Detecting plant stress by oil pollution using spectral reflectance and chlorophyll content: laboratory insights and field applications

Oil pollution resulting from human error, natural seepage, and accidental spills poses a major threat to ecosystems, biodiversity, and human health. Understanding its impact on vegetation is critical for environmental monitoring and remediation. The study aimed to assess the impact of oil pollution on plants by evaluating changes in physical condition, spectral reflectance characteristics, and chlorophyll (Chl) content. A laboratory experiment was conducted in which plants were exposed to crude oil spillage for 7 weeks (W0–W7), and the findings were subsequently validated in the Tugu Barat oil and gas field in the Northwest Java Basin, Indonesia. The results demonstrated three category responses to oil exposure: (1) plants that survived without fatal stress, (2) stressed plants that survived, and (3) plants that died (43%). Plant stress was characterized by spectral changes in visible and the red edge position (REP), reduced Chl content, the absence of leaf bud formation, leaf yellowing and inhibited root development. Field observations in the Tugu Barat oil and gas field confirmed micro seepage, consistent with laboratory results, with sugarcane exhibiting pale green and yellow leaves, dominant REP wavelength below 719 nm, and lower Chl levels compared to non-oil and gas areas. A multi-regression-based Chl algorithm using Landsat 8/9 bands 1, 3, and 5 successfully mapped spatial variation in Chl and effectively distinguished oil-impacted vegetation from unaffected areas. These findings demonstrate the potential of Chl-based remote sensing to detect plant stress associated with oil pollution, whether caused by human error or natural seepage.