Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks
Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on...
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oai:repositorio:1-14893 Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks Réjou-Méchain, Maxime Muller-Landau, Helene C. Detto, Matteo Thomas, Sean C. Le-Toan, Thuy Saatchi, Sassan S. Barreto-Silva, Juan Sebastian Bourg, Norman A. Bunyavejchewin, Sarayudh Butt, Nathalie Brockelman, Warren Y. Cao, Min Cárdenas, Dairón Chiang, Jyh-Min Chuyong, George Bindeh Clay, Keith Condit, Richard S. Dattaraja, Handanakere Shavaramaiah Davies, Stuart James Duque M, Alvaro J. Esufali, Shameema T. Ewango, Corneille E.N. Fernando, R. H S Fletcher, Christine Dawn N Gunatilleke, I. A.U. Hao, Zhanqing Harms, Kyle E. Hart, Terese B. Hérault, Bruno Howe, Robert W. Hubbell, Stephen P. Johnson, Daniel J. Kenfack, David Larson, Andrew J. Lin, Luxiang Lin, Yiching Lutz, James A. Makana, Jean Rémy Malhi, Yadvinder Singh Marthews, Toby R. McEwan, Ryan Walker McMahon, Sean M. McShea, William J. Muscarella, Robert A. Nathalang, Anuttara Noor, Nur Supardi Md Nytch, Christopher J. Oliveira, Alexandre Adalardo de Phillips, Richard P. Pongpattananurak, Nantachai Punchi-Manage, Ruwan Salim, R. Schurman, Jonathan S. Sukumar, Raman Suresh, Hebbalalu Sathyanarayana Suwanvecho, Udomlux Thomas, Duncan W. Thompson, Jill Uríarte, Ma?ia Valencia, Renato L. Vicentini, Alberto Wolf, Amy T. Yap, Sandra L. Yuan, Zuoqiang Zartman, Charles Eugene Zimmerman, Jess K. Chave, Jérôme Biomass Carbon Sequestration Forest Cover Remote Sensing Spatial Data Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha-1) at spatial scales ranging from 5 to 250 m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise. © Author(s) 2014. 2020-05-07T13:47:14Z 2020-05-07T13:47:14Z 2014 Artigo https://repositorio.inpa.gov.br/handle/1/14893 10.5194/bg-11-6827-2014 en Volume 11, Número 23, Pags. 6827-6840 Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ application/pdf Biogeosciences |
| institution |
Instituto Nacional de Pesquisas da Amazônia - Repositório Institucional |
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INPA-RI |
| language |
English |
| topic |
Biomass Carbon Sequestration Forest Cover Remote Sensing Spatial Data |
| spellingShingle |
Biomass Carbon Sequestration Forest Cover Remote Sensing Spatial Data Réjou-Méchain, Maxime Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks |
| topic_facet |
Biomass Carbon Sequestration Forest Cover Remote Sensing Spatial Data |
| description |
Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha-1) at spatial scales ranging from 5 to 250 m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise. © Author(s) 2014. |
| format |
Artigo |
| author |
Réjou-Méchain, Maxime |
| author2 |
Muller-Landau, Helene C. Detto, Matteo Thomas, Sean C. Le-Toan, Thuy Saatchi, Sassan S. Barreto-Silva, Juan Sebastian Bourg, Norman A. Bunyavejchewin, Sarayudh Butt, Nathalie Brockelman, Warren Y. Cao, Min Cárdenas, Dairón Chiang, Jyh-Min Chuyong, George Bindeh Clay, Keith Condit, Richard S. Dattaraja, Handanakere Shavaramaiah Davies, Stuart James Duque M, Alvaro J. Esufali, Shameema T. Ewango, Corneille E.N. Fernando, R. H S Fletcher, Christine Dawn N Gunatilleke, I. A.U. Hao, Zhanqing Harms, Kyle E. Hart, Terese B. Hérault, Bruno Howe, Robert W. Hubbell, Stephen P. Johnson, Daniel J. Kenfack, David Larson, Andrew J. Lin, Luxiang Lin, Yiching Lutz, James A. Makana, Jean Rémy Malhi, Yadvinder Singh Marthews, Toby R. McEwan, Ryan Walker McMahon, Sean M. McShea, William J. Muscarella, Robert A. Nathalang, Anuttara Noor, Nur Supardi Md Nytch, Christopher J. Oliveira, Alexandre Adalardo de Phillips, Richard P. Pongpattananurak, Nantachai Punchi-Manage, Ruwan Salim, R. Schurman, Jonathan S. Sukumar, Raman Suresh, Hebbalalu Sathyanarayana Suwanvecho, Udomlux Thomas, Duncan W. Thompson, Jill Uríarte, Ma?ia Valencia, Renato L. Vicentini, Alberto Wolf, Amy T. Yap, Sandra L. Yuan, Zuoqiang Zartman, Charles Eugene Zimmerman, Jess K. Chave, Jérôme |
| author2Str |
Muller-Landau, Helene C. Detto, Matteo Thomas, Sean C. Le-Toan, Thuy Saatchi, Sassan S. Barreto-Silva, Juan Sebastian Bourg, Norman A. Bunyavejchewin, Sarayudh Butt, Nathalie Brockelman, Warren Y. Cao, Min Cárdenas, Dairón Chiang, Jyh-Min Chuyong, George Bindeh Clay, Keith Condit, Richard S. Dattaraja, Handanakere Shavaramaiah Davies, Stuart James Duque M, Alvaro J. Esufali, Shameema T. Ewango, Corneille E.N. Fernando, R. H S Fletcher, Christine Dawn N Gunatilleke, I. A.U. Hao, Zhanqing Harms, Kyle E. Hart, Terese B. Hérault, Bruno Howe, Robert W. Hubbell, Stephen P. Johnson, Daniel J. Kenfack, David Larson, Andrew J. Lin, Luxiang Lin, Yiching Lutz, James A. Makana, Jean Rémy Malhi, Yadvinder Singh Marthews, Toby R. McEwan, Ryan Walker McMahon, Sean M. McShea, William J. Muscarella, Robert A. Nathalang, Anuttara Noor, Nur Supardi Md Nytch, Christopher J. Oliveira, Alexandre Adalardo de Phillips, Richard P. Pongpattananurak, Nantachai Punchi-Manage, Ruwan Salim, R. Schurman, Jonathan S. Sukumar, Raman Suresh, Hebbalalu Sathyanarayana Suwanvecho, Udomlux Thomas, Duncan W. Thompson, Jill Uríarte, Ma?ia Valencia, Renato L. Vicentini, Alberto Wolf, Amy T. Yap, Sandra L. Yuan, Zuoqiang Zartman, Charles Eugene Zimmerman, Jess K. Chave, Jérôme |
| title |
Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks |
| title_short |
Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks |
| title_full |
Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks |
| title_fullStr |
Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks |
| title_full_unstemmed |
Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks |
| title_sort |
local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks |
| publisher |
Biogeosciences |
| publishDate |
2020 |
| url |
https://repositorio.inpa.gov.br/handle/1/14893 |
| _version_ |
1787142933153054720 |
| score |
11.653393 |