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The prevalence of the invasive species African Lovegrass (Eragrostis curvula, ALG thereafter) in Australian landscapes presents significant challenges for land managers, including agricultural losses, reduced native species diversity, and heightened bushfire risks. Uncrewed aerial system (UAS) remote sensing combined with AI algorithms offer a powerful tool for accurately mapping the spatial distribution of invasive species and facilitating effective management strategies. However, segmentation of vegetations within mixed grassland ecosystems presents challenges due to spatial heterogeneity, spectral similarity, and seasonal variability. The performance of state-of-the-art artificial intelligence (AI) algorithms in detecting ALG in the Australian landscape remains unknown. This study compared the performance of four supervised AI models for segmenting ALG using multispectral (MS) imagery at four sites and developed segmentation models for two different seasonal conditions. UAS surveys were conducted at four sites in New South Wales, Australia. Two of the four sites were surveyed in two distinct seasons (flowering and vegetative), each comprised of different data collection settings. A comparative analysis was also conducted between hyperspectral (HS) and MS imagery at a single site within the flowering season. Of the five AI models developed (XGBoost, RF, SVM, CNN, and U-Net), XGBoost and the customized CNN model achieved the highest validation accuracy at 99%. The AI model testing used two approaches: quadrat-based ALG proportion prediction for mixed environments and pixel-wise classification in masked regions where ALG and other classes could be confidently differentiated. Quadrat-based ALG proportion ground truth values were compared against the prediction for the custom CNN model, resulting in 5.77% and 12.9% RMSE for the seasons, respectively, emphasizing the superiority of the custom CNN model over other AI algorithms. The comparison of the U-Net demonstrated that the developed CNN effectively captures ALG without requiring the more intricate architecture of U-Net. Masked-based testing results also showed higher F1 scores, with 91.68% for the flowering season and 90.61% for the vegetative season. Models trained on single-season data exhibited decreased performance when evaluated on data from a different season with varying collection settings. Integrating data from both seasons during training resulted in a reduction in error for out-of-season predictions, suggesting improved generalizability through multi-season data integration. Moreover, HS and MS predictions using the custom CNN model achieved similar test results with around 20% RMSE compared to the ground truth proportion, highlighting the practicality of MS imagery over HS due to operational limitations. Integrating AI with UAS for ALG segmentation shows great promise for biodiversity conservation in Australian landscapes by facilitating more effective and sustainable management strategies for controlling ALG spread. Full article

Oil spill detection plays an important role in marine environment protection. The technique of global navigation satellite system-reflectometry (GNSS-R) has the advantage of a short revisit time, which could help with timely cleanup of marine oil pollution. The conventional GNSS-R oil spill detection algorithm can resolve only the dielectric constant of oil based on power ratio measurements, while that of water cannot be realized. This is because the dielectric constant of water is much larger than that of oil such that the range of the equation used in the conventional algorithm is inadequate. To resolve this problem, we proposed a new algorithm containing a new equation with a larger scope, which has never been applied previously to GNSS-R oil spill detection. We derived a lookup method to resolve the dielectric constant of both oil and water. To validate our method, a drone-borne GNSS-R experiment based on dual-frequency QZSS reflection signals was conducted on 17 July 2023 using experimental pools simulating oil spills. Raw IF data in the L1 and L5 bands, collected using dual antennas and a data recorder, were processed using a software-defined receiver to deduce the power ratios and SNR of the GNSS signals. Results showed that the proposed algorithm is capable of resolving the dielectric constants of the reflected surface. In addition, the L5 signal was found to provide more detail and better contrast than the L1 C/A signal. Full article