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Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome.

Guitet S, Hérault B, Molto Q, Brunaux O, Couteron P - PLoS ONE (2015)

Bottom Line: Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest.Environmental variables accounted for a minor part of spatial variation.We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate "wall-to-wall" remote sensing signals provide reliable AGB predictions.

View Article: PubMed Central - PubMed

Affiliation: Office National des Forêts (ONF), R&D department, Cayenne, French Guiana; Institut National de la Recherche Agronomique (INRA), UMR Amap, Montpellier, France; Institut de Recherche pour le Développement (IRD), UMR Amap, Montpellier, France.

ABSTRACT
Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate "wall-to-wall" remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.

No MeSH data available.


Related in: MedlinePlus

Coefficients of the selected GLM that predict biomass from environmental variables.Grey bars and brackets indicate groups of modalities related to the different categorical variables. For continuous topographical and hydrographical variables (HAND, LOG, SLOpe and ALTitude) the coefficient value is multiplied by the mean of the variable.
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pone.0138456.g005: Coefficients of the selected GLM that predict biomass from environmental variables.Grey bars and brackets indicate groups of modalities related to the different categorical variables. For continuous topographical and hydrographical variables (HAND, LOG, SLOpe and ALTitude) the coefficient value is multiplied by the mean of the variable.

Mentions: The best GLM selected to predict biomass variation with environmental variables explained a small but significant proportion of variance when fitted on the training set (R² = 0.09, DF = 1228, p<0.001, AIC = 11 541 with intercept = 0). Geomorphological landforms, the dry season index and annual rainfall were excluded from the model (Fig 5). Geomorphological landscapes had the strongest effect on biomass (F = 6.0664, DF = 10, p<0.001) and displayed marked contrasts at large scales between (i) on the one hand, regions dominated by mountains (H), plateaus (E,F,G), or smoothed multi-convex landscapes (I) with high biomass; and (ii) on the other hand, plains (A), valleys (C), multi-concave (D) and marked multi-convex landscapes (B,J) with low biomass. Low HAND (height above the nearest drainage) and high LOG (logarithm of basin area), which mainly point to seasonally flooded forests, were also highly influential at the local scale with a significant negative effect (respectively F = 11.3495, p<0.001 and F = 9.9309, p<0.01). GEOL (Geology) had a significant but limited effect driven by the “dykes” category (G2), which corresponded to an extremely hard localized substrate with significantly lower biomass (F = 2.5477, DF = 7, p<0.05). Similarly, VEGET (vegetation type) had only a slight effect mainly driven by type 22 which exhibited very low biomass (F = 2.8287, DF = 5, p<0.05) and corresponded to “open forests mixed with palm forests”, mainly located in the southern part of French Guiana. Altitude and slope had the weakest effects (respectively F = 3.5036, p<0.1 and F = 2.5405, p = 0.111).


Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome.

Guitet S, Hérault B, Molto Q, Brunaux O, Couteron P - PLoS ONE (2015)

Coefficients of the selected GLM that predict biomass from environmental variables.Grey bars and brackets indicate groups of modalities related to the different categorical variables. For continuous topographical and hydrographical variables (HAND, LOG, SLOpe and ALTitude) the coefficient value is multiplied by the mean of the variable.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4581701&req=5

pone.0138456.g005: Coefficients of the selected GLM that predict biomass from environmental variables.Grey bars and brackets indicate groups of modalities related to the different categorical variables. For continuous topographical and hydrographical variables (HAND, LOG, SLOpe and ALTitude) the coefficient value is multiplied by the mean of the variable.
Mentions: The best GLM selected to predict biomass variation with environmental variables explained a small but significant proportion of variance when fitted on the training set (R² = 0.09, DF = 1228, p<0.001, AIC = 11 541 with intercept = 0). Geomorphological landforms, the dry season index and annual rainfall were excluded from the model (Fig 5). Geomorphological landscapes had the strongest effect on biomass (F = 6.0664, DF = 10, p<0.001) and displayed marked contrasts at large scales between (i) on the one hand, regions dominated by mountains (H), plateaus (E,F,G), or smoothed multi-convex landscapes (I) with high biomass; and (ii) on the other hand, plains (A), valleys (C), multi-concave (D) and marked multi-convex landscapes (B,J) with low biomass. Low HAND (height above the nearest drainage) and high LOG (logarithm of basin area), which mainly point to seasonally flooded forests, were also highly influential at the local scale with a significant negative effect (respectively F = 11.3495, p<0.001 and F = 9.9309, p<0.01). GEOL (Geology) had a significant but limited effect driven by the “dykes” category (G2), which corresponded to an extremely hard localized substrate with significantly lower biomass (F = 2.5477, DF = 7, p<0.05). Similarly, VEGET (vegetation type) had only a slight effect mainly driven by type 22 which exhibited very low biomass (F = 2.8287, DF = 5, p<0.05) and corresponded to “open forests mixed with palm forests”, mainly located in the southern part of French Guiana. Altitude and slope had the weakest effects (respectively F = 3.5036, p<0.1 and F = 2.5405, p = 0.111).

Bottom Line: Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest.Environmental variables accounted for a minor part of spatial variation.We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate "wall-to-wall" remote sensing signals provide reliable AGB predictions.

View Article: PubMed Central - PubMed

Affiliation: Office National des Forêts (ONF), R&D department, Cayenne, French Guiana; Institut National de la Recherche Agronomique (INRA), UMR Amap, Montpellier, France; Institut de Recherche pour le Développement (IRD), UMR Amap, Montpellier, France.

ABSTRACT
Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate "wall-to-wall" remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.

No MeSH data available.


Related in: MedlinePlus