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.
Broad-scale high-temporal frequency satellite imagery is increasingly used for environmental monitoring. While the normalized difference vegetation index (NDVI) is the most commonly used index to track changes in vegetation cover, newer spectral mixture approaches aim to quantify sub-pixel fractions of photosynthesizing vegetation, non-photosynthesizing vegetation, and exposed soil. Validation of the unmixing products is essential to enable confident use of the products for management and decision-making. The most frequently used validation method is by field data collection, but this is very time consuming and costly, in particular in remote regions where access is difficult. This study developed and demonstrates an alternative method for quantifying landcover fractions using high-spatial resolution satellite imagery. The research aimed to evaluate the bare soil fraction in a sub-pixel product, MODIS Fract-G, for the natural arid landscapes of the far west of South Australia. Twenty-two sample regions, of 3400 sampling points each, were investigated across several arid land types in the study area. Albedo thresholds were carefully determined in Advanced Land Observing Satellite Panchromatic Remote-sensing Instrument Stereo Mapping (ALOS PRISM) images (2.5 m spatial resolution), which separated predominantly bare soil from predominantly vegetated or covered soil, and created classified images. Correlation analysis was carried out between MODIS Fract-G bare soil fractional cover and ALOS PRISM bare soil proportions for the same areas. Results showed much lower correlations than expected, though limited agreement was found in some specific areas. It is posited that the Moderate Resolution Imaging Spectroradiometer (MODIS) fractional cover product, which is based on unmixing using the NDVI and a cellulose absorption index (CAI) proxy, may be generally unable to separate soil from vegetation in situations where both indices are low. In addition, separation is hampered by the lack of 'pure pixels' in this heterogeneous landscape. This suggests that the MODIS fractional cover product, at least in its present form, is unsuited to monitor sparsely vegetated arid landscapes.
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