Spectral Discrimination of the Invasive Plant Spartina alterniflora at Multiple Phenological Stages in a Saltmarsh Wetland.

Spartina alterniflora has widely invaded the saltmarshes of the Yangtze River Estuary and brought negative effects to the ecosystem. Remote sensing technique has recently been used to monitor its distribution, but the similar morphology and canopy structure among S. alterniflora and its neighbor species make it difficult even with high-resolution images. Nevertheless, these species have divergence on phenological stages throughout the year, which cause distinguishing spectral characteristics among them and provide opportunities for discrimination. The field spectra of the S. alterniflora community as well as its major victims, native Phragmites australis and Scirpus mariqueter, were measured in 2009 and 2010 at multi-phenological stages in the Yangtze River Estuary, aiming to find the most appropriate periods for mapping S. alterniflora. Collected spectral data were analyzed separately for every stage firstly by re-sampling reflectance curves into continued 5-nm-wide hyper-spectral bands and then by re-sampling into broad multi-spectral bands - the same as the band ranges of the TM sensor, as well as calculating commonly used vegetation indices. The results showed that differences among saltmarsh communities' spectral characteristics were affected by their phenological stages. The germination and early vegetative growth stage and the flowering stage were probably the best timings to identify S. alterniflora. Vegetation indices like NDVI, ANVI, VNVI, and RVI are likely to enhance spectral separability and also make it possible to discriminate S. alterniflora at its withering stage.

Identification of land-cover characteristics using MODIS time series data: an application in the Yangtze river estuary.

Land-cover characteristics have been considered in many ecological studies. Methods to identify these characteristics by using remotely sensed time series data have previously been proposed. However, these methods often have a mathematical basis, and more effort is required to better illustrate the ecological meanings of land-cover characteristics. In this study, a method for identifying these characteristics was proposed from the ecological perspective of sustained vegetation growth trend. Improvement was also made in parameter extraction, inspired by a method used for determining the hyperspectral red edge position. Five land-cover types were chosen to represent various ecosystem growth patterns and MODIS time series data were adopted for analysis. The results show that the extracted parameters can reflect ecosystem growth patterns and portray ecosystem traits such as vegetation growth strategy and ecosystem growth situations.