The data set expresses likelihood of inflow dependent landscapes (ID) from 1 -10 (low to high). IDs are landscapes that are wetter than surrounding areas either seasonally or permanently, because they receive water from inflows in addition to rainfall, such as groundwater. IDs include a large variety of natural ecosystems (e.g. rivers, wetland, and forests) as well as altered landscapes such as irrigated farming and channels. The ID layer was constructed from multiple satellite sensors, namely MODIS and Landsat. While MODIS estimates evapotranspiration from land surface (van Dijk et al., 2011) and Landsat measures vegetation dynamics sustained during a prolonged dry period (Barron et al., 2011), the two sources were combined by converting these metrics into a standard unit based on likelihood of an ID ranging from 1 to 10 (low to high). Within different landscapes, rules are applied to determine the combination of MODIS and Landsat data to develop a continental likelihood of ID expressed at 25m resolution (SKM, 2012).

The data set expresses likelihood of inflow dependent landscapes (ID) from 1 -10 (low to high). IDs are landscapes that are wetter than surrounding areas either seasonally or permanently, because they receive water from inflows in addition to rainfall, such as groundwater. IDs include a large variety of natural ecosystems (e.g. rivers, wetland, and forests) as well as altered landscapes such as irrigated farming and channels. The ID layer was constructed from multiple satellite sensors, namely MODIS and Landsat. While MODIS estimates evapotranspiration from land surface (van Dijk et al., 2011) and Landsat measures vegetation dynamics sustained during a prolonged dry period (Barron et al., 2011), the two sources were combined by converting these metrics into a standard unit based on likelihood of an ID ranging from 1 to 10 (low to high). Within different landscapes, rules are applied to determine the combination of MODIS and Landsat data to develop a continental likelihood of ID expressed at 25m resolution (SKM, 2012).

The presence and location of ID landscapes is a strong indicator for the presence of GDEs, and is the foundation for identifying GDEs. The purpose of this resource is to provide a continental ID layer for Australia which is used as a primary input into the National Water Commission's GDE Atlas.

The presence and location of ID landscapes is a strong indicator for the presence of GDEs, and is the foundation for identifying GDEs. The purpose of this resource is to provide a continental ID layer for Australia which is used as a primary input into the National Water Commission's GDE Atlas.

The limitation is that the scale and resolution of the mapped information is only as detailed as the input mapping used; features which do not influence a 30m Landsat pixel are not identifed on the map. In addition, the data is dependent on the time period of the data used (2000 - 2010) and can only be identified if active in the time period; it does not provide information on the future or the past.

The limitation is that the scale and resolution of the mapped information is only as detailed as the input mapping used; features which do not influence a 30m Landsat pixel are not identifed on the map. In addition, the data is dependent on the time period of the data used (2000 - 2010) and can only be identified if active in the time period; it does not provide information on the future or the past

The data set expresses likelihood of inflow dependent landscapes (ID) from 1 -10 (low to high). IDs are landscapes that are wetter than surrounding areas either seasonally or permanently, because they receive water from inflows in addition to rainfall, such as groundwater. IDs include a large variety of natural ecosystems (e.g. rivers, wetland, and forests) as well as altered landscapes such as irrigated farming and channels. The ID layer was constructed from multiple satellite sensors, namely MODIS and Landsat. While MODIS estimates evapotranspiration from land surface (van Dijk et al., 2011) and Landsat measures vegetation dynamics sustained during a prolonged dry period (Barron et al., 2011), the two sources were combined by converting these metrics into a standard unit based on likelihood of an ID ranging from 1 to 10 (low to high). Within different landscapes, rules are applied to determine the combination of MODIS and Landsat data to develop a continental likelihood of ID expressed at 25m resolution (SKM, 2012).

The data set expresses likelihood of inflow dependent landscapes (ID) from 1 -10 (low to high). IDs are landscapes that are wetter than surrounding areas either seasonally or permanently, because they receive water from inflows in addition to rainfall, such as groundwater. IDs include a large variety of natural ecosystems (e.g. rivers, wetland, and forests) as well as altered landscapes such as irrigated farming and channels. The ID layer was constructed from multiple satellite sensors, namely MODIS and Landsat. While MODIS estimates evapotranspiration from land surface (van Dijk et al., 2011) and Landsat measures vegetation dynamics sustained during a prolonged dry period (Barron et al., 2011), the two sources were combined by converting these metrics into a standard unit based on likelihood of an ID ranging from 1 to 10 (low to high). Within different landscapes, rules are applied to determine the combination of MODIS and Landsat data to develop a continental likelihood of ID expressed at 25m resolution (SKM, 2012).

The presence and location of ID landscapes is a strong indicator for the presence of GDEs, and is the foundation for identifying GDEs. The purpose of this resource is to provide a continental ID layer for Australia which is used as a primary input into the National Water Commission's GDE Atlas.

The presence and location of ID landscapes is a strong indicator for the presence of GDEs, and is the foundation for identifying GDEs. The purpose of this resource is to provide a continental ID layer for Australia which is used as a primary input into the National Water Commission's GDE Atlas.

The limitation is that the scale and resolution of the mapped information is only as detailed as the input mapping used; features which do not influence a 30m Landsat pixel are not identifed on the map. In addition, the data is dependent on the time period of the data used (2000 - 2010) and can only be identified if active in the time period; it does not provide information on the future or the past.

The limitation is that the scale and resolution of the mapped information is only as detailed as the input mapping used; features which do not influence a 30m Landsat pixel are not identifed on the map. In addition, the data is dependent on the time period of the data used (2000 - 2010) and can only be identified if active in the time period; it does not provide information on the future or the past