Detection of Aquatic Plants Using Multispectral UAV Imagery and Vegetation Index

Abstract

In this study, aquatic plants in a small reservoir were detected using multispectral UAV (Unmanned Aerial Vehicle) imagery and various vegetation indices. A Firefly UAV, which has both fixed-wing and rotary-wing flight modes, was flown over the study site four times. A RedEdge camera was mounted on the UAV to acquire multispectral images. These images were used to analyze the NDVI (Normalized Difference Vegetation Index), ENDVI (Enhance Normalized Difference Vegetation Index), NDREI (Normalized Difference RedEdge Index), NGRDI (Normalized Green-Red Difference Index), and GNDVI (Green Normalized Difference Vegetation Index). As for multispectral characteristics, waterside plants showed the highest reflectance in Rnir, while floating plants had a higher reflectance in Rre. During the hottest season (on 25 June), the vegetation indices were the highest, and the habitat expanded near the edge of the reservoir. Among the vegetation indices, NDVI was the highest and NGRDI was the lowest. In particular, NGRDI had a higher value on the water surface and was not useful for detecting aquatic plants. NDVI and GNDVI, which showed the clearest difference between aquatic plants and water surface, were determined to be the most effective vegetation indices for detecting aquatic plants. Accordingly, the vegetation indices using multispectral UAV imagery turned out to be effective for detecting aquatic plants. A further study will be accompanied by a field survey in order to acquire and analyze more accurate imagery information.

Hybrid Approach of Unmanned Aerial Vehicle and Unmanned Surface Vehicle for Assessment of Chlorophyll-a Imagery Using Spectral Indices in Stream, South Korea

“…(NIR * − red)/(NIR + red) [54] Normalized green-red difference index (NGRDI) (green − red)/(green + red) [55] Green normalized difference vegetation index (GNDVI) (NIR − green)/(NIR + green) [56,57] Normalized difference red edge index (NDRE) (NIR − red edge)/(NIR + red edge) [58] * NIR, near-infrared.…”

“…As a result of being compared with the actual data of the GNDVI, R 2 = 0.73 and RMSE = 3.41. The NDVI and GNDVI, which showed the most apparent difference between aquatic vegetation and water surface, were reported to be the most effective for detecting aquatic plants [54].…”

Modified Vegetation Detection Index Using Different-Spectral Signature

“…Although the blue channel has vegetation reflectance behavior similar to the red band, it has rarely been used in remote sensing as a vegetation indicator. The same applies to the green band, even though numerous researches have shown that it is better related to vegetation constraints [10]. The information obtained from vegetation indices varies due to spectrally similar features.…”

Applications of UAS in Crop Biomass Monitoring: A Review

“…Different spectral bands can reflect the characteristics of different plants and can be used to effectively distinguish different crops (Xu et al, 2019). Song and Park (2020) used the RedEdge multispectral camera from MicaSense to analyze the spectral characteristics of aquatic plants and found that waterside plants exhibited the highest reflectivity in the NIR band, while floating plants had high reflectivity in the red-edge band.…”