Climate change is acknowledged as a global threat to the environment and human well-being. Forest ecosystems are a significant factor in this regard as they act both as a sink and a source of carbon. Forest carbon evaluation has received more attention after the Paris Agreement. Pakistan has 5.1% forest cover of its total land area, which comprises nine forest types. This study covers the studies conducted on above-ground biomass and carbon stock in various forest types of Pakistan. Most of the studies on biomass and carbon stock estimation have been conducted during 2015–2020. The non-destructive method is mostly followed for carbon stock estimation, followed by remote sensing. The destructive method is used only for developing allometric equations and biomass expansion factors. The information available on the carbon stock and biomass of Pakistan forest types is fragmented and sporadic. Coniferous forests are more important in carbon sequestration and can play a vital role in mitigating climate change. Pakistan is a signatory of the Kyoto Protocol and still lacks regional and national level studies on biomass and carbon stock, which are necessary for reporting under the Kyoto Protocol. This study will help researchers and decision-makers to develop policies regarding Reducing Emissions from Deforestation and forest Degradation (REDD+), conservation, sustainable forest management and enhancement of forest carbon stocks

Accurately characterizing carbon stock is vital for reporting carbon emissions from forest ecosystems. We studied the estimation of biomass using Sentinel-2 remote sensing data in moist temperate forests in the Galies region of Abbottabad Pakistan. Above-ground biomass (AGB), estimated from 60 field plots, was correlated with vegetation indices obtained from Sentinel-2 image-to-map AGB using regression models. Furthermore, additional explanatory variables were also associated with AGB in the geo-statistical technique, and kriging interpolation was used to predict AGB. The results illustrate that the atmospherically resistant vegetation index (ARVI) is the best index (R² = 0.67) for estimating AGB. In spectral reflectance, Band 1(Coastal Aerosol 443 nm) performs better than other bands. Multiple linear regression models calibrated with ARVI, NNIR and NDVI yielded better results (R² = 0.46) with the lowest RMSE (48.53) and MAE (38.42) and were therefore considered better for biomass estimation. On the other hand, in the geo-statistical technique, distance to settlements, ARVI and annual precipitation were significantly correlated with biomass compared to others. In the stepwise regression method, the forward selection resulted in a very significant value (less than 0.000) for ARVI. Therefore, it can be considered best for prediction and used to interpolate AGB through kriging. Compared to the geo-statistical technique, the remote sensing-based models performed relatively well. Regarding potential sites for REDD+ implementation, temporal analysis of Landsat images showed a decrease in forest area from 8896.23 ha in 1988 to 7692.03 ha in 2018. Therefore, this study concludes that the state-of-the-art open-source sensor, the Sentinel-2 data, has significant potential for forest biomass and carbon stock estimation and can be used for robust regional AGB estimation with acceptable accuracy and frequent availability.

The growing population and the impacts of climate change present a major challenge to forests, which play a crucial role in regulating the carbon cycle. Pakistan, as a Kyoto Protocol signatory, has implemented afforestation initiatives such as the Khyber Pakhtunkhwa (KP) government’s Billion Tree Afforestation Project (BTAP). Quantifying the environmental impacts of such initiatives is very important; however, carbon pool data for BTAP plantation regions remain unavailable and are underexplored. This study aims to quantify aboveground biomass (AGB) and carbon sequestration potential (CSP) in the BTAP plantation regions using remote sensing and field data. Random sampling of 310 circular plots (17.84 m radius) provided measurements for tree height and diameter, from which AGB was calculated using allometric equations. Remote sensing data from Sentinel-1 and Sentinel-2, combined with polarization rasters and vegetation indices, were used to train and evaluate multiple regression models including multiple linear regression (MLR), support vector regression (SVR), and random forest regression (RFR). The RFR model outperformed the others (R² = 0.766) when using combined optical and radar data, yielding a mean AGB of 4.77 t/ha, carbon stock of 2.24 t/ha, and CO₂ equivalent of 10.36 t/ha. For BTAP plantations, the total biomass reached 1.19 million tons, with 2.06 million tons of CO₂ equivalent sequestered, corresponding to an annual sequestration of 0.47 tC/ha/yr and a potential of 99.18 ± 15 tC/ha. This research introduces innovative predictive models and a comprehensive carbon assessment framework for afforestation projects, providing critical insights for policymakers and climate change mitigation efforts.

| This study attempts to develop an allometric model for biomass estimation of Chirpine ( Pinus roxberghii) an important species of subtropical pine ecosystems in the Himalayas. Data was collected through destructive sampling of 32 sample trees growing in subtropical pine forests of Hazara Forest Region, Khyber Pakhtunkhwa (KP), Pakistan. Diameter at Breast Height (DBH) of the selected sample trees ranged from (12-93 cm) and height (H) varied between 9.50-40.10 m. Allometric equation was developed through testing different allometric models. The best fit model was obtained through logarithmic transformation of response and explanatory variables. It was found that square of DBH and H (D 2 H) was the best independent variable for predicting above ground biomass of an individual tree. The equation was also evaluated and compared with the existing generic allometric equations. Results showed that the allometric equation developed in this study gave more reliable estimates of aboveground biomass. A strong relationship (r 2 = 0.99) found for tree biomass as response variable and DBH with height as explanatory variables. The significance of the model was demonstrated by F and P values. The model was found a good fit on the basis of values of sum of square (SS) of the residuals and standard error. Homogeneity of variance and normality of residuals were also validated for checking accuracy of the model. The result can be used for estimation of the above ground biomass and carbon stocks in subtropical pine forests of the Khyber Pakhtunkhwa and other areas which represent similar ecological conditions.