Name
Abstract | ||
|---|---|---|
Biomass, as an indicator of vegetation productivity, can evaluate the contribution of wetland vegetation to carbon sink and carbon source. Long time and quantitative biomass study can help to acknowledge and understand the global carbon balance and carbon cycle. RADAR, which can work all day/weather and can penetrate vegetation in some extent, can be used to retrieve vegetation structure information, even the biomass. Here, the RADARSAT-2 data was used to retrieve vegetation biomass in Poyang Lake wetland. Based on the canopy scattering model, which is based on radioactive transfer model, the vegetation backscatter characteristics at C band were studied and good relationship between simulation results and backscatter in RADATSAT-2 image were achieved. Using the backscatter model, pairs of training data (backscatter coefficients in HH, VV, HV polarization mode and polarization decomposed components) were built and were used to train the Back Propagation (BP) artificial neural network (ANN). The biomass was inversed using this ANN, and compared to the field survey. It shows that the combination of the canopy scatter model and polarimetric decomposition components can improve the inversion precision efficiently. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IGARSS.2012.6351456 | IGARSS |
Keywords | Field | DocType |
global carbon cycle,lakes,bp ann,radioactive transfer model,polarimetric decomposition,carbon source,polarimetric sar,poyang lake wetland,biomass,canopy scatter model,remote sensing by radar,vegetation structure information,canopy scattering model,wetland vegetation biomass inversion,vegetation productivity indicator,carbon sink,global carbon balance,vegetation,neural nets,polarimetric radarsat-2 data,back propagation artificial neural network,polarimetric decomposition components,remote sensing,artificial neural networks,backscatter | Radar,Soil science,Biomass,Vegetation,Computer science,Remote sensing,Backscatter,Wetland,Enhanced vegetation index,Carbon cycle,Carbon sink | Conference |
Volume | Issue | ISSN |
null | null | 2153-6996 E-ISBN : 978-1-4673-1158-8 |
ISBN | Citations | PageRank |
978-1-4673-1158-8 | 0 | 0.34 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guozhuang Shen | 1 | 5 | 5.22 |
| Jingjuan Liao | 2 | 22 | 10.46 |
| Huadong Guo | 3 | 459 | 84.66 |
| Ju Liu | 4 | 0 | 0.34 |
| Lu Zhang | 5 | 22 | 4.93 |
| Jie Chen | 6 | 2487 | 353.65 |