Your search found 3 records
1 Hunt, E. D.; Singh, V. P.; Singh, A. N.; Rala, A.. 1995. Geographic information systems in agriculture: A tool for environmental characterization. In Ingram, K. T. (Ed.), Rainfed lowland rice: Agricultural research for high-risk environments. Manila, Philippines: IRRI. pp.19-30.
GIS ; Agricultural research ; Ecology ; Environmental effects ; Rice ; Case studies / India / Uttar Pradesh
(Location: IWMI-HQ Call no: 633.18 G000 ING Record No: H017756)
2 Gumma, M. K.; Thenkabail, P. S.; Hideto, F.; Nelson, A.; Dheeravath, V.; Busia, D.; Rala, A.. 2011. Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data. Remote Sensing, 3(4):816-835. [doi: https://doi.org/10.3390/rs3040816]
Remote sensing ; Methodology ; Mapping ; Irrigated land ; Irrigated farming ; Land use ; Land cover ; Satellite imagery ; Statistics / Ghana
(Location: IWMI HQ Call no: e-copy only Record No: H044267)
(1.69MB)
Maps of irrigated areas are essential for Ghana’s agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m time-series data was adopted to classify, group, and label classes. Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. An irrigated area derived from remote sensing (32,421 ha) was 20–57% higher than irrigated areas reported by Ghana’s Irrigation Development Authority (GIDA). This was because of the uncertainties involved in factors such as: (a) absence of shallow irrigated area statistics in GIDA statistics, (b) non-clarity in the irrigated areas in its use, under-development, and potential for development in GIDA statistics, (c) errors of omissions and commissions in the remote sensing approach, and (d) comparison involving widely varying data types, methods, and approaches used in determining irrigated area statistics using GIDA and remote sensing. Extensive field campaigns to help in better classification and validation of irrigated areas using high (30 m ) to very high (<5 m) resolution remote sensing data that are fused with multi temporal data like MODIS are the way forward. This is especially true in accounting for small yet contiguous patches of irrigated areas from dug-wells and dug-outs.
3 Gumma, M. K.; Kajisa, K.; Mohammed, I. A.; Whitbread, A. M.; Nelson, A.; Rala, A.; Kuppannan, Palanisami. 2015. Temporal change in land use by irrigation source in Tamil Nadu and management implications. Environmental Monitoring and Assessment, 187(1):1-17. [doi: https://doi.org/10.1007/s10661-014-4155-1]
Land use ; Land cover ; Groundwater irrigation ; Irrigated sites ; Irrigation canals ; Tank irrigation ; Spectral analysis ; Rain ; Crop management ; River basins ; Agriculture ; Remote sensing / India / Tamil Nadu
(Location: IWMI HQ Call no: e-copy only Record No: H047509)
(6.45 MB)
Interannual variation in rainfall throughout Tamil Nadu has been causing frequent and noticeable land use changes despite the rapid development in groundwater irrigation. Identifying periodically water-stressed areas is the first and crucial step to minimizing negative effects on crop production. Such analysis must be conducted at the basin level as it is an independent water accounting unit. This paper investigates the temporal variation in irrigated area between 2000–2001 and 2010–2011 due to rainfall variation at the state and sub-basin level by mapping and classifying Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day composite satellite imagery using spectral matching techniques. A land use/land cover map was drawn with an overall classification accuracy of 87.2 %. Area estimates between the MODISderived net irrigated area and district-level statistics (2000–2001 to 2007–2008) were in 95 % agreement. A significant decrease in irrigated area (30–40 %) was observed during the water-stressed years of 2002–2003, 2003–2004, and 2009–2010. Major land use changes occurred three times during 2000 to 2010. This study demonstrates how remote sensing can identify areas that are prone to repeated land use changes and pin-point key target areas for the promotion of drought-tolerant varieties, alternativewater management practices, and new cropping patterns to ensure sustainable agriculture for food security and livelihoods.
Powered by DB/Text WebPublisher, from
