Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data

Abstract

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 OPEN ACCESSRemote Sens. 2011, 3 817 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.

Urban Sprawl and Adverse Impacts on Agricultural Land: A Case Study on Hyderabad, India

“…A comprehensive methodology for mapping cropland areas using IRS-P6 and Landsat-8 data was taken from the literature [27,31,32,44]. Each image was classified using unsupervised ISOCLASS cluster Isodata classification, with 40 classes and 40 maximum iterations and with convergence threshold of 0.99.…”

Prioritization of Watersheds across Mali Using Remote Sensing Data and GIS Techniques for Agricultural Development Planning

“…Land use classes were mapped based on ground data and land cover classes inferred from Google Earth high resolution imagery [30]. Irrigated land was assigned a score of five because it is mostly associated with flood plains and buried channels, which are very good recharge zones, as indicated by field derived information in the Upper East Region [31]. One of the dominant land use/land cover categories in the area is Class 4, Savannas: grasslands, shrub lands, and woodlands mixed with rainfed agriculture.…”

Mapping Fractional Cropland Distribution in Mato Grosso, Brazil Using Time Series MODIS Enhanced Vegetation Index and Landsat Thematic Mapper Data

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