自然科学版
陕西师范大学学报(自然科学版)
资源与环境科学
祁连山典型小流域高寒草地生物量估算及空间分布特征
苏玉波1, 张福平1,2*, 冯起2, 魏永芬3, 李广文1, 马倩倩1, 宋智渊1
(1 陕西师范大学 旅游与环境学院, 陕西 西安 710119;2 中国科学院 寒区旱区环境与工程研究所, 甘肃 兰州 730000;3 日本国立岐阜大学 流域圈科学研究中心, 日本 岐阜 501-1193)
苏玉波,男,硕士研究生,研究方向为资源环境遥感与GIS应用。E-mail: 398444617@qq.com
摘要:
以祁连山八宝河流域为例,实地采集的数据为基础,利用统计分析方法,估算了流域内高寒草甸与高寒草原的生物量,并结合实测的归一化植被指数(NDVIGS)和同期的多光谱遥感影像(NDVILD),建立了流域内高寒草地生物量的估算模型,并对流域内草地生物量进行了估算。研究结果表明:研究区内草地地上平均生物量为216.78 g/m2,地下平均生物量为2 985.07 g/m2。基于遥感估算高寒草地地上生物量的最优模型为指数函数(n=40,R2=0.731,P<0.01),经与实地数据检验,精确度达到72%,模型适合高寒草地地上生物量的估算。基于遥感估算八宝河流域高寒草地地上生物量最高为459.54 g/m2,最低生物量为45.12 g/m2,地上总生物量为0.198×109 kg。
关键词:
高寒草地; 生物量; 模型; 空间分布
收稿日期:
2014-07-15
中图分类号:
S812; Q948
文献标识码:
A
文章编号:
1672-4291(2015)02-0079-06doi:10.15983/j.cnki.jsnu.2015.02.421
基金项目:
陕西省“百人计划”项目;国家科技支撑计划(2012BAC08B07);中国博士后科学基金资助项目 (2011M501496);中央高校基本科研业务费专项资金项目(GK201101002)
Doi:
Estimation of alpine grassland biomass and analysis of its spatial distribution characteristics in typical small watershed of Qilian Mountain
SU Yubo1, ZHANG Fuping1,2*, FENG Qi2, WEI Yongfen3,LI Guangwen1, MA Qianqian1 , SONG Zhiyuan1
(1 School of Tourism and Environment Sciences, Shaanxi Normal University, Xi′an 710119, Shaanxi, China; 2 Cold and Arid Regions Environment and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, Gansu, China;3 River Basin Research Center, Gifu University, Gifu 501-1193, Japan)
Abstract:
Based on the field survey data, the alpine meadow and alpine grass biomass were estimated with the statistical analysis method in Babao river basin located in Qilian Mountain firstly. Then, in combination with the normalized difference vegetation index (NDVIGS) examined by GreenSeeker and NDVILD, derived from multi-spectral remote sensing data of the same period, a regression model to estimate the alpine grassland biomass was established, and a quantitative assessment was also carried out.The result shows that the average of aboveground and belowground biomass were estimated to be 216.78 g/m2 and 2 985.07 g/m2, respectively. Through comparing models (linear, exponential, and logarithmic) used to estimate the aboveground biomass of alpine grassland, it found that the best fitting one is the exponential relation (n=40,R2=0.731,P
KeyWords:
alpine grass; biomass; model; spatial distribution