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

Qi Chen,Maiza Dos-Santos,Changwei Wang,Michael Keller,Dengsheng Lu,Yunyun Feng,Edson Bolfe

doi:10.3390/rs8010021

Abstract

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.

Highlights

Agroforestry denotes land use systems where woody perennials are cultivated on the same land units as agricultural crops and/or animals [1,2]
We examined the residual errors of the above model from the perspectives of both tree Diameter breast height (DBH)-H relationship and wood density at the plot scale
This study examined the potentials and challenges of using airborne lidar data for estimating aboveground biomass (AGB) in an agroforestry system in the Brazilian Amazon

Summary

Introduction

Agroforestry denotes land use systems where woody perennials (trees, shrubs, palms, etc.) are cultivated on the same land units as agricultural crops and/or animals [1,2]. We use agroforestry as a general term to refer to the land use system of cultivating woody perennials, either polyculture or monoculture (i.e., plantation) on agricultural land, regardless the current existence of crops or animals. 2016, 8, 21 many countries to offer a wide range of economic, social, and ecological benefits: (1) increasing the per capita farm income by planning high-value tree products [4]; (2) improving soil fertility and land productivity [5]; (3) increasing household resilience [4]; (4) mitigating the impacts of climate variability and change [6,7]; (5) conserving biodiversity [8,9,10]; and (6) improving air and water quality [11,12,13]. For agroforestry to be successful as a strategy for C sequestration, it is necessary to have sound detection and monitoring systems [20]

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