AbstractHyderabad is one of the fastest growing mega cities in India and it is facing many economic, social and environmental problems due to rapid urban growth. For the better planning of resources and to provide basic amenities to its residents, it is necessary to have sufficient knowledge about its urban growth activities. Also, it is necessary to monitor the changes in land use over time and to detect growth activities in different parts of the city. To accomplish these tasks with greater accuracy and easiest way, remote sensing and geographic information system (GIS) tools proved to be very advantageous. This study makes an attempt towards the mapping of land use classes for different time periods and analysis of apparent changes in land use using the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) data for the urban agglomeration of Hyderabad, India. In this study, three different time periods viz. 1989–2000, 2000–2005 and 2005–2011 are chosen for the analysis. The results have shown that high-density urban area had grown during 1989–2011 by encroaching into other land use classes. The urban growth has also affected water resources both, qualitatively and quantitatively in the region. The transformation of other land use types into urban area dynamically continued in the North-East and Southern parts of the city. In the North-East direction, the urban growth was mostly due to growth in industrial and residential area and in Southern part, mostly due to residential growth .

1. Land cover changes in the rural-urban interaction of Xi’an region using Landsat TM/ETM data October 2005

2. Monitoring The Turbidity and Surface Temperature Changes and Effects of the 17 August 1999 Earthquake in the İzmİt Gulf, Turkey by the Landsat TM/ETM Data September 2005

3. Spatial-temporal changes of tidal flats in the Huanghe River Delta using Landsat TM/ETM+ images July 2004

4. Verification of net primary production estimation method in the Mongolian Plateau using landsat ETM+data June 2004

5. Forest site classification using Landsat 7 ETM data: A case study of Maçka-Ormanüstü forest, Turkey

AbstractLandsat ETM/TM data and an artificial neural network (ANN) were applied to analyse the expansion of the city of Xi’an and land use/cover change of its surrounding area between 2000 and 2003. Supervised classification and normalized difference barren index (NDBI) were used respectively to retrieve its urban boundary. Results showed that the urban area increased by an annual rate of 12.3%, with area expansion from 253.37 km2 in 2000 to 358.60 km2 in 2003. Large areas of farmland in the north and

southwest were converted into urban construction land. The land use/cover changes of Xi’an were mainly caused by fast development of urban economy, population immigration from countryside, great development of infrastructure such as transportation, and huge demands for urban market. In addition, affected by the government policy of “returning farmland to woodland”, some farmland was converted into economic woodland, such as Chinese goosebeery garden, vineyard etc

AbstractThe temporal turbidity and surface temperature changes and effects of the 17 August 1999 earthquake in the İzmit Gulf, Turkey have been investigated using Landsat TM/ETM data. The gulf is in the Mediterranean–Black Sea transition climatic zone and is partially surrounded by green vegetation cover and degraded and densely urbanized-industrialized areas. Landsat TM/ETM data acquired in 1990–1999 confirms increase in turbidity. Turbidity is always low in the southern part and high in the northern part of the gulf, because the more urbanized and industrialized areas are located in the northern part. The Landsat-7 ETM data acquired in the same year (1999) shows seasonal changes in turbidity. Moreover, the two high turbidity and surface temperature anomalies, one of which is parallel to the 17 August 1999 earthquake surface rupture (east–west) and the other which is in the northwest–southeast direction were mapped from Landsat-5 TM data acquired the day (18.08.1999) following the earthquake in the east end of the gulf. On the basis of turbidity implying the sea bottom movement, it is possible to state that a second rupture in the northwest and southeast direction could have occurred at the sea bottom during the earthquake. The distribution of the seismicity centers and the orientation of the lineaments in the area support this finding.

AbstractIntegrating remote sensing, geographic information system (GIS) and fractal theory, change characteristics of tidal flats and tidal creeks in the Huanghe (Yellow) River Delta over the period of 1986–2001 were discussed. The results show that evolutions of tidal flats throughout the Huanghe River Delta are influenced by various factors, and that progressive succession and regression of tidal flats concur in different coastal segments of the delta. Human activities have played an increasingly important role in the succession process of tidal flats. Due to land reclamation in coastal zones of the delta in the last 15 years, lots of tidal flats were occupied, the artificial coastline migrated seaward (the maximum change rate was 0.8 kmyr−1) and tidal creeks became sparser (the highest decreasing rate of length of tidal creeks was 14.9 kmyr−1). Except for two coastal segments from the Tiaohe Estuary to the 106 Station and from the south of the Huanghe River mouth to the north of the 

AbstractWe plan to estimate global net primary production (NPP) of vegetation using the Advanced Earth Observing Satellite-II (ADEOS-II) Global Imager (GLI) multi-spectral data. We derive an NPP estimation algorithm from ground measurement data on temperate plants in Japan. By the algorithm, we estimate NPP using a vegetation index based on pattern decomposition (VIPD) for the Mongolian Plateau. The VIPD is derived from Landsat ETM+multi-spectral data, and the resulting NPP estimation is compared with ground data measured in a semi-arid area of Mongolia. The NPP estimation derived from satellite remote sensing data agrees with the ground measurement data within the error range of 15% when all above-ground vegetation NPP is calculated for different vegetation classifications.

AbstractAforestation activities, silvicultural prescription, forest management decisions and land use planning are based on site information to develop appropriate actions for implementation. Forest site classification has been one of the major problems of Turkish forestry for long time. Both direct and indirect methods can be used to determine forest site productivity. Indirect methods are usually reserved for practical applications as they are relatively simple, yet provide less accurate site estimation. However, direct method is highly time-demanding, expensive and hard to conduct, necessitating the use of information technologies such as Geographic Information Systems (GIS) and Remote Sensing (RS). This study, first of all, generated a forest site map using both direct and indirect methods based on ground measurements in 567.2 ha sample area. Then, supervised classification was conducted on Landsat 7 ETM image using forest site map generated from direct method as ground measurements to generate site map. The classification resulted in moist site of 262.5 ha, very moist site of 122.5 ha and highly moist site of 191.2 ha in direct method; sites I–II cover 38.9 ha, III 289.6 ha, IV–V 143.5 ha and treeless-degraded areas of 104.2 ha in indirect method; moist site of 203.5 ha, very moist site of 232.1 ha and highly moist site of 140.6 ha in remote sensing method. However, 104.2 ha treeless and degraded areas were not determined by indirect method, yet by the other methods. Secondly, forest site map for the whole area (5,980.8 ha) was generated based on the site map generated by the direct method for sampled area. The Landsat 7 ETM image was classified based on the forest site map of sample area. The site index (SI) map for the whole area was generated using conventional inventory measurements. The classification resulted in sites I–II cover 134.1 ha, III 1,643.6 ha, IV–V 1,396.5 ha, treeless-degraded

areas of 1,097.3 ha and settlement-agriculture areas of 1,709.3 ha in indirect method; moist site of 1,674.3 ha, very moist site of 853.6 ha, highly moist site of 1,729.6 ha and settlement-agriculture areas 1,723.3 ha in remote sensing method. Again the treeless- degraded areas of 1,097.3 ha were not determined by indirect method but by remote sensing method.

Obtaining SRTM DataIn accordance with NASA policy, the USGS Earth Resources Observation and Science (EROS) Center hosts and distributes SRTM data via Earth Explorer.

1-arc second (30 meter) SRTM data postings of the continental United States and 3 arc second (90 meter) international continental datasets can now be obtained via Earth Explorer.The Shuttle Radar Topography Mission (SRTM) obtained elevation data on a near-global scale to generate the most complete high-resolution digital topographic database of Earth. SRTM consisted of a specially modified radar system that flew onboard the Space Shuttle Endeavour during an 11-day mission in February of 2000. SRTM is an international project spearheaded by the National Geospatial-Intelligence Agency (NGA), NASA, the Italian Space Agency (ASI) and the German Aerospace Center (DLR). There are three resolution outputs available, including 1 kilometer and 90 meter resolutions for the world and a 30 meter resolution for the US. GLCF serves the main USGS editions, plus has 'enhanced' editions as well as provides editions in WRS-2 tiles to approximate Landsat scenes.

 > Data > SRTM 90m Digital Elevation Database v4.1

SRTM 90m Digital Elevation Database v4.1

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.

Introduction

The SRTM digital elevation data provided on this site has been processed to fill data voids, and to facilitate it’s 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 for 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.

The SRTM 90m DEM’s have a resolution of 90m at the equator, and are provided in mosaiced 5 deg x 5 deg tiles for easy download and use. All are produced from a seamless dataset to allow easy mosaicing. These are available in both ArcInfo ASCII and GeoTiff format to facilitate their ease of use in a variety of image processing and GIS applications. Data can be downloaded using a browser or accessed directly from the ftp site. If you find this digital elevation data useful, please let us know at csi@cgiar.org.The NASA Shuttle Radar Topographic Mission (SRTM) has provided digital elevation data (DEMs) for over 80% of the globe. This data is currently distributed free of charge by USGS and is available for download from the National Map Seamless Data Distribution System, or the USGS ftp site. The SRTM data is available as 3 arc second (approx. 90m resolution) DEMs. A 1 arc second data product was also produced, but is not available for all countries. The vertical error of the DEM’s is reported to be less than 16m. The data currently being distributed by NASA/USGS (finished product) contains “no-data” holes where water or heavy shadow prevented the quantification of elevation. These are generally small holes, which nevertheless render the data less useful, especially in fields of hydrological modeling.

Andy Jarvis and Edward Guevara of the CIAT Agroecosystems Resilience project, Hannes Isaak Reuter (JRC-IES-LMNH) and Andy Nelson (JRC-IES-GEM) have further processed the original DEMs to fill in these no-data voids. This involved the production of vector contours and points, and the re-interpolation of these derived contours back into a raster DEM. These interpolated DEM values are then used to fill in the original no-data holes within the SRTM data. These processes were implemented using Arc/Info and an AML script. The DEM files have been mosaiced into a seamless near-global coverage (up to 60 degrees north and south), and are available for download as 5 degree x 5 degree tiles, in geographic coordinate system – WGS84 datum. These files are available for download in both Arc-Info ASCII format, and as GeoTiff, for easy use in most GIS and Remote Sensing software appications. In addition, a binary Data Mask file is available for download, allowing users to identify the areas within each DEM which has been interpolated.

Methodology

The first release of Shuttle Radar Topography Mission (SRTM) data was provided in 1-degree digital elevation model (DEM) tiles from the USGS ftp server (ftp://e0srp01u.ecs.nasa.gov/srtm/) in 2003. The data was released continent by continent, as

and when the data was processed by NASA and the USGS. For the United States, data was made available at 1-arc second resolution (approximately 30m at the equator), but for the rest of the world the 1-arc second product is degraded to 3-arc seconds (approximately 90m at the equator). SRTM elevation data has now been released for the entire terrestrial surface, and a “Finished” product has now been released (ftp://e0srp01u.ecs.nasa.gov/srtm/version2/SRTM3/).In this web site, the Consortium for Spatial Information (CGIAR-CSI) of the Consultative Group for International Agricultural Research (CGIAR) is offering post-processed 3-arc second DEM data for the globe. The original SRTM data has been subjected to a number of processing steps to provide seamless and complete elevational surfaces for the globe. In its original release, SRTM data contained regions of no-data, specifically over water bodies (lakes and rivers), and in areas where insufficient textural detail was available in the original radar images to produce three-dimensional elevational data. There are a total of 3,436,585 voids accounting for 796,217 km2, and in extreme cases, such as Nepal they constitute 9.6% of the country area with some 32,688 voids totalling an area of 13,740 km2. No-data regions due to insufficient textural detail were especially found in mountainous regions (Himalayas and Andes, for example), or desertic regions (e.g. Sahara). The existence of no-data regions in a DEM cause significant problems in using SRTM DEMs, especially in the application of hydrological models which require continuous flow surfaces. For the CGIAR-CSI SRTM data product we apply a hole-filling algorithm to provide continuous elevational surfaces. The data is projected in a Geographic (Lat/Long) projection, with the WGS84 horizontal datum and the EGM96 vertical datum.

We follow the method described by Reuter et al. (2007). The first processing stage involves importing and 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. The processing is made using Arc/Info AML model. Specifically:

The original SRTM DEM (finished grade data downloaded fromftp://e0srp01u.ecs.nasa.gov/srtm/version2/SRTM3/ is used to produce contours or points (depending on the interpolation methodology to be used for the void). Processing was made on a 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 centre of each cell of the auxiliary DEM within void areas. When no high resolution auxiliary DEM is available, the 30 second SRTM30 DEM is used as an auxiliary for large voids.

For areas with a high resolution auxiliary DEM: The contours and points surrounding the hole and inside the hole are interpolated to produce a hydrologically sound DEM using the TOPOGRID algorithm in Arc/Info. TOPOGRID is based upon the established algorithms of Hutchinson (1988; 1989), designed to use contour data (and stream and point data if available) to produce hydrologically sound DEMs. This process interpolates through the no-data holes, producing a smooth elevational surface where no data was originally found. Drainage enforcement is activated, and the tolerances set at 5 for “tolerance 1”, representing the density and accuracy of input topographic data, and a horizontal standard error of 1m and vertical standard error of 0m.

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. The 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 surfaces without no-data regions. This entire process is performed for tiles with large overlap with neighbouring tiles, thus ensuring seamless and smooth transitions in topography in large void areas.

The resultant seamless dataset is then clipped along coastlines using the Shorelines and Water Bodies Database (SWBD). This dataset is very detailed along shorelines, and contains all small islands. More information about this dataset is available in USGS (2006c).

Auxiliary DEMs were available from the following sources:

1. The original SRTM DEM (finished grade data downloaded from ftp://e0srp01u.ecs.nasa.gov/srtm/version2/SRTM3/ is used to produce contours or points (depending on the interpolation methodology to be used for the void). Processing was made on a void by void basis.

2. In cases when a higher resolution auxiliary DEM was available, a point coverage is produced of the elevation values at the centre of each cell of the auxiliary DEM within void areas. When no high resolution auxiliary DEM is available, the 30 second SRTM30 DEM is used as an auxiliary for large voids.

3. For areas with a high resolution auxiliary DEM: The contours and points surrounding the hole and inside the hole are interpolated to produce a hydrologically sound DEM using the TOPOGRID algorithm in Arc/Info. TOPOGRID is based upon the established algorithms of Hutchinson (1988; 1989), designed to use contour data (and stream and point data if available) to produce hydrologically sound DEMs. This process interpolates through the no-data holes, producing a smooth elevational surface where no data was originally found. Drainage enforcement is activated, and the tolerances set at 5 for “tolerance 1”, representing the density and accuracy of input topographic data, and a horizontal standard error of 1m and vertical standard error of 0m.

4. 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. The 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.

5. The interpolated DEM for the no-data regions is then merged with the original DEM to provide continuous elevational surfaces without no-data regions. This entire process is performed for tiles with large overlap with neighbouring tiles, thus ensuring seamless and smooth transitions in topography in large void areas.

6. The resultant seamless dataset is then clipped along coastlines using the Shorelines and Water Bodies Database (SWBD). This dataset is very detailed along shorelines, and contains all small islands. More information about this dataset is available in USGS (2006c).Auxiliary DEMs were available from the following sources:

USA – NED 3-arc second digital elevation model for mainland USA, Alaska, Hawaii and Puerto Rico. Available fromhttp://seamless.usgs.gov

Mexico – 90m DEM available from http://www.inegi.gob.mx/geo/default.asp Canada – Canadian Digital Elevation Data Level 1derived from 1:50,000 and 1:250,000

topographic maps, available from http://www.geobase.ca/geobase/en/data/cded1.html New Zealand – 100m DEM made kindly available by Geographx

(http://www.geographx.co.nz/downloads.html) Australia – GEODATA TOPO 100k contour data, interpolated to produce a 90m DEM

available formhttp://www.ga.gov.au/products Mountainous areas in Central Asia, China, Europe, Caucasus, Northern Andes and Southern

Andes based on data from Jonathan de Ferranti’s webpage: http://www.viewfinderpanoramas.org/dem3.html

Costa Rica – 50m DEM derived from digitized topographic maps made available to CIAT by Antonio Trabucco.

Ecuador – a 90m DEM derived from digitized topographic maps available fromhttp://rslultra.star.ait.ac.th/~souris/ecuador.htm#DEM30

Global – Where other auxiliary DEMs were not available, the SRTM30 1km product was used as an auxiliary DEM (USGS, 2006d).This method produces a smooth elevational surface of no-data regions. Whilst micro-scale topographic variation is not captured using this method, most macro-scale features are captured in small-intermediate sized holes. Jarvis et al. (2004) (available here) make a detailed analysis of the accuracy of the interpolated elevational data in a region in Colombia with 43% of the region containing no-data in the original SRTM release. 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 257m in a region with approximately 1500m 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. For a complete review of methods for hole-filling in SRTM data, readers are referred to an article produced by the Alpine Mapping Guild, Gamache (2004). Martin Gamache has since produced some detailed analysis of the data offered here by the CSI, concluding that the hole-filling algorithm is quite successful in representing broad scale patterns in topography in data holes. A detailed evaluation of the hole-filling methodology is available at http://www.terrainmap.com/downloads/Gamache_final_web.pdf.The original SRTM DEM (finished grade data downloaded from ftp://e0srp01u.ecs.nasa.gov/srtm/version2/SRTM3/ is used to produce contours or

points (depending on the interpolation methodology to be used for the void). Processing was made on a 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 centre of each cell of the auxiliary DEM within void areas. When no high resolution auxiliary DEM is available, the 30 second SRTM30 DEM is used as an auxiliary for large voids.For areas with a high resolution auxiliary DEM: The contours and points surrounding the hole and inside the hole are interpolated to produce a hydrologically sound DEM using the TOPOGRID algorithm in Arc/Info. TOPOGRID is based upon the established algorithms of Hutchinson (1988; 1989), designed to use contour data (and stream and point data if available) to produce hydrologically sound DEMs. This process interpolates through the no-data holes, producing a smooth elevational surface where no data was originally found. Drainage enforcement is activated, and the tolerances set at 5 for “tolerance 1”, representing the density and accuracy of input topographic data, and a horizontal standard error of 1m and vertical standard error of 0m.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. The 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 surfaces without no-data regions. This entire process is performed for tiles with large overlap with neighbouring tiles, thus ensuring seamless and smooth transitions in topography in large void areas.The resultant seamless dataset is then clipped along coastlines using the Shorelines and Water Bodies Database (SWBD). This dataset is very detailed along shorelines, and contains all small islands. More information about this dataset is available in USGS (2006c).

Download

Official download interface (multiple 5 degree tiles): http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp

Bulk download: Request here (14 GB in total) Download interface in Chinese: http://srtm.datamirror.csdb.cn/search.jsp Google Earth interface (1 and 5 degree tiles): http://www.ambiotek.com/srtm Resampled data (250m, 500m, and 1

km): https://hc.box.com/shared/1yidaheouv (Password: ThanksCSI!)

Acknowledgements

King’s College London (Mark Mulligan) mirrors the data, and has created a Google Earth Interface for browsing and downloading SRTM tiles. it alsoprovides smaller (1 by 1 degree) tiles for users who have difficultywith the 5×5 degree tiles as well as 2D and 3D visualisation of the data.

Joint Research Center in the Institute for Environmental Research. We would like to thank the colleagues in the Land Management and Natural Hazards Unit and the Global Environmental Monitoring unit for their support to provide this data.

HarvestChoice provides the US-based mirror site and a Google Maps-mashup interface. The CGIAR-CSI SRTM website was created under the guidance of Robert Zomer, and the

support of the International Water Management Institute (IWMI). Database search, data display, and download programming was implemented by GENESIIS Software. Many thanks to CGNET for hosting the database and tech support.

Funding for this project has been provided by the CGIAR ICT-KM Program: ICT for Tomorrow’s Science Initiative.

Citation

Jarvis, A., H.I. Reuter, A. Nelson, E. Guevara, 2008, Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database (http://srtm.csi.cgiar.org).

Version History

Change from Version 3 to Version 4 Version 4 uses a number of interpolations techniques, described by Reuter et al. (2007) Version 4 uses extra auxiliary DEMs to fill the voids and SRTM30 for large voids Version 4 differs from Version 3 with a ½ grid pixel shift which definitively solves this

confusion.Change from Version 2 to Version 3

Version 3 includes Finished grade SRTM data Version 3 uses the SWBD database to clip the coastlines Version 3 uses auxiliary DEMs to fill the voids Version 3 differs from Version 2 with a ½ grid pixel shift

Changes from Version 1 to Version 2 Version 2 includes DEM data for Australasia and small islands in the Atlantic, Indian and

Pacific Oceans. Version 2 has the shorelines clipped. Version 2 have no “cliffs” on tile joins, brought about by insufficient overlap in interpolation

in Version 1.Known issues and future improvements

We plan to continue improving the data as and when high resolution auxiliary datasets become available. Updates are planned that will use high resolution ASTER DEMs for filling holes in particularly troublesome areas (Sahara, for example).

Disclaimer

DATASET: The data distributed here are in ARC GRID, ARC ASCII and Geotiff format, in decimal degrees and datum WGS84. They are derived from the USGS/NASA SRTM data. CIAT have processed this data to provide seamless continuous topography surfaces. Areas with regions of no data in the original SRTM data have been filled using interpolation methods described by Reuter et al. (2007).DISTRIBUTION: Users are prohibited from any commercial, non-free resale, or redistribution without explicit written permission from CIAT. Users should acknowledge CIAT as the source used in the creation of any reports, publications, new data sets, derived products, or services resulting from the use of this data set. CIAT also request reprints of any

publications and notification of any redistributing efforts. For commercial access to the data, send requests to Andy Jarvis.NO WARRANTY OR LIABILITY: 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.

ACKNOWLEDGEMENT AND CITATION: We kindly ask any users to cite this data in any published material produced using this data, and if possible link web pages to the CIAT-CSI SRTM website (http://srtm.csi.cgiar.org).REFERENCE: Reuter H.I, A. Nelson, A. Jarvis, 2007, An evaluation of void filling interpolation methods for SRTM data, International Journal of Geographic Information Science, 21:9, 983-1008 (PDF).

References

Gamache, M. (2004). Free and Low Cost Datasets for International Mountain Cartography,http://www.icc.es/workshop/abstracts/ica_paper_web3.pdf.

Hutchinson, M. (1988). Calculation of hydrologically sound digital elevation models. Third International Symposium on Spatial Data Handling, Columbus, Ohio, International Geographical Union.

Hutchinson, M. (1989). “A new procedure for gridding elevation and stream line data with automatic removal of spurious pits.” Journal of Hydrology 106: 211-232.

Jarvis, A., J. Rubiano, A. Nelson, A. Farrow and M. Mulligan (2004). Practical use of SRTM data in the tropics: Comparisons with digital elevation models generated from cartographic data. Working Document no. 198. Cali, International Centre for Tropical Agriculture (CIAT): 32.

Reuter H.I, A. Nelson, A. Jarvis, 2007, An evaluation of void filling interpolation methods for SRTM data, International Journal of Geographic Information Science, 21:9, 983-1008.

USGS, 2006a, Shuttle Radar Topography Mission (SRTM) “Finished” 3-arc second SRTM Format Documentation, Available online at: http://edc.usgs.gov/products/elevation/srtmbil.html (accessed 01/08/2006).

USGS, 2006b, Shuttle Radar Topography Mission DTED® Level 1 (3-arc second) documentation, Available online at: http://edc.usgs.gov/products/elevation/srtmdted.html (accessed 01/08/2006).

USGS, 2006c, Shuttle Radar Topography Mission Water Body Dataset, Available online at:http://edc.usgs.gov/products/elevation/swbd.html (accessed 01/08/2006).

USGS, 2006d, SRTM30 Documentation, Available online at:ftp://e0srp01u.ecs.nasa.gov/srtm/version2/SRTM30 (accessed 01/08/2006).

Wessel, P., and W. H. F. Smith, A Global Self-consistent, Hierarchical, High-resolution Shoreline Database, J. Geophys. Res., 101, #B4, pp. 8741-8743, 1996.

Publications

Recently published studies that used our SRTM data (let us know if we missed yours!):

Ricaurte, L., Jokela, J., Siqueira, A., Núñez-Avellaneda, M., Marin, C., Velázquez-Valencia, A., &

Wantzen, K. (n.d.). Wetland Habitat Diversity in the Amazonian Piedmont of

Colombia. Wetlands, 1–14. doi:10.1007/s13157-012-0348-y

Gorokhovich, Y., & Voustianiouk, A. (2006). Accuracy assessment of the processed SRTM-based

elevation data by CGIAR using field data from USA and Thailand and its relation to the

terrain characteristics. Remote Sensing of Environment, 104(4), 409–415.

doi:10.1016/j.rse.2006.05.012

Mouratidis, A., Briole, P., & Katsambalos, K. (2010). SRTM 3″ DEM (versions 1, 2, 3, 4) validation

by means of extensive kinematic GPS measurements: a case study from North

Greece. International Journal of Remote Sensing, 31(23), 6205–6222.

doi:10.1080/01431160903401403

Hirt, C., Filmer, M. S., & Featherstone, W. E. (2010). Comparison and validation of the recent

freely available ASTER-GDEM ver1, SRTM ver4.1 and GEODATA DEM-9S ver3 digital

elevation models over Australia. Australian Journal of Earth Sciences, 57(3), 337–347.

doi:10.1080/08120091003677553

Khan, A. (2012). Geology and Geomorphology of the Manipur Valley Using Digitally Enhanced

Satellite Image and SRTM DEM in the Eastern Himalaya, India. International Journal of

Geosciences, 03(25), 1010–1018. doi:10.4236/ijg.2012.325101

Plasencia Sánchez, E., & Fernandez de Villarán, R. (2012). SRTM 3" comparison with local

information: Two examples at national level in Peru. Journal of Applied Geodesy, 6(2).

doi:10.1515/jag-2011-0016

Drăguţ, L., & Eisank, C. (2012). Automated object-based classification of topography from SRTM

data. Geomorphology,141–142, 21–33. doi:10.1016/j.geomorph.2011.12.001

Lin, S., Jing, C., Coles, N. A., Chaplot, V., Moore, N. J., & Wu, J. (2013). Evaluating DEM source and

resolution uncertainties in the Soil and Water Assessment Tool. Stochastic Environmental

Research and Risk Assessment,27(1), 209–221. doi:10.1007/s00477-012-0577-x

Tags: DEM, Elevation, SRTM

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SRTM

The Shuttle Radar Topography Mission (SRTM) is an international research effort that obtained digital elevation models on a near-global scale from 56° S to 60° N,[2] to generate the most complete high-resolution digital topographic database of Earth prior to the release of the ASTER GDEM in 2009. SRTM consisted of a specially modified radar system that flew on board the Space Shuttle Endeavour during the 11-day STS-99mission in February 2000, based on the older Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar (SIR-C/X-SAR), previously used on the Shuttle in 1994. To acquire topographic (elevation) data, the SRTM payload was outfitted with two radar antennas.[2] One antenna was located in the Shuttle's payload bay, the other – a critical change from the SIR-C/X-SAR, allowing single-pass interferometry – on the end of a 60-meter (200-foot) mast[2] that extended from the payload bay once the Shuttle was in space. The technique employed is known as Interferometric Synthetic Aperture Radar.

The elevation models are arranged into tiles, each covering one degree of latitude and one degree of longitude, named according to their south western corners. It follows that "n45e006" stretches from 45°N 6°E to 46°N 7°E and "s45w006" from 45°S 6°W to 44°S 5°W. The resolution of the raw data is one arcsecond (30 m),

but this has only been released over United States territory. A derived one arcsecond dataset (with trees and other non-terrain features removed) covering Australia was made available in November 2011; the raw data are restricted for government use.[3] For the rest of the world, only three arcsecond (90 m) data are available.[4] Each one arcsecond tile has 3,601 rows, each consisting of 3,601 16 bitbigendian cells. The dimensions of the three arcsecond tiles are 1201 x 1201.

The elevation models derived from the SRTM data are used in Geographic Information Systems. They can be downloaded freely over the Internet, and their file format (.hgt) is supported by several software developments.

The Shuttle Radar Topography Mission is an international project spearheaded by the U.S. National Geospatial-Intelligence Agency (NGA) and the U.S. National Aeronautics and Space Administration (NASA)