Continuous Cover Forestry and Remote Sensing : A Review of Knowledge Gaps, Challenges, and Potential Directions
Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid
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Yn: Current Forestry Reports, Cyfrol 9, Rhif 6, 12.2023, t. 490-501.
Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid
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TY - JOUR
T1 - Continuous Cover Forestry and Remote Sensing : A Review of Knowledge Gaps, Challenges, and Potential Directions
AU - Stoddart, Jaz
AU - Suarez, Juan
AU - Mason, William
AU - Valbuena, Ruben
PY - 2023/12
Y1 - 2023/12
N2 - Purpose of ReviewContinuous cover forestry (CCF) is a sustainable management approach for forestry in which forest stands are manipulated to create irregular stand structures with varied species composition. This approach differs greatly from the traditional approaches of plantation-based forestry, in which uniform monocultures are maintained, and thus, traditional methods of assessment, such as productivity (yield class) calculations, are less applicable. This creates a need to identify new methods to succeed the old and be of use in operational forestry and research. By applying remote sensing techniques to CCF, it may be possible to identify novel solutions to the challenges introduced through the adoption of CCF.Recent FindingsThere has been a limited amount of work published on the applications of remote sensing to CCF in the last decade. Research can primarily be characterised as explorations of different methods to quantify the target state of CCF and monitor indices of stand structural complexity during transformation to CCF, using terrestrial and aerial data collection techniques.SummaryWe identify a range of challenges associated with CCF and outline the outstanding gaps within the current body of research in need of further investigation, including a need for the development of new inventory methods using remote sensing techniques. We identify methods, such as individual tree models, that could be applied to CCF from other complex, heterogenous forest systems and propose the wider adoption of remote sensing including information for interested parties to get started.
AB - Purpose of ReviewContinuous cover forestry (CCF) is a sustainable management approach for forestry in which forest stands are manipulated to create irregular stand structures with varied species composition. This approach differs greatly from the traditional approaches of plantation-based forestry, in which uniform monocultures are maintained, and thus, traditional methods of assessment, such as productivity (yield class) calculations, are less applicable. This creates a need to identify new methods to succeed the old and be of use in operational forestry and research. By applying remote sensing techniques to CCF, it may be possible to identify novel solutions to the challenges introduced through the adoption of CCF.Recent FindingsThere has been a limited amount of work published on the applications of remote sensing to CCF in the last decade. Research can primarily be characterised as explorations of different methods to quantify the target state of CCF and monitor indices of stand structural complexity during transformation to CCF, using terrestrial and aerial data collection techniques.SummaryWe identify a range of challenges associated with CCF and outline the outstanding gaps within the current body of research in need of further investigation, including a need for the development of new inventory methods using remote sensing techniques. We identify methods, such as individual tree models, that could be applied to CCF from other complex, heterogenous forest systems and propose the wider adoption of remote sensing including information for interested parties to get started.
KW - Biomass estimation
KW - Continuous cover forestry
KW - Forest inventory
KW - Individual tree growth models
KW - Remote sensing
U2 - 10.1007/s40725-023-00206-0
DO - 10.1007/s40725-023-00206-0
M3 - Article
VL - 9
SP - 490
EP - 501
JO - Current Forestry Reports
JF - Current Forestry Reports
SN - 2198-6436
IS - 6
ER -