Kilpatrick Adam D, Lewis Megan M, Ostendorf Bertram
School of Biological Sciences, The University of Adelaide, Adelaide, Australia.
PLoS One. 2015 Nov 13;10(11):e0142742. doi: 10.1371/journal.pone.0142742. eCollection 2015.
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.
在干旱牧场，需要对放牧管理对植被覆盖的影响进行有效监测。利用遥感影像的监测方法可能具有全面的空间和时间采样，但不一定能控制自然变量的空间变化，如土地系统、植被类型、土壤类型和降雨量。我们利用陆地卫星专题制图仪（Landsat TM）卫星影像的红波段倒数来确定南澳大利亚中部22672平方公里干旱牧场的植被覆盖水平。我们使用跨围栏比较方法来解读这些丰富的数据，从而能够根据放牧管理的有效性对研究区域内的围场（田地）进行排名。跨围栏比较方法通过成对的跨围栏采样点生成并求解每个围场与所有其他围场之间关系的联立方程。我们比较了相隔六年的两个影像日期的排名，在此期间已知管理措施发生了变化。跨围栏比较方法导致的围场排名变化与该地区放牧管理预测的变化高度一致，两种常见管理类型之间存在显著差异；从满负荷放牧率转变为轻载20%的放牧制度（大幅减少放牧）和维持满负荷100%的放牧制度（维持满负荷放牧）（P = 0.00000132）。虽然在研究期间没有围场的放牧率已知增加，但许多围场的牲畜数量减少或完全清除，许多围场还进行了有害物种（如兔子）的清除以及其他生态系统恢复活动。与放牧制度相对不变的围场相比，这些围场的排名总体上有所改善。由于跨围栏实验设计的性质以及卫星影像中可用的空间综合数据，这种方法首次使我们能够在控制降雨、土壤类型和植被群落分布等自然时空变量的同时，对牧场区域内不相邻的围场进行相互排名。这种方法为土地管理者在决策过程中，特别是在放牧率方面，提供了一个潜在的工具。
