Results

Assessment of Random forest models

For the Yanga ROI, there was a moderate correlation between LiDAR FTCC and predicted FTCC (Figure 2a; RFYanga), with the Random forest model underpredicting. The correlation was higher (0.8, p-value < 0.01) at Barmah (Figure 2b; RFBarmah). The model trained using both Barmah and Yanga data (RFall), derived significantly improved results, exhibiting a strong correlation between LiDAR FTCC and predicated FTCC for both ROI’s (Figure 2c and d). The RFall predictor model could explain 85% of the FTCC variation for Yanga and 91% for Barmah. The RMSE indicates the accuracy of RFall was higher than RFYanga and RFBarmah, strongly demonstrating the combined model (RFall) contains more information than RFYanga and RFBarmah individually.

Prediction of the fractional tree canopy cover for Yanga and Barmah

The percentage of FTCC per 20 m pixel was predicted for both Yanga (Figure 3a) and Barmah (Figure 3d) ROIs. High FTCC (> 65%) was found along the Murrumbidgee River and around the periphery of ‘Irrigation Lake’ at Yanga, in both the predicted and LiDAR FTCC images (Figure 3a and b). Low FTCC can be observed predominantly in the south of the ROI related to bare land and/or low shrubs such asMuehlenbeckia florulenta (Lignum). The patterns of predicted FTCC and LiDAR FTCC generally showed high consistency, although areas indicated within the red boxes suggest errors (Figure 3c). These are likely related to a mismatch between the spatial resolution of the training data (20 m) and LiDAR data (10 m).
Within the Barmah ROI, average FTCC was much higher across the ROI (72%) compared to Yanga (26%) which is not surprising given the ROI is in the heart of a forested area. Similar to Yanga, predicted FTCC (Figure 3d) displayed similar patterns to LiDAR FTCC (Figure 3e) when predicted using the RFall model. Measurement errors were detected in the LiDAR data (red areas; Figure 3e) which explains the missing section of river channel in that figure and it’s prominence as an error (yellow pixels) in Figure 3f, which is not an error of prediction.

Relative importance of remote sensing products in the predictive model

One of the most efficient functions of the Random forest model is to reveal the relative importance of imaging techniques and bands (spectral for multi-spectral or polarization for SAR) contributing to the developed predictive models (Fassnacht et al. , 2014). Sentinel-2 band B12, a shortwave infrared band with a central wavelength of 2202.4 nm (Vaudour et al. , 2019) proved most significant to predict FTCC across the three independent predictor models (Figure 4). Prior studies note the importance of SWIR with respect to leaf water content (Tucker, 1980; Han et al. , 2019) and remote sensing. Band B11, another SWIR band, contributed an average of 51% to the final result (Figure 4), while bands B2, 3, and 4 were all vital to the RFallmodel, with an average importance of 46%. The contribution of Sentinel-1 bands was less important than Sentinel-2 bands. However, of note is the ‘cross-polarized’ VH band, which contains information on complex volume scattering (a typically dominant radar scattering mechanism in tree canopy covers) which is the most information-rich of the two SAR bands.
SWIR bands are sensitive to variation in leaf area index and leaf water content (Asner and Lobell, 2000; Ghulam et al. , 2008). SWIR can account for up to 89% of leaf area index variation based on simulations of a radiative transfer model (Bowyer and Danson, 2004; Wang et al. , 2008). In addition, prior studies have noted the importance of leaf water thickness variations which are strongly presented in SWIR bands due to the low absorption of light by water (Asner and Lobell, 2000; Ghulam et al. , 2008). Hence, the obvious difference of leaf area index and leaf water content for canopy and soil contributes significantly in Random forest model training.
Considering the lower ranking of the Sentinel-1 bands, the RFall model was trained without Sentinel-1 bands. Correlations were weaker than the previous model at Yanga (R2 = 0.84, p-value < 0.01) and Barmah (R2 = 0.86, p-value < 0.01), respectively (data not shown). The results suggest that Sentinel-1 bands play an important role in the Random forest model training to accurately derive FTCC.