The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites
Open accessAbstract
Abstract Detailed knowledge of forest cover dynamics is crucial for many applications from resource management to ecosystem service assessments. Landsat data provides the necessary spatial, temporal and spectral detail to map and analyze forest cover and forest change processes. With the opening of the Landsat archive, new opportunities arise to monitor forest dynamics on regional to continental scales. In this study we analyzed changes in forest types, forest disturbances, and forest recovery for the Carpathian ecoregion in Eastern Europe. We generated a series of image composites at five year intervals between 1985 and 2010 and utilized a hybrid analysis strategy consisting of radiometric change classification, post-classification comparison and continuous index- and segment-based post-disturbance recovery assessment. For validation of the disturbance map we used a point-based accuracy assessment, and assessed the accuracy of our forest type maps using forest inventory data and statistically sampled ground truth data for 2010. Our Carpathian-wide disturbance map achieved an overall accuracy of 86% and the forest type maps up to 73% accuracy. While our results suggested a small net forest increase in the Carpathians, almost 20% of the forests experienced stand-replacing disturbances over the past 25 years. Forest recovery seemed to only partly counterbalance the widespread natural disturbances and clear-cutting activities. Disturbances were most widespread during the late 1980s and early 1990s, but some areas also exhibited extensive forest disturbances after 2000, especially in the Polish, Czech and Romanian Carpathians. Considerable shifts in forest composition occurred in the Carpathians, with disturbances increasingly affecting coniferous forests, and a relative decrease in coniferous and mixed forests. Both aspects are likely connected to an increased vulnerability of spruce plantations to pests and pathogens in the Carpathians. Overall, our results exemplify the highly dynamic nature of forest cover during times of socio-economic and institutional change, and highlight the value of the Landsat archive for monitoring these dynamics.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Ecosystems and Human Well-being: Synthesis
Assessment, M. E. (2005) · Island Press · doi:10.5281/zenodo.18167309 · Cited by 10,665
Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., et al. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.11.026 · Cited by 4,815
Status of land cover classification accuracy assessment
Foody, G. M. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(01)00295-4 · Cited by 4,580
Characteristics of maximum-value composite images from temporal AVHRR data
HOLBEN, B. N. (1986) · International Journal of Remote Sensing · doi:10.1080/01431168608948945 · Cited by 3,002
Object-based cloud and cloud shadow detection in Landsat imagery
Zhu, Z., & Woodcock, C. E. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.10.028 · Cited by 2,208
Kennedy, R. E., Yang, Z., & Cohen, W. B. (2010) · Remote Sensing of Environment · doi:10.1016/j.rse.2010.07.008 · Cited by 1,842
A Landsat Surface Reflectance Dataset for North America, 1990–2000
Masek, J. G., Vermote, E. F., Saleous, N. E., Wolfe, R., Hall, F. G., Huemmrich, K. F., Gao, F., Kutler, J., & Lim, T.-K. (2006) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2005.857030 · Cited by 1,627
Tropical Deforestation and Habitat Fragmentation in the Amazon: Satellite Data from 1978 to 1988
Skole, D., & Tucker, C. (1993) · Science · doi:10.1126/science.260.5116.1905 · Cited by 1,590
Land use, land use change and forestry
(2015) · Research Handbook on Climate Change Mitigation Law · doi:10.4337/9781849805834.00023 · Cited by 1,492
Cited by
Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine
Huang, H., Chen, Y., Clinton, N., Wang, J., Wang, X., Liu, C., Gong, P., Yang, J., Bai, Y., Zheng, Y., et al. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.02.021 · Cited by 441
Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring
Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., Hobart, G. W., & Campbell, L. B. (2016) · International Journal of Digital Earth · doi:10.1080/17538947.2016.1187673 · Cited by 293
Satellite Remote Sensing Contributions to Wildland Fire Science and Management
Chuvieco, E., Aguado, I., Salas, J., García, M., Yebra, M., & Oliva, P. (2020) · Current Forestry Reports · doi:10.1007/s40725-020-00116-5 · Cited by 259
Metadata
- Authors
- Griffiths, P., Kuemmerle, T., Baumann, M., Radeloff, V. C., Abrudan, I. V., Lieskovsky, J., Munteanu, C., Ostapowicz, K., & Hostert, P.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2014-08
- Volume
- 151
- Pages
- 72-88
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W1974329551
- Cited by
- 302
- References
- 51
- FWCI
- 18.13
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites. https://app.cochat.ai/papers/10.1016/j.rse.2013.04.022
- The paper
- Griffiths, P., Kuemmerle, T., Baumann, M., Radeloff, V. C., Abrudan, I. V., Lieskovsky, J., Munteanu, C., Ostapowicz, K., & Hostert, P. (2014). Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2013.04.022
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.