Improvements in airborne laser scanning-based forest structural type assessment
Accurate forest structural types (FSTs) assessment helps to provide valuable support tools to distinguish different structures in forest stands and formulate effective management decisions. We used data from -Boreal, Mediterranean and Atlantic- biogeographical regions and developed reliable methodologies for the FSTs assessment. First, we used the Gini coefficient (GC) of tree size inequality and evaluated the effects of plot size, stand density and point density of the airborne laser scanning (ALS) on the ALS-assisted GC estimations in Boreal conditions. Second, we used four structural variables -quadratic mean diameter (QMD), GC, basal area larger than mean (BALM) and stand density (N)- from the three biogeographical regions and developed region-independent methods for the FSTs assessment. Lastly, we detected FSTs directly from ALS data, predicted the aboveground biomass (AGB) at each FST, and compared it with the AGB prediction without pre-stratification. Results showed that (a) plot size had a greater effect on the ALS-assisted GC estimation as compared to the stand and point density and 250-450 m2 plot size is the optimal plot size for reliable ALS-assisted GC estimation. (b) GC and BALM were the most important descriptors for the FSTs assessment and single storey, multi-storey and reversed-J types of forest structures can be separated by lower, medium and high values of GC and BALM, respectively, while QMD and N were relevant to separate young/mature and sparse/dense subtypes.(c) We observed marginal improvements in the AGB predictions from the direct ALS-based FSTs but identified critical differences in the selection of ALS metrics by the prediction models such as higher percentiles are more relevant in the open canopies while cover metrics and average percentiles are important in the closed canopies. These results are thus very useful in improving our understanding on the causality behind the choice of ALS predictors in structurally complex forests. Keywords: Sustainable forest management, Monitoring and data collection, Biodiversity conservation, Research ID: 3621963
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| Format: | Document biblioteca |
| Language: | English |
| Published: |
FAO ;
2022
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| Online Access: | https://openknowledge.fao.org/handle/20.500.14283/cc4435en http://www.fao.org/3/cc4435en/cc4435en.pdf |
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| Summary: | Accurate forest structural types (FSTs) assessment helps to provide valuable support tools to distinguish different structures in forest stands and formulate effective management decisions. We used data from -Boreal, Mediterranean and Atlantic- biogeographical regions and developed reliable methodologies for the FSTs assessment. First, we used the Gini coefficient (GC) of tree size inequality and evaluated the effects of plot size, stand density and point density of the airborne laser scanning (ALS) on the ALS-assisted GC estimations in Boreal conditions. Second, we used four structural variables -quadratic mean diameter (QMD), GC, basal area larger than mean (BALM) and stand density (N)- from the three biogeographical regions and developed region-independent methods for the FSTs assessment. Lastly, we detected FSTs directly from ALS data, predicted the aboveground biomass (AGB) at each FST, and compared it with the AGB prediction without pre-stratification. Results showed that (a) plot size had a greater effect on the ALS-assisted GC estimation as compared to the stand and point density and 250-450 m2 plot size is the optimal plot size for reliable ALS-assisted GC estimation. (b) GC and BALM were the most important descriptors for the FSTs assessment and single storey, multi-storey and reversed-J types of forest structures can be separated by lower, medium and high values of GC and BALM, respectively, while QMD and N were relevant to separate young/mature and sparse/dense subtypes.(c) We observed marginal improvements in the AGB predictions from the direct ALS-based FSTs but identified critical differences in the selection of ALS metrics by the prediction models such as higher percentiles are more relevant in the open canopies while cover metrics and average percentiles are important in the closed canopies. These results are thus very useful in improving our understanding on the causality behind the choice of ALS predictors in structurally complex forests.
Keywords: Sustainable forest management, Monitoring and data collection, Biodiversity conservation, Research
ID: 3621963 |
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