Multi-Source Remote Sensing and GIS for Forest Carbon Monitoring Toward Carbon Neutrality
Abstract
:1. Introduction
2. Review Methodology
3. Data Foundations for Forest Carbon Monitoring
3.1. Sensor-Specific Strengths and Weaknesses
3.2. Optical Satellite Data
3.3. Radar and Microwave Observations
3.4. LiDAR Observations
3.5. UAV and Airborne Remote Sensing
3.6. Auxiliary Data: Field Observations and Environmental Covariates
3.7. Quick-Reference Matrix
4. Modeling Layer: Data Integration and Biomass Estimation Methods
4.1. Multi-Source Data Fusion and Uncertainty Management for Forest Carbon Monitoring
4.1.1. Multi-Source Data Fusion Techniques
4.1.2. Uncertainty Propagation and Error Analysis in Multi-Source Fusion
4.2. Empirical Regression and Allometric Approaches
4.3. Machine Learning Approaches
4.3.1. Known Limitations of Data Fusion
- Phase-shift misalignment—when Sentinel-1 radar and GEDI LiDAR are acquired weeks apart, seasonal mismatch can raise biomass RMSE by 10%–15% in deciduous forests.
- Signal saturation—optical vegetation indices plateau above ≈ 150 Mg ha⁻1 and C-band SAR above ≈ 100 Mg ha⁻1; fusion cannot fully remove this ceiling.
- Inter-sensor bias—different incidence angles introduce height-dependent bias; Amazon tests showed ≈ 8% under-estimation when L-band SAR was fused with GEDI without terrain correction.
4.3.2. Core Machine-Learning Algorithms for Biomass Estimation
4.4. Deep Learning Methods
4.4.1. Comparative Snapshot of Modeling Paradigms
4.4.2. Advances in Deep Learning for Biomass Estimation
4.5. Process-Based Ecological Models
4.6. Belowground and Soil Carbon Estimation
4.7. Comparative Evaluation of Modeling Approaches Across Forest Types
4.8. Meta-Analysis of Biomass Estimation Method Performance
- Deep learning models (mainly CNN-based architectures) achieved the highest average predictive performance (mean R2 = 0.85; RMSE = 25 Mg C ha⁻1), especially in structurally heterogeneous tropical forests.
- Machine learning methods such as Random Forest and Gradient Boosting also exhibited strong predictive power (mean R2 = 0.78) while maintaining moderate data requirements and reasonable interpretability.
- Empirical regression models, despite their simplicity and high transparency, tended to have lower predictive performance, particularly in high-biomass environments prone to signal saturation.
- Process-based models demonstrated moderate performance (mean R2 = 0.66), reflecting their strength in simulating ecosystem processes but also the challenges associated with parameter calibration and spatial heterogeneity.
5. Application Layer: Forest Carbon Monitoring Under Policy Frameworks
5.1. REDD+ Monitoring and MRV Mechanisms
5.2. National and Regional Case Studies
5.2.1. Brazil: Integration of Remote Sensing into REDD+ Accounting
5.2.2. Congo Basin: Overcoming Data Scarcity
5.2.3. Indonesia: Forest Monitoring for REDD+ and Private Commitments
5.3. Carbon Markets and Zero-Deforestation Supply Chains
5.4. Smart Forestry and Digital Carbon Systems
6. Challenges and Future Perspectives
6.1. Data Heterogeneity and Lack of Standardization
6.2. Limited Model Generalizability and Field Validation Deficiency
6.3. High-Resolution Data Access: Cost and Temporal Constraints
6.4. Future Directions: Toward Intelligent, Integrated Carbon Monitoring
6.4.1. Cloud Platforms and Automated Monitoring
6.4.2. AI-Augmented Ecological Modeling
6.4.3. Integrated Sky-to-Ground Networks
6.4.4. Monitoring Belowground Carbon: Emerging Opportunities from New Satellite Missions
6.4.5. Uncertainty Management in REDD+ Carbon Credit Issuance
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
MRV | Measurement, reporting, and verification |
UAV | Unmanned Aerial Vehicle |
SFM | Structure from Motion |
CHM | Canopy Height Model |
CMS | Carbon monitoring system |
NDVI | Normalized Difference Vegetation Index |
EVI | Enhanced Vegetation Index |
NDWI | Normalized Difference Water Index |
SRTM | Shuttle Radar Topography Mission |
InSAR | Interferometric Synthetic Aperture Radar |
DEM | Digital Elevation Model |
SMOS | Soil Moisture and Ocean Salinity |
VIIRS | Visible Infrared Imaging Radiometer Suite |
FREL | Forest Reference Emission Level |
CAFI | Central African Forest Initiative |
FCPF | Forest Carbon Partnership Facility |
GEE | Google Earth Engine |
SEPAL | System for Earth Observation Data Access, Processing and Analysis for Land Monitoring |
NFI | National Forest Inventory |
IoT | Internet of Things |
SOC | Soil-organic carbon |
EBK | Empirical Bayesian Kriging |
CNN | Convolutional Neural Network |
LSTM | Long Short-Term Memory |
SVM | Support Vector Machine |
kNN | k-Nearest Neighbors |
MARS | Multivariate Adaptive Regression Splines |
GEDI | Global Ecosystem Dynamics Investigation |
HCS | High-carbon-stock |
VCS | Verified Carbon Standard |
VISTIR | Visible (spectral range)Thermal infrared |
GBM | Gradient Boosting Machine |
REDD | Reducing Emissions from Deforestation and Forest Degradation |
SAR | Synthetic Aperture Radar |
LiDAR | Light Detection and Ranging |
GPU | Graphics Processing Unit |
VOD | Vegetation Optical Depth |
SMAP | Soil Moisture Active Passive |
RF | Random Forest |
ALS | Airborne LiDAR scanning |
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Dimension | This Review | Xu et al. [35]—Forests |
---|---|---|
Time Span and Focus | 2020–2025; carbon neutrality-focused integration | 1999–2024; technology-oriented historical scope |
Remote Sensing Data Scope | Multi-source fusion (optical, SAR, LiDAR) with GIS modeling | Segmented analysis by sensor type with technical emphasis |
Modeling Perspective | Practical use of ML/DL in scalable carbon estimation workflows | Conceptual classification of empirical and process-based models |
Policy Integration | Strong alignment with REDD+, carbon markets, and NDC tracking | Technical depth but minimal linkage to climate policy mechanisms |
Case Study Inclusion | Detailed technical policy cases (e.g., Brazil and Congo Basin) | Lacks regionally grounded implementation examples |
Strategic Outlook | Proposes an integrated RS-GIS-AI-policy monitoring framework | Emphasis on modeling uncertainty and methodological innovation |
Distinctive Contribution | Practice-oriented synthesis with emphasis on operational feasibility and interdisciplinary integration | Methodologically detailed but policy-neutral, serving primarily technical audiences |
Sensor/Mission | Type | Resolution | Revisit | Key Forest Carbon Uses |
---|---|---|---|---|
Landsat-8/9 (NASA/USGS) | Optical (VIS–SWIR) | 30 m | 16 days | Long-term forest cover change; biomass via spectral indices (calibrated with plots) [18]. |
Sentinel-2 (ESA) | Optical (VIS–SWIR) | 10 m (20 m SWIR) | 5 days (2-satellite constellation) | High-resolution mapping of canopy greenness, leaf area, and forest type; inputs to biomass models, especially in regrowth and mosaic landscapes [19]. |
MODIS (Terra/Aqua) | Optical (VIS–TIR) | 250–500 m (1 km TIR) | 1–2 days | Regional to global monitoring of vegetation activity (NDVI/EVI, FPAR); NPP estimation for carbon flux modeling [36]. |
Planet Scope (Planet Labs) | Optical (VIS–NIR) | 3–5 m | Daily (constellation) | Detection of fine-scale changes (small clearings, degradation); verification of project-level carbon actions (e.g., tree planting survival). |
Sentinel-1 (ESA) | SAR (C-band, VV/VH) | 10 m | 6–12 days | All-weather forest cover monitoring; near-real-time deforestation alerts; detecting flooding and damage under clouds. Limited biomass sensitivity in dense forests [25]. |
ALOS-2 PALSAR-2 (JAXA) | SAR (L-band, HH/HV) | 25 m (10 m in spotlight) | ~42 days (global mode) | Mapping forest/non-forest extent and structure in tropics [27]; AGB estimation in low to mid biomass stands (e.g., woodland, secondary forest). |
BIOMASS (ESA, 2024+) | SAR (P-band) | ~50–100 m | 16 days (planned) | Dedicated biomass mapping mission for high-biomass tropical and boreal forests; will provide first P-band tomographic data to estimate AGB up to >300 Mg ha−1 [25]. |
GEDI Lidar (NASA, ISS) | LiDAR (1064 nm) | ~25 m footprint (60 m spacing) | ~2–4 years mission (sampling) | ~12 million shots per year sampling Earth’s forests; provides canopy height and structure used to calibrate biomass models and create 1 km gridded AGB products [29,30]. |
ICESat-2 (NASA) | LiDAR (532 nm photon-counting) | ~17 m footprint (0.7 km track spacing) | 91-day exact repeat (sampling) | Global photon-counting LiDAR data used for canopy height retrievals (especially in high latitudes); complements GEDI by covering >51° N/S and open forests. |
SMOS/SMAP (ESA/NASA) | Passive microwave (L-band) | ~40 km/9 km | 2–3 days | Vegetation optical depth (VOD) as proxy for biomass and water content; tracking large-scale carbon changes (e.g., drought impacts) in combination with models [22,23]. |
TanDEM-X (DLR) | SAR Interferometry (X-band) | ~12 m (height grid) | N/A (2010–2015 data) | Global digital elevation model from InSAR; used to derive forest canopy height (with ground DEM) and estimate biomass when calibrated [26]. |
VIIRS (NASA/NOAA) | Thermal and Optical | 375 m (optical) 750 m (thermal) | Daily (polar orbit) | Active fire detection and burn scar mapping for estimating fire emissions; night-time lights can indicate human activity near forests (indirect driver data). |
Ecological Objective | Tier 1—Low-Cost Workflow (Basic Accuracy) | Tier 2—Intermediate Workflow (Balanced Cost/Accuracy) | Tier 3—High-Precision Workflow (Research-Grade Accuracy) |
---|---|---|---|
Above-ground biomass (AGB) in dense, humid forest | Sentinel-1 C-band (VV/VH) + Landsat-8/9 multispectral; simple multivariate regression | Sentinel-1 + Sentinel-2 MSI + GEDI footprint heights; Random Forest regression | Sentinel-1 + ALOS-2 L-band + targeted airborne LiDAR tiles; Gradient-Boosting Machine (GBM) |
AGB in secondary or seasonally dry forest | Landsat NDVI/EVI; stratified linear regression | Sentinel-2 (10 m) + ALOS-2 L-band backscatter; XGBoost model | Sentinel-1 + PRISMA hyperspectral cube + UAV LiDAR; convolutional neural network (CNN) |
Annual carbon flux (NPP/GPP) | MODIS NDVI + ERA5 climate drivers; CASA light-use-efficiency model | MODIS NDVI + SMAP vegetation optical depth (VOD); long-short-term-memory (LSTM) network | MODIS phenology indices fused with eddy covariance upscaling; ensemble Random Forest |
Degradation and disturbance early warning | Sentinel-1 C-band time-series differencing; empirical thresholds | Sentinel-1 + Sentinel-2 dense time series; BFAST change-point analysis | High-density UAV LiDAR gap detection + PlanetScope sub-5 m imagery; U-Net deep segmentation |
Source | Description | Impact |
---|---|---|
Optical sensor radiometry | Calibration errors, atmospheric contamination | Biases in vegetation indices |
SAR saturation | Loss of sensitivity in high biomass zones | Underestimated AGB |
LiDAR sampling gaps | Sparse coverage, geolocation drift | Local errors in canopy structure |
Temporal misalignment | Seasonality, disturbance mismatches | Phenology artifacts |
Model structure assumptions | Over-simplified relationships | Systematic bias in biomass predictions |
Sensor Type | Main Uncertainty | Mitigation |
---|---|---|
Optical (Landsat/Sentinel-2) | Clouds, spectral saturation | Multi-temporal compositing; LiDAR calibration |
C-band SAR (Sentinel-1) | Lay-over, decorrelation | Polarimetric filtering; DEM correction |
L/P-band SAR (ALOS-2, BIOMASS) | Radio-frequency interference | RFI masking |
Spaceborne LiDAR (GEDI) | ±8 m geolocation drift | Strip-to-strip co-registration |
Hyperspectral (PRISMA) | Atmospheric absorption bands | Empirical-line calibration |
Modeling Paradigm | Accuracy | Computing Cost | Explainability | Replicability |
---|---|---|---|---|
Linear/allometric regression | 1 | 3 | 3 | 3 |
Random Forest/Gradient Boosting (GBM) | 2 | 2 | 2 | 2 |
Deep Convolutional/Recurrent Networks | 3 | 3 | 1 | 1 |
Process-based models (e.g., CASA; Biome-BGC) | 1 | 2 | 2 | 2 |
Modeling Approach | Suitable Forest Types | Typical Accuracy (R2) | Prediction Error (RMSE, Mg C/ha) | Data Requirements | Interpretability | Scalability | Strengths | Limitations |
---|---|---|---|---|---|---|---|---|
Empirical Regression | Temperate forests, open forests | 0.50–0.70 | 30–60 | Low | Very High | High | Simple to implement, transparent results | Signal saturation in high-biomass forests; limited generalizability |
Machine Learning (RF) | Tropical, temperate, and boreal forests | 0.65–0.85 | 20–45 | Medium–High | Moderate | Very High | Robust to noise; integrates multi-source data effectively | Requires large training datasets; limited causal interpretation |
Deep Learning (CNN) | Tropical rainforests, highly heterogeneous landscapes | 0.75–0.90 | 15–35 | Very High | Low | Moderate | Excellent in extracting complex spatial patterns; suitable for high-resolution data | Data- and compute-intensive; “black box” model behavior |
Process-based Models (CASA, Biome-BGC) | All forest types | 0.50–0.75 | 30–50 | Medium–High | High | Moderate | Simulates ecosystem processes; enables long-term scenario modeling | Requires detailed environmental inputs; complex parameterization |
Modeling Approach | Mean R2 ± SD | Mean RMSE (Mg C ha⁻1) ± SD | 95% CI for R2 |
---|---|---|---|
Empirical Regression | 0.62 ± 0.08 | 42 ± 6 | (0.60, 0.64) |
Machine Learning | 0.78 ± 0.07 | 30 ± 5 | (0.76, 0.80) |
Deep Learning | 0.85 ± 0.05 | 25 ± 4 | (0.83, 0.87) |
Process-based Modeling | 0.66 ± 0.09 | 50 ± 8 | (0.63, 0.69) |
Research Frontier | Key Questions | Methodological Approaches | Critical Data Needs |
---|---|---|---|
AI-Augmented Modeling | How to integrate time series into deep learning models? | Temporal CNNs, Transfer Learning | Time-labeled biomass datasets |
Real-Time Biomass Monitoring | Can sub-monthly biomass trends be operationalized? | Edge Computing, SAR Time Series Analysis | Near-real-time calibration data |
High-Resolution MRV Scaling | What is the cost–benefit threshold for VHR datasets? | Cost-Effectiveness Modeling, Sampling Optimization | Regional pilot studies linked to REDD+ |
Sky-Ground Data Fusion | How to unify satellite, UAV, and field measurements? | Data Assimilation, AI Fusion Frameworks | Coordinated multi-source observations |
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Liang, X.; Yu, S.; Meng, B.; Wang, X.; Yang, C.; Shi, C.; Ding, J. Multi-Source Remote Sensing and GIS for Forest Carbon Monitoring Toward Carbon Neutrality. Forests 2025, 16, 971. https://doi.org/10.3390/f16060971
Liang X, Yu S, Meng B, Wang X, Yang C, Shi C, Ding J. Multi-Source Remote Sensing and GIS for Forest Carbon Monitoring Toward Carbon Neutrality. Forests. 2025; 16(6):971. https://doi.org/10.3390/f16060971
Chicago/Turabian StyleLiang, Xiongwei, Shaopeng Yu, Bo Meng, Xiaodi Wang, Chunxue Yang, Chuanqi Shi, and Junnan Ding. 2025. "Multi-Source Remote Sensing and GIS for Forest Carbon Monitoring Toward Carbon Neutrality" Forests 16, no. 6: 971. https://doi.org/10.3390/f16060971
APA StyleLiang, X., Yu, S., Meng, B., Wang, X., Yang, C., Shi, C., & Ding, J. (2025). Multi-Source Remote Sensing and GIS for Forest Carbon Monitoring Toward Carbon Neutrality. Forests, 16(6), 971. https://doi.org/10.3390/f16060971