Characterising inundation conditions for flood-pulsed wetlands is a critical first step towards assessment of flood risk as well as towards understanding hydrological dynamics that underlay their ecology and functioning. In this paper, we develop a series of inundation maps for the Okavango Delta, Botswana, based on the thresholding of the SWIR band (b7) MODIS MCD43A4 product. We show that in the Okavango Delta, SWIR is superior to other spectral bands or derived indices, and illustrate an innovative way of defining the spectral threshold used to separate inundated from dry land. The threshold is determined dynamically for each scene based on reflectances of training areas capturing end-members of the inundation spectrum. The method provides a very good accuracy and is suitable for automated processing.

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Knowledge of wetland vegetation spectral reflectance signatures can assist in spectral classification of remotely sensed images for monitoring of wetland hydroperiod. This study aimed at assessing the differences between wetland vegetation communities of varying species composition and density in terms of spectral reflectance. The investigation was carried out in floodplains at Nxaraga and Seronga in the Okavango Delta, Botswana. Spectral measurements were conducted during rising and receding flood stages. In each study area, 2 transects were located in homogeneous macrophyte stands, and in each transect 5-10 1 m 2 quadrats were randomly set. In each quadrat, water depth and cover percentages (green leaved and senescent) of macrophyte vegetation were recorded, and a full reflectance spectrum between 325 and 1,075 nm wavelengths captured using a handheld spectroradiometer. Multiple regression analysis with stepwise selection of significant variables based on AIC was performed to determine the influence of various stand characteristics on spectral reflectance and vegetation indices (NDVI and EVI). The results showed that stand characteristics explained a large proportion of variance in spectral vegetation indices with r 2 = 0.67 for NDVI and r 2 = 0.75 for EVI. For NDVI, the significant explanatory variables were percentage green cover, senescent cover and water depth, while for EVI it was the total vegetation cover. Analysis of regression residuals for the various vegetation community classes showed significant differences between the classes in their NDVI, but no differences in EVI, with the exception of the Panicum repens class. Reflectance in visible wavelengths did not vary significantly between sites and seasons, but the NIR reflectance differed across sites and seasons. The results indicate that the simple vegetation indices in the Okavango Delta respond strongly to stand characteristics and thus can be used to derive these from remote sensing imagery. The scope for determination of vegetation communities from these indices is limited, however.