Digital Terrain Modeling bv Using Real Time Kinematic GPS Data

Total Station (TS) survey is one of the most familiar and accurate technique used for high
resolution DTM data collection. However, some factors such as; intervisibility, prism
targeting and unfriendly weather condition are frequemly slowing down the speed of
survey which mostly leads to cost inflation. Real Time Kinematic GPS (RTK GPS) offers
an alternative where less surveyor needed for the survey, no intervisibility is required,
and survey execution is weather independent. The materials presented in this work are
based on the experiment to demonstrate the application of RTK G PS compared to TS
survey. The analysis covers the accuracy, productivity, efficiency and DTM quality
measure. Height and volumetric error analysis are the parameter of the DTM quality
measure. Result shows that this method is capable of generating high resolution DTM up
to 20cm of terrain details. RTK GPS provides sufficient accuracy as centimeter level can
be achieved on terrain with average sky view above 50%. It is proven to be more
productive and capable of yielding a lot more data. With the same survey duration, for an
area of 62500m2 with 75% sky view, RTK GPS could collect 568 more data and
approximately 1.4 times faster than TS survey. For area with 60% sky view, the survey
speed can be maintained at about 1.4 times faster than TS. For sky view 50%-60%, it
shows less productivity where the survey speed of both techniques nearly the same. The
application of RTK GPS on area with average sky view above 55% can produce a good
DTM quality, with height errors ranging from 0.3cm-6cm, absolute mean error of
2.44cm, and the volumetric error of 0.5%. Lower quality of DTM was generated for area
with average sky view between 50%-55% where the height errors and the absolute mean
error are in centimeter level, while volumetric error is about 1%. Applying RTK GPS for
area with average sky view less than 50% produced low quality DTM where height errors
are in decimeter level and volumetric error is almost 6%.