基于草地综合顺序分类系统(IOCSG)的中国北方草地地上生物量高精度模拟

High accuracy simulation of aboveground biomass in Northern China based on IOCSG

草地生态系统是陆地生态系统中分布最广泛的生态系统类型之一,草地生物量的精确估算一直是陆地生态学研究的重点问题。针对目前草地生物量估算方法的不确定性问题,提出了不依赖于遥感植被指数,而是通过分析草地生物量影响因素的方法去构建草地生物量估算模型。根据年积温(>0℃)和湿润度指标将研究区域划分为4种潜在植被类型,即微温干旱温带半荒漠类、微温微干温带典型草原类、微温微润草甸草原类和微温湿润森林草原类,然后对每一种潜在植被类型的草地生物量分析其内在影响因素,研究结果发现,微温干旱温带半荒漠类的草地生物量与年积温存在较好的线性关系,微温微干温带典型草原类的草地生物量可以用表层土壤粘粒含量的二次多项式来模拟,后两种潜在植被类型的草地生物量则随着潜在NPP的变化呈现先减小后增大的变化趋势。对4种潜在植被类型区域分别建立草地生物量与其影响因素之间的回归关系确定研究区域草地生物量的趋势面,结合HASM模型实现研究区域草地生物量的高精度模拟,结果显示上述4种潜在植被类型区的草地平均生物量分别为76.62、110.94、142.69、184.40 g/m2。

Since grassland ecosystem is one of the most widely distributed terrestrial ecosystems, the accurate statistic of the biomass of the grassland ecosystem is a key issue in terrestrial ecological studies. Presently, the primary methods to compute the biomass of the grassland are almost based on various vegetation indexes, of which the NDVI is employed most frequently. However, the calculation of vegetation indexes contains many uncertainties, which can be brought into the grassland biomass estimation. In order to avoid these uncertainties in the vegetation indexes, this paper tries to build a new model to compute grassland biomass which is independent of various vegetation indexes. Firstly, this research divides the study area into four kinds of potential vegetation types according to the annual cumulative temperature （ 〉 0℃ ） and the K-value （ humidity index） based on Integrated Orderly Classification System of Grassland （IOCSG）. The first kind of the potential vegetation type is Cool temperate-arid temperate zonal semi-desert, the second kind is Cool temperate-semiarid temperate typical steppe, the third is Cool temperate-subhumid meadow steppe and the last one is Cool temperate-humid forest steppe, deciduous broad leaved forest. Then, correlation analysis was applied between aboveground biomass of the grassland and some selected impacts for each potential vegetation type. The criteria of the impacts selection aim to make sure that the factor surely has some impacts on the biomass estimation, but not the one that reflects the biornass. For example, both the temperature and the rainfall belong to the former, while the NDVI belongs to the later. In this paper, annual cumulative temperature, the K-value, potential NPP, slope, aspect, clay content of the surface layer and sand content of the surface layer are selected, of which the potential NPP is calculated according to annual cumulative temperature and the K-value. The analysis results show that the main impact of the aboveground biomass of the grassland on each potential vegetation type is different. For Cool temperate-arid temperate zonal semi-desert, the main impact is annual cumulative temperature; On the part of Cool temperate-semiarid temperate typical steppe, the main impact is the clay content of the surface layer; and to Cool temperate-subhumid meadow steppe and Cool temperate-humid forest steppe, deciduous broad leaved forest, the main impact is potential NPP. However, the relationship between the biomass and the potential NPP is different for the above two potential vegetation types. Since the main impacts of the grassland biomass for each potential vegetation type is confirmed, mathematical relationship is built respectively. Finally, High Accuracy Surface Model （HASM） was employed to simulate the biomass of the grassland in the study area. Simulation results show that the aboveground biomass density of the grassland in the four potential vegetation types is 76.62, 110.94, 142.69 g/m2 and 184.40 g/m2 respectively.