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

Adam D Kilpatrick,Megan M Lewis,Bertram Ostendorf,Lalit Kumar

doi:10.1371/journal.pone.0142742

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 22,672km2 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.

Highlights

Rangelands are areas of managed grazing and are the largest landuse by area, worldwide
Amongst the 15 leases we identify four main changes that have occurred in management regime from the original region-wide full stocking, beginning in the mid-1980s: Ecosystem Restoration (ER); Complete Stocking Removal (CSR); Major Stocking Reduction (MSR); Full Stocking Maintained (FSM) (Fig 2)
Vegetation cover varies widely in magnitude and spatial distribution throughout the study area, with the same landscape patterns evident on both image dates, such as the stony tableland country in the northern third, high cover in creek lines and what appears to be low cover surrounding cane grass swamps in the western portion. Other features such as sand dune/swale repetition can be discerned in both epochs of imagery. Such spatially variable vegetation cover associated with landsystems means that using raw vegetation indices, or averages of indices for each paddock, would be a misleading guide to land condition, and certainly wouldn’t allow meaningful comparisons to be made between paddocks

Summary

Introduction

Rangelands are areas of managed grazing and are the largest landuse by area, worldwide. While it is possible to compare the condition of paddocks in rangelands using remotely-sensed imagery [3], [14] most methods have limitations because they cannot separate the effect of natural factors causing variation in condition from management influences. The limitations include lack of spatially comprehensive rainfall data or control for other factors that contribute to vegetation condition, such as spatial and temporal variability of rainfall, and landscape variables such as soil type and vegetation communities [15], [12], [16], [17]. One method recently developed to isolate the effects of rangeland management from remotely sensed vegetation cover measures, is the Dynamic Reference-Cover Method [18] This method identifies reference pixels of persistent high vegetation from a time series of imagery and a regional moving window in order to classify all pixels within the moving window, based on their potential difference in vegetation cover [18]. A second method involves modelling vegetation growth using available rainfall data, which is subtracted from measured vegetation growth in time series imagery, inferring grazing effects [14], [19], [20]

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Results

Discussion

Conclusion