Evaluating Disturbance Regime Stratification for Aboveground Biomass Estimation in a Heterogeneous Forest Landscape: Insights from the Atewa Landscape, Ghana.
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| Title: | Evaluating Disturbance Regime Stratification for Aboveground Biomass Estimation in a Heterogeneous Forest Landscape: Insights from the Atewa Landscape, Ghana. |
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| Authors: | Adams, Lukman B.1 (AUTHOR), Hayakawa, Yuichi S.1 (AUTHOR) hayakawa@eis.hokudai.ac.jp |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 5, p765. 23p. |
| Subjects: | Biomass estimation, Ecological disturbances, Remote sensing, Forest biodiversity, Machine learning, Forests & forestry, Anthropogenic effects on nature |
| Geographic Terms: | Ghana |
| Abstract: | Highlights: What are the main findings? Excessive heterogeneity or homogeneity in regimes may affect forest aboveground biomass modeling. Human-mediated disturbance factors exhibited weaker heteroscedastic behavior with increasing distance and showed intermediate importance in aboveground biomass modeling. What are the implications of the main findings? A combination of heterogeneous and homogeneous regimes overcomes the challenge of increased noise or reduced variance, thereby improving modeling accuracy. Human-mediated disturbance factors counter model biases introduced by other predictor variables. Optical and passive remote sensing-based estimation of aboveground biomass (AGB) using forest structural stratification has shown improvements over global models. This study investigated whether stratification by human-mediated disturbances improves prediction accuracy. Disturbance variables included proximity to mines, roads, and settlements, evaluated across three regimes: the full Atewa landscape ("FSR"), the Atewa Range Forest Reserve ("FR"), and the surrounding disturbed area ("SR"). Predictor selection for regimes was performed using recursive feature elimination with cross-validation, applied to random forest (RF) and support vector machine (SVM) algorithms. AGB was then estimated using local, global, and retuned global models, and the results were compared using the coefficient of determination (r2) and root mean square error (RMSE). The global RF model achieved the best performance (r2 = 0.54; RMSE = 57.71 Mg/ha), likely due to structured heterogeneity captured across combined regimes. The "SR" models, however, performed poorly, indicating that excessive unstructured heterogeneity introduces noise and redundancy that weaken predictions. The low performance of the "FR" regime was attributed to spectral saturation and limited variance in observed AGB. Although disturbance factors added minimal bias, heteroscedasticity was evident in the "SR" and "FSR" regimes. Overall, this study indicates that disturbance-based stratification may not necessarily improve AGB estimation accurately compared to global models. However, it highlights the value of disturbance information for AGB modeling in heterogeneous forest landscapes. [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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192640022 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Evaluating Disturbance Regime Stratification for Aboveground Biomass Estimation in a Heterogeneous Forest Landscape: Insights from the Atewa Landscape, Ghana. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Adams%2C+Lukman+B%2E%22">Adams, Lukman B.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hayakawa%2C+Yuichi+S%2E%22">Hayakawa, Yuichi S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hayakawa@eis.hokudai.ac.jp</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Mar2026, Vol. 18 Issue 5, p765. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Biomass+estimation%22">Biomass estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Ecological+disturbances%22">Ecological disturbances</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+biodiversity%22">Forest biodiversity</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Forests+%26+forestry%22">Forests & forestry</searchLink><br /><searchLink fieldCode="DE" term="%22Anthropogenic+effects+on+nature%22">Anthropogenic effects on nature</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Ghana%22">Ghana</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? Excessive heterogeneity or homogeneity in regimes may affect forest aboveground biomass modeling. Human-mediated disturbance factors exhibited weaker heteroscedastic behavior with increasing distance and showed intermediate importance in aboveground biomass modeling. What are the implications of the main findings? A combination of heterogeneous and homogeneous regimes overcomes the challenge of increased noise or reduced variance, thereby improving modeling accuracy. Human-mediated disturbance factors counter model biases introduced by other predictor variables. Optical and passive remote sensing-based estimation of aboveground biomass (AGB) using forest structural stratification has shown improvements over global models. This study investigated whether stratification by human-mediated disturbances improves prediction accuracy. Disturbance variables included proximity to mines, roads, and settlements, evaluated across three regimes: the full Atewa landscape ("FSR"), the Atewa Range Forest Reserve ("FR"), and the surrounding disturbed area ("SR"). Predictor selection for regimes was performed using recursive feature elimination with cross-validation, applied to random forest (RF) and support vector machine (SVM) algorithms. AGB was then estimated using local, global, and retuned global models, and the results were compared using the coefficient of determination (r2) and root mean square error (RMSE). The global RF model achieved the best performance (r2 = 0.54; RMSE = 57.71 Mg/ha), likely due to structured heterogeneity captured across combined regimes. The "SR" models, however, performed poorly, indicating that excessive unstructured heterogeneity introduces noise and redundancy that weaken predictions. The low performance of the "FR" regime was attributed to spectral saturation and limited variance in observed AGB. Although disturbance factors added minimal bias, heteroscedasticity was evident in the "SR" and "FSR" regimes. Overall, this study indicates that disturbance-based stratification may not necessarily improve AGB estimation accurately compared to global models. However, it highlights the value of disturbance information for AGB modeling in heterogeneous forest landscapes. [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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18050765 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 765 Subjects: – SubjectFull: Biomass estimation Type: general – SubjectFull: Ecological disturbances Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Forest biodiversity Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Forests & forestry Type: general – SubjectFull: Anthropogenic effects on nature Type: general – SubjectFull: Ghana Type: general Titles: – TitleFull: Evaluating Disturbance Regime Stratification for Aboveground Biomass Estimation in a Heterogeneous Forest Landscape: Insights from the Atewa Landscape, Ghana. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Adams, Lukman B. – PersonEntity: Name: NameFull: Hayakawa, Yuichi S. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 5 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
