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

作     者：James Halperin Valerie LeMay Emmanuel Chidumayo Louis Verchot Peter Marshall 

作者机构：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 Aereo 6713 Cali 763537. Colombia 

出 版 物：《Forest Ecosystems》 (森林生态系统（英文版）)

年 卷 期：2016年第3卷第4期

页      面：258-274页

核心收录：

学科分类：0710[理学-生物学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 0907[农学-林学] 0829[工学-林业工程] 09[农学] 0903[农学-农业资源与环境] 0901[农学-作物学] 0833[工学-城乡规划学] 0713[理学-生态学] 0834[工学-风景园林学（可授工学、农学学位）] 

基　　金：provided by the United States Agency for International Development under grant number 3FS-G-11-00002 to the Center for International Forestry Research,entitled the Nyimba Forest Project provided by The University of British Columbia 

主　　题：National Forest Inventory Above ground biomass Miombo REDD+ Generalized additive model Nonlinear model Landsat 8 OLI 

摘      要：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 *** many tropical developing countries,this information may be unreliable or at a scale too coarse for use at local *** is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development ***-based methods provide an efficient framework to estimate ***：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 *** 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 *** compared model fit statistics to a null model as a baseline estimation *** 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 ***：Canopy cover,soil moisture,and vegetation indices were consistently selected as predictor *** 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 *** 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 designbased ***：Our findings demonstrate that NFI