干旱区生态环境知识资源中心

Vegetation indexes were the most common and the most important parameters to characterizing large-scale terrestrial ecosystems. It is vital to get precise vegetation indexes for running land surface process models and computation of NPP change, moisture and heat fluxes over surface. Biological soil crusts (BSC) are widely distributed in arid and semi-arid, polar and sub-polar regions. The spectral characteristics of dry and wet BSCs were quite different, which could produce much higher vegetation indexes value for the wet BSC than for the dry BSC as reported. But no research was reported about whether the BSC would impact on regional vegetation indexes and how much dry and wet BSC had impact on regional vegetation indexes. In the present paper, the most common vegetation index NDVI were used to analyze how the moss soil crusts (MSC) dry and wet changes affect regional NDVI values. It was showed that 100% coverage of the wet MSC have a much higher NDVI value (0. 657) than the dry MSC NDVI value (0. 320), with increased 0. 337. Dry and wet MSC NDVI value reached significant difference between the levels of 0. 000. In the study area, MSC, which had the average coverage of 12. 25%, would have a great contribution to the composition of vegetation index. Linear mixed model was employed to analyze how the NDVI would change in regional scale as wet MSC become dry MSC inversion. The impact of wet moss crust than the dry moss crust in the study area can make the regional NDVI increasing by 0.04 (14. 3%). Due to the MSC existence and rainfall variation in arid and semi-arid zones, it was bound to result in NDVI change instability in a short time in the region. For the wet MSC’s spectral reflectance curve is similar to those of the higher plants, misinterpretation of the vegetation dynamics could be more severe due to the "maximum value composite" (MVC) technique used to compose the global vegetation maps in the study of vegetation dynamics. The researches would be useful for detecting and mapping MSC from remote sensing imagery. It also is to the advantage to employing vegetation index wisely.