Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review
Abstract
:1. Introduction
- Which satellite-derived independent biophysical variables and regression models are widely used and perform better than others?
- Based on the literature review, which high-resolution optical satellite sensors are best for AGB estimations?
2. Methodology
3. Geographical Areas
4. Satellite-Derived Independent Variables and Regression Techniques Used for AGB Modeling
4.1. Tree Crown
4.1.1. Linear Regression Models
4.1.2. Linear (Multiple) Regression Models
4.1.3. Non-Linear (Multiple and Exponential) Regression Models
4.1.4. Non-Linear (Machine Learning) Regression Models
4.2. Image Textures
4.2.1. Linear Regression Models
4.2.2. Linear (Multiple) Regression Models
4.3. Tree Shadow Fraction
Linear Regression Models
4.4. Canopy Height
4.4.1. Linear Regression Models
4.4.2. Linear (Multiple) Regression Models
4.5. Vegetation Indices
4.5.1. Linear Regression Models
4.5.2. Non-Linear (Machine Learning) Regression Models
4.6. Multiple Variables
4.6.1. Linear (Multiple) Regression Models
4.6.2. Non-Linear (Multiple and Exponential) Regression Models
4.6.3. Non-Linear (Machine Learning) Regression Models
5. AGB Estimations and Reporting
6. Accuracy Assessment of AGB Estimations
7. Limitations and Challenges
8. Applications of High-Resolution AGB Estimations and Maps
9. Alternatives to Optical High-Resolution Satellite Data for AGB Estimations
10. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Satellite Sensor * | Africa | Asia | Europe | North America | South America | Total |
---|---|---|---|---|---|---|
GeoEye-1 | 3 | 3 | ||||
GeoEye-1 and QuickBird | 1 | 1 | 2 | |||
IKONOS | 2 | 2 | 1 | 1 | 6 | |
IKONOS-2 | 1 | 1 | ||||
IKONOS and Cartosat-1 | 1 | 1 | ||||
Pleiades | 1 | 2 | 1 | 4 | ||
Pleiades-1A and GeoEye-1 | 1 | 1 | ||||
Pleiades-1A and WorldView-2 | 1 | 1 | ||||
QuickBird | 1 | 4 | 1 | 5 | 11 | |
QuickBird and WorldView-2 | 1 | 1 | ||||
SPOT-5 | 2 | 1 | 1 | 4 | ||
SPOT-6 | 1 | 1 | ||||
Cartosat-1a | 1 | 1 | ||||
WorldView-2 | 1 | 3 | 1 | 1 | 6 | |
WorldView-3 | 1 | 1 | ||||
Airborne Orthophotos | 1 | 1 | ||||
RapidEye | 1 | 1 | ||||
Grand total | 8 | 21 | 4 | 8 | 5 | 46 |
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Ahmad, A.; Gilani, H.; Ahmad, S.R. Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review. Forests 2021, 12, 914. https://doi.org/10.3390/f12070914
Ahmad A, Gilani H, Ahmad SR. Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review. Forests. 2021; 12(7):914. https://doi.org/10.3390/f12070914
Chicago/Turabian StyleAhmad, Adeel, Hammad Gilani, and Sajid Rashid Ahmad. 2021. "Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review" Forests 12, no. 7: 914. https://doi.org/10.3390/f12070914
APA StyleAhmad, A., Gilani, H., & Ahmad, S. R. (2021). Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review. Forests, 12(7), 914. https://doi.org/10.3390/f12070914