Discussion

Understanding vegetation water requirements and losses are important to inform environmental water management and underpin equitable water sharing plans. Given the significant advances in digital technology and high costs of in-situ monitoring, new innovative cost-effective methods are vital to monitor large land tracts both in Australia and other regions across the world (Manfreda et al. , 2018). Woody vegetation ET can provide a line of evidence to improve monitoring and inform water management, however downscaling of low spatial resolution data is required to provide robust remotely sensed ET estimates. The performance of the RFall predictor model presented within, indicates that a model has been developed that can accurately predict FTCC for both sparse and densely vegetated areas semi-arid and likely arid, floodplain environments.
As mentioned previously, while other fractional vegetation products are available (Guerschman et al. , 2015; Guerschman and Hill, 2018; DEE, 2019) the classification and spatial resolution of these did not suit the purpose of improving remotely sensed ET outputs. Guerschman and Hill (2018), for example, provide landscape fractional cover including percent photosynthetic vegetation, non-photosynthetic vegetation and bare soil across 250 m MODIS pixels. In contrast, the model presented here, provides FTCC in 10% increments of canopy cover related only to trees.

Important outcomes of method development

LiDAR imagery collected from the regions of interest (Yanga and Barmah National Parks) proved invaluable to the development of the reported method. LiDAR provides a proxy for field derived canopy cover, against which Sentinel data was trained. As the LiDAR output is composed of ‘point clouds’ representing 3-D land surface features, it was possible to separate trees over 2 m in height from other surface features, to provide ‘field-based’ canopy cover. The results, from a remote sensing perspective, are also important to understand critical bands that are required to monitor vegetation and water to inform future satellite development.

Additional method application

While remote sensing methods can be used to derive FTCC such as aerial imagery (Melville et al. , 2019), LiDAR (Wasser et al. , 2013) and fine resolution satellite imagery like WorldView2 and 3 (Immitzer et al. , 2018), acquiring imagery is costly and requires ‘tasking’ (i.e. imagery it is not collected regularly and needs to be ordered) for specific areas of interest. As a result, national scale imagery is not available and temporal availability is poor. In comparison, developing a method using open-access Sentinel-1 and -2 imagery, provides a mechanism to monitor vegetation cover change from 2015 and into the future at desired intervals such as monthly, seasonally or annually, depending on the application.
The FTCC method, is however, likely to be very valuable to other areas of catchment water management. The significant bushfires across southern Australia over the summer of 2019/2020 are likely to have significant future impacts on water resources and especially changes to water yield in both quality and quantity over the next decade (Brown, 1972; Lee, 2020; Moreno et al. , 2020). The FTCC method would enable accurate estimates of tree area, pre and post bushfires, to underpin future hydrological catchment yield forecasting. Current methods are unlikely to be suitable to disentangle woody tree vegetation, which is a dominant water user, from other vegetation sources. This may lead to errors in water yield estimation pre and post fires. Tree reduction also increases streamflow locally, although this is quickly reversed as regeneration occurs, particularly in Australia with bush tolerant native species (Kuczera, 1987; Brookhouse et al. , 2013). There is an opportunity to link broadscale FTCC predictions with modelling of water fluxes through Land Surface Models, enabling modelling to understand the effects of fires (or any land cover changes) on hydrologic fluxes (Barlage and Zeng, 2004; Fang et al. , 2018). As severe bushfires have also featured in other areas around the world such as the United States and Europe, the method is relevant internationally.

Sources of error

Sentinel data was trained against LiDAR which was collected between 2009-2015. The very high correlation between LiDAR FTCC and predicted FTCC, provides some confidence that although time has passed, substantial changes to vegetation crowns were not apparent in the trained areas. As vegetation might have changed slightly between the training data (LiDAR) and the covariates (multi-spectral and SAR bands), part of the error is actually not attributable to FTCC modelling, i.e. the discrepancy between the LiDAR FTCC and the predicted FTCC represent a maximum error margin. Yanga LiDAR, collected in 2009, occurred before the break in the Millennium Drought from 1997 to 2009 (Leblanc et al. , 2012), while Barmah LiDAR was collected after (2015). This might explain the poorer prediction at Yanga using the RFYangamodel as substantial improvement in tree canopy crowns occurred over the 2010-2012 flood period (Doody et al. , 2015) leading to a discrepancy between amount of crown cover pre and post flood. The match at Barmah was likely higher due to closer match between imagery dates (2015 LiDAR and 2016 Sentinel). While the date gap between Yanga imagery is not ideal, it was suitable for this project, however more recent LiDAR imagery is preferred. Additional sources of error could have been introduced to the model from use of two different LiDAR collecting platforms and differences in their acquisition altitudes as well as the spatial mismatch between 20 m training data and 10 m LiDAR data.

Further research

While the initial method shows considerable promise for widespread application and identification of FTCC, to scale the method across the MDB, additional areas will need to be trained to incorporate vegetation in different climate and especially rainfall zones. It is unclear if regions with higher rainfall will fit the RFall model, so further investigation is required. The objective moving forward, is to provide a universal model to predict FTCC across the MDB and examine reducing FTCC resolution further to <10 m. Building further upon that, will be investigation of the feasibility of producing FTCC timeseries over the period of Sentinel availability (~5 years), focusing on seasonal and annual predictions which will be valuable for monitoring of temporal vegetation canopy cover change at a fine resolution. As mentioned in relation to bushfire and water yield research applications, provision of <10 m woody tree canopy cover would substantially improve vegetation water use estimates based on tree area and aid forecasts of how water yield and hydrologic fluxes (ET, recharge and runoff) will change into the future.