Optimising Sampling Design with Semivariogram for Vegetation Survey of Derived Savannah, Ogun State, Nigeria (2024)
Vegetation survey is useful for biodiversity conservation and management. Sampling design strategies oftentimes fail to capture the heterogeneous vegetation structure of area being studied due to cost and time constraint. The study aimed to determine the optimum sampling design for vegetation assessment in the study area by characterizing spatial structure and identifying extent of spatial correlation in data points. Hypothetical sampling scenarios of low, medium and high density random and transect sample plots of (3 x 3 km) were laid on Normalised Difference Vegetation Index (NDVI) from Landsat 8 Operational Land Imager (OLI) satellite imagery of the study area. NDVI values were extracted for the respective sampling scenarios. Data were subjected to descriptive statistics and fitted to spherical, exponential and Gaussian’s semivariogram models. Best fitted models were evaluated by Root Mean Square Error (RMSE) values. Nugget, sill and range parameters of the best fitted semivariogram models described the spatial structure of the vegetation cover in the study area. Therefore, the parameter estimates guided the selection of medium density random sample plots and low density transect-laid sample plots as the optimized sampling design most suitable for vegetation survey in derived savannah ecosystem of Ogun State, Nigeria.