The essence of acquisition time of airborne hyperspectral and on-ground reference data for classification of highly invasive annual vine Echinocystis lobata (Michx.) Torr. & A. Gray

Abstract

Invasive alien species are one of the biggest threats to biodiversity today. Identifying their locations are mandatory parts of the strategies being developed to control them. Remote sensing along with machine learning are already proven and effective tools for monitoring invasive species, especially trees, shrubs, and tall perennials. However, annual vine species are particularly difficult to map using remote sensing because of their dynamic plant growth and the movement of shoots during the growing season. Therefore, the phenological phase in which the data is acquired, and the synchronization of airborne data acquisition with on-ground reference data, may be key factors for correct plant classification. This research aimed to answer the following questions: (i) What is the impact of acquiring synchronized on-ground data and hyperspectral data in different phases of plants’ phenological development on the annual vine IAPS (Invasive Alien Plant Species) classification results? (ii) How does the lack of synchronization while obtaining hyperspectral and on-ground data collection impact annual vine IAPS mapping results? (iii) Does multitemporal image fusion improve the results of annual vine IAPS classification? For this purpose, research was carried out on Echinocystis lobata, an annual vine species considered highly invasive in Europe. The obtained results indicate that the phenological phase in which the data is acquired has a very strong influence on the quality of the classification result. The period of flowering (summer) with the greatest coverage of the area with shoots was optimal for the classification of Echinocystis lobata with F1 classification accuracy of 0.87 ± 0.04. The accuracy of classifications was significantly less for spring (F1 = 0.64 ± 0.04) and autumn (F1 = 0.75 ± 0.05). Obtaining on-ground reference data that is mismatched temporally with hyperspectral data causes a decrease in the accuracy of the result up to 0.08 (from F1 = 0.64 to 0.56) in relation to data obtained synchronously. In the multitemporal image fusion method, using hyperspectral data linked from different phases of plants’ development to classify an image had a minimal improvement in classification accuracy compared to classifications trained on images from one phenological stage. The main conclusion is that mapping an annual vine using remote sensing and machine learning is possible and highly effective, provided the remote sensing and on-ground data are obtained in strict synchronization and the appropriate phenological phase. For the most efficient classification results, a single data acquisition per year is enough, even in the case of annual vine IAPS. Further research is needed to explore the possibility of mapping Echinocystis lobata using, i.e. multispectral or hyperspectral satellite data (e.g. EnMAP).

Keywords

• plant species classification
• multitemporal data fusion
• random forest
• time series
• plant phenology
• machine learning