Integrating UAV-Derived Information and WorldView-3 Imagery for Mapping Wetland Plants in the Old Woman Creek Estuary, USA

Abstract

The classification of wetland plants using unmanned aerial vehicle (UAV) and satellite synergies has received increasing attention in recent years. In this study, UAV-derived training and validation data and WorldView-3 satellite imagery are integrated in the classification of five dominant wetland plants in the Old Woman Creek (OWC) estuary, USA. Several classifiers are explored: (1) pixel-based methods: maximum likelihood (ML), support vector machine (SVM), and neural network (NN), and (2) object-based methods: Naïve Bayes (NB), support vector machine (SVM), and k-nearest neighbors (k-NN). The study evaluates the performance of the classifiers for different image feature combinations such as single bands, vegetation indices, principal components (PCs), and texture information. The results showed that all classifiers reached high overall accuracy (>85%). Pixel-based SVM and object-based NB exhibited the best performance with overall accuracies of 93.76% and 93.30%, respectively. Insignificantly lower overall accuracy was achieved with ML (92.29), followed by NN (90.95) and object-oriented SVM (90.61). The k-NN method showed the lowest (but still high) accuracy of 86.74%. All classifiers except for the pixel-based SVM required additional input features. The pixel-based SVM achieved low errors of commission and omission, and unlike the other classifiers, exhibited low variability and low sensitivity to additional image features. Our study shows the efficacy of combining very high spatial resolution UAV-derived information and the super spectral observation capabilities of WorldView-3 in machine learning for mapping wetland vegetation.

Species Classification and Carbon Stock Assessment of Mangroves in Qi’ao Island with Worldview-3 Imagery

“…In the study, the use of UAV allowed us to collect more information on mangroves on a larger scale, and the use of UAV imagery allowed for the precise selection of training and validation samples of satellite imagery, thereby increasing the accuracy of satellite-image classification, while minimizing or eliminating the interference of field surveys in mangrove ecosystems [65]. Islam et al [40] corroborated that UAV-derived information was effective in mapping wet vegetation. Research by Oldeland et al has shown that UAV can greatly assist field surveys for plant-species monitoring by providing accurate species maps as well [66].…”

“…The findings demonstrated a significant improvement in classification accuracy due to the incorporation of texture features. This improvement was attributed to the inherent variation among the different species within the mangrove ecosystem, which included a variety of leaf shapes, average heights, and branch structures [40]. In high-resolution remotely sensed imagery, these distinct attributes appear as texture features [50].…”

“…However, this approach undoubtedly necessitates a greater number of resources, thereby increasing the research's overall cost and demands. The WorldView-3 satellite offers a promising solution as it is equipped with a sensor that has eight multi-spectral bands and one panchromatic band with a high spatial resolution of 0.31 m [40]. With more bands and higher resolution, WorldView-3 is at the forefront of commercial-satellite technology.…”

Vegetation discrimination based on chlorophyll prediction in Marshy wetland using Unmanned Aerial Vehicles

“…However, they occupy a small portion of the earth but are essential and play a significant ecological function (Chakraborty et al, 2023). Wetlands provide socio-economic benefits to society in addition to fishing and recreational activities (Islam et al, 2023). They also maintain the nutrient cycle in neighbouring agricultural regions, improve water quality, sequester carbon, and reduce floods in coastal areas (Balwan & Kour, 2021;Mitsch & Gosselink, 2015;Reddy et al, 2010).…”

“…For integrated analysis of various datasets, ML provides advantageous computational and analytical methods (Marin et al, 2021). In addition to handling complex relationships between multisource input data and parameter estimation, ML algorithms can manage nonlinearity and complex training data (Islam et al, 2023). A wide range of algorithms has been developed and tested from parametric models such as maximum likelihood to nonparametric models such as support vector machine (SVM), artificial neural network (ANN), random forest (RF), and decision tree (DT) (Fu et al, 2022; Islam et al, 2023; Martínez Prentice et al, 2021).…”