the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Jan 2026
A global map of forest age for natural and planted forests at a fine spatial resolution of 30 meters
Abstract. Natural and planted forests differ substantially in ecological functions and economic values, with forest age serving as a key indicator of their developmental and carbon dynamics. However, existing global forest age datasets remain constrained by coarse spatial resolution and the lack of explicit forest type distinction. In this study, we developed a 30 m global forest age dataset for 1985–2024 by integrating Landsat time-series data with the Continuous Change Detection and Classification (CCDC) algorithm on the Google Earth Engine platform, thereby reducing reliance on ground-based forest inventory data. Building upon global forest distribution products, forest age was estimated from change points in long-term spectral trajectories, distinguishing the age of natural and planted forests at a global scale. Validation using 6,100 globally stratified samples demonstrated strong agreement with visually interpreted references (overall accuracy = 0.72, RMSE = 5.66 years), with higher accuracy for natural forests (0.73) than for planted forests (0.70). Globally, pronounced regional contrasts were observed: old-growth native forests (NF) dominate in Europe (84.38 %), South America (82.61 %), and North America (80.62 %), whereas Australia exhibits a bimodal age distribution driven by both old and regenerating stands. Planted forests (PF), by contrast, are consistently younger, with the youngest plantations concentrated in Australia (Age1–5: 65.77 %) and the most mature in Europe (Age36–40: 53.99 %). This 30 m global forest age map provides a consistent and high-resolution benchmark for improving forest carbon accounting, plantation yield modeling, and conservation strategy development.
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Status: final response (author comments only)
- RC1: 'Comment on essd-2025-674', Anonymous Referee #1, 03 Mar 2026
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RC2: 'Comment on essd-2025-674', Anonymous Referee #2, 05 Mar 2026
Overview and Significance
This manuscript presents a 30-meter resolution forest age map that distinguishes between natural and planted forests for the period 1985–2024. The global forest stand age was estimated based on forest disturbance and recovery process. The disturbance and recovery events was detected using Landsat archive on the Google Earth Engine platform by the Continuous Change Detection and Classification (CCDC) algorithm. This global forest age is valueable for improving carbon cycle modeling and for supporting sustainable forest management and climate mitigation policies. Although the method is somewhat coarse, it is basically appropriate. The manuscript is generally well-structured I am confident the authors can address these comments and I look forward to seeing this important work published.
Major Comments
Methodological Clarity for Age Assignment:
The core methodology—assigning age based on the "latest stable segment" from CCDC—is logical for post-disturbance regrowth or afforestation. However, the manuscript would benefit from a more explicit discussion of how the algorithm handles the age estimation for mature natural forests that have not experienced a detected disturbance within the Landsat era (1985-2024). If no breakpoint is detected, does the algorithm default to assigning an age of "40+" (i.e., the full timespan of the study)? The current description in Section 2.4.2 and Figure 2 implies a breakpoint is always present. A clarification on how "stable, old-growth" segments are treated is crucial for user interpretation of the dataset, especially given the high proportions of old-growth forests reported in Europe and the Americas.
Sensitivity of NBR in Deciduous Forests:
The study relies heavily on the Normalized Burn Ratio (NBR) as the primary vegetation index for detecting disturbance and recovery events (Section 2.3). While NBR is indeed sensitive to structural and moisture changes, its performance can be seasonally variable, particularly in deciduous forests. In these ecosystems, the natural annual senescence and leaf-off periods can produce spectral signals that may be misinterpreted as disturbance events, or conversely, may obscure subtle disturbance signals. Additionally, the rapid green-up in spring could complicate the precise identification of a stand's establishment year. The manuscript would be strengthened by acknowledging this potential limitation and discussing how the CCDC algorithm's harmonic model (which accounts for seasonality) mitigates this risk, or if any additional post-processing was applied to filter out false positives caused by phenological cycles in temperate and boreal deciduous stands.
Distinguishing Age from Ecological Succession:
The current methodology defines forest age as the time since the last major disturbance (breakpoint). However, in many natural forest ecosystems, particularly in boreal and temperate zones, tree establishment is not instantaneous following a disturbance. Ecological succession often involves a lag phase where grasses and shrubs colonize an area before trees become established. Consequently, the "stand age" (time since disturbance) may significantly overestimate the "tree age" (actual age of the dominant cohort). The manuscript should explicitly acknowledge this distinction and discuss its potential implications for carbon modeling, as carbon accumulation rates in early-successional non-forest vegetation differ substantially from those in young tree stands.
Partial Disturbance and Survivor Trees:
A core assumption of the breakpoint-based method is that a detected disturbance clears the forest stand, resetting the age to zero. This assumption is often violated in reality. Many disturbances, such as low-severity fires, selective logging, or insect outbreaks, do not result in complete mortality. Surviving trees (remnants) can be decades or centuries older than the post-disturbance cohort. In such cases, assigning the entire pixel an age based on the last disturbance will systematically underestimate the true biological age of the forest and its existing carbon stock. The authors should address this limitation, perhaps by discussing how the prevalence of such partial disturbances varies by region and forest type, and how this might contribute to the higher RMSE or bias observed in certain areas (e.g., natural forests with complex age structures).
Citation: https://doi.org/10.5194/essd-2025-674-RC2
Data sets
The Global 30m Forest Age Map (Natural vs. Planted forest) Y. Wang et al. https://doi.org/10.17632/yfm4sw8h25.1
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- 1
This study aims to produce a global 30-meter resolution dataset of natural and planted forest ages using the CCDC algorithm and Landsat time-series data, a research direction that holds significant scientific value and application potential. However, after careful review, I believe that the manuscript suffers from fundamental flaws in defining the scientific problem, methodological rigor, validation reliability, and uncertainty analysis of the results.
1. The scientific problem definition and product naming in this study are misleading. The study aims to develop a global 30-meter resolution dataset of natural and planted forest ages. However, the current paper inaccurately defines the scientific problem by estimating post-disturbance forest recovery age or young forest age instead of true "global forest age." The authors define the starting point (t_start) of the last time segment detected by CCDC as the starting point of forest age, which actually reflects the recovery time after the most recent disturbance event rather than the actual biological age of the forest stand. For old-growth forests that have not experienced significant disturbances, CCDC may fail to detect effective breakpoints, leading to incorrect age estimation or omission of these forests. Therefore, it is recommended to rename the product as "Global 30m Forest Recovery Age" or "Post-disturbance Forest Age" to avoid misleading users, or to clearly limit it to "young forest age" in the title. Additionally, the paper lacks a quantitative analysis of uncertainty in forest age estimation, and pixel-level confidence indicators or error ranges should be provided instead of only overall accuracy statistics.
2. The paper lacks a sensitivity analysis for CCDC algorithm parameters. When using the CCDC algorithm for breakpoint detection, the paper employs a combination of five bands plus the NBR index (Table 1) but does not conduct a sensitivity analysis of threshold values for key parameters. The original CCDC algorithm (Zhu & Woodcock, 2014) primarily recommends using raw reflectance data, and adding vegetation indices may introduce additional noise, reducing detection accuracy. The paper provides no sensitivity tests on how different band combinations and index selections affect forest age estimation results, making the robustness of the algorithm questionable.
3. The applicability of global uniform CCDC parameters is questionable. The paper uses a set of globally uniform CCDC parameters (Table 1), but this approach may not be suitable for all regions. Spectral characteristics and phenological rhythms vary significantly across different climate zones and forest types, necessitating regionally adaptive parameter optimization. For example, tropical regions with severe cloud interference require more lenient observation count thresholds, while high-latitude regions with short growing seasons need adjustments to seasonal fitting parameters. Therefore, it is recommended to test parameter sensitivity at least in typical ecological zones (tropical, temperate, boreal) or adopt an adaptive parameter selection strategy to improve the algorithm's global applicability.
4. There are differences between the GEE version CCDC and the original algorithm that are not adequately discussed. The GEE version of CCDC used in the paper adopts the disturbance judgment criteria of the COLD (Continuous Monitoring of Land Disturbance) algorithm, which fundamentally differs from the original CCDC algorithm. The key question is whether the judgment criteria for t_start are consistent with the original CCDC or use the COLD standard. Even for the COLD algorithm, t_start corresponds to the starting point where the time-series model can be stably fitted, not the true starting point of forest growth, which inevitably leads to systematic underestimation of forest age. The paper does not adequately discuss the impact of this difference on forest age estimation results, requiring further clarification and validation.
5. The reliability of using Google Earth visual interpretation for validation data is questionable. The authors obtain validation samples solely through visual interpretation of Google Earth, a method with serious flaws. The coverage of historical imagery on Google Earth varies greatly across years and regions, making it difficult to ensure the availability of imagery for early years (1985-2000). Additionally, varying image quality means that blurry images often indicate the absence of high-quality imagery at that time point, making it difficult to accurately determine the exact establishment year of forests. The lack of uniform interpretation standards is also a problem, as accurately determining forest age from intermittent image sequences, especially for slow natural regeneration processes, poses significant challenges.
6. The representativeness of validation samples is insufficient.The 6,100 validation samples may not adequately represent various forest types and age structures on a global scale. As seen in Figure 4b, there is significant dispersion between predicted and observed values, particularly in the 15-30 year range, where predicted values exhibit obvious "banding," indicating systematic bias in the algorithm. Therefore, it is recommended to supplement the validation with National Forest Inventory data as an independent validation source, conduct field surveys in typical regions to obtain real stand age data, and provide detailed metadata for validation samples, including image availability and interpretation confidence, to improve the reliability of validation results.
7. The paper lacks a comprehensive literature review and product comparison. The paper does not adequately review existing forest age products, particularly global forest age products with 0.5° resolution like GFAD (Global Forest Age Dataset), as well as medium-to-high resolution forest age products at national scales in Canada, Europe, and China. The strengths, weaknesses, and relationships of these products to the current study are not sufficiently discussed, making it difficult for readers to assess the unique contributions and relative advantages of this research.
8. The comparison with existing products is insufficient. The paper only conducts limited comparisons with Xiao et al.'s (2023) Chinese young forest age product (FAP) and Besnard et al.'s (2024) GAMI product. The comparison with FAP is "of the same kind and origin," as both are based on the CCDC algorithm, and high correlation does not prove the independent reliability of the product. The comparison with GAMI only selects three favorable examples for display (Figures 6 and 9), lacking a global-scale quantitative comparison and failing to provide systematic comparisons of statistical indicators (e.g., RMSE, Bias, correlation coefficient) or explain significant differences in certain regions. Therefore, it is recommended to supplement systematic comparisons with GFAD and national-scale forest age products, provide global-scale error spatial distribution maps, and analyze the unique value and applicable scenarios of this product relative to existing products.
9. There are logical issues in breakpoint detection and forest age estimation. The paper's core assumption is that the starting point (t_start) of the last time segment detected by CCDC is the starting point of current forest age, a hypothesis with multiple problems. First, it confuses disturbance and recovery processes; misjudging a recovery breakpoint as a disturbance breakpoint will lead to systematic underestimation of forest age. Second, the lag in the starting point of stable segments is also an issue; even if a disturbance is correctly detected, t_start represents the starting point of spectral signal stability, not the true establishment time of the forest, potentially leading to several years of age underestimation. Finally, for old-growth forests over 40 years old that were already stable before 1985, CCDC may not detect any breakpoints, and the paper does not clearly explain how to estimate the age of these forests.
10. The claim of innovation in distinguishing natural and planted forests is unfounded. The paper claims that its innovation lies in being the "first global forest age product that distinguishes between natural and planted forests," but in reality, it does not adopt different processing methods for the two forest types. Both use CCDC detection results, and the "distinction" is only made in the final stage by displaying results and statistical accuracy separately based on the GNPF mask. This "distinction" is a posteriori and superficial rather than methodologically innovative. Natural and planted forests differ fundamentally in disturbance frequency, spectral characteristics, and age structure, warranting differentiated algorithm strategies to improve the accuracy of forest age estimation.
11. There are credibility issues with the results. Figure 4b shows significant dispersion between predicted and observed values, particularly in high-age intervals (>25 years), where predicted values are significantly lower, consistent with the inherent limitations of the CCDC algorithm (inability to detect disturbances before 1985). However, the paper does not adequately discuss this systematic bias and its impact on the results, reducing the credibility and explanatory power of the results. Additionally, the uncertainty analysis of the forest age product is completely lacking.
12. The paper exhibits non-compliance with writing standards and inadequate adaptation of submission templates. The paper's writing is relatively rough, with multiple non-standard aspects. Formatting issues include non-compliance with the ESSD submission template requirements for author affiliations, paragraph indentation, line spacing, and figure caption formatting. Repetition issues include duplicate content in "Acknowledgements" and "Financial support" (pages 21-22). Chart quality issues include insufficient resolution and unclear legend explanations for some charts. Language expression issues include multiple grammatical errors and unidiomatic expressions, suggesting the need for professional language polishing. The overall impression is that of a student's initial draft submitted without careful review by a supervisor. It is recommended that the author team conduct internal quality control to improve the overall quality of the paper.
In conclusion, the scientific contributions and innovation claims of the current manuscript are not adequately supported due to fundamental flaws in defining the scientific problem, methodological rigor, validation reliability, and uncertainty analysis of the results. Therefore, publication is not recommended.