Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing

Abstract

1. This study evaluates the efficacy of remote sensing technology to monitor species composition, areal extent and density of aquatic plants (macrophytes and filamentous algae) in impoundments where their presence may violate water-quality standards. 2. Multispectral satellite (IKONOS) images and more than 500 in situ hyperspectral samples were acquired to map aquatic plant distributions. By analyzing field measurements, we created a library of hyperspectral signatures for a variety of aquatic plant species, associations and densities. We also used three vegetation indices. Normalized Difference Vegetation Index (NDVI), near-infrared (NIR)-Green Angle Index (NGAI) and normalized water absorption depth (D H ) , at wavelengths 554, 680, 820 and 977 nm to differentiate among aquatic plant species composition, areal density and thickness in cases where hyperspectral analysis yielded potentially ambiguous interpretations. 3. We compared the NDVI derived from IKONOS imagery with the in situ, hyperspectralderived NDVI. The IKONOS-based images were also compared to data obtained through routine visual observations. Our results confirmed that aquatic species composition alters spectral signatures and affects the accuracy of remote sensing of aquatic plant density. The results also demonstrated that the NGAI has apparent advantages in estimating density over the NDVI and the D H . 4. In the feature space of the three indices, 3D scatter plot analysis revealed that hyperspectral data can differentiate several aquatic plant associations. High-resolution multispectral imagery provided useful information to distinguish among biophysical aquatic plant characteristics. Classification analysis indicated that using satellite imagery to assess Lemna coverage yielded an overall agreement of 79% with visual observations and >90% agreement for the densest aquatic plant coverages. 5. Interpretation of biophysical parameters derived from high-resolution satellite or airborne imagery should prove to be a valuable approach for assessing the effectiveness of management practices for controlling aquatic plant growth in inland waters, as well as for routine monitoring of aquatic plants in lakes and suitable lentic environments.

Mapping Aquatic Vegetation in a Large, Shallow Eutrophic Lake: A Frequency-Based Approach Using Multiple Years of MODIS Data

“…Water quality degradation is often associated with the disappearance of aquatic vegetation [4,5] and the increase of algal bloom [6]; thus, aquatic vegetation has been widely used as an indicator of water quality and nutrient loading in aquatic systems [7,8]. The efficient and accurate estimation of the distribution of aquatic vegetation is important for lake and water resource management [3].…”

“…Traditional site-specific sampling and analysis are limited in both time and spatial resolution, whereas remote sensing offers a potentially convenient way to assess aquatic vegetation in a synoptic manner [7]. The aquatic vegetation and floating algae are known to be co-existent in many aquatic systems [6,9].…”

Unmanned aircraft systems help to map aquatic vegetation

“…; Tian et al. ; Yang & Everitt ), but processing is time‐consuming, even when small areas are covered (Adam et al. ).…”

“…The higher spectral resolution of hyperspectral imagery increases the potential for automated species discrimination (e.g. Hamada et al 2007;Tian et al 2010;Yang & Everitt 2010), but processing is time-consuming, even when small areas are covered (Adam et al 2010). In a direct comparison, aquatic vegetation mapping based on visual interpretation yielded more species-specific information compared to automated classification (Valta-Hulkkonen et al 2003b).…”

The Potential Link Between El Nino and Water Hyacinth Blooms in Winam Gulf of Lake Victoria, East Africa: Evidence from Satellite Imagery

“…A number of studies (e.g. Kiage and Walker 2009;Jiang and Huete 2010;Tian et al 2010;Anyamba et al 2001) have shown that NDVI can be a sensitive indicator of presence or absence of vegetation in both aquatic and terrestrial environments. The excellent efficacy of the NDVI for detecting vegetation is attributed to the fact that chlorophyll a in plants absorb red light for photosynthesis and reflect highly in the near infrared wavelengths because of scattering caused by internal leaf structure.…”