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Relating Time-Series of Meteorological and Remote Sensing Indices to Monitor Vegetation Moisture Dynamics

The monitoring of vegetation moisture content (VWC) is a critical part of any fire danger rating system, since VWC determines fire ignition and propagation. The relationship between time-series of the normalized difference infrared index (NDWI) and Keetch-Byram drought index (KBDI), both related to VWC dynamics, is studied. Serial correlation of time-series, however, is a major issue when the relationship between time-series is analyzed. Serial correlation affects correlation between variables measured in time, and violates the basic regression assumption of independence. This chapter therefore focuses on the extraction of non-serially correlated seasonal metrics from time-series to evaluate quantitative relationships between remote sensing and meteorological time-series, using as test area the savanna ecosystem of Kruger National Park, South Africa. It was demonstrated that serial correlation between extracted seasonal metrics was minimal and that seasonality in time-series was an important factor influencing the relationship between the NDWI and KBDI time-series. Statistical analysis of the temporal occurrence of extracted metrics revealed that NDWI and KBDI time-series are not significantly different (p > 0.1), except at the beginning of the rain season (p < 0.01). The actual values of extracted metrics of NDWI and KBDI, however, only were related significantly at the end of the rain season (R 2 = 0.49). Furthermore, results have shown that correlation coefficients estimated by ordinary least squares regression were overestimated when serial correlation of the residuals was not taken into account resulting in a mean R 2-value of 0.7. The serial correlation effect was taken into account by extraction of seasonal metrics from time-Signal and Image Processing for Remote Sensing 3 series. Seasonal metrics can be used to study the relationship between remote sensing and ground-based time-series (i.e. meteorological or field measurements). A better understanding of the relationship between remote sensing and in situ observations at regular time intervals will contribute to the use of remotely sensed spectral data in the development of an index to represent seasonal vegetation moisture dynamics. The repeated occurrence of severe wildfires, which affect various fire-prone ecosystems of the world, has highlighted the need to develop effective tools for monitoring fire-related parameters. Vegetation water content (VWC), which influences the biomass burning processes, is an example of one such parameter [1-3].The physical definitions of VWC vary from water volume per leaf or ground area (equivalent water thickness) to water mass per mass of vegetation dry or fresh matter (fuel moisture content) [4]. VWC therefore also …