srtm 4.1 data

The data originate in the NASA Shuttle Radar Topographic Mission (SRTM) data held at the This is version of the CSI-SRTM data with improved hole-filling. The Shuttle Radar Topography Mission (SRTM) is an international research effort that obtained To acquire topographic data, the SRTM payload was outfitted with two radar antennas. One antenna was located in the Shuttle's payload bay. (left) and SRTM Void Filled (right) data (February 11, ). The Shuttle Radar Topography Mission (SRTM) was flown aboard the space shuttle Endeavour. The Shuttle Radar Topography Mission (SRTM) is an international research effort that obtained To acquire topographic data, the SRTM payload was outfitted with two radar antennas. One antenna was located in the Shuttle's payload bay. The data originate in the NASA Shuttle Radar Topographic Mission (SRTM) data held at the This is version of the CSI-SRTM data with improved hole-filling. Citations should be made as follows: Jarvis A., H.I. Reuter, A. Nelson, E. Guevara , , Hole-filled seamless SRTM data V4, International. srtm 4.1 data

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SRTM Tile Grabber

It all started with delicious pancakes and a glorified misconception. The experiment compared the variation in surface elevation obtained from a laser scan of an IHOP pancake and an elevation transect across the State of Kansas. First, the scale the analysis shrunk the mile-long Kansas elevation transect down to the 18 cm width of the pancake, thereby significantly reducing the variability of the elevation data. Second, pancakes have edges, which creates some significant relief relative to the size of the pancake, approximately 70 miles!

Using this approach, there is no place on earth that is not flatter than a pancake. Now, I can take a joke, and at the time thought the article was clever and funny. And while I still think it was clever, it began to bother me that the erroneous and persistent view that Kansas is flat, and therefore boring, would have negative economic consequences for the state. I grew up on the High Plains of southwestern Kansas, where there are broad stretches srtm 4.1 data very flat uplands.

The joke of it is that the official Physiographic Regions of Kansas Map describes the majority of the state in terms of hills: Not to mention the very hilly Glaciated Region of northeastern Kansas, anyone who attended classes on Mount Oread can confirm that for you. As part of my PhD coursework I was investigating the utility of open source geospatial software as a replacement for proprietary Srtm 4.1 data and needed a topic that could actually test the processing power of the software.

What emerged from that work was published last year in the Geographical Review as a paper coauthored with Dr. First, create a measure of flatness that reflected the human perception of flat. This measure needed to be based on how humans perceive flatness, quantitatively based, repeatable, and globally applicable.

Second, srtm 4.1 data how the general population of the U. There were many measures of flat that had been developed in the geomorphological srtm 4.1 data, but they tended to be localized measures, meant for hydrological and landscape modeling. I wanted something that could capture the srtm 4.1 data of expanse that you feel in a very flat place. Beginning with that thought, I tried to imagine a perfect model of flatness.

It had to expand in all directions and be vast. The mental model was that of being on a boat in flat seas and looking out at nothing but horizon in all directions. With a little research, I discovered there is an equation for determining how far you can see at sea. In terms of software, the underlying goal srtm 4.1 data this project was to use only open source software to srtm 4.1 data the analysis.

However, it is worthwhile to become facile in another software as it reinforces that you have to think about what you are doing before you start pushing buttons. The open source stack has come a long way since I started this project back inwith usability being the greatest improvement. It is a lot easier now for a mere mortal to get up and running with open source than it was then, and the community continues to make big strides on that front. It seems there are only so many ways to perform map algebra; note, I discuss the new game-changing approaches to distributed raster processing at the end.

The first attempts to model flatness used a nested approach of slope and relief calculations run at different focal window sizes that were then combined into an index score. To start I was only working on a Kansas subset and compared various model outputs to places I knew well. To model flat, r.

Additionally it needed to be run for every raster cell. The output was then 16 different layers, one for each azimuth, with the intersection angle of the ray and the terrain. Next I had to determine at what angular measurement flat stopped being flat.

This is a subjective decision and one srtm 4.1 data on my experience growing up on the High Plains. Now this measure is completely arbitrary, and it would be interesting to get how others would classify it. I did review it with a few other western Kansas natives who agreed with me.

Note, we were not concerned with down elevation at all. The data processing for this project was massive, i dreamed a dream score scribd er downloading all the individual tiles of the SRTM for the Lower 48 55 tiles, over 4GB in total sizeimporting r. Each segment of the DEM took about 36 hours to process in r.

In the final step, each of the 16 individual azimuth scores were added together r. This index score was divided into four groupings, with Not FlatFlat megaman x7 1 part color, FlatterFlattest categories.

Zonal statistics r. A water bodies data layer was used as a mask in the zonal statistics r. A second mask was also used to eliminate the influence of two areas of bad data located in the southeastern U. Both total number of flat pixels and percent area flat pixels were calculated and ranked for the flat, flatter, and flattest categories.

See the article below for a table of results. Below are a series of maps that display the final Flat Index. The spatial distribution of flat areas is intriguing, with some confirmations and surprises to our initial srtm 4.1 data. A complete table of the state rankings is available in the article, srtm 4.1 data there are several more zoomed in maps available below. Each image is clickable and will open a much larger version.

Jerry was always confident it would be well received, but the range of international, national, and regional coverage it received was beyond anything I imagined…and it keeps going. With over 4. I recommend the entire video, and the Flat Map section begins around the I would like to thank Dr.

When I was swamped with work at the State Department, Jerry pushed forward on srtm 4.1 data write up srtm 4.1 data talking with the media. In terms of the future, there is much more that can be done here. New distributed raster processing tools Mr. Geo and GeoTrellis could rapidly increase processing speeds, and provide an opportunity for using a more refined, multi-scalar approach to flatness. New global elevation datasets are also becoming available, and could potentially reduce the error of the analysis through lower margins of srtm 4.1 data in forested areas.

I would also like to get the data hosted on web mapping server somewhere, so people could interact with it directly. If anyone is interested in working together, let me know. Below is a pre-publication version of the article submitted to Geographical Review. Please cite the published version for any academic works. Download the PDF file. RT disruptivegeo: New DisruptiveGeo blog: The Flatness of U. States http: I bet people perceive plains states to be flatter because srtm 4.1 data can see farther.

RT AmericanGeo: Your email address will not be published. Skip to content Ummmm…. Official Physiographic Regions for the State of Kansas. Methodology There were many measures of flat that had been developed in the geomorphological literature, but they tended to be localized measures, meant for hydrological and landscape modeling. A srtm 4.1 data model of flatness. Graphic displays how the Flat Index is calculated for every 90 meter cell, using independent measures collected across 16 different directions.

Graphic displays the angular measure criteria 0. Results Below are a series of maps that display the final Flat Index. Map shows the Flat Index, a. Useful for visualizing the patterns of flat lands within the continental United States. Map displays the Flattest Category of the Flat Index. Does the perception of flatness impact tourism?

Seems the State of Kansas is interested in projecting that the Kansas landscape has hills. Interesting correlation between flat areas and physiographic boundaries. Map displays the Flat Index over Florida, note large areas of flat land in southern half of the state and along the panhandle coast.

Large areas of flat lands occur within the river valley and along the coastal areas. Map shows the Flat Index over Illinois and Indiana. Note the huge area of Illinois within srtm 4.1 data Brantley gilbert whats left of a small town mp3 class, the result of glacial outwash geomorphic processes. Map shows the Flat Index over northern Texas and Oklahoma.

Srtm 4.1 data large tracts of flat land in the western High Plains region. Map shows the Flat Index over Kansas. Note large areas of flat land in the western High Plains region and in the central area of the state corresponding to the Arkansas River valley and McPherson-Wellington Lowlands physiographic province.

Thanks I would like to thank Dr. Future In terms of the future, there is much more that can be done here.

Article Below is a pre-publication version of the article submitted to Geographical Review. States GeoNe. Flatness — beyond the maps. Leave a Reply Cancel reply Your email address will not be published. Previous Previous post: Moving forward….

The SRTM digital elevation data provided on this site has been processed to fill data voids and to facilitate its ease of use by a wide group of potential users. This data is provided in an effort to promote the use of geospatial science and applications srtm 4.1 data sustainable development and resource conservation in the developing world.

Digital elevation models DEM for the entire globe, covering all of the countries of the world, are available for download on this site. All are produced from a seamless srtm 4.1 data to allow easy mosaicing. Data can be downloaded using a browser or accessed directly from the FTP site.

The SRTM data is available as 3 arc second approx. A 1 arc second data product was also produced latest hindi album video songs is not available for all countries.

These are generally small holes, which nevertheless render the data less useful, especially in fields of hydrological modeling. This involved the production of vector contours and points and the re-interpolation of these derived contours back into a raster DEM. The DEM files have been mosaiced into a seamless near-global coverage up to 60 degrees north and southand are available for download as 5-degree x 5-degree tiles, in a geographic coordinate system — WGS84 datum. In addition, a binary Data Mask file is available for download, allowing users to identify the areas within each DEM which has been interpolated.

For the United States, data was made available at 1-arc second resolution approximately 30m at the equatorbut for the rest of the world, the 1-arc second product is degraded to srtm 4.1 data seconds approximately 90m at the equator. The original SRTM data has been subjected to a number of processing steps to provide seamless srtm 4.1 data complete elevational surfaces for the globe. In its original release, SRTM data contained regions of no-data, specifically over water bodies lakes and riversand in areas where insufficient textural detail was available in the original radar images to produce three-dimensional elevational data.

There are a total of 3, voids accounting forkm2, and in srtm 4.1 data cases, such as Nepal they constitute 9. No-data regions due to insufficient textural detail were especially found in mountainous regions Himalayas and Andes, for exampleor desertic regions e. We follow the method described by Reuter et al.

The first processing stage involves importing srtm 4.1 data merging the 1-degree tiles into continuous elevational surfaces in ArcGRID format. The second process fills small holes iteratively, and the cleaning of the surface to reduce pits and peaks.

The third stage then interpolates through the holes using a range of methods. The method used is based on the size of the hole, and the landform that surrounds it. This dataset is very detailed along shorelines and contains all small islands.

More information about this dataset is available in USGS c. This method produces a smooth elevational surface of no-data regions. Whilst micro-scale topographic variation is not captured srtm 4.1 data this method, most macro-scale features are captured in small-intermediate sized holes. Jarvis et al. They find an average vertical error of just 5m in interpolated regions when compared with a DEM derived from cartographic maps, though the maximum error stretches to m in a region with approximately m elevation.

When hydrological models are applied to the interpolated DEM and the cartographic DEM, little difference is found in hydrological response in terms of overland flow and discharge.

The method presented here for filling in the no-data holes in the original SRTM release is by no means the only method available. Martin Gamache has since produced some detailed analysis of the data offered here by the CSI, concluding srtm 4.1 data the hole-filling algorithm is quite successful in representing broad-scale patterns in topography in data holes.

CIAT have processed this data to provide seamless continuous topography surfaces. Users are prohibited from any commercial, non-free resale, or redistribution without explicit written permission from CIAT. Users should acknowledge Srtm 4.1 data as the source used in the creation of any reports, publications, new datasets, derived products, or services resulting from the use of this dataset.

CIAT also request reprints of any publications and notification of any redistributing efforts. CIAT provides these data without any warranty of any kind whatsoever, either express or implied, including warranties of merchantability and fitness for a particular purpose. CIAT shall not be liable for incidental, consequential, or special damages arising out of the use of any data.

Reuter H. Nelson, A. Skip to content Close Search for: Close Menu. The SRTM digital elevation data, produced by NASA originally, is a major breakthrough in digital mapping of the world, and provides a major advance in the accessibility of high quality elevation data for large portions of the tropics and other areas of the developing world. Processing was made on ios flat icons void by void basis.

In cases when a higher resolution auxiliary DEM was available, a point coverage is produced of the elevation values at the center of each cell of the auxiliary DEM within void areas. For areas with a high-resolution auxiliary DEM: This process interpolates through the no-data holes, producing a smooth elevational surface where no data was originally found.

For areas without a high-resolution auxiliary DEM: The most appropriate interpolation technique is selected based on void size and landform typology, and applied on the data immediately surrounding the hole, using SRTM30 derived points inside the hole should it be of a certain size or greater. Srtm 4.1 data best interpolations methods can be generalised as Kriging or Inverse Distance Weighting interpolation for small and medium-size voids in relatively flat low-lying areas; Spline interpolation for small and medium-sized voids in high altitude and dissected terrain; Triangular Irregular Network or Inverse Distance Weighting interpolation for large voids in very flat areas, and an advanced Spline Method ANUDEM for large voids in other terrains.

The interpolated DEM for the no-data regions is then merged with the original DEM to provide continuous elevational srtm 4.1 data without no-data regions. This entire process is performed srtm 4.1 data tiles with the large overlap with neighboring tiles, thus ensuring seamless and smooth transitions in topography in large void areas. Auxiliary DEMs were available from the following sources: Available from http: Canadian Digital Elevation Data Level 1derived from 1: Download Official download interface multiple 5-degree tiles: Request here Download interface in Chinese: We would like empetri adobe thank the colleagues in the Srtm 4.1 data Management and Natural Hazards Unit and the Global Environmental Monitoring unit for their support to provide this data.

Citation Jarvis, A. Reuter, A. Nelson, E. Version History Change from Version 3 to Version 4 Version 4 uses a number of interpolations techniques, described by Reuter et al. Version 2 has the shorelines clipped. Known issues and future improvements We plan to continue improving the data as and when high-resolution auxiliary datasets become available. References Gamache, M. Hutchinson, M. Calculation of hydrologically sound digital elevation models. Jarvis, A. Rubiano, A. Farrow and M.

Mulligan Practical use of SRTM data in the tropics: Comparisons with digital elevation models generated from cartographic data. Working Document no. Wessel, P. Lin, S.

Automated object-based classification of topography from SRTM data. Two examples at the national level in Peru. Wetlands, 1— Post to Cancel. Post srtm 4.1 data not sent - check your email addresses! Sorry, your blog cannot share posts by email.

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3 Comments

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  2. Grokree

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  3. Gobei

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