Abstract
Remote sensing of terrestrial vegetation has been successful thanks to the unique spectral characteristics of green vegetation, low reflectance in red and high reflectance in Near-InfraRed (NIR). These spectral characteristics were used to develop vegetation indices, including Normalized Difference Vegetation Index (NDVI). However, the NIR absorption by water and light scattering from suspended particles reduces the practical application of such indices in aquatic vegetation studies, especially for the Submerged Aquatic Vegetation (SAV) that grows below water surface. We experimentally tested if NDVI can be used to depict canopies of aquatic plants in shallow waters. A 100-gallonoutdoor tank was lined with black pond liners, a black panel or SAV shoots were mounted on the bottom, and filled with water up to 0.5 m. We used a GER 1500 spectroradiometer to collect spectral data over floating waterhyacinth (Eichhornia crassipes) and also over the tanks that contain SAV and black panel at varying water depths. The measured upwelling radiance was converted to % reflectance; and we integrated the hyperspectral reflectance to match the Red and NIR bands of three satellite sensors: Landsat 7 ETM, SPOT 5 HRG, and ASTER. NDVI values ranged 0.6-0.65 when the SAV canopy was at the water level, then they decreased linearly (slope of 0.013 NDVI/meter) with water depth increases in clear water. When corrected for water attenuation using the data obtained from the black panel, the NDVI values significantly increased at all depths that we tested (0.1 - 0.5 m). Our results suggest the conventional NDVI: (1) can be used to depict SAV canopies at water surface; (2) is not a good indicator for SAV that is adapted to live underwater or other aquatic plants that are submerged during flooding even at shallow waters (0.3 m); and (3) the index values can significantly improve if information on spectral reflectance attenuation caused by water volume increases is collected simultaneously through ground-truthing and integrated.
Highlights
Remote sensors detect electromagnetic radiation (EMR) that is reflected or emitted from an object; and users can derive information about the object by studying the EMR signals
When corrected for water attenuation using the data obtained from the black panel, the Normalized Difference Vegetation Index (NDVI) values significantly increased at all depths that we tested (0.1 – 0.5 m) (Fig. 10; p
As demonstrated in previous studies [13, 14], our results support that NDVI can serve as a good indicator to delineate healthy waterhyacinth (Table 2) and other emergent/floating water plants because of the high reflectance in the NIR regions and low red reflectance (Fig. 4), the spectral responses that resemble the terrestrial vegetation spectral signals
Summary
Remote sensors detect electromagnetic radiation (EMR) that is reflected or emitted from an object; and users can derive information about the object by studying the EMR signals. Remote sensing of terrestrial vegetation has been successful due to the unique spectral characteristics of green plants: low reflectance in red and high reflectance in Near-InfraRed (NIR) [1]. The important plant pigments, Chlorophyll a and b, strongly absorb the energy in the blue (centered at 450 nm) and the red (centered at 670 nm) wavelengths for photosynthesis; and the internal spongy mesophyll structure of the plants’ leaves is responsible for the high reflectance in the nearinfrared (NIR) region (700 – 1300 nm) [2]. Spectral indices for vegetation, including Simple Vegetation Index (SVI = NIR reflectance – Red reflectance) and Normalized Difference Vegetation Index (NDVI), have been created by utilizing the characteristics of vegetation reflectance in the red and NIR regions. NDVI, (NIR reflectance – Red reflectance) / (NIR reflectance + Red reflectance), has been extensively applied in vegetation studies using multi-spectral satellite wavebands. The index values have been correlated with diverse plant characteristics, such as vegetation cover [3], vegetation type [4], water content [5], biomass & productivity [6], chlorophyll amount [1], PAR (Photosynthetically Active Radiation) absorbed by a crop canopy [7], and flooded biomass [8] at a broad span of scales from individual leaf areas to global vegetation dynamics
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