Raster Meets Point Data - Open Source Geospatial Tools: Applications in Earth Observation - page 137

Chapter 10
Raster Meets Point Data

This chapter deals with GDAL utilities that link raster to point data. With
gdal_grid , point data can be converted to raster grids. The conversion of
raster datasets to point data can be performed with gdallocationinfo and
gdal2xyz.py . Other utilities discussed in this chapter are: gdal_fillnodata.
py , gdal_proximity.py and gdaldem . We recall that three dimensional point
clouds, as a special case of point data, are covered in Chap. 15 .

10.1 gdal_grid

The gdal_grid utility converts point data, stored in anOGR supported input vector
format, to a regular output grid in a GDAL supported raster format. Because of the
typical irregular spatial distribution of point data, the number of points covered in each
grid cell of the output data can be arbitrary. Some cells might not be covered by points
at all and a value must be interpolated from surrounding point data. The grid cell
size must therefore be chosen carefully; the optimal size depends on the application
at hand and it has to be in relation to the density of the point cloud. Sparse point data
typically require larger grid cells resulting in a coarser spatial resolution. Dense point
clouds typically allow for smaller grid cells resulting in a finer spatial resolution.

Usage: gdal_grid [-ot {Byte/ Int16/ UInt16/ UInt32/ Int32/
ₒ

Float32/Float64/ CInt16/ CInt32/ CFloat32/ CFloat64}]

ₒ

[-of format] [-co "NAME=VALUE"] [-zfield field_name]

ₒ

[-a_srs srs_def] [-spat xmin ymin xmax ymax] [-clipsrc

ₒ <xmin ymin xmax ymax>| WKT| datasource| spat_extent]

ₒ [-clipsrcsql sql_statement] [-clipsrclayer layer]

ₒ [-clipsrcwhere expression] [-l layername]* [-where

ₒ expression] [-sql select_statement] [-txe xmin xmax]

ₒ [-tye ymin ymax] [-outsize xsize ysize] [-a algorithm

ₒ [:parameter1 = value1]*] [-q] <src_datasource>

ₒ <dst_filename>