Geography Reference
In-Depth Information
Serrano et al. (
2000
) compared different techniques developed to create a
homogeneous time series of LANDSAT images from 1984 to 2007 for the Middle
Ebro Valley in Spain. Mahmood and Easson (
2006
) explored the capability of
using ASTER imagery integrated with LANDSAT-7-ETM+ imagery of south-
western Bangladesh to detect equivalent measurements for change detection
studies. The used methods were regression with Discrete Fourier Transform (DFT)
and the cross-calibration method using digital number ratios. French et al. (
2008
)
demonstrated and confirmed a method using ASTER-imagery obtained between
2001 and 2003 over the Jornada Experimental Range, to map the LULC-changes
in a semi-arid area in southern New Mexico, USA. The results emphasize the
importance of multispectral thermal infrared data that contains observations at
wavelengths within 8-9.5 lm. Alberga (
2009
) proposed a technique for probable
change detectors in multi-sensor configurations, based on similarity measures that
did not rely totally on radiometric values. A chain of such measures was used for
automatic change detection of optical and SAR-images and an evaluation of their
functioning were carried out to detect the limits of their applicability and their
understanding to the occurred changes.
2.5 Remote Sensing for Irrigated Agriculture
Exact information on irrigation spatial coverage is the foundation of many sides of
the knowledge of the Earth's systems and global change research. Ozdogan and
Gutman (
2008
) defined irrigation as ''agricultural area that receives full or partial
application of water to the soil to offset periods of precipitation shortfalls under dry
land conditions''. The remote sensing techniques offer a unique approach to the
gathering of various data across place and time, facilitating the application of
various methods to obtain irrigated area statistics. In addition, time-series remotely
sensed data allow the dynamics of irrigated agriculture to be clearly researched, as
differing from other land uses (mapping). To date, a number of researchers have
used remote sensing to observe irrigated agriculture (Ozdogan
2010
). Initial efforts
focused on applying remote sensing in mapping and to update irrigated land areas
mostly in the US and India (Draeger
1976
; Rundquist et al.
1989
). More recently,
studies on classification irrigated areas were carried out based on advanced clas-
sification algorithms (Abuzar et al.
2001
). These researchers concluded that irri-
gation monitoring and mapping using remote sensing were at an advanced phase of
improvement (Ozdogan et al.
2006
) and that multi-temporal data were more
effective rather than single-date data in determining individual irrigated crop
classes (Thiruvengadachari
1981
). Spatial resolution of used remotely sensed data
for irrigation mapping was seen as vital to obtaining sufficient spatial details about
the irrigated fields (Pax-Lenney and Woodcock
1997
), as was the potential of
vegetation indices in classification irrigated fields, if suitable time-series are
obtainable. This latter fact was proved in several studies (Martinez-Beltran and
Calera-Belmonte
2001
).
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