Lessons learned from applying a forest gap model to understand ecosystem and carbon dynamics of complex tropical forests

Fischer, Rico; Bohn, Friedrich J.; Paula, Mateus Dantas de; Dislich, Claudia; Groeneveld, Jürgen; Gutiérrez, Álvaro G.; Kazmierczak, Martin; Knapp, Nikolai; Lehmann, Sebastian; Paulick, Sebastian; Pütz, Sandro; Rödig, Edna; Taubert, Franziska; Köhler, Peter; Huth, Andreas

doi:10.1016/j.ecolmodel.2015.11.018

Cited by 144 publications

(180 citation statements)

References 92 publications

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“…Having said that, modeling high complexity increases time to find the parameters and possible errors from the lack of empirical data for a lot of species may occur (Fischer et al, 2016).…”

Section: Plant Functional Type Groupingmentioning

confidence: 99%

“…According to field studies, species generally have comparable attributes (seed production rates, growth rate, mortality rates) making it possible to organize similar tree species of extremely rich BAF into species group (Fischer et al, 2016). In the light of this knowledge, trees are grouped according to their light regeneration strategies and their potential maximum height.…”

Section: Plant Functional Type Groupingmentioning

confidence: 99%

“…In that case, the light availability decreases from higher to smaller heights within the canopy because the radiation is progressively attenuated. This vertical distribution of light availability within a cell is named "light climate" (Fischer et al, 2015).…”

Section: Tree Physiognomymentioning

confidence: 99%

“…In the light of this knowledge, trees are grouped according to their light regeneration strategies and their potential maximum height. These two physiological attributes are presumed to be independent from each other (Fischer et al, 2016). Each group is a Plant Functional Type or PFT (Whitmore, 1998).…”

Section: Plant Functional Type Groupingmentioning

confidence: 99%

“…When there are few trees per cohort, a stochastic approach is applied to calculate the number of trees dying (Fischer et al, 2015). Then numAleat is compared to the mortality rate ‫ܽܯ‬ ( , ,௧,௫,௬) .…”

Section: Growth Of a Treementioning

confidence: 99%

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Carbon sequestration: mathematical model of the Brazilian Atlantic Forest.

Villacampa¹

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“…Having said that, modeling high complexity increases time to find the parameters and possible errors from the lack of empirical data for a lot of species may occur (Fischer et al, 2016).…”

Section: Plant Functional Type Groupingmentioning

confidence: 99%

Section: Plant Functional Type Groupingmentioning

confidence: 99%

Section: Tree Physiognomymentioning

confidence: 99%

Section: Plant Functional Type Groupingmentioning

confidence: 99%

Section: Growth Of a Treementioning

confidence: 99%

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Carbon sequestration: mathematical model of the Brazilian Atlantic Forest.

Villacampa¹

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How to map forest structure from aircraft, one tree at a time

Dalponte

Frizzera

Gianelle

2018

Ecology and Evolution

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Forest structure is strongly related to forest ecology, and it is a key parameter to understand ecosystem processes and services. Airborne laser scanning (ALS) is becoming an important tool in environmental mapping. It is increasingly common to collect ALS data at high enough point density to recognize individual tree crowns (ITCs) allowing analyses to move beyond classical stand‐level approaches. In this study, an effective and simple method to map ITCs, and their stem diameter and aboveground biomass (AGB) is presented. ALS data were used to delineate ITCs and to extract ITCs’ height and crown diameter; then, using newly developed allometries, the ITCs’ diameter at breast height (DBH) and AGB were predicted. Gini coefficient of DBHs was also predicted and mapped aggregating ITCs predictions. Two datasets from spruce dominated temperate forests were considered: one was used to develop the allometric models, while the second was used to validate the methodology. The proposed approach provides accurate predictions of individual DBH and AGB (R 2 = .85 and .78, respectively) and of tree size distributions. The proposed method had a higher generalization ability compared to a standard area‐based method, in particular for the prediction of the Gini coefficient of DBHs. The delineation method used detected more than 50% of the trees with DBH >10 cm. The detection rate was particularly low for trees with DBH below 10 cm, but they represent a small amount of the total biomass. The Gini coefficient of the DBH distribution was predicted at plot level with R 2 = .46. The approach described in this work, easy applicable in different forested areas, is an important development of the traditional area‐based remote sensing tools and can be applied for more detailed analysis of forest ecology and dynamics.

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