Your search found 29 records
1 Thenkabail, P. S.; Nolte, C. 1995. Regional characterization of Inland Valley agroecosystems in Save, Bante, Bassila, and Parakou regions in South-Central Republic of Benin through integration of remote sensing, global positioning system, and ground-truth data in a geographic information systems framework. Ibadan, Nigeria: IITA. 46p. (Inland valley characterization report 1)
GIS ; Remote sensing ; Mapping ; Land use ; Soil management / Benin / Save / Bante / Bassila / Parakou
(Location: IWMI-HQ Call no: 006 G196 THE Record No: H018071)
2 Thenkabail, P. S.. 2002. Biophysical and yield information for precision farming from near-real-time and historical Landsat TM images. International Journal of Remote Sensing, 24(14):839-877.
(Location: IWMI-HQ Call no: P 6619 Record No: H033319)
The main goal of this study was to quantify within and between field variability in mapping agricultural crop types, their biophysical characteristics, and yield for precision-farming applications using near-real-time and historical
(archival) Landsat Thematic Mapper (TM) images. Data for six crops (wheat, barley, chickpea, lentil, vetch and cumin) were gathered from a representative benchmark study area in the semi-arid environment of the world. Spectrobiophysical and yield models were established for each crop using a near-realtime TM image of 6 April 1998 acquired to coincide with an extensive ground data collection campaign. The models developed using this near-real-time acquisition were then used to extrapolate and quantify characteristics in the historical Landsat TM images of 5 April 1986 and 4 May 1988 acquired for the same area with limited ground data, thus adding scientific and commercial value to archival TM images. A farm-by-farm (or pixel-by-pixel) within and between field variability
in agricultural land cover, biophysical quantities [e.g. biomass and Leaf Area Index (LAI)] and yield was established and illustrated. For the near-realtime image of 1998: (a) quantitative biophysical characteristics such as LAI and biomass were mapped at 81% overall accuracy (Khat=0.76) or higher; (b) within field variability (commission errors) was mapped with an accuracy between
74–100%; and (c) between field variability (omission errors) was mapped with an accuracy between 76–100%. Temporal variability in biomass and LAI were mapped for the study area and highlighted for individual farms. Significant relationships existed between grain yields measured using field-based combinemounted sensors and Landsat TM derived indices. The results demonstrate the ability of using near-real-time and historical Landsat TM images for obtaining quantitative biophysical and yield information that highlight within and between field variability, which is of critical importance in precision-farming applications.
(archival) Landsat Thematic Mapper (TM) images. Data for six crops (wheat, barley, chickpea, lentil, vetch and cumin) were gathered from a representative benchmark study area in the semi-arid environment of the world. Spectrobiophysical and yield models were established for each crop using a near-realtime TM image of 6 April 1998 acquired to coincide with an extensive ground data collection campaign. The models developed using this near-real-time acquisition were then used to extrapolate and quantify characteristics in the historical Landsat TM images of 5 April 1986 and 4 May 1988 acquired for the same area with limited ground data, thus adding scientific and commercial value to archival TM images. A farm-by-farm (or pixel-by-pixel) within and between field variability
in agricultural land cover, biophysical quantities [e.g. biomass and Leaf Area Index (LAI)] and yield was established and illustrated. For the near-realtime image of 1998: (a) quantitative biophysical characteristics such as LAI and biomass were mapped at 81% overall accuracy (Khat=0.76) or higher; (b) within field variability (commission errors) was mapped with an accuracy between
74–100%; and (c) between field variability (omission errors) was mapped with an accuracy between 76–100%. Temporal variability in biomass and LAI were mapped for the study area and highlighted for individual farms. Significant relationships existed between grain yields measured using field-based combinemounted sensors and Landsat TM derived indices. The results demonstrate the ability of using near-real-time and historical Landsat TM images for obtaining quantitative biophysical and yield information that highlight within and between field variability, which is of critical importance in precision-farming applications.
3 Thenkabail, P. S.; Hall, J.; Lin, T.; Ashton, M. S.; Harris, D.; Enclona, E. A. 2003. Detecting floristic structure and pattern across topographic and moisture gradients in a mixed species Central African forest using IKONOS and Landsat - 7 ETM + images. International Journal of Applied Earth Observation, 4:255-270.
(Location: IWMI-HQ Call no: P 6620 Record No: H033320)
4 Thenkabail, P. S.. 2003. Inter-sensor relationships between IKONOS and Landsat – 7 ETM + NDVI data in three ecoregions of Africa. International Journal of Remote Sensing, 25(2):389-408.
(Location: IWMI-HQ Call no: P 6621 Record No: H033321)
The goal of this research was to establish inter-sensor relationships between IKONOS and Landsat-7 ETMz data. Dry and wet season images were acquired on the same date or about the same date from IKONOS and ETMz sensors to enable direct comparison between the two distinctly different data types. The images were from three distinct ecoregions located in African rainforests and savannas that encompass a wide range of land use/land cover classes and ecological units. The IKONOS NDVI had a high degree of correlation with ETMz NDVI with R2 values between 0.67 and 0.72. Intersensor model equations relating IKONOS NDVI with ETMz NDVI were
determined. The characteristics that contribute to the increased sensitivity in dynamic ranges of IKONOS NDVI relative to ETMz NDVI were attributed to: (1) radiometric resolution that adds more bits per data point (11-bit IKONOS data as opposed to 8-bit ETMz); and (2) spatial resolution that helped in resolving spectral characteristics at micro landscape units. Spectral bandwidths of the two sensors had no effect on the dynamic ranges of NDVIs. Overall, the IKONOS data showed greater sensitivity to landscape units and ecological characteristics when compared with Landsat-7 ETMz data. Across ecoregions and land use/land cover classes, the IKONOS NDVI dynamic range (20.07 to 0.71) was considerably greater than the ETMz NDVI dynamic range (20.24 to 0.46). IKONOS data explained greater variability (R2~0.73) in agroforest biomass when compared with ETMz data (R2~0.66). The inter-sensor relationships presented in this paper are expected to facilitate better understanding and
proper interpretation of terrestrial characteristics studied using multiple sensors over time periods.
determined. The characteristics that contribute to the increased sensitivity in dynamic ranges of IKONOS NDVI relative to ETMz NDVI were attributed to: (1) radiometric resolution that adds more bits per data point (11-bit IKONOS data as opposed to 8-bit ETMz); and (2) spatial resolution that helped in resolving spectral characteristics at micro landscape units. Spectral bandwidths of the two sensors had no effect on the dynamic ranges of NDVIs. Overall, the IKONOS data showed greater sensitivity to landscape units and ecological characteristics when compared with Landsat-7 ETMz data. Across ecoregions and land use/land cover classes, the IKONOS NDVI dynamic range (20.07 to 0.71) was considerably greater than the ETMz NDVI dynamic range (20.24 to 0.46). IKONOS data explained greater variability (R2~0.73) in agroforest biomass when compared with ETMz data (R2~0.66). The inter-sensor relationships presented in this paper are expected to facilitate better understanding and
proper interpretation of terrestrial characteristics studied using multiple sensors over time periods.
5 Enclona, E. A.; Thenkabail, P. S.; Celis, D.; Diekmann, J. 2003. Within-field wheat yield prediction from IKONOS data: a new matrix approach. International Journal of Remote Sensing, 25(2):377-388.
(Location: IWMI-HQ Call no: P 6622 Record No: H033322)
This study demonstrates a unique matrix approach to determine within-field variability in wheat yields using fine spatial resolution 4m IKONOS data. The matrix approach involves solving a system of simultaneous equations based on IKONOS data and post-harvest yields available at entire field scale.This approach was compared with a regression-based modelling approach involving field-sensor measured yields and the corresponding IKONOS
measured indices and wavebands. The IKONOS data explained 74–78% variability in wheat yield. This is a significant result since the finer spatial resolution leads to capturing greater spatial variability and detail in landscape
relative to coarser spatial resolution data. A pixel-by-pixel mapping of wheat yield variability highlights the fine spatial detail provided by IKONOS data for precision farming applications.
measured indices and wavebands. The IKONOS data explained 74–78% variability in wheat yield. This is a significant result since the finer spatial resolution leads to capturing greater spatial variability and detail in landscape
relative to coarser spatial resolution data. A pixel-by-pixel mapping of wheat yield variability highlights the fine spatial detail provided by IKONOS data for precision farming applications.
6 Gumma, Murali Krishna; Thenkabail, P. S.; Velpuri, N. M. 2009. Vegetation phenology to partition groundwater- from surface water-irrigated areas using MODIS 250-m time-series data for the Krishna River basin. In Bloschl, G.; van de Giesen, N.; Muralidharan, D.; Ren, L.; Seyler, F.; Sharma, U.; Vrba, J. (Eds.). Improving integrated surface and groundwater resources management in a vulnerable and changing world: proceedings of Symposium JS.3 at the Joint Convention of the International Association of Hydrological Sciences (IAHS) and the International Association of Hydrogeologists (IAH), Hyderabad, India, 6-12 September 2009. Wallingford, UK: International Association of Hydrological Sciences (IAHS) pp.271-281. (IAHS Publication 330)
Vegetation ; Phenology ; River basins ; Vegetation ; Maps ; Land cover ; Land use ; Groundwater irrigation ; Surface irrigation ; Canals ; Reservoirs ; Irrigated land ; Time series analysis ; Remote sensing / India / Krishna River basin
(Location: IWMI HQ Call no: e-copy only Record No: H042217)
(1.15 MB)
This paper describes a remote sensing based vegetation-phenology approach to accurately separate out and quantify groundwater irrigated areas from surface-water irrigated areas in the Krishna River basin (265 752 km2), India, using MODIS 250-m every 8-day near continuous time series for 2000–2001. Temporal variations in the Normalized Difference Vegetation Index (NDVI) pattern, depicting phenology, obtained for the irrigated classes enabled demarcation between: (a) irrigated surface-water double crop, (b) irrigated surface-water continuous crop, and (c) irrigated groundwater mixed crops. The NDVI patterns were found to be more consistent in areas irrigated with groundwater due to the continuity of water supply. Surface water availability, however, was dependent on canal water release that affected time of crop sowing and growth stages, which was in turn reflected in the NDVI pattern. Double-cropped (IDBL) and light irrigation (IL) have relatively late onset of greenness, because they use canal water from reservoirs that drain large catchments and take weeks to fill. Minor irrigation and groundwater-irrigated areas have early onset of greenness because they drain smaller catchments where aquifers and reservoirs fill more quickly. Vegetation phonologies of nine distinct classes consisting of irrigated, rainfed, and other land-use classes were derived using MODIS 250-m near continuous time-series data that were tested and verified using groundtruth data, Google Earth very high resolution (sub-metre to 4 m) imagery, and state-level census data. Fuzzy classification accuracies for most classes were around 80% with class mixing mainly between various irrigated classes. The areas estimated from MODIS were highly correlated with census data (R-squared value of 0.86).
7 Gumma, Murali Krishna; Thenkabail, P. S.; Fujii, Hideto; Namara, Regassa. 2009. Spatial models for selecting the most suitable areas of rice cultivation in the Inland Valley Wetlands of Ghana using remote sensing and geographic information systems. Journal of Applied Remote Sensing, 3(1):21p. [doi: https://doi.org/10.1117/1.3182847]
Remote sensing ; Models ; Wetlands ; Rice ; Cultivation / Africa / West Africa / Ghana / Africa South of Sahara
(Location: IWMI HQ Call no: e-copy only Record No: H042218)
http://remotesensing.spiedigitallibrary.org/data/Journals/APPRES/20338/033537_1.pdf
https://vlibrary.iwmi.org/pdf/H042218.pdf
https://vlibrary.iwmi.org/pdf/H042218.pdf
(4.64 MB)
The overarching goal of this research was to develop spatial models and demonstrate their use in selecting the most suitable areas for the inland valley (IV) wetland rice cultivation. The process involved comprehensive sets of methods and protocols involving: (1) Identification and development of necessary spatial data layers; (2) Providing weightages to these spatial data layers based on expert knowledge, (3) Development of spatial models, and (4) Running spatial models for determining most suitable areas for rice cultivation. The study was conducted in Ghana. The model results, based on weightages to 16-22 spatial data layers, showed only 3-4 % of the total IV wetland areas were “highly suitable” but 39-47 % of the total IV wetland areas were “suitable” for rice cultivation. The outputs were verified using field-plot data which showed accuracy between 84.4 to 87.5% with errors of omissions and commissions less than 23%. Given that only a small fraction (<15% overall) of the total IV wetland areas (about 20-28% of total geographic area in Ghana) are currently utilized for agriculture and constitute very rich land-units in terms of soil depth, soil fertility, and water availability, these agroecosystems offer an excellent opportunity for a green and a blue revolution in Africa.
8 Cai, Xueliang; Thenkabail, P. S.; Biradar, C. M.; Platonov, Alexander; Gumma, Murali Krishna; Dheeravath, V.; Cohen, Y.; Goldlshleger, F.; Ben-Dor, E.; Alchanatis, V.; Vithanage, Jagath; Anputhas, Markandu. 2009. Water productivity mapping using remote sensing data of various resolutions to support more crop per drop. Journal of Applied Remote Sensing, 3(033557). 23p. [doi: https://doi.org/10.1117/1.3257643]
Water productivity ; Crops ; Water use ; Evapotranspiration ; Mapping ; Remote sensing ; Models / Central Asia / Kyrgyzstan / Tajikistan / Uzbekistan / Kazakhstan / Syr Darya River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H042408)
(4.07 MB)
The overarching goal of this research was to map crop water productivity using satellite sensor data at various spectral, spatial, radiometric, and temporal resolutions involving: (a) Moderate Resolution Imaging Spectroradiometer (MODIS) 500m, (b) MODIS 250m, (c) Landsat enhanced thematic mapper plus (ETM+) 60m thermal, (d) Indian Remote Sensing Satellite (IRS) 23.5 m, and (e) Quickbird 2.44 m data. The spectro-biophysical models were developed using IRS and Quickbird satellite data for wet biomass, dry biomass, leaf area index, and grain yield for 5 crops: (a) cotton, (b) maize, (c) winter wheat, (d) rice, and (e) alfalfa in the Sry Darya basin, Central Asia. Crop-specific productivity maps were developed by applying the best spectro-biophysical models for the respective delineated crop types. Water use maps were produced using simplified surface energy balance (SSEB) model by multiplying evaporative fraction derived from Landsat ETM+ thermal data by potential ET. The water productivity (WP) maps were then derived by dividing the crop productivity maps by water use maps. The results of cotton crop, an overwhelmingly predominant crop in Central Asian Study area, showed that about 55% area had low WP of < 0.3 kg/m3, 34% had moderate WP of 0.3-0.4 kg/m3, and only 11% area had high WP > 0.4 kg/m3. The trends were similar for other crops. These results indicated that there is highly significant scope to increase WP (to grow "more crop per drop") through better water and cropland management practices in the low WP areas, which will substantially enhance food security of the ballooning populations without having to increase: (a) cropland areas, and\or (b) irrigation water allocations.
9 Thenkabail, P. S.; Biradar, C. M.; Noojipady, P.; Dheeravath, V.; Li, Yuan Jie; Velpuri, N. M.; Gumma, Murali Krishna; Gangalakunta, O. R. P.; Turral, H.; Cai, Xueliang; Vithanage, Jagath; Schull, M. A.; Dutta, R. 2009. Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium. International Journal of Remote Sensing, 30(14):3679-3733. [doi: https://doi.org/10.1080/01431160802698919]
(Location: IWMI HQ Call no: e-copy only Record No: H042409)
(18.23 MB)
A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3-band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time-series for 1997–1999, (b) Syste`me pour l’Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50km monthly time series for 1961–2000, (d) Global 30 Arc-Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite-1 Synthetic Aperture Radar (JERS-1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992–1993. A single mega-file data-cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re-sampling different data types into a common 1 km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments. Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space–time spiral curve (ST-SC) plots, (b) brightness–greenness–wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very-high-resolution imagery (VHRI) ‘zoom-in views’ in over 11 000 locations, (e) groundtruth data broadly sourced from the degree confluence project (3 864 sample locations) and from the GIAM project (1 790 sample locations), (f) high-resolution Landsat-ETM+ Geocover 150m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re-classify the MDFC, and the class identification and labelling protocol repeated. The sub-pixel area (SPA) calculations were performed by multiplying full-pixel areas (FPAs) with irrigated area fractions (IAFs) for every class. A 28 class GIAMwas produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year-round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but ‘equipped for irrigation’ at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 467million hectares (Mha), which is sum of the non-overlapping areas of: (a) 252Mha from season one, (b) 174Mha from season two and (c) 41Mha from continuous yearround crops. The TAAI at the end of the last millennium was 399 Mha. The distribution of irrigated areas is highly skewed amongst continents and countries. Asia accounts for 79% (370 Mha) of all AIAs, followed by Europe (7%) and North America (7%). Three continents, South America (4%), Africa (2%) and Australia (1%), have a very low proportion of the global irrigation. The GIAM had an accuracy of 79–91%, with errors of omission not exceeding 21%, and the errors of commission not exceeding 23%. The GIAM statistics were also compared with: (a) the United Nations Food and Agricultural Organization (FAO) and University of Frankfurt (UF) derived irrigated areas and (b) national census data for India. The relationships and causes of differences are discussed in detail. The GIAM products are made available through a web portal (http://www.iwmigiam.org).
10 Thenkabail, P. S.; Lyon, J. G.; Turral, H.; Biradar, C. M. (Eds.) 2009. Remote sensing of global croplands for food security. Boca Raton, FL, USA: CRC Press. 476p. (Taylor & Francis Series in Remote Sensing Applications)
Remote sensing ; Mapping ; Irrigated land ; Irrigated farming ; Rainfed farming ; Evapotranspiration ; Cropping systems ; Food security / Asia / Central Asia / China / India / Pakistan / USA / UK / Africa / Middle East
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042416)
(2.65 MB)
Provides a comprehensive knowledge base in the use of satellite sensor-based maps and statisics for irrigated and rainfed croplands and available water that will ensure food security.
11 Turral, H.; Thenkabail, P. S.; Lyon, J. G.; Biradar, C. M. 2009. Context, needed: the need and scope for mapping global irrigated and rain-fed areas. 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.3-11. (Taylor & Francis Series in Remote Sensing Applications)
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042417)
12 Li, Y. J.; Thenkabail, P. S.; Biradar, C. M.; Noojipady, P.; Dheeravath, V.; Velpuri, M.; Gangalakunta, O. R. P.; Cai, Xueliang. 2009. A history of irrigated areas of the world. 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.13-37. (Taylor & Francis Series in Remote Sensing Applications)
Irrigated land ; History ; Irrigation programs ; Statistics / China / India / Egypt / Peru / Indus River Basin / Tigris River Basin / Euphrates River Basin
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042418)
(1.04 MB)
13 Thenkabail, P. S.; Biradar, C. M.; Noojipady, P.; Dheeravath, V.; Gumma, Murali Krishna; Li, Y. J.; Velpuri, M.; Gangalakunta, O. R. P. 2009. Global irrigated area maps (GIAM) 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.41-117. (Taylor & Francis Series in Remote Sensing Applications)
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042419)
(2.98 MB)
14 Gangalakunta, O. R. P.; Dheeravath, V.; Thenkabail, P. S.; Chandrakantha, G.; Biradar, C. M.; Noojipady, P.; Velpuri, M.; Kumar, M. A. 2009. Irrigated areas of India derived from satellite sensors and national statistics: a way forward from GIAM experience. 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.139-176. (Taylor & Francis Series in Remote Sensing Applications)
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042421)
15 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)
16 Thenkabail, P. S.; Lyon, J. G.; Turral, H.; Biradar, C. M. (Eds.) 2009. Remote sensing of global croplands for food security. Boca Raton, FL, USA: CRC Press. 476p. (Taylor & Francis Series in Remote Sensing Applications)
Remote sensing ; Mapping ; Irrigated land ; Irrigated farming ; Rainfed farming ; Evapotranspiration ; Cropping systems ; Food security / Asia / Central Asia / China / India / Pakistan / USA / UK / Africa / Middle East
(Location: IWMI HQ Call no: 631.7.1 G000 THE c2 Record No: H042543)
Provides a comprehensive knowledge base in the use of satellite sensor-based maps and statisics for irrigated and rainfed croplands and available water that will ensure food security.
17 Cai, Xueliang; Thenkabail, P. S.. 2010. Using remote sensing to assess crop water productivity. SPIE, 3p. [doi: https://doi.org/10.1117/2.1201002.002576]
Water productivity ; Assessment ; Estimation ; Remote sensing ; Models ; Mapping ; Crop management ; Water use ; Cotton
(Location: IWMI HQ Call no: e-copy only Record No: H042729)
http://spie.org/x39199.xml?highlight=x2420&ArticleID=x39199
https://vlibrary.iwmi.org/pdf/H042729.pdf
https://vlibrary.iwmi.org/pdf/H042729.pdf
(1.47 MB)
Crop consumptive water use, biophysical parameters, and water productivity values can be mapped to support ‘more crops per drop.’
18 Gumma, M. K.; Thenkabail, P. S.; Barry, Boubacar. 2010. Delineating shallow ground water irrigated areas in the Atankwidi Watershed (Northern Ghana, Burkina Faso) using Quickbird 0.61 - 2.44 meter data. African Journal of Environmental Science and Technology, 4(7):455-464.
Groundwater irrigation ; Irrigated sites ; Watersheds ; River basins ; Remote sensing ; Land use ; Land cover ; Wells ; Satellite imagery / West Africa / Ghana / Burkina Faso / Volta River Basin / Atankwidi Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H043080)
http://www.academicjournals.org/AJEST/PDF/pdf%202010/Jul/Krishna%20et%20al.pdf
https://vlibrary.iwmi.org/pdf/H043080.pdf
https://vlibrary.iwmi.org/pdf/H043080.pdf
(1.38 MB)
The major goal of this research was to delineate the shallow groundwater irrigated areas (SGI) in the Atankwidi Watershed in the Volta River Basin of West Africa. Shallow ground water irrigation is carried out using very small dug-wells all along the river banks or shallow dug-outs all along the river bed. Each of these dug-wells and dug-outs are highly fragmented small water bodies that irrigate only a fraction of an acre. However, these are contiguous dug-wells and dug-outs that are hundreds or thousands in number. Very high spatial resolution (VHSR) Quickbird imagery (0.61 to 2.44 m) was used to identify: (a) dug-wells that hold small quantities of water in otherwise dry stream; and (b) dug-outs that are just a meter or two in depth but have dug-out soils that are dumped just next to each well. The Quickbird VHSR imagery was found ideal to detect numerous: (i) dug-wells through bright soils that lay next to each dug-well, and (ii) water bodies all along the dry stream bed. We used fusion of 0.61 m Quickbird panchromatic data with 2.44 Quickbird multispectral data to highlight SGI and delineate their boundaries. Once this was achieved, classification techniques using Quickbird imagery was used within the delineated areas to map SGI and other land use/land cover (LULC) areas. Results obtained showed that SGI is practiced on a land area of 387 ha (1.4%), rainfed areas is 15638 ha (54.7%) and the remaining area in other LULC. These results were verified using field-plot data which showed an accuracy of 92% with errors of omissions and commissions less than 10%.
19 Fujii, H.; Dawuni, B.; Kulawardhana, Wasantha; Thenkabail, P. S.; Namara, Regassa E. 2009. Features of river flow in inland valleys in semi-deciduous forest zone in Ghana. Transactions of the Japanese Society of Irrigation, Drainage and Rural Engineering, 77(6):637-644.
Watersheds ; Rivers ; Stream flow ; Hydrology ; Runoff ; Forest land ; Rice / Ghana / West Africa / Mankran Watershed / Offinso Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H043148)
(0.96 MB)
There are about 2.8 million ha of inland valleys in Ghana and 20 million ha of inland valley in West Africa. Although inland valleys are suitable for lowland rice due to the abundance of water resources and higher soil fertility compared with the upland, they have not been well utilized as agricultural land in West Africa. Further utilization of inland valley for lowland rice will improve the productivity of rice in West Africa. In this study water resources of small rivers in inland valleys in West Africa are evaluated. Two study watersheds with 1,400-1,500mm of annual rainfall in Semi-Deciduous Forest Zone in Ghana were selected and analyzed on slope distribution in the study watershed to grasp suitable area for lowland rice and on hydrological characteristics such as specific discharge and runoff ratio. The following findings are obtained from the study. 1) Most of the rivers in the study watershed are seasonal rivers. Non-flow period of some rivers were shown for around five months from middle of December to early May. However the term of non-flow period varies much depending on characteristics of sub-watersheds. 2) Runoff ratio for 5 years from 2000 to 2004 in Offinso watershed which is a typical watershed in semi deciduous forest zone in Ghana was indicated only 12%. It ranges from 0.08 to 0.16 depending on the year. The monthly runoff ratio indicated little value in March, April and May which is beginning of rainy season and high value in November and December which is beginning of dry season. 3) The gentle slope area with less than 2%, which seems suitable area for lowland rice, occupies 22 % of inland valley.
20 Fujii, H.; Gumma, M. K.; Thenkabail, P. S.; Namara, Regassa E. 2010. Suitability evaluation for lowland rice in inland valleys in West Africa. In Japanese. Transactions of the Japanese Society of Irrigation, Drainage and Rural Engineering, 78(4):47-55.
(Location: IWMI HQ Call no: e-copy only Record No: H043176)
(2.83 MB)
A GIS based model developed by the authors are applied for selecting suitable rice cultivation area in inland valleys that has high potential for rice production in West Africa where rice consumption is increasing very rapidly. The model has the following features. 1. The model is to evaluate the suitability of the land for lowland rice based on score distribution maps respectively made by the data of 29 evaluation parameters. 2. The parameters are classified into 4 categories; bio-physical, technical, socio-economic and health-environmental parameters. 3. Each scored map(layer)is integrated to obtain total scores by multiplying a weight which is determined by the importance of parameters. The suitability for rice in two study sites was evaluated using the model. Mankran and Jolo-Kwaha watershed selected as the study sites from different agro-ecological zone in Ghana. Applying the data of 12 parameters acquired in the study sites to the model, “very suitable” or “suitable” occupies around 30% in Mankran study site and around 60% in Jolo-Kwaha study site.
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