Mapping Forest Aboveground Biomass with Phenological Information Extracted from Remote Sensing Images in Subtropical Evergreen Broadleaf Forests
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Ground Measured Data
2.3. Remote Sensing Data and Pre-Processing
3. Methods
3.1. Spectral and Texture Features
3.2. Phenological Features
3.3. Variables Sets
3.4. Models and Accuracy Evaluation in Mapping Forest AGB
4. Results
4.1. The Results of Spectral Response in Subtropical Evergreen Broadleaf Forests
4.2. The Sensitivity Between the Forest AGB and the Extracted Phenological Features
4.3. The Results of Mapping AGB
5. Discussion
5.1. Interpretation the Phenological Features in Broadleaf Evergreen Forests
5.2. Potential of Phenological Features on AGB Mapping
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Sample | Number | Mean (t/hm2) | SD | CV (%) |
---|---|---|---|---|
0 t/hm2 < AGB ≤ 50 t/hm2 | 4 | 42.02 | 2.52 | 5.99 |
50 t/hm2 < AGB ≤ 100 t/hm2 | 12 | 78.66 | 16.04 | 20.39 |
100 t/hm2 < AGB ≤ 150 t/hm2 | 46 | 127.93 | 12.13 | 9.48 |
150 t/hm2 < AGB ≤ 200 t/hm2 | 28 | 169.51 | 14.11 | 14.11 |
AGB > 200 t/hm2 | 10 | 225.47 | 15.29 | 15.29 |
Total | 100 | 139.97 | 45.46 | 32.48 |
Number | Variable Set | Description |
---|---|---|
A | Bs, VI, TFs | Images acquired on 21 December 2021 |
B | A + PFs1 | EVI2 time series |
C | A + PFs2 | IRECI time series |
D | A + PFs3 | NDPI time series |
E | A + PFs4 | NDVI time series |
F | A + All PFs | Composite time series |
Phenological Features | EVI2 Time Series | IRECI Time Series | NDPI Time Series | NDVI Time Series |
---|---|---|---|---|
SOS (day of year) | 56 | 62 | 63 | 68 |
EOS (day of year) | 312 | 317 | 318 | 323 |
LOS (day) | 256 | 255 | 255 | 255 |
BV | 0.18 | 0.17 | 0.40 | 0.58 |
MAXMUN | 0.47 | 0.77 | 0.66 | 0.86 |
AP | 0.26 | 0.55 | 0.25 | 0.28 |
A | B | C | D | E | F | ||
---|---|---|---|---|---|---|---|
MLR | R2 | 0.34 | 0.39 | 0.48 | 0.54 | 0.48 | 0.54 |
RMSE (t/hm2) | 36.69 | 35.28 | 32.5 | 30.52 | 32.64 | 30.56 | |
rRMSE (%) | 26.21 | 25.20 | 23.22 | 21.81 | 23.32 | 21.83 | |
SVM | R2 | 0.29 | 0.33 | 0.33 | 0.51 | 0.52 | 0.58 |
RMSE (t/hm2) | 38.20 | 37.08 | 36.91 | 31.55 | 31.37 | 29.41 | |
rRMSE(%) | 27.29 | 26.49 | 26.37 | 22.54 | 22.41 | 21.01 | |
KNN | R2 | 0.29 | 0.29 | 0.27 | 0.35 | 0.40 | 0.40 |
RMSE (t/hm2) | 38.19 | 38.19 | 38.59 | 36.51 | 35.07 | 35.07 | |
rRMSE (%) | 27.28 | 27.28 | 27.57 | 26.08 | 25.06 | 25.06 | |
RF | R2 | 0.23 | 0.22 | 0.23 | 0.40 | 0.44 | 0.50 |
RMSE (t/hm2) | 39.72 | 40.05 | 39.72 | 34.97 | 33.99 | 32.00 | |
rRMSE (%) | 28.38 | 28.61 | 28.38 | 24.98 | 24.28 | 22.86 |
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Yang, P.; Long, J.; Lin, H.; Zhang, T.; Ye, Z.; Liu, Z. Mapping Forest Aboveground Biomass with Phenological Information Extracted from Remote Sensing Images in Subtropical Evergreen Broadleaf Forests. Remote Sens. 2025, 17, 1599. https://doi.org/10.3390/rs17091599
Yang P, Long J, Lin H, Zhang T, Ye Z, Liu Z. Mapping Forest Aboveground Biomass with Phenological Information Extracted from Remote Sensing Images in Subtropical Evergreen Broadleaf Forests. Remote Sensing. 2025; 17(9):1599. https://doi.org/10.3390/rs17091599
Chicago/Turabian StyleYang, Peisong, Jiangping Long, Hui Lin, Tingchen Zhang, Zilin Ye, and Zhaohua Liu. 2025. "Mapping Forest Aboveground Biomass with Phenological Information Extracted from Remote Sensing Images in Subtropical Evergreen Broadleaf Forests" Remote Sensing 17, no. 9: 1599. https://doi.org/10.3390/rs17091599
APA StyleYang, P., Long, J., Lin, H., Zhang, T., Ye, Z., & Liu, Z. (2025). Mapping Forest Aboveground Biomass with Phenological Information Extracted from Remote Sensing Images in Subtropical Evergreen Broadleaf Forests. Remote Sensing, 17(9), 1599. https://doi.org/10.3390/rs17091599