Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks
| Autor: | Yiching Lin, Alexandre Adalardo de Oliveira, Anuttara Nathalang, Alvaro Duque, Keith Clay, Yadvinder Malhi, Nantachai Pongpattananurak, Sean C. Thomas, S.S. Saatchi, William J. McShea, Sarayudh Bunyavejchewin, James A. Lutz, Matteo Detto, Amy Wolf, Stuart J. Davies, Andrew J. Larson, Charles E. Zartman, Stephen P. Hubbell, Ryan W. McEwan, H. S. Suresh, Zhanqing Hao, Ruwan Punchi-Manage, Shameema Esufali, H. S. Dattaraja, Helene C. Muller-Landau, Raman Sukumar, María Uriarte, Udomlux Suwanvecho, Jess K. Zimmerman, George B. Chuyong, Jill Thompson, Jérôme Chave, David Kenfack, Toby R. Marthews, Corneille E. N. Ewango, Nathalie Butt, Luxiang Lin, Nur Supardi Md. Noor, Daniel J. Johnson, Christopher J. Nytch, Warren Y. Brockelman, Bruno Hérault, I. A. U. N. Gunatilleke, Zuoqiang Yuan, Jonathan S. Schurman, Richard Condit, Duncan W. Thomas, Richard P. Phillips, R. H. S. Fernando, Juan Sebastian Barreto-Silva, Terese B. Hart, R. Salim, Norman A. Bourg, Min Cao, Alberto Vicentini, Sandra L. Yap, Dairon Cárdenas, Kyle E. Harms, Robert W. Howe, Maxime Réjou-Méchain, Jean-Remy Makana, Christine Fletcher, Sean M. McMahon, Robert Muscarella, T. Le Toan, Jyh-Min Chiang, Renato Valencia |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
Forest Cover 010504 meteorology & atmospheric sciences LIVE BIOMASS lcsh:Life TROPICAL FORESTS forêt tropicale 01 natural sciences Remote Sensing K01 - Foresterie - Considérations générales Biomasse Forest plot Biomass forest biomass carbon stocks Évaluation des stocks ALOS PALSAR DATA Biomass (ecology) lcsh:QE1-996.5 Sampling (statistics) DESMATAMENTO séquestration du carbone AIRBORNE LIDAR Forêt Échantillonnage P01 - Conservation de la nature et ressources foncières Modèle mathématique ABOVEGROUND BIOMASS Carbon Sequestration Carbone Méthodologie P40 - Météorologie et climatologie Télédétection Topographie MODELS 010603 evolutionary biology Ecology and Environment Deforestation lcsh:QH540-549.5 REGRESSION Reducing emissions from deforestation and forest degradation Spatial Data Spatial analysis Modélisation environnementale Ecology Evolution Behavior and Systematics atténuation des effets du changement climatique 0105 earth and related environmental sciences Earth-Surface Processes Remote sensing Changement climatique ERROR PROPAGATION Cartographie 15. Life on land Field (geography) lcsh:Geology lcsh:QH501-531 AMAZONIAN FOREST 13. Climate action Environmental science Spatial variability lcsh:Ecology DEFORESTATION U30 - Méthodes de recherche |
| Zdroj: | Rejou-Mechain, M, Muller-Landau, H C, Detto, M, Thomas, S C, Le Toan, T, Saatchi, S S, Barreto-Silva, J S, Bourg, N A, Bunyavejchewin, S, Butt, N, Brockelman, W Y, Cao, M, Cardenas, D, Chiang, J-M, Chuyong, G B, Clay, K, Condit, R, Dattaraja, H S, Davies, S J, Duque, A, Esufali, S, Ewango, C, Fernando, R H S, Fletcher, C D, Gunatilleke, I A U N, Hao, Z, Harms, K E, Hart, T B, Herault, B, Howe, R W, Hubbell, S P, Johnson, D J, Kenfack, D, Larson, A J, Lin, L, Lin, Y, Lutz, J A, Makana, J-R, Malhi, Y, Marthews, T R, McEwan, R W, McMahon, S M, McShea, W J, Muscarella, R, Nathalang, A, Noor, N S M, Nytch, C J, Oliveira, A A, Phillips, R P, Pongpattananurak, N, Punchi-Manage, R, Salim, R, Schurman, J, Sukumar, R, Suresh, H S, Suwanvecho, U, Thomas, D W, Thompson, J, Uriarte, M, Valencia, R, Vicentini, A, Wolf, A T, Yap, S, Yuan, Z, Zartman, C E, Zimmerman, J K & Chave, J 2014, ' Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks ' Biogeosciences, vol 11, no. 23, pp. 6827-6840 ., 10.5194/bg-11-6827-2014 Biogeosciences Repositório Institucional do INPA Instituto Nacional de Pesquisas da Amazônia (INPA) instacron:INPA Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP Biogeosciences, Vol 11, Iss 23, Pp 6827-6840 (2014) |
| ISSN: | 1726-4189 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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