Spatial Scale Effect and Correction of Forest Aboveground Biomass Estimation Using Remote Sensing
"> Figure 1
<p>Study area and sample site location. (<b>A</b>) the distribution of single trees in the plot of the broadleaf forest; (<b>B</b>) the distribution of single trees in the plot of coniferous forest; (<b>C</b>) the distribution of single trees in the plot of the coniferous and broadleaf mixed forest.</p> "> Figure 2
<p>Schematic flowchart of the two upscaling methods of the AGB estimation. (<span class="html-italic">f<sub>i</sub></span> is the remote sensing inversion model of forest aboveground biomass; <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mrow> <mrow> <mo>(</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>4</mn> </mrow> <mo>)</mo> </mrow> </mrow> </msub> </mrow> </semantics></math> is the characteristic variable of the high-resolution remote sensing image; <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>m</mi> </msub> </mrow> </semantics></math> is the average value of <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>i</mi> </msub> </mrow> </semantics></math>, namely the pixel V value of the coarse-resolution image; <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>G</mi> <msub> <mi>B</mi> <mrow> <mi>e</mi> <mi>x</mi> <mi>a</mi> </mrow> </msub> </mrow> </semantics></math> is the biological value of the high-resolution image, namely the relative truth value; <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>G</mi> <msub> <mi>B</mi> <mrow> <mi>a</mi> <mi>p</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math> is the biological value of the coarse-resolution image, namely the biological values with scale errors).</p> "> Figure 3
<p>Schematic diagram of true mean AGB scale invariance. (<b>A</b>) the mass of the AGB per unit surface area of coarse resolution remote sensing data; (<b>B</b>) the mass of the AGB per unit surface area of high resolution remote sensing data.</p> "> Figure 4
<p>Scatter plot between measured and estimated AGB (black dotted line indicates <span class="html-italic">y</span> = <span class="html-italic">x</span>). (<b>a</b>) the scattering plot of measured and estimated AGB of GF-2 by using random forest model; (<b>b</b>) the scattering plot of measured and estimated AGB of Sentinel-2 by using random forest model; (<b>c</b>) the scattering plot of measured and estimated AGB of Landsat-8 by using random forest model; (<b>d</b>) the scattering plot of measured and estimated AGB of GF-2 by using multiple linear model; (<b>e</b>) the scattering plot of measured and estimated AGB of Sentinel-2 by using multiple linear model; (<b>f</b>) the scattering plot of measured and estimated AGB of Landsat-8 by using multiple linear model.</p> "> Figure 5
<p>AGB estimation using the random forest model for various remote sensing data: (<b>a</b>) AGB estimation result using GF-2; (<b>b</b>) AGB estimation result using Sentinel-2; (<b>c</b>) AGB estimation result using Landsat-8. (Unit was t/ha).</p> "> Figure 6
<p>Relative values of the AGB extracted by using estimated AGB of GF-2 at 10 m and 30 m spatial resolution. (<b>a</b>) Relative value of the AGB of 10 m; (<b>b</b>) Relative value of the AGB of 30 m. (Unit was t/ha).</p> "> Figure 7
<p>Scatter plot of the pre- and post-correction results and true AGB and the accuracy comparison of 10 m. (<b>a</b>) Scatter plot of the pre-correction results and true AGB at 10 m; (<b>b</b>) scatter plot of the post-correction results and true AGB at 10 m; (<b>c</b>) accuracy comparison.</p> "> Figure 8
<p>Scatter plot of the pre- and post-correction results and true AGB and the accuracy comparison of 30 m. (<b>a</b>) Scatter plot of the pre-correction results and true AGB at 30 m; (<b>b</b>) scatter plot of the post-correction results and true AGB at 30 m; (<b>c</b>) accuracy comparison.</p> "> Figure 9
<p>The scale error-corrected AGB distribution at 10 m and 30 m spatial resolution. (<b>a</b>) Corrected AGB at 10 m; (<b>b</b>) corrected AGB at 30 m. (Unit was t/ha).</p> "> Figure 10
<p>Scatter plot between measured and geostatistical scale-corrected AGB at 10 m resolution.</p> "> Figure 11
<p>Accuracy evaluation among geostatistical scale-corrected AGB results at 30 m resolution.</p> ">
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data
2.2.1. Remote Sensing Data and Pre-Processing
2.2.2. Field Measurement
3. Methodology
3.1. Method of Aboveground Forest Biomass Estimation at Different Spatial Scales
3.1.1. Remote Sensing Variable Selection
3.1.2. Method of AGB Modeling
3.2. Scale Error Calculation
3.3. Scale Error Measurement of Mixed Pixels
3.3.1. Determination of the True Mean Value
3.3.2. Method of Scale Error Correction
3.3.3. Accuracy Evaluation
4. Results
4.1. Results of the AGB Modeling at Various Spatial Resolutions of Remote Sensing
4.2. Retriveved AGB at Various Spatial Resolutions
4.3. Verification of the Scale Error Correction
5. Discussion
6. Conclusions
- (1)
- The random forest model had better AGB estimation accuracy for three different spatial resolutions of remote sensing. This indicates that the nonlinear machine learning method would be promising candidate for AGB estimation.
- (2)
- With the assumption of the law of conservation of mass, a scale error correction method using the information entropy of land use type was developed and successfully applied to the upscaling of AGB estimation for data of different resolution. Compared with other geostatistical interpolation methods, this method can obtain a high-accuracy AGB estimation and reduce the effect of the scale error on AGB estimation. The results indicated that this method can reduce the scale effect caused by the heterogeneity of the surface feature.
Author Contributions
Funding
Conflicts of Interest
References
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Index | GF-2 (n = 70) | Sentinel-2 (n = 70) | Landsat-8 (n = 55) |
---|---|---|---|
Mean | 112.7623 | 98.4261 | 105.2296 |
Standard deviation | 23.1844 | 31.0125 | 31.8978 |
Range | 47.2768–181.5890 | 47.1336–174.2340 | 47.9736–193.5593 |
Sensor | Variable | Formular | Description |
---|---|---|---|
GF-2 | ME2 | Mean of the four directional textural features of GF-2 band 2 | |
Var4 | Sum variance of the gray-level co-occurrence matrix of GF-2 band 4 | ||
Ho2 | Homogeneity of the gray-level co-occurrence matrix of GF-2 band 2 | ||
B1 | Blue, 450–520 nm | Reflectance of the GF-2 blue band | |
B431 [56] | (B4 + B3)/B1 | A vegetation index calculated by GF-2 band 1, 3 and 4 | |
B4 | Near-infrared band, 770–890 nm | Reflectance of the GF-2 near-infrared band | |
B13 | B1 + B3 [56] | A vegetation index calculated by GF-2 band 1 and 3 | |
PX | Slope | Slope extracted from DEM data resampled to GF-2 spatial resolution | |
Sentinel-2 | Var7 | Sum variance of gray-level co-occurrence matrix of Sentinel-2 band 7 | |
Cor8 | The correlation texture between the grey levels and those neighboring pixels of Sentinel-2 band 8 | ||
IRECI [57] | (B7 − B4)/(B5/B6) | Inverted red-edge chlorophyll index | |
B3 | Green, 560 nm | Reflectance of the Sentinel-2 green light band | |
PX | Slope | Slope extracted from DEM data resampled to Sentinel-2 spatial resolution | |
Wetness [58] | 0.2578 × B2 + 0.2305 × B3 + 0.0883 × B4 + 0.1071 × B8 − 0.7611 × B11 − 0.5308 × B12 [59] | Tasseled Cap (KT) transformation wetness | |
REIP [57] | 700 + 40 × [(B4 + B7)/2 − B5]/(B6 − B5) | Red-edge infection point index | |
Brightness [58] | 0.351 × B2 + 0.3813 × B3 + 0.3437 × B4 + 0.7196 × B8 + 0.2396 × B11 + 0.1949 × B12 [59] | Tasseled Cap (KT) transformation brightness | |
Landsat-8 | B4/Albedo [60] | B4/(0.246 × B2 + 0.146 × B3 + 0.191 × B4 + 0.304 × B5 + 0.105 × B6 + 0.008 × B7) | Band combination vegetation index |
PX | Slope | Slope extracted from DEM data resampled to Landsat-8 spatial resolution | |
B4 | Red, 640–670 nm | Reflectance of the Landsat-8 red light band | |
ND563 [60] | (B5 + B6 − B3) × (B5 + B6 + B3) | Normalized difference vegetation index | |
Cor5 | The correlation texture between the grey levels and those neighboring pixels of Landsat-8 band 5 | ||
SM5 | Angular second moment of Landsat-8 band 5 | ||
Var5 | Sum variance of gray-level co-occurrence matrix of Landsat-8 band 5 | ||
ME2 | Mean of the four directional textural features of Landsat-8 band 2 |
Image | Resolution | Model | R2 | RMSE | MAE | rRMSE |
---|---|---|---|---|---|---|
GF-2 | 4 m | Multiple Linear | 0.5110 | 19.4328 | 15.7262 | 0.1737 |
Random Forest | 0.5943 | 17.5056 | 14.2677 | 0.1282 | ||
Sentinel-2 | 10 m | Multiple Linear | 0.5409 | 21.4195 | 17.7690 | 0.2442 |
Random Forest | 0.4971 | 20.2485 | 14.9274 | 0.2308 | ||
Landsat-8 | 30 m | Multiple Linear | 0.3034 | 28.8477 | 23.7357 | 0.2778 |
Random Forest | 0.4235 | 28.6546 | 25.2203 | 0.2759 |
Image | Resolution | Model | R2 | RMSE | MAE | rRMSE |
---|---|---|---|---|---|---|
GF-2 | 4 m | Multiple Linear | 0.4072 | 20.2781 | 16.9451 | 0.1759 |
Random Forest | 0.5711 | 16.9586 | 12.7153 | 0.1471 | ||
Sentinel-2 | 10 m | Multiple Linear | 0.3344 | 23.6606 | 19.7257 | 0.2353 |
Random Forest | 0.4819 | 19.4657 | 14.3562 | 0.1936 | ||
Landsat-8 | 30 m | Multiple Linear | 0.2892 | 32.8565 | 25.6158 | 0.3034 |
Random Forest | 0.4321 | 29.7677 | 28.0137 | 0.2749 |
Index | AGBGF-2 | AGBSentinel-2 | AGBLandsat-8 |
---|---|---|---|
Mean | 101.30 | 102.52 | 94.70 |
Standard deviation | 40.25 | 43.95 | 40.02 |
Index | AGBGF-2 | AGBexa-10 | AGBexa-30 |
---|---|---|---|
Mean | 101.30 | 101.29 | 101.24 |
Standard deviation | 40.25 | 39.31 | 37.98 |
Index | AGBSentinel-2 | AGBexa-10 | AGBLandsat-8 | AGBexa-30 |
---|---|---|---|---|
MBE | 11.4635 | 1.2378 | 6.0725 | −6.0069 |
RMSE | 16.3102 | 10.7745 | 9.0367 | 8.2139 |
MAPE | 12.0822 | 7.4743 | 7.0241 | 6.3071 |
R2 | 0.6226 | 0.6725 | 0.5910 | 0.6704 |
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Yu, Y.; Pan, Y.; Yang, X.; Fan, W. Spatial Scale Effect and Correction of Forest Aboveground Biomass Estimation Using Remote Sensing. Remote Sens. 2022, 14, 2828. https://doi.org/10.3390/rs14122828
Yu Y, Pan Y, Yang X, Fan W. Spatial Scale Effect and Correction of Forest Aboveground Biomass Estimation Using Remote Sensing. Remote Sensing. 2022; 14(12):2828. https://doi.org/10.3390/rs14122828
Chicago/Turabian StyleYu, Ying, Yan Pan, Xiguang Yang, and Wenyi Fan. 2022. "Spatial Scale Effect and Correction of Forest Aboveground Biomass Estimation Using Remote Sensing" Remote Sensing 14, no. 12: 2828. https://doi.org/10.3390/rs14122828