Cargando…

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

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, ther...

Descripción completa

Detalles Bibliográficos
Autores principales: Guitet, Stéphane, Hérault, Bruno, Molto, Quentin, Brunaux, Olivier, Couteron, Pierre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4581701/
https://www.ncbi.nlm.nih.gov/pubmed/26402522
http://dx.doi.org/10.1371/journal.pone.0138456
_version_ 1782391603436978176
author Guitet, Stéphane
Hérault, Bruno
Molto, Quentin
Brunaux, Olivier
Couteron, Pierre
author_facet Guitet, Stéphane
Hérault, Bruno
Molto, Quentin
Brunaux, Olivier
Couteron, Pierre
author_sort Guitet, Stéphane
collection PubMed
description 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.
format Online
Article
Text
id pubmed-4581701
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-45817012015-10-01 Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome Guitet, Stéphane Hérault, Bruno Molto, Quentin Brunaux, Olivier Couteron, Pierre PLoS One Research Article 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. Public Library of Science 2015-09-24 /pmc/articles/PMC4581701/ /pubmed/26402522 http://dx.doi.org/10.1371/journal.pone.0138456 Text en © 2015 Guitet et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Guitet, Stéphane
Hérault, Bruno
Molto, Quentin
Brunaux, Olivier
Couteron, Pierre
Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome
title Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome
title_full Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome
title_fullStr Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome
title_full_unstemmed Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome
title_short Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome
title_sort spatial structure of above-ground biomass limits accuracy of carbon mapping in rainforest but large scale forest inventories can help to overcome
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4581701/
https://www.ncbi.nlm.nih.gov/pubmed/26402522
http://dx.doi.org/10.1371/journal.pone.0138456
work_keys_str_mv AT guitetstephane spatialstructureofabovegroundbiomasslimitsaccuracyofcarbonmappinginrainforestbutlargescaleforestinventoriescanhelptoovercome
AT heraultbruno spatialstructureofabovegroundbiomasslimitsaccuracyofcarbonmappinginrainforestbutlargescaleforestinventoriescanhelptoovercome
AT moltoquentin spatialstructureofabovegroundbiomasslimitsaccuracyofcarbonmappinginrainforestbutlargescaleforestinventoriescanhelptoovercome
AT brunauxolivier spatialstructureofabovegroundbiomasslimitsaccuracyofcarbonmappinginrainforestbutlargescaleforestinventoriescanhelptoovercome
AT couteronpierre spatialstructureofabovegroundbiomasslimitsaccuracyofcarbonmappinginrainforestbutlargescaleforestinventoriescanhelptoovercome