Limits...
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

Flowchart followed for statistical analyses.The grey colours indicate the different steps of analysis. Input data are represented in rectangles, analysis in ellipse and outputs in rounded boxes.
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pone.0138456.g002: Flowchart followed for statistical analyses.The grey colours indicate the different steps of analysis. Input data are represented in rectangles, analysis in ellipse and outputs in rounded boxes.

Mentions: Statistical analyses followed three steps that are summarized in Fig 2. In the first step we used all our data to produce variograms in order to examine the spatial structure of biomass and its consequences in terms of accuracy for interpolating from field data (dark grey on Fig 2). In a second step we used half of our data (training dataset) to calibrate prediction models in order to produce AGB maps at different resolutions and to evaluate the influence of environment factors on AGB variation (medium grey on Fig 2). In the last step we used the second half of our data (test dataset) to compute Residual Mean Square Error of Prediction (RMSEP) and regressions in order to test the accuracy of our maps at different resolution and to compare it with the accuracy of extant global maps (from Baccini [15] and Saatchi [16]).


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)

Flowchart followed for statistical analyses.The grey colours indicate the different steps of analysis. Input data are represented in rectangles, analysis in ellipse and outputs in rounded boxes.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0138456.g002: Flowchart followed for statistical analyses.The grey colours indicate the different steps of analysis. Input data are represented in rectangles, analysis in ellipse and outputs in rounded boxes.
Mentions: Statistical analyses followed three steps that are summarized in Fig 2. In the first step we used all our data to produce variograms in order to examine the spatial structure of biomass and its consequences in terms of accuracy for interpolating from field data (dark grey on Fig 2). In a second step we used half of our data (training dataset) to calibrate prediction models in order to produce AGB maps at different resolutions and to evaluate the influence of environment factors on AGB variation (medium grey on Fig 2). In the last step we used the second half of our data (test dataset) to compute Residual Mean Square Error of Prediction (RMSEP) and regressions in order to test the accuracy of our maps at different resolution and to compare it with the accuracy of extant global maps (from Baccini [15] and Saatchi [16]).

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