Environmental zonation across the Australian arid region based on long-term vegetation dynamics

Lawley, Evertje Frederika; Lewis, Mervyn K.; Ostendorf, Bertram

doi:10.1016/j.jaridenv.2011.01.003

Cited by 13 publications

(13 citation statements)

References 21 publications

(5 reference statements)

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“…The AWNRM region in the far west of South Australia covers 261,180 km 2 , approximately 5.4% of the entire Australian arid zone, which was delimited by Lawley et al (2011) as the modified Koeppen arid zone classification, but with the dryland cultivated areas masked out. The climate is hot and dry.…”

Section: Study Regionmentioning

confidence: 99%

“…The components and classes of the zonation created by Lawley et al (2011) are here used to show the distribution of variability across the AW landscape. That zonation was based on principal component analysis (PCA) of 25 years of twicemonthly maximum value composite (MVC) AVHRR data, which had been calculated, at 8 km spatial resolution to the NDVI.…”

Section: Datamentioning

confidence: 99%

“…The Lawley et al (2011) zonation consists of twenty-four classes created by unsupervised iso-classification of the first 14 principal components (85% of variability). These new classes represent areas of distinct variability in vegetation response across the Australian arid zone.…”

Section: Datamentioning

confidence: 99%

“…This paper uses the Australian arid zone vegetation classification of Lawley et al (2011) as a framework of landscape spatio-temporal variability within which to interpret ecological indicator data. This framework is based on long-term patterns of vegetation growth across the Australian arid zone, derived from satellite remote sensing.…”

Section: Introductionmentioning

confidence: 99%

“…This framework is based on long-term patterns of vegetation growth across the Australian arid zone, derived from satellite remote sensing. Lawley et al (2011) stratified the Australian arid landscape based on variability in long-term vegetation response, as detected in high temporal frequency AVHRR Normalised Difference Vegetation Index (NDVI) images. They used principal component analysis (PCA) of these images to determine the key components that define vegetation dynamics.…”

Section: Introductionmentioning

confidence: 99%

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A remote sensing spatio-temporal framework for interpreting sparse indicators in highly variable arid landscapes

Lawley

Lewis

Ostendorf

2016

Ecological Indicators

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a b s t r a c tThe world's extensive and often remote arid landscapes are receiving increasing attention to maintain their ecological and productive values. Monitoring and management of these lands requires indicators and evidence of ecosystem condition and trend, generally derived from widely distributed and infrequently repeated site-based records. However adequate geographic representation and frequent site revisits are difficult to achieve because of the remoteness and vast extent of these landscapes. Interpreting such sparse ecological indicators is difficult, particularly within landscapes that are highly variable in space and time. To interpret ecological indicator data collected in such environments long-term patterns of natural landscape variability need to be understood. This paper presents a framework of landscape spatio-temporal variability within which to interpret ecological indicator data. This framework is based on long-term patterns of vegetation growth across the Australian arid zone, derived from twenty-five years of high temporal resolution National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellite imagery. We present a case study of the extensive Alinytjara Wilurara (AW) Natural Resource Management (NRM) region in far western South Australia to illustrate new insights about landscape function gained from this approach, and their implications for collection and interpretation of ecological indicator data. We illustrate how variability in vegetation response is expressed across the region, and how stratification based on active vegetation response differs from more commonly used biogeographic stratifications in this region. Lastly we demonstrate the unique patterns of long-term vegetation response for the major vegetation response classes. Average amount, seasonality, magnitude, timing and variability of vegetation response over time are used to characterise the natural "envelope" of variability of the new landscape classes.The study region showed low vegetation response in summer and higher response in winter. Onset of growth was earlier in the north and in ecosystems dominated by mallee vegetation. Cyclonic influence from the west was evident at the southern margin of the study region. The study demonstrates the landscape functional response of the study region, and presents a method whereby remote sensing reveals the landscape context within which to better interpret ecological indicator data collected in a highly variable landscape.

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Section: Study Regionmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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