Carbon Stocks In Tropical Forests Research Articles

Aboveground tree carbon stocks of Lowland evergreen mixed vegetation at Gannoruwa

Conservation of global forests is being considered as one of the sustainable ways of mitigating global warming, because forests are widely perceived to act as major sinks for atmospheric carbon. Estimation of carbon stocks of tropical forests are important for management, planning and for REDD initiatives.Therefore, this study was initiated to estimate existing carbon stocks of a Lowland evergreen mixed forest at Gannoruwa and how it has changed overtime.The sampling points were selected using an unsupervised classified Landsat TM satellite image and topograhpic maps, considering the elevation level and the vegetation type. A GPS (Global Position System) was used to detect the correct location of sampling points and sampling plot size was set as 30m×30m. The dbh and height of each individual in the sampling plot which is greater than 5cm dbh was measured.The dbh and height values were applied to a pre-derived regression equation and the above ground biomass was calculated. The equation was Y=exp {-3.3012+0.9439 ln(D2H)},(r2=0.9) where, Y is aboveground biomass, D is dbh and H is height. Assuming the carbon stock as 50% of the biomass, the above ground carbon stock was calculated. It was 146.4 mt/ha of carbon and the total aboveground tree carbon stock was 39.1×103 tons. The estimated basal area of the forest has declined compared to 25year old recorded data and it reflects the carbon reduction of the forest due to anthropogenic activities.

Aboveground tree carbon stocks of Lowland evergreen mixed vegetation at Gannoruwa

Conservation of global forests is being considered as one of the sustainable ways of mitigating global warming, because forests are widely perceived to act as major sinks for atmospheric carbon. Estimation of carbon stocks of tropical forests are important for management, planning and for REDD initiatives.Therefore, this study was initiated to estimate existing carbon stocks of a Lowland evergreen mixed forest at Gannoruwa and how it has changed overtime.The sampling points were selected using an unsupervised classified Landsat TM satellite image and topograhpic maps, considering the elevation level and the vegetation type. A GPS (Global Position System) was used to detect the correct location of sampling points and sampling plot size was set as 30m×30m. The dbh and height of each individual in the sampling plot which is greater than 5cm dbh was measured.The dbh and height values were applied to a pre-derived regression equation and the above ground biomass was calculated. The equation was Y=exp {-3.3012+0.9439 ln(D2H)},(r2=0.9) where, Y is aboveground biomass, D is dbh and H is height. Assuming the carbon stock as 50% of the biomass, the above ground carbon stock was calculated. It was 146.4 mt/ha of carbon and the total aboveground tree carbon stock was 39.1×103 tons. The estimated basal area of the forest has declined compared to 25 year old recorded data and it reflects the carbon reduction of the forest due to anthropogenic activities.

Above ground biomass estimation across forest types at different degradation levels in Central Kalimantan using LiDAR data

The quantification of tropical forest carbon stocks is a key challenge in creating a basic methodology for REDD (reducing emissions from deforestation and degradation in developing countries) projects. Small-footprint LiDAR (light detection and ranging) systems have proven to successfully correlate to above ground biomass (AGB) estimates in boreal and temperate forests. Their applicability to two different tropical rainforest types (lowland dipterocarp and peat swamp forest) in Central Kalimantan, Indonesia, was tested by developing multiple regression models at plot level using full waveform LiDAR point cloud characteristics. Forest inventory data is barely available for Central Kalimantan's forests. In order to sample a high number of field plots the angle count method was applied which allows fast sampling. More laborious fixed-area plots (three nests of circular shape) were used as a control and approved the use of the angle count method. AGB values, calculated by using existing allometric models, were in the range of 15–547Mgha−1 depending on forest type, degradation level and the model used for calculation. As expected, logging resulted in significant AGB losses in all forest types. AGB-prediction models were established for each forest type using statistical values of the LiDAR point clouds and the forest inventory plots. These regression models were then applied to six LiDAR tracks (altogether with a size of 5241ha) covering unlogged, logged and burned lowland dipterocarp and peat swamp forest. The regression analysis showed that the 45th and 65th percentiles and the standard error of the mean explain 83% of the variation in lowland dipterocarp forest plots (RMSE=21.37%). The best model for peat swamp forest could only explain 32% of the AGB variation (RMSE=41.02%). Taking both forest types together explained 71% (RMSE=33.85%). Calculating AGB for whole LiDAR tracks demonstrated the ability of this approach to quantify not only deforestation but also especially forest degradation and its spatial variability in terms of biomass change in different forest ecosystems using LiDAR transects. Concluding it can be stated that the combined approach of extensive field sampling and LiDAR point cloud analysis have high potential to significantly improve current estimates of carbon stocks across different forest types and degradation levels and its spatial variation in highly inaccessible tropical rainforests in the framework of REDD.

A universal airborne LiDAR approach for tropical forest carbon mapping

Airborne light detection and ranging (LiDAR) is fast turning the corner from demonstration technology to a key tool for assessing carbon stocks in tropical forests. With its ability to penetrate tropical forest canopies and detect three-dimensional forest structure, LiDAR may prove to be a major component of international strategies to measure and account for carbon emissions from and uptake by tropical forests. To date, however, basic ecological information such as height-diameter allometry and stand-level wood density have not been mechanistically incorporated into methods for mapping forest carbon at regional and global scales. A better incorporation of these structural patterns in forests may reduce the considerable time needed to calibrate airborne data with ground-based forest inventory plots, which presently necessitate exhaustive measurements of tree diameters and heights, as well as tree identifications for wood density estimation. Here, we develop a new approach that can facilitate rapid LiDAR calibration with minimal field data. Throughout four tropical regions (Panama, Peru, Madagascar, and Hawaii), we were able to predict aboveground carbon density estimated in field inventory plots using a single universal LiDAR model (r ( 2 )=0.80, RMSE=27.6MgCha(-1)). This model is comparable in predictive power to locally calibrated models, but relies on limited inputs of basal area and wood density information for a given region, rather than on traditional plot inventories. With this approach, we propose to radically decrease the time required to calibrate airborne LiDAR data and thus increase the output of high-resolution carbon maps, supporting tropical forest conservation and climate mitigation policy.

Evaluating uncertainty in mapping forest carbon with airborne LiDAR

Airborne LiDAR is increasingly used to map carbon stocks in tropical forests, but our understanding of mapping errors is constrained by the spatial resolution (i.e., plot size) used to calibrate LiDAR with field data (typically 0.1–0.36 ha). Reported LiDAR errors range from 17 to 40 Mg C ha − 1 , but should be lower at coarser resolutions because relative errors are expected to scale with (plot area) –1/2. We tested this prediction empirically using a 50-ha plot with mapped trees, allowing an assessment of LiDAR prediction errors at multiple spatial resolutions. We found that errors scaled approximately as expected, declining by 38% (compared to 40% predicted from theory) from 0.36- to 1-ha resolution. We further reduced errors at all spatial resolutions by accounting for tree crowns that are bisected by plot edges (not typically done in forestry), and collectively show that airborne LiDAR can map carbon stocks with 10% error at 1-ha resolution — a level comparable to the use of field plots alone.

Within-individual variation of trunk and branch xylem density in tropical trees

Wood density correlates with mechanical and physiological strategies of trees and is important for estimating global carbon stocks. Nonetheless, the relationship between branch and trunk xylem density has been poorly explored in neotropical trees. Here, we examine this relationship in trees from French Guiana and its variation among different families and sites, to improve the understanding of wood density in neotropical forests. Trunk and branch xylem densities were measured for 1909 trees in seven sites across French Guiana. A major-axis fit was performed to explore their general allometric relationship and its variation among different families and sites. Trunk xylem and branch xylem densities were significantly positively correlated, and their relationship explained 47% of the total variance. Trunk xylem was on average 9% denser than branch xylem. Family-level differences and interactions between family and site accounted for more than 40% of the total variance, whereas differences among sites explained little variation. Variation in xylem density within individual trees can be substantial, and the relationship between branch xylem and trunk xylem densities varies considerably among families and sites. As such, whole-tree biomass estimates based on nondestructive branch sampling should correct for both taxonomic and environmental factors. Furthermore, detailed estimates of the vertical distribution of wood density within individual trees are needed to determine the extent to which relying solely upon measures of trunk wood density may cause carbon stocks in tropical forests to be overestimated.

High-resolution forest carbon stocks and emissions in the Amazon

Efforts to mitigate climate change through the Reduced Emissions from Deforestation and Degradation (REDD) depend on mapping and monitoring of tropical forest carbon stocks and emissions over large geographic areas. With a new integrated use of satellite imaging, airborne light detection and ranging, and field plots, we mapped aboveground carbon stocks and emissions at 0.1-ha resolution over 4.3 million ha of the Peruvian Amazon, an area twice that of all forests in Costa Rica, to reveal the determinants of forest carbon density and to demonstrate the feasibility of mapping carbon emissions for REDD. We discovered previously unknown variation in carbon storage at multiple scales based on geologic substrate and forest type. From 1999 to 2009, emissions from land use totaled 1.1% of the standing carbon throughout the region. Forest degradation, such as from selective logging, increased regional carbon emissions by 47% over deforestation alone, and secondary regrowth provided an 18% offset against total gross emissions. Very high-resolution monitoring reduces uncertainty in carbon emissions for REDD programs while uncovering fundamental environmental controls on forest carbon storage and their interactions with land-use change.

Bushmeat Hunting and Climate: An Indirect Link

J. F. BRODIE AND H. K. GIBBS (“BUSHMEATHUNTING AS CLIMATE THREAT,”Letters, 16 October 2009, p. 364) argue that bushmeat extractionthreatens the carbon stocks of tropical forests because (i) bushmeathunting reduces abundances of large-bodied vertebrates; (ii) treespecies with large seeds reproduce poorly without large-bodied verte-brates on which they depend for seed dispersal; (iii) large seed size iscorrelated with high wood density in tropical trees; and (iv) trees withhigh wood density contribute dispro-portionately to the carbon stock. Their first point is well-established,but evidence regarding the others ismixed. Killing animals reduces seeddispersal of vertebrate-dispersed trees(

Vulnerability of carbon stocks in tropical forests

This article was submitted without an abstract, please refer to the full-text PDF file.

Total carbon stocks in a tropical forest landscape of the Porce region, Colombia

Detailed ground-based quantifications of total carbon stocks in tropical forests are few despite their importance in science and ecosystem management. Carbon stocks in live aboveground and belowground biomass, necromass, and soils were measured in a heterogeneous landscape composed of secondary and primary forest. A total of 110 permanent plots were used to estimate the size of these carbon pools. Local biomass equations were developed and used to estimate aboveground biomass and coarse root biomass for each plot. Herbaceous vegetation, fine roots, coarse and fine litter, and soil carbon to 4 m depth were measured in subplots. In primary forests, mean total carbon stocks (TCS) were estimated as 383.7 ± 55.5 Mg C ha −1 (±S.E.). Of this amount, soil organic carbon to 4 m depth represented 59%, total aboveground biomass 29%, total belowground biomass 10%, and necromass 2%. In secondary forests, TCS was 228.2 ± 13.1 Mg C ha −1, and soil organic carbon to 4 m depth accounted for 84% of this amount. Total aboveground biomass represented only 9%, total belowground biomass 5%, and total necromass 1% of TCS in secondary forests. Monte Carlo methods were used to assess the uncertainty of the biomass measurements and spatial variation. Of the total uncertainty of the estimates of TCS, the variation associated with the spatial variation of C pools between plots was higher than measurement errors within plots. From this study it is concluded that estimates of aboveground biomass largely underestimate total carbon stocks in forest ecosystems. Additionally, it is suggested that heterogeneous landscapes impose additional challenges for their study such as sampling intensity.

TEMPERATURE INFLUENCES CARBON ACCUMULATION IN MOIST TROPICAL FORESTS

Evergreen broad-leaved tropical forests can have high rates of productivity and large accumulations of carbon in plant biomass and soils. They can therefore play an important role in the global carbon cycle, influencing atmospheric CO2 concentrations if climate warms. We applied meta-analyses to published data to evaluate the apparent effects of temperature on carbon fluxes and storages in mature, moist tropical evergreen forest ecosystems. Among forests, litter production, tree growth, and belowground carbon allocation all increased significantly with site mean annual temperature (MAT); total net primary productivity (NPP) increased by an estimated 0.2-0.7 Mg C x ha(-1) x yr(-1) x degrees C(-1). Temperature had no discernible effect on the turnover rate of aboveground forest biomass, which averaged 0.014 yr(-1) among sites. Consistent with these findings, forest biomass increased with site MAT at a rate of 5-13 Mg C x ha(-1) x degrees C(-1). Despite greater productivity in warmer forests, soil organic matter accumulations decreased with site MAT, with a slope of -8 Mg C x ha(-1) x degrees C(-1), indicating that decomposition rates of soil organic matter increased with MAT faster than did rates of NPP. Turnover rates of surface litter also increased with temperature among forests. We found no detectable effect of temperature on total carbon storage among moist-tropical evergreen forests, but rather a shift in ecosystem structure, from low-biomass forests with relatively large accumulations of detritus in cooler sites, to large-biomass forests with relatively smaller detrital stocks in warmer locations. These results imply that, in a warmer climate, conservation of forest biomass will be critical to the maintenance of carbon stocks in moist tropical forests.

Tropical forests and the global carbon cycle: impacts of atmospheric carbon dioxide, climate change and rate of deforestation.

The remaining carbon stocks in wet tropical forests are currently at risk because of anthropogenic deforestation, but also because of the possibility of release driven by climate change. To identify the relative roles of CO2 increase, changing temperature and rainfall, and deforestation in the future, and the magnitude of their impact on atmospheric CO2 concentrations, we have applied a dynamic global vegetation model, using multiple scenarios of tropical deforestation (extrapolated from two estimates of current rates) and multiple scenarios of changing climate (derived from four independent offline general circulation model simulations). Results show that deforestation will probably produce large losses of carbon, despite the uncertainty about the deforestation rates. Some climate models produce additional large fluxes due to increased drought stress caused by rising temperature and decreasing rainfall. One climate model, however, produces an additional carbon sink. Taken together, our estimates of additional carbon emissions during the twenty-first century, for all climate and deforestation scenarios, range from 101 to 367 Gt C, resulting in CO2 concentration increases above background values between 29 and 129 p.p.m. An evaluation of the method indicates that better estimates of tropical carbon sources and sinks require improved assessments of current and future deforestation, and more consistent precipitation scenarios from climate models. Notwithstanding the uncertainties, continued tropical deforestation will most certainly play a very large role in the build-up of future greenhouse gas concentrations.

Impacts of simulated shifting cultivation on deforestation and the carbon stocks of the forests of central Africa

Research on shifting cultivation in the tropics has focused primarily on the issues of agricultural productivity and soil degradation. Few have attempted to quantify its impact on forest losses and the carbon cycle in the context of the global change debate. A simulation, which incorporated historic and projected rural population growth, was conducted to investigate the impacts of shifting cultivation on deforestation and the carbon stocks of the humid tropical forests of central Africa. The study demonstrated that in 1950, the central African forest region subjected to shifting cultivation was composed of 92% forest (including primary and secondary forests) and that in 1990 the forest cover had been reduced to 80% of the total simulated land area. The results of the simulation showed that with the current level of technology and consumptive habitats, shifting cultivation under projected rates of rural population growth would increase the annual rate of deforestation to 1.3%, converting 94% of the land area into cropland, fallow and secondary forests by 2050. At that time, only 40% of the potential above-ground biomass carbon (i.e., assuming all the simulated area is occupied by dense forests) will remain, compared to 85 and 74% in 1950 and 1990, respectively. The simulation suggests that there is a need for alternatives to shifting cultivation such as integration of cultivation intensification, fallow management and agroforestry if population pressure on the tropical forests of central Africa is to be alleviated. This preliminary regional study shows that modeling could provide an important tool to explore alternative scenarios of land use and to assist decision makers with forest management.