Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Field Study Location
2.2. Satellite Data Collection
2.3. Image Preprocessing
2.3.1. Synthetic Aperture Radar Image Preprocessing
2.3.2. SAR Image Reconstruction
2.3.3. Image Fusion
2.3.4. Feature Parameter Extraction
2.4. Biomass Estimation Methods
3. Results
3.1. Image Reconstruction and Fusion
3.2. Above Ground Biomass (AGB)AGB Estimation Models and Uncertainty
4. Discussion
4.1. Relationship of the Measured AGB with Remote Sensing Variables
4.2. The Accuracy and Uncertainty of the AGB Estimation Model
5. Conclusions
- High correlation values between biomass and remote-sensing data were obtained from the fused-image band 8 (0.874), EVI (0.876), VH polarization (0.504), and the mean texture parameter of band 7 (0.760) at a 0.01 significance level.
- The developed regression model based on the fused-image band 8 input provides a higher accuracy in biomass estimation compared to models using Worldview-3 or SAR data alone, which have prediction errors of 22.82 g m−2 and 24.29 g m−2, and accuracies of 74.64% and 73.12%, respectively.
- Higher values of uncertainty were found in the reclamation areas of 2013 and 2015 with an average AGB of over 100 g m−2. The combination of the WV-3 and Sentinel-1 SAR data can reduce the uncertainty of the mean value from the saturation level by a decrement of 2.42 g m−2 to eventually become 9.68 g m−2.
Author Contributions
Acknowledgments
Conflicts of Interest
References
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Reclamation Year | Vegetation Cover | Dominant Species | Number of Sampling Sites | ||
---|---|---|---|---|---|
2016 | 2018 | 2019 | |||
2009 | 50% | Elymus dahuricus Turcz. | 3 | 0 | 0 |
Heteropappus altaicus (Willd.) Novopokr. | |||||
2010 | 50% | Elymus dahuricus Turcz. | 2 | 0 | 0 |
2011 | 60% | Elymus dahuricus Turcz. | 6 | 2 | 1 |
Artemisia sieversiana | |||||
2012 | 62% | Artemisia sieversiana | 6 | 1 | 2 |
Scutellaria baicalensis Georgi | |||||
Bupleurum chinensis DC. | |||||
2013 | 70% | Medicago falcata L. | 4 | 4 | 4 |
2014 | 57% | Elymus dahuricus Turcz. | 4 | 3 | 3 |
2015 | 75% | Medicago falcata L. | 3 | 1 | 2 |
2016 | 50% | Elymus dahuricus Turcz. | 4 | 1 | 2 |
Sensor | Spatial Resolution | Band Name | Wavelength (nm) |
---|---|---|---|
Coastline | 400–450 | ||
Blue | 450–510 | ||
Green | 510–580 | ||
Worldview-3 | 1.24 m | Yellow | 585–625 |
Red | 630–690 | ||
Red edge | 705–745 | ||
NIR1 | 770–895 | ||
NIR2 | 860–1040 |
Acquisition Time | Polarization Mode | Radiation Precision | Resolution |
---|---|---|---|
29 July 2016 | VV+VH | 20 m (Azimuth resolution) | |
5 August 2016 | |||
10 August 2016 | 5 m (Range resolution) | ||
17 August 2016 |
Vegetative Index | Calculation Formula | Advantages | Reference |
---|---|---|---|
NDVI | The most widely used vegetation index; the best indicator of vegetation growth and coverage. | Rouse et al., 1974 [20] | |
DVI | Sensitive to changes in soil background, using areas with early- or mid-vegetation development or low-vegetation coverage. | Richardson and Weigand, 1977 [21] | |
RVI | Sensitive to lush, high-coverage vegetation, with high RVI values for green vegetation and low RVI values for nonvegetation. | Pearson and Miller, 1972 [22] | |
NDGI | Tests different forms of vital vegetation. | Gitelson et al., 1996 [23] | |
ARVI | Reduces the impact of the atmosphere on the vegetation index. | Kanfman and Tanre, 1992 [24] | |
EVI | Weakens the influence of the soil background. | Liu and Huete, 1995 [25] | |
An improved version of the NDVI index that is very sensitive to small changes in vegetation canopy, forest window fragments, and aging changes. | Sims et al., 2002 [26] |
Worldview-3 | Sentinel-1 SAR | Fusion | |||||
---|---|---|---|---|---|---|---|
Spectral Information | Texture Information | Texture Information | Spectral Information | ||||
Variable | Correlation | Variable | Correlation | Variable | Correlation | Variable | Correlation |
Coastline (B1) | −0.323 | B4ME | −0.741 ** | VH | 0.504 ** | B1 | −0.759 ** |
Blue (B2) | −0.474 * | B5ME | −0.792 ** | VV | 0.410 * | B2 | −0.806 ** |
Green (B3) | −0.372 | B5CR | −0.358 * | VVME | 0.412 ** | B3 | −0.837 ** |
Yellow (B4) | −0.620 ** | B6ME | 0.511 ** | VVVA | 0.174 | B4 | −0.834 ** |
Red (B5) | −0.657 ** | B6CR | −0.412 * | VVHO | −0.238 | B5 | −0.800 ** |
Red edge (B6) | 0.215 | B7ME | 0.760 ** | VVCO | 0.165 | B6 | 0.564 ** |
NIR1 (B7) | 0.694 ** | B7VA | 0.700 ** | VVDI | 0.201 | B7 | 0.842 ** |
NIR2 (B8) | 0.718 ** | B7HO | −0.593 ** | VVEN | 0.257 | B8 | 0.874 ** |
RVI | 0.790 ** | B7DI | 0.535 * | VVSM | −0.219 | ||
NGVI | 0.871 ** | B7EN | 0.510 ** | VVCR | 0.169 | ||
NDVI705 | 0.835 ** | B7SM | −0.366 * | VHME | 0.503 ** | ||
NDVI | 0.874 ** | B8ME | 0.748 ** | VHVA | −0.061 | ||
EVI | 0.876 ** | B8VA | 0.669 ** | VHHO | 0.069 | ||
DVI | 0.833 ** | B8HO | −0.600 ** | VHCO | 0.064 | ||
B8DI | 0.558 ** | VHDI | −0.016 | ||||
B8EN | 0.612 ** | VHEN | −0.133 | ||||
B8SM | 0.520 ** | VHSM | 0.298 | ||||
VHCR | −0.195 |
Label | Variable | Model | Model Accuracy (n = 11) | ||
---|---|---|---|---|---|
R2 | RMSE g m−2 | Ac% | |||
a | EVI | y = 2.8408×EVI + 16.829 | 0.7098 | 24.2018 | 73.12 |
b | VH | y = 2.2726×VH2 + 96.275×VH + 1094.4 | 0.3240 | 42.1104 | 53.22 |
c | NDVI VHME | y = 3.2563×NDVI – 0.6224×VHME + 8.593 | 0.6963 | 23.6801 | 73.69 |
d | FusedB8 | y = 1.0557×Fusion B8 – 26.676 | 0.7983 | 22.8283 | 74.64 |
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Bao, N.; Li, W.; Gu, X.; Liu, Y. Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery. Remote Sens. 2019, 11, 2855. https://doi.org/10.3390/rs11232855
Bao N, Li W, Gu X, Liu Y. Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery. Remote Sensing. 2019; 11(23):2855. https://doi.org/10.3390/rs11232855
Chicago/Turabian StyleBao, Nisha, Wenwen Li, **aowei Gu, and Yanhui Liu. 2019. "Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery" Remote Sensing 11, no. 23: 2855. https://doi.org/10.3390/rs11232855