Aboveground Carbon Density Research Articles

Aboveground carbon emissions from gold mining in the Peruvian Amazon

In the Peruvian Amazon, high biodiversity tropical forest is underlain by gold-enriched subsurface alluvium deposited from the Andes, which has generated a clash between short-term earnings for miners and long-term environmental damage. Tropical forests sequester important amounts of carbon, but deforestation and forest degradation continue to spread in Madre de Dios, releasing carbon to the atmosphere. Updated spatially explicit quantification of aboveground carbon emissions caused by gold mining is needed to further motivate conservation efforts and to understand the effects of illegal mining on greenhouse gases. We used satellite remote sensing, airborne LiDAR, and deep learning models to create high-resolution, spatially explicit estimates of aboveground carbon stocks and emissions from gold mining in 2017 and 2018. For an area of ∼750 000 ha, we found high variations in aboveground carbon density (ACD) with mean ACD of 84.6 (±36.4 standard deviation) Mg C ha−1 and 83.9 (±36.0) Mg C ha−1 for 2017 and 2018, respectively. An alarming 1.12 Tg C of emissions occurred in a single year affecting 23,613 hectares, including in protected zones and their ecological buffers. Our methods and findings are preparatory steps for the creation of an automated, high-resolution forest carbon emission monitoring system that will track near real-time changes and will support actions to reduce the environmental impacts of gold mining and other destructive forest activities.

Agricultural land use reduces plant biodiversity and carbon storage in tropical West African savanna ecosystems: Implications for sustainability

Savanna ecosystems in tropical West Africa undergo severe land use pressure, resulting in ecosystem degradation and biogenic carbon emissions. In such context, highlighting the key ecological attributes of land degradation and the underlying processes are essential within the national adaptation and mitigation plans. This study analyzed the impacts of land use on plant biodiversity, stand structure and carbon storage. Inventories of ligneous species were conducted on 240 plots laid out along four levels of land disturbance in Burkina Faso. Dendrometric data collected from 6035 shrubs and trees were converted to aboveground biomass and carbon density. The results revealed a γ–diversity of 107 woody species belonging to 73 genera and 35 families. Significant effect of land use was found on species diversity, stand structure and carbon density (p < 0.001). Agricultural lands had the lowest diversity, density and carbon stocks, whereas protected areas held the highest values. Carbon density ranged from 10.362 ± 1.209 Mg C.ha−1 in fallows to 42.663 ± 1.982 Mg C.ha−1 in protected areas. Principal Component Analysis showed tight links between carbon storage, species diversity and stand structure. The multiple linear regression revealed that tree density explained 21.25% of the variation in the plot-level total carbon stocks (α = 19.301; p < 3.38e-14). Similarly, tree diameter and height together accounted for 45.43% of the variation in mean carbon stocks (α = 19.301; p < 2.2 e−16). This study demonstrated that the higher the land use pressure, the lower the species diversity and carbon storage in woody vegetation. The findings highlight the importance of accounting for improved or smart agricultural practices within the intended nationally determined contributions’ framework.

Woody Species Diversity and Carbon Stock Estimation of Dorze Ayira Natural Forest, Gamo Zone, Southern Ethiopia

Forests play a significant role in mitigating climate change by sequestering CO2 from the atmosphere. This study was aimed to investigate woody species diversity and to estimate the carbon stock potential at Dorze Ayira Natural Forest at Chencha District, Gamo Zone, Southern Ethiopia. Systematic sampling techniques were used to collected vegetation data from twenty 20m×20m quadrats. Aboveground carbon and belowground carbon were estimated using allometric equations. Additional 1m×1m subplot was laid for literal and soil samples data collection using destructive methods. A total of 26 woody species represented by 25 genera and 19 families were identified. The mean aboveground biomass and carbon density were 50.87±9.98 and 23.91 ± 4.69 t/ha, respectively. On the other hand, the mean belowground biomass and carbon density were 10.18 ± 1.996 and 4.78 ± 0.94 t/ha.. The average sum of all carbon pools in the study area was 91.74t/ha. The corresponding average CO2 equivalents of all carbon pools was 336.69 ton/ha. The mean leaf literal, herb and grass biomass and carbon stock were estimated to be 2.96 ± 0.49 and 1.39 ± 0.23 t/ha, respectively. Furthermore, the mean soil organic carbon was 60.95±13.49ton/ha (0-10cm, 10-20cm and 20-30cm depth). The result of the current total carbon stock capacity of Dorze Ayra forest revealed its contribution to climate change mitigation. Therefore, the forest needs more protection and due attention to reduce its current anthropogenic pressures.

Monitoring tropical forest carbon stocks and emissions using Planet satellite data

Tropical forests are crucial for mitigating climate change, but many forests continue to be driven from carbon sinks to sources through human activities. To support more sustainable forest uses, we need to measure and monitor carbon stocks and emissions at high spatial and temporal resolution. We developed the first large-scale very high-resolution map of aboveground carbon stocks and emissions for the country of Peru by combining 6.7 million hectares of airborne LiDAR measurements of top-of-canopy height with thousands of Planet Dove satellite images into a random forest machine learning regression workflow, obtaining an R2 of 0.70 and RMSE of 25.38 Mg C ha−1 for the nationwide estimation of aboveground carbon density (ACD). The diverse ecosystems of Peru harbor 6.928 Pg C, of which only 2.9 Pg C are found in protected areas or their buffers. We found significant carbon emissions between 2012 and 2017 in areas aggressively affected by oil palm and cacao plantations, agricultural and urban expansions or illegal gold mining. Creating such a cost-effective and spatially explicit indicators of aboveground carbon stocks and emissions for tropical countries will serve as a transformative tool to quantify the climate change mitigation services that forests provide.

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Dynamic monitoring of carbon storage in forests resources is important for tracking ecosystem functionalities and climate change impacts. In this study, we used multi-year Landsat data combined with a Random Forest (RF) algorithm to estimate the forest aboveground carbon (AGC) in a forest area in China (Hang-Jia-Hu) and analyzed its spatiotemporal changes during the past two decades. Maximum likelihood classification was applied to make land-use maps. Remote sensing variables, such as the spectral band, vegetation indices, and derived texture features, were extracted from 20 Landsat TM and OLI images over five different years (2000, 2004, 2010, 2015, and 2018). These variables were subsequently selected according to their importance and subsequently used in the RF algorithm to build an estimation model of forest AGC. The results showed the following: (1) Verification of classification results showed maximum likelihood can extract land information effectively. Our land cover classification yielded overall accuracies between 86.86% and 89.47%. (2) Additionally, our RF models showed good performance in predicting forest AGC, with R2 from 0.65 to 0.73 in the training and testing phase and a RMSE range between 3.18 and 6.66 Mg/ha. RMSEr in the testing phase ranged from 20.27 to 22.27 with a low model error. (3) The estimation results indicated that forest AGC in the past two decades increased with density at 10.14 Mg/ha, 21.63 Mg/ha, 26.39 Mg/ha, 29.25 Mg/ha, and 44.59 Mg/ha in 2000, 2004, 2010, 2015, and 2018. The total forest AGC storage had a growth rate of 285%. (4) Our study showed that, although forest area decreased in the study area during the time period under study, the total forest AGC increased due to an increment in forest AGC density. However, such an effect is overridden in the vicinity of cities by intense urbanization and the loss of forest covers. Our study demonstrated that the combined use of remote sensing data and machine learning techniques can improve our ability to track the forest changes in support of regional natural resource management practices.

Relationship of forest biomass carbon with biophysical parameters in north Kashmir region of Himalayas

Biophysical parameters affecting biomass carbon have been emphasized in the Paris Agreement for realizing climatic benefits from mitigation projects. The present study was conducted to assess the relation of biophysical parameters with forest biomass carbon in north Kashmir region of Himalayas. The relation of biomass carbon was assessed with (1) species type or strata including Cedrus deodara, mixed I (Cedrus deodara-Pinus wallichiana), mixed II (Abies pindrow-Picea smithiana) and Pinus wallichiana, (2) altitude (1292-2911m amsl), (3) crown density, (4) aspect, (5) tree count or density and (6) location. Using a stratified sampling design, a total of 188 quadrats of 0.1ha were laid across the entire region representing different biophysical parameters. Field observation including diameter at breast height and height were recorded and sample biomass (tha-1) was estimated using volumetric equations. The observed relation of aboveground biomass carbon with species revealed a trend of mixed II ˃ Cedrus deodara ˃ mixed I ˃ Pinus wallichiana. A positive but weak correlation (R2 = 0.02) was found between aboveground biomass carbon and altitude. A reasonably good correlation (R2 = 0.40) was observed to exist between aboveground biomass carbon and crown density. The highest value of average biomass carbon (72.63tha-1) was recorded for the north-eastern aspect whereas the lowest value (44.60tha-1) was recorded for the eastern aspect. The aboveground biomass carbon and tree count was found positively correlated (+ 0.475, R2 = 0.48). Forest biomass carbon fluctuates within the same geographical region with a variety of biophysical factors. The growth rate of species, photosynthetic ability under different crown densities and climatic conditions could address the reasons for this variability. Biophysical relations of forest biomass carbon can be viewed as an important input for guidelines and policy matters on climate change.

Sensitivity of L-band vegetation optical depth to carbon stocks in tropical forests: a comparison to higher frequencies and optical indices

Monitoring vegetation carbon in tropical regions is essential to the global carbon assessment and to evaluate the actions oriented to the reduction of forest degradation. Mainly, satellite optical vegetation indices and LiDAR data have been used to this purpose. These two techniques are limited by cloud cover and are sensitive only to the top of vegetation. In addition, the vegetation attenuation to the soil microwave emission, represented by the vegetation optical depth (VOD), has been applied for biomass estimation using frequencies ranging from 4 to 30 GHz (C- to K-bands). Atmosphere is transparent to microwaves and their sensitivity to canopy layers depends on the frequency, with lower frequencies having greater penetration depths. In this regard, L-band VOD (1.4 GHz) is expected to enhance the ability to estimate carbon stocks. This study compares the sensitivity of different VOD products (from L, C, and X-bands) and an optical vegetation index (EVI) to the above-ground carbon density (ACD). It quantifies the contribution of ACD and forest cover proportion to the VOD/EVI signals. The study is conducted in Peru, southern Colombia and Panama, where ACD maps have been derived from airborne LiDAR. Results confirm the enhanced sensitivity of L-band VOD to ACD when compared to higher frequency bands, and show that the sensitivity of all VOD bands decreases in the densest forests. ACD explains 34% and forest cover 30% of L-band VOD variance, and these proportions gradually decrease for EVI, C-, and X-band VOD, respectively. Results are consistent through different categories of altitude and carbon density. This pattern is found in most of the studied regions and in flooded forests. Results also show that C-, X-band VOD and EVI provide complementary information to L-band VOD, especially in flooded forests and in mountains, indicating that synergistic approaches could lead to improved retrievals in these regions. Although the assessment of vegetation carbon in the densest forests requires further research, results from this study support the use of new L-band VOD estimates for mapping the carbon of tropical forests.

Effect of climate change and local management on aboveground carbon dynamics (1987–2015) in Zagros oak forests using Landsat time-series imagery

Forest carbon stocks are a time-integrated manifestation of various phenomena and processes ranging from tree growth and mortality to natural and human disturbances. Understanding the effects of environmental and human activities is critically important in vulnerable ecosystems like arid and semi-arid forests, given climate variability coupled with historical human activities. Zagros forests are one of the largest vegetation communities in the Middle East. This region is highly affected by dust storms which are mainly a result of the loss of vegetation. This current study is an exploration of changes to aboveground carbon (AGC) density as affected by climate change (CC) and local management from 1987 to 2015 at 5-year intervals based on Landsat imagery and field data, analyzed with stochastic gradient boosting regression. Two sites with different local management practices were selected in Zagros coppice oak forests and AGC density was measured using field sampling. Four climate variables including mean annual temperature, rainfall, and evaporation were used to explore the effect of CC on AGC density dynamics over the study period. Results revealed an upward trend of AGC density in all intervals for both sites (from 6.36 to 7.5 ton ha−1) except for the 2010–2015 interval. The decrease of AGC density between 2010 and 2015 was likely related to dieback of oak trees due to different factors such as dust storms, pests, and other diseases. The trend of increasing AGC density in SarfiruzAbad forests was more than in Gahvareh forests, which is indicative of the young forest stand growth created by conservation and regeneration of damaged forests. Due to the large effect of local management on AGC density, our findings indicate that it was difficult to assess the effect of CC on AGC density. Our results suggest that other forests with a high degree of human disturbance, like Zagros forests, can play a role in mitigating global warming if the local management strategies are optimized. Additionally, these results demonstrate that in a region with no historical information about carbon stored in forests and woodlands, like most developing countries, Landsat-based AGC monitoring can provide information for forest managers and policy makers for understanding carbon accounting under human disturbances and CC, which could guide forest management strategies.

Stand carbon density drivers and changes under future climate scenarios across global forests

Climate and soil factors drive forest carbon (C) density. However, their impacts on the aboveground carbon density (ACD) and belowground carbon density (BCD) of global forest types have not been quantified in previous studies. In this study, we compiled global forest biomass data that included 8800 plots to better quantify the changes in total carbon density (TCD) of five forest types under current climate and future climate change scenarios (e.g., RCP2.6, RCP4.5 and RCP8.5) using the boosted regression tree method and TCD-stand age-climate models. Tropical forests have the largest amount of C density (130.0 ± 3.2 Mg C ha−1; 173.60 ± 17.24 Mg C ha−1), while boreal forests have the lowest TCDs (59.4 ± 0.9 Mg C ha−1; 62.34 ± 15.10 Mg C ha−1) in the current and future. For the current forest scenarios, the dryness index (DI) for tropical forests exhibited a negative mean change rate for the ACD (4.5 Mg C ha−1/0.1 DI) and BCD (0.9 Mg C ha−1/0.1 DI). The mean annual precipitation (MAP) had a positive mean change rate for the ACD (7.1 Mg C ha−1/100 mm) and BCD (1.3 Mg C ha−1/100 mm) when the MAP was ≤1100 mm in the temperate forests. The MAP was negative for the ACD (1.9 Mg C ha−1/100 mm) and BCD (0.4 Mg C ha−1/100 mm) when the MAP was >1100 mm. The mean annual temperature (MAT) in boreal forests had a positive mean change rate for the ACD (1.78 Mg C ha−1/1°C) and BCD (0.4 Mg C ha−1/1°C). We found that in contrast to 2050, there were positive impacts of the DI on the TCD change in tropical forests in 2070, and the DI was the most important driver for tropical forests. There were strong positive MAP impacts on the TCD change in temperate forests in both 2050 and 2070, where the MAP was the most important driver for temperate forests. There were negative MAT impacts on the TCD change in boreal forests under RCP4.5 (−0.22 ± 10.53 Mg C ha−1) and RCP8.5 (−4.75 ± 10.80 Mg C ha−1) in 2070, and the MAT was the most important driver in boreal forests. Our results could be used to improve the accuracy of C density estimates and assess climate change impacts on the C budgets of global forests.

Estimating the Aboveground Carbon Density of Coniferous Forests by Combining Airborne LiDAR and Allometry Models at Plot Level.

Forest carbon density is an important indicator for evaluating forest carbon sink capacities. Accurate carbon density estimation is the basis for studying the response mechanisms of forest ecosystems to global climate change. Airborne light detection and ranging (LiDAR) technology can acquire the vertical structure parameters of forests with a higher precision and penetration ability than traditional optical remote sensing. Combining top of canopy height model (TCH) and allometry models, this paper constructed two prediction models of aboveground carbon density (ACD) with 94 square plots in northwestern China: one model is plot-averaged height-based power model and the other is plot-averaged daisy-chain model. The correlation coefficients (R2) were 0.6725 and 0.6761, which are significantly higher than the correlation coefficients of the traditional percentile model (R2 = 0.5910). In addition, the correlation between TCH and ACD was significantly better than that between plot-averaged height (AvgH) and ACD, and Lorey’s height (LorH) had no significant correlation with ACD. We also found that plot-level basal area (BA) was a dominant factor in ACD prediction, with a correlation coefficient reaching 0.9182, but this subject requires field investigation. The two models proposed in this study provide a simple and easy approach for estimating ACD in coniferous forests, which can replace the traditional LiDAR percentile method completely.

Geografía del carbono en alta resolución en bosque tropical amazónico del Ecuador utilizando tecnología LiDAR aerotransportada

La estimación de la biomasa de la vegetación terrestre en bosque tropical es un tema de gran interés para reducir las emisiones de carbono asociadas a la deforestación y la degradación forestal (REDD+). Las estimaciones de densidad de carbono sobre el suelo (ACD) en base a inventarios de campo y datos provenientes de sensores aerotransportados, en especial con sensores LiDAR, han conducido a un progreso sustancial en el cartografiado a gran escala de las reservas de carbono forestal. Sin embargo, estos mapas de carbono tienen incertidumbres considerables, asociadas generalmente al proceso de calibración del modelo de regresión utilizado para producir los mapas. En este estudio se establece una metodología para la calibración y validación de un modelo general de estimación de ACD usando LiDAR en el Parque Nacional Yasuní en Ecuador. En el proceso de calibración del modelo se considera el tamaño y la ubicación de las parcelas, la influencia de la topografía y la distribución espacial de la biomasa. Para el ajuste y validación del modelo se propone un esquema de muestreo estratificado por posiciones topográficas (valle, ladera y cima). La validación del modelo general para toda la zona de estudio presentó valores de RMSE= 5.81 Mg C ha-1, R2 = 0.94 y sesgo= 0.59, mientras que, al considerar las posiciones topográficas, el modelo presentó valores de RMSE= 1.67 Mg C ha-1, R2= 0.98 y sesgo= 0.23 para el valle; RMSE= 3.13 Mg C ha-1, R2= 0.98 y sesgo= - 0.34 para la ladera; y RMSE= 2.33 Mg C ha-1, R2= 0.97 y sesgo= 0.74 para la cima. Los resultados muestran el potencial de los datos LiDAR para caracterizar la estructura vertical de la vegetación en un bosque altamente diverso, permitiendo realizar estimaciones precisas de ACD, y conocer patrones espaciales continuos de la distribución de la biomasa aérea

Modeling and Mapping Forest Fire Occurrence from Aboveground Carbon Density in Mexico

Understanding the spatial patterns of fire occurrence is key for improved forest fires management, particularly under global change scenarios. Very few studies have attempted to relate satellite-based aboveground biomass maps of moderate spatial resolution to spatial fire occurrence under a variety of climatic and vegetation conditions. This study focuses on modeling and mapping fire occurrence based on fire suppression data from 2005–2015 from aboveground biomass—expressed as aboveground carbon density (AGCD)—for the main ecoregions in Mexico. Our results showed that at each ecoregion, unimodal or humped relationships were found between AGCD and fire occurrence, which might be explained by varying constraints of fuel and climate limitation to fire activity. Weibull equations successfully fitted the fire occurrence distributions from AGCD, with the lowest fit for the desert shrub-dominated north region that had the lowest number of observed fires. The models for predicting fire occurrence from AGCD were significantly different by region, with the exception of the temperate forest in the northwest and northeast regions that could be modeled with a single Weibull model. Our results suggest that AGCD could be used to estimate spatial fire occurrence maps; those estimates could be integrated into operational GIS tools for assistance in fire danger mapping and fire and fuel management decision-making. Further investigation of anthropogenic drivers of fire occurrence and fuel characteristics should be considered for improving the operational spatial planning of fire management. The modeling strategy presented here could be replicated in other countries or regions, based on remote-sensed measurements of aboveground biomass and fire activity or fire suppression records.

Spatiotemporal dynamic simulation on aboveground carbon storage of bamboo forest and its influence factors in Zhejiang Province, China.

Bamboo forests have an efficient carbon sequestration capacity and play an important role in responding to global climate change. However, the current estimation of bamboo carbon storage has some errors, leading to uncertainty in the spatiotemporal pattern of bamboo forest carbon storage. This study simulated aboveground carbon storage of Zhejiang Province, China, during 1984-2014 based on the combination of an improved BIOME-BGC (biogeochemical cycles) model and remote sensing data, with the accuracy being verified with forest resource inventory data. The spatio-temporal distribution and environmental factors of aboveground carbon storage were analyzed. The results showed that the simulated carbon storage was accurate, with average correlation coefficient (r), root mean square error (RMSE) and relative bias (rBIAS) being 0.75, 7.24 Mg C·hm-2 and -2.57 Mg C·hm-2, respectively. Generally, the aboveground carbon storage of bamboo forests in the whole province tended to increase from 1984 to 2014, the range of aboveground carbon density was 13.10-17.14 Mg C·hm-2, and that of the total aboveground carbon storage was between 9.94-17.19 Tg C. The high aboveground carbon storage of bamboo was mainly distributed in developed bamboo industry areas, such as Anji, Lin'an, and Longyou. The change of aboveground carbon storage in bamboo forest was significantly correlated with temperature, precipitation, radiation, CO2 concentration and nitrogen deposition, with higher partial correlation coefficients between precipitation and temperature and carbon storage.

A critique of general allometry-inspired models for estimating forest carbon density from airborne LiDAR.

There is currently much interest in developing general approaches for mapping forest aboveground carbon density using structural information contained in airborne LiDAR data. The most widely utilized model in tropical forests assumes that aboveground carbon density is a compound power function of top of canopy height (a metric easily derived from LiDAR), basal area and wood density. Here we derive the model in terms of the geometry of individual tree crowns within forest stands, showing how scaling exponents in the aboveground carbon density model arise from the height−diameter (H−D) and projected crown area−diameter (C−D) allometries of individual trees. We show that a power function relationship emerges when the C−D scaling exponent is close to 2, or when tree diameters follow a Weibull distribution (or other specific distributions) and are invariant across the landscape. In addition, basal area must be closely correlated with canopy height for the approach to work. The efficacy of the model was explored for a managed uneven−aged temperate forest in Ontario, Canada within which stands dominated by sugar maple (Acer saccharum Marsh.) and mixed stands were identified. A much poorer goodness−of−fit was obtained than previously reported for tropical forests (R2 = 0.29 vs. about 0.83). Explanations for the poor predictive power on the model include: (1) basal area was only weakly correlated with top canopy height; (2) tree size distributions varied considerably across the landscape; (3) the allometry exponents are affected by variation in species composition arising from timber management and soil conditions; and (4) the C-D allometric power function was far from 2 (1.28). We conclude that landscape heterogeneity in forest structure and tree allometry reduces the accuracy of general power-function models for predicting aboveground carbon density in managed forests. More studies in different forest types are needed to understand the situations in which power functions of LiDAR height are appropriate for modelling forest carbon stocks.

Accurate Measurement of Tropical Forest Canopy Heights and Aboveground Carbon Using Structure From Motion

Unmanned aerial vehicles are increasingly used to monitor forests. Three-dimensional models of tropical rainforest canopies can be constructed from overlapping photos using Structure from Motion (SfM), but it is often impossible to map the ground elevation directly from such data because canopy gaps are rare in rainforests. Without knowledge of the terrain elevation, it is, thus, difficult to accurately measure the canopy height or forest properties, including the recovery stage and aboveground carbon density. Working in an Indonesian ecosystem restoration landscape, we assessed how well SfM derived the estimates of the canopy height and aboveground carbon density compared with those from an airborne laser scanning (also known as LiDAR) benchmark. SfM systematically underestimated the canopy height with a mean bias of approximately 5 m. The linear models suggested that the bias increased quadratically with the top-of-canopy height for short, even-aged, stands but linearly for tall, structurally complex canopies (&gt;10 m). The predictions based on the simple linear model were closely correlated to the field-measured heights when the approach was applied to an independent survey in a different location ( R 2 = 67% and RMSE = 1.85 m), but a negative bias of 0.89 m remained, suggesting the need to refine the model parameters with additional training data. Models that included the metrics of canopy complexity were less biased but with a reduced R 2 . The inclusion of ground control points (GCPs) was found to be important in accurately registering SfM measurements in space, which is essential if the survey requirement is to produce small-scale restoration interventions or to track changes through time. However, at the scale of several hectares, the top-of-canopy height and above-ground carbon density estimates from SfM and LiDAR were very similar even without GCPs. The ability to produce accurate top-of-canopy height and carbon stock measurements from SfM is game changing for forest managers and restoration practitioners, providing the means to make rapid, low-cost surveys over hundreds of hectares without the need for LiDAR.

Characterizing forest carbon dynamics using multi-temporal lidar data

Recent years have seen a rapid surge in the use of remote sensing technologies for characterizing the structure, composition and function of forested landscapes. Yet with few exceptions these studies have only provided snapshots of the ecosystem at one point in time, thereby limiting our ability to understand how forests are responding to global change. New approaches for characterizing forest dynamics using multi-temporal remotely sensed data are therefore urgently needed if we are to integrate these new technologies into conservation and management decision making processes. By combining data from a network of forest plots with repeat airborne lidar, here we develop an approach to (i) map fine-scale variation in aboveground carbon density (ACD) and its change over time across the landscape, and (ii) link these changes in ACD to forest structural attributes, species composition, disturbance regimes and local topography. We tested this framework on a temperate forest in the Alps characterized by the presence of three dominant species: spruce (Picea abies), silver fir (Abies alba) and beech (Fagus sylvatica). We found that between 2007 and 2011 the majority of the landscape (61.0%) increased in ACD, with a much smaller fraction of the study area exhibiting evidence of small-scale natural disturbances (3.7%) or ACD loss as a result of logging activities (13.7%). On average, areas of the landscape that actively sequestered carbon did so at a rate of 3.6% per year. However, rates of ACD accumulation varied considerably across the landscape, being greatest in forest stands characterized by multi-layered heterogeneous canopies, in ones dominated by spruce and at lower elevations. We demonstrated the potential of repeat lidar for characterizing not only the structure, but also the composition and aboveground carbon dynamics of forests. In doing so we open the door to monitor forests across large and inaccessible landscapes in order to better understand how they are responding to rapid global change and refine how we manage and conserve these critical ecosystems.

Elephants limit aboveground carbon gains in African savannas.

Understanding the drivers of vegetation carbon dynamics is essential for climate change mitigation and effective policy formulation. However, most efforts focus on abiotic drivers of plant biomass change, with little consideration for functional roles performed by animals, particularly at landscape scales. We combined repeat airborne Light Detection and Ranging with measurements of elephant densities, abiotic factors, and exclusion experiments to determine the relative importance of drivers of change in aboveground woody vegetation carbon stocks in Kruger National Park, South Africa. Despite a growing elephant population, aboveground carbon density (ACD) increased across most of the landscape over the 6-year study period, but at fine scales, bull elephant density was the most important factor determining carbon stock change, with ACD losses recorded only where bull densities exceeded 0.5 bulls/km2 . Effects of bull elephants were, however, spatially restricted and landscape dependent, being especially pronounced along rivers, at mid-elevations, and on steeper slopes. In contrast, elephant herds and abiotic drivers had a comparatively small influence on the direction or magnitude of carbon stock change. Our findings demonstrate that animals can have a substantive influence on regional-scale carbon dynamics and warrant consideration in carbon cycling models and policy formulation aimed at carbon management and climate change mitigation.

Combining behavioural and LiDAR data to reveal relationships between canopy structure and orangutan nest site selection in disturbed forests

Primary tropical forests are becoming increasingly disturbed and fragmented, making it critically important to understand the conservation value of degraded forests. Many populations of even the largest and most iconic species are now found outside of primary habitats, and the long-term survival of these and many other species depends on appropriate management of degraded areas, whether protected or not. However, for conservation in degraded habitats to be successful, an adequate understanding of the minimal ecological requirements necessary for species persistence within them is required. We combined ground and helicopter nest surveys of critically endangered Bornean orangutans with high-resolution measurements of forest canopy structure from airborne Light Detection and Ranging (LiDAR) to understand orangutan nest site selection across multiple spatial scales in degraded forests of the Lower Kinabatangan region, Malaysian Borneo. We found orangutans to be selective when choosing nest sites, with nests more likely to be observed in canopies of tall and uniform height and closer to full canopy gaps, which was consistent across spatial scales and orangutan age and sex classes. These sites likely offer orangutans an improved vantage point and/or shelter from wind and rain. In contrast, no discernible relationships between nest site selection and canopy complexity, or nest abundance and landscape forest structure or aboveground carbon density were recorded. Our findings suggest that although orangutans do nest across a range of forest conditions, their optimum requirement for nesting strongly depends on forest patches with sufficient tall canopy of uniform height. These results serve to inform degraded forest conservation strategies across Borneo, particularly where orangutans are a focal species.

Development and dominance of Douglas-fir in North American rainforests

One of the five tallest tree species, Pseudotsuga menziesii has enormous economic and ecological importance, but rainforests dominated by this species are not as well understood as their drier montane counterparts. We climbed and measured 30 trees up to 97 m tall growing in coastal forests of the Olympic Peninsula and northern California to quantify structural attributes—leaves, bark, cambium, sapwood, heartwood, deadwood, biomass, growth increments, and age—and combined these with an equal number of trees up to 85 m tall growing in forests of the Cascade Mountains to develop allometric equations based on ground-level predictors. After comparing new equations to those previously published, we applied the best available equations for tall forests to predict aboveground quantities of all vascular plant species in 12 ha of Olympic and Cascade forests. The largest (117 Mg) and one of the oldest (615 years) trees we studied had the highest biomass increment (305 kg yr−1), but age had a negative effect on current and long-term growth increments. After accounting for variation in tree size and aboveground vigor, older trees produced less wood annually and grew less efficiently than younger trees. Size of P. menziesii trees increased more rapidly, and the proportion of biomass and leaf area in P. menziesii decreased more slowly, in Olympic than Cascade forests over six centuries following stand-replacing fire. Maximum aboveground biomass (1999 Mg ha−1) and carbon density (994 Mg ha−1) occurred in a Cascade forest with high abundance of three conifer species (P. menziesii, Tsuga heterophylla, Thuja plicata), but maximum P. menziesii biomass (1289 Mg ha−1) occurred in an Olympic forest with 50 trees ha−1 up to 90 m tall. Vulnerability to wood decay fungi and dependence on fire for stand dominance limit P. menziesii biomass accumulation in rainforests.

Estimating and Analyzing the Spatiotemporal Pattern of Aboveground Carbon in Bamboo Forest by Combining Remote Sensing Data and Improved BIOME-BGC Model

Estimating the carbon stock of forests, and further studying their spatiotemporal variations is an important prerequisite for assessing forest carbon sequestration capability. This paper simulated aboveground carbon (AGC) of bamboo forest in Zhejiang Province, China, during 2003–2014 based on the combination of an improved BIOME-BGC (BioGeochemical Cycles) model and bamboo forest map extracted from moderate resolution imaging spectroradiometer data, and analyzed the characteristic and impact factors of the AGC spatiotemporal variations based on the geostatistical method. The modeled AGC density significantly correlated with the plot data from continuous forest resource inventory, and led to an average correlation coefficient and normalized root mean square error (NRMSE) of 0.81% and 17.15%, respectively. The stands with high AGC density were distributed in the mountainous areas in the northwest, southwest, and northeast of the province, while those with low AGC density were located in the basin, high altitude areas, and coastal areas. During the study period, the AGC density and total AGC storage increased from 12.02 to 18.15 Mg C ha−1 and 9.22 to 16.41 Tg, respectively. The spatial heterogeneity of AGC was revealed by an exponential semivariance model, and characterized by significant autocorrelation. The dominant factors contributing to the spatial heterogeneity of AGC were structure relevant factors, especially meteorological and terrain factors. The autocorrelation range of AGC in 2003 was 24.90 km, but decreased dramatically after that, indicating that the influence of external factors (i.e., management measures) on AGC increased gradually.