# Improving Estimates and Change Detection of Forest Above-Ground Biomass Using Statistical Methods

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## 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?

^{2}) [78,80].

#### 6.2. Problems with These Validation Methods?

^{2}, estimated by cross-validation, only quantify the prediction error relative to the mean of the the predictive distribution and hence ignore the uncertainty of the prediction. Gelman et al. [80] therefore recommends to also consider the log score and the log likelihood. In particular, the log score is also useful for different non-normal distributions (e.g., in logistic regression) and when the focus is on the accuracy of the whole predictive distribution rather than just point estimates.

#### 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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**Figure 1.**Satellite imagery downloaded using Google Earth Engine over the same location in Black Forest, Germany [centre point: 48.528N, 8.228E]. (

**a**) Lidar, GEDI. Footprints of data are shown by coloured dots ranging from pink (low height) to blue (large height). (

**b**) SAR HV, ALOS PALSAR-2 annual mosaic, shown with no filtering. No backscatter is black and high backscatter is white. (

**c**) Optical Imagery, Sentinel-2. Bands 2, 3 and 4 are shown to create a RGB image, with a 90% stretch applied. (

**a**) GEDI footprint data, lidar imagery; (

**b**) ALOS-PALSAR-2 annual mosaic, SAR imagery; (

**c**) Sentinel-2 bands: RGB, optical imagery.

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**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Turton, 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