Exploring the possibility of estimating the aboveground biomass of Vallisneria spiralis L. using Landsat TM image in Dahuchi, Jiangxi Province, China

The provision of food to breeding and migrating waterfowl is one of the major functions of submerged aquatic vegetation in shallow lakes. Vallisneria spiralis L. is a submerged aquatic plant species widely distributed within Jiangxi Poyang Lake National Nature Reserve, China. More than 95% of the world population of the endangered Siberian crane as well as significant numbers of Bewick's swan and swan goose over winter in this area, while foraging on the tubers of Vallisneria. The objective of this paper was to explore the possibility of estimating the aboveground biomass of Vallisneria in Dahuchi Lake using Landsat TM image. The relations between aboveground biomass and the bands of a Landsat TM image and their derived variables were investigated using uni- and multivariate linear and non-linear regression models. The results revealed significant but very weak relations between aboveground biomass and the remotely sensed variables. Hence Landsat TM imagery offered little potential to predict aboveground biomass of Vallisneria in this particular region. Possible reasons which could have caused these results were discussed, including: 1) the possible influence of suspended matter in the water; 2) the less accurate field sampling; 3) the limitations of spatial and spectral resolutions of Landsat TM image; 4) the methods used are not appropriate; 5) the homogeneously spatial distribution of aboveground biomass. We propose considering two alternative methods to improve the estimation of aboveground biomass of Vallisneria. First of all, results might be improved while combining alternative data sources (hyperspectral or high spatial resolution images) with innovative methods and more accurate sampling data; Secondly we propose assessing aboveground biomass while using productivity simulation models of submerged aquatic vegetation integrated with geographic information system (GIS) and remote sensing.