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Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data

Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, School of Environmental & Resource Sciences, Zhejiang A&F University, Lin An 311300, China
Department of Geography, University of Hawaii at Manoa, Honolulu, HI 96822, USA
Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48823, USA
Brazilian Agricultural Research Corporation—Embrapa, Campinas, SP 13070-115, Brazil
USDA Forest Service, International Institute of Tropical Forestry, San Juan, PR 00926, USA
Author to whom correspondence should be addressed.
Academic Editors: Guomo Zhou, Conghe Song, Guangxing Wang, Nicolas Baghdadi and Prasad S. Thenkabail
Remote Sens. 2016, 8(1), 21;
Received: 30 September 2015 / Revised: 16 December 2015 / Accepted: 20 December 2015 / Published: 30 December 2015
(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)
PDF [6728 KB, uploaded 30 December 2015]


Agroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology for mapping aboveground biomass (AGB) over landscape levels. However, little research in the past has been done to study AGB of agroforestry systems using airborne lidar data. Focusing on an agroforestry system in the Brazilian Amazon, this study first predicted plot-level AGB using fixed-effects regression models that assumed the regression coefficients to be constants. The model prediction errors were then analyzed from the perspectives of tree DBH (diameter at breast height)—height relationships and plot-level wood density, which suggested the need for stratifying agroforestry fields to improve plot-level AGB modeling. We separated teak plantations from other agroforestry types and predicted AGB using mixed-effects models that can incorporate the variation of AGB-height relationship across agroforestry types. We found that, at the plot scale, mixed-effects models led to better model prediction performance (based on leave-one-out cross-validation) than the fixed-effects models, with the coefficient of determination (R2) increasing from 0.38 to 0.64. At the landscape level, the difference between AGB densities from the two types of models was ~10% on average and up to ~30% at the pixel level. This study suggested the importance of stratification based on tree AGB allometry and the utility of mixed-effects models in modeling and mapping AGB of agroforestry systems. View Full-Text
Keywords: agroforestry; aboveground biomass; lidar; mixed-effects models; allometry; wood density agroforestry; aboveground biomass; lidar; mixed-effects models; allometry; wood density

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Chen, Q.; Lu, D.; Keller, M.; Dos-Santos, M.N.; Bolfe, E.L.; Feng, Y.; Wang, C. Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data. Remote Sens. 2016, 8, 21.

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