the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Oct 2025
Drivers of drought-induced canopy mortality in conifer and broadleaf forests across Luxembourg
Abstract. Climate change-induced weather extremes are increasing the intensity and frequency of disturbance events, posing a major threat to forests globally. In Central Europe, hotter and drier summers, such as those during the 2018–2020 drought period, have led to widespread forest damage. To adapt forests to a hotter and drier future it is important to identify sites more vulnerable to canopy mortality during drought, but high-resolution tree mortality data across a continuous landscape is still sparse.
This study aimed at filling this research gap by utilising a high-resolution (single-tree-level), spatially continuous dataset covering the entire Central European country of Luxembourg. We used generalized additive models (GAMs) to explore the contribution of biotic and abiotic site characteristics to the observed canopy mortality of conifer and broadleaf forests during the 2018–2020 summer droughts.
Our model explained 44.6 % of canopy mortality variation in conifers and 25.3 % in broadleaf forests. Clustered mortality patches spreading from one year to the other, typical for bark beetle infestation, were the strongest predictor of canopy mortality in conifer trees. Forest height also emerged as a strong predictor of mortality in both forest types. Surprisingly, we found limited influence of topography on canopy mortality. Our study highlights the potential of using high-resolution canopy mortality data across a national-scale study area to unravel the influence of site characteristics driving spatial variation in forest mortality during drought events.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-5021', Anonymous Referee #1, 25 Nov 2025
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RC2: 'Comment on egusphere-2025-5021', Anonymous Referee #2, 04 Dec 2025
The author's work addresses an important topic of drivers of drought-induced tree mortality at the country scale (Luxembourg). The study is well written, and the results are supported by appropriate figures. However, there are several major points regarding the data selection and their corresponding methodologies that need clarification before publication.
Major points to clarify:
- The advantage of using high-resolution (sub 1 m) data compared to freely available data, such as Sentinel-2 and Landsat is not clearly stated. Especially, as the authors do a large-scale country-wide study, Sentinel-2 could already have sufficiently high resolution to observe patterns, and the results would be less noisy. Additionally, more frequent acquisitions would be possible.
- I do not understand why the bark beetle distribution and patches of already dead trees are used as an environmental driver for drought-induced mortality. I agree that bark beetle infestations can happen more often after trees are already damaged by a drought, but the beetle itself is not a driver for drought-induced mortality.
- I assume that meteorological factors such as temperature and precipitation also play a major role in the drought-induced mortality, but no climatic variables are discussed in this study. Is there any temperature and precipitation data, or other climatic data available to examine local patterns that could also correlate with the drought-induced mortality?
- Please give more information about the used data, such as the data source, sensor type, resolution, date/year of acquisition. This could be done, e.g., as a table at the beginning of the Methods section.
Minor:
- 35 Can you give examples here of what you understand by abiotic/biotic factors?
- 47 How do topographic variables differ from abiotic factors, or are they the same?
- 75 What do you understand as "high-resolution" canopy data? You mention Landsat data, but could you also use 10 m Sentinel-2 data for your analysis?
- 87 If you are interested in understanding the mortality drivers at the landscape scale, why is it necessary to look at individual trees?
- 95 From your hypotheses, the main aim of the paper is not clear to me. Can you link it more to the title of the paper?
- 108: How much of the whole country area is forest (in %). E.g., add it after the 900 km².
- 139: 2.3.1 Why is the mortality occurrence and spread under "environmental data" and not under 2.2 where you describe the mortality data? I also do not understand why the distribution of the bark beetle patches is an environmental factor influencing the drought-induced mortality of other trees.
- 140: Is this the distance between the centroids (i.e. patchA_2017 to patchA_2019?)? If your assumption of "spreading" is true, the distance between the centroids between the years would not necessarily change that much (for the same patches).
- 142: data point = centroid position?
- 174: Why did you use northness/eastness instead of only one parameter “aspect”? Would this not be easier to interpret?
- 239: I do not agree that no large-scale patterns are visible in Figure 3. In the coniferous forest, I see 2 "bands" of high mortality, and in the broadleaf forest, a North-South gradient.
- 251: Where can I find these %-numbers in Figure 4?
- 266: …this is not surprising given...
- 417 What about the large-scale patterns? Are they related to meteorological drivers?
- 423 The authors do not discuss the influence of climate variables on the tree mortality.
Figures:
- Figure 1: I think it would be easier to interpret if the pixel size is in km and the color in % forest area. Is it possible to color the non-forest areas in a different color than white? I find it hard to distinguish it from the color of the low forest area.
In L123, you write that you only use the “pure” forest sites >75% but some pixels are colored in both maps. Is this map still representing the mixed forest sites as well? - Figure 2: Please use another color combination. The red polygons over the forest image are not colorblind-safe.
- Figure 6: What does a negative deltaArea indicate? Would this indicate forest recovery, or can it also be that areas are no longer forest?
- Figure 7: Add the diste and distr in the caption (L295).
- Figure 8: It should be “elevation”.
Citation: https://doi.org/10.5194/egusphere-2025-5021-RC2
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- 1
The authors tackle a timely and relevant research topic, that is a driver analysis of tree mortality in Luxembourg across several years. The study is well written, the proposed methodology is cleanly executed, and the related work is reviewed in depth. The application of GAMs in this specific context is also novel.
However, there are two major methodological design concerns:
The authors report the vicinity of existing mortality to be the best predictor of new mortality, which is not a driver, as promised in the paper title. The only covariate with relevant explanatory power was forest height. In the case of conifer trees, that bark beetles spread within some radius and do not normally travel cross-country as a random process is expected. Analogous, potentially for broadleaf, similar locations have similar trees in similar settings causing them to be potentially similarly susceptible to environmental stress. This raises the questions: "What makes a site vulnerable to initiating mortality versus experiencing spread?" or "Why does mortality spread in one region but not another?" if clustering is the only effect you observe. The currently chosen co-location variables might also already be somewhat representing the local conditions and overshadowing the actual drivers, e.g., slope, aspect, soil, etc.. It might make sense to attempt to drop mortality spread covariates in an additional analysis.
Please discuss the impact of different model performances and thus map accuracies of broadleaf (F1=46%-52%) vs conifer (F1=72%-75%) (Schwarz et al., 2024) on the results, especially given that 71% of the studied forest is broadleaf. The implications also depend on precision vs. recall, which I was not able to extract from the previous study.
Minor:
- Line 87: I'd suggest sticking to the wording "landscape-scale". Countries are only a political structure and heavily vary in size.
- Line 125: Please reference Figure 2a earlier. Without the figure, the described binarization process is very difficult to understand.
- Line 125: How exactly is the centroid of the dead canopy polygon computed? In the naive way, i.e., (maxX-minX)/2, (maxY-minY)/2, this point can very easily land outside the actual deadwood polygon, e.g., with L-shaped polygons. This is especially important since this appears to be a semantic segmentation and not an instance segmentation mask.
- Line 143: Are these distances calculated between the above-defined centroids or between polygon edges?
- Line 146: Writing "other dead canopy" and "spread from tree to tree" is misleading as the polygons are not at the tree-level, but represent canopy groups (Figure 2). I understand the use of this metric, but the implication is misleading.
- Line 160: How accurate is Lis-L at detecting forest roads? This is a non-trivial task and the displayed polygon in Figure 2 appears fairly coarse, suggesting that (small) forest roads might not be captured. This might also explain the high correlation between both variables.
- Figure 3: Why is the colorbar scaled the way it is?
- For readability, I'd replace "dist_e" and "dist_r" with "Distance_Edge" and "Distance_Road", and similarly for dist_19, ...
- I think it is called "deadtrees.earth" and not "deadtrees.org".
Suggested additional citations:
- Line 30: Most recent study supporting this: "Gazol, A., Pizarro, M., Hammond, W. M., Allen, C. D., & Camarero, J. J. (2025). Droughts preceding tree mortality events have increased in duration and intensity, especially in dry biomes. _Nature Communications_, _16_(1), 5779."
- Line 87:
- "Cheng, Y., Oehmcke, S., Brandt, M., Rosenthal, L., Das, A., Vrieling, A., ... & Horion, S. (2024). Scattered tree death contributes to substantial forest loss in California. _Nature Communications_, _15_(1), 641."
- "Junttila, S., Blomqvist, M., Laukkanen, V., Heinaro, E., Polvivaara, A., O'Sullivan, H., ... & Peltola, H. (2024). Significant increase in forest canopy mortality in boreal forests in Southeast Finland. _Forest Ecology and Management_, _565_, 122020."