the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The European Forest Disturbance Atlas: a forest disturbance monitoring system using the Landsat archive
Abstract. Forests in Europe are undergoing complex changes that require a comprehensive monitoring of disturbance occurrence. Here we present the European Forest Disturbance Atlas (EFDA), a Landsat-based approach for mapping annual forest disturbances across continental Europe since 1985. We built a consistent Landsat data cube of summer composites and compiled reference data on forest land use and forest disturbances. A classification-based approach was developed to detect forest disturbances annually, therefore accounting for multiple disturbance events per time-series. The EFDA contains annual layers on disturbance occurrence, severity and agent, as well as aggregated layers on the number of disturbances and the latest and greatest disturbance year. Based on the annual disturbance estimates (1985–2023), we quantified a total forest disturbed area of 439,000 km2, increasing to 610,000 km2 when accounting for multiple disturbance events. Map accuracies of the disturbance classification showed an overall F1-score of 0.89, with very low errors (<1 %) for the undisturbed class and with commission and omission errors for the disturbed class of 17.3 % and 22.5 %, respectively. Further, temporal validation revealed errors decreased over time, with commission substantially decreases to 10.6 % after the year 2000. The workflow implemented to create annual forest disturbance maps was designed for easy updating when new data arrives and in an open access framework to facilitate reproducibility, thus paving the road for an operational forest disturbance system in Europe. EFDA products are available at https://doi.org/10.5281/zenodo.13333034 (Viana-Soto and Senf, 2024).
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RC1: 'Comment on essd-2024-361', Anonymous Referee #1, 20 Dec 2024
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Review for manuscript entitled: “The European Forest Disturbance Atlas: a forest disturbance monitoring system using the Landsat archive' submitted to the journal Earth System Science Data (Manuscript ID: essd-2024-361).
Overall:
In the manuscript the authors report the creation of an impressive dataset with multiple forest disturbances in Europe using multi-temporal Landsat data. The authors discuss an important topic that should be of interest to the readers of ESSD and establish large data set that includes a way to detect multiple disturbances within a single timeseries. I think the paper is well-written, but the methods description could be improved. Variable importance/selection is not discussed, and the reader is referred to other published work to assess the validity of the evaluation datasets. I commend the authors on producing such large dataset, that is consistently processed, well evaluated (at least I assume), and made openly available.
Major comments:
L125 onwards: the Explanation of the reference data collection is somewhat inadequate. I assume that interpreters used higher resolution data to interpret disturbance occurrence within Landsat pixels. If not, I think it would be very hard (except maybe for clear cuts) to detect and attribute a disturbance. The reader is referred to a couple of other papers, but since this is a critical step, I recommend the authors to discuss this step in more detail.
Methods: The methodology seems pretty solid, but there is no discussion of variable selection, variable importance, or reduction of auto-correlated variables. I agree that random forest is somewhat robust against overfitting (although opinions vary on this), the authors should at a minimum discuss the variable importance and detail a bit more what variables where chosen and why no model reduction was performed.
Note: In reviewing the final maps (which is an impressive dataset!) I found in some parts of Europe (in particular northern Europe/Scandinavia) >50% of the forest was disturbed and I wonder if this is realistic or whether the model is oversensitive/has many commission errors. Not sure how to test this as the accuracy metrics seem pretty balances. I just wonder if this is realistic… See also Sweden, Fig 5.
Minor comments:
L15: Could add “overlapping” to “…accounting for multiple overlapping disturbance events”. To be a bit more clear.
L24: Change to: “…spanning from timber production and carbon storage (Lindner et al., 2010), to water purification and regulation (Orsi et al., 2020), to recreation and spiritual value (Saarikoski et al., 2015).”
L67: “Shortly after, Senf and Seidl (2021a) created the first pan-European characterization of forest disturbance by combining a trajectory-segmentation algorithm (LandTrendr; Kennedy et al., 2010) with a random forest classification approach.” this might need a bit more info since about you categorized LandTrendr into “(1) trajectory segmentation approaches”. What was the RF classification used for? Maybe also explain why this method cannot detect multiple disturbances per timeseries.
Figure 1: why does the validation stop -- connect an arrow to accuracy assessment?
L189-L191: How was this applied? Are all forested pixels smaller than 6 adjacent pixels in all configurations set to non-forest? Please clarify.
L197-199: Please provide formulas for the different spectral indices. Could be added to Fig. A5, if you needed to keep appendices to a minimum. Note that there are different TC indices for different Landsat sensors and the DI is somewhat less known.
L211: So the minimum forest patch is 6 pixels and within that the minimum disturbed patch is 3 pixels, if I understand this correctly. You might want to restate the minimum forest patch size again here to remind the readers.
L255-258: This needs more information -- “We thus used an approach developed in Senf and Seidl, 2021b and selected a random background sample from all disturbance patches. As harvest is assumed to be the major disturbance agent in Europe (Patacca et al., 2023; Seidl and Senf, 2024). This background sample will represent harvest conditions in contrast to the agent information available in the existing databases.”
- What do you mean be “random background sample”?
- How did you ensure this is harvest, please add more details here?
L267: I do not agree here: there are many bark beetle outbreaks (and other biotic disturbances) throughout Europe prior to 2017. For instance, a large outbreak in the 1990s and early 2000s on the German and Czech border. See Kautz at al. (2017) and this paper: https://annforsci.biomedcentral.com/articles/10.1007/s13595-013-0279-7 and the many references therein. In my opinion its ok to combine the 2 classes but I would stay away from stating the bark beetle disturbance was minor without much evidence. For example: Fire and Bark beetles are of the same magnitude from 1980 to 2015 (Patacca et al, 2023, Fig. 5).
Kautz, M., Meddens, A.J., Hall, R.J. and Arneth, A., 2017. Biotic disturbances in Northern Hemisphere forests–a synthesis of recent data, uncertainties and implications for forest monitoring and modelling. Global Ecology and Biogeography, 26(5), pp.533-552.
Figure 10. There are very large discrepancies in 2018 across the different data sets, is there a way to improve this? In L449 this is attributed to dry years, but I assume there are many more “dry” years in the time series. Could you relate (correlate?) drought years to this overestimation?
Note some minor spelling comments/edits in the attached pdf.
Data sets
European Forest Disturbance Atlas (v2.1.1.) Alba Viana-Soto and Cornelius Senf https://doi.org/10.5281/zenodo.13333034
European Forest Disturbance Atlas viewer Alba Viana-Soto and Cornelius Senf https://albaviana.users.earthengine.app/view/european-forest-disturbance-map
Model code and software
Code for European-Forest-Disturbance-Atlas Alba Viana Soto https://github.com/albaviana/European-Forest-Disturbance-Atlas
FORCE: Framework for Operational Radiometric Correction for Environmental monitoring David Franzt https://force-eo.readthedocs.io/en/latest/
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