the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Jan 2025
A dataset on the structural diversity of European forests
Abstract. Forest structural diversity, defined as the heterogeneity of canopy structural elements in space, is an important axis of functional diversity and is central to understanding the relationship between canopy structure, biodiversity, and ecosystem functioning. Despite the recognised importance of forest structural diversity, the development of specific data products has been hindered by the challenges associated with collecting information on forest structure over large spatial scales. However, the advent of novel spaceborne LiDAR sensors like the Global Ecosystem Dynamics Investigation (GEDI) is now revolutionising the assessment of forest structural diversity by providing high-quality information on forest structural parameters with a quasi-global coverage. Whilst the availability of GEDI data and the computational capacity to handle large datasets have opened up new opportunities for mapping structural diversity, GEDI only collects sparse measurements of vegetation structure. Continuous information of forest structural diversity over large spatial domains may be needed for a variety of applications. The aim of this study was to create wall-to-wall maps of canopy structural diversity in European forests using a predictive modelling framework based on machine learning. We leverage multispectral and Synthetic Aperture Radar (SAR) data to create a series of input features that were related to eight different structural diversity metrics, calculated using GEDI. The models proved to be robust, indicating that active radar and passive optical data can effectively be used to predict structural diversity. Our dataset finds applications in a range of disciplines, including ecology, hydrology, and climate science. As our models can be regularly rerun as new images become available, it can be used to monitor the impacts of climate change and land use management on forest structural diversity.
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Status: open (until 20 Jun 2025)
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RC1: 'Comment on essd-2024-471', Anonymous Referee #1, 10 Mar 2025
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This manuscript presents the results of a case study to produce a continental dataset on vegetation (forest) heterogeneity via associating Sentinel-1 and -2 driven variables with sparsely distributed GEDI-derived structural metrics. The case study per se is not new, as all the underlying data, methods (RF modeling, cross-validation) have been extensively used in a plethora of previous studies at different spatial levels. In this regard, the manuscript can only be considered as a pure data description paper with no technical innovative aspects associated with the underlying case study. There are currently many other modeling approaches via both statistical and deep learning techniques that can be used to increase the performance of the results and their applicability for large-scale analysis. In addition, the fact that the turnover of a number of spectral variables extracted from active and passive remote sensing data are directly associated with 3D structural heterogeneity has been confirmed in the literature for a while. Examples are DOIs. 10.1088/1748-9326/ac5f6d, 10.1016/j.foreco.2023.120987, 10.1186/s13021-023-00228-9, 10.3390/rs14143345, 10.5194/bg-18-1234-2021, 10.1002/eap.2567, 10.1109/TGRS.2022.3156789 and many more.
Citation: https://doi.org/10.5194/essd-2024-471-RC1 -
AC1: 'Reply on RC1', Marco Girardello, 31 Mar 2025
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We thank the reviewer for their comments and the opportunity to clarify the scope of our work. The reviewer notes that the manuscript does not employ deep learning methods and combines data from passive optical and SAR satellites in a way they consider not novel. We would like to clarify that our study does not claim novelty in the combined use of optical and SAR data, nor does it focus on the application of deep learning techniques. Rather, the main contribution - as explicitly outlined in the manuscript - lies in the development and adaptation of eight distinct metrics to generate, for the whole of Europe, a comprehensive dataset describing structural diversity at multiple spatial resolutions. To our knowledge, this represents the first attempt to systematically map structural complexity at a quasi-continental scale.
The review also includes DOIs referencing studies that are presented as closely related to our work. After careful examination, we found that some of the provided DOIs appear to be incorrect or point to studies that differ substantially in scope and methodology, particularly in relation to the construction of a dataset describing the structural diversity of European forests.
We hope this clarifies the scope and contribution of our work.
Below, we have pasted the DOI verification performed using the DOI Foundation’s resolver (https://www.doi.org/), along with the corresponding bibliographic information where available:
- 10.1088/1748-9326/ac5f6d
DOI NOT FOUND - 10.1016/j.foreco.2023.120987
McKinney, Caleb M., Ronald E. Masters, Arjun Adhikari, Bijesh Mishra, Omkar Joshi, Chris B. Zou, and Rodney E. Will. "Forage quantity and protein concentration changes across a forest-savanna gradient with management implications for white-tailed deer." Forest Ecology and Management 538 (2023): 120987. - 10.3390/rs14143345
Shao, Z., Zhang, X., Zhang, T., Xu, X., & Zeng, T. (2022). RBFA-Net: a rotated balanced feature-aligned network for rotated SAR ship detection and classification. Remote Sensing, 14(14), 3345. - 10.5194/bg-18-1234-2021
DOI NOT FOUND - 10.1002/eap.2567
Grinde, Alexis R., Melissa B. Youngquist, Robert A. Slesak, Stephen R. Kolbe, Josh D. Bednar, Brian J. Palik, and Anthony W. D'Amato. "Potential impacts of emerald ash borer and adaptation strategies on wildlife communities in black ash wetlands." Ecological Applications 32, no. 4 (2022): e2567. - 10.1109/TGRS.2022.3156789
DOI NOT FOUND
Citation: https://doi.org/10.5194/essd-2024-471-AC1 - 10.1088/1748-9326/ac5f6d
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AC1: 'Reply on RC1', Marco Girardello, 31 Mar 2025
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RC2: 'Comment on essd-2024-471', Anonymous Referee #2, 26 May 2025
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General comments
The preprint introduces wall-to-wall structural complexity/diversity metrics for Europe which were derived from Sentinel-1, Sentinel-2, ALOS-Palsar and are based on GEDI structural metrics. Both vertical and horizontal, as well as combined structural diversity is addressed. For this, a standard random forest machine learning approach was performed.
There is definitely a gap and the need for spatially-explicit structural complexity assessments for European forests to advance our understanding of the impact of structural complexity or changes of complexity on the carbon cycle and the ecological functions of a forest. Hence, this study is highly appreciated.
However, I feel this study lacks clarity in the choices that were made: e.g. why these particular eight structure metrics? Why Sentinel-1/2 and ALOS-Palsar? I think clarification on a couple of methodological steps and decisions will help to assess whether the metrics and datasets presented here, fill the existing gap of structural diversity metrics for European forests and for which purposes and models the datasets can be used.
Specific comments
Specific general comments:
I appreciate this study and the focus on structural diversity of European forests, as well as the integrated use of several openly-available EO-datasets. I would welcome a couple of clarifications on general decisions taken and processing steps. I think the preprint and the usability of the dataset would be enhanced by this. These points do not necessarily correspond to one line or particular paragraph in the preprint, hence this ‘specific general comment’-section:
1. Existing structural diversity indices or datasets
While this study presents quite a complex assessment of structural diversity, I am missing a paragraph on existing structural diversity indices and datasets. Not all of the existing datasets are wall-to-wall or some only address vertical or horizontal structural diversity but I still think these have to be mentioned and their advantages and disadvantages should be addressed. This would help to identify the current knowledge gap and underline the need for other structural diversity metrics, such as those presented here.
I would expect that at least (but not limited to these): the GEDI L2B products (PAI, FHD, PAVD, …), GEDI L4C WSCI (also z, xy), Forest canopy structural complexity (CSC), are addressed.
1.5. Especially concerning the provided GEDI products (L2B and L4C): Did you assess these products as potential target variables? Why did you choose to calculate 8 new metrics?
2. Choice of 8 metrics
I understand that these 8 metrics address vertical, horizontal and combined structural diversity and therefore depict the multidimensional structural diversity. However, I feel the manuscript misses a paragraph that explains why these metrics where chosen, and a little more detail on what the individual metric depicts. I am aware that this is briefly mentioned in 2.1.2, 2.1.3 and 2.1.4 (error in preprint, line 186 the subsection should be 2.1.4) but I think it would be good to elaborate on this to ensure that every reader can follow understand the choice made.
3. 10km, 5km, and 1km spatial resolution
Can you please specify how you chose these 3 spatial resolutions? Can you also address the potential application/uses of structural diversity metrics at these spatial resolutions? Do modelling studies operate at these resolutions? I am just wondering, as the all the inputs have a much higher spatial resolution and also certain potential applications that I can think of (besides modelling) would benefit from a higher spatial resolution, e.g. disturbances (small-scale disturbances are very common), edge effects, forests become more fragmented, …
Specific comments:
4. line 74/75: give sources/citations from other studies.
5. Figure 1: difference between solid edge boxes and dashed boxes is not clear. I am not sure what the difference between ‘data that were directly utilised’ and ‘raw, original data’ means pratically
6. line 128: What is M?
7. line 132: What are ‘our calculations’ referring to? The calculation of the eight individual structural metrics?
8. line 128 to 136: I do not understand this paragraph. Is this the GEDI shot selection? Is the reference to pixels or areas? What is the median value based on? Same question for the Z-score ?
9. Line 152-153: definition not clear. What is the expected CV of VP? I think ‘latter’ is not correct in this sentence anything. What is central tendency?
10. Line 158, reference to Figure S2: be more specific in the main text and also in the Figure S2 caption. There are so many panels in this figure with different foci; please clarify which panel refers to what
11. Line 168: reference to Figure S2: same comment as above. Be more specific in Figure S2, which panel/s illustrate what (skewness, kurtosis?); this will increase understandability
12. Figure 4: PCA (panel D) displayed but not mentioned in text. Why was a PCA performed? What does the result show us?
13. Table B1, B2: Model validation paragraph in main text (starting line 415). Some of the validation results are quite poor (both for the random and cross-validation method); e.g. 𝜎 of Canopy Cover (𝜏𝐶𝐶 ) 0.16 (random validation). I think this should be discussed more. Are the provided datasets on figshare those that showed these poor validation results? This is not clear. I am not sure if maps/datasets with such a low validation score should be included/used, e.g. from modellers as input to their models. Or what do you think? Maybe I am misunderstanding this. I think this is not highlighted/marked sufficiently in the text.
14. Table S1: GEDI points; I think conventionally it is GEDI shots or GEDI footprints but not points.
Technical corrections
15. line 186: the subsection should be 2.1.4
I did not focus on this section, as I think that the previous comments and questions should be addressed first before technical corrections make sense.
Citation: https://doi.org/10.5194/essd-2024-471-RC2
Data sets
A dataset on the structural diversity of European forests M. Girardello, G. Oton, M. Piccardo, and G. Ceccherini https://figshare.com/s/daa9b652c12beb42e518
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