Aim  Encroachment or densification by woody plants affects natural ecosystems around the world. Many studies have reported encroachment in temperate Australia, particularly in coastal ecosystems and grassy woodlands. However, the degree to which published studies reflect broad‐scale changes is unknown because most studies intentionally sampled areas with conspicuous densification. We aimed to estimate changes in woody vegetation cover within lowland grassy woodland and coastal ecosystems in Victoria from 1989 to 2005 to determine whether published reports of recent encroachment are representative of broad‐scale ecosystem changes.Location  All lowland grassy woodland and coastal ecosystems (c. 6.11 × 10⁵ ha) in Victoria, Australia. Four major ecosystems were analysed: Plains woodlands, Herb‐rich woodlands, Riverine woodlands and Coastal vegetation.Methods  Changes in woody vegetation cover from 1989 to 2005 were assessed based on state‐wide vegetation maps and Landsat analyses of woody vegetation cover conducted by the Australian Greenhouse Office’s National Carbon Accounting System. The results show changes in woody cover within mapped patches of native vegetation, rather than changes in the extent of woody vegetation resulting from clearing and revegetation.Results  When pooled across all ecosystems, woody vegetation increased by 18,730 ha from 1989 to 2005. Woody cover within Riverine woodlands and within Plains woodlands each increased by >7000 ha. At the patch scale, the mean percentage cover of woody vegetation in each polygon increased by >5% in all four ecosystems: Riverine woodlands (+9.2% on average), Herb‐rich woodlands (+7.6%), Plains woodlands (+6.7%) and Coastal vegetation (+5.9%). Regression models relating degree of encroachment to geographic and climatic variables were extremely weak (r² ≤ 0.026), indicating that most variation occurred at local scales rather than across broad geographic gradients.Main conclusions  At the scale of observation, woody vegetation cover increased in all lowland woodland and coastal ecosystems over the 16‐year period. Thus, published examples of encroachment in selected coastal and woodland patches do appear to reflect widespread increases in woody vegetation cover in these ecosystems. This densification appears to be associated with changes in land management rather than with post‐fire vegetation recovery and is likely to be ongoing and long‐lasting, with substantial implications for biodiversity conservation and ecosystem services.

Some of the remnants of the Cumberland Plain woodland, an endangered dry sclerophyllous forest type of New South Wales, Australia, host large populations of mistletoe. In this study, the extent of mistletoe infection was investigated based on a forest inventory. We found that the mistletoe infection rate was relatively high, with 69% of the Eucalyptus fibrosa and 75% of the E. moluccana trees being infected. Next, to study the potential consequences of the infection for the trees, canopy temperatures of mistletoe plants and of infected and uninfected trees were analyzed using thermal imagery acquired during 10 flights with an unmanned aerial vehicle (UAV) in two consecutive summer seasons. Throughout all flight campaigns, mistletoe canopy temperature was 0.3–2 K lower than the temperature of the eucalypt canopy it was growing in, suggesting higher transpiration rates. Differences in canopy temperature between infected eucalypt foliage and mistletoe were particularly large when incoming radiation peaked. In these conditions, eucalypt foliage from infected trees also had significantly higher canopy temperatures (and likely lower transpiration rates) compared to that of uninfected trees of the same species. The study demonstrates the potential of using UAV-based infrared thermography for studying plant-water relations of mistletoe and its hosts.

Mid-season nitrogen (N) application in rice crops can maximize yield and profitability. This requires accurate and efficient methods of determining rice N uptake in order to prescribe optimal N amounts for topdressing. This study aims to determine the accuracy of using remotely sensed multispectral data from satellites to predict N uptake of rice at the panicle initiation (PI) growth stage, with a view to providing optimum variable-rate N topdressing prescriptions without needing physical sampling. Field experiments over 4 years, 4–6 N rates, 4 varieties and 2 sites were conducted, with at least 3 replicates of each plot. One WorldView satellite image for each year was acquired, close to the date of PI. Numerous single- and multi-variable models were investigated. Among single-variable models, the square of the NDRE vegetation index was shown to be a good predictor of N uptake (R ${}^2$ = 0.75, RMSE = 22.8 kg/ha for data pooled from all years and experiments). For multi-variable models, Lasso regularization was used to ensure an interpretable and compact model was chosen and to avoid over fitting. Combinations of remotely sensed reflectances and spectral indexes as well as variety, climate and management data as input variables for model training achieved R ${}^2$ < 0.9 and RMSE < 15 kg/ha for the pooled data set. The ability of remotely sensed data to predict N uptake in new seasons where no physical sample data has yet been obtained was tested. A methodology to extract models that generalize well to new seasons was developed, avoiding model overfitting. Lasso regularization selected four or less input variables, and yielded R ${}^2$ of better than 0.67 and RMSE better than 27.4 kg/ha over four test seasons that weren’t used to train the models.

Rice growers are reluctant to physically sample their crops, with many relying on visual estimates of crop nitrogen status to determine mid-season nitrogen topdressing requirements. There is increased interest in using remote sensing to determine how accurately mid-season rice crop nitrogen status (nitrogen concentration and content in shoots) can be predicted. Field-derived hyperspectral reflectance measurements were collected over three seasons to evaluate the optimal wavelengths for the prediction of in-crop nitrogen at panicle initiation. Rice plots were planted and differing nitrogen rates applied to establish a range of crop nitrogen uptake levels at the panicle initiation stage of crop development. Hyperspectral canopy reflectance (350–2500 nm) data were collected from each plot at the panicle initiation growth stage and plant samples were collected at each scanning location. Calibration models were developed for plant nitrogen concentration, dry matter and nitrogen uptake using partial least squares regression and the influential wavelengths used in the nitrogen uptake prediction model identified. When the nitrogen uptake model, which included samples collected in three crop seasons, was used to predict nitrogen uptake in an independent set of samples, r² was 0.84 and the root-mean-square error of prediction ( RMSEP) was 16.5 kg N ha⁻¹. The important wavelengths utilised for predicting nitrogen uptake were determined from the regression coefficients and a new calibration created by multiple linear regression (MLR) using only four wavelengths (738 nm, 1362 nm, 1835 nm and 1859 nm). The MLR calibration using the four wavelengths had a prediction accuracy of r² = 0.82 and RMSEP = 18.4 kg N ha⁻¹, which shows potential for commercial applications. These findings will encourage the development of a remote sensing method for predicting nitrogen uptake for rice crops using the identified wavelengths in an inexpensive multiband instrument.