Study on Models for Monitoring of Grassland Biomass around Qinghai Lake Assisted by Remote Sensing

  • ege of Geographical Sciences, Nanjing Normal University, Nanjing 210097, China

Received date: 2002-11-18

  Revised date: 2003-04-28

  Online published: 2003-09-25

Supported by

National Natural Science Foundation of China, No.49971056


Taking the region around the Qinghai Lake as the study area and using the Landsat Thematic Mapper data and the measured grass yield data, the monadic linear regression models and the non-linear regression models were established, respectively, to express the relations between grassland biomass and the vegetation indices. There are two types of sampling site, i.e., the larger one is 30 m×30 m and the smaller one is 1 m×1 m. Each larger sampling site includes one smaller one which was randomly selected. The major conclusions from this study are: 1) the fitting accuracies of the non-linear regression models are much higher than those of the non-linear regression models, namely, the results obtained from the non-linear regression models are more accordant with the measured grassland biomass data in comparison with those from the monadic linear regression models; 2) the comparison of different forms of the non-linear regression analysis on the relations between the vegetation indices and the measured grassland biomass data indicates that the cubic equation is the best one in terms of the suitability of use in the study area; 3) the results from the non-linear regression analysis show that the order is RVI, NDVI, SAVI, MSAVI and DVI in terms of the fitting accuracy between these vegetation indices and grassland biomass data; and 4) the non-linear model Y = -18.626RVI3+220.317RVI2-648.271RVI+691.093 is the best model which can be used in monitoring grassland biomass based on the vegetation indices in the region around the Qinghai Lake.

Cite this article

NIU Zhichun, NI Shaoxiang . Study on Models for Monitoring of Grassland Biomass around Qinghai Lake Assisted by Remote Sensing[J]. Acta Geographica Sinica, 2003 , 58(5) : 695 -702 . DOI: 10.11821/xb200305007


[1] Liu Haiqi, Jin Minyu, Gong Weipeng. Applications of remote sensing in agriculture in the United States. Journal of China Agricultural Resources and Regional Planning, 1999, 20(2): 56-60.
[刘海启, 金敏毓, 龚维鹏. 美国农业遥感技术应用状况概述. 中国农业资源与区划, 1999, 20(2): 56-60.]

[2] Taylor B F et al. Determination of seasonal and interannual variation in New Zealand pasture growth from NOAA 7 data. Remote Sensing of Environment, 1985, 18: 177-192.

[3] Li Jianlong, Jiang Ping, Liang Tiangang. The developing processes, contents and prospects of grassland remote sensing science in China. Grassland of China, 1998, (3): 53-56.
[李建龙, 蒋平, 梁天刚. 我国草地遥感科学发展的轨迹、内涵及展望. 中国草地, 1998, (3): 53-56.]

[4] Huang Jingfeng, Wang Xiuzhen, Hu Xinbo. Studies on grass yield monitoring for different natural grassland types using remote sensing data in northern Xinjiang. Grassland of China, 1999, (1): 7-11, 18.
[黄敬峰, 王秀珍, 胡新博. 新疆北部不同类型天然草地产草量遥感监测模型. 中国草地, 1999, (1): 7-11, 18.]

[5] Huang Jingfeng, Wang Xiuzhen, Wang Renchao et al. Relation analysis between the production of natural grassland and satellite vegetation indices. Research of Agricultural Modernization, 2000, 21(1): 33-36.
[黄敬峰, 王秀珍, 王人潮 等. 天然草地牧草产量与气象卫星植被指数的相关分析. 农业现代化研究, 2000, 21(1): 33-36.]

[6] Huang Jingfeng, Wang Xiuzhen, Wang Renchao et al. A study on monitoring and predicting models of grass yield in natural grassland using remote sensing data and meteorological data. Journal of Remote Sensing, 2001, 5(1): 71-76.
[黄敬峰, 王秀珍, 王人潮 等. 天然草地牧草产量遥感综合监测预测模型研究. 遥感学报, 2001, 5(1): 71-76.]

[7] Li Jianlong, Jiang Ping. The study on the remote sensing technology in estimating and forecasting grassland field applications. Journal of Wuhan Technical University of Surveying and Mapping, 1998, 23(2): 153-157.
[李建龙, 蒋平. 遥感技术在大面积天然草地估产和预报中的应用探讨. 武汉测绘科技大学学报, 1998, 23(2): 153-157.]

[8] Zhang Jianhua. Imitation methods of remote sensing. Journal of Arid Land Resources and Environment, 2000, 14(2): 82-86.
[张建华. 作物估产的遥感——数值模拟方法. 干旱区资源与环境, 2000, 14(2): 82-86.]

[9] Lanzhou Institute of Geology of CAS, Institute of Hydrobiology of CAS, Institute of Microbiology of CAS, Nanjing Institute of Geology and Paleontology of CAS. The Report of Integrated Survey of Qinghai Lake. Beijing: Science Press, 1979.
[中国科学院兰州地质研究所, 中国科学院水生物所, 中国科学院微生物所, 中国科学院南京地质古生物所 合编. 青海湖综合考察报告. 北京: 科学出版社, 1979.]

[10] Jensen J R. Remote Sensing of the Environment: An Earth Resource Perspective. New Jersey: Prentice Hall, 2000, 361-365.

[11] Tian Qingjiu, Min Xiangjun. Advances in study on vegetation indices. Advance in Earth Sciences, 1998, 13(4): 327-333.
[田庆久, 闵祥军. 植被指数研究进展. 地球科学进展, 1998, 13(4): 327-333.]

[12] Zhang Hongliang, Ni Shaoxiang, Jiang Jianjun et al. An analysis of natural grassland TM images vegetation index in the region around Qinghai Lake. Grassland of China, 2002, 24(2): 6-11.
[张洪亮, 倪绍祥, 蒋建军 等. 环青海湖地区天然草地TM影像植被指数分析. 中国草地, 2002, 24(2): 6-11.]

[13] Wen Jun, Wang Jiemin. A modified soil-adjusted vegetation index obtained from satellite remote sensing data. Climatic and Environmental Research, 1997, 2(3): 302-309.
[文军, 王介民. 一种由卫星遥感资料获得的修正的土壤调整植被指数. 气候与环境研究, 1997, 2(3): 302-309.]

[14] Li Rendong, Liu Jiyuan. An estimation of wetland vegetation biomass in the Poyang Lake using Landsat ETM data. Acta Geographica Sinica, 2001, 56(5): 532-540.
[李仁东, 刘纪远. 应用Landsat ETM 数据估算鄱阳湖湿生植被生物量. 地理学报, 2001, 56(5): 532-540.]

[15] Huete A F, H Q Liu. An error and sensitivity analysis of the atmospheric- and soil-correcting variants of the Normalized Difference Vegetation Index for the MODES-EOS. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(4): 897-905.