Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China

Exploring the spatial and temporal dynamic characteristics of regional forest net primary productivity (NPP) in the context of global climate change can not only provide a theoretical basis for terrestrial carbon cycle studies, but also provide data support for medium- and long-term sustainable management planning of regional forests. In this study, we took Shaoguan City, Guangdong Province, China as the study area, and used Landsat images and National Forest Continuous Inventory (NFCI) data in the corresponding years as the main data sources. Random forest (RF), multiple linear regression (MLR), and BP neural network were the three models applied to estimate forest NPP in the study area. Theil–Sen estimation, Mann–Kendall trend analysis and the standard deviation ellipse (SDE) were chosen to analyze the spatial and temporal dynamic characteristics of NPP, whereas structural equation modeling (SEM) was used to analyze the driving factors of NPP changes. The results show that the performance of the RF model is better than the MLR and BP neural network models. The NPP in the study area showed an increasing trend, as the NPP was 5.66 t·hm−2·a−1, 7.68 t·hm−2·a−1, 8.17 t·hm−2·a−1, 8.25 t·hm−2·a−1, and 10.52 t·hm−2·a−1 in 1997, 2002, 2007, 2012, and 2017, respectively. Spatial aggregation of NPP was increased in the period of 1997–2017, and the center shifted from the mid-west to the southwest. In addition, the forest stand factors had the greatest effect on NPP in the study area. The forest stand factors and environmental factors had a positive effect on NPP, and understory factors had a negative effect. Overall, although forest NPP has fluctuated due to the changes of forestry policies and human activities, forest NPP in Shaoguan has been increasing. In the future, the growth potential of NPP in Shaoguan City can be further increased by continuously expanding the area proportion of mixed forests and rationalizing the forest age group structure.

Optimization of the land use pattern in Horqin Sandy Land by using the CLUMondo model and Bayesian belief network

Land use and cover change is an important concept in the study of ecosystem services, especially in ecologically fragile areas. This study generated three scenarios, namely historical trend (HT), national planning (NP), and windbreak and sand fixation (WS), by using the CLUMondo model and Bayesian belief network (BBN) to explore land use with diverse demands. The CLUMondo model was utilized to simulate the land use probability surface of Horqin Sandy Land in 2025 under different scenarios. A BBN was constructed to investigate the net primary productivity (NPP), crop production (CP), and wind protection and sand fixation (WPSF) of Horqin Sandy Land in 2025 under uncertain land use to identify the short board areas of various services. The following results were obtained from the analysis. (1) The land use pattern of Horqin Sandy Land in 2025 under the HT scenario will be dominated by cultivated land expansion and grassland reduction. Under the NP scenario, forest will increase, and unused land and grassland will decrease considerably. Under the WS scenario, cultivated land will still maintain a similar growth state, but the difference is that forest and grassland will significantly increase. (2) NPP had the highest probability of being the Highest and the lowest probability of being Low, whereas CP and WPSF obtained the highest probability of being Medium and the lowest probability of being Higher. (3) Tuquan County and Wengniute Banner with a high probability of providing few ecosystem services should be regarded as key areas for ecological restoration. Kailu County and Horqin Left-wing Middle Banner can provide higher ecosystem services. The methodology adopted in this study establishes the connection between the land use probability surface and the optimized pattern of ecosystem services and can therefore be applied in areas where multi-objective comprehensive improvement of ecosystem services is expected.

Tree Stands and Liana Community in Royal Belum State Park, Malaysia

The diversity of lianas and trees were studied in five study sites of 100 x 20 m within the Royal Belum State Park, Malaysia with a view to provide baseline information on their incidence, taxonomy and ecological distributions. The sites include Sungai Kejar, Sungai Papan, Sungai Papan 2, Teluk Gopal and Sungai Kooi with at least 1000 m apart. These plots were further sub-divided into five sub-plots of 20 x 20 m each. Lianas with a diameter at breast height (dbh) ≥ 1 cm and trees with dbh ≥ 10 cm were identified and frequencies of occurrence were determined. Lianas comprising 92 species from 23 families while trees comprising 221 species and 48 families were enumerated. Annonaceae was the richest family of lianas and trees (19 species and 23 species respectively). Connarus (Connaraceae) and Spatholobus (Fabaceae) had the highest number of lianas (six species) whilst Syzygium (Myrtaceae) had the highest number of trees (11 species). There are significant differences in all the diversity indices among the study sites, except between Sungai Papan and Teluk Gopal which were the richest and most diverse in liana species. These two sites also showed high similarity index in liana species (0.50) followed by Sungai Kejar and Sungai Papan 2 (0.37). Sungai Kejar was however observed to have the highest tree species richness. These study sites could be described as very rich with a high diversity of lianas and trees. Although, it is richer in trees than lianas which means that the level of disturbance of the park is very low. Keywords: Annonaceae, Connarus, forests, lianas diversity, Perak, Syzygium

Spatial Responses of Net Ecosystem Productivity of the Yellow River Basin under Diurnal Asymmetric Warming

The net ecosystem productivity (NEP) of drainage basins plays an important role in maintaining the carbon balance of those ecosystems. In this study, the modified CASA (Carnegie Ames Stanford Approach) model and a soil microbial respiration model were used to estimate net primary productivity (NPP) and NEP of the Yellow River Basin’s (YRB) vegetation in the terrestrial ecosystem (excluding rivers, floodplain lakes and other freshwater ecosystems) from 1982 to 2015. After analyzing the spatiotemporal variations in the NEP using slope analysis, the coefficient of variation, and the Hurst exponent, precipitation was identified as the main factor limiting vegetation growth in the YRB. Hence, precipitation was treated as the control variable and a second-order partial correlation method was used to determine the correlation between diurnal asymmetric warming and the YRB’s NEP. The results indicate that: (i) diurnal asymmetric warming occurred in the YRB from 1982 to 2015, with nighttime warming (Tmin) being 1.50 times that of daytime warming (Tmax). There is a significant correlation between variations in NPP and diurnal warming; (ii) the YRB’s NEP are characterized by upward fluctuations in terms of temporal variations, large differences between the various vegetation types, high values in the western and southeastern regions but low values in the northern region in terms of spatial distribution, overall relative stability in the YRB’s vegetation cover, and changes in the same direction being more dominant than those in the opposite direction (although the former is not sustained); and (iii) positive correlations between the NEP and nighttime and daytime warming are approximately 48.37% and 67.51% for the YRB, respectively, with variations in nighttime temperatures having more extensive impacts on vegetation cover.

Unexpectedly large impact of forest management and grazing on global vegetation biomass

Carbon stocks in vegetation play a key role in the climate system1–4, but their magnitude and patterns, their uncertainties, and the impact of land use on them remain poorly quantified. Based on a consistent integration of state-of-the art datasets, we show that vegetation currently stores ~450 PgC. In the hypothetical absence of land use, potential vegetation would store ~916 PgC, under current climate. This difference singles out the massive effect land use has on biomass stocks. Deforestation and other land-cover changes are responsible for 53-58% of the difference between current and potential biomass stocks. Land management effects, i.e. land-use induced biomass stock changes within the same land cover, contribute 42-47% but are underappreciated in the current literature. Avoiding deforestation hence is necessary but not sufficient for climate-change mitigation. Our results imply that trade-offs exist between conserving carbon stocks on managed land and raising the contribution of biomass to raw material and energy supply for climate change mitigation. Efforts to raise biomass stocks are currently only verifiable in temperate forests, where potentials are limited. In contrast, large uncertainties hamper verification in the tropical forest where the largest potentials are located, pointing to challenges for the upcoming stocktaking exercises under the Paris agreement.

Assessment of Threats to Forest Ecosystems Using Geospatial Technology in Jharkhand State of India

Conservation of forest biodiversity is vital for mankind as it provides enormous benefits such as biological resources and ecosystem services. Of late, the forests are facing risk and threats such as fragmentation, degradation and forest fires which are responsible for the deteriorating condition. The progress in the field of science and technology like satellite remote sensing and GIS since the past few decades in India and the world provide an opportunity to track and monitor the changes taking place on the Earth’s surface. Besides, analysis of large spatial data in GIS can also provide insight into the various driving factors which lead to the loss of biodiversity in the threatened ecosystems i.e forests. This study has attempted to obtain information about the spatial extent of the three forest ecosystem degradation indicators viz. deforestation, fragmentation of forest and forest fires using methodical approach in the Jharkhand state of India. The satellite remote sensing data sets belonging to Landsat-8 were used to analyse the forest cover of Jharkhand state. To identify the areas of threat, grid cells (5KmX5Km) were generated in GIS domain. Analysis of deforestation was conducted using multi source data of the time periods 1935 and 2015. Evaluation of deforestation spanning over a time period reveals that vital changes have occurred in the forests of Jharkhand and determined 1224 extinct, 248 critically endangered, 318 endangered and 396 vulnerable ecosystem grid cells. The fragmentation analysis has determined 148 critically endangered, 296 endangered and 402 vulnerable ecosystem grid cells. Forest fire point’s data from the year 2005 to 2016 were utilized and analysis was executed. Further frequency of forest fires for each grid was noted. The result indicates that 67.3% of grid cell of Jharkhand forest was affected with forest fire. Conservation status has been evaluated based on the value of threat for each grid which was the fundamental criteria for conservation priority hotspot. About 2.1% of Jharkhand forest ecosystem grids are defined as extremely high ecosystem risk stage and have been designated in the category of conservation priority hotspot-1 followed by 19.7% conservation priority hotspot-2, 41.3% conservation priority hotspot-3, 27.8% conservation priority hotspot-4 and 9.1% lowest conservation priority hotspot-5. This study highlights the capability of integrating remote sensing and GIS data for mapping the forest degradation, which can be useful in formulating the strategies and policies for protection and conservation of forests.

Evaluation of modelled net primary production using MODIS and landsat satellite data fusion

BACKGROUND

To improve estimates of net primary production for terrestrial ecosystems of the continental United States, we evaluated a new image fusion technique to incorporate high resolution Landsat land cover data into a modified version of the CASA ecosystem model. The proportion of each Landsat land cover type within each 0.004 degree resolution CASA pixel was used to influence the ecosystem model result by a pure-pixel interpolation method.

RESULTS

Seventeen Ameriflux tower flux records spread across the country were combined to evaluate monthly NPP estimates from the modified CASA model. Monthly measured NPP data values plotted against the revised CASA model outputs resulted in an overall R2 of 0.72, mainly due to cropland locations where irrigation and crop rotation were not accounted for by the CASA model. When managed and disturbed locations are removed from the validation, the R2 increases to 0.82.

CONCLUSIONS

The revised CASA model with pure-pixel interpolated vegetation index performed well at tower sites where vegetation was not manipulated or managed and had not been recently disturbed. Tower locations that showed relatively low correlations with CASA-estimated NPP were regularly disturbed by either human or natural forces.

Histology and cell wall biochemistry of stone cells in the physical defence of conifers against insects

Conifers possess an array of physical and chemical defences against stem-boring insects. Stone cells provide a physical defence associated with resistance against bark beetles and weevils. In Sitka spruce (Picea sitchensis), abundance of stone cells in the cortex of apical shoots is positively correlated with resistance to white pine weevil (Pissodes strobi). We identified histological, biochemical and molecular differences in the stone cell phenotype of weevil resistant (R) or susceptible (S) Sitka spruce genotypes. R trees displayed significantly higher quantities of cortical stone cells near the apical shoot node, the primary site for weevil feeding. Lignin, cellulose, xylan and mannan were the most abundant components of stone cell secondary walls, respectively. Lignin composition of stone cells isolated from R trees contained a higher percentage of G-lignin compared with S trees. Transcript profiling revealed higher transcript abundance in the R genotype of coumarate 3-hydroxylase, a key monolignol biosynthetic gene. Developing stone cells in current year apical shoots incorporated fluorescent-tagged monolignol into the secondary cell wall, while mature stone cells of previous year apical shoots did not. Stone cell development is an ephemeral process, and fortification of shoot tips in R trees is an effective strategy against insect feeding.

Estimation of fine-root production using rates of diameter-dependent root mortality, decomposition and thickening in forests

Current studies indicate that fine roots of different diameter classes show different rates of decomposition. This study developed a new method to estimate fine-root production by considering the difference in the production of fine roots of two size classes, fine roots thinner than 1 mm and those between 1 and 2 mm, and their corresponding rates of decomposition. A litter bag experiment was used to estimate the decomposition rates, while the sequential soil core technique was used to identify mass values of live roots and dead roots at a given period of observation. The continuous inflow method was applied to estimate the amount of root decomposition, mortality and production with a framework of two diameter classes of fine roots and for quantification of the amount of mass transfer from the thicker fine-root class to the coarser root category (>2 mm). The results indicated that the estimate of fine-root production was greater when two size classes of fine roots were distinguished. Using a framework of two size classes developed in this study resulted in 21.3% higher fine-root production than a method that did not recognize fine-root size classes or mass transfer to the category of coarse roots. In addition, using shorter collection intervals led to higher production estimates than longer intervals. The production estimate with a 1-month interval was 21.4% higher than that with a 6-month interval. We consider that the use of the sequential soil core technique with continuous inflow estimate method by differentiating size classes of fine roots is likely to minimize the underestimation of the parameters of fine-root dynamics by accounting for decomposition and mortality of fine roots more appropriately.

The natural science underlying big history

Nature's many varied complex systems—including galaxies, stars, planets, life, and society—are islands of order within the increasingly disordered Universe. All organized systems are subject to physical, biological, or cultural evolution, which together comprise the grander interdisciplinary subject of cosmic evolution. A wealth of observational data supports the hypothesis that increasingly complex systems evolve unceasingly, uncaringly, and unpredictably from big bang to humankind. These are global history greatly extended, big history with a scientific basis, and natural history broadly portrayed across ∼14 billion years of time. Human beings and our cultural inventions are not special, unique, or apart from Nature; rather, we are an integral part of a universal evolutionary process connecting all such complex systems throughout space and time. Such evolution writ large has significant potential to unify the natural sciences into a holistic understanding of who we are and whence we came. No new science (beyond frontier, nonequilibrium thermodynamics) is needed to describe cosmic evolution's major milestones at a deep and empirical level. Quantitative models and experimental tests imply that a remarkable simplicity underlies the emergence and growth of complexity for a wide spectrum of known and diverse systems. Energy is a principal facilitator of the rising complexity of ordered systems within the expanding Universe; energy flows are as central to life and society as they are to stars and galaxies. In particular, energy rate density—contrasting with information content or entropy production—is an objective metric suitable to gauge relative degrees of complexity among a hierarchy of widely assorted systems observed throughout the material Universe. Operationally, those systems capable of utilizing optimum amounts of energy tend to survive, and those that cannot are nonrandomly eliminated.

Comparability of red/near-infrared reflectance and NDVI based on the spectral response function between MODIS and 30 other satellite sensors using rice canopy spectra

Long-term monitoring of regional and global environment changes often depends on the combined use of multi-source sensor data. The most widely used vegetation index is the normalized difference vegetation index (NDVI), which is a function of the red and near-infrared (NIR) spectral bands. The reflectance and NDVI data sets derived from different satellite sensor systems will not be directly comparable due to different spectral response functions (SRF), which has been recognized as one of the most important sources of uncertainty in the multi-sensor data analysis. This study quantified the influence of SRFs on the red and NIR reflectances and NDVI derived from 31 Earth observation satellite sensors. For this purpose, spectroradiometric measurements were performed for paddy rice grown under varied nitrogen levels and at different growth stages. The rice canopy reflectances were convoluted with the spectral response functions of various satellite instruments to simulate sensor-specific reflectances in the red and NIR channels. NDVI values were then calculated using the simulated red and NIR reflectances. The results showed that as compared to the Terra MODIS, the mean relative percentage difference (RPD) ranged from −12.67% to 36.30% for the red reflectance, −8.52% to −0.23% for the NIR reflectance, and −9.32% to 3.10% for the NDVI. The mean absolute percentage difference (APD) compared to the Terra MODIS ranged from 1.28% to 36.30% for the red reflectance, 0.84% to 8.71% for the NIR reflectance, and 0.59% to 9.32% for the NDVI. The lowest APD between MODIS and the other 30 satellite sensors was observed for Landsat5 TM for the red reflectance, CBERS02B CCD for the NIR reflectance and Landsat4 TM for the NDVI. In addition, the largest APD between MODIS and the other 30 satellite sensors was observed for IKONOS for the red reflectance, AVHRR1 onboard NOAA8 for the NIR reflectance and IKONOS for the NDVI. The results also indicated that AVHRRs onboard NOAA7-17 showed higher differences than did the other sensors with respect to MODIS. A series of optimum models were presented for remote sensing data assimilation between MODIS and other sensors.

Alaska ecosystem carbon fluxes estimated from MODIS satellite data inputs from 2000 to 2010

BACKGROUND

Trends in Alaska ecosystem carbon fluxes were predicted from inputs of monthly MODerate resolution Imaging Spectroradiometer (MODIS) vegetation index time-series combined with the NASA-CASA (Carnegie Ames Stanford Approach) carbon cycle simulation model over the past decade. CASA simulates monthly net ecosystem production (NEP) as the difference in carbon fluxes between net primary production (NPP) and soil microbial respiration (Rh).

RESULTS

Model results showed that NEP on a unit area basis was estimated to be highest (> +10 g C m-2 yr-1) on average over the period 2000 to 2010 within the Major Land Resource Areas (MRLAs) of the Interior Brooks Range Mountains, the Arctic Foothills, and the Western Brooks Range Mountains. The lowest (as negative land C source fluxes) mean NEP fluxes were predicted for the MLRAs of the Cook Inlet Lowlands, the Ahklun Mountains, and Bristol Bay-Northern Alaska Peninsula Lowlands. High levels of interannual variation in NEP were predicted for most MLRAs of Alaska.

CONCLUSIONS

The relatively warm and wet years of 2004 and 2007 resulted in the highest positive NEP flux totals across MLRAs in the northern and western coastal locations in the state (i.e., the Brooks Range Mountains and Arctic Foothills). The relatively cold and dry years of 2001 and 2006 were predicted with the lowest (negative) NEP flux totals for these MLRAs, and likewise across the Ahklun Mountains and the Yukon-Kuskokwim Highlands.

Forest production predicted from satellite image analysis for the Southeast Asia region

BACKGROUND

The objective of this study was to demonstrate a new, cost-effective method to define the sustainable amounts of harvested wood products in Southeast Asian countries case studies, while avoiding degradation (net loss) of total wood carbon stocks. Satellite remote sensing from the MODIS sensor was used in the CASA (Carnegie Ames Stanford Approach) carbon cycle model to map forest production for the Southeast Asia region from 2000 to 2010. These CASA model results have been designed to be spatially detailed enough to support carbon cycle assessments in different wooded land cover classes, e.g., open woodlands, wetlands, and forest areas.

RESULTS

The country with the highest average forest net primary production (NPP greater than 950 g C m-2 yr-1) over the period was the Philippines, followed by Malaysia and Indonesia. Myanmar and Vietnam had the lowest average forest NPP among the region's countries at less than 815 g C m-2 yr-1. Case studies from throughout the Southeast Asia region for the maximum harvested wood products amount that could be sustainably extracted per year were generated using the CASA model NPP predictions.

CONCLUSIONS

The method of using CASA model's estimated annual change in forest carbon on a yearly basis can conservatively define the upper limit for the amount of harvested wood products that can be removed and still avoid degradation (net loss) of the total wood carbon stock over that same time period.

Carbon fluxes in ecosystems of Yellowstone National Park predicted from remote sensing data and simulation modeling

BACKGROUND

A simulation model based on remote sensing data for spatial vegetation properties has been used to estimate ecosystem carbon fluxes across Yellowstone National Park (YNP). The CASA (Carnegie Ames Stanford Approach) model was applied at a regional scale to estimate seasonal and annual carbon fluxes as net primary production (NPP) and soil respiration components. Predicted net ecosystem production (NEP) flux of CO2 is estimated from the model for carbon sinks and sources over multi-year periods that varied in climate and (wildfire) disturbance histories. Monthly Enhanced Vegetation Index (EVI) image coverages from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) instrument (from 2000 to 2006) were direct inputs to the model. New map products have been added to CASA from airborne remote sensing of coarse woody debris (CWD) in areas burned by wildfires over the past two decades.

RESULTS

Model results indicated that relatively cooler and wetter summer growing seasons were the most favorable for annual plant production and net ecosystem carbon gains in representative landscapes of YNP. When summed across vegetation class areas, the predominance of evergreen forest and shrubland (sagebrush) cover was evident, with these two classes together accounting for 88% of the total annual NPP flux of 2.5 Tg C yr-1 (1 Tg = 1012 g) for the entire Yellowstone study area from 2000-2006. Most vegetation classes were estimated as net ecosystem sinks of atmospheric CO2 on annual basis, making the entire study area a moderate net sink of about +0.13 Tg C yr-1. This average sink value for forested lands nonetheless masks the contribution of areas burned during the 1988 wildfires, which were estimated as net sources of CO2 to the atmosphere, totaling to a NEP flux of -0.04 Tg C yr-1 for the entire burned area. Several areas burned in the 1988 wildfires were estimated to be among the lowest in overall yearly NPP, namely the Hellroaring Fire, Mink Fire, and Falls Fire areas.

CONCLUSIONS

Rates of recovery for burned forest areas to pre-1988 biomass levels were estimated from a unique combination of remote sensing and CASA model predictions. Ecosystem production and carbon fluxes in the Greater Yellowstone Ecosystem (GYE) result from complex interactions between climate, forest age structure, and disturbance-recovery patterns of the landscape.

Sources of variability in canopy reflectance and the convergent properties of plants

How plants interact with sunlight is central to the existence of life and provides a window to the functioning of ecosystems. Although the basic properties of leaf spectra have been known for decades, interpreting canopy-level spectra is more challenging because leaf-level effects are complicated by a host of stem- and canopy-level traits. Progress has been made through empirical analyses and models, although both methods have been hampered by a series of persistent challenges. Here, I review current understanding of plant spectral properties with respect to sources of uncertainty at leaf to canopy scales. I also discuss the role of evolutionary convergence in plant functioning and the difficulty of identifying individual properties among a suite of interrelated traits. A pattern that emerges suggests a synergy among the scattering effects of leaf-, stem- and canopy-level traits that becomes most apparent in the near-infrared (NIR) region. This explains the widespread and well-known importance of the NIR region in vegetation remote sensing, but presents an interesting paradox that has yet to be fully explored: that we can often gain more insight about the functioning of plants by examining wavelengths that are not used in photosynthesis than by examining those that are.

Net primary productivity of forest stands in New Hampshire estimated from Landsat and MODIS satellite data

BACKGROUND

A simulation model that relies on satellite observations of vegetation cover from the Landsat 7 sensor and from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate net primary productivity (NPP) of forest stands at the Bartlett Experiment Forest (BEF) in the White Mountains of New Hampshire.

RESULTS

Net primary production (NPP) predicted from the NASA-CASA model using 30-meter resolution Landsat inputs showed variations related to both vegetation cover type and elevational effects on mean air temperatures. Overall, the highest predicted NPP from the NASA-CASA model was for deciduous forest cover at low to mid-elevation locations over the landscape. Comparison of the model-predicted annual NPP to the plot-estimated values showed a significant correlation of R2 = 0.5. Stepwise addition of 30-meter resolution elevation data values explained no more than 20% of the residual variation in measured NPP patterns at BEF. Both the Landsat 7 and the 250-meter resolution MODIS derived mean annual NPP predictions for the BEF plot locations were within +/- 2.5% of the mean of plot estimates for annual NPP.

CONCLUSION

Although MODIS imagery cannot capture the spatial details of NPP across the network of closely spaced plot locations as well as Landsat, the MODIS satellite data as inputs to the NASA-CASA model does accurately predict the average annual productivity of a site like the BEF.

Africa and the global carbon cycle

The African continent has a large and growing role in the global carbon cycle, with potentially important climate change implications. However, the sparse observation network in and around the African continent means that Africa is one of the weakest links in our understanding of the global carbon cycle. Here, we combine data from regional and global inventories as well as forward and inverse model analyses to appraise what is known about Africa's continental-scale carbon dynamics. With low fossil emissions and productivity that largely compensates respiration, land conversion is Africa's primary net carbon release, much of it through burning of forests. Savanna fire emissions, though large, represent a short-term source that is offset by ensuing regrowth. While current data suggest a near zero decadal-scale carbon balance, interannual climate fluctuations (especially drought) induce sizeable variability in net ecosystem productivity and savanna fire emissions such that Africa is a major source of interannual variability in global atmospheric CO2. Considering the continent's sizeable carbon stocks, their seemingly high vulnerability to anticipated climate and land use change, as well as growing populations and industrialization, Africa's carbon emissions and their interannual variability are likely to undergo substantial increases through the 21st century.