基于MODIS数据和植被特征估算草地生物量

发布时间：2018-06-05 15:24

本文选题：云南省 + MODIS数据　；参考：《中国生态农业学报》2017年04期

【摘要】：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。
[Abstract]:Accurate estimation of grassland biomass is of great significance to the study of terrestrial ecosystem carbon cycle in the context of global climate change. In the past few decades, the research on biomass estimation of grassland is mostly concentrated in the north, while the grassland in the south has the characteristics of various types and scattered distribution, so there are few reports to estimate the biomass of grassland. Taking Yunnan Province as an example, a remote sensing estimation model of grassland aboveground biomass is established by using field survey data of grassland biomass from 2012 to 2014 and MODIS remote sensing data of the same period. Then the statistical model was optimized by introducing the information of grassland vegetation community characteristics (height and coverage) and the biomass spatial inversion was carried out. The results show that the estimation accuracy of the optimized model is improved from 35.0% to 43.7%, and the average annual AGB of Yunnan Province for 3 years is between 10.2686 million ~ 14.08 million t, with an average of 12.2111 million t, from the point of view of spatial distribution, the estimated accuracy of the optimized model is increased from 35.0% to 43.7%. The AGB density of grassland in Yunnan Province showed a pattern of high in the west and low in the north and high in the south. In this study, remote sensing vegetation index data were first combined with measured vegetation community characteristic parameters. The accuracy of estimation is 24. 9% higher than that of traditional simulation method of pure optical remote sensing. However, it is necessary to estimate accurately the AGBs of large areas of grassland. It is necessary to further explore how to extract vegetation feature information from lidar data or remote sensing stereoscopic images. It is used in the study of grassland AGB estimation.
【作者单位】：北京大学深圳研究生院城市规划与设计学院;云南省草原监督管理站;北京大学城市与环境学院;
【基金】：国家自然科学基金项目(41401096) 云南草地生态红线研究课题资助~~
【分类号】：S812

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