Multivariate Modelling Of Land Use On
In-stream Salinity Over Multiple Spatial Scales
Author(s)
V. Versace, D. Ierodiaconou, S. Salzman, F. Stagnitti,
M. Leblanc, A. Boland, L. Laurenson, T. March & L. Thwaites
Abstract
An impediment to sustainable dryland salinity management is the lack of
information on contributing factors. GIS and satellite imagery now offer a
cost-effective means of generating relevant land and water resource information
for integrated regional management of salinity. In this paper the relationships
between patterns in land use/cover distribution and base flow salt concentration
in streams (indicated by EC) are investigated and modelled. The
Glenelg-Hopkins area is a large regional watershed in southwest Victoria,
Australia, covering approximately 2.6 million ha. It is currently estimated that
27,400 ha of land is affected by dryland salinity and this is predicted to rapidly
increase in the next decade if current conditions prevail. Salt concentration data
from five gauging stations were analysed with multi-temporal land use maps
obtained from satellite imagery. Multiple regression analyses demonstrated that
the variables Native Vegetation and Dryland Grain Cropping were the most
significant influences on in-stream salinity in the whole catchment (r2=88.9%)
and 500 m (r2=88.3%) and 100 m riparian buffers (r2=86.9%) during times of
base flow. The implications for future land use planning, effectiveness of
riparian zones and revegetation programmes is discussed. This work also
demonstrates the utility of applying multivariate statistical analyses, spatial
statistics, and remote sensing with data integrated in a GIS framework for the
purpose of predicting and managing the regional salinity threat.
Keywords: dryland salinity, salt concentration, remote sensing, multiple
regression, land use.
Keywords
dryland salinity, salt concentration, remote sensing, multipleregression, land use.
Related Book
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