The Potential of Fully Polarized ALOS-2 Data for Estimating Forest Above-Ground Biomass
SAR data have a longer wavelength and stronger penetrating power compared with traditional optical remote sensing. Therefore, SAR data are more suitable for the estimation of the above-ground biomass (AGB) of forests. This study was aimed at evaluating the sensitivity of L-band full polarization dat...
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MDPI AG
2022-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/3/669 |
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| author | Zhihui Liu Opelele Omeno Michel Guoming Wu Yu Mao Yifan Hu Wenyi Fan |
| author_facet | Zhihui Liu Opelele Omeno Michel Guoming Wu Yu Mao Yifan Hu Wenyi Fan |
| author_sort | Zhihui Liu |
| collection | DOAJ |
| description | SAR data have a longer wavelength and stronger penetrating power compared with traditional optical remote sensing. Therefore, SAR data are more suitable for the estimation of the above-ground biomass (AGB) of forests. This study was aimed at evaluating the sensitivity of L-band full polarization data to AGB. L-band data were improved to estimate the saturation point produced by AGB, and were found to be suitable for estimating a wide range of AGB. This study extracted backscattering coefficients, polarization decomposition variables, and terrain factors. New parameters were constructed from these variables, and their performance in predicting AGB was evaluated. Significant variables found with AGB were added to the multivariate linear model. A statistical analysis showed the presence of multicollinearity between the variables. Therefore, ridge regression, random forest method (RF), and principal component analysis (PCA) were introduced to solve the problem of collinearity. In all the three methods, the saturation of the ridge regression model was low, reaching it at 150 t/ha. Better accuracy was obtained with the RF model. No obvious saturation incident was detected in the model established using the principal component analysis. This could be attributed to the low biomass levels observed in our study area. This model provided accurate results (adjusted r<sup>2</sup> = 0.90 rmse = 14.24 t/ha), indicating that L-band data have the potential to estimate AGB. Additionally, suitable variables and models were selected in this study, with the principal component analysis being more helpful in combining various SAR parameters. The achievement of these accurate results could be attributed to the synergy among variables. |
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| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T23:12:32Z |
| publishDate | 2022-01-01 |
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| series | Remote Sensing |
| spelling | doaj.art-3b2e29b7b692486e8c00a254b816ef1e2023-11-23T17:41:35ZengMDPI AGRemote Sensing2072-42922022-01-0114366910.3390/rs14030669The Potential of Fully Polarized ALOS-2 Data for Estimating Forest Above-Ground BiomassZhihui Liu0Opelele Omeno Michel1Guoming Wu2Yu Mao3Yifan Hu4Wenyi Fan5Key Laboratory of Sustainable Forest Ecosystem Management—Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Sustainable Forest Ecosystem Management—Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Sustainable Forest Ecosystem Management—Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Sustainable Forest Ecosystem Management—Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Sustainable Forest Ecosystem Management—Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Sustainable Forest Ecosystem Management—Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, ChinaSAR data have a longer wavelength and stronger penetrating power compared with traditional optical remote sensing. Therefore, SAR data are more suitable for the estimation of the above-ground biomass (AGB) of forests. This study was aimed at evaluating the sensitivity of L-band full polarization data to AGB. L-band data were improved to estimate the saturation point produced by AGB, and were found to be suitable for estimating a wide range of AGB. This study extracted backscattering coefficients, polarization decomposition variables, and terrain factors. New parameters were constructed from these variables, and their performance in predicting AGB was evaluated. Significant variables found with AGB were added to the multivariate linear model. A statistical analysis showed the presence of multicollinearity between the variables. Therefore, ridge regression, random forest method (RF), and principal component analysis (PCA) were introduced to solve the problem of collinearity. In all the three methods, the saturation of the ridge regression model was low, reaching it at 150 t/ha. Better accuracy was obtained with the RF model. No obvious saturation incident was detected in the model established using the principal component analysis. This could be attributed to the low biomass levels observed in our study area. This model provided accurate results (adjusted r<sup>2</sup> = 0.90 rmse = 14.24 t/ha), indicating that L-band data have the potential to estimate AGB. Additionally, suitable variables and models were selected in this study, with the principal component analysis being more helpful in combining various SAR parameters. The achievement of these accurate results could be attributed to the synergy among variables.https://www.mdpi.com/2072-4292/14/3/669backscatter coefficientspolarization decompositioncollinearityridge regressionRFPCA |
| spellingShingle | Zhihui Liu Opelele Omeno Michel Guoming Wu Yu Mao Yifan Hu Wenyi Fan The Potential of Fully Polarized ALOS-2 Data for Estimating Forest Above-Ground Biomass Remote Sensing backscatter coefficients polarization decomposition collinearity ridge regression RF PCA |
| title | The Potential of Fully Polarized ALOS-2 Data for Estimating Forest Above-Ground Biomass |
| title_full | The Potential of Fully Polarized ALOS-2 Data for Estimating Forest Above-Ground Biomass |
| title_fullStr | The Potential of Fully Polarized ALOS-2 Data for Estimating Forest Above-Ground Biomass |
| title_full_unstemmed | The Potential of Fully Polarized ALOS-2 Data for Estimating Forest Above-Ground Biomass |
| title_short | The Potential of Fully Polarized ALOS-2 Data for Estimating Forest Above-Ground Biomass |
| title_sort | potential of fully polarized alos 2 data for estimating forest above ground biomass |
| topic | backscatter coefficients polarization decomposition collinearity ridge regression RF PCA |
| url | https://www.mdpi.com/2072-4292/14/3/669 |
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