Near-global coverage of 1 arc second SRTM is becoming available

The near-global SRTM digital elevation model has been an extremely valuable data set for many years now. That data set has a resolution of 3 arc seconds, equalling roughly 90 m. A higher resolution (1 arc second or approximately 30 m) had been availbale for the USA, and higher resolution data had been available for small areas. For many years, researchers around the world have been hoping and waiting for a higher resolution near global or DEM to become available. The ASTER global DEM, with a nominal resolution of 30 m, is great because it does offer global coverage (in contrast to the SRTM DEM which does not cover regions north of 60° North and south of 58° South). However, the ASTER DEM suffers from data artefacts, and quality assessment showed that (despite a pixel size of 30 m) the effective spatial resultion is only approximately 120 m. In late 2012 I was told at a conference that a 1 arc second SRTM DEM was forthcoming within a year or two.

Now, NASA has begun to release the near-global (i.e. limited to between 60° North and 58° South) 1 arc second (approximately 30 m) SRTM data set. At least Africa, Europe and South America are now available through the USGS’s Earth Explorer. Already at first glance, this new data set is in moast areas far superior to both the 3 arc second SRTM DEM and the ASTER global DEM. Of course, as in the 3 arc second SRTM DEM, desert sand dunes (at least in hyperarid deserts) are recorded poorly because such surfaces simply did not backscatter enough of the radar signal. No-data areas in the 1 arc second STRM DEM are even slightly larger than in the 3 arc second SRTM DEM. The ASTER DEM still is the superior data set for such surfaces.

3 arc seond SRTM DEM. Note extensive data gaps in the dune field.

3 arc seond SRTM DEM (shaded relief image) of the Dunas Pamps Blanca – Palpa region. Note extensive data gaps in the dune field.

ASTER global DEM (ASTER GDEM is a product of METI and NASA). Note good representation of dune field and intense elevation noise on flat terrain in the northwestern portion of the image.

ASTER global DEM (ASTER GDEM is a product of METI and NASA). Note good representation of the dune field and intense elevation noise on flat terrain in the northwestern portion of the image.

1 arc second SRTM DEM. Note extensive data gaps in the large dune field and good representation of flat terrain in northwestern portion of the image.

1 arc second SRTM DEM. Note extensive data gaps in the dune field and good representation of flat terrain in northwestern portion of the image.

To assess elevation accuracy, I compared the 1 arc second SRTM DEM with the state-wide high-resolution (lidar-based) DEM of Germany’s federal state Baden-Württemberg (35,752 sq. km or 56.4 million SRTM 1 arcsec data pixels). That data set has a reolution of 1 m, so it had to be resampled to match the 1 arc seond resolution of the new SRTM data. I used both the DTM (digital terrain model, “bare earth model”) and DSM (digital surface model, including vegetation) and also looked at land use types (forest, agricultural land and grassland).

Shaded relief images of a small subset of Baden-Württemberg comparing lidar-based DTM and DSM with SRTM 1 arc second DEM.

Shaded relief images of a small subset of Baden-Württemberg comparing lidar-based DTM and DSM (both resampled to 1 arc second) with SRTM 1 arc second DEM. Lidar data (c) Landesamt für Geoinformation und Landentwicklung Baden-Württemberg.

Elevation difference between SRTM 1 arc second DEM and lidar-based DTM. The SRTM DEM is approximately 1 m too low in this region. Comparison between different landuse types clearly shows that the radar-based SRTM DEM records vegetation canopy.

Elevation difference between SRTM 1 arc second DEM and lidar-based DTM.

On average, the SRTM DEM is approximately 1 m too low in Baden-Württemberg. Comparison between different land use types clearly illustrates that the radar-based SRTM DEM records vegetation canopy rather than ground surface. This is also (but less clearly) visible in the shaded relief images. These images also show that the lidar-based DSM has an overall clearer appearance and shows more detail. Effective spatial resolution of the 1 arc second SRTM DEM does therefore not equal 1 arc second.

Elevation difference between SRTM 1 arc second DEM and lidar-based DSM for open land grid cells (open land grid cells here defined

Elevation difference between SRTM 1 arc second DEM and lidar-based DSM for open land grid cells (open land grid cells here defined as those for which the difference between lidar DSM and DTM is less than 0.5 m).

Absolute difference between lidar-based DEM and SRTM 1 arc second.

Absolute difference between lidar-based DEM and SRTM 1 arc second.

Because the SRTM DEM corresponds roughly to vegetation canopy, its apparent accuracy in forested areas is relatively low when compared with a lidar-based DTM (e.g. 10% of all SRTM grid cell elevations differ more than 15 m from the lidar DTM). However, when looking at open land grid cells only (i.e. those grid cells where the difference between lidar DSM and DTM is very low), almost two thirds of all SRTM grid cell elevations are within 2 m and more than 90% are within 7 m of the lidar DSM elevations.

All in all, the new SRTM DEM is a big step forward in the field of near-global elevation data sets.

Rotation is the enemy

Last week I have published a simple tool that calculates (among a few other things) motion blur resulting from camera movement relative to the photographed object. Looking at the results of those calculations, one could say that motion blur is a very minor issue in UAV photography: at a platform speed of 30 km/h and a shutter speed of 1/1000 s, motion blur is as low as 0.8 cm. Flying a Canon G12 at the wide angle limit (28 mm) and 200 m above ground, this amounts to only 0.25 image pixels. From the calculation results of UAVphoto, motion blur does not appear to be a relevant issue. The need to take images at short intervals to achive sufficient overlap appears to be much more important when using a UAV. But why do I even get blurred images when using a kite that is almost immobile relative to the ground?

The point is that motion blur due to translation (i.e. linear movement of the camera relative to the object) is only one reason for blurred images. Another (and much more relevant) reason is rotation of the camera. Unfortunately, this is also much harder to measure and to control. To show how important rotation is for image blur, I have added the calculation of rotation blur to the new version of UAVphoto. Two types of rotation have to be distinguished: rotation about the lens axis and rotation about the focal point but perpendicular to the lens axis. I am not using the terms pitch, roll and yaw here because the relation of platform pitch, roll and yaw to rotation about different camera axes depends on how the camera is mnounted to the platform.

Rotation about the lens axis results in rotation blur that is zero at the image centre and reaches a maximum at the image corners. Rotation about an axis orthogonal to the lens axis results in rotation blur that is at first sight indistinguishable from motion blur due to high speed translation movement. Of course, all types of blur combine to the total image blur. Rotation blur about the lens axis is independend of focal length. Orthogonal rotation blur, on the other hand, increases with increasing focal length. In both cases an increase in shutter speed will result in a proportional decrease in image blur.

Most UAV rotation movements are due to short-term deflections by wind gusts or steering. Wind gusts are also the main source of rotation movements of kite-carried cameras. Let’s say we’re using a Canon G12 at the wide angle limit (28 mm). The maximum rotation rate which will not result in image blur (using a 0.5 pixel threshold) is 12.4 °/s (or 29 s for a full circle) for rotation about the lens axis and 8.1 °/s (or 44 s for a full cirlce) for rotation orthogonal to the lens axis. At a focal length of 140 mm, the maximum rotation rate orthogonal to the lens axis is only 1.9 (or 189 s for a full circle). If all this sounds very slow to you, you’ve got the point: even slow rotation of the camera during image capture is a serious issue for UAV photography, in most cases much more important than flying speed.

UAVphoto – a simple calculation tool for aerial photography

I have to admit that I am sometimes a bit lazy. Rather than solving a problem once and working with the solution, in some cases I keep twiddling with the same problem again and again. Calculating things like viewing angles, ground resolution, motion blur or image overlap for aerial photography is a case in point. There must be a dozen or so spreadsheet files on my various computers which I used to do such calculations. I kept re-inventing the wheel again and again for myself and when others asked me for help.

UAVphoto_1.0.0.0_screenshot

Now I finally got around writing a small piece of software for this specific task. It is a simple tool that allows to calculate parameters like ground pixel size, motion blur and sequential image overlap from UAV flight parameters (velocity and altitude) and camera parameters (focal length, shutter time, image interval etc.). Calculation assumes a vertical camera view for simplicity. Image y dimensions are those in flight direction, image x dimensions are those perpendicular to flight direction. Default camera values are for Canon G12 at the wide angle limit. Five to six seconds is the approximate minimum image interval using a CHDK interval script. In continous shooting mode, a minimum interval of approximately one second can be achieved.

Now that I created this tool, why not share it? UAVphoto is published under the GNU General Public License and can be downloaded from Sourceforge.