High-Resolution Forest Biomass Mapping in Japan Using Canopy Height Estimation from Remote Sensing and Machine Learning.
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| Title: | High-Resolution Forest Biomass Mapping in Japan Using Canopy Height Estimation from Remote Sensing and Machine Learning. |
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| Authors: | Kawamura, Akito Davis1 (AUTHOR) akito_davis@archeda.inc, Kodama, Tomoya1,2 (AUTHOR), Tadono, Takeo1,2 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1845. 21p. |
| Subjects: | Forest biomass, Tree height, Allometric equations, Remote sensing, Machine learning, Forest mapping, Carbon cycle, Forests & forestry |
| Geographic Terms: | Japan |
| Abstract: | Highlights: What are the main findings? Addressing the "Saturation Problem" in ExistingWide-Area Maps and High-Biomass Regions. The importance of AGB estimation using methods that consider Japan's regional characteristics and tree species. What is the implication of the main finding? In Japanese forests, estimates tended to saturate at approximately 300–400 Mg/ha, leading to a significant underestimation of high-biomass areas. Global biomass maps (such as ESA CCI) exhibit a similar trend. This study ensures consistency and robustness against saturation, even in high AGB (Above-Ground Biomass) regions, by adopting a hybrid approach that combines tree height estimation with forest-type-specific allometric equations, rather than relying solely on machine learning. We demonstrated that accuracy can be maintained in high-biomass areas, reproducing an AGBD distribution close to actual measurements. To account for Japan's complex topography and diverse tree species (e.g., mixed evergreen/deciduous and needleleaf/broadleaf forests), we achieved improved accuracy by using optimized parameters for each forest type instead of a uniform national model. This high-precision, 10m-resolution national map enables precise carbon sink assessments reflecting regional differences in forest structure. It will serve as highly practical foundational information for transparent carbon credit trading and the formulation of municipal-level forest management plans. Continuous monitoring of forest biomass is indispensable for establishing transparent carbon budgets and ensuring sustainable forest management toward achieving carbon neutrality. While satellite data has traditionally been used for wide-area biomass estimation, signal saturation in high-biomass regions has posed a significant challenge to accuracy. To address this saturation issue and enhance the precision of carbon budget estimations, this study develops a new methodology for estimating forest above-ground biomass (AGB). First, a training dataset was constructed by integrating airborne LiDAR data from across Japan with various satellite datasets, such as PALSAR-2 and Sentinel-2. Machine learning (XGBoost) was then employed to generate a nationwide canopy height map, achieving a high coefficient of determination ( R 2 = 0.594 ). Subsequently, allometric equations with parameters optimized for specific forest types (evergreen coniferous, evergreen broadleaf, deciduous coniferous, and deciduous broadleaf) were derived from the relationship between estimated canopy height and AGB to create a nationwide AGB map. Validation results indicated that the resulting AGB map demonstrated higher estimation accuracy ( R 2 = 0.265 ) compared to existing global products (ESA CCI Biomass), with significant improvements in mitigating underestimation (saturation) in high-biomass areas. By combining canopy height estimation with forest-type-specific allometry, this approach enables high-precision mapping that reflects the unique characteristics of Japanese forests and is expected to contribute to more reliable carbon budget assessments. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Addressing the "Saturation Problem" in ExistingWide-Area Maps and High-Biomass Regions. The importance of AGB estimation using methods that consider Japan's regional characteristics and tree species. What is the implication of the main finding? In Japanese forests, estimates tended to saturate at approximately 300–400 Mg/ha, leading to a significant underestimation of high-biomass areas. Global biomass maps (such as ESA CCI) exhibit a similar trend. This study ensures consistency and robustness against saturation, even in high AGB (Above-Ground Biomass) regions, by adopting a hybrid approach that combines tree height estimation with forest-type-specific allometric equations, rather than relying solely on machine learning. We demonstrated that accuracy can be maintained in high-biomass areas, reproducing an AGBD distribution close to actual measurements. To account for Japan's complex topography and diverse tree species (e.g., mixed evergreen/deciduous and needleleaf/broadleaf forests), we achieved improved accuracy by using optimized parameters for each forest type instead of a uniform national model. This high-precision, 10m-resolution national map enables precise carbon sink assessments reflecting regional differences in forest structure. It will serve as highly practical foundational information for transparent carbon credit trading and the formulation of municipal-level forest management plans. Continuous monitoring of forest biomass is indispensable for establishing transparent carbon budgets and ensuring sustainable forest management toward achieving carbon neutrality. While satellite data has traditionally been used for wide-area biomass estimation, signal saturation in high-biomass regions has posed a significant challenge to accuracy. To address this saturation issue and enhance the precision of carbon budget estimations, this study develops a new methodology for estimating forest above-ground biomass (AGB). First, a training dataset was constructed by integrating airborne LiDAR data from across Japan with various satellite datasets, such as PALSAR-2 and Sentinel-2. Machine learning (XGBoost) was then employed to generate a nationwide canopy height map, achieving a high coefficient of determination ( R 2 = 0.594 ). Subsequently, allometric equations with parameters optimized for specific forest types (evergreen coniferous, evergreen broadleaf, deciduous coniferous, and deciduous broadleaf) were derived from the relationship between estimated canopy height and AGB to create a nationwide AGB map. Validation results indicated that the resulting AGB map demonstrated higher estimation accuracy ( R 2 = 0.265 ) compared to existing global products (ESA CCI Biomass), with significant improvements in mitigating underestimation (saturation) in high-biomass areas. By combining canopy height estimation with forest-type-specific allometry, this approach enables high-precision mapping that reflects the unique characteristics of Japanese forests and is expected to contribute to more reliable carbon budget assessments. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111845 |
