Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

This study examined the integration of two satellite data sets, that is Landsat 7 ETM+ and ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture RADAR) in estimating carbon stocks in mopane woodlands of north-western Zimbabwe. Mopane woodlands cover large spatial extents and provide ecosystem benefits to the rural economies and grazing resources for both livestock and wildlife. In this study, artificial neural networks (ANN) were used to estimate carbon stocks based on spectral metrics derived from Landsat 7 ETM+ and ALOS PALSAR. To determine the utility of the two satellite-derived metrics, a two-pronged modelling framework was adopted. Firstly, we used spectral bands and vegetation indices from the two satellite data sets independently, and subsequently, we integrated the metrics from the two satellite data sets into the final model. Results showed that the ALOS PALSAR (R 2 = 0.75 and nRMSE = 0.16) and Landsat ETM+ (R 2 = 0.78 and nRMSE = 0.14) derived spectral bands and vegetation indices comparatively yielded accurate estimations of carbon stocks. Integrating spectral bands and vegetation indices from both sensors significantly improved the estimation of carbon stocks (R 2 = 0.84 and nRMSE = 0.12). These findings underscore the importance of integrating satellite data in vegetation biophysical assessment and monitoring in poorly documented ecosystems such as the mopane woodlands.

This study explored the feasibility of using the terrestrial laser scanner (TLS) and quantitative structure modelling (QSM) to estimate the above-ground biomass (AGB) of individual trees in the tropical rainforest using data from the Ayer Hitam Forest Reserve, Malaysia. We also tested the influence of varying the runs, the cover set's diameter and nmin values on the ABG derived. To achieve our objectives, we estimated diameter at breast height (DBH) and height of 100 trees from 26 plots using both TLS and QSM, and field measurements then AGB based on both methods. We observed a powerful positive linear correlation between TLS and field-measured DBH (R 2 = 0.97) and between TLS and field-measured height (R 2 = 0.77), and a moderately strong relationship between TLS and field-based AGB (R 2 = 0.56). TLS and QSM overestimated AGB by 30% of field-based estimates. Varying the number of runs had no significant influence on the AGB derived from the TLS and QSM (one-way ANOVA, p > 0.05) while increasing the cover set diameter led to an increase in the derived AGB (one-way ANOVA, p < 0.05). The QSM was very sensitive to variations in the nmin (one-way ANOVA, p < 0.05). Our study has demonstrated that the TLS and QSM can be used for estimation of AGB for individual trees but with varying reliability. Regardless, our study provides yet another non-destructive approach to the suite of methods for estimating carbon of individual trees for various applications, including Reducing Emissions from Deforestation and Forest Degradation.