UAV-based modelling of vegetation recovery under extreme habitat stresses in the water level fluctuation zone of the Three Gorges Reservoir, China

Impoundment of the Three Gorges Reservoir on the upper Yangtze River has remarkably altered hydrological regime within the dammed reaches, triggering structural and functional changes of the riparian ecosystem. Up to date, how vegetation recovers in response to compound habitat stresses in the water level fluctuation zone remains inexplicitly understood. In this study, plant above-ground biomass (AGB) in a selected water level fluctuation zone was quantified to depict its spatial and temporal pattern using unmanned aerial vehicle (UAV)-derived multispectral images and screened empirical models. The contribution of multiple habitat stressors in governing vegetation recovery dynamics along the environmental gradient were further explored. Screened random forest models indicated relatively higher accuracy in AGB estimation, with R2 being 0.68, 0.79 and 0.62 during the sprouting, growth, and mature period, respectively. AGB displayed a significant linear increasing trend along the elevational gradient during the sprouting and early growth period, while it showed an inverted U-shaped pattern during late growth and mature period. Flooding duration, magnitude and timing was found to exert greater negative effects on plant sprouting and biomass accumulation and acted as decisive factors in governing the elevation-dependent pattern of AGB. Localized spatial variations in AGB were modulated by other stressors such as sediment burial, soil erosion, soil moisture and nutrient content. Occurrence of episodic summer floods and vegetation distribution were responsible for an inverted U-shaped pattern of AGB during the late growth and mature period. Generally, AGB reached its peak in August, thereafter an obvious decline by a unprecedent dry-hot climatic event. The water level fluctuations with cumulative flooding effects exerted substantial control on AGB temporal dynamics, while climatic condition played a secondary role. Herein, further restorative efforts need to be directed to screening suitable species, maintaining favorable soil condition, and improving vegetation pattern to balance the many trade-offs.

Biomass patterns in Srivilliputhur Wildlife Sanctuary: exploring factors and gradients with machine learning approach

Forest biomass plays a crucial role in the global carbon cycle as a significant contributor derived from both soil and trees. This study focuses on investigating tree carbon stock (TCS) and estimating aboveground biomass (AGB) based on elevation within the Srivilliputhur Wildlife Sanctuary forest, while also exploring the various factors that influence their contribution. Utilizing a non-destructive approach for carbon estimation, we found that the total tree biomass in this region ranged from 220.9 Mg/ha (in Z6) to 720.6 Mg/ha (Z2), while tree carbon stock ranged from 103.8 to 338.7 Mg/ha. While Kruskal-Wallis tests did not reveal a significant relationship (p = 0.09) between TCS and elevation, linear regression showed a weak correlation (R2 = 0.002, p < 0.05) with elevation. To delve deeper into the factors influencing TCS and biomass distribution, we employed a random forest (RF) machine learning algorithm, demonstrating that stand structural attributes, such as basal area (BA), diameter at breast height (DBH), and density, held a more prominent role than climatic variables, including temperature, precipitation, and slope. Generalized linear models (GLM) were also utilized, confirming that BA, mean DBH, and elevation significantly influenced AGB (p ≤ 0.001), with species richness, precipitation, and temperature having lower significance (p ≤ 0.01) comparatively. Overall, the RF model exhibited superior performance (R2 = 0.92, RMSE = 0.12) in terms of root mean square error (RMSE) compared to GLM (R2 = 0.88, RMSE = 0.35). These findings shed light on the intricate dynamics of biomass distribution and the importance of both stand structural and climatic factors in shaping forest ecosystems.

Allometric equations for selected Acacia species (Vachellia and Senegalia genera) of Ethiopia

BACKGROUND

Allometric equations are used to estimate biomass and carbon stock of forests. In Ethiopia, despite the presence of large floral diversity, only a few site-specific allometric equations have been developed so far. This study was conducted in the Omo-Gibe woodland of south-western Ethiopia to develop an allometric equation to estimate the Above-ground Biomass (AGB) of the four Acacia species (Senegalia polyacantha, Vachellia seyal, Vachellia etbaica and Vachellia tortilis). Fifty-four (54) Acacia trees were sampled and measured within 35 temporarily established square plots. In each plot, dendrometric variables were measured to derive the models based on combinations of Diameter at Breast Height (DBH), height, and wood density as predictor variables. Model performance was evaluated using goodness-of-fit statistics. The biomass was compared using four allometric biomass models that have been widely used in the tropics.

RESULTS

The model containing DBH alone was more accurate to estimate AGB compared to the use of multiple predictor variables. This study, therefore, substantiated the importance of site-specific allometric equations in estimating the AGB of Acacia woodlands. This is because a site-specific allometric equation recognizes the environmental factors, vegetation types and management practices.

CONCLUSIONS

The results of this study contribute to a better understanding of allometric equations and an accurate estimate of AGB of Acacia woodlands in Ethiopia and similar ecosystems elsewhere.

Old forests and old carbon: A case study on the stand dynamics and longevity of aboveground carbon

Most information on the ecology of oak-dominated forests in Europe comes from forests altered for centuries because remnants of old-growth forests are rare. Disturbance and recruitment regimes in old-growth forests provide information on forest dynamics and their effects on long-term carbon storage. In an old-growth Quercus petraea forest in northwestern Spain, we inventoried three plots and extracted cores from 166 live and dead trees across canopy classes (DBH ≥ 5 cm). We reconstructed disturbance dynamics for the last 500 years from tree-ring widths. We also reconstructed past dynamics of above ground biomass (AGB) and recent AGB accumulation rates at stand level using allometric equations. From these data, we present a new tree-ring-based approach to estimate the age of carbon stored in AGB. The oldest tree was at least 568 years, making it the oldest known precisely-dated oak to date and one of the oldest broadleaved trees in the Northern Hemisphere. All plots contained trees over 400 years old. The disturbance regime was dominated by small, frequent releases with just a few more intense disturbances that affected ≤20% of trees. Oak recruitment was variable but rather continuous for 500 years. Carbon turnover times ranged between 153 and 229 years and mean carbon ages between 108 and 167 years. Over 50% of AGB (150 Mg·ha^-1) persisted ≥100 years and up to 21% of AGB (77 Mg·ha^-1) ≥300 years. Low disturbance rates and low productivity maintained current canopy oak dominance. Absence of management or stand-replacing disturbances over the last 500 years resulted in high forest stability, long carbon turnover times and long mean carbon ages. Observed dynamics and the absence of shade-tolerant species suggest that oak dominance could continue in the future. Our estimations of long-term carbon storage at centennial scales in unmanaged old-growth forests highlights the importance of management and natural disturbances for the global carbon cycle.

Golden carbon of Sargassum forests revealed as an opportunity for climate change mitigation

Marine climate change mitigation initiatives have recently attracted a great deal of interest in the role of natural carbon sinks, particularly on coastal systems. Brown seaweeds of the genus Sargassum are the largest canopy-forming algae in tropical and subtropical environments, with a wide global distribution on rocky reefs and as floating stands. Because these algae present high amounts of biomass, we suggest their contribution is relevant for global carbon stocks and consequently for mitigating climate change as CO2 remover. We modelled global distributions and quantified carbon stocks as above-ground biomass (AGB) with machine learning algorithms and climate data. Sargassum AGB totaled 13.1 Pg C at the global scale, which is a significant amount of carbon, comparable to other key marine ecosystems, such as mangrove forests, salt marshes and seagrass meadows. However, specific techniques related to bloom production and management, or the utilization of biomass for biomaterials, should be fostered.

Evaluation of Macaranga tanarius as a biomass feedstock for fermentable sugars production

Macaranga tanarius is a fast-growing tree species that could be potentially utilized as a biomass feedstock for biorefinery. The average productivity of M. tanarius biomass was estimated to be ~19.2 ton/ha if the above-ground biomass is harvested bi-annually. Different pretreatment approaches were investigated to increase the enzymatic digestibility of foliage and woody biomass. The results indicated that no pretreatment was required for the foliage biomass while sequential acid/alkali pretreatment was necessary for the woody biomass before enzymatic hydrolysis. For the woody biomass, the delignification was 34.5% after sequential dilute acid/alkali pretreatment. The reducing sugar yields from enzymatic hydrolysis of foliage and pretreated woody biomass were 0.31 and 0.42 g/g dry biomass, respectively. The results also showed that both hydrolysates were fermentable by lactic acid bacteria. Overall, the results suggested that M. tanarius could be a potential feedstock for biorefinery based on the findings and processes derived from this study.

Estimating urban above ground biomass with multi-scale LiDAR

BACKGROUND

Urban trees have long been valued for providing ecosystem services (mitigation of the "heat island" effect, suppression of air pollution, etc.); more recently the potential of urban forests to store significant above ground biomass (AGB) has also be recognised. However, urban areas pose particular challenges when assessing AGB due to plasticity of tree form, high species diversity as well as heterogeneous and complex land cover. Remote sensing, in particular light detection and ranging (LiDAR), provide a unique opportunity to assess urban AGB by directly measuring tree structure. In this study, terrestrial LiDAR measurements were used to derive new allometry for the London Borough of Camden, that incorporates the wide range of tree structures typical of an urban setting. Using a wall-to-wall airborne LiDAR dataset, individual trees were then identified across the Borough with a new individual tree detection (ITD) method. The new allometry was subsequently applied to the identified trees, generating a Borough-wide estimate of AGB.

RESULTS

Camden has an estimated median AGB density of 51.6 Mg ha-1 where maximum AGB density is found in pockets of woodland; terrestrial LiDAR-derived AGB estimates suggest these areas are comparable to temperate and tropical forest. Multiple linear regression of terrestrial LiDAR-derived maximum height and projected crown area explained 93% of variance in tree volume, highlighting the utility of these metrics to characterise diverse tree structure. Locally derived allometry provided accurate estimates of tree volume whereas a Borough-wide allometry tended to overestimate AGB in woodland areas. The new ITD method successfully identified individual trees; however, AGB was underestimated by ≤ 25% when compared to terrestrial LiDAR, owing to the inability of ITD to resolve crown overlap. A Monte Carlo uncertainty analysis identified assigning wood density values as the largest source of uncertainty when estimating AGB.

CONCLUSION

Over the coming century global populations are predicted to become increasingly urbanised, leading to an unprecedented expansion of urban land cover. Urban areas will become more important as carbon sinks and effective tools to assess carbon densities in these areas are therefore required. Using multi-scale LiDAR presents an opportunity to achieve this, providing a spatially explicit map of urban forest structure and AGB.

An alternative approach for estimating above ground biomass using Resourcesat-2 satellite data and artificial neural network in Bundelkhand region of India

Determination of above ground biomass (AGB) of any forest is a longstanding scientific endeavor, which helps to estimate net primary productivity, carbon stock and other biophysical parameters of that forest. With advancement of geospatial technology in last few decades, AGB estimation now can be done using space-borne and airborne remotely sensed data. It is a well-established, time saving and cost effective technique with high precision and is frequently applied by the scientific community. It involves development of allometric equations based on correlations of ground-based forest biomass measurements with vegetation indices derived from remotely sensed data. However, selection of the best-fit and explanatory models of biomass estimation often becomes a difficult proposition with respect to the image data resolution (spatial and spectral) as well as the sensor platform position in space. Using Resourcesat-2 satellite data and Normalized Difference Vegetation Index (NDVI), this pilot scale study compared traditional linear and nonlinear models with an artificial intelligence-based non-parametric technique, i.e. artificial neural network (ANN) for formulation of the best-fit model to determine AGB of forest of the Bundelkhand region of India. The results confirmed the superiority of ANN over other models in terms of several statistical significance and reliability assessment measures. Accordingly, this study proposed the use of ANN instead of traditional models for determination of AGB and other bio-physical parameters of any dry deciduous forest of tropical sub-humid or semi-arid area. In addition, large numbers of sampling sites with different quadrant sizes for trees, shrubs, and herbs as well as application of LiDAR data as predictor variable were recommended for very high precision modelling in ANN for a large scale study.

Comparison of calculation methods for estimating annual carbon stock change in German forests under forest management in the German greenhouse gas inventory

BACKGROUND

The German greenhouse gas inventory in the land use change sector strongly depends on national forest inventory data. As these data were collected periodically 1987, 2002, 2008 and 2012, the time series on emissions show several "jumps" due to biomass stock change, especially between 2001 and 2002 and between 2007 and 2008 while within the periods the emissions seem to be constant due to the application of periodical average emission factors. This does not reflect inter-annual variability in the time series, which would be assumed as the drivers for the carbon stock changes fluctuate between the years. Therefore additional data, which is available on annual basis, should be introduced into the calculations of the emissions inventories in order to get more plausible time series.

RESULTS

This article explores the possibility of introducing an annual rather than periodical approach to calculating emission factors with the given data and thus smoothing the trajectory of time series for emissions from forest biomass. Two approaches are introduced to estimate annual changes derived from periodic data: the so-called logging factor method and the growth factor method. The logging factor method incorporates annual logging data to project annual values from periodic values. This is less complex to implement than the growth factor method, which additionally adds growth data into the calculations.

CONCLUSION

Calculation of the input variables is based on sound statistical methodologies and periodically collected data that cannot be altered. Thus a discontinuous trajectory of the emissions over time remains, even after the adjustments. It is intended to adopt this approach in the German greenhouse gas reporting in order to meet the request for annually adjusted values.