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

Spatial distribution of inventory blocks from CTFT (1974–1976) and ONF (2006–2013).Inventory blocks from CTFT (1974–1976) in pale grey polygons. Complementary inventory campaigns from ONF (2006–2013) in white circles (size represent the effective area covered by transects). Areas disturbed by harvesting or mining between 1974 and 2007 (in black) were removed from the dataset.
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pone.0138456.g001: Spatial distribution of inventory blocks from CTFT (1974–1976) and ONF (2006–2013).Inventory blocks from CTFT (1974–1976) in pale grey polygons. Complementary inventory campaigns from ONF (2006–2013) in white circles (size represent the effective area covered by transects). Areas disturbed by harvesting or mining between 1974 and 2007 (in black) were removed from the dataset.

Mentions: We used two different forest inventories produced by French public organizations (Fig 1). The first inventory was done by CTFT (Centre Technique Forestier Tropical) between 1974 and 1976 in the northern part of the French Guiana [36]. CTFT data were scanned between 2006 and 2010 and positioned on GIS using original maps. This dataset corresponded to 126,880 trees (DBH≥20cm) in 1,172 plots 0.5 ha in size.


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)

Spatial distribution of inventory blocks from CTFT (1974–1976) and ONF (2006–2013).Inventory blocks from CTFT (1974–1976) in pale grey polygons. Complementary inventory campaigns from ONF (2006–2013) in white circles (size represent the effective area covered by transects). Areas disturbed by harvesting or mining between 1974 and 2007 (in black) were removed from the dataset.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0138456.g001: Spatial distribution of inventory blocks from CTFT (1974–1976) and ONF (2006–2013).Inventory blocks from CTFT (1974–1976) in pale grey polygons. Complementary inventory campaigns from ONF (2006–2013) in white circles (size represent the effective area covered by transects). Areas disturbed by harvesting or mining between 1974 and 2007 (in black) were removed from the dataset.
Mentions: We used two different forest inventories produced by French public organizations (Fig 1). The first inventory was done by CTFT (Centre Technique Forestier Tropical) between 1974 and 1976 in the northern part of the French Guiana [36]. CTFT data were scanned between 2006 and 2010 and positioned on GIS using original maps. This dataset corresponded to 126,880 trees (DBH≥20cm) in 1,172 plots 0.5 ha in size.

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