This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m²/year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.

Over the past few decades, there has been a rapid change in forest and land cover, especially in tropical forests due to massive deforestation. The major factor responsible for the changes is to fulfill the growing demand of increasing population through agricultural intensification, rural settlements, and urbanization. Monitoring forest cover and vegetation are essential for detecting regional and global environmental changes. The present study evaluates the influence of deforestation on land surface temperature (LST) in the states of Kedah and Perak, Malaysia, between 1988 and 2017. The trend in forest cover change over the time span of 29 years, was analyzed using Landsat 5 and Landsat 8 satellite images to map the sequence of forest cover change. With the measurement of deforestation and its relationship with LST as an end goal, the Normalized Difference Vegetation Index (NDVI) was used to determine forest health, and the spectral radiance model was used to extract the LST. The findings of the study show that nearly 16% (189,423 ha) of forest cover in Perak and more than 9% (33,391 ha) of forest cover in Kedah have disappeared within these 29 years as a result of anthropogenic activities. The correlation between the LST and NDVI is related to the distribution of forests, where LST is inversely related to NDVI. A strong correlation between LST and NDVI was observed in this study, where the average mean of LST in Kedah (25 °C) is higher than in Perak (22.6 °C). This is also reflected by the decreased NDVI value from 0.6 to 0.5 in 2017 at both states. This demonstrated that a decrease in the vegetation area leads to an increase in the surface temperature. The resultant forest change map would be helpful for forest management in terms of identifying highly vulnerable areas. Moreover, it could help the local government to formulate a land management plan.

Cancer is the second-ranked disease and a cause of death for millions of people around the world despite many kinds of available treatments. Phytochemicals are considered a vital source of cancer-inhibiting drugs and utilize specific mechanisms including carcinogen inactivation, the induction of cell cycle arrest, anti-oxidant stress, apoptosis, and regulation of the immune system. Family Fabaceae is the second most diverse family in the plant kingdom, and species of the family are widely distributed across the world. The species of the Fabaceae family are rich in phytochemicals (flavonoids, lectins, saponins, alkaloids, carotenoids, and phenolic acids), which exhibit a variety of health benefits, especially anti-cancer properties; therefore, exploration of the phytochemicals present in various members of this family is crucial. These phytochemicals of the Fabaceae family have not been explored in a better way yet; therefore, this review is an effort to summarize all the possible information related to the phytochemical status of the Fabaceae family and their anti-cancer properties. Moreover, various research gaps have been identified with directions for future research.

Rabigh Lagoon, located on the eastern coast of the Red Sea, is an ecologically rich zone in Saudi Arabia, providing habitat to Avicennia marina mangrove trees. The environmental quality of the lagoon has been decaying since the 1990s mainly from sedimentation, road construction, and camel grazing. However, because of remedial measures, the mangrove communities have shown some degree of restoration. This study aims to monitor mangrove health of Rabigh Lagoon during the time it was under stress from road construction and after the road was demolished. For this purpose, time series of EVI (Enhanced Vegetation Index), MSAVI (Modified, Soil-Adjusted Vegetation Index), NDVI (Normalized Difference Vegetation Index), and NDMI (Normalized Difference Moisture Index) have been used as a proxy to plant biomass and indicator of forest disturbance and recovery. Long-term trend patterns, through linear, least square regression, were estimated using 30 m annual Landsat surface-reflectance-derived indices from 1986 to 2019. The outcome of this study showed (1) a positive trend over most of the study region during the evaluation period; (2) most trend slopes were gradual and weakly positive, implying subtle changes as opposed to abrupt changes; (3) all four indices divided the times series into three phases: degraded mangroves, slow recovery, and regenerated mangroves; (4) MSAVI performed best in capturing various trend patterns related to the greenness of vegetation; and (5) NDMI better identified forest disturbance and recovery in terms of water stress. Validating observed patterns using only the regression slope proved to be a challenge. Therefore, water quality parameters such as salinity, pH/dissolved oxygen should also be investigated to explain the calculated trends.

This study evaluates the Billion Tree Afforestation Project (BTAP) in Pakistan's Khyber Pakhtunkhwa (KPK) province using remote sensing and machine learning. Applying Random Forest (RF) classification to Sentinel‐2 imagery, we observed an increase in tree cover from 25.02% in 2015 to 29.99% in 2023 and a decrease in barren land from 20.64% to 16.81%, with an accuracy above 85%. Hotspot and spatial clustering analyses revealed significant vegetation recovery, with high‐confidence hotspots rising from 36.76% to 42.56%. A predictive model for the Normalized Difference Vegetation Index (NDVI), supported by SHAP analysis, identified soil moisture and precipitation as primary drivers of vegetation growth, with the ANN model achieving an R ² of 0.8556 and an RMSE of 0.0607 on the testing dataset. These results demonstrate the effectiveness of integrating machine learning with remote sensing as a framework to support data‐driven afforestation efforts and inform sustainable environmental management practices.

The Juniper forest reserve of Ziarat is one of the biggest Juniperus forests in the world. This study assessed the land-use changes and carbon stock of Ziarat. Different types of carbon pools were quantified in terms of storage in the study area in tons/ha i.e., above ground, soil, shrubs and litter. The Juniper species of this forest is putatively called Juniperus excelsa Beiberstein. To estimate above-ground biomass, different allometric equations were applied. Average above ground carbon stock of the forest was estimated as 8.34 ton/ha, 7.79 ton/ha and 8.4 ton/ha using each equation. Average carbon stock in soil, shrubs and litter was calculated as 24.35 ton/ha, 0.05 ton/ha and 1.52 ton/ha, respectively. Based on our results, soil carbon stock in the Juniper forest of Ziarat came out to be higher than the living biomass. Furthermore, the spatio-temporal classified maps for Ziarat showed that forest area has significantly decreased, while agricultural and barren lands increased from 1988 to 2018. This was supported by the fact that estimated carbon stock also showed a decreasing pattern between the evaluation periods of 1988 to 2018. Furthermore, the trend for land use and carbon stock was estimated post 2018 using a linear prediction model. The results corroborate the assumption that under a business as usual scenario, it is highly likely that the Juniperus forest will severely decline.