Your search found 13 records
1 Thenkabail, Prasad S.; Enclona, E. A.; Ashton, M. S.; Legg, C.; de Dieu, M. J. 2004. Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests. Remote Sensing of Environment, 90(1):23-43.
(Location: IWMI-HQ Call no: IWMI 634.9 G100 THE Record No: H033904)
2 Thenkabail, Prasad S.; Enclona, E. A.; Ashton, M. S.; Van Der Meer, B. 2004. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing of Environment, 91(3-4):354-376.
(Location: IWMI-HQ Call no: IWMI 621.3678 G190 THE Record No: H034569)
3 Thenkabail, Prasad S.; Nolte, C. 2003. Regional characterization of inland valley agroecosystems in West and Central Africa using high-resolution remotely sensed data. In Lyon, J. G. (Ed.), GIS for water resources and watershed management. London, UK: Taylor & Francis. pp.77-99.
Remote sensing ; Satellite surveys ; Mapping ; Land use ; Ecosystems / Africa / Benin / Ivory Coast / Burkina Faso
(Location: IWMI-HQ Call no: 006 G000 LYO Record No: H035213)
(5.41 MB)
4 Thenkabail, Prasad. S.; Stucky, N.; Griscom, B. W.; Ashton, M. S.; Diels, J.; van der Meer, B.; Enclona, E. 2004. Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data. International Journal of Remote Sensing, 25(23):5447-5472.
(Location: IWMI-HQ Call no: IWMI 621.3678 G100 THE Record No: H035473)
5 Thenkabail, Prasad S.; Schull, Mitchell; Turral, Hugh. 2005. Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data. Remote Sensing of Environment, 95:317-341.
(Location: IWMI-HQ Call no: IWMI 631.7.1 G000 THE Record No: H036374)
6 Thenkabail, Prasad S.; Biradar, C. M.; Noojipady, P.; Cai, Xueliang; Dheeravath, Venkateswarlu; Li, Y. J.; Velpuri, M.; Gumma, Murali Krishna; Pandey, Suraj. 2007. Sub-pixel area calculation methods for estimating irrigated areas. Sensors, 7: 2519-2538.
(Location: IWMI HQ Call no: IWMI 631.7.1 G000 THE Record No: H040450)
7 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%.
8 Melesse, A. M.; Weng, Q.; Thenkabail, Prasad S.; Senay, G. B. 2007. Remote sensing sensors and applications in environmental resources mapping and modelling. Sensors, 7:3209-3241.
Remote sensing ; Sensors ; Imagery ; Models ; Environmental effects ; Drought ; Soil water ; Mapping ; Hydrology ; Forecasting ; Early warning systems
(Location: IWMI HQ Call no: IWMI 621.3678 G000 MEL Record No: H040633)
The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land- cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhausted application of the remote sensing technique rather summary of some important applications in environmental studies and modeling.
9 Woodcock, C. E.; Allen, R.; Anderson, M.; Belward, A.; Bindschadler, R.; Cohen, W.; Gao, F.; Goward, S. N.; Helder, D.; Helmer, E.; Nemani, R.; Oreopoulos, L.; Schott, J.; Thenkabail, Prasad, S.; Vermote, E. F.; Vogelmann, J.; Wulder, M. A.; Wynne, R. 2008. Free access to Landsat imagery. Science, 320: 1011-1012.
(Location: IWMI HQ Call no: IWMI 621.3678 G430 WOO Record No: H041184)
http://www.fs.fed.us/global/iitf/pubs/ja_iitf_2008_woodcock001.pdf
https://vlibrary.iwmi.org/pdf/H041184.pdf
https://vlibrary.iwmi.org/pdf/H041184.pdf
10 Gumma, Murali Krishna; Thenkabail, Prasad S.; Gautam, N. C.; Gangadhara Rao, Parthasaradhi; Manohar, Velpuri. 2008. Irrigated area mapping using AVHRR, MODIS and LANDSAT ETM+ data for the Krishna River Basin, India. Technology Spectrum, 2(1): 1-11.
River basins ; Water scarcity ; Irrigation programs ; Irrigated land ; Remote sensing ; Mapping ; Time series analysis / India / Krishna River Basin
(Location: IWMI HQ Call no: IWMI 631.7.1 G635 GUM Record No: H041432)
Net irrigated area in the Krishna river basin is varying quiet frequently due to water scarcity. Accurate area and extent of irrigated area in the Krishna River Basin is not available. State Irrigation Department projects large area under irrigation in the Krishna River Basin, which is attributed to its prestigious irrigation projects. However, the irrigation projects do not fulfill the demand in the basin consequently the tail Enders grow dry crops. Remote sensing replaces costly and tedious data collection on the ground, which is non-destructive. The aim of the present study is to prepare a comprehensive land use/land cover (LU/LC) map using continuous time-series data of multiple resolutions. A methodology is developed to map irrigated area categories using LANDSAT ETM+ along with coarse resolution time series imagery from AVHRR and MODIS, SRTM elevation, and other secondary data. Major stress was towards discrimination of ground-water irrigated area from surface-water irrigated area, determining of cropping patterns in irrigated area using MODIS NDVI time- series, and use of non-traditional methods of accuracy assessment using, ancillary datasets like SRTM-DEM, precipitation and state census statistics. A regression of the 9 class areas against agricultural census data explained 73% and 74% of the variance in groundwater and surface water irrigated area, respectively.
11 Biradar, C. M.; Thenkabail, Prasad S.; Platonov, Alexander; Xiao, X.; Geerken, R.; Noojipady, P.; Turral, H.; Vithanage, Jagath. 2008. Water productivity mapping methods using remote sensing. Journal of Applied Remote Sensing, 2(1):023544. 22p. (Published online only)
Water productivity ; Mapping ; Remote sensing ; Vegetation index ; Evapotranspiration ; Wheat ; Rice ; Cotton ; Irrigated farming / Central Asia / Syr Darya River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H041669)
The goal of this paper was to develop methods and protocols for water productivity mapping (WPM) using remote sensing data at multiple resolutions and scales in conjunction with field-plot data. The methods and protocols involved three broad categories: (a) Crop Productivity Mapping (CPM) (kg/m2); (b) Water Use (evapotranspiration) Mapping (WUM)(m3/m2); and (c) Water Productivity Mapping (WPM) (kg/m3). First, the CPMs were determined using remote sensing by: (i) Mapping crop types; (ii) modeling crop yield; and (iii) extrapolating models to larger areas. Second, WUM were derived using the Simplified Surface Energy Balance (SSEB) model. Finally, WPMs were produced by dividing CPMs and WUMs. The paper used data from Quickbird 2.44m, Indian Remote Sensing (IRS) Resoursesat-1 23.5m, Landsat-7 30m, and Moderate Resolution Imaging Spectroradiometer (MODIS) 250m and 500m, to demonstrate the methods for mapping water productivity (WP). In terms of physical water productivity (kilogram of yield produced per unit of water delivered), wheat crop had highest water productivity of 0.60 kg/m3 (WP), followed by rice with 0.5 kg/m3, and cotton with 0.42 kg/m3. In terms of economic value (dollar per unit of water delivered), cotton ranked highest at $ 0.5/m3 followed by wheat with $ 0.33/m3 and rice at $ 0.10/m3. The study successfully delineated the areas of low and high WP. An overwhelming proportion (50+%) of the irrigated areas were under low WP for all crops with nly about 10% area in high WP.
12 Thenkabail, Prasad S.; Biradar, Chandrashekhar M.; Noojipady, P.; Dheeravath, Venkateswarlu; Li, Yuan Jie; Velpuri, M.; Reddy, G. P. O.; Cai, Xueliang; Gumma, Murali Krishna; Turral, Hugh; Vithanage, Jagath; Schull, M.; Dutta, R. 2008. A Global Irrigated Area Map (GIAM) using remote sensing at the end of the last millennium. Colombo, Sri Lanka: International Water Management Institute (IWMI) 62p. [doi: https://doi.org/10.5337/2011.0024]
(Location: IWMI HQ Call no: e-copy only Record No: H042115)
(3.00 MB) (3MB)
13 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%.
Powered by DB/Text WebPublisher, from
