Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia
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 unreli...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2016-07-01
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| Series: | Forest Ecosystems |
| Subjects: | |
| Online Access: | https://forestecosyst.springeropen.com/articles/10.1186/s40663-016-0077-4 |
| _version_ | 1797965364079886336 |
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| author | James Halperin Valerie LeMay Emmanuel Chidumayo Louis Verchot Peter Marshall |
| author_facet | James Halperin Valerie LeMay Emmanuel Chidumayo Louis Verchot Peter Marshall |
| author_sort | James Halperin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T01:57:45Z |
| format | Article |
| id | doaj.art-ae6d5dafd49c4caba0f3e1e699f2835e |
| institution | Directory Open Access Journal |
| issn | 2095-6355 2197-5620 |
| language | English |
| last_indexed | 2024-04-11T01:57:45Z |
| publishDate | 2016-07-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Forest Ecosystems |
| spelling | doaj.art-ae6d5dafd49c4caba0f3e1e699f2835e2023-01-03T04:49:46ZengKeAi Communications Co., Ltd.Forest Ecosystems2095-63552197-56202016-07-01310.1186/s40663-016-0077-4Model-based estimation of above-ground biomass in the miombo ecoregion of ZambiaJames Halperin0Valerie LeMay1Emmanuel Chidumayo2Louis Verchot3Peter Marshall4Department of Forest Resources Management, The University of British ColumbiaDepartment of Forest Resources Management, The University of British ColumbiaMakeni Savanna Research ProjectInternational Center for Tropical AgricultureDepartment of Forest Resources Management, The University of British ColumbiaBackground 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.https://forestecosyst.springeropen.com/articles/10.1186/s40663-016-0077-4National Forest InventoryAbove-ground biomassMiomboREDD+Generalized additive modelNonlinear modelLandsat 8 OLI |
| spellingShingle | James Halperin Valerie LeMay Emmanuel Chidumayo Louis Verchot Peter Marshall Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia Forest Ecosystems National Forest Inventory Above-ground biomass Miombo REDD+ Generalized additive model Nonlinear model Landsat 8 OLI |
| title | Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia |
| title_full | Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia |
| title_fullStr | Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia |
| title_full_unstemmed | Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia |
| title_short | Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia |
| title_sort | model based estimation of above ground biomass in the miombo ecoregion of zambia |
| topic | National Forest Inventory Above-ground biomass Miombo REDD+ Generalized additive model Nonlinear model Landsat 8 OLI |
| url | https://forestecosyst.springeropen.com/articles/10.1186/s40663-016-0077-4 |
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