SRTM 90m Digital Elevation Database

The SRTM digital elevation data, originally produced by NASA, 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. 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 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.

Dr. Andy Jarvis and Edward Guevara of the CIAT Agroecosystems Resilience project, Dr. Hannes Isaak Reuter (JRC-IES-LMNH) and Dr. 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.

MIRROR DOWNLOAD SITE: Dr. Mark Mulligan (King’s College London) mirrors the data, and has created a Google Earth Interface for browsing and downloading SRTM tiles. it also provides smaller (1 by 1 degree) tiles for users who have difficulty with the 5×5 degree tiles as well as 2D and 3D visualisation of the data.

Google Link: http://www.ambiotek.com/topoview
Download SRTM KML file click here
Download Google-Earth click here

The CGIAR-CSI has produced this website under the guidance of Dr. 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.

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COMING SOON: VERSION 5

The SRTM data available here is version 4. It was last updated in November 2018. Version 5 is planned for 2019.

Visit the CGIAR CSI website

Download SRTM Data

Frequently Asked Questions (FAQ)

What is the source of this data, and why is it different from the NASA data?

The current dataset (Version 4) has been produced based on the finished-grade 3 arc-second SRTM data released by NASA and distributed by the USGS through ftp access (ftp://edcsgs9.cr.usgs.gov/pub/data/srtm/version1/). The original data came with data voids, where insufficient contrast was available in the radar data to extract the elevation. These data voids tend to occur over water bodies (lakes and rivers), areas with snow cover and in mountainous regions (for example, the Himalayas has the greatest concentration of no data voids in the original data). The CGIAR-CSI SRTM dataset has undergone post-processing of the NASA data to “fill in” the no data voids through interpolation techniques (see the Data Processing and Methodology page for detailed description). The result is seamless, complete coverage of elevation for the globe.

What formats are available?

The data comes in two formats: arc-formatted ASCII and GeoTIFF.

Arc ASCII: This is a text-based data array, with a five-line header providing the information on spatial extent and resolution.The coordinates in the header represent the lower left corner of the lower left pixel. No data values are represented by values of –9999.
This data can be imported into ArcView 3.2 through use of the Spatial Analysis extension and File->Import Data Source.
Within ArcGIS, there are different methods available for importing ASCII files. You can access toolboxes from the ArcToolbox window or the ArcCatalog tree. The Conversion toolbox contains tools that convert data between various formats. Select Ascii to raster. Next a dialog box appears where you supply the parameter values for the tool, then click OK. Alternatively, the command line syntax is:

ASCIIToRaster_conversion {INTEGER | FLOAT}

Many other GIS software can read ARC ASCIIs through import functions.

GeoTIFF: The GeoTiff data are georeferenced images (with the geographic data embedded – there is no .gtif file) in signed 16 bit (-32768 to 32767) TIF format. They have been produced through export from ArcGIS 8. In the GeoTIFF files, values of –32768 represent no data regions.

When I import GeoTIFF data, values of –32768 appear. What does this mean?

When the GeoTiff files are imported into some software programs, it has been reported that values of –32768 result. This is due to the software failing to identify values of –32768 as pixels with no data. Users can either search for a solution that fixes this or should explore the ASCII files that so far have not presented any similar problems.

This problem can be solved by reclassifying (reclassifying –32768 as NO DATA) or querying all pixels with values greater than –32768, and recalculating the grid based only on those regions. One command in Arc/Info can also solve this (the user should specify the name of the grid for “outgrid” and “srtmdem”)::

OUTGRID = setnull(SRTMDEM == -32768, SRTMDEM)

Why do some mountain regions have peaks significantly lower than they should be?

As mentioned earlier, many original data voids are concentrated in mountainous areas and in snow-covered regions. Hence, many peaks in high-mountain areas are actually interpolated. Without using a high resolution co-variable for the interpolation, the interpolation fails to identify that the data void is actually a peak, and tends to “flatten” the peak, leading to underestimates in the true elevation for that region. This issue is largely resolved in Version 4.

Why do some small islands not appear in the dataset?

The dataset available online has been cut for shorelines using the Shorelines and Water Bodies dataset (SWBD) that accompanies SRTM topography data. In some cases this dataset fails to include small islands. The result is that these small islands, originally captured in the SRTM data, are lost from the dataset during the cutting process with shorelines. Uncut data is available upon request – this includes large areas of radar backscatter over water bodies.

Why do some regions have very smooth looking topography?

Interpolated regions in the dataset can often be spotted as they appear to be very smooth, visibly lacking low-scale detail. These are the areas with original no data voids, that have undergone interpolation.

When is the next update likely to be available, and what modifications are expected?

The current release is Version 4. It is envisaged that CGIAR-CSI SRTM Version 4 is our definitive and final release of the 3-arc-second product. However, we do plan some improvements which will be updated as patches. These will include areas where new high-resolution DEMs become available for the interpolation of holes. This may include the use of ASTER scenes to fill holes.

Can I use this data for commercial use?

f interested in using this data for commercial purposes please email Andy Jarvis.

What is the point of origin and placement of a pixel in the dataset?

The below diagram shows how an example SRTM tile should be read. This is an example for SRTM_38_03 and SRTM_38_02.

Disclaimer

Processed SRTM Data

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.

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 SRTM page (http://srtm.csi.cgiar.org).

Citations should be made as follows: Jarvis A., H.I. Reuter, A. Nelson, E. Guevara, 2008, Hole-filled seamless SRTM data V4, International Centre for Tropical Agriculture (CIAT), available from http://srtm.csi.cgiar.org.

References

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.

Contact us

Andy Jarvis, Ph.D.
Research Area Director – Decision and Policy Analysis
International Centre for Tropical Agriculture (CIAT)
Email: a.jarvis@cgiar.org

Edward Guevara
Research Associate – Decision and Policy Analysis Research Area
International Centre for Tropical Agriculture (CIAT)
Email: e.d.guevara@cgiar.org

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AgroFIMS: Your new companion for easy standardization of data collection and description

The Agronomy Field Information Management System (AgroFIMS) allows users to create fieldbooks to collect agronomic data that is already tied to a metadata standard (the CG Core Metadata Schema, aligned with the standard Dublin Core), and semantic standards like the Agronomy Ontology (AgrO), generating data that is Findable, Accessible, Interoperable, and Reusable (FAIR) at collection. AgroFIMS therefore standardizes data collection and description for easy aggregation and inter-linking across disparate datasets. The fieldbooks you create can be exported to the Android-based KDSmart data collection application, and collected data imported back to AgroFIMS for statistical analysis and reports. In 2021 AgroFIMS will allow you to set up agronomic survey questionnaires, for data collection via ODK. It will also allow easy upload of your “born FAIR” data to Dataverse repository platforms with Dublin Core-compliant metadata schemas. Funding for AgroFIMS was provided by the Bill and Melinda Gates Foundation’s Open Access, Open Data Initiative, and the CGIAR Platform for Big Data in Agriculture. AgroFIMS is under GPL license. Go to AGROFIMS →

Responsible Data Management Guidelines to protect privacy

CGIAR Platform for Big Data in Agriculture advocates open data for agricultural research for development. It considers that opening up research data for scrutiny and reuse confers significant benefits to society.

However, the Platform appreciates that not all research data can be open and that a broad range of legitimate circumstances may require data to be restricted.

As an integral component of its advocacy for open data, the Platform promotes responsible data management through the entire research data lifecycle from planning, collecting, storing, disclosing or publishing, transferring, discovery and archiving.

These guidelines were created from information collected from: review on best and emerging practices across various sectors in the fast changing landscape of privacy and ethics (130 external resources); privacy and ethic materials sourced from seven CGIAR centers; first draft was circulated for input and feedback across CGIAR and incorporated into this edition. It’s important to note that this is an evolving document, the next stage is to consult externally for further input.

These Guidelines are intended to assist agricultural researchers handle privacy and personally identifiable information (PII) in the research project data lifecycle.

Check the guidelines →

REUSE / TRANSFER

Ensure consistency with the DMP-PII and the purpose for which prior informed consent has been obtained
Revaluate likelihood of (re-)identification and risk of harm, particularly if it involves a public data-set containing PII (as above)
Ensure PII is stored securely to protect privacy (as above)
Minimize use of PII and risk of disclosure through pro-privacy access controls and analytical tools (as above)

Don’t transfer data containing PII unless have explicit consent
Don’t transfer data containing PII in the absence of a data sharing agreement identifying aspects such as purpose and scope of use, privacy protections measures, confidentiality and any limitations)
Don’t reuse or transfer PII until any inconsistencies with the DMP-PII and/or purpose compatibility have been resolved (e.g. through updated ethics review or consent from participant)

ARCHIVING / DISCARDING

Plan for archiving or data destruction early in the process. Destroying data can be more secure, however, archiving can be beneficial if the data has ongoing evidentiary, scientific or cultural value. If archiving, identify where and how, the budget require
Ensure DMP-PII and purpose compatibility (as above)
Ensure adequate security measures to protect privacy (as above)

Don’t wait until the end of the project to assess archiving needs when time and resources may be limited
Don’t assume the longevity of a particular format, future-proof your archives data
Don’t forget to budget for archiving data, this should be done as part of your Data Management Plan

PUBLISHING AND DISCOVERY

Ensure DMP-PII and purpose compatibility (as above)
Revaluate likelihood of (re-)identification and risk of harm, particularly if it involves a public data-set containing PII
Indicate in metadata the availability of raw data or minimized data containing PII, if available bilaterally
Minimize use of PII and risk of disclosure through pro-privacy access controls and analytical tools

Don’t include PII in public datasets unless absolutely necessary to preserve the data’s analytic potential, scientific utility or benefit to the participant (and subject to participants informed consent and a rigorous risk assessment)

STORAGE AND ANALYSIS

Ensure compatibility with the DMP-PII (as above) and also the purpose for which prior informed consent has been obtained

Ensure PII is stored securely to protect privacy, through organizational or project specific safeguards to prevent unauthorized access, accidental disclosure or breach of data (physical & technical)

encryption for the storage and transmission of PII
access control measures to limited access to PII
two-factor or multifactor authentication
cloud services & back-end security

Don’t store data in unsecured locations or on unsecured devices or servers

Don’t store encrypted data and encryption keys in locations where they can be easily accessed simultaneously

Don’t underestimate the importance and value of administrative safeguards to standardize practices (i.e. organizational policies, procedures and maintenance of security measures that are designed to protect private information, data and access)

COLLECTION

Ensure compatibility with the DMP-PII
De-identify data to anonymize by default unless it will impair the data’s analytic potential, scientific utility or benefit to the participant,
If you cannot anonymize, minimize the PII and pseudonymize to reduce the disclosure risk
Provide research participants sufficient information to use reasoned judgment to decide whether or not they wish to participate in the project
Ensure informed consent is designed to address the following elements:
- competence, comprehension, full disclosure, voluntariness
- legitimate scientific purpose for which the PII is collected and scope of use (e.g. stored, transferred, published and whether as anonymized, minimized or raw data)
- foreseeable risk of privacy loss and consequences
- meaningful alternatives including opt-in protection/anonymization
- safeguards to protect privacy, conditions on which PII may be shared and any limitations on reuse or third- party access and use of PII
- permission to follow-up or contact the participant and for what purpose (including by third- parties)
- participant’s right to withdraw and rights regarding their data (e.g. to be informed; to access; to rectify; to object; to erase)
- inclusion of physical, phone and/or electronic contact (at least two forms of contact) that participant can reach to exert her/rights
- explicit consent and participant’s acknowledgement of understanding
- if written, provide the participant a copy of processed informed consent
Use plain language and adapt informed consent to meet the needs of vulnerable populations (e.g. obtain orally or in local language)

Don’t collect PII unless you have a Data Management Plan and any necessary approvals in place, including the recorded approval of the potential participant
Don’t collect PII unless you absolutely need it
Don’t assume that removal of direct identifiers is sufficient to anonymize data or that all de-identification techniques will result in anonymized data. Consider the risk of re-identification of a research participant, particularly if datasets are combined. If there is a reasonable risk of re-identification the information should be handled as PII (i.e. undertake risk analysis, evaluate stronger anonymization techniques, seek informed consent for the disclosure of data and explain its possible consequences)
Don’t include vulnerable participants or communities if their ability or capacity to provide voluntary informed consent is genuinely in question
Don’t underestimate the potential of quasi or indirect identifiers to identify an individual, particularly the inherent ability of location-based data to identify participants and their communities, and the increased risk of harm this may pose to potentially vulnerable individuals/communities
Avoid seeking overly broad consent that may call into question transparency or a research participant’s understanding regarding the use of their PII, be specific regarding the activities, purpose and limitations associated with PII so that the participant can make a genuinely informed decision and downstream users can evaluate purpose compatibility and seek fresh consent if needed

PLANNING AND APPROVAL

Develop a Data Management Plan which governs the handling of PII in the research project and beyond (DMP-PII). It should address:
- the type and nature of PII
- compliance requirements (including necessary forms for obtaining consent, and ethics clearance, if applicable)
- legitimate research objectives that will be advanced by the PII
- foreseeable risks and consequences if participants are identified from the data
- privacy protection measures (or lack thereof) for collection, storage, transfer and publishing
- process for obtaining informed consent
- timeframe or trigger for archiving or deletion of PII
Employ stricter standards for research involving vulnerable populations such as children or illiterate participants or sensitive data such as ethnicity or religious beliefs
Undertake due-diligence of datasets previously collected by you or third parties to ensure you are entitled/permitted to use for your research project
Consult the legal, IRB or ethics clearance committee or any other relevant institutional group for specific institutional, local, regional or national policies and regulatory frameworks that may apply to PII in the context of your work

Don’t leave the handling of PII and privacy protection as an after-thought, plan ahead!
Don’t forget to check local laws and donor or third-party requirements in addition to institutional policies governing research ethics and privacy protection (seek expert support if unsure!)
Don’t ignore ethical practices/standards, if your institution does not have an ethics framework or clearance process in place self-assess!
In assessing whether information is capable of identifying someone (i.e. PII) don’t limit your focus to direct identifiers, also consider indirect/quasi identifiers. Appreciate this will depend on the context of the research project, the data in question and external data which is or may become otherwise available (i.e. there is no exhaustive list).
In assessing risk of harm don’t forget to consider potential harm to the participant’s community or groups of individuals that can otherwise be identified or associated with the participant