Physically Based Canopy Interception Model for a Beech Forest Using Remote Sensing Data Article Swipe
Katalin Anita Zagyvai‐Kiss
,
Zoltán Gribovszki
,
Péter Kalicz
,
Katarina Zabret
,
József Szilágyi
,
András Herceg
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/f16091469
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/f16091469
Precipitation retained by tree canopies (~25%) is a key part of the forest water cycle, making our understanding of it vital for ecohydrological studies. Canopy interception varies seasonally with changes in canopy storage capacity. While remote sensing for rainfall partitioning is still developing, there is a strong demand for the calibration of canopy interception models based on ground measurements. In addition, most studies cover no longer than a few years, limiting long-term insights. Therefore, the main objective of this study is to upgrade a Merriam-type canopy interception model (derived partly from physical parameters) for European beech (Fagus sylvatica L.), which takes into account the dynamically changing reservoir capacity of the canopy (based on remotely sensed LAI data). The model was tested with annual precipitation totals for the period of 2017–2022. The results indicate that interception has a significant partitional effect on rainfall, which is notable in terms of its proportions, especially for small precipitation events (0–5 mm). The value of yearly interception was 20% (13% in dormancy and 24% in the growing season). The calibrated model can provide beech interception values in areas with similar climatic parameters to those of the study area.
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- article
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- en
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- https://doi.org/10.3390/f16091469
- https://www.mdpi.com/1999-4907/16/9/1469/pdf?version=1758018876
- OA Status
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- References
- 54
- OpenAlex ID
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Physically Based Canopy Interception Model for a Beech Forest Using Remote Sensing DataWork title
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2025Year of publication
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2025-09-16Full publication date if available
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Katalin Anita Zagyvai‐Kiss, Zoltán Gribovszki, Péter Kalicz, Katarina Zabret, József Szilágyi, András HercegList of authors in order
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https://doi.org/10.3390/f16091469Publisher landing page
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https://www.mdpi.com/1999-4907/16/9/1469/pdf?version=1758018876Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1999-4907/16/9/1469/pdf?version=1758018876Direct OA link when available
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0Total citation count in OpenAlex
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| abstract_inverted_index.growing | 171 |
| abstract_inverted_index.notable | 144 |
| abstract_inverted_index.provide | 177 |
| abstract_inverted_index.results | 131 |
| abstract_inverted_index.sensing | 36 |
| abstract_inverted_index.similar | 184 |
| abstract_inverted_index.storage | 32 |
| abstract_inverted_index.studies | 62 |
| abstract_inverted_index.upgrade | 82 |
| abstract_inverted_index.(derived | 88 |
| abstract_inverted_index.European | 94 |
| abstract_inverted_index.canopies | 4 |
| abstract_inverted_index.capacity | 107 |
| abstract_inverted_index.changing | 105 |
| abstract_inverted_index.climatic | 185 |
| abstract_inverted_index.dormancy | 166 |
| abstract_inverted_index.indicate | 132 |
| abstract_inverted_index.limiting | 70 |
| abstract_inverted_index.physical | 91 |
| abstract_inverted_index.rainfall | 38 |
| abstract_inverted_index.remotely | 113 |
| abstract_inverted_index.retained | 1 |
| abstract_inverted_index.season). | 172 |
| abstract_inverted_index.studies. | 23 |
| abstract_inverted_index.addition, | 60 |
| abstract_inverted_index.capacity. | 33 |
| abstract_inverted_index.insights. | 72 |
| abstract_inverted_index.long-term | 71 |
| abstract_inverted_index.objective | 76 |
| abstract_inverted_index.rainfall, | 141 |
| abstract_inverted_index.reservoir | 106 |
| abstract_inverted_index.sylvatica | 97 |
| abstract_inverted_index.Therefore, | 73 |
| abstract_inverted_index.calibrated | 174 |
| abstract_inverted_index.especially | 150 |
| abstract_inverted_index.parameters | 186 |
| abstract_inverted_index.seasonally | 27 |
| abstract_inverted_index.calibration | 50 |
| abstract_inverted_index.developing, | 42 |
| abstract_inverted_index.dynamically | 104 |
| abstract_inverted_index.parameters) | 92 |
| abstract_inverted_index.partitional | 138 |
| abstract_inverted_index.significant | 137 |
| abstract_inverted_index.2017–2022. | 129 |
| abstract_inverted_index.Merriam-type | 84 |
| abstract_inverted_index.interception | 25, 53, 86, 134, 161, 179 |
| abstract_inverted_index.partitioning | 39 |
| abstract_inverted_index.proportions, | 149 |
| abstract_inverted_index.Precipitation | 0 |
| abstract_inverted_index.measurements. | 58 |
| abstract_inverted_index.precipitation | 123, 153 |
| abstract_inverted_index.understanding | 17 |
| abstract_inverted_index.ecohydrological | 22 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5029161314 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I85686399 |
| citation_normalized_percentile.value | 0.44400315 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |