Environmental Research Letters · 2021 · 20 citations · 57 references
Natural ResourcesEcologyForestryForest InventoryForest ManagementTerrestrial EcologyCarbon Stock AssessmentCarbon Stock EstimationDeforestationRemote Sensing For Forest InventoryTropical Forest InventoryForest Biomass InventoryForest Cover DynamicsForest Inventory MappingForest Resource InventoryNational Forest Biomass InventoriesSatellite Image Time Series AnalysisSoil Carbon StocksWater Resource Availability
Abstract Disturbed African tropical forests and woodlands have the potential to contribute to climate change mitigation. Therefore, there is a need to understand how carbon stocks of disturbed and recovering tropical forests are determined by environmental conditions and human use. In this case study, we explore how gradients in environmental conditions and human use determine aboveground biomass (AGB) in 1958 national forest inventory (NFI) plots located in forests and woodlands in mainland Tanzania. Plots were divided into recovering forests (areas recovering from deforestation for <25years) and established forests (areas consistently defined as forests for ⩾25 years). This division, as well as the detection of year of forest establishment, was obtained through the use of dense satellite time series of forest cover probability. In decreasing order of importance, AGB in recovering forests unexpectedly decreased with water availability, increased with surrounding tree cover and time since establishment, and decreased with elevation, distance to roads, and soil phosphorus content. AGB in established forests unexpectedly decreased with water availability, increased with surrounding tree cover, and soil nitrogen content, and decreased with elevation. AGB in recovering forests increased by 0.4 Mg ha −1 yr −1 during the first 20 years following establishment. Our results can serve as the basis of carbon sink estimates in African recovering tropical forests and woodlands, and aid in forest landscape restoration planning.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Fitting Linear Mixed-Effects Models Using <b>lme4</b>
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