| 03 Jun 2026
Using remote sensing to support forest policies in Bavaria, Germany
Abstract. In order to mitigate climate change, the German Federal Government set goals to reduce greenhouse gas emissions and reach climate neutrality by 2045. To implement it, the federal and local governments have made a series of policies to improve the forest conditions in Bavaria. In this paper, we generated annual high-resolution dominant leaf type (DLT) and above-ground biomass density maps over Bavaria to support policy-making. Specifically, two U-Net-based models were trained to predict the DLT and biomass density separately from multispectral Sentinel-2 data based on deep learning. The model achieved 92.5 % DLT segmentation accuracy and an R2 of 0.62 biomass estimation accuracy on the test set. Then, the trained model is used to derive annual DLT and biomass density maps from 2015 to 2025, where a post-processing step was proposed to exclude noisy fluctuating predictions. The results show a clear increase in tree area and broadleaved area, but this has slowed down since 2020. Besides, biomass loss due to tree degradation is higher than that due to deforestation, as suggested by the results. Subsequently, the time-series maps are used to identify hotspots in Bavaria, which is of interest to policymakers. We analyzed the tree cover and biomass loss for different administrative regions, and found that for most administrative areas, the increase of broadleaf tree areas is noticeably larger than the loss of that, except for Upper Franconia. Besides, continuous increases in both forest area and biomass amount in mountainous regions were observed. A landscape metrics-based analysis suggests that forest cover across the entire state has become increasingly fragmented. The results provide good insights into the tree status in Bavaria and suggest a new focus for forest management policies.
For final publication, the manuscript should be reconsidered after major revisions. I would be willing to review the revised version.
On originality, I rate the paper Fair. A U-Net trained on Sentinel-2 for dominant leaf type and above-ground biomass is a well-established approach, and the authors themselves cite prior work along these lines (Waser et al., 2021; Song et al., 2024). The genuinely new part is the policy framing in Table 1 and the annual 2015 to 2025 time series for Bavaria, rather than the method. Relying on optical multispectral data alone for biomass, without any 3-D information (GEDI, LiDAR) or SAR (Sentinel-1), is also a fairly conservative choice given what is now available.
On scientific quality (rigour) I rate it Poor. Two issues are each serious on their own. First, the headline changes are smaller than the model's own uncertainty. Second, the resolution of the biomass reference and the saturation of the optical signal are not addressed. Taken together, they undercut most of the quantitative conclusions as they currently stand.
On significance, I rate it Fair. Linking the results to forest policy is worthwhile, but the high prediction uncertainty means the specific statements about how much biomass was gained or lost could mislead the very stakeholders the paper is written for.
On presentation, I rate it Poor. There are a lot of typos, at least one unresolved LaTeX citation, inconsistent handling of numbers and units, and several figures that are hard to read (overlapping legends, and different-unit metrics forced onto one rescaled axis).
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