Improving Estimates and Change Detection of Forest Above-Ground Biomass Using Statistical Methods
Abstract
:1. Introduction
- (i)
- Spatial modelling: Spatial modelling techniques can account explicitly for the spatial correlation which is exhibited in satellite data.
- (ii)
- Combining data sources: Interpolation techniques to combine data sources available at different spatial scales.
- (iii)
- Model validation: Validation methods which can be used effectively for spatially correlated data.
- (iv)
- Uncertainty measurements: Methods used to appropriately recognise and visualize final estimates of error, when errors arise from multiple sources.
- (v)
- Change detection and quantification: Methods which may help to detect and quantify AGB change over time.
2. Background Information
2.1. Field Data
2.2. Optical Remote Sensing
Name | Type of Data | Years Available | Life Span | Revisit Period | Cost | Provider | Resolution |
---|---|---|---|---|---|---|---|
Sentinel 1 | SAR C-band | 2014-Present | Continuous | 6/12 days | Open access | ESA | 10m |
TanDEM-X | SAR X-band | 2010-Present | 5+ years | 11 days | Private | DLR and AirBus | 25 cm–40 m |
ALOS-2 PALSAR-2 | SAR L-band | 2014-Present | 5+ years | 14 days | Yearly mosaic open access | JAXA | 10m |
RADARSAT 1-2 | SAR C-band | 1995-Present | continuous | 24 days | Limited open access | CSA | 1–100 m |
BIOMASS | SAR P-band | 2023 Launch | 5.5 years | 3 days | Open access | ESA | 200 m |
Landsat 4–9 | Optical | 1984-Present | Continuous | 8 days | Open access | NASA | 30 m |
Sentinel-2 | Optical | 2015-Present | Continuous | 2–5 days | Open access | ESA | 10–60 m |
MODIS | Optical | 1999-Present | Beyond life span | 16 days | Open access | NASA | 250–1000 m |
ICESat-2 | Lidar | 2018-Present | 3–7 years | <33 days | Open access | NASA | 2 m |
GEDI | Lidar | 2018-Present | 2+ years | Not guaranteed | Open access | NASA | 25 m circular footprints |
2.3. Synthetic Aperture Radar (SAR) Remote Sensing
2.4. Lidar Remote Sensing
3. Data
Owner | Map | Reference | Spatial Resolution | Input Data | Data Used to Train or Validate Models | Method to Obtain Estimate | Method to Combine Data | Method Used to Validate Model | Uncertainty Estimates |
---|---|---|---|---|---|---|---|---|---|
ESA, JAXA | GlobBiomass 2010 | Santoro et al. [39] | 100 m | SAR C-band, SAR L-band, Optical | Spaceborne lidar, Forest Inventory field data | Water cloud model | Weighted combination of two predictions | RMSE | Standard deviation available |
NCEO | Africa Aboveground biomass map 2017 | Rodriguez-Veiga and Balzter [40] | 100 m | SAR L-band, and Optical Percent Tree cover | Spaceborne lidar, Airborne lidar | Random forest for canopy height, empirical model for AGB | Tree cover used to constrain predictions to areas with tree cover | Spatial k-fold cross validation | N/A |
NASA | GEDI Level 4A Footprint AGB 2020 | Duncanson et al. [41] | 25 m- available at footprints | Spaceborne lidar | Airborne lidar and field data | OLS regression | N/A | Geographic cross validation | N/A |
NASA | JPL Benchmark map | Saatchi et al. [42] | 1 km | Optical vegetation indices, Microwave, digital elevation map | Field data and GLAS lidar | Maximum entropy machine learning | Variables in model | Cross validation with separated data-set | Available at pixel level |
NASA | Mangrove canopy height and biomass map 2000 | Simard et al. [43] | 100 m | Digital elevation map (DEM), spaceborne lidar, | Field data | Allometric equations, regression models | N/A | RMSE | N/A |
ESA | CCI Biomass 2017, 2020 | Santoro [44] | 100 m | SAR C-band, SAR L-band | Spaceborne lidar | Water cloud model, Least squares regression and self calibration | Weighted combination of two predictions | RMSE | Standard deviation available |
_ | Tropical carbon density map 2003-14 | Baccini et al. [45], Baccini et al. [46] | 500 m | Optical mosaic imagery | Field data and GLAS lidar | Random forest | N/A | RMSE validation with separated data set | Available at national scale |
_ | Integrated pan-tropical biomass map | Avitabile et al. [47] | 1 km | multiple AGB maps | Sepated reference data-set | Regression model | Linear weighted average of predictors | RMSE with separated data set | Map available for most regions |
4. Large-Scale Spatial Modelling
4.1. Current Global Modelling Approaches
4.2. What Problems Are Faced When Modelling AGB Data?
4.3. Methods to Model Spatial Data
5. Data Combination
5.1. Why Use Combinations of Data Sources?
5.2. How Are Global Data Sources Currently Combined?
5.3. Problems Faced When Combining Data
5.4. Methods to Tackle Spatial Misalignment
5.5. Models to Improve Data Combination
6. Model Validation
6.1. How Are Global Maps Currently Assessed?
6.2. Problems with These Validation Methods?
6.3. Alternative Validation Methods
7. Uncertainty Measurements
7.1. The Importance of Uncertainty Measurements
7.2. How Is Uncertainty of Global Maps Currently Presented?
7.3. Problems Faced When Providing Uncertainty Estimates
7.4. Alternative Uncertainty Measurement Methods
8. Change Detection and Quantification
8.1. The Importance of Change Detection
8.2. How Is Global Biomass Change Currently Detected?
8.3. Problems Faced When Detecting AGB Change
8.4. How Can Change Detection Be Improved?
9. Discussion of Future Research and Potential Solutions
10. Summary
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AGB | Above ground biomass |
AGC | Above-Ground Carbon |
ALOS | Advanced Land Observing Satellite |
CCI | ESA Climate Change Initiative |
CSA | Canadian Space Agency |
DBH | Diameter at Breast Height |
DEM | Digital Elevation Map |
DLR | German Aerospace Center |
ECV | Essential climate variable |
ESA | European Space Agency |
GAMs | Generalised Additive Models |
GEDI | Global Ecosystem Dynamics Investigation |
GLAS | Geoscience Laser Altimeter System |
GLM | generalised linear model |
GLMMs | Generalised Linear Mixed Models |
HV | Horizontal vertical |
INLA | Integrated nested Laplace approximation |
JAXA | Japan Aerospace Exploration Agency |
Lidar | Light Detection and Ranging |
LLO | Leave Location Out |
MAAP | Multi-Mission ALgorithm and Analysis Platform |
MCMC | MArkov Chain Monte Carlo |
MODIS | Moderate Resolution Imaging Spectroradiometer |
MSE | Mean Squared Error |
NASA | National Aeronautics and Space Administration |
NCEO | National Centre for Earth Observation |
NFIs | National Forest Inventories |
OLS | Ordinary Least Squares |
PALSAR-2 | Phased Array L-band Synthetic Aperture Radar |
REDD | Reducing Emissions from Deforestation and Forest Degradation |
RF | Random Forest |
RMSE | Root Mean Squared Error |
SAR | Synthetic Aperture Radar |
SDGs | Sustainable Development Goals |
SGCS | Sequential Gaussian Cosimulation |
SIS | Sequential indicator simulation |
SPDE | Stochastic Partial Differential Equations |
UAV | Unmanned Aerial Vehicle |
UN | United Nations |
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Turton, A.E.; Augustin, N.H.; Mitchard, E.T.A. Improving Estimates and Change Detection of Forest Above-Ground Biomass Using Statistical Methods. Remote Sens. 2022, 14, 4911. https://doi.org/10.3390/rs14194911
Turton AE, Augustin NH, Mitchard ETA. Improving Estimates and Change Detection of Forest Above-Ground Biomass Using Statistical Methods. Remote Sensing. 2022; 14(19):4911. https://doi.org/10.3390/rs14194911
Chicago/Turabian StyleTurton, Amber E., Nicole H. Augustin, and Edward T. A. Mitchard. 2022. "Improving Estimates and Change Detection of Forest Above-Ground Biomass Using Statistical Methods" Remote Sensing 14, no. 19: 4911. https://doi.org/10.3390/rs14194911
APA StyleTurton, A. E., Augustin, N. H., & Mitchard, E. T. A. (2022). Improving Estimates and Change Detection of Forest Above-Ground Biomass Using Statistical Methods. Remote Sensing, 14(19), 4911. https://doi.org/10.3390/rs14194911