Remote Sensing Technologies for Assessing Climate-Smart Criteria in Mountain Forests
| Autor: | Torresan, Chiara, Luyssaert, Sebastiaan, Filippa, Gianluca, Imangholiloo, Mohammad, Gaulton, Rachel, Tognetti, Roberto, Smith, Melanie, Panzacchi, Pietro |
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| Přispěvatelé: | Tognetti, Roberto, Smith, Melanie, Panzacchi, Pietro, Department of Forest Sciences, Systems Ecology |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
0106 biological sciences
4112 Forestry Forest cover LiDAR 010504 meteorology & atmospheric sciences SMART criteria Growing stock Climate-smart indicators Forest biodiversity education 15. Life on land 010603 evolutionary biology 01 natural sciences RADAR Multifunctional sensors Managing Forest Ecosystems 13. Climate action Remote sensing (archaeology) Satellite Environmental science Forest health 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Torresan, C, Luyssaert, S, Filippa, G, Imangholiloo, M & Gaulton, R 2022, Remote Sensing Technologies for Assessing Climate-Smart Criteria in Mountain Forests . in R Tognetti, M Smith & P Panzacchi (eds), Climate-Smart Forestry in Mountain Regions . Managing Forest Ecosystems (MAFE), vol. 40, Springer, Cham, Cham, pp. 399-433 . https://doi.org/10.1007/978-3-030-80767-2_11 Climate-Smart Forestry in Mountain Regions, edited by Tognetti R., Smith M., Panzacchi P., pp. 399–433. Cham Heidelberg New York Dordrecht London: Springer, 2022 info:cnr-pdr/source/autori:Torresan C., Luyssaert S., Filippa G., Imangholiloo M., Gaulton R./titolo:Remote Sensing Technologies for Assessing Climate-Smart Criteria in Mountain Forests/titolo_volume:Climate-Smart Forestry in Mountain Regions/curatori_volume:Tognetti R., Smith M., Panzacchi P./editore: /anno:2022 Climate-Smart Forestry in Mountain Regions ISBN: 9783030807665 Climate-Smart Forestry in Mountain Regions, 399-433 STARTPAGE=399;ENDPAGE=433;TITLE=Climate-Smart Forestry in Mountain Regions |
| DOI: | 10.1007/978-3-030-80767-2_11 |
| Popis: | Monitoring forest responses to climate-smart forestry (CSF) is necessary to determine whether forest management is on track to contribute to the reduction and/or removal of greenhouse gas emissions and the development of resilient mountain forests. A set of indicators to assess “the smartness” of forests has been previously identified by combining indicators for sustainable forest management with the ecosystem services. Here, we discuss the remote sensing technologies suitable to assess those indicators grouped in forest resources, health and vitality, productivity, biological diversity, and protective functions criteria. Forest cover, growing stock, abiotic, biotic, and human-induced forest damage, and tree composition indicators can be readily assessed by using established remote sensing techniques. The emerging areas of phenotyping will help track genetic resource indicators. No single existing sensor or platform is sufficient on its own to assess all the individual CSF indicators, due to the need to balance fine-scale monitoring and satisfactory coverage at broad scales. The challenge of being successful in assessing the largest number and type of indicators (e.g., soil conditions) is likely to be best tackled through multimode and multifunctional sensors, increasingly coupled with new computational and analytical approaches, such as cloud computing, machine learning, and deep learning. |
| Databáze: | OpenAIRE |
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