Elizabeth Morse-McNabb

VLUIS, a land use data product for Victoria, Australia, covering 2006 to 2013.

Land Use Information is a key dataset required to enable an understanding of the changing nature of our landscapes and the associated influences on natural resources and regional communities. The Victorian Land Use Information System (VLUIS) data product has been created within the State Government of Victoria to support land use assessments. The project began in 2007 using stakeholder engagement to establish product requirements such as format, classification, frequency and spatial resolution. Its genesis is significantly different to traditional methods, incorporating data from a range of jurisdictions to develop land use information designed for regular on-going creation and consistency. Covering the entire landmass of Victoria, the dataset separately describes land tenure, land use and land cover. These variables are co-registered to a common spatial base (cadastral parcels) across the state for the period 2006 to 2013; biennially for land tenure and land use, and annually for land cover. Data is produced as a spatial GIS feature class.

Managing Wheat From Space: Linking MODIS NDVI and Crop Models for Predicting Australian Dryland Wheat Biomass

This study explored the relationships between moderate resolution imaging spectroradiometer (MODIS) NDVI observations with both measured and simulated fractional green cover (FGrC), leaf area index (LAI), and above ground biomass (AGB) for dryland wheat in Australia. A total of 37 paddocks in north-western Victoria, Australia, were sampled during 2003-2006 for AGB at anthesis, and for FGrC, NDVI (from an active optical sensor), and AGB during 2012. The 2012 FGrC and NDVI measurements were fitted to MODIS NDVI, resulting in positive, linear relationships when the MODIS NDVI values were ≤ 0.80. Measured AGB was also positively, linearly related to MODIS summed NDVI, resulting in an overall R2 of 0.81 and root mean square error (RMSE) of 1397 kg/ha. Crop simulations were run for the fourteen paddocks from 2003 to 2006, and six paddocks from 2012. Four crop phenological points were selected to extract corresponding NDVI and simulated crop parameters: emergence, peak LAI, the mid-point between emergence and peak LAI, and anthesis. Linear models were fit between the MODIS NDVI and simulated values of FGrC, LAI, and AGB. Overall, the highest R2 values corresponded to using all of the dates for FGrC (R2 = 0.82) and AGB (R2 = 0.92), and anthesis dates for LAI (R2 = 0.74). For FGrC and AGB, the RMSE with simulated parameters were comparable or better than the equivalent results from the in situ measurements (note that there were no LAI in situ measurements to compare with). The results support the notion for extending the value of the MODIS NDVI using crop simulation models. The combination of remotely sensed and simulation data might offer regional maps of spatial AGB and ultimately grain yield, which would have high value for research, resource management, policy, and potentially, crop management.

Mapping dominant annual land cover from 2009 to 2013 across Victoria, Australia using satellite imagery.

There is a demand for regularly updated, broad-scale, accurate land cover information in Victoria from multiple stakeholders. This paper documents the methods used to generate an annual dominant land cover (DLC) map for Victoria, Australia from 2009 to 2013. Vegetation phenology parameters derived from an annual time series of the Moderate Resolution Imaging Spectroradiometer Vegetation Indices 16-day 250 m (MOD13Q1) product were used to generate annual DLC maps, using a three-tiered hierarchical classification scheme. Classification accuracy at the broadest (primary) class level was over 91% for all years, while it ranged from 72 to 81% at the secondary class level. The most detailed class level (tertiary) had accuracy levels ranging from 61 to 68%. The approach used was able to accommodate variable climatic conditions, which had substantial impacts on vegetation growth patterns and agricultural production across the state between both regions and years. The production of an annual dataset with complete spatial coverage for Victoria provides a reliable base data set with an accuracy that is fit-for-purpose for many applications.

Managing wheat from space: Linking MODIS NDVI and crop models for Australian dryland wheat

The purpose of this study was to begin to explore the relationships between Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI observations with both measured and simulated above ground biomass (AGB), fractional green cover (FGrC), and leaf area index (LAI) for wheat in Victoria. These initial comparisons of MODIS NDVI with both measurements and simulation results indicate positive, linear relationships for FGrC, LAI, and AGB and either NDVI or summed NDVI. Measurements of AGB from 2012 were fitted with a linear model to summed NDVI from MODIS, resulting in an R2 of 0.83. The results support the potential for extending the value of the MODIS NDVI using crop simulation models. The combination of remotely sensed and simulation data might offer regional maps of AGB and yield, which would have high value for research, resource management, policy, and potentially, crop management.

Time series analysis of MODIS EVI data for regular land cover mapping in Victoria, Australia

The objectives of this research were to develop a MOD13Q1 EVI based land cover classification protocol to generate a land cover map of Victoria and evaluate the accuracy of this map. In 2009, an extensive geographically stratified random sampling design was constructed and undertaken across the whole state. Twenty-three MOD13Q1 EVI image products were stacked and then smoothed using the Asymmetric Gaussian filter within the TIMESAT program where 11 phenology metrics that characterised the smoothed seasonal growth curves were created. In addition, the standard deviation of the 23 smoothed time slices was computed. Values for each of these 12 metrics were extracted for each homogenous area sampled on the ground and were used to create a rule set for classification using the C5.0 program. These rules were applied to the 12 metrics to produce a land cover classification of Victoria with 11 discrete classes. A 100-fold cross validation trial was used (as part of the C5.0 program) and determined the predictive accuracy of the rule set to be 76.8%.

Creating an historical land cover data set for the Wimmera region, Victoria, Australia from the USGS Landsat archive

The availability of the USGS Landsat archive, which spans nearly 40 years, has presented many opportunities for land cover mapping [1]. This study aimed to develop an approach using the USGS Landsat archive to produce land cover maps which could be used to assess changes in land cover over time. The study area was located in the Wimmera region of north-western Victoria, Australia. Initially, Normalized Vegetation Difference Index (NDVI) was used to generate coarse land cover maps of water, bare ground and low and high vegetation cover. These classes were refined using a Multiple Endmember Spectral Mixing Analysis (MESMA) approach. The production of this baseline data now presents opportunities for further investigation of land management, land cover changes and trends, and links to other biophysical data such as soil characteristics.