基于MODIS数据和植被特征估算草地生物量

准确估算草地生物量,对全球气候变化背景下的陆地生态系统碳循环研究具有重要意义。过去几十年,草地生物量估算研究大多集中在北方,而南方草地具有类型繁多和分布零散等特征,对其生物量进行评估的报道较少。本文以云南省为例,应用2012—2014年草地生物量野外调查资料和同期MODIS遥感数据,建立草地地上生物量(AGB)遥感估算模型;再引入草地植被群落特征(高度和盖度)信息对统计模型进行优化,并进行生物量空间反演。结果表明:优化后模型的估算精度由原来的35.0%提升为43.7%;反演得到云南省3年年均AGB的总量介于1 026.86万~1 408.54万t,平均为1 221.11万t;从空间分布上看,云南省草地AGB密度总体上呈现西部高东部低、南部高北部低的格局。本研究首次将遥感植被指数数据与实测植被群落特征参数结合,使估算精度比传统的纯粹光学遥感模拟方法显著提升24.9%,但精确估算大面积的草地AGB,需要进一步探索如何将激光雷达数据或遥感立体影像中提取的植被特征信息应用于草地AGB估算研究。

Estimation of grassland biomass using MODIS data and plant community characteristics

In the context of global climate change, the accurate estimation of grassland biomass is critical for terrestrial carbon cycling research. In China, most studies in this area have focused on grasslands in North China over the past decades. Only a few studies have estimated grassland biomass in South China, mainly due to difficulties in spatial complexity of plant species in the region. Therefore, it is necessary to develop a model for the estimation of grassland biomass in South China in order to analyze the spatial distribution of this vegetation type. In this study, we first developed a model for the estimation of aboveground grassland biomass (AGB) in Yunnan Province using field sample and NDVI (normalized difference vegetation index) data (2012-2014), derived from MODIS sensor. The derived grassland characteristics (height and coverage) were then inputted into the model to improve the estimation accuracy. With the improved model, we used remote sensing and GIS platforms to map the spatial pattern of AGB in Yunnan Province. Finally, we carried out statistical analysis of AGB of grassland in a district in Yunnan Province and calculated the average density of AGB in multiple types of grassland. The results indicated that:1) the model for the estimation of AGB of grassland was improved by the use of field data on plant community. Thus the goodness-of-fit (R²) of the model increased by 0.289 and the estimation accuracy of the model also increased (35.0%-43.7%) significantly. 2) During 2012-2014, annual total AGB in the study area was 1.03×107-1.41×107 tons, with an average value of 1.22×107 tons that accounted for 4.1% of total AGB in China. The results suggested that the area of grasslands in South China is not negligible. The density of AGB of grassland in Yunnan was highest in the eastern and southern regions of the province. 3) The density of AGB of grassland in the districts of Yunnan was 1 130.12-2 116.03 kg·hm^-2. Grasslands with high AGB densities were in southern and southwestern areas of the province, including Xishuangbanna, Dehong and Puer. Grasslands with low densities were in northwestern and eastern areas of the province, including Diqing and Qujing. Moreover, AGB density of mutiple grassland types had a clear pattern, with an increasing trend from montane meadow to tropical herbosa. The order of the AGB density increase was:montane meadow (1 071.73 kg·hm^-2) < lowland meadow (1 552.45 kg·hm^-2) < tropical shrub herbosa (1 579.80 kg·hm^-2) < warm-temperate shrub herbosa (1 588.12 kg·hm^-2) < warm-temperate herbosa (1 771.02 kg·hm^-2) < tropical herbosa (2 004.37 kg·hm^-2). In the study, a remotely sensed vegetation index was first combined with field data on plant community. Using this approach, the accuracy of the results increased with 24.9%, compared with the traditional approach which relies only on remote sensing data. Thus in order to improve the accuracy of AGB estimation for grasslands at a large scale, it was recommended that future studies attempt to incorporate grassland height derived from Light Detection and Ranging equipment (LiDAR) data or optical stereo images.