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Estimation of above-ground biomass using MODIS satellite imagery of multiple land-cover types in China

(2016) REMOTE SENSING LETTERS. 7(12). p.1141-1149
Author
Organization
Abstract
Estimations of above-ground biomass (AGB) at various spatial and temporal scales remain a large uncertainty when quantifying the global carbon cycle. In this work, we assessed the ability of the Enhanced Vegetation Index (EVI), obtained from the Moderate-Resolution Imaging Spectroradiometer, for estimating AGB across multiple land cover types in China. Our results indicate that, with the exception of mixed forests at the site level, annual integrated EVI (iEVI) can be used for estimating AGB. We were also able to reduce the permissible margin of error for the total AGB of mixed forests at the biome level. Estimated total AGB for China was determined to be 21.19 Pg (1 Pg = 1 x 10(12) Kg), with 69.8% attributed to forests and 31.2% to non-forests. Overall, we concluded that iEVI has great potential for dynamically estimating spatial AGB. Here, we also describe the correspondence between analysed parameters.
Keywords
GRASSLANDS

Citation

Please use this url to cite or link to this publication:

MLA
Yuan, Xiuliang et al. “Estimation of Above-ground Biomass Using MODIS Satellite Imagery of Multiple Land-cover Types in China.” REMOTE SENSING LETTERS 7.12 (2016): 1141–1149. Print.
APA
Yuan, X., Li, L., Tian, X., Luo, G., & Chen, X. (2016). Estimation of above-ground biomass using MODIS satellite imagery of multiple land-cover types in China. REMOTE SENSING LETTERS, 7(12), 1141–1149.
Chicago author-date
Yuan, Xiuliang, Longhui Li, Xin Tian, Geping Luo, and Xi Chen. 2016. “Estimation of Above-ground Biomass Using MODIS Satellite Imagery of Multiple Land-cover Types in China.” Remote Sensing Letters 7 (12): 1141–1149.
Chicago author-date (all authors)
Yuan, Xiuliang, Longhui Li, Xin Tian, Geping Luo, and Xi Chen. 2016. “Estimation of Above-ground Biomass Using MODIS Satellite Imagery of Multiple Land-cover Types in China.” Remote Sensing Letters 7 (12): 1141–1149.
Vancouver
1.
Yuan X, Li L, Tian X, Luo G, Chen X. Estimation of above-ground biomass using MODIS satellite imagery of multiple land-cover types in China. REMOTE SENSING LETTERS. 2016;7(12):1141–9.
IEEE
[1]
X. Yuan, L. Li, X. Tian, G. Luo, and X. Chen, “Estimation of above-ground biomass using MODIS satellite imagery of multiple land-cover types in China,” REMOTE SENSING LETTERS, vol. 7, no. 12, pp. 1141–1149, 2016.
@article{8534488,
  abstract     = {Estimations of above-ground biomass (AGB) at various spatial and temporal scales remain a large uncertainty when quantifying the global carbon cycle. In this work, we assessed the ability of the Enhanced Vegetation Index (EVI), obtained from the Moderate-Resolution Imaging Spectroradiometer, for estimating AGB across multiple land cover types in China. Our results indicate that, with the exception of mixed forests at the site level, annual integrated EVI (iEVI) can be used for estimating AGB. We were also able to reduce the permissible margin of error for the total AGB of mixed forests at the biome level. Estimated total AGB for China was determined to be 21.19 Pg (1 Pg = 1 x 10(12) Kg), with 69.8% attributed to forests and 31.2% to non-forests. Overall, we concluded that iEVI has great potential for dynamically estimating spatial AGB. Here, we also describe the correspondence between analysed parameters.},
  author       = {Yuan, Xiuliang and Li, Longhui and Tian, Xin and Luo, Geping and Chen, Xi},
  issn         = {2150-704X},
  journal      = {REMOTE SENSING LETTERS},
  keywords     = {GRASSLANDS},
  language     = {eng},
  number       = {12},
  pages        = {1141--1149},
  title        = {Estimation of above-ground biomass using MODIS satellite imagery of multiple land-cover types in China},
  url          = {http://dx.doi.org/10.1080/2150704x.2016.1219458},
  volume       = {7},
  year         = {2016},
}

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