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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

Comparison of AGB values predicted by the different maps at 2-km resolution with test dataset.Aboveground biomass (AGB) means at cell level for the test set are compared to the values predicted by the different maps at 2-km resolution: from the top left to the bottom right—KR, GLM, Baccini [15] and Saatchi [16]. The red line indicates the 1:1 relationship (expected slope). The size of the circles indicates the number of plots for each cell in the test set (from 3 for the smallest to 12 for the biggest).
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pone.0138456.g008: Comparison of AGB values predicted by the different maps at 2-km resolution with test dataset.Aboveground biomass (AGB) means at cell level for the test set are compared to the values predicted by the different maps at 2-km resolution: from the top left to the bottom right—KR, GLM, Baccini [15] and Saatchi [16]. The red line indicates the 1:1 relationship (expected slope). The size of the circles indicates the number of plots for each cell in the test set (from 3 for the smallest to 12 for the biggest).

Mentions: The same results were obtained with 2-km resolution cells (Table 3 and Fig 8). Thanks to coarse-graining, our models were more accurate than at the finest resolution (RMSEP was reduced by 25% for GLM and 30% for KR compared with 1-km resolution), with better precision (r² = 0.19 and 0.48 for GLM and KR respectively with p<0.001 in both cases). However the regression slopes kept the same value as at 1-km resolution (i.e. about 0.2 for GLM and about 0.5 for KR), indicating a dilution bias effect that could not be reduced. As a result, bias was zero for mean values but systematically negative for the highest values and positive for the lowest values (Fig 8). At and above 4-km resolution, all statistics of adjustment were degraded or saturated on all maps (RMSEP, R² and the same or worse slope than previously).


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)

Comparison of AGB values predicted by the different maps at 2-km resolution with test dataset.Aboveground biomass (AGB) means at cell level for the test set are compared to the values predicted by the different maps at 2-km resolution: from the top left to the bottom right—KR, GLM, Baccini [15] and Saatchi [16]. The red line indicates the 1:1 relationship (expected slope). The size of the circles indicates the number of plots for each cell in the test set (from 3 for the smallest to 12 for the biggest).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0138456.g008: Comparison of AGB values predicted by the different maps at 2-km resolution with test dataset.Aboveground biomass (AGB) means at cell level for the test set are compared to the values predicted by the different maps at 2-km resolution: from the top left to the bottom right—KR, GLM, Baccini [15] and Saatchi [16]. The red line indicates the 1:1 relationship (expected slope). The size of the circles indicates the number of plots for each cell in the test set (from 3 for the smallest to 12 for the biggest).
Mentions: The same results were obtained with 2-km resolution cells (Table 3 and Fig 8). Thanks to coarse-graining, our models were more accurate than at the finest resolution (RMSEP was reduced by 25% for GLM and 30% for KR compared with 1-km resolution), with better precision (r² = 0.19 and 0.48 for GLM and KR respectively with p<0.001 in both cases). However the regression slopes kept the same value as at 1-km resolution (i.e. about 0.2 for GLM and about 0.5 for KR), indicating a dilution bias effect that could not be reduced. As a result, bias was zero for mean values but systematically negative for the highest values and positive for the lowest values (Fig 8). At and above 4-km resolution, all statistics of adjustment were degraded or saturated on all maps (RMSEP, R² and the same or worse slope than previously).

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