Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands

High-resolution images obtained by multispectral cameras mounted on Unmanned Aerial Vehicles (UAVs) are helping to capture the heterogeneity of the environment in images that can be discretized in categories during a classification process. Currently, there is an increasing use of supervised machine learning (ML) classifiers to retrieve accurate results using scarce datasets with samples with non-linear relationships. We compared the accuracies of two ML classifiers using a pixel and object analysis approach in six coastal wetland sites. The results show that the Random Forest (RF) performs better than K-Nearest Neighbors (KNN) algorithm in the classification of pixels and objects and the classification based on pixel analysis is slightly better than the object-based analysis. The agreement between the classifications of objects and pixels is higher in Random Forest. This is likely due to the heterogeneity of the study areas, where pixel-based classifications are most appropriate. In addition, from an ecological perspective, as these wetlands are heterogeneous, the pixel-based classification reflects a more realistic interpretation of plant community distribution.

Automated summary

In the study, high-resolution images obtained by multispectral cameras mounted on Unmanned Aerial Vehicles were used to classify coastal wetland sites. The Random Forest classifier outperformed the K-Nearest Neighbors algorithm, especially in pixel-based classification. The findings suggest that for heterogeneous environments like wetlands, pixel-based classification provides a more realistic interpretation of plant community distribution.