Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imagery

Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April-November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.

New SNPs for population genetic analysis reveal possible cryptic speciation of eastern Australian sea mullet (Mugil cephalus)

Sustainable management of sea mullet (Mugil cephalus) fisheries needs to account for recent observations of regional-scale differentiation. Population genetic analysis is sought to assess the situation of this ecologically and economically important fish species in eastern Australian waters. Here, we report (i) new population genetic markers [single nucleotide polymorphisms (SNPs) and potential microsatellites], (ii) first estimates of spatial genetic differentiation and (iii) prospective power tests for designing more comprehensive studies. Six DNA samples from three sampling regions (North Queensland, South Queensland and central New South Wales) on the eastern coast of Australia were used to prepare restriction site associated DNA (RAD) tag libraries from genomic DNA digested with EcoRI and MseI. A pooled sample of regional RAD tag libraries was sequenced using the Roche GS-FLX Titanium platform. A total of 172837 raw reads (17.4Mbp) were retrieved, 95500 of which were used to discover 1267 SNPs and 1417 microsatellites. A subset of 161 SNPs was validated based on 63 additional DNA samples genotyped using the Sequenom MassArray (iPLEX Gold chemistry). Altogether 92 SNPs (57%) were confirmed, with 40% of these marking fixed variants between northern and southern sampling regions. Our preliminary findings indicate a multispecies fishery stock of M. cephalus in eastern Australian waters, but suggest that strong genetic differentiation occurs north of major fishing grounds. Low potential differentiation within major fishing grounds (e.g. FST=0.0025) can be resolved with a likely power 67% by using standard sample sizes of 50 and validated subsets of available markers.

Quantification of the inevitable: the influence of soil macrofauna on soil water movement in rehabilitated open-cut mined lands

Recolonisation of soil by macrofauna (especially ants, termites and earthworms) in rehabilitated open-cut mine sites is inevitable and, in terms of habitat restoration and function, typically of great value. In these highly disturbed landscapes, soil invertebrates play a major role in soil development (macropore configuration, nutrient cycling, bioturbation, etc.) and can influence hydrological processes such as infiltration, seepage, runoff generation and soil erosion. Understanding and quantifying these ecosystem processes is important in rehabilitation design, establishment and subsequent management to ensure progress to the desired end goal, especially in waste cover systems designed to prevent water reaching and transporting underlying hazardous waste materials. However, the soil macrofauna is typically overlooked during hydrological modelling, possibly due to uncertainties on the extent of their influence, which can lead to failure of waste cover systems or rehabilitation activities. We propose that scientific experiments under controlled conditions and field trials on post-mining lands are required to quantify (i) macrofauna–soil structure interactions, (ii) functional dynamics of macrofauna taxa,and (iii) their effects on macrofauna and soil development over time. Such knowledge would provide crucial information for soil water models, which would increase confidence in mine waste cover design recommendations and eventually lead to higher likelihood of rehabilitation success of open-cut mining land.