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Lookup NU author(s): Dr Rachel GaultonORCiD
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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.
Author(s): Torresan C, Luyssaert S, Filippa G, Imangholiloo M, Gaulton R
Editor(s): Tognetti, R; Smith, M; Panzacchi, P;
Publication type: Book Chapter
Publication status: Published
Book Title: Climate-Smart Forestry in Mountain Regions
Year: 2022
Volume: 40
Pages: 399-433
Online publication date: 25/11/2021
Acceptance date: 01/09/2021
Series Title: Managing Forest Ecosystems
Publisher: Springer
Place Published: Cham
URL: https://doi.org/10.1007/978-3-030-80767-2
DOI: 10.1007/978-3-030-80767-2
Library holdings: Search Newcastle University Library for this item
ISBN: 9783030807665