Multi-Sensor Fusion and Machine Learning for Forest Age Mapping in Southeastern Tibet
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Strategy
- Data Collection
- Data Fusion and Preprocessing
- Field Surveys and Target Analysis
- Model Selection and Evaluation
- Detailed Classification Using the CODED Algorithm
- Feature Combination and Model Performance Analysis
- Results Visualization and Systematic Evaluation
2.2. Research Area
2.3. Field Data and Forest Age Groups
2.4. Multi-Source Time-Series Remote-Sensing Data Fusion
- Spectroscopy, vegetation indices, and ecological remote-sensing product data:
- LiDAR forest structure and biomass data
- Climate and Soil Data
- SAR Data
- Topographic data:
2.5. Forest Change Detection and Age Identification
- Change Detection
- Classification of Disturbances
- Age Inference
2.6. Model Selection and Evaluation
2.7. Methods of Evaluation
3. Results
3.1. Correlation Analysis of Forest Age with Biomass and Ecological Variables
3.2. Forest Age Group—Scale Variability Relationship of Remote Sensing Spectroscopy
3.3. Model Performance
3.4. Evaluation of Forest Age Group Classification Results and Analysis of Characteristics of Predictors
3.5. Spatial Distribution of Forest Age
4. Discussion
4.1. Ecological Characteristics of Forest Age
4.2. The Influence of Forest Age on Spectral Reflectance Characteristics
4.3. Spectral Reflectivity Characteristics Depend on the Scale Variability of Spatial Resolution
4.4. Error Sources and Model Generalizability Discussion
4.5. Limitations of the Study and Future Outlook
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Classification of Forest Parameters | Parameter Name | Describe |
---|---|---|
Age | AGEyr. | Forest age (years) is the average age of the dominant tree species. |
Leaf area | TREE-LAI | Tree leaf-area index |
Volume | VOLUMEm3/ha | Volume of wood per hectare |
Tree Density | DENSITYtrees/ha | Density of trees per hectare |
Biomass | STEM_MASSt.DM/ha | Biomass per hectare of tree trunk (dry matter) |
BRANCH_MASSt./ha | Biological number of branches per hectare | |
LEAF_MASSt./ha | Leaf biomass per hectare | |
ROOT_MASSt.DM/ha | Root biomass (dry matter) per hectare | |
TOTAL_TREE_MASSt.DM/ha | Total tree biomass per hectare | |
HERB-MASSt.DM/ha | Herbaceous biomass per hectare | |
SHRUB-MASSt./ha | Shrub biomass per hectare | |
TOTAL-MASSt.DM/ha | Total biomass per hectare (trees, shrubs, herbs) | |
Primary productivity | STEM_NPPt.DM/ha/yr. | Primary productivity per hectare of tree trunk (dry matter) |
BRANCH_NPPt.DM/ha/yr. | Primary productivity per hectare of branches | |
LEAF_NPPt.DM/ha/yr. | Primary productivity per hectare of leaves | |
ROOT_NPPt.DM/ha/yr. | Primary productivity per hectare of roots | |
TOTAL_TREE_NPPt.DM/ha/yr. | Total tree primary productivity per hectare | |
HERB-NPPt./ha/yr. | Primary productivity per hectare of herbs | |
SHRUB-NPPt.DM/ha/yr. | Primary productivity per hectare of shrub | |
TOTAL-NPPt.DM/ha/yr. | Total primary productivity per hectare | |
Geographic information | Altitude | Altitude |
LATITUDE,deg. | North latitude | |
LONGITUDEE,deg. | East longitude |
Data Set | Abbreviated Name | Describe | Dataset Id (GEE) |
---|---|---|---|
ETH Global Sentinel-2 10 m Canopy Height (2020) | CAPHEI | Fusing GEDI with Sentinel-2 to generate probabilistic deep learning models for global retrieval of canopy height [83]. | users/nlang/ETH_GlobalCanopyHeight_2020_10m_v1 |
Sensor-Independent MODIS and VIIRS LAI/FPAR CDR 2000 to 2022 (8d500m) | LAI | Leaf-Area Index (LAI), used to characterize the state of terrestrial ecosystems [84]. | projects/sat-io/open-datasets/BU_LAI_FPAR/wgs_500m_8d |
Sensor-Independent MODIS & VIIRS LAI/FPAR CDR 2000 to 2022 (8d500m) | FPAR | Fraction of Photosynthetically Active Radiation (FPAR), used to characterize terrestrial ecosystem states. | projects/sat-io/open-datasets/BU_LAI_FPAR/wgs_500m_8d |
Global Aboveground and Belowground Biomass Carbon Density Maps at 300 m Resolution (2010) | AGB | Living biomass carbon reserve density on wood and grassy vegetation in 2010. This includes the carbon stored in the living plant tissue (stems, bark, branches, twigs) located above the surface. This does not include fallen leaves or rough woody fragments that once attached to living plants but were later deposited and are no longer viable [85]. | NASA/ORNL/biomass_carbon_density/v1 |
MOD17A3HGF.061: Terra Net Primary Production Gap-Filled Yearly Global 500 m (2001–2023) | GPP | Gross primary productivity [86]. | MODIS/061/MOD17A3HGF |
SBIO1_Annual_Mean_Temperature (1000 m) | AMT | Average annual temperature, which indicates the average temperature at soil depth of 0–5 cm during the year (ecosystem conditions below the vegetation crown and near the surface) [87]. | projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm |
SBIO2_Mean_Diurnal_Range (1000 m) | MDR | Mean daily range of temperature, i.e., –5 cm of soil depth. The average difference between the highest and lowest temperatures on a day. | projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm |
SBIO3_Isothermality (1000 m) | S_I | The isotherm of soil depth of 0–5 cm, the formula is BIO2/BIO7 × 100, which reflects the uniformity of temperature change. | projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm |
SBIO4_Temperature_Seasonality (1000 m) | STS | The seasonal temperature change of 0–5 cm soil depth, measured as a standard deviation, indicates the dispersion of temperature changes over a year. | projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm |
SBIO5_Max_Temperature_of_Warmest_Month (1000 m) | SMTWM | The hottest monthly maximum temperature of 0–5 cm of soil depth indicates the maximum temperature in the hottest month of the year. | projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm |
SBIO6_Min_Temperature_of_Coldest_Month (1000 m) | SMTCM | The coldest monthly minimum temperature of 0–5 cm of soil depth indicates the lowest temperature in the coldest month of the year. | projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm |
SBIO7_Temperature_Annual_Range (1000 m) | TARa | The annual temperature range of 0–5 cm soil depth, the formula BIO5-BIO6, that is, the difference between the highest temperature of the hottest month and the lowest temperature of the coldest month. | projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm |
SBIO8_Mean_Temperature_of_Wettest_Quarter (1000 m) | MTWQ | The wettest quarterly mean temperature of 0–5 cm of soil depth indicates the average temperature of the wettest quarter of the year. | projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm |
SBIO9_Mean_Temperature_of_Driest_Quarter (1000 m) | MTDQ | The driest quarterly mean temperature of 0–5 cm of soil depth indicates the average temperature of the driest quarter of the year. | projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm |
SBIO10_Mean_Temperature_of_Warmest_Quarter (1000 m) | MTWQ_1 | The warmest quarterly mean temperature of 0–5 cm of soil depth indicates the average temperature of the warmest quarter of the year. | projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm |
SBIO11_Mean_Temperature_of_Coldest_Quarter (1000 m) | MTCQ | The coldest quarterly mean temperature of 0–5 cm of soil depth indicates the average temperature of the coldest quarter of the year. | projects/crowtherlab/soil_bioclim/SBIO_v2_0_5cm |
Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected, log scaling (2015–2023) | VV | Single-covalent polarization, vertical emission/vertical reception | COPERNICUS/S1_GRD |
Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected, log scaling (2015–2023) | VH | Two-band cross polarization, vertical emission/horizontal reception | COPERNICUS/S1_GRD |
Global PALSAR-2/PALSAR Yearly Mosaic, version 1 | HHLSAR | HH polarization backscattering coefficient, 16-bit DN [88]. | JAXA/ALOS/PALSAR/YEARLY/SAR |
Global PALSAR-2/PALSAR Yearly Mosaic, version 1 | HVLSAR | HV polarization backscattering coefficient, 16-bit DN. | JAXA/ALOS/PALSAR/YEARLY/SAR |
Gridded GEDI Vegetation Structure Metrics and Biomass Density, 1 km pixel size (2019–2023) | MEAN | The mean of GEDI shot metric values within a pixel [89]. | LARSE/GEDI/GRIDDEDVEG_002/V1/1KM |
Gridded GEDI Vegetation Structure Metrics and Biomass Density, 1 km pixel size (2019–2023) | MEANBASE | Standard error of the mean calculated using bootstrap resampling. | LARSE/GEDI/GRIDDEDVEG_002/V1/1KM |
Gridded GEDI Vegetation Structure Metrics and Biomass Density, 1 km pixel size (2019–2023) | MEDIAN | The median value (50th percentile) of GEDI shot metric values within a pixel. | LARSE/GEDI/GRIDDEDVEG_002/V1/1KM |
Gridded GEDI Vegetation Structure Metrics and Biomass Density, 1 km pixel size (2019–2023) | SD | The standard deviation of GEDI shot metric values within a pixel. | LARSE/GEDI/GRIDDEDVEG_002/V1/1KM |
Gridded GEDI Vegetation Structure Metrics and Biomass Density, 1 km pixel size (2019–2023) | IQR | The interquartile range (75th percentile minus 25th percentile) of GEDI shot metric values within a pixel. | LARSE/GEDI/GRIDDEDVEG_002/V1/1KM |
Gridded GEDI Vegetation Structure Metrics and Biomass Density, 1 km pixel size (2019–2023) | P95 | The 95th percentile value of GEDI shot-metric values within a pixel. | LARSE/GEDI/GRIDDEDVEG_002/V1/1KM |
Gridded GEDI Vegetation Structure Metrics and Biomass Density, 1 km pixel size (2019–2023) | Mountain | Shannon’s diversity index (H) of GEDI shot metric values within a pixel. Calculated as:-1(sum(plog(p))) where p is the proportion of GEDI shot values per bin. | LARSE/GEDI/GRIDDEDVEG_002/V1/1KM |
Gridded GEDI Vegetation Structure Metrics and Biomass Density, 1 km pixel size (2019–2023) | COUNTF | The count of GEDI shot metric values within a pixel. | LARSE/GEDI/GRIDDEDVEG_002/V1/1KM |
PROBA-V C1 Top Of Canopy Daily Synthesis 100 m (2013-) | RED | Top of canopy reflectance RED channel | VITO/PROBAV/C1/S1_TOC_100M |
PROBA-V C1 Top Of Canopy Daily Synthesis 100 m (2013-) | NIR | Top of canopy reflectance NIR channel | VITO/PROBAV/C1/S1_TOC_100M |
PROBA-V C1 Top Of Canopy Daily Synthesis 100 m (2013-) | BLUE | Top of canopy reflectance BLUE channel | VITO/PROBAV/C1/S1_TOC_100M |
PROBA-V C1 Top Of Canopy Daily Synthesis 100 m (2013-) | SWIR | Top of canopy reflectance SWIR channel | VITO/PROBAV/C1/S1_TOC_100M |
PROBA-V C1 Top Of Canopy Daily Synthesis 100 m (2013-) | NDVI | Normalized Difference Vegetation Index | VITO/PROBAV/C1/S1_TOC_100M |
Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-2A (2017–2024) | “B1”, “B2”, “B3”, “B4”, “B5”, “B6”, “B7”, “B8”, “B8A”, “B9”, “B11”, “B12” | COPERNICUS/S2_SR_HARMONIZED | |
Landsat Collection 2 (1990–2020) | “blue”, “green”, “red”, “nir”, “swir1”, “swir2” [90] | ||
MOD09A1.061 Terra Surface Reflectance 8-Day Global 500 m (2000–2024) | "sur_refl_b01", "sur_refl_b02", "sur_refl_b03", "sur_refl_b04", "sur_refl_b05", "sur_refl_b06", "sur _ refl _ b07" [91] | MODIS/061/MOD09A1 | |
NASADEM: NASA 30 m Digital Elevation Model | ELEVATION | Altitude [92] | NASA/NASADEM_HGT/001 |
NASADEM: NASA NASADEM Digital Elevation 30 m | SLOPE | Slope | NASA/NASADEM_HGT/001 |
NASADEM: NASA NASADEM Digital Elevation 30 m | ASPECT | The slopes are sloped | NASA/NASADEM_HGT/001 |
Model Name | Description |
---|---|
Linear regression | Linear regression [93] models the relationship between the dependent variable (forest age) and the independent variables using a linear equation. |
Ridge regression | Predicts forest age by shrinking coefficients with L2 regularization [94], suitable for handling multicollinearity. |
Lasso regression | Predicts forest age by selecting important features with L1 regularization [95], suitable for high-dimensional data. |
ElasticNet Regression | Predicts forest age by combining L1 and L2 regularization [96], suitable for high-dimensional data with multicollinearity. |
Decision Tree Regression | Predicts forest age by recursively splitting data based on a forest structure [97], suitable for nonlinear relationships. |
Random Forest Regression | Predicts forest age by averaging results from multiple decision forests [98], suitable for high-dimensional data requiring high accuracy. |
Gradient Boosting Regression | Predicts forest age by sequentially adding weak learners to optimize the loss function [99], suitable for complex nonlinear relationships. |
Extra forests Regression | Predicts forest age using random thresholds for splitting data [100], with faster training speed. |
K-Nearest Neighbor Regression | Predicts forest age by averaging the values of the K nearest neighbors [101], suitable for low-dimensional data. |
Support vector regression | Predicts forest age using a hyperplane and kernel functions [102], suitable for high-dimensional data. |
Multilayer Perceptron Regression | Predicts forest age by fitting complex relationships with a neural network [103], suitable for large-scale data. |
AdaBoost Regressor | Predicts forest age by training multiple weak learners with adjusted sample weights [104], suitable for improving model performance. |
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Age Groups | Age Range | Description |
---|---|---|
Young forest | <40 years | Trees in early growth stage with underdeveloped canopies and unstable stand structure. |
Intermediate-aged forest | 40–60 years | Vigorously growing trees forming closed canopies with rapid height and diameter increases. |
Sub-mature forest | 60–80 years | Trees approaching physiological maturity with slowing growth but accumulating economic value. |
Mature forest | 80–120 years | Trees at peak utilization stage with optimal timber quality and ecological functions. |
Overmature forest | >120 years | Declining trees with stagnant growth, increased vulnerability to pests and diseases. |
Model Name | RMSE | R2 |
---|---|---|
Linear Regression | 24.77 | 0.444 |
Ridge Regression | 25.26 | 0.422 |
Lasso Regression | 26.44 | 0.366 |
ElasticNet Regression | 29.28 | 0.223 |
Decision Tree Regression | 24.77 | 0.444 |
Random Forest Regression | 25.39 | 0.466 |
Gradient Boosting Regression | 24.77 | 0.444 |
Extra Forests Regression | 24.77 | 0.444 |
K-Nearest Neighbor Regression | 24.41 | 0.410 |
Support Vector Regression | 34.13 | −0.056 |
Multilayer Perceptron Regression | 59.15 | −2.171 |
AdaBoost Regressor | 29.81 | 0.195 |
Feature Combination | Validation Accuracy |
---|---|
C-band radar band: VV, VH | 0.45 |
L-band radar and GEDI feature combinations: HHLSAR, HVLSAR, mean, meanbase, median, sd, iqr, p95, shan, countf | 0.42 |
Optical feature combinations: RED, NIR, BLUE, SWIR, NDVI | 0.28 |
Radar and terrain feature combinations: VV, VH, HHLSAR, HVLSAR, mean, meanbase, median, sd, iqr, p95, shan, countf, elevation, slope, aspect | 0.57 |
Ecological characteristics combination: CAPHEI, LAI, FPAR, Agb _, Gpp, AMT, MDR, S _ I, STS, SMTWM, SMTCM, TARa, MTWQ, MTDQ, MTWQ _ 1, MTCQ | 0.55 |
Comprehensive combination of characteristics1 (Includes GEDI Data): CAPHEI, LAI, FPAR, agb_, AMT, MDR, S_I, STS, SMTWM, SMTCM, TARa, MTWQ, MTDQ, MTWQ_1, MTCQ, VV, VH, RED, NIR, BLUE, SWIR, NDVI, HHLSAR, HVLSAR, mean, meanbase, median, sd, iqr, p95, shan, countf, elevation, slope, aspect | 0.65 |
Comprehensive combination of characteristics2 (Not including GEDI data)CAPHEI, LAI, FPAR, Agb_, AMT, MDR, S _ I, STS, SMTWM, SMTCM, TARa, MTWQ, MTDQ, MTWQ_1, MTCQ, VV, VH, RED, NIR, Blue, SWIR, NDVI, Gpp, HHLSAR,HVLSAR,elevation,slope,aspect | 0.62 |
Forest Age Group | Total Area (m2) | Percentage of Total Area |
---|---|---|
Young forest | 233,797,514.40 | 1% |
Intermediate-aged forest | 833,776,911.90 | 2% |
Sub-mature forest | 492,678,667.95 | 1% |
Mature forest | 3,275,570,584.65 | 9% |
Overmature forest | 33,472,546,497.30 | 87% |
Total | 38,308,370,176.20 | 100% |
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Chi, Z.; Xu, K. Multi-Sensor Fusion and Machine Learning for Forest Age Mapping in Southeastern Tibet. Remote Sens. 2025, 17, 1926. https://doi.org/10.3390/rs17111926
Chi Z, Xu K. Multi-Sensor Fusion and Machine Learning for Forest Age Mapping in Southeastern Tibet. Remote Sensing. 2025; 17(11):1926. https://doi.org/10.3390/rs17111926
Chicago/Turabian StyleChi, Zelong, and Kaipeng Xu. 2025. "Multi-Sensor Fusion and Machine Learning for Forest Age Mapping in Southeastern Tibet" Remote Sensing 17, no. 11: 1926. https://doi.org/10.3390/rs17111926
APA StyleChi, Z., & Xu, K. (2025). Multi-Sensor Fusion and Machine Learning for Forest Age Mapping in Southeastern Tibet. Remote Sensing, 17(11), 1926. https://doi.org/10.3390/rs17111926