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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18050765 |
