[Objective] This study was to investigate the Suaeda salsa community characteristics,further getting the soil chemical properties.[Method] The paired-data of field spectra and corresponding soil physical-chemical property of seventeen samples was used to reveal the relationship between soil chemical property and field spectra（visible and near infra-red spectra）of S.salsa.[Result] The second derivative spectrum of S.salsa at 1 121 nm could reflect the changes of soil organic matter and soil total nitrogen,and that at 1 208 nm could commendably indicate changes of soil total phosphorus and at 724 nm could indicate changes of soil pH.The first derivative spectrum of S.salsa at 353 nm can indicate changes of soil available potassium,and that at 950 nm could commendably reflect the changes of soil salt content.[Conclusion] Our results laid basis for monitoring chemical property of soil covered with S.salsa using remote sensing technology.
Types and structure of plant communities in the Yellow River Delta were investigated by using detrended canonical correspon-dence analyses(DCCAs) and a two-way indicator species analysis(TWINSPAN).The distribution pattern and influential factors of the plant communities were also analyzed by testing elevation, slope, soil characteristics, longitude and latitude of 134 vegetation samples collected by representative plot sampling methods.Results showed that all the 134 vegetation samples could be divided into seven vegetation groups, separately dominated by Robinia pseucdoacacia, Imperata cylindrical, Miscanthus saccharifleus, Suaeda salsa, Aeluropus sinensis, Phragmites australis and Tamarix chinensis.The vegetation distribution pattern was mainly related to elevation, ground water depth and soil characteristics such as salinity and soluble potassium.Among the factors affecting distribution pattern of the plant communities, the species matrix explained by non-spatial environmental variation accounts for 45.2% of total variation.Spatial variation and spatial-structured environmental variation explain 11.8%, and 2.2%, respectively.Remained 40.8% of undetermined variation is attributed to biological and stochastic factors.
SONG Chuang-ye1, 2, LIU Gao-huan1, LIU Qing-sheng1 1 Institute of Geographic Sciences and Nature Resources Research, Chinese Academy of Sciences, Beijing 100101, China 2 Graduated School of Chinese Academy of Sciences, Beijing 100039, China
