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Rangeland Condition Monitoring: A New Approach Using Cross-Fence Comparisons of Remotely Sensed Vegetation.

Kilpatrick AD, Lewis MM, Ostendorf B - PLoS ONE (2015)

Bottom Line: We interpret this wealth of data using a cross-fence comparison methodology, allowing us to rank paddocks (fields) in the study region according to effectiveness of grazing management.While no paddocks had a known increase in stocking rate during the study period, many had a reduction or complete removal in stock numbers, and many also experienced removals of pest species, such as rabbits, and other ecosystem restoration activities.These paddocks generally showed an improvement in rank compared to paddocks where the stocking regime remained relatively unchanged.

View Article: PubMed Central - PubMed

Affiliation: School of Biological Sciences, The University of Adelaide, Adelaide, Australia.

ABSTRACT
A need exists in arid rangelands for effective monitoring of the impacts of grazing management on vegetation cover. Monitoring methods which utilize remotely-sensed imagery may have comprehensive spatial and temporal sampling, but do not necessarily control for spatial variation of natural variables, such as landsystem, vegetation type, soil type and rainfall. We use the inverse of the red band from Landsat TM satellite imagery to determine levels of vegetation cover in a 22,672 km(2) area of arid rangeland in central South Australia. We interpret this wealth of data using a cross-fence comparison methodology, allowing us to rank paddocks (fields) in the study region according to effectiveness of grazing management. The cross-fence comparison methodology generates and solves simultaneous equations of the relationship between each paddock and all other paddocks, derived from pairs of cross-fence sample points. We compare this ranking from two image dates separated by six years, during which management changes are known to have taken place. Changes in paddock rank resulting from the cross-fence comparison method show strong correspondence to those predicted by grazing management in this region, with a significant difference between the two common management types; a change from full stocking rate to light 20% stocking regime (Major Stocking Reduction) and maintenance of full 100% stocking regime (Full Stocking Maintained) (P = 0.00000132). While no paddocks had a known increase in stocking rate during the study period, many had a reduction or complete removal in stock numbers, and many also experienced removals of pest species, such as rabbits, and other ecosystem restoration activities. These paddocks generally showed an improvement in rank compared to paddocks where the stocking regime remained relatively unchanged. For the first time, this method allows us to rank non-adjacent paddocks in a rangeland region relative to each other, while controlling for natural spatio-temporal variables such as rainfall, soil type, and vegetation community distributions, due to the nature of the cross-fence experimental design, and the spatially comprehensive data available in satellite imagery. This method provides a potential tool to aid land managers in decision making processes, particularly with regard to stocking rates.

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Layout of the GIS sampling regime for cross-fence comparisons.
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pone.0142742.g003: Layout of the GIS sampling regime for cross-fence comparisons.

Mentions: Four adjoining Landsat Enhanced Thematic Mapper (ETM) and Thematic Mapper (TM) tiles were acquired for each date from the USGS GLOVIS website. The tiles were from the square of Rows 99 and 100 and Paths 80 and 81. In order to minimise cloud cover, dates for each set of four images were chosen from within the same month, though not necessarily from the same acquisition date. Landsat ETM imagery was used for November 2002, while TM was used for October 2008, due to the SLC error in later ETM imagery. No rainfall fell between acquisition dates for the set of four images. A correction to top of atmosphere reflectance [34] was applied. No additional atmospheric or radiometric corrections, such as BRDF corrections, need to be performed, as the nature of the cross-fence analysis means that image pixel pairs are subjected to the same atmospheric and sensor conditions, and if multiplicative, mathematically cancel out in the calculation of cross fence ratios (r in Eq 1 below). The fact that such image corrections are not an required for this methodology is indeed one of its great strengths. Imagery can be used in relatively raw forms, and can even include radiometrically or temporally un-matched mosaics, as long as image joining is not conducted along fence lines causing bias in the calculation of r. Further geometric corrections were not performed; instead we allowed for possible imagery misalignment in our sample design (Fig 3). Mosaicking of image tiles was performed with selection of overlapping areas preferring tiles with lower levels of cloud cover.


Rangeland Condition Monitoring: A New Approach Using Cross-Fence Comparisons of Remotely Sensed Vegetation.

Kilpatrick AD, Lewis MM, Ostendorf B - PLoS ONE (2015)

Layout of the GIS sampling regime for cross-fence comparisons.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4643980&req=5

pone.0142742.g003: Layout of the GIS sampling regime for cross-fence comparisons.
Mentions: Four adjoining Landsat Enhanced Thematic Mapper (ETM) and Thematic Mapper (TM) tiles were acquired for each date from the USGS GLOVIS website. The tiles were from the square of Rows 99 and 100 and Paths 80 and 81. In order to minimise cloud cover, dates for each set of four images were chosen from within the same month, though not necessarily from the same acquisition date. Landsat ETM imagery was used for November 2002, while TM was used for October 2008, due to the SLC error in later ETM imagery. No rainfall fell between acquisition dates for the set of four images. A correction to top of atmosphere reflectance [34] was applied. No additional atmospheric or radiometric corrections, such as BRDF corrections, need to be performed, as the nature of the cross-fence analysis means that image pixel pairs are subjected to the same atmospheric and sensor conditions, and if multiplicative, mathematically cancel out in the calculation of cross fence ratios (r in Eq 1 below). The fact that such image corrections are not an required for this methodology is indeed one of its great strengths. Imagery can be used in relatively raw forms, and can even include radiometrically or temporally un-matched mosaics, as long as image joining is not conducted along fence lines causing bias in the calculation of r. Further geometric corrections were not performed; instead we allowed for possible imagery misalignment in our sample design (Fig 3). Mosaicking of image tiles was performed with selection of overlapping areas preferring tiles with lower levels of cloud cover.

Bottom Line: We interpret this wealth of data using a cross-fence comparison methodology, allowing us to rank paddocks (fields) in the study region according to effectiveness of grazing management.While no paddocks had a known increase in stocking rate during the study period, many had a reduction or complete removal in stock numbers, and many also experienced removals of pest species, such as rabbits, and other ecosystem restoration activities.These paddocks generally showed an improvement in rank compared to paddocks where the stocking regime remained relatively unchanged.

View Article: PubMed Central - PubMed

Affiliation: School of Biological Sciences, The University of Adelaide, Adelaide, Australia.

ABSTRACT
A need exists in arid rangelands for effective monitoring of the impacts of grazing management on vegetation cover. Monitoring methods which utilize remotely-sensed imagery may have comprehensive spatial and temporal sampling, but do not necessarily control for spatial variation of natural variables, such as landsystem, vegetation type, soil type and rainfall. We use the inverse of the red band from Landsat TM satellite imagery to determine levels of vegetation cover in a 22,672 km(2) area of arid rangeland in central South Australia. We interpret this wealth of data using a cross-fence comparison methodology, allowing us to rank paddocks (fields) in the study region according to effectiveness of grazing management. The cross-fence comparison methodology generates and solves simultaneous equations of the relationship between each paddock and all other paddocks, derived from pairs of cross-fence sample points. We compare this ranking from two image dates separated by six years, during which management changes are known to have taken place. Changes in paddock rank resulting from the cross-fence comparison method show strong correspondence to those predicted by grazing management in this region, with a significant difference between the two common management types; a change from full stocking rate to light 20% stocking regime (Major Stocking Reduction) and maintenance of full 100% stocking regime (Full Stocking Maintained) (P = 0.00000132). While no paddocks had a known increase in stocking rate during the study period, many had a reduction or complete removal in stock numbers, and many also experienced removals of pest species, such as rabbits, and other ecosystem restoration activities. These paddocks generally showed an improvement in rank compared to paddocks where the stocking regime remained relatively unchanged. For the first time, this method allows us to rank non-adjacent paddocks in a rangeland region relative to each other, while controlling for natural spatio-temporal variables such as rainfall, soil type, and vegetation community distributions, due to the nature of the cross-fence experimental design, and the spatially comprehensive data available in satellite imagery. This method provides a potential tool to aid land managers in decision making processes, particularly with regard to stocking rates.

Show MeSH
Related in: MedlinePlus