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Forest Ecosystems 2016, 3(4): 14 https://doi.org/10.1186/s40663-016-0077-4
Research | Open Access | Issue | Published: 10 July 2016

Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia

Show Author's Information James Halperin1 ( ),  Valerie LeMay1,  Emmanuel Chidumayo2,  Louis Verchot3,  Peter Marshall1
Department of Forest Resources Management, The University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada
Makeni Savanna Research Project, P.O. Box 50323, Ridgeway, Lusaka, Zambia
International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, Apartado Aéreo 6713, Cali 763537, Colombia
Keywords:
Generalized additive model, National Forest Inventory, Above-ground biomass, Miombo, REDD+, Nonlinear model, Landsat 8 OLI
Cite this article:
Halperin J, LeMay V, Chidumayo E, et al. Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia. Forest Ecosystems, 2016, 3(4): 14. https://doi.org/10.1186/s40663-016-0077-4
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Abstract Full text About this article
Background

Information on above-ground biomass (AGB) is important for managing forest resource use at local levels,land management planning at regional levels,and carbon emissions reporting at national and international levels. In many tropical developing countries,this information may be unreliable or at a scale too coarse for use at local levels. There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements. Model-based methods provide an efficient framework to estimate AGB.

Methods

Using National Forest Inventory (NFI) data for a ~1,000,000 ha study area in the miombo ecoregion,Zambia,we estimated AGB using predicted canopy cover,environmental data,disturbance data,and Landsat 8 OLI satellite imagery. We assessed different combinations of these datasets using three models,a semiparametric generalized additive model (GAM) and two nonlinear models (sigmoidal and exponential),employing a genetic algorithm for variable selection that minimized root mean square prediction error (RMSPE),calculated through cross-validation. We compared model fit statistics to a null model as a baseline estimation method. Using bootstrap resampling methods,we calculated 95 % confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data.

Results

Canopy cover,soil moisture,and vegetation indices were consistently selected as predictor variables. The sigmoidal model and the GAM performed similarly; for both models the RMSPE was ~36.8 tonnes per hectare (i.e.,57 % of the mean). However,the sigmoidal model was approximately 30 % more efficient than the GAM,assessed using bootstrapped variance estimates relative to a null model. After selecting the sigmoidal model,we estimated total AGB for the study area at 64,526,209 tonnes (+/- 477,730),with a confidence interval 20 times more precise than a simple design-based estimate.

Conclusions

Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty,while also providing spatially explicit AGB maps useful for management,planning,and reporting purposes.


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About this article

Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia

Show Author's information James Halperin1 ( ), Valerie LeMay1, Emmanuel Chidumayo2, Louis Verchot3, Peter Marshall1
Department of Forest Resources Management, The University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada
Makeni Savanna Research Project, P.O. Box 50323, Ridgeway, Lusaka, Zambia
International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, Apartado Aéreo 6713, Cali 763537, Colombia

Abstract

Background

Information on above-ground biomass (AGB) is important for managing forest resource use at local levels,land management planning at regional levels,and carbon emissions reporting at national and international levels. In many tropical developing countries,this information may be unreliable or at a scale too coarse for use at local levels. There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements. Model-based methods provide an efficient framework to estimate AGB.

Methods

Using National Forest Inventory (NFI) data for a ~1,000,000 ha study area in the miombo ecoregion,Zambia,we estimated AGB using predicted canopy cover,environmental data,disturbance data,and Landsat 8 OLI satellite imagery. We assessed different combinations of these datasets using three models,a semiparametric generalized additive model (GAM) and two nonlinear models (sigmoidal and exponential),employing a genetic algorithm for variable selection that minimized root mean square prediction error (RMSPE),calculated through cross-validation. We compared model fit statistics to a null model as a baseline estimation method. Using bootstrap resampling methods,we calculated 95 % confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data.

Results

Canopy cover,soil moisture,and vegetation indices were consistently selected as predictor variables. The sigmoidal model and the GAM performed similarly; for both models the RMSPE was ~36.8 tonnes per hectare (i.e.,57 % of the mean). However,the sigmoidal model was approximately 30 % more efficient than the GAM,assessed using bootstrapped variance estimates relative to a null model. After selecting the sigmoidal model,we estimated total AGB for the study area at 64,526,209 tonnes (+/- 477,730),with a confidence interval 20 times more precise than a simple design-based estimate.

Conclusions

Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty,while also providing spatially explicit AGB maps useful for management,planning,and reporting purposes.

Keywords: Generalized additive model, National Forest Inventory, Above-ground biomass, Miombo, REDD+, Nonlinear model, Landsat 8 OLI

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Publication history

Received: 02 June 2016
Accepted: 10 July 2016
Published: 10 July 2016
Issue date: December 2016

Copyright

© 2016 The Author(s).

Acknowledgements

Acknowledgements

The authors wish to thank Dr. Nicholas Coops, Dr. Davison Gumbo, Mr. Kaala Moombe, Mr. Joel Lwambo, Mr. Martin Lyambai, Mr. Andrew Goods Nkoma, Chief Nyalugwe, Chief Ndake, Chieftaness Mwape, Chief Luembe, Mrs. Bertha Kauseni, Ms. Rhoda Chiluba, Mr. Shadreck Ngoma, Mr. Sylvester Siame, Mr. Geoffrey Tebuho, Mr. Susiku Muyapekwa, Mr. Smart Lungu, Mr. Saule Lungu, Mr. Moses Ngulube, Dr. Julian Fox, Mr. Abel Siampale, and the residents of Nyimba District, Zambia. We also wish to thank two anonymous reviewers for their helpful comments and suggestions.

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