Exploring characteristics of national forest inventories for integration with global space-based forest biomass data

Nesha, Karimon; Herold, Martin; Bruin, S. de; Araza, Arnan; Málaga, Natalia; Gamarra, Javier G. P.; Hergoualc’h, Kristell; Pekkarinen, Anssi; Ramirez, Carla María Ordinola; Morales-Hidalgo, David; Tavani, Rebecca

doi:10.1016/j.scitotenv.2022.157788

Cited by 14 publications

(14 citation statements)

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“…As well, data coverage is uneven with tropical forests under and temperate forests overrepresented in both inventories and functional trait measurements. Increased sampling effort and funding to establish permanent sample plots in poorly documented areas are needed to rectify this discrepancy (Nesha et al., 2022). We acknowledge that additional variation in forest functional composition may be present due to genetic variation and phenotypic plasticity within species (Fridley et al., 2007; Fridley & Grime, 2010), especially in species‐poor communities dominated by conifers (Anderegg et al., 2018; Siefert et al., 2015).…”

Section: Discussionmentioning

confidence: 99%

Global patterns and environmental drivers of forest functional composition

Bouchard,

Searle,

Drapeau

et al. 2023

Global Ecol Biogeogr

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AimTo determine the relationships between the functional trait composition of forest communities and environmental gradients across scales and biomes and the role of species relative abundances in these relationships.LocationGlobal.Time periodRecent.Major taxa studiedTrees.MethodsWe integrated species abundance records from worldwide forest inventories and associated functional traits (wood density, specific leaf area and seed mass) to obtain a data set of 99,953 to 149,285 plots (depending on the trait) spanning all forested continents. We computed community‐weighted and unweighted means of trait values for each plot and related them to three broad environmental gradients and their interactions (energy availability, precipitation and soil properties) at two scales (global and biomes).ResultsOur models explained up to 60% of the variance in trait distribution. At global scale, the energy gradient had the strongest influence on traits. However, within‐biome models revealed different relationships among biomes. Notably, the functional composition of tropical forests was more influenced by precipitation and soil properties than energy availability, whereas temperate forests showed the opposite pattern. Depending on the trait studied, response to gradients was more variable and proportionally weaker in boreal forests. Community unweighted means were better predicted than weighted means for almost all models.Main conclusionsWorldwide, trees require a large amount of energy (following latitude) to produce dense wood and seeds, while leaves with large surface to weight ratios are concentrated in temperate forests. However, patterns of functional composition within‐biome differ from global patterns due to biome specificities such as the presence of conifers or unique combinations of climatic and soil properties. We recommend assessing the sensitivity of tree functional traits to environmental changes in their geographic context. Furthermore, at a given site, the distribution of tree functional traits appears to be driven more by species presence than species abundance.

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Section: Discussionmentioning

confidence: 99%

Global patterns and environmental drivers of forest functional composition

Bouchard,

Searle,

Drapeau

et al. 2023

Global Ecol Biogeogr

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“…Limit the accounting classes. If the global carbon flux disagrees with the UN-FRA data especially for top-tier countries according to UNFRA standards (Nesha et al, 2022), the last resort would be the exclusion of certain UNSEEA classes such as mixed forests and other woody vegetation in our case. These classes have minimal carbon stocks among the carbon accounting classes.…”

Section: Implications Of Reference Data Sample To Carbon Accountingmentioning

confidence: 88%

“…In cases where NFIs and ΔAGB maps need integration for national ΔAGB estimates and rigorous map accuracy assessments, the NFI sampling design should be considered (Nesha et al, 2022). Further caution is needed because NFIs are not primarily designed for map comparisons and plots do not properly sample mapped ΔAGB especially at coarse aggregation levels.…”

Section: δAgb Spatial Analysis and Map-map Comparisonsmentioning

confidence: 99%

“…Direct mapping often uses machine learning predictive models that relate in-situ biomass change to auxiliary remote sensing variables. In the absence of repeated in-situ data, countries can rely on forest change maps and associated land use and land cover (LULC) changes, and this case is more common especially in developing countries (McRoberts et al, 2020;Nesha et al, 2022). There are around 24 countries that have relied on remote sensing data to generate their carbon stocks and flows in compliance with the UNSEEA framework (Hein et al, 2020a).…”

Section: Spatial Predictions and Uncertainties Of Forest Carbon Fluxe...mentioning

confidence: 99%

“…Carbon stocks and fluxes maps are always subject to map assessments and integration with reference data especially now that country NFIs are increasing even in developing countries in the tropics (Nesha et al, 2022). Given these country data in addition to the upcoming LiDAR sites (Chave et al, 2019a), sufficient global data would allow map errors of the global carbon fluxes to be modelled and minimized using our bias modelling approach (Araza et al, 2022a).…”

Section: Outlooks In Carbon Accounting Based On Remote Sensingmentioning

confidence: 99%

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Assessment, uncertainties and applications of global Above-ground biomass maps from earth observation

Araza

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Chapter 2 A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps Chapter 3 Past decade above-ground biomass change comparisons from four multi-temporal global maps Chapter 4 Spatial predictions and uncertainties of forest carbon fluxes for environmental-economic accounting Chapter 5 Rapid remote monitoring reveals spatial and temporal hotspots of carbon loss in Africa's rainforests Chapter 6 Synthesis

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