Discriminating between C3, C4, and Mixed C3/C4 Pasture Grasses of a Grazed Landscape Using Multi-Temporal Sentinel-1a Data

In livestock grazing environments, the knowledge of C3/C4 species composition of a pasture field is invaluable, since such information assists graziers in making decisions around fertilizer application and stocking rates. The general aim of this research was to explore the potential of multi-temporal Sentinel-1 (S1) Synthetic Aperture Radar (SAR) to discriminate between C3, C4, and mixed-C3/C4 compositions. In this study, three Random Forest (RF) classification models were created using features derived from polarimetric SAR (polSAR) and grey-level co-occurrence textural metrics (glcmTEX). The first RF model involved only polSAR features and produced a prediction accuracy of 68% with a Kappa coefficient of 0.49. The second RF model used glcmTEX features and produced prediction accuracies of 76%, 62%, and 75% for C3, C4, and mixed C3/C4 grasses, respectively. The glcmTEX model achieved an overall prediction accuracy of 73% with a Kappa coefficient of 0.57. The polSAR and glcmTEX features were then combined (COMB model) to improve upon their individual classification performances. The COMB model produced prediction accuracies of 89%, 81%, and 84% for C3, C4, and mixed C3/C4 pasture grasses, and an overall prediction accuracy of 86% with a Kappa coefficient of 0.77. The contribution of the various model features could be attributed to the changes in dominant species between sampling sites through time, not only because of climatic variability but also because of preferential grazing

Discriminating between C3, C4, and Mixed C3/C4 Pasture Grasses of a Grazed Landscape Using Multi-Temporal Sentinel-1a Data

In livestock grazing environments, the knowledge of C3/C4 species composition of a pasture field is invaluable, since such information assists graziers in making decisions around fertilizer application and stocking rates. The general aim of this research was to explore the potential of multi-temporal Sentinel-1 (S1) Synthetic Aperture Radar (SAR) to discriminate between C3, C4, and mixed-C3/C4 compositions. In this study, three Random Forest (RF) classification models were created using features derived from polarimetric SAR (polSAR) and grey-level co-occurrence textural metrics (glcmTEX). The first RF model involved only polSAR features and produced a prediction accuracy of 68% with a Kappa coefficient of 0.49. The second RF model used glcmTEX features and produced prediction accuracies of 76%, 62%, and 75% for C3, C4, and mixed C3/C4 grasses, respectively. The glcmTEX model achieved an overall prediction accuracy of 73% with a Kappa coefficient of 0.57. The polSAR and glcmTEX features were then combined (COMB model) to improve upon their individual classification performances. The COMB model produced prediction accuracies of 89%, 81%, and 84% for C3, C4, and mixed C3/C4 pasture grasses, and an overall prediction accuracy of 86% with a Kappa coefficient of 0.77. The contribution of the various model features could be attributed to the changes in dominant species between sampling sites through time, not only because of climatic variability but also because of preferential grazing

Monitoring Pasture Species, Biomass and Canopy Heterogeneity Using Sentinel-1 Synthetic Aperture Radar Data

Pasture grasses are an important feed-base for the livestock industry. The ability to identify and characterise pasture type, species composition and quantify the available biomass in fields is invaluable to the sustainability and profitability of our livestock industries. Pasture species composition, biomass and canopy structural variations have been measured at different spatial scales using varied optical methods/tools such as active optical sensors, as well as aerial and spaceborne passive optical sensors. At large spatial scale, optical satellite sensors are often used. However, the utilisation of these sensors is affected by cloudy weather conditions and the fact that they are really only responsive to photosynthetically active biomass. Satellite-based Synthetic Aperture Radar (SAR) sensors, though not popular yet in pasture studies, have the potential to offset this limitation of optical sensing as the microwave energy emitted by these sensors penetrate clouds and that these wavelengths are also sensitive to volumetric scattering processes rendering them, potentially useful to situations involving significant, senesced, plant material (e.g. during drought) . This thesis predominantly focussed on Sentinel-1 C-band SAR with the whole research project comprising of three main components: (i) discrimination of pasture species based on C3 and C4 photosynthetic mechanisms and diversity of the botanical composition; (ii) estimating pasture biophysical variables with emphasis on aboveground biomass; and (iii) detection of surface heterogeneity due to selective grazing in pasture fields. In discriminating pasture species into C3, C4 and mixed C3/C4 classes, Random Forest classification was used and the highest overall classification accuracy (86%) was achieved with a combination of grey-level co-occurrence textural metrics and polarimetric SAR metrics. Moreover, the combined strengths of Sentinel-1 SAR and Sentinel-2 optical information parameterised into K-Nearest Neighbours, Random Forest and Support Vector Machine classifiers, produced the highest overall accuracy estimates of 89%, 96% and 95%, respectively. Regression models such as the generalised additive model estimated pasture biomass with a root mean square error of prediction of 392 kg/ha over AGB estimates between 443–2642 kg/ha. Here pasture LAI ranged from 0.27 to 1.87, and sward height from 3.25 cm to 13.75 cm. In the final study, canopy heterogeneity due to selective grazing was detectable with the Sentinel-1 SAR. Particularly, the range estimates (dispersion measure) of the polarimetric scattering entropy produced the strongest, statistically significant, linear correlation with a metric of patchiness (R2 =0.74). Altogether, this thesis has demonstrated that Sentinel-1 SAR on its own as well as when integrated with optical data, could be a useful tool providing data to aid in pasture management

A Preliminary Investigation of the Potential of Sentinel-1 Radar to Estimate Pasture Biomass in a Grazed Pasture Landscape

Knowledge of the aboveground biomass (AGB) of large pasture fields is invaluable as it assists graziers to set stocking rate. In this preliminary evaluation, we investigated the response of Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data to biophysical variables (leaf area index, height and AGB) for native pasture grasses on a hilly, pastoral farm. The S1 polarimetric parameters such as backscattering coefficients, scattering entropy, scattering anisotropy, and mean scattering angle were regressed against the widely used morphological parameters of leaf area index (LAI) and height, as well as AGB of pasture grasses. We found S1 data to be more responsive to the pasture parameters when using a 1 m digital elevation model (DEM) to orthorectify the SAR image than when we employed the often-used Shuttle Radar Topography 30 m and 90 m Missions. With the 1m DEM analysis, a significant quadratic relationship was observed between AGB and VH cross-polarisation (R2 = 0.71), and significant exponential relationships between polarimetric entropy and LAI and AGB (R2 = 0.53 and 0.45, respectively). Similarly, the mean scattering angle showed a significant exponential relationship with LAI and AGB (R2 = 0.58 and R2 = 0.83, respectively). The study also found a significant quadratic relationship between the mean scattering angle and pasture height (R2 = 0.72). Despite a relatively small dataset and single season, the mean scattering angle in conjunction with a generalised additive model (GAM) explained 73% of variance in the AGB estimates. The GAM model estimated AGB with a root mean square error of 392 kg/ha over a range in pasture AGB of 443 kg/ha to 2642 kg/ha with pasture LAI ranging from 0.27 to 1.87 and height 3.25 cm to 13.75 cm. These performance metrics, while indicative at best owing to the limited datasets used, are nonetheless encouraging in terms of the application of S1 data to evaluating pasture parameters under conditions which may preclude use of traditional optical remote sensing systems

Enabling Regenerative Agriculture Using Remote Sensing and Machine Learning

The emergence of cloud computing, big data analytics, and machine learning has catalysed the use of remote sensing technologies to enable more timely management of sustainability indicators, given the uncertainty of future climate conditions. Here, we examine the potential of “regenerative agriculture”, as an adaptive grazing management strategy to minimise bare ground exposure while improving pasture productivity. High-intensity sheep grazing treatments were conducted in small fields (less than 1 ha) for short durations (typically less than 1 day). Paddocks were subsequently spelled to allow pasture biomass recovery (treatments comprising 3, 6, 9, 12, and 15 months), with each compared with controls characterised by lighter stocking rates for longer periods (2000 DSE/ha). Pastures were composed of wallaby grass (Austrodanthonia species), kangaroo grass (Themeda triandra), Phalaris (Phalaris aquatica), and cocksfoot (Dactylis glomerata), and were destructively sampled to estimate total standing dry matter (TSDM), standing green biomass, standing dry biomass and trampled biomass. We invoked a machine learning model forced with Sentinel-2 imagery to quantify TSDM, standing green and dry biomass. Faced with La Nina conditions, regenerative grazing did not significantly impact pasture productivity, with all treatments showing similar TSDM, green biomass and recovery. However, regenerative treatments significantly impacted litterfall and trampled material, with high-intensity grazing treatments trampling more biomass, increasing litter, enhancing surface organic matter and decomposition rates thereof. Pasture digestibility and sward uniformity were greatest for treatments with minimal spelling (3 months), whereas both standing senescent and trampled material were greater for the 15-month spelling treatment. TSDM prognostics from machine learning were lower than measured TSDM, although predictions from the machine learning approach closely matched observed spatiotemporal variability within and across treatments. The root mean square error between the measured and modelled TSDM was 903 kg DM/ha, which was less than the variability measured in the field. We conclude that regenerative grazing with short recovery periods (3–6 months) was more conducive to increasing pasture production under high rainfall conditions, and we speculate that – in this environment - high-intensity grazing with 3-month spelling is likely to improve soil organic carbon through increased litterfall and trampling. Our study paves the way for using machine learning with satellite imagery to quantify pasture biomass at small scales, enabling the management of pastures within small fields from afar