Remote sensing from optical radiometers in space offers a nondestructive approach to estimating above ground biomass (AGB) with high spatial and temporal resolution, but the application is challenged by cloud cover and differences in soil background and crop phenology. We present a framework based on Sentinel-2 imagery for relating the adjusted summed NDVI measurements to the AGB. The resulting R² values for the measured and estimated AGB ranged from 0.79 to 0.98 for individual paddocks, and the R² from a pooled dataset (multiple crops, years, and locations) was 0.86. Application of the pooled dataset model to a separate validation dataset resulted in an R² of 0.88; however, there was a bias that resulted in the underestimation of the measured biomass. Analysis of the impacts of the gaps in the time series showed a decrease of 0.43% per gap day for the summed NDVI values. To address the impacts of clouds, we demonstrate the use of active optical and additional satellite imagery to fill the gaps due to clouds in the Sentinel-2 imagery. The framework presented results of the spatial daily estimates of the AGB and crop growth rates.

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.

Alligator weed (Alternanthera philoxeroides (Mart.) Griseb) forms dense infestations in aquatic environments and is the focus of intensive management programs in many jurisdictions within Australia, including Victoria. A critical component of weed biosecurity programs is surveillance to find the location and extent of the target weed so that control strategies can be implemented. Current approaches within Victoria rely heavily on ground surveys and community reporting. However, these methods do not provide a systematic approach to surveillance across landscapes, resulting in undiscovered infestations. The aim of this study was to detect alligator weed from aerial photography and demonstrate the potential use of remote sensing data to support existing ground surveys and monitoring programs. Two random forest algorithms were trained based on data from 2010 and 2016. Both classifiers had high levels of accuracy, with an overall pixel-based classification accuracy of 96.8% in 2010 and 98.2% in 2016. The trained classifiers were then applied to imagery acquired annually between 2010 and 2016. The classification outputs were combined with class probability and water proximity data to produce a weighted, normalised alligator weed likelihood data layer. These datasets were evaluated by assessing alligator weed patch detection rates, using manually delineated areas of weed for each year. The patch detection rates for each year ranged from 76.5% to 100%. The results also demonstrate the use of this approach for monitoring alligator weed infestations at a site over time. The key outcome of the study is an approach to support existing biosecurity monitoring and surveillance efforts at a landscape scale and at known infested localised sites.

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.