patterns serve as a useful basis for prioritizing areas susceptible to microseepage. They also help reduce uncertainty during early-stage hydrocarbon exploration and support reconnaissance-scale assessments in petroleum-rich basins. At the same time, the observed vegetation responses provide important insight into environmental pressure exerted by chronic hydrocarbon exposure, enabling the identification of areas where ecosystem health may gradually deteriorate.This is particularly important in dry areas, where plants are highly sensitive to persistent contamination and conducting extensive field-based monitoring can often be difficult. Consequently, the framework presented in this study is transferable to other hydrocarbon-bearing sedimentary basins with comparable climatic conditions and can be applied as a complementary approach alongside geological and geochemical investigations for both exploration-oriented and environmental monitoring purposes.\u003c/p\u003e"},{"header":"List of Acronyms and Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"426\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eFull Term\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 310px;\"\u003e\n \u003cp\u003eVegetation Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eHCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 310px;\"\u003e\n \u003cp\u003eHydrocarbons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 310px;\"\u003e\n \u003cp\u003eRemote Sensing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eVNIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 310px;\"\u003e\n \u003cp\u003eVisible-Near Infrared\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSWIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eShort-Wave Infrared\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNDVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNormalized Difference Vegetation Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eChlorophyll Transmittance Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNDCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNormalized Difference Chlorophyll Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSum of Squared Errors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAnalytical Spectral Devices\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNear-Infrared\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePSSRa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePlant Stress Spectral Ratio a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRENDVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRed-Edge Normalized Difference Vegetation Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLeaf-Level Chlorophyll Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVegetation Optical Greenness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSimple Ratio Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOSAVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eOptimized Soil-Adjusted Vegetation Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eARVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAtmospherically Resistant Vegetation Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGNDVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eGreen Normalized Difference Vegetation Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGNIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eGreen-Near Infrared Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIPNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eInternational Plant Names Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLeaf Area Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTotal Bitumen Content\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY.E.: Conceptualization, investigation, methodology, writing\u0026mdash;original draft, data analysis (spectral ,statistical and spatial), figure preparation, review \u0026amp; editing. K.R.: Sample collection, spectroscopy measurements, conceptualization, supervision, review \u0026amp; editing. M.K.: Review \u0026amp; editing. S.A.: Data analysis support, conceptualization of results, review \u0026amp; editing. A.A.: Review \u0026amp; editing, support in data analysis. M.S.: Review \u0026amp; editing. All authors have read and approved the final manuscript.\u003c/p\u003e\u003cp\u003eThe authors declare that they have no known financial interests, commercial affiliations, or other competing interests that could have appeared to influence the work reported in this paper. AI-assisted copy editing was used solely to improve readability and language quality.\u003c/p\u003e\n\u003cp\u003eCode, Data, and Materials Availability\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not publicly available. They can be requested from the corresponding author at [email protected]\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the plant sampling team at the Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, for their valuable assistance in collecting the plant samples. We sincerely thank Prof. Dr. Sabine Chabrillat at the Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, for her insightful guidance and support during Yasmin Elhaei\u0026rsquo;s visit as a PhD researcher. Additionally, an AI-based language tool was used to improve the clarity and grammar of the manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdelbaki, A., Udelhoven, T., 2022. 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GeoArabia 18, 49\u0026ndash;80.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Petroleum contamination, Plant reflectance characteristics, Remote sensing, Spectral indices, Environmental monitoring","lastPublishedDoi":"10.21203/rs.3.rs-8756547/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8756547/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVegetation stress is a common phenomenon in sensitive ecosystems affected by petroleum-related activities and natural seepage, making its early detection essential for both environmental management and exploration purposes. Reflectance spectroscopy provides an effective tool for monitoring subtle physiological changes in vegetation at different scales, outperforming traditional approaches. This study applies a statistical framework based on spectral indices derived from point spectroscopy to identify hydrocarbon-induced stress in vegetation samples from the South Dezful Embayment, Iran—an area within the Zagros Fold-Thrust Belt that has long experienced continuous seepage due to intensive oil production and exploration activities. Leaf-level spectral measurements were collected from 68 vegetation samples using an ASD FieldSpec Pro spectroradiometer. Fourteen vegetation indices, including the CTR, PSSRa, RENDVI, NDCI, LIC, VOG, starch index, NDVI, SRI, OSAVI, ARVI, GNDVI, GNIR, and phenolic index, were assessed for this purpose. In addition, principal component analysis (PCA) was used to identify stress-responsive indices, and K-Means clustering was employed to objectively classify vegetation into high, moderate, and low stress levels. The results show that the CTR2 and NDCI exhibit pronounced sensitivity to stress-induced spectral variations. K-Means clustering effectively separated the samples into three stress categories, with highly stressed samples corresponding to productive oil fields. There were significant differences (p \u0026lt; 0.05) in the CTR and NDCI values for highly stressed samples, which was consistent with the reduction in chlorophyll content under hydrocarbon exposure. This was corroborated by visual inspection of spectral plots from stressed plants, which revealed increased visible-region reflectance, a blueshifted red edge, and decreased near-infrared reflectance. Overall, the findings demonstrate the ability of reflectance spectroscopy—supported by robust statistical validation—to detect vegetation stress induced by hydrocarbon seepage/leakage in arid and semiarid regions. This approach is recommended as a complementary tool for early environmental assessments and long-term ecological monitoring in hydrocarbon-rich provinces.\u003c/p\u003e","manuscriptTitle":"Reflectance Spectroscopic Study of Hydrocarbon-Induced Vegetation Stress Assessment in Chalinger-Garangan and Surrounding Oil Fields, Zagros Fold-Thrust Belt, Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 13:26:26","doi":"10.21203/rs.3.rs-8756547/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-19T02:04:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-17T14:33:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-17T14:30:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2026-02-01T13:29:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a0b2bee4-22a7-4e5f-aa7b-572e7bfca102","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T13:26:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 13:26:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8756547","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8756547","identity":"rs-8756547","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}