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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 extent of wildfires cannot be easily mapped using field-based methods in areas with complex topography, and in those areas the use of remote sensing is an alternative. This study first obtained images from the Sentinel-2 satellites for the period 2015–2020 with the objective of applying multi-temporal spectral indices to assess areas burned in wildfires and prescribed fires in the Margalla Hills of Pakistan using the Google Earth Engine (GEE). Using those images, the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), which are often used to assess the severity of fires, were calculated for wildfires and prescribed fires. For each satellite image, spectral indices values were extracted for the 5th, 20th, 40th, 60th, 80th and 95th percentiles of pixels of each burned area. Then, boxplots representing the distribution of these values were plotted for each satellite image to identify whether the regeneration time subsequent to a fire, also known as the burn scar, and the severity of the fire differed between the autumn and summer wildfires, and with prescribed fires. A statistical test revealed no differences for the regeneration time amongst the three categories of fires, but that the severity of summer wildfires was significantly different from that of prescribed fire, and this, for both indices. Second, SAR images were obtained from the Sentinel-1 mission for the same period as that of the optical imagery. A comparison of the response of 34 SAR variables with official data on wildfires and prescribed fires from the Capital Development Authority revealed that the 95th percentile of the Normalized Signal Ratio (NSR p_95) was found to be the best variable to detect fire events, although only 50% of the fires were correctly detected. Nonetheless, when the occurrence of fire events according to the SAR variable NSR p_95 was compared to that from the two spectral indices, the SAR variable was found to correctly identify 95% of fire events. The SAR variable NSR p_95 is thus a suitable alternative to spectral indices to monitor the progress of wildfires and assess their severity when there are limitations to the use of optical images due to cloud coverage or smoke, for instance.

This paper synthesizes research studies on spatial forest assessment and mapping using remote sensing data and techniques in Pakistan. The synthesis states that 73 peer-reviewed research articles were published in the past 28 years (1993–2021). Out of all studies, three were conducted in Azad Jammu & Kashmir, one in Balochistan, three in Gilgit-Baltistan, twelve in Islamabad Capital Territory, thirty-one in Khyber Pakhtunkhwa, six in Punjab, ten in Sindh, and the remaining seven studies were conducted on national/regional scales. This review discusses the remote sensing classification methods, algorithms, published papers’ citations, limitations, and challenges of forest mapping in Pakistan. The literature review suggested that the supervised image classification method and maximum likelihood classifier were among the most frequently used image classification and classification algorithms. The review also compared studies before and after the 18th constitutional amendment in Pakistan. Very few studies were conducted before this constitutional amendment, while a steep increase was observed afterward. The image classification accuracies of published papers were also assessed on local, regional, and national scales. The spatial forest assessment and mapping in Pakistan were evaluated only once using active remote sensing data (i.e., SAR). Advanced satellite imageries, the latest tools, and techniques need to be incorporated for forest mapping in Pakistan to facilitate forest stakeholders in managing the forests and undertaking national projects like UN’s REDD+ effectively.

A global challenge to sustainable development is land degradation, and to achieve land degradation neutrality, monitoring, mapping, and impact assessment of ongoing ecological restoration efforts is necessary. Here, we analyze the desertification process and role of restoration projects at a spatial and temporal scale in Mu Us Desert from 2001 to 2018. We used 17 years of data to (1) assess the vegetation trend including its significance and map land degradation based on Sustainable Development Goal Indicator 15.3.1 (2) address how vegetation activity has changed under the influence of restoration programs and climate change (3) estimate how successful are the positive human interventions to achieve Land degradation neutrality. Results showed an overall increasing vegetation trend (85.69% significant increasing) and a partial decreasing vegetation trend (1.33% significant decreasing) in Mu Us desert. Ecological restoration activities are found to be one of the key driving forces of vegetation restoration in the desert, however, limited impact of climatic factors on vegetation cover change was observed. Results revealed that 41.42% of total significant restoration is attributed to ecological restoration programs out of which 40.42% area has shown improvement in all three sub-indicators of land degradation.