High-Resolution Forest Biomass Mapping in Japan Using Canopy Height Estimation from Remote Sensing and Machine Learning.
Saved in:
| Title: | High-Resolution Forest Biomass Mapping in Japan Using Canopy Height Estimation from Remote Sensing and Machine Learning. |
|---|---|
| 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] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 194587066 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: High-Resolution Forest Biomass Mapping in Japan Using Canopy Height Estimation from Remote Sensing and Machine Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kawamura%2C+Akito+Davis%22">Kawamura, Akito Davis</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> akito_davis@archeda.inc</i><br /><searchLink fieldCode="AR" term="%22Kodama%2C+Tomoya%22">Kodama, Tomoya</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tadono%2C+Takeo%22">Tadono, Takeo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 11, p1845. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Forest+biomass%22">Forest biomass</searchLink><br /><searchLink fieldCode="DE" term="%22Tree+height%22">Tree height</searchLink><br /><searchLink fieldCode="DE" term="%22Allometric+equations%22">Allometric equations</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+mapping%22">Forest mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Carbon+cycle%22">Carbon cycle</searchLink><br /><searchLink fieldCode="DE" term="%22Forests+%26+forestry%22">Forests & forestry</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Japan%22">Japan</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194587066 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18111845 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 1845 Subjects: – SubjectFull: Forest biomass Type: general – SubjectFull: Tree height Type: general – SubjectFull: Allometric equations Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Forest mapping Type: general – SubjectFull: Carbon cycle Type: general – SubjectFull: Forests & forestry Type: general – SubjectFull: Japan Type: general Titles: – TitleFull: High-Resolution Forest Biomass Mapping in Japan Using Canopy Height Estimation from Remote Sensing and Machine Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kawamura, Akito Davis – PersonEntity: Name: NameFull: Kodama, Tomoya – PersonEntity: Name: NameFull: Tadono, Takeo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 11 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
