Imagery for Everyone…
Timeline Set to Release Entire USGS Landsat Archive at No Charge.
Data
Available over the Internet
Landsat 7 – all new global acquisitions
July 2008
Landsat 7 – all data
September 2008
Landsat 5 – all TM data
December 2008
Landsat 4 – all TM data
January 2009
Landsat 1-5 – all MSS data
January 2009
The USGS Landsat archive is an unequaled 35-year record of the Earth’s surface that is valuable for a broad range of uses, ranging from climate change science to forest management to emergency response, plus countless other user applications. Under a transition toward a National Land Imaging Program sponsored by the Secretary of the Interior, the USGS is pursuing an aggressive schedule to provide users with electronic access to any Landsat scene held in the USGS-managed national archive of global scenes dating back to Landsat 1, launched in 1972. By February 2009, any archive scene selected by a user – with no restriction on cloud cover – will be processed automatically to a standard product recipe, using such parameters as the Universe Transverse Mercator projection, and staged for electronic retrieval. In addition, newly acquired scenes meeting a cloud cover threshold of 20% or below will be processed to the standard recipe and placed on line for at least six months, after which they will remain available for selection from the archive.
Newly acquired, minimally cloudy Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data covering North America and Africa are already being distributed by the USGS over the Internet at no charge, with expansion to full global coverage of incoming Landsat 7 data to be completed by July 2008 (see timeline below). The full archive of historical Landsat 7 ETM+ data acquired by the USGS since launch in 1999 will become available for selection and downloading by the end of September 2008. At that time, all Landsat 7 data purchasing options from the USGS, wherein users pay for on-demand processing to various parameters will be discontinued.
By the end of December of 2008, both incoming Landsat 5 Thematic Mapper (TM) data and all Landsat 5 TM data acquired by the USGS since launch (1984) will become available, with all Landsat 4 TM (1982-1985) and Landsat 1-5 Multi-Spectral Scanner (MSS) (1972-1994) data becoming available by the end of January 2009. All Landsat data purchasing options from the USGS will be discontinued by February 2009, once the entire Landsat archive can be accessed at no charge.
Landsat scenes can be previewed and downloaded using the USGS Global Visualization Viewer at http://glovis.usgs.gov [under “Select Collection” choose Landsat archive: L7 SLC-off (2003-present)]. Scenes can also be selected using the USGS Earth Explorer tool at http://earthexplorer.usgs.gov [under “Select Your Dataset” choose Landsat Archive: L7 SLC-off (2003-present)]. For further information on Landsat satellites and products, see http://landsat.usgs.gov
For further information on USGS Land Remote Sensing please visit thier website: landremotesensing@usgs.gov
Wednesday, October 28, 2009
Friday, October 2, 2009
Climatic data Free down load for whole globe
Climatic data Free down load for whole globe
The IWMI World Water and Climate Atlas gives mean climatic parameters for whole globe. It includes monthly and annual means for precipitation, temperature, humidity, hours of sunshine, evaporation estimates, wind speed, total number of days with and without rainfall, days without frost and Penman-Montieth reference evapotranspiration rates. The core of the Atlas is data assembled from weather stations around the world for the period 1961-1990. Data can be extracted in summary, manipulated using the Atlas software, or exported in a standardized format for use in other applications. Users can also include their own local data for a more detailed picture of a particular area Please see following link for main page http://www.iwmi.cgiar.org/WAtlas/Default.aspx
Please try through following links , if fails let me know
http://dw.iwmi.org/dataplatform/ClickandPlot.aspx
The IWMI Climate and Water Atlas is a free global public good. You can use it as long as you want. Here are the links to the 2 CDs zipped and the upgrade of the Atlas program called the synthetizer. I have also attached some guidelines on how to get started with the Atlas. If you are interested in reusing this data into a GIS please see the lower part of this email where links to the ArcInfo grid ascii files are provided.
http://dw.iwmi.org/atlas/CD1.zip
http://dw.iwmi.org/atlas/CD2.zip
http://dw.iwmi.org/atlas/Synthesizer2005.zip
About the Atlas software, here are links to the guidelines on how to use it:
http://dw.iwmi.org/atlas/Atlasguide.pdf
http://dw.iwmi.org/atlas/Atlasguide.zip
http://dw.iwmi.org/atlas/Atlasguide.doc
In case you want to use the Atlas data in a GIS here is the parameter list and the download link for each one of them. Each compressed file contains the arcinfo ascii grids for each parameter.
1. Pcp50 -- 50% Probability Precipitation (mm/month)
http://dw.iwmi.org/atlas/pcp50.exe
2. Pcp75 -- 75% Probability Precipitation (mm/month)
http://dw.iwmi.org/atlas/pcp75.exe
3. Rd0 -- Days with rainfall (days/month)
http://dw.iwmi.org/atlas/rd0.exe
4. TMP -- Mean Daily Air Temperature (deg. C)
http://dw.iwmi.org/atlas/tmp.exe
5. DTR -- Mean Diurnal Temperature Range (deg. C)
http://dw.iwmi.org/atlas/dtr.exe
6. FRS -- Days with Ground Frost (days/month)
http://dw.iwmi.org/atlas/frs.exe
7. REH -- Relative Humidity (%)
http://dw.iwmi.org/atlas/reh.exe
8. SUNP -- Sunlight Hours (% of Maximum Sunlight Hours)
http://dw.iwmi.org/atlas/sunp.exe
9. WND -- Mean Wind Speed (m/s)
http://dw.iwmi.org/atlas/wnd.exe
10. MAI -- Hargreaves Moisture Availability Index
http://dw.iwmi.org/atlas/mai.exe
11. ETPen -- Evapo-transpiration (mm/day)
http://dw.iwmi.org/atlas/etpen.exe
The IWMI World Water and Climate Atlas gives mean climatic parameters for whole globe. It includes monthly and annual means for precipitation, temperature, humidity, hours of sunshine, evaporation estimates, wind speed, total number of days with and without rainfall, days without frost and Penman-Montieth reference evapotranspiration rates. The core of the Atlas is data assembled from weather stations around the world for the period 1961-1990. Data can be extracted in summary, manipulated using the Atlas software, or exported in a standardized format for use in other applications. Users can also include their own local data for a more detailed picture of a particular area Please see following link for main page http://www.iwmi.cgiar.org/WAtlas/Default.aspx
Please try through following links , if fails let me know
http://dw.iwmi.org/dataplatform/ClickandPlot.aspx
The IWMI Climate and Water Atlas is a free global public good. You can use it as long as you want. Here are the links to the 2 CDs zipped and the upgrade of the Atlas program called the synthetizer. I have also attached some guidelines on how to get started with the Atlas. If you are interested in reusing this data into a GIS please see the lower part of this email where links to the ArcInfo grid ascii files are provided.
http://dw.iwmi.org/atlas/CD1.zip
http://dw.iwmi.org/atlas/CD2.zip
http://dw.iwmi.org/atlas/Synthesizer2005.zip
About the Atlas software, here are links to the guidelines on how to use it:
http://dw.iwmi.org/atlas/Atlasguide.pdf
http://dw.iwmi.org/atlas/Atlasguide.zip
http://dw.iwmi.org/atlas/Atlasguide.doc
In case you want to use the Atlas data in a GIS here is the parameter list and the download link for each one of them. Each compressed file contains the arcinfo ascii grids for each parameter.
1. Pcp50 -- 50% Probability Precipitation (mm/month)
http://dw.iwmi.org/atlas/pcp50.exe
2. Pcp75 -- 75% Probability Precipitation (mm/month)
http://dw.iwmi.org/atlas/pcp75.exe
3. Rd0 -- Days with rainfall (days/month)
http://dw.iwmi.org/atlas/rd0.exe
4. TMP -- Mean Daily Air Temperature (deg. C)
http://dw.iwmi.org/atlas/tmp.exe
5. DTR -- Mean Diurnal Temperature Range (deg. C)
http://dw.iwmi.org/atlas/dtr.exe
6. FRS -- Days with Ground Frost (days/month)
http://dw.iwmi.org/atlas/frs.exe
7. REH -- Relative Humidity (%)
http://dw.iwmi.org/atlas/reh.exe
8. SUNP -- Sunlight Hours (% of Maximum Sunlight Hours)
http://dw.iwmi.org/atlas/sunp.exe
9. WND -- Mean Wind Speed (m/s)
http://dw.iwmi.org/atlas/wnd.exe
10. MAI -- Hargreaves Moisture Availability Index
http://dw.iwmi.org/atlas/mai.exe
11. ETPen -- Evapo-transpiration (mm/day)
http://dw.iwmi.org/atlas/etpen.exe
Tuesday, September 29, 2009
Remote sensing to assess reservoir sedimentation
Remote sensing to assess reservoir sedimentation
Management of water resources in a country often requires construction of dams across rivers for creating storages and regulating flows. This practice is widely prevalent in the country due to variations of the rainfall distribution with respect to time and space. Water flowing into a dam from surface run-off always carries sediments, which settle down on the bed of the reservoir due to variation of flow velocity. Continued deposition of sediments in course of time leads to reduction in the storage capacity of the dam. In some of the reservoirs constructed in earlier periods it is found from capacity surveys that the design assumption of sedimentation rate was quite low causing reduction in the useful life of reservoir (Rajan,1982)Timely assessment of sedimentation in a reservoir is required to determine the useful life of a reservoir and for downstream planning. The conventional method of reservoir capacity survey is laborious, time consuming and costly in comparison to the capacity survey based on remote sensing techniques. The water-spread area of the reservoir reduces with the sedimentation at a given level and this fact is utilized in estimation of reservoir capacity. In remote sensing based reservoir survey, water-spread area at the time of satellite pass is mapped and used to calculate the incremental reservoir capacity between two levels. The use of this technique has been many attempted by many researchers (Mohanty et al, 1986; Vibulshresth et al., 1988; Manavalan, 1991 ; Goel and Jain, 1996; Bryant et al., 1999; Goel et al., 2002; Jain et al, 2002; Bothale et al, 2003; Durbude et al, 2005).
The results obtained by remote sensing technique have been found to be all most similar to that obtained from conventional method. This technique has the limitation of estimating sedimentation below the dead storage levels, as measurement of water spread area below this level using satellite imagery is not possible
Management of water resources in a country often requires construction of dams across rivers for creating storages and regulating flows. This practice is widely prevalent in the country due to variations of the rainfall distribution with respect to time and space. Water flowing into a dam from surface run-off always carries sediments, which settle down on the bed of the reservoir due to variation of flow velocity. Continued deposition of sediments in course of time leads to reduction in the storage capacity of the dam. In some of the reservoirs constructed in earlier periods it is found from capacity surveys that the design assumption of sedimentation rate was quite low causing reduction in the useful life of reservoir (Rajan,1982)Timely assessment of sedimentation in a reservoir is required to determine the useful life of a reservoir and for downstream planning. The conventional method of reservoir capacity survey is laborious, time consuming and costly in comparison to the capacity survey based on remote sensing techniques. The water-spread area of the reservoir reduces with the sedimentation at a given level and this fact is utilized in estimation of reservoir capacity. In remote sensing based reservoir survey, water-spread area at the time of satellite pass is mapped and used to calculate the incremental reservoir capacity between two levels. The use of this technique has been many attempted by many researchers (Mohanty et al, 1986; Vibulshresth et al., 1988; Manavalan, 1991 ; Goel and Jain, 1996; Bryant et al., 1999; Goel et al., 2002; Jain et al, 2002; Bothale et al, 2003; Durbude et al, 2005).
The results obtained by remote sensing technique have been found to be all most similar to that obtained from conventional method. This technique has the limitation of estimating sedimentation below the dead storage levels, as measurement of water spread area below this level using satellite imagery is not possible
The Computations of reservoir capacity at various Elevations have been made using the following prismoidal formula.
V = h/3 (AI+A2+ Sqrt(Al*A2)) ----------------- eq 01.
Where,
V = Reservoir Capacity between two successive elevations hl and h2.
H = Elevation difference (hl - h2)
AI&A2 = Water spread area at elevation hi& h2.
The difference between the original and revised cumulative capacities represents the loss in capacity of the reservoir.
V = h/3 (AI+A2+ Sqrt(Al*A2)) ----------------- eq 01.
Where,
V = Reservoir Capacity between two successive elevations hl and h2.
H = Elevation difference (hl - h2)
AI&A2 = Water spread area at elevation hi& h2.
The difference between the original and revised cumulative capacities represents the loss in capacity of the reservoir.
Monday, September 28, 2009
Data for world's glaciers free down load
Data for world's glaciers free down load
GLIMS (Global Land Ice Measurements from Space) is a project designed to monitor the world's glaciers primarily using data from optical satellite instruments, such as ASTER (Advanced Space borne Thermal Emission and reflection Radiometer)
http://•www.glims.org/
This web contains GLIMSVIew software and you will be able to see the data. http://astrogeology.usgs.gov/Projects/GLIMSView/
You will be able to search places where you want to download the data. Data are available in various formats. You can down loads as ESRI *.shp format which is so fast. It gives a vector polygon file for the glaciers extents which I found very useful for hydrological modeling works.
For data please visit here : http://glims.colorado.edu/glacierdata/
GLIMS (Global Land Ice Measurements from Space) is a project designed to monitor the world's glaciers primarily using data from optical satellite instruments, such as ASTER (Advanced Space borne Thermal Emission and reflection Radiometer)
http://•www.glims.org/
This web contains GLIMSVIew software and you will be able to see the data. http://astrogeology.usgs.gov/Projects/GLIMSView/
You will be able to search places where you want to download the data. Data are available in various formats. You can down loads as ESRI *.shp format which is so fast. It gives a vector polygon file for the glaciers extents which I found very useful for hydrological modeling works.
For data please visit here : http://glims.colorado.edu/glacierdata/
Remote Sensing and GIS for hydrological modeling
Remote Sensing and GIS for hydrological modeling
Usage of remote sensing and GIS for hydrological modeling is increasing. Remotely sensed data (aerial photography and satellite imagery) provide spatial information about the processes of the land phase of the hydrological cycle. The land cover maps derived by remote sensing are the basis of hydrologic response units for modeling units. For an understanding of the hydrology of areas with little available data, a better insight into the distribution of the physical characteristics of the catchments is provided by image processing techniques. Some of the new measurement methods (photographic systems, active radar systems etc.) could yield assessment of areal distribution or at least to some extent reliable areal totals or averages of hydrologic variable such as precipitation, evapotranspiration and soil moisture.
Original remote sensing imagery can be directly use to identify hydrologically important spatial phenomena and processed remote sensing data can be used as forcings of hydrological models. Further, using multispectral data, such as vegetation (land cover) types and density are most useful to quantify surface parameters. Direct calculation of evapotranspiration distribution in terms of spectral data of satellite remote sensing based on surface energy balance is a new invention in the modeling world. Some remote sensing derived fields, such as soil moisture, use to improve the model simulations through data assimilations. Remotely sensed data can, at least, improve model simulations by Providing more accurate spatially distributed model parameters such as vegetation information over a study region, land cover, LAI, NDVI, Albedo etc and also Helps to identify potential model structure weaknesses through validation, data assimilation, etc.
More to come
Usage of remote sensing and GIS for hydrological modeling is increasing. Remotely sensed data (aerial photography and satellite imagery) provide spatial information about the processes of the land phase of the hydrological cycle. The land cover maps derived by remote sensing are the basis of hydrologic response units for modeling units. For an understanding of the hydrology of areas with little available data, a better insight into the distribution of the physical characteristics of the catchments is provided by image processing techniques. Some of the new measurement methods (photographic systems, active radar systems etc.) could yield assessment of areal distribution or at least to some extent reliable areal totals or averages of hydrologic variable such as precipitation, evapotranspiration and soil moisture.
Original remote sensing imagery can be directly use to identify hydrologically important spatial phenomena and processed remote sensing data can be used as forcings of hydrological models. Further, using multispectral data, such as vegetation (land cover) types and density are most useful to quantify surface parameters. Direct calculation of evapotranspiration distribution in terms of spectral data of satellite remote sensing based on surface energy balance is a new invention in the modeling world. Some remote sensing derived fields, such as soil moisture, use to improve the model simulations through data assimilations. Remotely sensed data can, at least, improve model simulations by Providing more accurate spatially distributed model parameters such as vegetation information over a study region, land cover, LAI, NDVI, Albedo etc and also Helps to identify potential model structure weaknesses through validation, data assimilation, etc.
More to come
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