Savannas and adjacent vegetation types like gallery forests are highly valuable ecosystems contributing to several ecosystem services including carbon budgeting. Financial mechanisms such as REDD+ (Reduced Emissions from Deforestation and Forest Degradation) can provide an opportunity for developing countries to alleviate poverty through conservation of its forestry resources. However, for availing such opportunities carbon stock assessments are essential. Therefore, a research study for this purpose was conducted at two protected areas (Nazinga Game Ranch and Bontioli Nature Reserve) in Burkina Faso. Similarly, analysis of various vegetation parameters was also conducted to understand the overall vegetation structure of these two protected areas. For estimating above ground biomass, existing allometric equations for dry tropical woody vegetation types were used. Compositional structure was described by applying tree species and family importance indices. The results show that both sites collectively contain a mean carbon stock of 3.41 ± 4.98 Mg·C·ha⁻¹. Among different savanna vegetation types, gallery forests recorded the highest mean carbon stock of 9.38 ± 6.90 Mg·C·ha⁻¹. This study was an attempt at addressing the knowledge gap particularly on carbon stocks of protected savannas—it can serve as a baseline for carbon stocks for future initiatives such as REDD+ within these areas.

Accurate estimations of aboveground biomass (AGB) in tropical forests are crucial for maintaining carbon stocks and ensuring effective forest management. By combining remote sensing (RS) data with ensemble algorithms, reliable AGB estimates in forests can be obtained. In this context, the freely available Sentinel-1 (S-1 SAR) and Sentinel 2 multispectral imagery (S-2 MSI) data have a significant role in enhancing accurate AGB estimations at a lower cost, which is relevant for the tropical dry forest (TDF) regions where AGB estimation is uncertain or there is a lack of comprehensive exploration. This study aims to address this gap by presenting a cost-effective and reliable AGB estimation approach in the TDF region of Colombia. For this purpose, we modeled and compared the performance of two ensemble algorithms, random forest (RF) and extreme gradient boosting (XGBoost), to estimate AGB using three predictor categories (polarizations/textures, spectral bands/vegetation indices, and a combination of both). We then examined the modeling potential of S-1 SAR and S-2 MSI imagery in predicting forest AGB and subsequently identified the most suitable variables. To construct AGB models’ field data, we employed a clustered distributed sampling approach involving 100 subsample plots, each with an area of 400 m². Stepwise multiple linear regression was applied to identify suitable predictors from the original satellite bands, vegetation indices, and texture metrics. To produce a map of AGB, predicted AGB values were calculated for every pixel within a specific satellite subscene using the most effective ensemble algorithm. Our study findings show that the RF model, which employed combined predictor sets, displayed superior performance when evaluated against the independent validation set. The RF model successfully estimated AGB with a high degree of accuracy, achieving an R² value of 0.78 and an RMSE value of 42.25 Mg/ha⁻¹. In contrast, the XGBoost model performed less accurately, obtaining an R² value of only 0.60 and an RMSE value of 48.41 Mg/ha⁻¹. The results also indicate that S-2 vegetation indices data were more appropriate for this purpose than S-1 texture data. Despite this, S-1 cross-polarized textures were necessary during the dry season for the combined datasets. The top predictive variables for S-2 images were cab and cw, as well as red-edge bands during the wet season. As for S-1 images, texture D_VH _Hom during the dry season was the most important variable for explaining performance. Overall, the proposed approach of using freely available Sentinel data seems to improve the accuracy of AGB estimation in heterogeneous forest cover and, as such, they should be recommended as a data source for forest AGB assessment.

Forests offer high potential for the fight against climate change. However, forests are faced with increased deforestation. REDD+ is a financial mechanism that offers hope to developing countries for tackling deforestation. Aboveground (AGB) estimation, however, is necessary for such financial mechanisms. Remote sensing methods offer various advantages for AGB estimation. A study, therefore, was conducted for the estimation of AGB using a combination of remote sensing Sentinel-1 (S1) and Sentinel-2 (S2) satellite data and field inventorying. The mean AGB for Sub-tropical Chir Pine Forest was recorded as 146.73 ± 65.11 Mg ha⁻¹, while for Sub-tropical Broadleaved Evergreen Forest it was 33.77 ± 51.63 Mg ha⁻¹. Results revealed weak associations between the S1 and S2 data with the AGB. Nonetheless, S1 and S2 offer advantages such as free data resources that can be utilized by developing countries for forest biomass and carbon monitoring.

Estimations of Leaf Area Index (LAI) have recently gained attention due to the sensitivity to the effects of climate change and its impact on forest ecosystems. Hence, a study was conducted on the LAI estimation of four vegetation types: (i) gallery forests, (ii) woodland savannas, (iii) tree savannas, and (iv) shrub savannas, at two protected areas of Nazinga Game Ranch and Bontioli Nature Reserve, Burkina Faso. A relationship between LAI and Crown Diameter was also investigated at these two sites. Digital hemispherical photography was used for the LAI estimation. Crown diameters (CD) were determined perpendicular to each other and averaged for each tree and shrub. Overall results revealed that LAI ranged from 0-1.33 and the CD was recorded in the range of 0.46-11.01 m. The gallery forests recorded the highest mean LAI 1.33 ± 0.32 as well as the highest mean CD 7.69 ± 1.90 m. The LAI for the vegetation types were at their lower ends as the study was conducted in summer season, higher values are therefore expected in the wet season, as a significant correlation between LAI and precipitation has been emphasized by various studies. Continuous LAI monitoring and studies on various growth parameters of different vegetation types at the study sites are recommended towards enhanced monitoring and an ecologically feasible forest- and savanna-use and management to maintain essential ecosystem functions and services

Forests can play an important role in climate change mitigation. However, limited information is available worldwide regarding forest carbon and biomass stocks. Financial mechanisms such as ‘reducing emissions from deforestation and forest degradation and the role of conservation of forest carbon, sustainable management of forests and enhancement of forest carbon stocks’ (REDD+) also emphasize the quantification of forest biomass and carbon. This study aimed to estimate the forest biomass in two forests of Margalla Hills National Park (MHNP): Sub-tropical Chir Pine Forest (SCPF) and Sub-tropical Broadleaved Evergreen Forest (SBEF). For this, circular sampling plots of a 20 m radius were used for the collection of the variables, “diameter at breast height (DBH) and height”. Statistical analysis was done for exploring regression relationships between the variables. We found a mean Aboveground Carbon (AGC) of 73.36 ± 32.55 Mg C ha⁻¹ in SCPF and a mean AGC of 16.88 ± 25.81 Mg C ha⁻¹ in SBEF. The mean Aboveground Biomass (AGB) for SCPF was recorded as 146.73 ± 65.11 Mg ha⁻¹, while for SBEF it was 33.77 ± 51.63 Mg ha⁻¹. It was therefore concluded that the SCPF had higher mean AGB and mean AGC than the SBEF. Similar differences were also noticed in the structural characteristics of the two forests. These could be valuable information while designing sustainable management plans and afforestation programmes for the future and also for accessing nature-based funding such as REDD+.