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This study is aimed at investigating the relationship between landform classification and vegetation in the southwest of Fars province, Iran. First, topographic position index (TPI) is used to perform landform classification using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) with resolution of 30 m. The classification has ten classes; high ridges, midslope ridges, upland drainage, upper slopes, open slopes, plains, valleys, local ridges, midslope drainage and streams. Visual interpretation indicates that for the local, midslope and high ridge landforms, normalized difference vegetation index (NDVI) values and tree heights are higher as compared to the other landforms. In addition, it is found that there are positive and significant correlations between NDVI and tree height (r = 0.923), and landform and NDVI (r = 0.640). This shows that landform classification and NDVI can be used to predict tree height in the area. High correlation of determination (R 2 ) 0.909 is obtained for the prediction of tree height using landform classification and NDVI.

In this study, Global positioning system (GPS) simulation is employed to study the effect of radio frequency interference (RFI) on the accuracy of two handheld GPS receivers; Garmin GPSmap 60CSx (evaluated GPS receiver) and Garmin GPSmap 60CS (reference GPS receiver). Both GPS receivers employ the GPS L1 coarse acquisition (C/A) signal. It was found that with increasing interference signal power level, probable error values of the GPS receivers increase due to decreasing carrier-to-noise density (C/N 0 ) levels for GPS satellites tracked by the receivers. Varying probable error patterns are observed for readings taken at different locations and times. This was due to the GPS satellite constellation being dynamic, causing varying GPS satellite geometry over location and time, resulting in GPS accuracy being location/time dependent. In general, the highest probable error values were observed for readings with the highest position dilution of precision (PDOP) values, and vice versa.