A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

@article{Lu2016ASO,
  title={A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems},
  author={Dengsheng Lu and Qi Chen and Guangxing Wang and Lijuan Liu and Guiying Li and Emilio Moran},
  journal={Int. J. Digital Earth},
  year={2016},
  volume={9},
  pages={63-105}
}
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems Dengsheng Lu, Qi Chen, Guangxing Wang, Lijuan Liu, Guiying Li & Emilio Moran a Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, School of Environmental & Resource Sciences, Zhejiang A&F University, Lin'An, China b Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, USA c Department of Geography, University of… CONTINUE READING
Highly Cited
This paper has 65 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 45 extracted citations

65 Citations

0102020152016201720182019
Citations per Year
Semantic Scholar estimates that this publication has 65 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 202 references

Lidar Remote Sensing of Vegetation Biomass.

Q. Chen
Remote Sensing of Natural Resources, • 2013
View 15 Excerpts
Highly Influenced

Benchmark map of forest carbon stocks in tropical regions across three continents.

Proceedings of the National Academy of Sciences of the United States of America • 2011
View 8 Excerpts
Highly Influenced

The potential and challenge of remote sensing-based biomass estimation

LU DENGSHENG
2006
View 20 Excerpts
Highly Influenced

Aboveground Biomass Estimation Using Landsat TM Data in the Brazilian Amazon.

D. Lu
International Journal of Remote Sensing • 2005
View 8 Excerpts
Highly Influenced

Error propagation and scaling for tropical forest biomass estimates.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences • 2004
View 4 Excerpts
Highly Influenced

Achieving Accuracy Requirements for Forest Biomass Mapping: A Spaceborne Data Fusion Method for Estimating Forest Biomass and LiDAR Sampling Error.

P. M. Montesano, B. D. Cook, +5 authors S. Luthcke
Remote Sensing of Environment • 2013
View 4 Excerpts
Highly Influenced

Optical Remote Sensing of Forest Leaf Area Index and Biomass.

C. Song
Progress in Physical Geography • 2013
View 5 Excerpts
Highly Influenced

Capabilities and Limitations of Landsat and Land Cover Data for Aboveground Woody Biomass Estimation of Uganda.

V. Avitabile, A. Baccini, M. A. Friedl, C. Schmullius
Remote Sensing of Environment • 2012
View 5 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…