复杂地形草地植被碳储量遥感估算研究进展

草地生态系统是我国最大的陆地生态系统,其植被碳储量的准确评估对维护国家生态安全和指导畜牧发展有重要作用。植被生物量和草地面积是草地植被碳储量估算的关键,随着遥感技术的发展,两者估算精度和效率显著提高,先后发展出多种草地生物量遥感估算模型和土地覆被产品,并已在平坦地区取的较好估算结果。然而,复杂地形区迥异于平地的几何形态和水热分布所产生的不均一的生态系统结构和功能,给草地生物量和草地面积的遥感估算带来诸多困难,影响对草地植被碳储量的准确判定。本文在回顾国内外草地植被碳储量遥感估算方法与所需关键参数的基础上,对遥感估算复杂地形草地植被碳储量过程中所面临“遥感影像地形效应的去除和尺度选择”、“植被指数与地形指标的选取”、“过程模型植被生长参数的率定”、“草地面积估算”以及“气象数据与复杂地形上微气候的匹配”等问题进行了总结并提出相应的解决思路,以期为草地植被碳储量遥感估算模型的合理构建以及估算精度的提高提供参考。

Research progress on estimation of vegetation carbon storage of grasslands on complex terrain by remote sensing technology

Grassland is the largest terrestrial ecosystem in China. The accurate assessment of grassland vegetation carbon stocks plays an important role in maintaining national ecological security and guiding the development of animal husbandry. Vegetation biomass and grassland area are the key parameters to the estimation of grassland carbon storage. With the development of remote sensing technology, the estimation accuracy and efficiency of grassland biomass and area have been significantly improved. Various remote sensing estimation models of grassland biomass and land cover products have been developed, with high accuracy of estimation results in flat areas. However, in complex terrain area, due to the high heterogeneity in ecosystem structure and functions caused by the geometry and consequently water and heat distribution, it is difficult to accurately make out vegetation types as well as the biomass and area of each type. Therefore, it is difficult to adopt remote sensing methods suitable for flat land directly to estimate grassland biomass and area in complex terrain, affecting the accuracy of grassland vegetation carbon storage determination. This paper reviews remote sensing methods and the key parameters in estimation of vegetation carbon storage of grasslands in complex terrain. It points out that LAI (Leaf Area Index) inversion is moderately affected by topographic effect at slope above 30o and introduction of topographic parameters obviously promotes the accuracy of NPP (net primary productivity) estimation as compared to that with NDVI (Normalized Difference Vegetation Index) alone. In process models based on remote sensing, topography affected the determination of key parameters including optimal temperature of photosynthesis, soil water content, grazing intensity, vegetation type and phenology, and carbon allocation. Ignoring rolling topography underestimates grassland area especially with slope above 30o. With a thorough analyses of the fundamental issues, including "topographic effect removal and scale selection of remote sensing image", "selection of vegetation indexes and topographic parameters", "calibration of vegetation growth parameters in process model", "estimation of grassland area", "matching of meteorological data with microclimate in complex terrain", the paper proposes corresponding solutions. Among the diverse vegetation indexes, EVI (Enhanced Vegetation Index) is more sensitive to topographic effect, which is better used in smooth surface with high plant coverage. NDVI is recommended for terrains with slope less than 25° and moderate plant coverage. However, all the vegetation indexes should be corrected in terms of topographic effect in rough terrains. For topographic data, TWI (topographic wetness index) or indexes of terrain complexity is needed to characterize rough terrain. For climate data, it is recommended to combine fine DEM and re-analysis of climate data to fit micro-climate. The paper emphasizes the importance of characteristic length scale of remote sensing image and suggests it is much larger than the mean distance among the ridges in rough terrains. To dampen topographic effect, C correction is proposed to be a simple and effective method that is applicable to estimation of vegetation carbon storage in grasslands on complex terrains.