Spatial distribution of above ground carbon density in Harana Forest, Ethiopia

Background The need for understanding spatial distribution of forest aboveground carbon density (ACD) has increased to improve management practices of forest ecosystems. This study examined spatial distribution of the ACD in the Harana Forest. A grid sampling technique was employed and three nested circular plots were established at each point where grids intersected. Forest-related data were collected from 1122 plots while the ACD of each plot was estimated using the established allometric equation. Environmental variables in raster format were downloaded from open sources and resampled into a spatial resolution of 30 m. Descriptive statistics were computed to summarize the ACD. A Random Forest classification model in the R-software package was used to select strong predictors, and to predict the spatial distribution of ACD. Results The mean ACD was estimated at 131.505 ton per ha in this study area. The spatial prediction showed that the high class of the ACD was confined to eastern and southwest parts of the Harana Forest. The Moran’s statistics depicted similar observations showing the higher clustering of ACD in the eastern and southern parts of the study area. The higher ACD clustering was linked with the higher species richness, species diversity, tree density, tree height, clay content, and SOC. Conversely, the lower ACD clustering in the Harana Forest was associated with higher soil cation exchange capacity, silt content, and precipitation. Conclusions The spatial distribution of ACD in this study area was mainly influenced by attributes of the forest stand and edaphic factors in comparison to topographic and climatic factors. Our findings could provide basis for better management and conservation of aboveground carbon storage in the Harana Forest, which may contribute to Ethiopia’s strategy of reducing carbon emission.

Estimating Aboveground Carbon Stock at the Scale of Individual Trees in Subtropical Forests Using UAV LiDAR and Hyperspectral Data

Accurate estimation of aboveground carbon stock for individual trees is important for evaluating forest carbon sequestration potential and maintaining ecosystem carbon balance. Airborne light detection and ranging (LiDAR) data has been widely used to estimate tree-level carbon stock. However, few studies have explored the potential of combining LiDAR and hyperspectral data to estimate tree-level carbon stock. The objective of this study is to explore the potential of integrating unmanned aerial vehicle (UAV) LiDAR with hyperspectral data for tree-level aboveground carbon stock estimation. To achieve this goal, we first delineated individual trees by a CHM-based watershed segmentation algorithm. We then extracted structural and spectral features from UAV LiDAR and hyperspectral data respectively. Then, Pearson correlation analysis was conducted to assess the correlation between LiDAR features, hyperspectral features, and tree-level carbon stock, based on which, features were selected for model development. Finally, we developed tree-level carbon stock estimation models based on the Schumacher–Hall formula and stepwise multiple regression. Results showed that both LiDAR and hyperspectral features were strongly correlated to tree-level carbon stock. Both tree height (H, r = 0.75) and Green index (GI, r = 0.83) had the highest correlation coefficients with tree-level carbon stock in LiDAR and hyperspectral features, respectively. The best model using LiDAR features alone includes the metrics of H, the 10th height percentile of points (PH10), and mean height of points (Hmean), and can explain 74% of the variations in tree-level carbon stock. Similarly, the best model using hyperspectral data includes GI and modified normalized differential vegetation index (mNDVI), and has similar explanatory power (r2 = 0.75). The model that integrates predictors, namely, GI and the 95th height percentile of points (PH95) from hyperspectral and LiDAR data, substantially improves the explanatory power (r2 = 0.89). These results indicated that while either LiDAR data or hyperspectral data alone can estimate tree-level carbon stock with reasonable accuracy, combining LiDAR and hyperspectral features can substantially improve the explanatory power of the model. Such results suggested that tree-level carbon stock estimation can greatly benefit from the complementary nature of LiDAR-detected structural characteristics and hyperspectral-captured spectral information of vegetation.

Opposing Ecological Strategies Together Promote Biomass Carbon Storage in Homegardens Agroforestry of Southern Bangladesh

Exploration of the biodiversity–environmental factors–carbon storage relationships have been a central research question in the changing global climate over the last few decades. However, in comparison to other forest ecosystems, very few studies have been conducted in homegarden agroforestry plantations, which have a tremendous capacity to battle global climate change sustainably. We hypothesized that (i) soil organic matter content has both a direct and indirect effect on aboveground carbon storage through species richness, structural diversity, functional diversity (FD) and functional composition (FC); (ii) some facets of diversity (structural diversity, FD and FC) would be more important in linking species richness to aboveground carbon; (iii) species richness, FC, structural diversity and FD would have a positive impact on aboveground carbon storage (AGC) after considering the effect of soil fertility; and (iv) FC would have a greater effect on AGC than the other three components of biodiversity. These hypotheses were tested using structural equation modeling with field data obtained from 40 homesteads in southwestern Bangladesh. We observed that species richness, FC of maximum canopy height and structural diversity had significant effects on AGC, while soil organic matter and FD of wood density had an insignificant effect. Among the four biodiversity components, the structural diversity had a greater influence on AGC. Contrary to our hypothesis, soil fertility and species richness did not have a significant indirect effect on AGC through their mediators. These four components of biodiversity, along with soil organic matter together explained 49% of the variance in AGC. Our findings indicate that both niche complementarity and selection effects regulate AGC in homegardens, where the former theory had stronger control of AGC in homegardens. Therefore, we need to maintain not only the species diversity but also structural diversity (DBH) and functional composition (canopy height) for enhancing aboveground carbon storage on a sustainable basis in homegardens and other restoration programs under nature-based solution.

ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE

Remote sensing can make seagrass aboveground carbon stock (AGCseagrass) information spatially extensive and widely available. Therefore, it is necessary to develop a rapid approach to estimate AGCseagrass in the field to train and assess its remote sensing-based mapping. The aim of this research is to (1) analyze the Percent Cover (PCv)-AGCseagrass relationship in seagrass at the species and community levels to estimate AGCseagrass from PCv and (2) perform AGCseagrass mapping at both levels using WorldView-2 image and assess the accuracy of the resulting map. This research was conducted in Karimunjawa and Kemujan Islands, Indonesia. Support Vector Machine (SVM) classification was used to map seagrass species composition, and stepwise regression was used to model AGCseagrass using deglint, water column corrected, and principle component bands. The results were a rapid AGCseagrass estimation using an easily measured parameter, the seagrass PCv. At the community level, the AGCseagrass map had 58.79% accuracy (SEE = 5.41 g C m−2), whereas at the species level, the accuracy increased for the class Ea (64.73%, SEE = 6.86 g C m−2) and EaThCr (70.02%, SEE = 4.32 g C m−2) but decreased for ThCr (55.08%, SEE = 2.55 g C m−2). The results indicate that WorldView-2 image reflectance can accurately map AGCseagrass in the study area in the range of 15–20 g C m−2 for Ea, 10–15 g C m−2 for EaThCr, and 4–8 g C m−2 for ThCr. Based on our model, the AGCseagrass in the study area was estimated at 13.39 t C.

Carbon and ecohydrological priorities in managing woody encroachment: An UAV perspective 63 years after a control treatment

Woody encroachment, including both woody species expansion and density increase, is a globally observed phenomenon that deteriorates arid and semi-arid rangeland health, biodiversity, and ecosystem services. Mechanical and chemical control treatments are commonly performed to reduce woody cover and restore ecohydrologic function. While the immediate impacts of woody control treatments are well documented in short-term studies, treatment impacts at decadal scales are not commonly studied. Using a controlled herbicide treatment from 1954 in the Sierra Ancha Experimental Forest in central Arizona, USA, we quantify woody encroachment and associated aboveground carbon accumulation in treated and untreated watersheds. Woody encroachment and aboveground carbon are estimated using high resolution multispectral images and photogrammetric data from a fixed-wing unmanned aerial vehicle (UAV). We then combine the contemporary UAV image-derived estimates with historical records from immediately before and after the treatment to consider long-term trends in woody vegetation cover, aboveground carbon, water yield, and sedimentation. Our results indicate that the treatment has had a lasting impact. More than six decades later, woody cover in two treated watersheds are still significantly lower compared to two control watersheds, even though woody cover increased in all four drainages. Aboveground woody carbon in the treated watersheds is approximately one half that accumulated in the control watersheds. The historical records indicate that herbicide treatment also increased water yield and reduced annual sedimentation. Given the sustained reduction in woody cover and aboveground woody biomass in treated watersheds, we infer that the herbicide treatment has had similarly long lasting impacts on ecohydrological function. Land managers can consider legacy impacts from control treatments to better balance carbon and ecohydrological consequences of woody encroachment and treatment activities.