Your search found 8 records
1 de Silva, R. P. (Ed.) 2004. Global positioning systems: theory, application and practice: proceedings of the GPS day. Peradeniya, Sri Lanka: Geo-Informatics Society of Sri Lanka. 60p.
(Location: IWMI HQ Call no: 526.0285 G744 DES Record No: H040890)
2 Xiao, B.; Wan, F.; Wu, C.; Zhang, K. 2006. River cross-section surveying using RTK technology: the Yangtze River Project case study [China]. Coordinates, 11(12):12-17.
Rivers ; Case studies ; Technology ; Global positioning systems ; Surveys ; Flooding ; Water levels / China / Yangtze River
(Location: IWMI HQ Call no: e-copy only Record No: H044777)
http://mycoordinates.org/river-cross-section-surveying-using-rtk-technology/all/1/
https://vlibrary.iwmi.org/pdf/H044777.pdf
https://vlibrary.iwmi.org/pdf/H044777.pdf
(1.03 MB)
3 Centre for Space Science and Technology Education in Asia and the Pacific (CSSTEAP). 2012. International Training Course: Application of Space Technology for Disaster Risk Reduction. Lecture notes. Dehradun, India: Centre for Space Science and Technology Education in Asia and the Pacific (CSSTEAP). 432p.
Natural disasters ; Risk management ; Socioeconomic development ; Remote sensing ; Mapping ; Meteorology ; GIS ; Satellite surveys ; Forecasting ; Image processing ; Data analysis ; Global positioning systems ; Flooding ; Drought ; Monitoring ; Earthquakes ; Landslides ; Tsunamis ; Early warning systems ; Weather forecasting ; Hydrometeorology ; Space ; Technology
(Location: IWMI HQ Call no: 363.34 G000 CEN Record No: H044954)
(0.41 MB)
4 Sahlu, D.; Pfeifer, Catherine; Abebe, Yenenesh; Omolo, A. 2011. Geographic information system: practical training manual for agricultural research centers. Bahir Dar, Ethiopia: Amhara Regional Agricultural Research Institute (ARARI); Addis Ababa, Ethiopia: International Water Management Institute (IWMI); Nairobi, Kenya: International Livestock Research Institute (ILRI) 98p.
GIS ; Information systems ; Training ; Agricultural research ; Data ; Analytical methods ; Global positioning systems
(Location: IWMI HQ Call no: e-copy only Record No: H045181)
(4.17 MB)
5 Wernecke, J. 2009. The KML handbook: geographic visualization for the web. Boston, MA, USA: Addison-Wesley. 339p.
(Location: IWMI HQ Call no: 551.49 G000 WER Record No: H045416)
(0.47 MB)
6 Gangodagamage, C.; Biradar, C. M.; Islam, A.; Thenkabail, P. S. 2004. Shuttle Radar Topography Mission (SRTM) data for Sri Lanka: potential contributions in river basin research. In De Silva, R. P. (Ed.). Geo-informatics research and applications: proceedings of the First Symposium on Geo-informatics, Peradeniya, Sri Lanka, 30 July 2004. Peradeniya, Sri Lanka: Geo-Informatics Society of Sri Lanka (GISSL). pp.19-30.
River basins ; Catchment areas ; Radar ; Models ; Global Positioning Systems) ; Hydrology / Sri Lanka
(Location: IWMI HQ Call no: 621.3678 G000 DES Record No: H045954)
(1.11 MB)
7 Roy, P. S.; Behera, M. D.; Murthy, M. S. R.; Roy, A.; Singh, S.; Kushwaha, S. P. S.; Jha, C. S.; Sudhakar, S.; Joshi, P. K.; Reddy, S.; Gupta, S.; Pujar, G.; Dutt, C. B. S.; Srivastava, V. K.; Porwal, M. C.; Tripathi, P.; Singh, J. S.; Chitale, V.; Skidmore, A. K.; Rajshekhar, G.; Kushwaha, D.; Karnatak, H.; Saran, S.; Amarnath, Giriraj; Padalia, H.; Kale, M.; Nandy, S.; Jeganathan, C.; Singh, C. P.; Biradar, C. M.; Pattanaik, C.; Singh, D. K.; Devagiri, G. M.; Talukdar, G.; Panigrahy, R. K.; Singh, H.; Sharma, J. R.; Haridasan, K.; Trivedi, S.; Singh, K. P.; Kannan, L.; Daniel, M.; Misra, M. K.; Niphadkar, M.; Nagabhatla, N.; Prasad, N.; Tripathi, O. P.; Prasad, P. R. C.; Dash, P.; Qureshi, Q.; Tripathi, S. K.; Ramesh, B. R.; Gowda, B.; Tomar, S.; Romshoo, S.; Giriraj, S.; Ravan, S. A.; Behera, S. K.; Paul, S.; Das, A. K.; Ranganath, B. K.; Singh, T. P.; Sahu, T. R.; Shankar, U.; Menon, A. R. R.; Srivastava, G.; Sharma, N. S.; Mohapatra, U. B.; Peddi, A.; Rashid, H.; Salroo, I.; Krishna, P. H.; Hajra, P. K.; Vergheese, A. O.; Matin, S.; Chaudhary, S. A.; Ghosh, S.; Lakshmi, U.; Rawat, D.; Ambastha, K.; Malik, A. H.; Devi, B. S. S.; Gowda, B.; Sharma, K. C.; Mukharjee, P.; Sharma, A.; Davidar, P.; Raju, R. R. V.; Katewa, S. S.; Kant, S.; Raju, V. S.; Uniyal, B. P.; Debnath, B.; Rout, D. K.; Thapa, R.; Joseph, S.; Chhetri, P.; Ramachandran, R. M. 2015. New vegetation type map of India prepared using satellite remote sensing: comparison with global vegetation maps and utilities. International Journal of Applied Earth Observation and Geoinformation, 39:142-159. [doi: https://doi.org/10.1016/j.jag.2015.03.003]
Satellite imagery ; Remote sensing ; Vegetation ; Climate change ; Temperature ; Precipitation ; Scrublands ; Grasslands ; Ecology ; Global positioning systems ; Land cover ; Assessment ; Cultivation / India
(Location: IWMI HQ Call no: e-copy only Record No: H047008)
(2.48 MB)
A seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. Weused 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover, Holdridge’s life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal (http://bis.iirs.gov.in).
8 Sarkar, T.; Karunakalage, Anuradha; Kannaujiya, S.; Chaganti, C. 2022. Quantification of groundwater storage variation in Himalayan & Peninsular river basins correlating with land deformation effects observed at different Indian cities. Contributions to Geophysics and Geodesy, 52(1):1-56. [doi: https://doi.org/10.31577/congeo.2022.52.1.1]
Groundwater ; Water storage ; River basins ; Observation ; Towns ; Global positioning systems ; SAR (radar) ; Precipitation ; Drought ; Rain ; Aquifers ; Time series analysis ; Models / India
(Location: IWMI HQ Call no: e-copy only Record No: H051083)
(16.80 MB) (16.8 MB)
Groundwater is a significant resource that supports almost one-fifth population globally, but has been is diminishing at an alarming rate in recent years. To delve into this objective more thoroughly, we calculated interannual (2002–2020) GWS (per grid) distribution using GRACE & GRACE-FO (CSR-M, JPL-M and SH) Level 3 RL06 datasets in seven Indian river basins and found comparatively higher negative trends (-20.10 ± 1.81 to -8.60 ± 1.52 mm/yr) in Basin 1–4 than in Basin 5–7 (-7.11 ± 0.64 to -0.76 ± 0.47 mm/yr). After comparing the Groundwater Storage (GWS) results with the CHIRPS (Climate Hazards Group Infrared Precipitation with Stations) derived SPI (Standardized Precipitation Index) drought index, we found that GWS exhausts analogously in the same period (2005–2020) when SPI values show improvement (~ 1.89–2), indicating towards wet condition. Subsequently, the GWSA time series is decomposed using the STL (Seasonal Trend Decomposition) (LOESS Regression) approach to monitor long-term groundwater fluctuation. The long term GWS rate (mm/yr) derived from three GRACE & GRACE-FO solutions vary from -20.3 ± 5.52 to -13.19 ± 3.28 and the GWS mass rate (km3 /yr) lie in range of -15.17 ± 4.18 to -1.67 ± 0.49 for basins 1–3. Simultaneously, in basin 4–7 the GWS rate observed is -8.56 ± 8.03 to -0.58 ± 7.04 mm/yr, and the GWS mass rate differs by -1.71 ± 0.64 to -0.26 ± 3.19 km3 /yr. The deseasonalized GWS estimation (2002–2020) states that Himalayan River basins 1,2,3 exhibit high GWS mass loss (-260 to -35.12 km3 ), with Basin 2 being the highest (-260 km3 ). Whereas the Peninsular River basin 4,6,7 gives moderate mass loss value from -26.72 to -23.58 km3 . And in River basin 5, the GWS mass loss observed is the lowest, with a value of -8 km3 . Accordingly, GPS (Global Positioning System) and SAR (Synthetic Aperture Radar) data are considered to examine the land deformation as an effect due to GWS mass loss. The GPS data acquired from two IGS stations, IISC Bengaluru and LCK3 Lucknow, negatively correlates with GWS change, and the values are ~ -0.90 to ~-0.21 and ~-0.7 to -0.4, respectively. Consequently, correlation between GWS mass rate (km3 /yr) and the SAR (Sentinel-1A, SBAS) data procured from Chandigarh, Delhi, Mehsana, Lucknow, Kolkata and Bengaluru shows ~ 72 – 48% positively correlated area (PCA). The vertical velocity ranges within ~ -94 to -25 mm/yr estimated from PCA. There is an increase in population (estimated 2008–2014) in Basin 1 & 2. Likewise, the correlation coefficient ( ) between GWS change and the irrigational area is positive in all seven basins indicating significant depletion in GWS due to an uncalled hike in population or irrigational land use. Similarly, the positive linear regression (R 2 ) in Basins 1–3 also indicates high depletion in GWS. But basins 4–7 observe negative linear regression even after increasing population, which implies a control on the irrigational land use, unable to determine the GWS change at local scale and heterogeneous aquifer distribution. Therefore, if such unsystematic groundwater storage variation is not controlled on time, then very soon in the future, India might reach a deadlock state of water shortage.
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