Detection and Discrimination of Stress in Bean (Phaseolus vulgaris Tendergreen) Caused by Oil Pollution and Waterlogging Using Combined Spectral and Thermal Remote Sensing

摘要

Remote sensing of plant stress holds promise for the detection of pollution caused by oil; however, plant stress can be caused by a range of abiotic and biotic factors present to varying degrees within a given environment. Thus, for the accurate application of remedial measures, it is important to be able to detect and discriminate between different causes of plant stress. In oil-rich delta regions of the world, waterlogging is a frequent source of plant stress that has similar physiological effects to those of oil pollution. Hence, this study investigated the dual capabilities of spectral and thermal remote sensing for detecting and discriminating between plant stress caused by a combination of oil pollution and waterlogging. In a glasshouse, plants of pot grown bean (Phaseolus vulgaris Tendergreen) were subjected to oil pollution, waterlogging and combined oil and waterlogging treatments. Canopy physiological, spectral, and thermal measurements were taken every 2 to 3 days following treatment to follow the development of stress responses. For plants treated with oil, spectral and thermal responses were evident 6 days before symptoms could be observed visually. However, in waterlogged plants only spectral responses were observed, and up to 8 days before visual symptoms. Based on timing and consistency in sensitivity, a narrowband reflectance ratio R673/R545 was most efficient in detecting stress symptoms caused by oil and waterlogging. The absolute canopy temperature and derived thermal index (Ig) were good indicators of developing oil and combined oil and waterlogging stress in bean, but were insensitive to waterlogging alone. Thus, this paper reports that by combining spectral and thermal remote sensing, plant stress caused by oil pollution can be detected and discriminated from stress caused by waterlogging. The findings justify further research to investigate the wider applicability of this approach and its potential as the basis for an operational monitoring technique for oil pollution.