Published February 5, 2018 | Version v1
Journal article Open

DIGITAL ELEVATION MODELING OF SAINT MARTIN ISLAND, BANGLADESH: A METHOD BASED ON OPEN SOURCE GOOGLE EARTH DATA.

  • 1. Assistant Director, Geological Survey of Bangladesh.
  • 2. Deputy Director, Geological Survey of Bangladesh.

Description

Many method or sources of data of the earth surfaces are well established for the digital elevation modeling. But all the traditional available high resolution data have not availability to easy access of public.Though the SRTM (90-meter, 30-meter) & Global ASTER (30-meter) DEM are now available for free of cost but resolution is not enough for large-scale small area.Many researcher different corner of the world try to established alternative way of Digital Elevation Modeling (DEM) through open sources data. Google earth is one of the most open sources tool of vast and diversifies free access data source. In this paper an attempt has taken to high resolution Digital Elevation Modeling of Saint martin Island, southern most territory of Bangladesh, based on open source Google earth data and a comparative assessment has done with SRTM 30, SRTM 90 and ASTER 30-meter resolution DEM. DEM was generated through extracted 58540 elevation points from Google earth of the study area. A comparative assessment through elevation profiling in same direction for all DEM and also a statistical correlation has performed. It was found that maximum elevation from DEM extracted from Google earth and SRTM (30-meter, 90-meter) DEM and ASTER 30-meter DEM shows respectively 13, 13.92, 12.39, 18.40 meter and average elevation respectively as 5.053,5.259, 5.014, 9.523. Total sum, mean and median elevation of four different DEM shows that Google DEM strongly tie with SRTM 90 and SRTM 30, but deviation of ASTER 30-meter DEM with another DEM, as well as Google DEM are not negligible. Analysis of the result revealed that elevation extracted from Google Earth can be a good alternative source of elevation data, in some cases where as unavailability of enough data for small scale large area.

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