Research Papers

Estimating grassland aboveground biomass using multitemporal MODIS data in the West Songnen Plain, China

[+] Author Affiliations
Fei Li

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

University of Chinese Academy of Sciences, Beijing 100049, China

Lei Jiang

University of Chinese Academy of Sciences, Beijing 100049, China

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science, Beijing 100101, China

Xufeng Wang

Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Cold and Arid Regions Remote Sensing Observation System Experiment Station, Lanzhou 730000, China

Xiaoqiang Zhang

Nagoya University, Department of Earth and Environmental Sciences, Graduate School and Environmental Studies, Nagoya 464-8602, Japan

Jiajia Zheng

Chuzhou University, College of Geographic Information and Tourism, Chuzhou 239000, China

Qianjun Zhao

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

J. Appl. Remote Sens. 7(1), 073546 (Jun 05, 2013). doi:10.1117/1.JRS.7.073546
History: Received November 7, 2012; Revised April 18, 2013; Accepted May 10, 2013
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Abstract.  The West Songnen Plain is an ecologically fragile area. The grasslands on the plain have been seriously degraded over the past five decades and this process is continuing. The reliable estimation of grassland aboveground biomass (AGB) provides scientific data for determining the livestock stocking rate on rangeland. AGB is also of considerable significance for biodiversity and environmental protection in this region. Remote sensing is the most effective way to estimate grassland AGB on a regional scale. Multitemporal, remotely sensed data were used for grassland AGB estimation with statistical models and an artificial neural network (ANN), and the accuracy of estimation for these methods was compared. The results demonstrate that the use of multi-temporal remotely sensed data has advantages for grassland AGB estimation, whether with statistical models or ANN methods, compared with single-temporal remotely sensed data, although the ANN had a higher accuracy of estimation for grassland AGB. Finally, the grassland AGB on the Songnen Plain was estimated with the ANN using multitemporal MODIS data. The spatial distribution pattern of grassland AGB showed large variations, and grassland productivity was generally low. The maximum green weight of the grassland AGB was 927.22g/m2 and was mainly distributed on the northeast of the West Songnen Plain. The minimum green weight of the grassland AGB was 194.82g/m2 and was mainly distributed on the central and southwestern West Songnen Plain. Most of the areas had medium- and low-yielding grasses. The significant increases of population and livestock number were the primary and direct reasons for the decrease in grassland quality. This study will contribute to policy making for the control of grazing and for biodiversity and environmental protection.

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© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Fei Li ; Lei Jiang ; Xufeng Wang ; Xiaoqiang Zhang ; Jiajia Zheng, et al.
"Estimating grassland aboveground biomass using multitemporal MODIS data in the West Songnen Plain, China", J. Appl. Remote Sens. 7(1), 073546 (Jun 05, 2013). ; http://dx.doi.org/10.1117/1.JRS.7.073546


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