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

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.

[1]  S. Ismail,et al.  Carbon and nitrogen storage in agroforests, tree plantations, and pastures in western Oregon, USA , 2004, Agroforestry Systems.

[2]  K. Moffett,et al.  Remote Sens , 2015 .

[3]  Robert J. McGaughey,et al.  Monitoring selective logging in western Amazonia with repeat lidar flights , 2014 .

[4]  E. Næsset,et al.  Indirect and direct estimation of forest biomass change using forest inventory and airborne laser scanning data , 2015 .

[5]  Qi Chen,et al.  LiDAR Remote Sensing of Vegetation Biomass , 2013 .

[6]  M. Helmers,et al.  Targeting Perennial Vegetation in Agricultural Landscapes for Enhancing Ecosystem Services , 2022 .

[7]  A. Homma.  História da agricultura na Amazônia: da era pré-colombiana ao terceiro milênio. , 2003 .

[8]  Jon Atli Benediktsson,et al.  Land-cover classification using both hyperspectral and LiDAR data , 2015 .

[9]  P. Nair.  An introduction to agroforestry. , 1993 .

[10]  Scott X. Chang,et al.  Carbon Sequestration in Agroforestry Systems , 2014 .

[11]  Henry Neufeldt,et al.  Reducing subsistence farmers’ vulnerability to climate change: evaluating the potential contributions of agroforestry in western Kenya , 2012, Agriculture & Food Security.

[12]  Qi Chen,et al.  Modeling aboveground tree woody biomass using national-scale allometric methods and airborne lidar , 2015 .

[13]  David A. Coomes,et al.  Global wood density database , 2009 .

[14]  Qi Chen.  Airborne Lidar Data Processing and Information Extraction , 2007 .

[15]  M. Noordwijk,et al.  Multipurpose agroforestry as a climate change resiliency option for farmers: an example of local adaptation in Vietnam , 2013, Climatic Change.

[16]  Fabian Ewald Fassnacht,et al.  Forest structure modeling with combined airborne hyperspectral and LiDAR data , 2012 .

[17]  David Saah,et al.  Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass , 2012 .

[18]  S. Jose.  Agroforestry for conserving and enhancing biodiversity , 2012, Agroforestry Systems.

[19]  H. Gholz,et al.  An evaluation of agroforestry systems as a rural development option for the Brazilian Amazon , 2002, Agroforestry Systems.

[20]  Erik Næsset,et al.  The uncertainty of biomass estimates from LiDAR and SAR across a boreal forest structure gradient , 2014 .

[21]  Yong Q. Tian,et al.  Estimating Basal Area and Stem Volume for Individual Trees from Lidar Data , 2007 .

[22]  Lijuan Liu,et al.  A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems , 2016, Int. J. Digit. Earth.

[23]  É. L. Bolfe,et al.  Correlação de variáveis espectrais e estoque de carbono da biomassa aérea de sistemas agroflorestais , 2012 .

[24]  Thomas J. Sauer,et al.  Odor mitigation with tree buffers: Swine production case study , 2012 .

[25]  M. Kanninen,et al.  Modeling biomass and soil carbon sequestration of indigenous agroforestry systems using CO2FIX approach , 2015 .

[26]  C. Doscher,et al.  Using satellite image data to estimate aboveground shelterbelt carbon stocks across an agricultural landscape , 2012 .

[27]  Behnaz Bigdeli,et al.  A decision fusion method based on multiple support vector machine system for fusion of hyperspectral and LIDAR data , 2014 .

[28]  M. d'Oliveira,et al.  Estimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest, Acre State, Western Brazilian Amazon , 2012 .

[29]  P. Nair,et al.  Carbon sequestration studies in agroforestry systems: a reality-check , 2011, Agroforestry Systems.

[30]  E. Luedeling,et al.  Agroforestry solutions to address food security and climate change challenges in Africa , 2014 .

[31]  R. Valentini,et al.  Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels , 2015 .

[32]  O. Mutanga,et al.  Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas , 2015 .

[33]  Catherine Potvin,et al.  Variation in carbon storage among tree species: Implications for the management of a small-scale carbon sink project , 2007 .

[34]  R. Valentini,et al.  Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data , 2014 .

[35]  Alain Albrecht,et al.  Carbon sequestration in tropical agroforestry systems , 2003 .

[36]  Tonny J. Oyana,et al.  Mapping and spatial uncertainty analysis of forest vegetation carbon by combining national forest inventory data and satellite images , 2009 .

[37]  R. Zomer,et al.  Trees on farm: analysis of global extent and geographical patterns of agroforestry. , 2009 .

[38]  David Saah,et al.  Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates , 2012 .

[39]  M. Keller,et al.  Biomass estimation in the Tapajos National Forest, Brazil: Examination of sampling and allometric uncertainties , 2001 .

[40]  J. Chave,et al.  Towards a Worldwide Wood Economics Spectrum 2 . L E a D I N G D I M E N S I O N S I N W O O D F U N C T I O N , 2022 .

[41]  Rattan Lal,et al.  Soil organic carbon sequestration in agroforestry systems. A review , 2014, Agronomy for Sustainable Development.

[42]  Guoqing Sun,et al.  Combining satellite lidar, airborne lidar, and ground plots to estimate the amount and distribution of aboveground biomass in the boreal forest of North America1 , 2015 .

[43]  J. Meira‐Neto,et al.  Environmental filtering of agroforestry systems reduces the risk of biological invasion , 2014, Agroforestry Systems.

[44]  G. Asner,et al.  High-resolution mapping of forest carbon stocks in the Colombian Amazon , 2012 .

[45]  J. R. N. F. Gama,et al.  Caracterização e classificação dos solos do Município de Tomé-Açu, PA. , 2001 .

[46]  H. E. Garrett,et al.  Soil water content and infiltration in agroforestry buffer strips , 2008, Agroforestry Systems.

[47]  Ronald E. McRoberts,et al.  Effects of uncertainty in model predictions of individual tree volume on large area volume estimates , 2014 .

[48]  J. Mcneely,et al.  Nature vs. nurture: managing relationships between forests, agroforestry and wild biodiversity , 2004, Agroforestry Systems.

[49]  E. Moran.  Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery. , 2010, Photogrammetric engineering and remote sensing.

[50]  M. Keller,et al.  Tree height and tropical forest biomass estimation , 2013 .

[51]  Leila Fathi,et al.  Structural and mechanical properties of the wood from coconut palms, oil palms and date palms , 2014 .

[52]  Sassan Saatchi,et al.  A novel application of satellite radar data: measuring carbon sequestration and detecting degradation in a community forestry project in Mozambique , 2013 .

[53]  B. Nelson,et al.  Improved allometric models to estimate the aboveground biomass of tropical trees , 2014, Global change biology.

[54]  Oliver L. Phillips,et al.  Amazon palm biomass and allometry , 2013 .

[55]  William F. Laurance,et al.  Total aboveground biomass in central Amazonian rainforests: a landscape-scale study , 2002 .

[56]  N. H. Ravindranath,et al.  Agriculture, Forestry and Other Land Use (AFOLU) , 2014 .