Next Article in Journal
Previous Article in Journal
Remote Sens. 2014, 6(2), 1496-1513; doi:10.3390/rs6021496
Article

Remote Sensing-Based Biomass Estimation and Its Spatio-Temporal Variations in Temperate Grassland, Northern China

1
, 1
, 1
, 1
, 2
, 3
, 1
, 1
, 1
 and 1,*
Received: 28 December 2013; in revised form: 5 February 2014 / Accepted: 9 February 2014 / Published: 19 February 2014
View Full-Text   |   Download PDF [802 KB, uploaded 19 June 2014]
Abstract: Grassland biomass is essential for maintaining grassland ecosystems. Moreover, biomass is an important characteristic of grassland. In this study, we combined field sampling with remote sensing data and calculated five vegetation indices (VIs). Using this combined information, we quantified a remote sensing estimation model and estimated biomass in a temperate grassland of northern China. We also explored the dynamic spatio-temporal variation of biomass from 2006 to 2012. Our results indicated that all VIs investigated in the study were strongly correlated with biomass (α < 0.01). The precision of the model for estimating biomass based on ground data and remote sensing was greater than 73%. Additionally, the results of our analysis indicated that the annual average biomass was 11.86 million tons and that the average yield was 604.5 kg/ha. The distribution of biomass exhibited substantial spatial heterogeneity, and the biomass decreased from the eastern portion of the study area to the western portion. The interannual biomass exhibited strong fluctuations during 2006–2012, with a coefficient of variation of 26.95%. The coefficient of variation of biomass differed among the grassland types. The highest coefficient of variation was found for the desert steppe, followed by the typical steppe and the meadow steppe.
Keywords: biomass; vegetation index; MODIS; temperate grassland; Xilingol biomass; vegetation index; MODIS; temperate grassland; Xilingol
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Jin, Y.; Yang, X.; Qiu, J.; Li, J.; Gao, T.; Wu, Q.; Zhao, F.; Ma, H.; Yu, H.; Xu, B. Remote Sensing-Based Biomass Estimation and Its Spatio-Temporal Variations in Temperate Grassland, Northern China. Remote Sens. 2014, 6, 1496-1513.

AMA Style

Jin Y, Yang X, Qiu J, Li J, Gao T, Wu Q, Zhao F, Ma H, Yu H, Xu B. Remote Sensing-Based Biomass Estimation and Its Spatio-Temporal Variations in Temperate Grassland, Northern China. Remote Sensing. 2014; 6(2):1496-1513.

Chicago/Turabian Style

Jin, Yunxiang; Yang, Xiuchun; Qiu, Jianjun; Li, Jinya; Gao, Tian; Wu, Qiong; Zhao, Fen; Ma, Hailong; Yu, Haida; Xu, Bin. 2014. "Remote Sensing-Based Biomass Estimation and Its Spatio-Temporal Variations in Temperate Grassland, Northern China." Remote Sens. 6, no. 2: 1496-1513.


Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert