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Abstract The Manchar Lake wetland complex, Pakistan’s largest freshwater-lake, faces unprecedented ecological challenges amidst climate change and human pressures, necessitating urgent, data-driven conservation strategies. This study employs cutting-edge multi-sensor remote sensing techniques to quantify and analyze the dynamic changes in this critical ecosystem from 2015 to 2023, aiming to provide a comprehensive understanding of wetland dynamics for informed management decisions. Integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 multispectral imagery, we assessed changes in wetland extent, vegetation health, and land-use patterns using spectral indices and topographic data. Our methodology achieved classification accuracies exceeding 92% across all study years, revealing significant ecosystem fluctuations. Water body extent exhibited a non-linear trend, expanding from 318.5 km² (5%) in 2015 to 397.0 km² (7%) in 2019, before contracting to 369.9 km² (6%) in 2023. This pattern was corroborated by MNDWI values. Concurrently, vegetation covers dramatically increased from 405.5 km² (7%) in 2019 to 1081.6 km² (18%) in 2023. The Enhanced Vegetation Index (EVI) reflected this trend, decreasing from 0.61 in 2015 to 0.41 in 2019, before recovering to 0.53 in 2023. Land use changes were substantial, with agricultural areas increasing from 118.4 km² (2%) in 2015 to 498.0 km² (8%) in 2023. SAR data consistently supported these observations. Topographic analysis, including the Topographic Wetness Index (TWI), provided crucial insights into wetland distribution and resilience. This comprehensive analysis highlights the complex interplay between natural processes and human influences shaping the Manchar-Lake ecosystem, underscoring the urgent need for adaptive management strategies in the face of rapid environmental change.

Abstract The plant extract-mediated method is eco-friendly, simple, safe, and low-cost, using biomolecules as a reducing agent to separate nanoparticles. Lanthanum (La) is a rare earth metal that positively affects plant growth and agriculture. Citrus limon is a leading citrus fruit with many varieties. Conventional vegetative propagation methods depend on season, availability of plant material and are time-consuming. It is the main reason for limiting the acceptance of new varieties. So, In-vitro propagation of the lemon method is practiced overcoming all these problems. Lanthanum oxide nanoparticles (La2O3-NPs) were synthesized using plant extract of C. aurantium. Ultraviolet (UV)-Visible Spectroscopy, Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Fourier Transform Infrared (FTIR) spectroscopy, and Thermal Gravimetric Analysis (TGA) were used to characterize the synthesized La2O3-NPs. Fabricated La2O3-NPs were oval and spherical, with an average size of 51.1 nm. UV-visible absorption spectra of La2O3-NPs were shown at a sharp single peak at 342 nm and FTIR showed stretching frequency at 455 cm−1-516 cm−1. In the TGA outcome, mass loss was 9.1%. In vitro experiments demonstrated that La2O3-NPs significantly enhanced the germination and growth of C. limon seeds, achieving an 83% germination rate at 5 mg/L concentration, with uncoated seeds showing root initiation at 10 days and shoot formation at 15 days. Furthermore, La2O3-NPs effectively stimulated callus induction and maturation, with optimal responses observed in media containing MS and 2 mg/L 2,4-D, resulting in a maximum callus frequency of 100% from leaves and 87.5% from shoots at 5 mg/L concentration. These findings underscore the potential of La2O3-NPs to improve seed germination rates, seedling vigor, and callogenesis efficiency, suggesting their promising integration into agricultural practices for sustainable crop production, especially in suboptimal growing conditions. Future research is recommended to explore the mechanisms and broader applications of La2O3-NPs across various plant species and environments.

Abstract The management of groundwater systems is essential for nations that rely on groundwater as the principal source of communal water supply (e.g., Mohmand District of Pakistan). The work employed Remote Sensing and GIS datasets to ascertain the groundwater recharge zones (GWRZ) in the Mohmand District of Pakistan. Subsequently, a sensitivity analysis was conducted to examine the impact of geology and hydrologic factors on the variability of the GWRZ. The GWRZ was determined by employing weighted overlay analysis on thematic maps derived from datasets about drainage density, slope, geology, rainfall, lineament density, land use/land cover, and soil types. The use of multi-criteria decision analysis (MCDA) involves the utilization of the multi-influencing factor (MIF) and analytical hierarchy procedure (AHP) to allocate weights to the selected influencing factors. The MIF data found that very high groundwater recharge spanned 1.20%, high zones covered 40.44%, moderate zones covered 50.81%, and low zones covered 7.54%. In comparison, the AHP technique results suggest that 1.81% of the whole area is very high, 33.26 is high, 55.01% is moderate, and 9.92% has low groundwater potential. The geospatial-assisted multi-influencing factor approach helps increase conceptual knowledge of groundwater resources and evaluate possible groundwater zones.

Abstract Matricaria chamomilla (Asteraceae), commonly known as chamomile can tolerate freezing temperatures and grows in many soil types. This plant is found on all continents and has significant medicinal value. There are more than 120 chemicals detected in chamomile flowers, with the majority found in the essential oil. In this study, M. chamomilla was given the NaCl stress of 0 mM, 1 mM, 100 mM, and 150 mM concentrations This study was the first to assess the efficacy of German chamomile upon exposure to salt stress hence plant particles that had been dried and powdered were analyzed using, phytochemical tests, Fourier Transform Infrared and UV–Vis spectroscopy, thin layer chromatography, fluorescence recovery after photobleaching assay, antibacterial and antioxidant activity. The characterization and results of these activities show amazing results which enhance their antibacterial property with an increased zone of inhibition when the samples of salt stress of the above-given concentrations were compared to the control samples. More graph analysis indicates an effective impact of salt stress on the phytoconstituents of M. chamomilla. Other than that, there was a clear flower induction upon salt stress, as a variety of compounds are regarded as essential to the biological functions of chamomile flowers according to the phytoconstituent screening which can be further used in the cosmetic industry, pharmaceutical industry, and all other fields as well for various application as a nano-drug or bio-drug. Due to this, this plant became essential for plant biotechnology research.

Abstract Dragon fruit (Selenicereus undatus), known for its captivating appearance and remarkable nutritional profile, has garnered considerable attention in recent years. Despite its popularity, there's a dearth of research on optimal conditions for seed germination and early growth stages such as seedling shoot length, which are crucial for optimal crop yield. This study aims to bridge this gap by evaluating various growing media's performance on dragon fruit germination and early growth stages. Dragon fruit seeds were obtained from local markets in Pakistan and evaluated in five different growing media: cocopeat, peat moss, sand, vermiculite, and compost. Germination parameters were observed for 45 days, including seed germination percentage, mean germination time, and mean daily germination percentage, among others while early growth was monitored for 240 days. Statistical analysis was conducted using ANOVA and Tukey’s HSD test. Significant differences were found among the growing media regarding germination percentage, mean germination time, and mean daily germination. Vermiculite exhibited the highest germination rate (93.33%), while compost showed the least (70%). Peat moss and sand media facilitated rapid germination, while compost showed slower rates. Stem length was significantly influenced by the growth media, with compost supporting the longest stems. Vermiculite emerged as the most effective medium for dragon fruit seed germination, while compost showed slower but steady growth. These findings provide valuable insights for optimizing dragon fruit cultivation, aiding commercial growers and enthusiasts in achieving higher yields and quality. Further research could explore additional factors influencing dragon fruit growth and development.

Abstract An experimental setup was developed for simulating the field conditions to determine the force and power required for cutting cumin crops in dynamic conditions. The effect of cutter bar speeds, forward speeds, and blade type on cutting force and power requirement for cutting cumin were also studied. Experiments were carried out at three levels: cutter bar speeds, forward speeds, and blade type. The results showed that all the factors significantly affected cutting force. The cutting force followed a decreasing trend with the increase in cutter bar speed. Whereas it followed an increasing trend with the increase in forward speed. The maximum cutting force for all three blades was observed at a cutter bar speed of 2.00 strokes.s-1 and forward speed of 0.46 m.s−1. The idle power and actual power required for cutting the cumin crop were also determined based on the cutting force. The results obtained were validated by the power drawn from the power source while operating the cutter bar blades. The R2 values for Blade-B1, Blade-B2, and Blade-B3 were 0.90, 0.82, and 0.88, respectively. The cutting force was primarily affected by the cutter bar speed, resulting in PCR values of 74.20%, 82.32%, and 81.75% for Blade-B1, Blade-B2, and Blade-B3, respectively, followed by the forward speed, which also had an impact on PCR values of 16.60%, 15.27%, and 18.25% for Blade-B1, Blade-B2, and Blade-B3, respectively. The cutting force for Blade-B1, Blade-B2, and Blade-B3 varied from 15.96 to 58.97 N, 21.08 to 76.64 N, and 30.22 to 85.31, respectively, for the selected range of cutter bar speed and forward speed. Blade-B1 had 18 and 30% less power consumption than Blade-B2 and Blade-B3, respectively.

Abstract Background Genetic diversity, population structure, agro-morphological traits, and molecular characteristics, are crucial for either preserving genetic resources or developing new cultivars. Due to climate change, water availability for agricultural use is progressively diminishing. This study used 100 molecular markers (25 TRAP, 22 SRAP, 23 ISTR, and 30 SSR). Additionally, 15 morphological characteristics were utilized to evaluate the optimal agronomic traits of 12 different barley genotypes under arid conditions. Results Substantial variations, ranging from significant to highly significant, were observed in the 15 agromorphological parameters evaluated among the 12 genotypes. The KSU-B101 barley genotype demonstrated superior performance in five specific traits: spike number per plant, 100-grain weight, spike number per square meter, harvest index, and grain yield. These results indicate its potential for achieving high yields in arid regions. The Sahrawy barley genotype exhibited the highest values across five parameters, namely leaf area, spike weight per plant, spike length, spike weight per square meter, and biological yield, making it a promising candidate for animal feed. The KSU-B105 genotype exhibited early maturity and a high grain count per spike, which reflects its early maturity and ability to produce a high number of grains per spike. This suggests its suitability for both animal feed and human food in arid areas. Based on marker data, the molecular study found that the similarity coefficients between the barley genotypes ranged from 0.48 to 0.80, with an average of 0.64. The dendrogram constructed from these data revealed three distinct clusters with a similarity coefficient of 0.80. Notably, the correlation between the dendrogram and its similarity matrix was high (0.903), indicating its accuracy in depicting the genetic relationships. The combined analysis revealed a moderate correlation between the morphological and molecular analysis, suggesting alignment between the two characterization methods. Conclusions The morphological and molecular analyses of the 12 barley genotypes in this study effectively revealed the varied genetic characteristics of their agro-performance in arid conditions. KSU-B101, Sahrawy, and KSU-B105 have emerged as promising candidates for different agricultural applications in arid regions. Further research on these genotypes could reveal their full potential for breeding programs.

Congenital myasthenia gravis syndrome (CMS) is a rare genetic heterogeneous disorder due to abnormal neuromuscular transmission caused by several genes. CMS caused by choline acetyltransferase (ChAT) gene mutations have been reported in 53 cases since 2020, and only three of them have been identified as hemizygous mutations.

Abstract Pea, member of the plant family Leguminosae, play a pivotal role in global food security as essential legumes. However, their production faces challenges stemming from the detrimental impacts of abiotic stressors, leading to a concerning decline in output. Salinity stress is one of the major factors that limiting the growth and productivity of pea. However, biochar amendment in soil has a potential role in alleviating the oxidative damage caused by salinity stress. The purpose of the study was to evaluate the potential role of biochar amendment in soil that may mitigate the adverse effect of salinity stress on pea. The treatments of this study were, (a) Pea varieties; (i) V1 = Meteor and V2 = Green Grass, Salinity Stress, (b) Control (0 mM) and (ii) Salinity (80 mM) (c) Biochar applications; (i) Control, (ii) 8 g/kg soil (56 g) and (iii) 16 g/kg soil (112 g). Salinity stress demonstrated a considerable reduction in morphological parameters as Shoot and root length decreased by (29% and 47%), fresh weight and dry weight of shoot and root by (85, 63%) and (49, 68%), as well as area of leaf reduced by (71%) among both varieties. Photosynthetic pigments (chlorophyll a, b, and carotenoid contents decreased under 80 mM salinity up to (41, 63, 55 and 76%) in both varieties as compared to control. Exposure of pea plants to salinity stress increased the oxidative damage by enhancing hydrogen peroxide and malondialdehyde content by (79 and 89%), while amendment of biochar reduced their activities as, (56% and 59%) in both varieties. The activities of catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD) were increased by biochar applications under salinity stress as, (49, 59, and 86%) as well as non-enzymatic antioxidants as, anthocyanin and flavonoids improved by (112 and 67%). Organic osmolytes such as total soluble proteins, sugars, and glycine betaine were increased up to (57, 83, and 140%) by biochar amendment. Among uptake of mineral ions, shoot and root Na+ uptake was greater (144 and 73%) in saline-stressed plants as compared to control, while shoot and root Ca2+ and K+ were greater up to (175, 119%) and (77, 146%) in biochar-treated plants. Overall findings revealed that 16 g/kg soil (112 g) biochar was found to be effective in reducing salinity toxicity by causing reduction in reactive oxygen species and root and shoot Na+ ions uptake and improving growth, physiological and anti-oxidative activities in pea plants (Fig. 1). Figure 1 A schematic diagram represents two different mechanisms of pea under salinity stress (control and 80 mM NaCl) with Biochar (8 and 16 g/kg soil).

Abstract Heavy metals (HMs) contamination, owing to their potential links to various chronic diseases, poses a global threat to agriculture, environment, and human health. Nickel (Ni) is an essential element however, at higher concentration, it is highly phytotoxic, and affects major plant functions. Beneficial roles of plant growth regulators (PGRs) and organic amendments in mitigating the adverse impacts of HM on plant growth has gained the attention of scientific community worldwide. Here, we performed a greenhouse study to investigate the effect of indole-3-acetic acid (IAA @ 10− 5 M) and compost (1% w/w) individually and in combination in sustaining cauliflower growth and yield under Ni stress. In our results, combined application proved significantly better than individual applications in alleviating the adverse effects of Ni on cauliflower as it increased various plant attributes such as plant height (49%), root length (76%), curd height and diameter (68 and 134%), leaf area (75%), transpiration rate (36%), stomatal conductance (104%), water use efficiency (143%), flavonoid and phenolic contents (212 and 133%), soluble sugars and protein contents (202 and 199%), SPAD value (78%), chlorophyll ‘a and b’ (219 and 208%), carotenoid (335%), and NPK uptake (191, 79 and 92%) as compared to the control. Co-application of IAA and compost reduced Ni-induced electrolyte leakage (64%) and improved the antioxidant activities, including APX (55%), CAT (30%), SOD (43%), POD (55%), while reducing MDA and H2O2 contents (77 and 52%) compared to the control. The combined application also reduced Ni uptake in roots, shoots, and curd by 51, 78 and 72% respectively along with an increased relative production index (78%) as compared to the control. Hence, synergistic application of IAA and compost can mitigate Ni induced adverse impacts on cauliflower growth by immobilizing it in the soil.

The essential oils extracted from the leaves of five Eucalyptus species: E. astringens, E. camaldulensis, E. lehmannii, E. leucoxylon, and E. sideroxylon, were investigated for their antimicrobial properties. These species were growing in the same plantation area, exposed to identical conditions, and subjected to uniform agronomic practices. Processed and analyzed under consistent parameters, the essential oil yields ranged from 0.14 to 0.96% (w/w). Chromatographic analysis were resolved into 48 compounds, with 11 common to all oils. Terpenoids (oxygenated mono- and sesquiterpenes) dominated the oil profiles, constituting 55.66–76.67% of the composition. Major components identified included 1,8-cineole (21.97–50.93%), α-pinene (2.18–15.95%), p-cymene (0.83–15.94%), spathulenol (0–20.49%), globulol (4.09–14.26%), and aromadendrene (2.37–15.03%). Genetically driven interspecific variation in composition was observed through Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and heatmap clustering. Moreover, distinctive components were identified for each essential oil, offering a valuable tool for discriminating between Eucalyptus species and ensuring authentication and quality control in commercial samples. Results from antimicrobial disc-diffusion assays indicated robust antimicrobial activity in all essential oils, with those derived from E. camaldulensis, E. lehmannii, and E. leucoxylon exhibiting the highest effectiveness.

The aim of this study was to evaluate the carcass characteristics, meat quality and fatty acid profile of lambs fed different levels of sprouted barley (SB) compared to a control group fed a diet based on barley grain and alfalfa hay. Forty-five intact male Awassi lambs (3 months old) were included in the study and were fed for 75 days, divided equally into five feeding groups based on their average body weight. The groups were fed diets containing 25%, 50%, 75% and 100% SB replacing the control diet of barley grain and alfalfa hay (SB25, SB50, SB75 and SB100, respectively). The results showed that SB (SB25–SB75) had no effect on body weight gain while a significant gradual decrease in dry matter, crude protein and crude fat intake with the inclusion of SB (SB50–SB100). Higher rates of SB, especially 50%, 75% and 100%, resulted in a remarkable decrease in body wall fat. In addition, the treatment groups receiving SB50, SB75 and SB100 had a significantly lighter ultimate meat color compared to the control diet (p

Cellular automata (CA) models are employed for simulating geographical distributions, while Markov-Chain models are utilized for simulating temporal changes. This study aims to forecast the dynamics of land use and land cover (LULC) change and land surface temperature (LST) using a CA and Markov-Chain model for the period 1992–2022 in Islamabad, Pakistan. This research innovatively predicts future LULC and LST and examines their correlations with various vegetation indexes. LULC maps were generated from time series data of Landsat satellite images (Landsat 5, 7, and 8) for the years 1992, 2002, 2012, and 2022, utilizing the random forest algorithm. Five satellite indexes were employed: normalized difference vegetation index (NDVI), bare soil index (BSI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), and soil adjusted vegetation index (SAVI). Through the CA-Markov-Chain analysis, the results showed significant new findings, such as a rise in the built-up area of Islamabad, which increased from 105.63 km2 (11.66%) in 1992 to 447.39 km2 (49.38%) in 2022. The study predicted that the built-up area would further increase to 531.82 km2 (55.38%) by 2042. The study analyzed the enhancement of LST, which was about 2.40 °C from 1992 to 2022, ultimately because of the expansion of uncontrolled urban areas. The correlation of LST with the vegetation indexes NDVI, BSI, NDWI, NDBI, and SAVI was also analyzed through regression analysis. Furthermore, the surface temperature was predicted well by the urban index (UI), a nonvegetation index, demonstrating the positive correlation of R2 = 0.87 with respect to retrieved surface temperature. Using the UI as a predictor of LST, our projections indicate that regions with temperatures ranging from 20 to 24 °C and from 25 to 28 °C will decrease in coverage from 4.63% to 3.85% and from 25.33% to 21.08%, respectively, between 2032 and 2042.

Rapid urbanization and industrialization in Lahore and Faisalabad have intensified air pollution issues, influencing nitrogen dioxide (NO2) concentrations, land surface temperature (LST), and vegetation. The study aims to comprehensively assess changes in NO2, LST, and vegetation induced by industrialization, focusing on seasonal variations from 2019 to 2022. The study evaluates NO2 concentrations vegetation health using indices normalized difference vegetation index, enhanced vegetation index, atmospherically resistant vegetation index, and LST variations. The analysis reveals a notable increase in NO2 during both summer and winter, with approximately 0.021 (×103 mol/m2) and 0.03 (×103 mol/m2) rises observed in Lahore. In comparison, Faisalabad experienced more modest increases of around 0.0034 (×103 mol/m2) and 0.007 (×103 mol/m2) in the respective seasons. Simultaneously, vegetation indices decline in both cities, indicating substantial vegetation health deterioration. Moreover, a notable upward trend in LST occurred, with Lahore experiencing an increase of approximately 1.59 °C in summer and 0.92 °C in winter. Faisalabad also showed rises of around 1.64 and 0.54 °C in the corresponding seasons. Pearson correlation analysis highlights a robust negative correlation between NO2 and vegetation indices, underlining the impact of declining vegetation on air quality. A positive correlation between NO2 and LST indicates the interconnected nature of rising temperatures and air pollution. The findings emphasize the need for environmental regulations in Lahore and Faisalabad. Addressing rising NO2 levels and temperatures is critical for policymakers and urban planners. These insights contribute to the sustainable development goal 11, fostering strategies for sustainable cities and communities to combat pressing environmental challenges in these urban areas.

In this research, we used the Revised Universal Soil Loss Equation (RUSLE) and Geographical Information System (GIS) to predict the annual rate of soil loss in the District Chakwal of Pakistan. The parameters of the RUSLE model were estimated using remote sensing data, and the erosion probability zones were determined using GIS. The estimated length slope ($LS$), crop management ($C$), rainfall erosivity ($R$), soil erodibility ($K$), and support practice ($P$) range from 0–68,227, 0–66.61%, 0–0.58, 495.99–648.68 $\; MJ/mm.t.ha^{-1}.year^{-1}\comma \;$ 0.15–0.25 $MJ/mm.t.ha^{-1}.year^{-1}$, and 1 respectively. The results indicate that the estimated total annual potential soil loss of approximately 4,67,064.25 $t.ha^{-1}.year^{-1}$ is comparable with the measured sediment loss of 11,631 $t.ha^{-1}.year^{-1}\;$ during the water year 2020. The predicted soil erosion rate due to an increase in agricultural area is approximately 164,249.31 $t.ha^{-1}.year^{-1}$. In this study, we also used Landsat imagery to rapidly achieve actual land use classification. Meanwhile, 38.13% of the region was threatened by very high soil erosion, where the quantity of soil erosion ranged from 365487.35 $t.ha^{-1}.year^{-1}$. Integrating GIS and remote sensing with the RUSLE model helped researchers achieve their final objectives. Land-use planners and decision-makers use the result's spatial distribution of soil erosion in District Chakwal for conservation and management planning.

Precision agriculture which facilitates and enables crop management through site-specific recommendations, is essential to optimize agricultural inputs in space and time. In this study, we used Landsat and MODIS-NDVI product data with climatic, topographic data and laboratory-analyzed soil samples to map the spatial distribution of seven soil properties; soil texture (T), electrical conductivity (EC), potential hydrogen (pH), nitrogen (N), phosphorus (P), potassium (K), and organic matter (OM) in the Punjab, Pakistan from 2000 to 2020. We examined and compared three statistical prediction models: the support vector machine (SVM), the random forest regression (RFR), and the multiple linear regression (MLR). The predictions were validated against a separate set of soil samples while considering the modeling region and an extrapolation area. Model performance statistics showed that the RFR often provided the highest accuracy, with the machine learning algorithms performing slightly better than the MLR. It was discovered that one obstacle to accurately forecasting soil parameters at unsampled areas with MLR was its inability to handle non-linear connections between independent and dependent variables. The results indicate that the cultivated area decreased from 43.16 % in 2000 to 38.24% in 2020. The soil has a high level of EC due to salinity. In general, the soils contained

Groundwater contamination poses a severe public health risk in Lahore, Pakistan’s second-largest city, where over-exploited aquifers are the primary municipal and domestic water supply source. This study presents the first comprehensive district-wide assessment of groundwater quality across Lahore using an innovative integrated approach combining geographic information systems (GIS), multi-criteria decision analysis (MCDA), and water quality indexing techniques. The core objectives were to map the spatial distributions of critical pollutants like arsenic, model their impacts on overall potability, and evaluate targeted remediation scenarios. The analytic hierarchy process (AHP) methodology was applied to derive weights for the relative importance of diverse water quality parameters based on expert judgments. Arsenic received the highest priority weight (0.28), followed by total dissolved solids (0.22) and hardness (0.15), reflecting their significance as health hazards. Weighted overlay analysis in GIS delineated localized quality hotspots, unveiling severely degraded areas with very poor index values (>150) in urban industrial zones like Lahore Cantt, Model Town, and parts of Lahore City. This corroborates reports of unregulated industrial effluent discharges contributing to aquifer pollution. Prospective improvement scenarios projected that reducing heavy metals like arsenic by 30% could enhance quality indices by up to 20.71% in critically degraded localities like Shalimar. Simulating advanced multi-barrier water treatment processes showcased an over 95% potential reduction in arsenic levels, indicating the requirement for deploying advanced oxidation and filtration infrastructure aligned with local contaminant profiles. The integrated decision support tool enables the visualization of complex contamination patterns, evaluation of remediation options, and prioritizing risk-mitigation investments based on the spatial distribution of hazard exposures. This framework equips urban planners and utilities with critical insights for developing targeted groundwater quality restoration policies through strategic interventions encompassing treatment facilities, drainage infrastructure improvements, and pollutant discharge regulations. Its replicability across other regions allows for tackling widespread groundwater contamination challenges through robust data synthesis and quantitative scenario modeling capabilities.

Multi-temporal unmanned aerial vehicle (UAV) imagery and topographic data were used to characterize and evaluate the geomorphic changes of two active landslides (Nara and Nokot) in Pakistan. Ortho-mosaic images and field-based investigations were utilized to assess the geomorphological changes, including the Topographic Wetness Index, slope, and displacement. Volumetric changes in specific areas of the landslides were measured using the Geomorphic Change Detection (GCD) tool. The depletion zone of the Nara landslide was characterized by failures of the main scarps, resulting in landslides causing erosional displacements exceeding 201.6 m. In contrast, for the Nokot landslide, the erosional displacement ranged from −201.05 m to −64.98 m. The transition zone of the slide experienced many slow earth flows that re-mobilized displaced material from the middle portion of the landslide, ultimately reaching the accumulation zone. Volumetric analysis of the Nara landslide indicated overall erosion of landslide material with a volume of approximately 4,565,274.96 m3, while the accumulated and surface-raising material volume was approximately 185,544.53 m3. Similarly, for the Nokot landslide, the overall erosion of landslide material was estimated to be 6,486,121.30 m3, with an accumulated volume and surface-raising material of 117.98 m3. This study has demonstrated the efficacy of the GCD tool as a robust and repeatable method for mapping and monitoring landslide dynamics with UAVs over a relatively long time series.