Your search found 11 records
1 Kulawardhana, Wasantha; Thenkabail, Prasad; Masiyandima, Mutsa; Biradar, Chandrashekhar; Vithanage, Jagath; Finlayson, Max; Gunasinghe, Sarath; Alankara, Ranjith. 2006. Evaluation of different methods for delineation of wetlands in Limpopo River Basin using Landsat ETM + and SRTM data. In Proceedings, GlobWetland: Looking at Wetlands from Space, Frascati, Italy, 19-20 October 2006. 4p.
Wetlands ; Mapping ; Remote sensing / South Africa / Mozambique / Zimbabwe / Botswana / Limpopo River Basin
(Location: IWMI-HQ Call no: IWMI 333.918 G000 REB Record No: H039731)
2 Thenkabail, Prasad; Biradar, Chandrashekhar; Noojipady, Praveen; Islam, Aminul; Vithanage, Jagath; Velpuri, Manohar; Dheeravath, Venkateswarlu; Kulawardhana, Wasantha; Jie, Li Yuan; Gunasinghe, Sarath; Alankara, Ranjith. 2006. International Water Management Institute’s Data Storehouse Pathway (IWMIDSP): A unique data and knowledge gateway of spatial data with emphasis on river basins. In Li, D.; Xia, L. (Eds.). Geoinformatics 2006: GNSS and Integrated Geospatial applications. Proceedings of SPIE Vol.6418, Wuhan, China, 28-29 October 2006. Bellingham, Washington, USA: SPIE – The International Society for Optical Engineering. 6418(64181R):1-6.
(Location: IWMI-HQ Call no: IWMI 621.3678 G000 THE Record No: H039738)
3 Thenkabail, Prasad; Biradar, Chandrashekhar; Noojipady, Praveen; Islam, Aminul; Velpuri, Manohar; Vithanage, Jagath; Kulawardhana, Wasantha; Jie, Li Yuan; Venkateswarlu, Dheeravath; Gunasinghe, Sarath; Alankara, Ranjith. 2006. The spatial data and knowledge gateways at the International Water Management Institute (IWMI) In Li, D.; Xia, L. (Eds.). Geoinformatics 2006: GNSS and Integrated Geospatial applications. Proceedings of SPIE Vol.6421, Wuhan, China, 28-29 October 2006. Bellingham, Washington, USA: SPIE – The International Society for Optical Engineering. 64211Z. 10p.
(Location: IWMI-HQ Call no: IWMI 621.3678 G000 THE Record No: H039739)
4 Kulawardhana, Wasantha; Thenkabail, Prasad; Vithanage, Jagath; Biradar, Chandrashekhar; Islam, Aminul; Gunasinghe, Sarath; Alankara, Ranjith. 2007. Evaluation of the wetland mapping methods using Landsat ETM+ and SRTM data. Journal of Spatial Hydrology, 2. 47p.
Wetlands ; Remote sensing ; Mapping ; River basins / Botswana / Zimbabwe / South Africa / Mozambique / Limpopo River Basin
(Location: IWMI HQ Call no: IWMI 333.918 G178 KUL Record No: H040222)
5 Islam, Aminul; Thenkabail, Prasad S.; Kulawardhana, Wasantha; Alankara, Ranjith; Gunasinghe, Sarath; Edussuriya, C.; Gunawardana, A. 2008. Semi-automated methods for mapping wetlands using Landsat ETM+ and SRTM data. International Journal of Remote Sensing, 29:(24):7077-7106.
(Location: IWMI HQ Call no: IWMI 333.918 G744 ISL Record No: H040452)
The overarching goal of this study was to develop a comprehensive methodology for mapping natural and human-made wetlands using fine resolution Landsat enhanced thematic mapper plus (ETM+), space shuttle radar topographic mission digital elevation model (SRTM DEM) data and secondary data. First, automated methods were investigated in order to rapidly delineate wetlands; this involved using: (a) algorithms on SRTM DEM data, (b) thresholds of SRTM-derived slopes, (c) thresholds of ETM+ spectral indices and wavebands and (d) automated classification techniques using ETM+ data. These algorithms and thresholds using SRTM DEM data either over-estimated or under-estimated stream densities (S d) and stream frequencies (S f), often generating spurious (non-existent) streams and/or, at many times, providing glaring inconsistencies in the precise physical location of the streams. The best of the ETM+-derived indices and wavebands either had low overall mapping accuracies and/or high levels of errors of omissions and/or errors of commissions. Second, given the failure of automated approaches, semi-automated approaches were investigated; this involved the: (a) enhancement of images through ratios to highlight wetlands from non-wetlands, (b) display of enhanced images in red, green, blue (RGB) false colour composites (FCCs) to highlight wetland boundaries, (c) digitizing the enhanced and displayed images to delineate wetlands from non-wetlands and (d) classification of the delineated wetland areas into various wetland classes. The best FCC RGB displays of ETM+ bands for separating wetlands from other land units were: (a) ETM+4/ETM+7, ETM+4/ETM+3, ETM+4/ETM+2, (b) ETM+4, ETM+3, ETM+5 and (c) ETM+3, ETM+2, ETM+1. In addition, the SRTM slope threshold of less than 1% was very useful in delineating higher-order wetland boundaries. The wetlands were delineated using the semi-automated methods with an accuracy of 96% as determined using field-plot data. The methodology was evaluated for the Ruhuna river basin in Sri Lanka, which has a diverse landscape ranging from sea shore to hilly areas, low to very steep slopes (0° to 50°), arid to semi-arid zones and rain fed to irrigated lands. Twenty-four per cent (145 733 ha) of the total basin area was wetlands as a result of a high proportion of human-made irrigated areas, mainly under rice cropping. The wetland classes consisted of irrigated areas, lagoons, mangroves, natural vegetation, permanent marshes, salt pans, lagoons, seasonal wetlands and water bodies. The overall accuracies of wetland classes varied between 87% and 94% (K hat = 0.83 to 0.92) with errors of omission less than 13% and errors of commission less than 1%.
6 Biradar, Chandrashekhar; Thenkabail, Prasad; Islam, Aminul; Anputhas, Markandu; Tharme, Rebecca; Vithanage, Jagath; Alankara, Ranjith; Gunasinghe, Sarath. 2007. Establishing the best spectral bands and timing of imagery for land use-land cover (LULC) class separability using Landsat ETM+ and Terra MODIS data. Canadian Journal of Remote Sensing, 33(5):431-444.
Remote sensing ; Land use ; Land cover mapping ; Irrigated farming ; Irrigation programs / Sri Lanka / Uda Walawe River Basin
(Location: IWMI HQ Call no: IWMI 333.918 G744 BIR Record No: H040453)
7 Biradar, C. M.; Thenkabail, P. S.; Noojipady, P.; Dheeravath, V.; Velpuri, M.; Turral, H.; Cai, Xueliang; Gumma, Murali Krishna; Gangalakunta, O. R. P.; Schull, M. A.; Alankara, Ranjith; Gunasinghe, Sarath; Xiao, X. 2009. Global map of rainfed cropland areas (GMRCA) and statistics using remote sensing. In Thenkabail, P. S.; Lyon, J. G.; Turral, H.; Biradar, C. M. (Eds.). Remote sensing of global croplands for food security. Boca Raton, FL, USA: CRC Press. pp.357-389. (Taylor & Francis Series in Remote Sensing Applications)
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042430)
(1.40 MB)
8 Biradar, Chandrashekhar M.; Thenkabail, Prasad S.; Noojipady, P.; Li, Yuan Jie; Dheeravath, Venkateswarlu; Turral, Hugh; Velpuri, Manohar; Gumma, Murali Krishna; Gangalakunta, O. R. P.; Cai, X. L.; Xiao, X.; Schull, M. A.; Alankara, Ranjith; Gunasinghe, Sarath; Mohideen, Sadir. 2009. A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing. International Journal of Applied Earth Observation and Geoinformation, 11(2):114-129. [doi: https://doi.org/10.1016/j.jag.2008.11.002]
(Location: IWMI HQ Call no: e-copy only Record No: H042769)
The overarching goal of this study was to produce a global map of rainfed cropland areas (GMRCA) and calculate country-by-country rainfed area statistics using remote sensing data. A suite of spatial datasets, methods and protocols for mapping GMRCA were described. These consist of: (a) data fusion and composition of multi-resolution time-series mega-file data-cube (MFDC), (b) image segmentation based on precipitation, temperature, and elevation zones, (c) spectral correlation similarity (SCS), (d) protocols for class identification and labeling through uses of SCS R2-values, bi-spectral plots, space-time spiral curves (ST-SCs), rich source of field-plot data, and zoom-in-views of Google Earth (GE), and (e) techniques for resolving mixed classes by decision tree algorithms, and spatial modeling. The outcome was a 9-class GMRCA from which country-by-country rainfed area statistics were computed for the end of the last millennium. The global rainfed cropland area estimate from the GMRCA 9-class map was 1.13 billion hectares (Bha). The total global cropland areas (rainfed plus irrigated) was 1.53 Bha which was close to national statistics compiled by FAOSTAT (1.51 Bha). The accuracies and errors of GMRCA were assessed using field-plot and Google Earth data points. The accuracy varied between 92 and 98% with kappa value of about 0.76, errors of omission of 2–8%, and the errors of commission of 19–36%.
9 Matin, Mir Abdul; Smakhtin, Vladimir; Palliyaguruge, Mahendra N.; Mohideen, Sadir; Yapa, Nishath; Alankara, Ranjith; Gunasinghe, Sarath; Jayakody, Priyantha. 2010. Development of a water resources assessment and audit framework for Sri Lanka. In Jinapala, K.; De Silva, Sanjiv; Aheeyar, M. M. M. (Eds.). Proceedings of the National Conference on Water, Food Security and Climate Change in Sri Lanka, BMICH, Colombo, Sri Lanka, 9-11 June 2009. Vol. 3. Policies, institutions and data needs for water management. Colombo, Sri Lanka: International Water Management Institute (IWMI). pp.95-111.
Water resources ; Water availability ; Assessment ; Databases ; Decision support tools ; Water scarcity ; Water demand ; Water use ; Disasters ; Climate change / Sri Lanka
(Location: IWMI HQ Call no: IWMI 631.7 G744 JIN Record No: H042808)
(0.36 MB)
The demand and use of water resources is permanently increasing, while the quality of water is dropping and the availability of water in the context of climate change is becoming uncertain. To meet these growing problems it is necessary to carefully assess the existing water stocks and future trends in a country. The accuracy of such an assessment highly depends on the quality of data and information used. In other words – we cannot manage what we do not measure. In most developing countries, the lack of readily accessible and quality controlled data is the major obstacle for scientifically-based assessments on water resources, water development planning and evaluating the status and trends of water resources. Sri Lanka too faces similar obstacles. Recently IWMI initiated the development of a prototype system for managing national water resources data and information, which can be accessed online by various users and interested stakeholders. The data and information in the system is being organized in modules to provide user-friendly access. The ‘overview’ module includes information on topography, soil, land use, land cover, river network and settlement patterns. The ‘water availability’ module contains data on various components of the hydrological cycle, including rainfall, runoff, evaporation, ground- water, river basin characteristics, per capita water availability and trends, and water scarcity. The ‘Demand and use’ module focuses on the factors that affect demand, such as population growth, sectoral demand, irrigation requirements and withdrawals. The ‘water quality’ module provides information on salinity, water quality constituents and Water related diseases. The ‘governance and management’ module contains information on institutions, legislation and finances in the Sri Lankan water sector. The ‘disaster and risk’ module focuses on the characteristics of floods, land slides, tsunami etc. Finally, the ‘climate change’ module covers the impacts of climate change on rainfall, salinity and sea level rise to guide adaptation planning. The system is designed with a view to facilitate assessments of water resources at various administrative (e.g., province, district) and hydrological (e.g., river basin) units. The map-based interface ensures quick access to available data and allows the date to be downloaded and displayed. The system is currently a ‘work in progress’ and only an illustration of what can be achieved. It is envisaged that by cooperating with national agencies, the system will be enhanced into a unified platform for maintaining and sharing data by various participating agencies and will be used to conduct a systematic assessment of water resources in Sri Lanka. By developing a comprehensive and national water audit, Sri Lanka may provide as an example to other developing countries too.
10 Eriyagama, Nishadi; Chemin, Yann; Amarasinghe, Upali; Alankara, Ranjith; Hoanh, Chu Thai. 2011. Estimation of consumptive water use and vulnerability mapping of coffee: a global analysis. Project report submitted to Nestle Ltd. under the project “Global Consumptive Water Use of Coffee”. Colombo, Sri Lanka: International Water Management Institute (IWMI). 43p.
Water use ; Beverages ; Coffee ; Species ; Research projects ; Mapping ; Water stress ; Water scarcity ; Water footprint ; Rainfed farming ; Irrigated farming
(Location: IWMI HQ Call no: e-copy only Record No: H044551)
(2.18 MB)
11 Eriyagama, Nishadi; Chemin, Yann; Alankara, Ranjith. 2014. A methodology for quantifying global consumptive water use of coffee for sustainable production under conditions of climate change. Journal of Water and Climate Change, 5(2):128-150.
Climate change ; Coffee industry ; Water use ; Crop yield ; Water scarcity ; Water stress ; Water management ; Irrigation
(Location: IWMI HQ Call no: e-copy only Record No: H046249)
(1.30 MB) (1 MB)
Coffee is the second most traded commodity in the world after oil. A sustainable coffee industry is crucial to maintaining global agriculture, trade, human and environmental well-being, and livelihoods. With increasing water scarcity and a changing climate, understanding and quantifying the risks associated with water, a primary input in coffee production, is vital. This methodological paper examines the means of quantifying: (a) ‘current’ consumptive water use (CWU) of green coffee (coffee beans at harvest time) globally; (b) coffee ‘hot spots’ and ‘bright spots’ with respect to levels of CWU, yields and water stress; and (c) possible impacts of climate change on the CWU of coffee. The methodology employs satellite-derived monthly evapotranspiration data, and climate projections from two global circulation models for three future scenarios. Initial estimates suggest that currently (on average) 18.9 m3/kg of water is consumed in producing one unit of green coffee. The same estimate for irrigated coffee is 8.6 m3/kg, while that for rain fed coffee is 19.6 m3/kg. Climate scenarios show that effective mean annual rainfall in many major coffee areas may decrease by the 2050s. The generic methodology presented here may be applied to other crops, too, if crop data are available.
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