Above-Ground Biomass Estimation for Coniferous Forests in Northern China Using Regression Kriging and Landsat 9 Images
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Field Measurement of AGB
2.3. Landsat Images Preprocessing
2.4. Extraction and Selection of Feature Variables
2.5. Model Development and Assessment
2.5.1. Parametric and Nonparametric Models
2.5.2. Regression Kriging
2.5.3. Model Accuracy Assessment
3. Results
3.1. Band Analysis for Landsat 8 and Landsat 9 Images
3.2. Correlation and Importance Analysis
3.3. Comparison of Original AGB Estimation Results
3.4. Regression Kriging for AGB Estimation
3.5. Spatial Distribution of AGB in Wangyedian
4. Discussion
4.1. Comparison of AGB Estimation Models
4.2. Limitations and Prospection
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Tree Species | Number | Value Range (t/ha) | Mean (t/ha) | Standard Deviation (t/ha) | Coefficient of Variation (%) |
---|---|---|---|---|---|
Larch | 41 | 24.56–207.17 | 115.95 | 49.14 | 42.3 |
Chinese pine | 45 | 28.48–459.62 | 138.29 | 64.63 | 46.7 |
Scots pine | 2 | 91.04–148.38 | 119.71 | 40.55 | 33.9 |
Total | 88 | 24.56–459.62 | 127.46 | 58.01 | 45.5 |
Spectral Bands | Wavelength Range (nm) | Spatial Resolution (m) | |
---|---|---|---|
Landsat 8/OLI-1 | Landsat 9/OLI-2 | ||
Band 1—Coastal | 435–451 | 435–450 | 30 |
Band 2—Blue | 452–512 | 452–512 | 30 |
Band 3—Green | 533–590 | 532–589 | 30 |
Band 4—Red | 636–673 | 636–672 | 30 |
Band 5—NIR | 851–879 | 850–879 | 30 |
Band 6—SWIR 1 | 1566–1651 | 1565–1651 | 30 |
Band 7—SWIR 2 | 2107–2294 | 2105–2294 | 30 |
Band 8—Panchromatic | 504–676 | 503–675 | 15 |
Band 9—Cirrus | 1363–1384 | 1363–1384 | 30 |
Landsat 8/TIRS-1 | Landsat 9/TIRS-2 | ||
Band 10—TIRS 1 | 10.60–11.18 | 10.45–11.20 | 100 |
Band 11—TIRS 2 | 11.51–12.50 | 11.58–12.50 | 100 |
Variable Type | Feature Variable | Reference |
---|---|---|
Spectral variable | Band reflectance (Band i, i = 1, 2, …7) | [43] |
Normalized difference vegetation index (NDVI) | [51] | |
Red–green vegetation index (RGVI) | [52] | |
Atmospherically resistant vegetation index (ARVI) | [52] | |
Enhanced vegetation index (EVI) | [53] | |
Visible atmospherically resistant index (VARI) | [54] | |
Soil-adjusted vegetation index (SAVI) | [55] | |
Modified soil-adjusted vegetation index (MSAVI) | [56] | |
Texture feature | Mean | [59] |
Variance | [59] | |
Homogeneity | [59] | |
Contrast | [59] | |
Dissimilarity | [59] | |
Entropy | [59] | |
Second moment | [59] | |
Correlation | [59] | |
Topographic factor | Elevation | [9] |
Slope | [9] | |
Aspect | [9] |
Bands | Landsat 8 | Landsat 9 | Correlation Coefficient | ||||
---|---|---|---|---|---|---|---|
Mean | Standard Deviation | Coefficient of Variation (%) | Mean | Standard Deviation | Coefficient of Variation (%) | ||
B1 | 0.10 | 0.13 | 127.21 | 0.23 | 0.24 | 104.29 | 0.77 |
B2 | 0.09 | 0.13 | 135.17 | 0.21 | 0.23 | 108.53 | 0.79 |
B3 | 0.11 | 0.13 | 120.85 | 0.23 | 0.24 | 104.37 | 0.82 |
B4 | 0.13 | 0.14 | 107.60 | 0.24 | 0.24 | 99.75 | 0.85 |
B5 | 0.19 | 0.13 | 69.27 | 0.29 | 0.21 | 74.41 | 0.89 |
B6 | 0.14 | 0.11 | 78.69 | 0.09 | 0.06 | 64.21 | 0.85 |
B7 | 0.11 | 0.08 | 78.20 | 0.07 | 0.04 | 61.17 | 0.87 |
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Jiang, F.; Sun, H.; Chen, E.; Wang, T.; Cao, Y.; Liu, Q. Above-Ground Biomass Estimation for Coniferous Forests in Northern China Using Regression Kriging and Landsat 9 Images. Remote Sens. 2022, 14, 5734. https://doi.org/10.3390/rs14225734
Jiang F, Sun H, Chen E, Wang T, Cao Y, Liu Q. Above-Ground Biomass Estimation for Coniferous Forests in Northern China Using Regression Kriging and Landsat 9 Images. Remote Sensing. 2022; 14(22):5734. https://doi.org/10.3390/rs14225734
Chicago/Turabian StyleJiang, Fugen, Hua Sun, Erxue Chen, Tianhong Wang, Yaling Cao, and Qingwang Liu. 2022. "Above-Ground Biomass Estimation for Coniferous Forests in Northern China Using Regression Kriging and Landsat 9 Images" Remote Sensing 14, no. 22: 5734. https://doi.org/10.3390/rs14225734