Unmanned aerial vehicles (UAV) are increasingly used for spatiotemporal monitoring of invasive plants in coastal wetlands. Early identification of invasive species is necessary in planning, restoring, and managing wetlands. This study assessed the effectiveness of UAV technology to identify invasive Phragmites australis in the Old Woman Creek (OWC) estuary using machine learning (ML) algorithms: Neural network (NN), support vector machine (SVM), and k-nearest neighbor (kNN). The ML algorithms were compared with the parametric maximum likelihood classifier (MLC) using pixel- and object-based methods. Pixel-based NN was identified as the best classifier with an overall accuracy of 94.80% and the lowest error of omission of 1.59%, the outcome desirable for effective eradication of Phragmites. The results were reached combining Sequoia multispectral imagery (green, red, red edge, and near-infrared bands) combined with the canopy height model (CHM) acquired in the mid-growing season and normalized difference vegetation index (NDVI) acquired later in the season. The sensitivity analysis, using various vegetation indices, image texture, CHM, and principal components (PC), demonstrated the impact of various feature layers on the classifiers. The study emphasizes the necessity of a suitable sampling and cross-validation methods, as well as the importance of optimum classification parameters.

(1) Background: Strategies aimed at managing freshwater eutrophication should be based on practices that consider cropland invertebrates, climatic change, and soil nutrient cycling. Specifically, detritivores play a crucial role in the biogeochemical processes of soil through their consumptive and burrowing activities. Here, we evaluated the effectiveness of increasing detritivore abundance as a strategy for nutrient management under varied rainfall. (2) Methods: We manipulated soil macroinvertebrate abundance and rainfall amount in an agricultural mesocosms. We then measured the phosphorus, nitrogen, and carbon levels within the soil, corn, invertebrates, and soil solution. (3) Results: Increasing detritivore abundance in our soil significantly increased corn biomass by 2.49 g (p < 0.001), reduced weed growth by 18.2% (p < 0.001), and decreased soil solution nitrogen and total organic carbon (p < 0.05) and volume by 31.03 mL (p < 0.001). Detritivore abundance also displayed a significant interaction effect with rainfall treatment to influence soil total P (p = 0.0019), total N (p < 0.001), and total C (p = 0.0146). (4) Conclusions: Soil detritivores play an important role in soil nutrient cycling and soil health. Incorporating soil macroinvertebrate abundance into management strategies for agricultural soil may increase soil health of agroecosystems, preserve freshwater ecosystems, and protect the valuable services they both provide for humans.