Role of communal and private forestland tenure regimes in regulating forest ecosystem goods and services in Rombo district, Tanzania

This study was undertaken to compare provisioning of forest ecosystem goods and services in Manuo Hill Communal Forest and Shirima Private Forest in Rombo District, Tanzania. Fuel wood was a key forest ecosystem good and biodiversity protection was a key forest ecosystem service identified. Manuo Hill communal forest had lower endowments values in terms of number of stems (1376 stems/ha), basal area (2.6 m2/ha),  volume (7.3 m3/ha) and carbon stock (2.1 tons/ha) compared to the Shirima private forest with 2214 stems/ha, basal area of 3.2 m2/ha, volume of 11.2 m3/ha and carbon stock of 3.2 tons/ha. Only volume andcarbon stock were significantly different between the forests. Species diversity was more or less similar between the forests. Tree removals were higher in communal (1.5 m3/ha) than in private (1.0 m3/ha) but they were not significantly different. Endowments in terms of tenure rights were better in communal forest than in private. More people were entitled to fuel woodfrom communal forest (78%) than from private (32%). Environmental benefits of biodiversity protection were entitled to everybody in both forests. It was concluded that no single tenure regime can achieve all objectives of forest management. Instead, balancing between different tenures is recommended.Key words: forest ecosystems, tenure regimes, endowment and  entitlement, goods and services

Charcoal Supply In Dar Es Salaam City, Tanzania

In Tanzania, charcoal is the primary source of energy particularly in urban areas. Dar es Salaam, being the largest urban center in the country, is also the largest consumer of charcoal. Assuming that all charcoal transported in the city is consumed, an investigation to estimate the amount of charcoal supplied daily was undertaken through monitoring at checkpoints the amount of charcoal transported daily to the city of Dar es Salaam. The study reveals that on average about 6,000 bags of charcoal are transported daily to the city. The figure may be an underestimation by four fold as most charcoal enters the city unrecorded. The highest amount of charcoal comes from North-West (34 %) and South (31 %) of Dar es Salaam. Open trucks transport the highest amount of charcoal (88 %) into the city. However, bicycles are the most frequent means of charcoal transportation constituting on average about 64 % of all individuals engaged daily in charcoal transportation. Though there are some new vehicles, the greatest percentage of vehicles involved in charcoal transportation are old (mainly registered in the 1980's). Most of the charcoal is transported during morning hours (56 %). Most of the charcoal transported to the city is for commercial use. The revenues from charcoal transportation taxes contribute a significant amount of money to both Local and Central Governments. If properly collected and used, they can effectively contribute to the development of the country and sustainable management of the catchment areas for charcoal. TJFNC Vol. 75 2004: pp. 108-11

Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania

Abstract Field surveys are often a primary source of aboveground biomass (AGB) data, but plot-based estimates of parameters related to AGB are often not sufficiently precise, particularly not in tropical countries. Remotely sensed data may complement field data and thus help to increase the precision of estimates and circumvent some of the problems with missing sample observations in inaccessible areas. Here, we report the results of a study conducted in a 15,867 km² area in the dry miombo woodlands of Tanzania, to quantify the contribution of existing canopy height and biomass maps to improving the precision of canopy height and AGB estimates locally. A local and a global height map and three global biomass maps, and a probability sample of 513 inventory plots were subject to analysis. Model-assisted sampling estimators were used to estimate mean height and AGB across the study area using the original maps and then with the maps calibrated with local inventory plots. Large systematic map errors – positive or negative – were found for all the maps, with systematic errors as great as 60–70 %. The maps contributed nothing or even negatively to the precision of mean height and mean AGB estimates. However, after being calibrated locally, the maps contributed substantially to increasing the precision of both mean height and mean AGB estimates, with relative efficiencies (variance of the field-based estimates relative to the variance of the map-assisted estimates) of 1.3–2.7 for the overall estimates. The study, although focused on a relatively small area of dry tropical forests, illustrates the potential strengths and weaknesses of existing global forest height and biomass maps based on remotely sensed data and universal prediction models. Our results suggest that the use of regional or local inventory data for calibration can substantially increase the precision of map-based estimates and their applications in assessing forest carbon stocks for emission reduction programs and policy and financial decisions

A data support infrastructure for Clean Development Mechanism forestry implementation: an inventory perspective from Cameroon

Clean Development Mechanism (CDM) forestry project development requires highly multi-disciplinary and multiple-source information that can be complex, cumbersome and costly to acquire. Yet developing countries in which CDM projects are created and implemented are often data poor environments and unable to meet such complex information requirements. Using Cameroon as an example, the present paper explores the structure of an enabling host country data support infrastructure for CDM forestry implementation, and also assesses the supply potential of current forestry information. Results include a conceptual data model of CDM project data needs; the list of meso- and macro-level data and information requirements (Demand analysis); and an inventory of relevant data available in Cameroon (Supply analysis). From a comparison of demand and supply, we confirm that data availability and the relevant infrastructure for data or information generation is inadequate for supporting carbon forestry at the micro, meso and macro-levels in Cameroon. The results suggest that current CDM afforestation and reforestation information demands are almost impenetrable for local communities in host countries and pose a number of cross-scale barriers to project adoption. More importantly, we identify proactive regulatory, institutional and capacity building policy strategies for forest data management improvements that could enhance biosphere carbon management uptake in poor countries. CDM forestry information research needs are also highlighted

Aboveground forest biomass varies across continents, ecological zones and successional stages: Refined IPCC default values for tropical and subtropical forests

For monitoring and reporting forest carbon stocks and fluxes, many countries in the tropics and subtropics rely on default values of forest aboveground biomass (AGB) from the Intergovernmental Panel on Climate Change (IPCC) guidelines for National Greenhouse Gas (GHG) Inventories. Default IPCC forest AGB values originated from 2006, and are relatively crude estimates of average values per continent and ecological zone. The 2006 default values were based on limited plot data available at the time, methods for their derivation were not fully clear, and no distinction between successional stages was made. As part of the 2019 Refinement to the 2006 IPCC Guidelines for GHG Inventories, we updated the default AGB values for tropical and subtropical forests based on AGB data from >25 000 plots in natural forests and a global AGB map where no plot data were available. We calculated refined AGB default values per continent, ecological zone, and successional stage, and provided a measure of uncertainty. AGB in tropical and subtropical forests varies by an order of magnitude across continents, ecological zones, and successional stage. Our refined default values generally reflect the climatic gradients in the tropics, with more AGB in wetter areas. AGB is generally higher in old-growth than in secondary forests, and higher in older secondary (regrowth >20 years old and degraded/logged forests) than in young secondary forests (20 years old). While refined default values for tropical old-growth forest are largely similar to the previous 2006 default values, the new default values are 4.0-7.7-fold lower for young secondary forests. Thus, the refined values will strongly alter estimated carbon stocks and fluxes, and emphasize the critical importance of old-growth forest conservation. We provide a reproducible approach to facilitate future refinements and encourage targeted efforts to establish permanent plots in areas with data gaps

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Aboveground forest biomass varies across continents, ecological zones and successional stages: refined IPCC default values for tropical and subtropical forests

For monitoring and reporting forest carbon stocks and fluxes, many countries in the tropics and subtropics rely on default values of forest aboveground biomass (AGB) from the Intergovernmental Panel on Climate Change (IPCC) guidelines for National Greenhouse Gas (GHG) Inventories. Default IPCC forest AGB values originated from 2006, and are relatively crude estimates of average values per continent and ecological zone. The 2006 default values were based on limited plot data available at the time, methods for their derivation were not fully clear, and no distinction between successional stages was made. As part of the 2019 Refinement to the 2006 IPCC Guidelines for GHG Inventories, we updated the default AGB values for tropical and subtropical forests based on AGB data from >25 000 plots in natural forests and a global AGB map where no plot data were available. We calculated refined AGB default values per continent, ecological zone, and successional stage, and provided a measure of uncertainty. AGB in tropical and subtropical forests varies by an order of magnitude across continents, ecological zones, and successional stage. Our refined default values generally reflect the climatic gradients in the tropics, with more AGB in wetter areas. AGB is generally higher in old-growth than in secondary forests, and higher in older secondary (regrowth >20 years old and degraded/logged forests) than in young secondary forests (⩽20 years old). While refined default values for tropical old-growth forest are largely similar to the previous 2006 default values, the new default values are 4.0–7.7-fold lower for young secondary forests. Thus, the refined values will strongly alter estimated carbon stocks and fluxes, and emphasize the critical importance of old-growth forest conservation. We provide a reproducible approach to facilitate future refinements and encourage targeted efforts to establish permanent plots in areas with data gaps

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available