Depth‐variant spectral characteristics of submersed aquatic vegetation detected by Landsat 7 ETM+

Abstract

Spectral variations along depth profiles were compared using two subsets of a Landsat 7 Enhanced Thematic Mapper (ETM + ) scene to test the difference between submersed aquatic vegetation (SAV) and non-vegetated bare substrate in their depth-induced spectral variation. Field-surveyed water depth and SAV cover along transects were overlaid with the satellite image of Lake Pontchartrain, LA, USA. Digital numbers on the survey transects for each band and for band ratios were correlated with depth and vegetation cover. Band 1/band 3 correlated well with depth in both SAV and bare substrates, indicating that this ratio least reflects the effect of SAV. The ratio of bands 2 and 1 correlated best with vegetation cover within the shallow estuarine waters. Correlations between depth and the ratio of band 2/band3 showed contrasting results between the two substrate types (SAV and bare), suggesting that the depth-induced variations in the band ratio can be used as indicators of SAV.

A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data

“…curve still had an absorption valley in band 3 with the curve around this inflection point either concave or convex with small convexity, which is consistent with the experimental results obtained by Cho et al [39]. To distinguish between SAV and water, a concave-convex decision function was constructed, expressed as follows: Equation (2) denotes k1 − k2.…”

“…2018, 10, 1279 7 of 16 of band 4 was lower than the reflectance of band 3, making its spectral characteristic curves similar to the spectral characteristic curves of water, increasing the difficulty of identifying the macrophytes using NDVI. The water spectral curve inflection point of band 3 remained convex, while the SAV curve still had an absorption valley in band 3 with the curve around this inflection point either concave or convex with small convexity, which is consistent with the experimental results obtained by Cho et al [39]. To distinguish between SAV and water, a concave-convex decision function was constructed, expressed as follows:…”

Remote Sensing of Submerged Aquatic Vegetation

“…The aforementioned conventional vegetation indices also are not effectively used for plants that grow underwater or that are temporarily flooded (Beget & Di Bella, 2007;Cho et al 2008) because the water overlying the vegetation canopies reduces the vegetation effects of 'red absorption' and the 'NIR reflectance' (Han & Rundquist, 2003;Cho, 2007;Cho et al, 2008; Fig. 1).…”

A water-depth correction algorithm for submerged vegetation spectra

“…The vegetation spectral indices are simple but versatile and can be used to compare data obtained under varying illumination conditions. However, the conventional vegetation indices may not be effectively used for plants that grow underwater or that are temporarily flooded (Beget and Di Bella 2007) because the water overlying the vegetation canopies reduces the vegetation effects of 'red absorption' and the 'NIR reflectance' (Han and Rundquist 2003, Cho 2007, Cho et al 2008.…”