Search

Urban sprawl, also widely known as urbanization, is one of the significant problems in the world. This research aims to assess and predict the urban growth and impact on Land Surface Temperature (LST) of Lahore as well as Land-Use and Land-cover (LULC) with a Cellular–Automata–Markov–Chain (CA–Markov–Chain). LULC and LST distributions were mapped using Landsat (5, 7, and 8) data from 1990, 2004, and 2018. Long–term changes to the landscape were simulated using a CA-Markov model at 14-year intervals from 2018 to 2046. Results indicate that the built-up area was increased from 342.54 (18.41%) km2 to 720.31 (38.71%) km2. Meanwhile, barren land, water and vegetation area was decreased from 728.63 (39.16%) km2 to 544.83 (29.28%) km2, from 64.85 (3.49%) km2 to 34.78 (1.87%) km2 and from 724.53 (38.94%) km2 to 560.63 (30.13%) km2 respectively. In addition, Urban Index, a non-vegetation index, accurately predicted LST, showing the maximum correlation R2 = 0.87 with respect to retrieved LST. According to CA–Markov Chain analysis, we can predict the growth of built-up area from 830.22 km2 to 955.53 km2 between 2032 and 2046, based on the development from 1990 to 2018. As Urban Index as the predictor anticipated that the LST 20–23°C, and 24–27°C, regions would all decline in coverage from 5.30–4.79% and 15.79–13.77% in 2032 and 2046, while the temperature 36–39°C regions would all grow in coverage from 15.60–17.21% of the city. These findings are significant for the planning and development division to ensure the long-term usage of land resources for urbanization expansion projects in the future.

The leaf chlorophyll content (Chlleaf) is a crucial vegetation parameter in carbon cycle modelling and agricultural monitoring at local, regional and global scales. The red-edge spectral region is sensitive to variations in Chlleaf. An increasing number of sensors are capable of sampling red-edge bands, providing opportunities to estimate Chlleaf. However, the contributions of canopy/foliar/soil factors are always combined in the reflectance signal, which limits the generalizability of vegetation index (VI)-based Chlleaf inversions. This study aims to propose a new red-edge chlorophyll index to decouple the effects of the canopy and soil background from the Chlleaf estimation.The chlorophyll sensitive index (CSI) was proposed, and the regression equations between the CSI and Chlleaf were acquired using PROSAIL (PROSPECT + SAIL) and the 4-Scale-PROSPECT model.Sensitivity analyses showed that the CSI is resistant to variations in the canopy structure and soil background. Validation results obtained using 308 ground-measured samples over nine sites world-wide revealed that CSI improves the Chlleaf retrieval accuracy (root mean square error (RMSE = 9.39 μg cm−2) compared with the existing Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI; RMSE = 13.00 μg cm−2). Moreover, the CSI method steadily achieves a highly accurate inversion under different LAI and Chlleaf conditions. Based on the CSI regression method, a Chlleaf product with a 30-m/10-day resolution across China was generated.The CSI is sensitive to Chlleaf but resistant to canopy structure and soil moisture parameters, and it has the potential to explicitly retrieve leaf-scale biochemistry in ecosystem modelling and ecological applications.

Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security, notably from climate change and, for that purpose, remote sensing is routinely used. However, identifying specific crop types, cropland, and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similar spectral signatures. This study applied a methodology to identify cropland and specific crop types, including tobacco, wheat, barley, and gram, as well as the following cropping patterns: wheat-tobacco, wheat-gram, wheat-barley, and wheat-maize, which are common in Gujranwala District, Pakistan, the study region. The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning (ML) methods, namely a Decision Tree Classifier (DTC) and a Random Forest (RF) algorithm. The best time periods for differentiating cropland from other land cover types were identified, and then Sentinel-2 and Landsat 8 NDVI-based time series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms. The methodology was subsequently evaluated using Landsat images, crop statistical data for 2020 and 2021, and field data on cropping patterns. The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images, together with ML techniques, for mapping not only the distribution of cropland but also crop types and cropping patterns when validated at the county level. Hence, this methodology has benefits for the assessment and monitoring of food security in Pakistan, adding to the evidence base of studies on the use of remote sensing to identify crop types and cropping patterns from other countries.

Background: In the era of globalization and industrialization, socioeconomic challenges are increasing day by day. In developing countries including Pakistan, such challenges are of high magnitude which is not only responsible for the financial crisis but also linked with psychosocial and mental health issues. In Pakistan, the area of clinical including neuropsychology is being neglected which has resulted in lethal consequences on psychosocial and mental health. Purpose: The current study was aimed to explore challenges associated with clinical and neuropsychology practice in Pakistan and propose a strategic vision to enhance the scope of clinical psychology in Pakistan. Methods: For this, an in-depth literature review was conducted to explore the challenges and issues linked with the clinical psychology practice. The literature was searched using Google scholar, Pubmed, and Sciences Direct. Result and Conclusion: The article reviewed the past, current situation with futuristic aspects of clinical psychology practice in Pakistan concerning history, higher education content, legislation, training, and credential required for practice. The paper also reflects the status of psychology in Pakistan in comparison to other countries of the world. The stigma that is related to seeking help from clinical psychologists, awareness-related campaigns, and policies for the betterment of society is discussed. Future strategies for the developments and the areas that need to amend have also been recommended in the light of arguments and analysis presented in the paper.

This study emphasizes the awareness and training of farmers on sustainable agricultural practices for restrictive use of pesticides to reduce water pollution caused by pesticides in rural areas. For this assessment, 150 feasible farmers were selected randomly using a convenient sampling technique. Data was collected through interviews (n=30) and questionnaires (n=120). Data was scrutinized in SPSS software. To examine freshwater contamination with pesticides, freshwater samples were collected from open water bodies (5 wells) in the area of tehsil Hasilpur (29.6902° N, 72.5796° E) and Qaimpur of Bahawalpur, Punjab, and analyzed for eight pesticides which are frequently used. ECD: Electron Capture Detector was used to detect the organochlorine pesticides, herbicides, and various halogenated hydrocarbons. NPD: Nitrogen Phosphorus Detector was used to detect nitrogen- or phosphorus[1]containing compounds. Approximately 80% of farmers revealed that they never got any training or informative 64 sessions for pest management other than using chemical pesticides. The remaining 20% were trained by some private non-toxic pesticide manufacturing firms operating in Pakistan. Although non-significant cooperation from the government bodies of the state, the correlation among respondents’ profiles and the extent of training they have received in total was positive. Results for pesticide contamination were astonishing as 6 out of 8 pesticides were detected in water samples with alarming concentrations of Carbofuran (23.1µg/l) and Monocrotophos (8.3µg/l) which can be fatal for animals and humans in prolonged usage. The use of pesticides in a preventive and sustainable way gives more protection against pests and minimizes pollution such as water pollution or air pollution, etc., caused by the chemicals (pesticides). Hence such sustainable practices must be adopted for better production and conservation of the environment.

Urbanization is a global phenomenon that caused many regions worldwide to face dramatic land use land cover (LULC) changes associated with urban sprawl and significant consequences. This study uses satellite images from the last two decades to examine Peshawar's geographical and temporal changes in urban expansion. Analysis of urban sprawl was carried by using the Maximum Likelihood Classification (MLC). Land change modeler combined with the Markov chain model was used to explore the patterns of urban sprawl. Later, the Cellular automata (CA) model was combined with the Markov chain model. Finally, the kappa coefficient and confusion matrix were used to validate the models and LULC maps accuracy. Results indicate a substantial expansion in built-up and vegetation class for Peshawar city replacing bare land. Peshawar's vegetation cover has increased by 25.6% throughout the study period, especially 12.7% under the billion-tree project (from 2013 to 2018). The urban area has increased by 16.3% and will continue to grow in the future, costing the bare land and affecting the water land. Classified images had an overall accuracy rate of almost 80%. According to a 2017 study by the Peshawar Development Authority (PDA), the entire urban built-up area was 264 square kilometers. As a result of our research, the total constructed area in 2018 was 272 km2. Also, the results of 2028 and 2038 indicate that in the future, the vegetation cover will increase from 50.63% in 2018 to 59.23% by 2038. But also the built-up area would be increased from 21.52–25.23% respectively. Education, health, and small-business centers were essential to the development. In addition, the population of the Peshawar District relocated from nearby tribal areas due to military operations.

Zeolites are crystalline microporous aluminosilicates that are extensively used as adsorbents and catalysts in the chemical and petrochemical industry. This is due to the reason they exhibit distinct properties such as hydrothermal stability, large surface area, strong acidity, shape selectivity, and ability to accommodate various metal ions. However, due to their microporous nature, they pose a difficulty in transporting large feed and product molecules. Such limitations can be avoided by creating hierarchical zeolites which then have shorter diffusion paths and larger pore diameters for the movement of bulky molecules. There are numerous approaches by which hierarchical zeolites can be prepared. Generally, these can be categorized as bottom-up approaches (hard templating, soft templating, and zeolitization) and top-down approaches (demetallation, recrystallization, and irradiation). In the present contribution, the aim is to review the important strategies used in synthesizing hierarchical zeolites and to survey the recent advances in the field.

The novel infectious disease (COVID-19) took only a few weeks from its official inception in December 2019 to become a global pandemic in early 2020. Countries across the world went to lockdown, and various strict measures were implemented to reduce the further spread of the infection. Although, the strict lockdown measures were aimed at stopping the spread of COVID-19, however, Its positive implications were also observed for the environmental conditions across the global regions. The present study attempted to explore the eco-restoration of coastal marine system in response to reduced deposition of atmospheric nitrogen (NO2) emission during the substantial shift in human activities across the global metropolitan cities. Remotely data of NO2 emission were taken from Ozone Monitoring Instrument and the coastal water quality along the marine system was estimated from MODIS-Aqua Level-3 using Semi-Analytic Sediment Model (SASM). The changes in tropospheric NO2 in 2020s were also compared with the long-term average changes over the baseline period 2015 − 2019. A significant reduction in anthropogenic mobility (85 − 90%) has been observed in almost all countries over different places, especially grocery, parks, workplaces, and transit stations. A massive reduction in tropospheric NO2 was detected in Wuhan (53%), Berlin (42%), London (41%), Karachi (40%), Paris (38%), Santiago (35%), and Chennai (34%) during the strict lockdown period of the early 2020 as compared to the last five years. However, after the partial lockdown was lifted, tropospheric NO2 values bounced back and slightly increased over Karachi (6%) and Bremen (12%). For water turbidity, the rate of reduction was found to be the highest along the different coastal regions of the Mediterranean Sea and Black Sea (51%), West Atlantic Ocean (32%), East Atlantic Ocean (29%), and Indian Ocean (21%) from Apr to Jun 2020. The monthly comparison of overland-runoff in 2020 compared to 2019 across the different costal watersheds indicates that the observed decline in turbidity might have been due to the reduced deposition of atmospheric nitrogen. The findings of this study suggest that the recent decline in tropospheric NO2 and water turbidity might be associated with reduced emissions from fossil fuels and road transports followed by COVID-19 forced restrictions in the twenty-first century. The inferences made here highlight the hope of improving the global environmental quality by reducing greenhouse gas emissions using innovative periodic confinement measures on heavy transport and industries while securing public health and socioeconomics.

Land use and land cover transition is continue process due to various anthropogenic activities and altering the landscape pattern of the metropolitan area of Lahore over the last two decade. LULC has been emerging masses of environmental problems including land use issues for the inhabitants, city planners, and managers. This research has been focused on the LULC change from 1998 to 2018 and their impact to forecast the landscape pattern of 2023 and 2028 in metropolitan area of Lahore. Numerous satellite imageries including land sat 5 TM and landsat 8 OLI has been undergone by supervised classification for the preparation of LULC map, land change moderler MCL to calculate the transition in LULC and CA-Markov model for predicting upcoming transitions. Results have validated by transition matrix, Google Earth data and kappa statistics. Derived results has depicted the decreased in water bodies 2.70% to 0.60%, vegetation 24.90% to 22.60% and barren land 42.50% to 35.70% and increased in built up area 29.80 % to 41% between 1998 to 2018. Finding clearly represents the loss of ecological and barren landscape over the last two decade and therefore urban expansion will likely to continue the change in landscape. This study will provide a baseline reference to urban planners and policymakers to make informed decision for management of land resources, urban land planning and for maintain sustainable land development.

Active distributed generations (ADGs) are more prevalent near consumer premises. However, the ADG penetration contribute a lot of dynamic changes in power distribution networks which cause different protection and control issues. Islanding is one of the crucial problems related to such ADGs; on the other hand, islanding detection is also a challenging aspect. Therefore, an extensive review of islanding real-time depiction and islanding detection strategies (IDS) is provided in this work. Initially, the focus is on islanding detection concept depiction, islanding detection standardization, benchmark test systems for IDS validation, and software/tools and an analysis of their pros and cons. Then, the detailed classification of IDSs is presented with an emphasis on remote and local methods. Passive, active, and hybrid can be used further to categorize local IDSs. Moreover, the statistical comparative analysis of the IDSs based on the non-detection-zone (NDZ), cost-effectiveness, and false operation are mentioned. The research gap and loopholes in the existing work based on limitations in the existing work are presented. Finally, the paper is concluded with detailed recommendations.

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.

Global land cover dynamics alter energy, water, and greenhouse gas exchange between land and atmosphere, affecting local to global weather and climate change. Although reforestation can provide localized cooling, ongoing land use land cover (LULC) shifts are expected to exacerbate urban heat island impacts. In this study, we monitored spatiotemporal changes in green cover in response to land use transformation associated with the Khyber Pakhtunkhwa (KPK) provincial government’s Billion Tree Tsunami Project (BTTP) and the Ravi Urban Development Plan (RUDP) initiated by the provincial government of Punjab, both in Pakistan. The land change modeler (LCM) was used to assess the land cover changes and transformations between 2000 and 2020 across Punjab and KPK. Furthermore, a curve fit linear regression model (CFLRM) and sensitivity analysis were employed to analyze the impacts of land cover dynamics on land surface temperature (LST) and carbon emissions (CE). Results indicated a significant increase in green fraction of +5.35% under the BTTP, achieved by utilizing the bare land with an effective transition of 4375.87 km2. However, across the Punjab province, an alarming reduction in green fraction cover by −1.77% and increase in artificial surfaces by +1.26% was noted. A significant decrease in mean monthly LST by −4.3 °C was noted in response to the BTTP policy, while an increase of 5.3 °C was observed associated with the RUDP. A substantial increase in LST by 0.17 °C was observed associated with transformation of vegetation to artificial surfaces. An effective decrease in LST by −0.21 °C was observed over the opposite transition. Furthermore, sensitivity analysis suggested that LST fluctuations are affecting the % of CO2 emission. The current findings can assist policymakers in revisiting their policies to promote ecological conservation and sustainability in urban planning.

This research aims to assess the urban growth and impact on land surface temperature (LST) of Lahore, the second biggest city in Pakistan. In this research, various geographical information system (GIS) and remote sensing (RS) techniques (maximum likelihood classification (MLC)) LST, and different normalized satellite indices have been implemented to analyse the spatio-temporal trends of Lahore city; by using Landsat for 1990, 2004, and 2018. The development of integrated use of RS and GIS and combined cellular automata-Markov models has provided new means of assessing changes in land use and land cover and has enabled the projection of trajectories into the future. Results indicate that the built-up area and bare area increased from 15,541 (27%) to 23,024 km

Microgrids (MGs) offers grid-connected (GC) and islanded (ID) operational modes. However, these dynamic modes of operation pose different microgrid protection challenges. This paper presents a new protection method for MGs based on a discrete one-dimensional recursive Median filter (1-DRMF). In the first step, the 1-DRMF is applied on a measured current signal on every single phase individually for targeted feature extraction. Then, the median filter deviation (MFD) and the selected harmonic distortion (SHD) are computed from the current signals of all phases independently. In the second step, the upsurges in the MFD and the SHD of all phases are cross-checked with the pre-established threshold value of 0.3 to identify and categorize fault incidents. Finally, the directional properties of three-phase (3-p) reactive energy are employed in order to pinpoint the faulty line section (LS). Many simulations were executed on MATLAB/Simulink to validate the sustainable performance of the established method. Results prove that the scheme can detect, classify, and locate the solid and high impedance faults (HIF) in the GC as well as the ID modes under radial and meshed scenarios.

Nighttime light (NTL) imagery, as an excellent data source for monitoring urban development and human activity, has been used in research concerning land surface thermal environment. However, previous studies have only taken NTL data as a measure of urbanization degree or to obtain intermediate products for LST studies, such as urban impervious surface area and building density. Few studies have systematically analyzed the spatial and temporal variations of different light levels and surface temperatures. In this study, a LST–NTL rank correlation model was constructed based on MOD11C3 and DMSP/OLS data to analyze the characteristics and trends of the relationship between LST and NTL, during both daytime and nighttime over China in 2012. The findings revealed a strong positive correlation between NTL level and LST, with the peak correlation (R2 > 0.85) observed during July–September, which indicated that, with the increase of the light intensity, the LST has an overall upward trend. The trend was relatively more stable during nighttime (variation range of R2 was 0.3) than during daytime (variation range of R2 was 0.44). Moreover, it was greatly enhanced without considering NTL intensity saturation pixels in the NTL partition system, and was further improved after removing pixels from urban centers, which was particularly evident at nighttime. This provided evidence that NTL intensity can be a good indicator for the distribution of urban thermal environment, particularly outside the urban centers.

Many physical phenomena are localized in space, where physical quantities of interest extend to large distances (effectively to infinity) where they vanish smoothly. Thus, its modeling necessitates the imposition of vanishing boundary conditions (BCs) to the formulated differential equations (DEs). Numerical solutions for such DEs can be complicated as they involve operations on physical quantities expanding infinitely in space. Currently, many approaches have been implemented in this context, where they mostly use techniques like domain truncation, using functions intrinsic to unbounded domains, and domain transformation. However, since the actual solutions have a decaying nature, solving such problems with high accuracy and efficiency while taking into consideration a wide range of decaying rates and oscillation frequencies is a major challenge. In this work, we present an efficient high-order method, based on Galerkin method, to solve DEs in unbounded domains using properly designed domain transformations where the physical unbounded domain ( − ∞ , ∞ ) is mapped into a computational bounded domain ( 0 , 1 ) . The mapped sine series is then used to solve the differential equation in the computational domain ( 0 , 1 ) where the vanishing quantities at infinities are mapped to 0 (corresponding to physical −∞) and 1 (corresponding to physical ∞). The designed transformations maintain the orthogonality in both physical and computational spaces. Moreover, using sine series basis allows – in most cases – analytical integrations. For verification, the proposed method is applied to solve 1) Helmholtz equation in one-dimension, where the exact solutions for various cases (decaying exponential or algebraic function) are known, and 2) three-dimensional Schrodinger wave equation with harmonic oscillator. For the Helmholtz problem, the error in the approximate solution was less than 10 − 40 using 100 basis functions, while for the second problem, the error is less than 10 − 25 using the same number of bases. All results and the analyses demonstrate how the properly designed mapped basis sets can be used to develop efficient high-order methods to solve DEs with vanishing boundary conditions and with wide range of decaying rates and oscillation frequencies simultaneously.

Due to the scarcity of studies linking the variability of rainfall and population growth in the capital cities of Northeastern Brazil (NEB), the purpose of this study is to evaluate the variability and multiscale interaction (annual and seasonal), and in addition, to detect their trends and the impact of urban growth. For this, monthly rainfall data between 1960 and 2020 were used. In addition, the detection of rainfall trends on annual and seasonal scales was performed using the Mann–Kendall (MK) test and compared with the phases of El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). The relationship between population growth data and rainfall data for different decades was established. Results indicate that the variability of multiscale urban rainfall is directly associated with the ENSO and PDO phases, followed by the performance of rain-producing meteorological systems in the NEB. In addition, the anthropic influence is shown in the relational pattern between population growth and the variability of decennial rainfall in the capitals of the NEB. However, no capital showed a significant trend of increasing annual rainfall (as in the case of Aracaju, Maceió, and Salvador). The observed population increase in the last decades in the capitals of the NEB and the notable decreasing trend of rainfall could compromise the region’s water security. Moreover, if there is no strategic planning about water bodies, these changes in the rainfall pattern could be compromising.

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.

Land surface temperature (LST) is an essential parameter for the urban thermal environment and global climate change studies. The significant of LST is being acknowledged gradually and indicates that Vegetation cover has an immediate impact of Land surface temperature (LST). The main objective of this study is, retrieval of Land Surface Temperature (LST) and its relationship with Normalized difference vegetation index (NDVI) by using the different satellite imageries including Landsat 5 (TM); Landsat 7 (ETM+); and Landsat 8 (OLI) for 1998, 2003, 2008, 2013 and 2018 in Lahore and Peshawar city (Pakistan). Combined techniques of GIS and Remote Sensing has been applied for retrieval of LST and NDVI, and further, their relationship was checked by using correlation regression. Results indicate a rising trend of LST and decreasing trend of NDVI in Lahore, while the pattern of LST in Peshawar shows declining trend and NDVI shows an increasing trend. R2 Coefficient value between LST and NDVI is very significant. The areas with low vegetation cover have a high temperature as compared to the areas which contain vegetation cover.

Climate change is likely to have serious social, economic, and environmental impacts on farmers whose subsistence depends on nature. Land Use Land Cover (LULC) changes were examined as a significant tool for assessing changes at diverse temporal and spatial scales. Normalized Difference Vegetation Index (NDVI) has the potential ability to signify the vegetation structures of various eco-regions and provide valuable information as a remote sensing tool in studying vegetation phenology cycles. In this study, we used remote sensing and Geographical Information System (GIS) techniques with Maximum Likelihood Classification (MLC) to identify the LULC changes for 40 years in the Sahiwal District. Later, we conducted 120 questionnaires administered to local farmers which were used to correlate climate changes with NDVI. The LULC maps were prepared using MLC and training sites for the years 1981, 2001, and 2021. Regression analysis (R2) was performed to identify the relationship between temperature and vegetation cover (NDVI) in the study area. Results indicate that the build-up area was increased from 7203.76 ha (2.25%) to 31,081.3 ha (9.70%), while the vegetation area decreased by 14,427.1 ha (4.5%) from 1981 to 2021 in Sahiwal District. The mean NDVI values showed that overall NDVI values decreased from 0.24 to 0.20 from 1981 to 2021. Almost 78% of farmers stated that the climate has been changing during the last few years, 72% of farmers stated that climate change had affected agriculture, and 53% of farmers thought that rainfall intensity had also decreased. The R2 tendency showed that temperature and NDVI were negatively connected to each other. This study will integrate and apply the best and most suitable methods, tools, and approaches for equitable local adaptation and governance of agricultural systems in changing climate conditions. Therefore, this research outcome will also meaningfully help policymakers and urban planners for sustainable LULC management and strategies at the local level.

Water quality is highly affected by riverside vegetation in different regions. To comprehend this research, the study area was parted into wet and dry regions. The WASP8 was applied for the simulations of water quality profile over both Waterways selected from each region. It was found that the Ara Waterway, located in the wet regions, has a higher water quality variation in seasonal scale than that of the Yamuna Waterway, which is in the dry region. The interrelationship between river water quality variables and NDVI produce higher association for water quality variables with Pearson correlation coefficient values of about 0.66, 0.68 and −0.58, respectively, over the annual and seasonal scales in the energy limited regions. This approach will help in monitoring the seasonal variation and effect of the vegetation biomass on water quality for the sustainable water environment.

Soil moisture content (SMC) plays an essential role in geoscience research. The SMC can be retrieved using an artificial neural network (ANN) based on remote sensing data. The quantity and quality of samples for ANN training and testing are two critical factors that affect the SMC retrieving results. This study focused on sample optimization in both quantity and quality. On the one hand, a sparse sample exploitation (SSE) method was developed to solve the problem of sample scarcity, resultant from cloud obstruction in optical images and the malfunction of in situ SMC-measuring instruments. With this method, data typically excluded in conventional approaches can be adequately employed. On the other hand, apart from the basic input parameters commonly discussed in previous studies, a couple of new parameters were optimized to improve the feature description. The Sentinel-1 SAR and Landsat-8 images were adopted to retrieve SMC in the study area in eastern Austria. By the SSE method, the number of available samples increased from 264 to 635 for ANN training and testing, and the retrieval accuracy could be markedly improved. Furthermore, the optimized parameters also improve the inversion effect, and the elevation was the most influential input parameter.

Due to its unique natural conditions and agricultural tradition, northeast China (NEC) has formed a distinctive open biomass burning habit with local-specific biomass burning aerosol features. In this research, with the help of a newly optimized biomass burning aerosol identification method, which combines satellite aerosol and fire observational products with the HYSPLIT model forward trajectories, a systematic and quantitative analysis of aerosol emitted from open biomass burning in the NEC region are conducted to determine in detail its local-specific features, such as influence region, aging characteristics, and seasonal variation. During the 72-h aging process after biomass burning emission, aerosol particle size growth found with the Angstrom exponent declines from 1.6 to 1.54. Additionally, the volume fraction of black carbon decreases from 4.5% to 3.1%, leading to the Single Scattering Albedo (SSA) increasing from the fresh state of 0.84 to the aged state of 0.89. The cooling effect at TOA, due to the existence of aerosol, is enhanced by more than 70%, indicating its severe and dynamic influence on climate change. The average AOD in spring is 0.63, which is higher than autumn’s value of 0.52, indicating that biomass burning is more intensive in spring. Compared to autumn, aerosols emitted from spring biomass burning in the NEC region have lower sphere fraction, smaller particle size, higher volume fraction of black carbon, higher absorbability, and weaker cooling effect at TOA, which can be partly explained by the drier ambient environment and lower water content of the burned crop straw in spring.

Land use–land cover (LULC) alteration is primarily associated with land degradation, especially in recent decades, and has resulted in various harmful changes in the landscape. The normalized difference vegetation index (NDVI) has the prospective capacity to classify the vegetative characteristics of many ecological areas and has proven itself useful as a remote sensing (RS) tool in recording vegetative phenological aspects. Likewise, the normalized difference built-up index (NDBI) is used for quoting built-up areas. The current research objectives include identification of LULC, NDVI, and NDBI changes in Jhelum District, Punjab, Pakistan, during the last 30 years (1990–2020). This study targeted five major LULC classes: water channels, built-up area, barren land, forest, and cultivated land. Satellite imagery classification tools were used to identify LULC changes in Jhelum District, northern Punjab, Pakistan. The perception data about the environmental variations as conveyed by the 500 participants (mainly farmers) were also recorded and analyzed. The results depict that the majority of farmers (54%) believe in the appearance of more drastic changes such as less rainfall, drought, and decreased water availability for irrigation during 2020 compared to 30 years prior. Overall accuracy assessment of imagery classification was 83.2% and 88.8% for 1990, 88.1% and 85.7% for 2000, 86.5% and 86.7% for 2010, and 85.6% and 87.3% for 2020. The NDVI for Jhelum District was the highest in 1990 at +0.86 and the lowest in 2020 at +0.32, similarly, NDBI values were the highest in 2020 at +0.72 and the lowest in 1990 at −0.36. LULC change showed a clear association with temperature, NDBI, and NDVI in the study area. At the same time, variations in the land area of barren soil, vegetation, and built-up from 1990 to 2020 were quite prominent, possibly resulting in temperature increases, reduction in water for irrigation, and changing rainfall patterns. Farmers were found to be quite responsive to such climatic variations, diverting to framing possible mitigation approaches, but they need government assistance. The findings of this study, especially the causes and impacts of rapid LULC variations in the study area, need immediate attention from related government departments and policy makers.

This paper presents an application of fractional order controller for the control of multi input multi output twin rotor aerodynamic system. Dynamics of the considered system are highly nonlinear and there exists a significant cross-coupling between the horizontal and vertical axes (pitch & yaw). In this paper, a fractional order model of twin rotor aerodynamic system is identified using input output data from nonlinear system. Based upon identified fractional order model, a fractional order PID controller is designed to control the angular position of level bar of twin rotor aerodynamic system. The parameters of controller are tuned using Nelder-Mead optimization and compared with particle swarm optimization techniques. Simulation results on the nonlinear model show a significant improvement in the performance of fractional order PID controller as compared to a classical PID controller.

The study of power flow analysis for microgrids has gained importance where several methods have been proposed to solve these problems. However, these schemes are complicated and not easy to implement due to the absence of a slack bus as well as the dependence of the power on frequency as a result of the droop characteristics. This paper proposes simple and effective modifications to the conventional method (Newton Raphson) to compute the power flow for microgrids. The presented method provides a simple, easy to implement, and accurate approach to solve the power flow equations for microgrids. The proposed method is applied to two test systems: a 6-bus system and a 38-bus system. The results are compared against simulation results from PSCAD/EMTDC which validate the effectiveness of the developed method. The proposed technique can be easily integrated in current commercially available power system software and can be applied for power system studies.

Actual evapotranspiration (ET) with high spatiotemporal resolution is very important for the research on agricultural water resource management and the water cycle processes, and it is helpful to realize precision agriculture and smart agriculture, and provides critical references for agricultural layout planning. Due to the impact of the clouds, weather environment, and the orbital period of optical satellite, there are difficulties in providing daily remote sensing data that are not contaminated by clouds for estimating daily ET with high spatial-temporal resolution. By improving the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), this manuscript proposes the method to fuse high temporal and low spatial resolution Weather Research and Forecasting (WRF) model surface skin temperature (TSK) with the low temporal and high spatial resolution remote sensing surface temperature for obtaining high spatiotemporal resolution daily surface temperature to be used in the estimation of the high spatial resolution daily ET (ET_WRFHR). The distinction of this study from the previous literatures can be summarized as the novel application of the fusion of WRF-simulated TSK and remote sensing surface temperature, giving full play to the availability of model surface skin temperature data at any time and region, making up for the shortcomings of the remote sensing data, and combining the high spatial resolution of remote sensing data to obtain ET with high spatial (Landsat-like scale) and temporal (daily) resolution. The ET_WRFHR were cross-validated and quantitatively verified with MODIS ET products (MOD16) and observations (ET_Obs) from eddy covariance system. Results showed that ET_WRFHR not only better reflects the difference and dynamic evolution process of ET for different land types but also better identifies the details of various fine geographical objects. It also represented a high correlation with the ET_Obs by the R2 amount reaching 0.9186. Besides, the RMSE and BIAS between ET_WRFHR and the ET_Obs are obtained as 0.77 mm/d and −0.08 mm/d respectively. High R2, as well as the small RMSE and BIAS amounts, indicate that ET_WRFHR has achieved a very good performance.

The authors wish to make the following corrections to this paper [...]

Land use land cover (LULC) of city regions is strongly affected by urbanization and affects the thermal environment of urban centers by influencing the surface temperature of core city areas and their surroundings. These issues are addressed in the current study, which focuses on two provincial capitals in Pakistan, i.e., Lahore and Peshawar. Using Landsat data, LULC is determined with the aim to (a) examine the spatio-temporal changes in LULC over a period of 20 years from 1998 to 2018 using a CA-Markov model, (b) predict the future scenarios of LULC changes for the years 2023 and 2028, and (c) study the evolution of different LULC categories and investigate its impacts on land surface temperature (LST). The results for Peshawar city indicate the significant expansion in vegetation and built-up area replacing barren land. The vegetation cover and urban area of Peshawar have increased by 25.6%, and 16.3% respectively. In contrast, Lahore city urban land has expanded by 11.2% while vegetation cover decreased by (22.6%). These transitions between LULC classes also affect the LST in the study areas. Transformation of vegetation cover and water surface into built-up areas or barren land results in the increase in the LST. In contrast, the transformation of urban areas and barren land into vegetation cover or water results in the decrease in LST. The different LULC evolutions in Lahore and Peshawar clearly indicate their effects on the thermal environment, with an increasing LST trend in Lahore and a decrease in Peshawar. This study provides a baseline reference to urban planners and policymakers for informed decisions.

An industrial-scale dead-end ultrafiltration system was optimized using statistically designed experiments. Given a certain level of pollutant, a two-level full factorial design and a central composite design were used to optimize the filtrate production of a single 8-inch industrial ultrafiltration membrane while manipulating the levels of four factors: feed pressure, backwash pressure, forward filtration time, and backwash time. Analysis of variance and residual analysis were used to validate and check the adequacy of the developed regression models. The optimal levels were later validated experimentally. The predicted filtrate production was in reasonable agreement with the experimental data.

Recent advances in energy storage and power electronics technologies are offering promising solutions to improve the grid resilience and allow higher renewable energy penetration. Energy storage systems (ESSs) act as energy buffers to aid the operations and lifetime of the grid assets and bridge the gap between supply and demand for renewable energy generation. Currently, there are more than 650 active ESS projects around the globe with a total capacity of 3.83 GW, representing a significant market potential for companies. To that end, this chapter aims to provide a comprehensive overview and classification of ESSs, underlying technologies and working principles, current and future applications, and economic analysis.

This paper focused on the application of electro hydraulic actuator in positioning of aircraft control surface. The dynamic of the considered system is highly nonlinear due to nonlinear pressure-flow relationship, orifice dynamics, and friction effect. Such nonlinearities offer variation of system parameters. So in order to account for such variations, a linear parameter varying (LPV) model of the system is developed first and fixed structure H ∞ controller is designed that can operate within predefine parameter range. The proposed controller is simple structured and has low order compared to conventional ∞ controllers. To test the controller according to real flight condition, an external disturbance (due to air load acting on control surface) is applied. Simulation results on nonlinear system validate the dominant performance of proposed controller as compared to classical PID. Moreover, the proposed scheme found robust under the variation of system parameter.