Potential to monitor plant stress using remote sensing tools

Abstract

The growing energy crisis has necessitated the expansion of thermal power stations to meet South Africa's electricity needs. Possessing vast amounts of coal deposits, the Waterberg region of the Limpopo Province is set to undergo rapid transformation as new power stations and coal mines are built, expected to exacerbate water shortages. Detailed baseline information to assess future impacts on key plant species is lacking compromising biodiversity conservation efforts in a region where eco e tourism is a major source of livelihood. In this study we evaluated the spatio e temporal distribution plant status during wet and dry seasons using two measures of plant stress namely the midday leaf water potential (LWP), and leaf nitrogen (N) concentrations. At leaf level, spectral indices such as the moisture stress index (MSI), normalized difference water index (NDWI), and the water index (WI) predicted more than 70% of LWP variation using leaf reflectance data. At landscape level, red edge based simple ratio indices were selected for mapping leaf water potential and leaf N for wet and dry season using RapidEye data. We conclude that remote sensing images can be applied for the long term vegetation monitoring for future biodiversity conservation efforts.