Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data
Abstract
:1. Introduction
2. Materials
2.1. Field Data
2.2. ICESat/GLAS Data
2.3. Landsat5/TM Data
- Surface reflectance: bands 1, 2, 3, 4, 5 and 7;
- Spectral indices: normalized difference vegetation index (NDVI) [42], Enhanced Vegetation index (EVI) [43] perpendicular vegetation index (PVI) [44], soil-adjusted vegetation index (SAVI) [45], normalized difference infrared vegetation index (NDIIB6) [46], normalized difference infrared vegetation index (NDIIB7) [46], [47], [47], and [47];
3. Methods
3.1. Variable Selection
3.2. Bootstrapping
- (1)
- The original data (AGB field data and corresponding predictor variables) of size 108 was sorted by ascending AGB values.
- (2)
- After that, we divided the dataset into four equal-sized subgroups (size = 27).
- (3)
- For each subgroup, random sampling with replacement was performed and repeated 27 times. Therefore, there were 27 data for each subgroup and a total of 108 data were obtained, which was our first bootstrap sample.
- (4)
- The process (3) was repeated 300 times to obtain 300 bootstrap samples.
3.3. Modeling Approach
3.4. Independent Validation
4. Results
4.1. ICESat/GLAS Data Processing Results
4.2. AGB Model Results
4.3. Wall-to-Wall AGB Prediction over the Daxing’anling Mountains in Heilongjiang Province
5. Discussion
5.1. Spatio-Temporal Matching between GLAS Data and Measured Data
5.2. GLAS Data and Terrain Effects
5.3. Influence of Regional Coverage Types on Estimation
5.4. Effects of TM Data on Regional Biomass Mapping
5.5. Effects of range of AGB values on validation
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Data | Acquisition Time | |
---|---|---|
Field data | 2005, 2006 and 2007 | |
GLAS data | L2A | 25 September 2003–19 November 2003 |
L2D | 25 November 2008–17 December 2008 | |
L3A | 3 October 2004–8 November 2004 | |
L3B | 17 February 2005–24 March 2005 | |
L3C | 20 May 2005–23 June 2005 | |
L3D | 21 October 2005–24 November 2005 | |
L3F | 24 May 2006–26 June 2006 | |
L3G | 25 October 2006–27 November 2006 | |
Landsat5/TM data | July 2005 |
Types | GLAS Metric Abbreviations | Descriptions |
---|---|---|
The height metrics | Extent | The distance from signal beginning to signal ending [34]. |
Treeht | The distance from signal beginning to ground peak [34,38]. | |
Treeht2 Treeht3 | Top tree heights with corrections [34]. | |
H25 H75 | Quartile heights calculated by subtracting the ground elevation from elevation at which 25% or 75% of the returned energy occurs [34,39]. | |
H10 H20 H100 | Decimal heights calculated by subtracting the ground elevation from elevation at which 10% (20%...100%) of the returned energy occurs [34]. | |
LEE | The distance from the elevation of signal beginning to the first elevation at which the signal strength of the waveform is half of the maximum signal [38]. | |
TEE | The distance from the last elevation at which the signal strength of the waveform is half of the maximum signal to the elevation of signal ending [38]. | |
HOME | The height of median energy (HOME) [9]. | |
Meanh Medh | Mean canopy height, median canopy height [40]. | |
QMCH | Quadratic mean canopy height (QMCH) calculated from the canopy height profiles [40]. | |
The intensity metrics | Canopy cover | The ratio of the canopy echo area to the total wave area [41]. |
AVAW | The area under the waveform from vegetation [41]. |
Spectral Variables | Formula |
---|---|
NDVI | |
EVI | |
PVI | |
SAVI | |
NDIIB6 | |
NDIIB7 | |
TCB | |
TCG | |
TCW | |
TCdistance | |
TCangle |
Method | RMSE | SNR(dB) | r |
---|---|---|---|
wavelet transform | 0.73 | 35.67 | 0.53 |
Gaussian filter | 1.02 | 33.15 | 0.54 |
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Liu, K.; Wang, J.; Zeng, W.; Song, J. Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data. Remote Sens. 2017, 9, 341. https://doi.org/10.3390/rs9040341
Liu K, Wang J, Zeng W, Song J. Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data. Remote Sensing. 2017; 9(4):341. https://doi.org/10.3390/rs9040341
Chicago/Turabian StyleLiu, Kaili, Jindi Wang, Weisheng Zeng, and Jinling Song. 2017. "Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data" Remote Sensing 9, no. 4: 341. https://doi.org/10.3390/rs9040341