Comparative geospatial approach for agricultural crops identification in interfluvial plain - A case study of Sahiwal district, Pakistan

Agricultural crop cover identification is a major issue and time-consuming effort to verify the crop type through surveys of the individual field or using prehistoric methods. To establish the scenario of crop identification, the stage of crop provides diverse spatial information about the variety of crops due to its spectral changes. The main aim of this study was to the identify the crop types and their behavior using remote sensing and geographical information system-based approach. Moreover, two main methods were applied to the Sentinel-2 satellite data in which one is random forest based supervised classification and another was Normalize Difference Vegetation Index (NDVI) density estimation method through the google earth engine to procure the data in time-efficient way. This study also established the comparison between classified and vegetation index based seasonal compositional datasets for wheat, cotton, maize, and fodder crops. Study discussed the best fit technique for crops identification in the light of observed methods. Furthermore, the vegetation index ranges by the zonal statistics of the field samples were established according to crop precision. Results showed that -22.94, -43.72, 20.61, and 32.49 % dissimilarities existed in wheat, fodder, cotton, and maize results respectively, after comparison of both techniques. Although, the accuracy assessment was performed on the classified dataset for validation of results by confusion matrix accuracy assessment process using field sample data. Moreover, the vegetation index was used to evaluate crop land surface temperature to estimate the crop growth stage valuation that revealed noticeably enthralling outcomes. The results determined that the classified accuracies of wheat, cotton, maize and fodder were 84, 80, 81 and 71 % respectively. This study also revealed that the random forest classifier has used more features and information potentially during the classifier trainings but vegetation index just implies the limited number of features such as crop growing status

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Monitoring the population change and urban growth of four major Pakistan cities through spatial analysis of open source data

ABSTRACTCities are complex and dynamic entities in close proximity of people, implying multi temporal observations to analyse and understand the urban context. At present, open-source data and geospatial intelligence are becoming the important means of exploring, monitoring and predicting urban status of area growth and population increase. In last few decades, unemployment and absence of infrastructures in the rural areas promoted the unplanned and haphazard urbanization across the urban centres in Pakistan. This study focuses on exploring the potential of open-source/freely available datasets for city mapping and monitoring spatially. The study gives a spatial perspective of rapidly growing cities of Pakistan using Google Earth Engine to classify Landsat images over last four decades, and discovers sprawl patterns across cities. The study works out that the built-up area is significantly increasing with population growth over four decades and there is a strong positive correlation between population growth and built-up expansion. Using Open-Source Data (Landsat images and LandScan data), this study has offered a technical solution of Google Earth Engine-supported analysis of statistics and machine learning to spatially monitoring the population change and urban growth of four major Pakistan cities. It is undoubted that our working results will provide the timely and cost-effective information to policymakers, Govt Officials and citizens for more sustainable urbanization

Flood Disaster Mapping Using Geospatial Techniques: A Case Study of the 2022 Pakistan Floods

Remote sensing images are an essential tool for mapping the amount of flood inundation after flood events. For early flood estimation, flood mapping is a crucial component. This study used an integration of geospatial techniques to evaluate the flood extent in District Dera Ghazi Khan, Pakistan. The modified normalized difference water index (MNDWI) was utilized to estimate the flood extent using Landsat data. For a thorough flood investigation, pre-flood, during, and post-flood images were obtained. The analysis enabled us to delineate flood extent as well as flood duration. The result showed that the flood continued for nearly 5 weeks in the study area. This proposed geospatial technique provides a framework for the identification of inundated areas, which allows emergency responses to be focused on newly flooded areas. Hence, the current study offers a novel perspective on flood mapping and significantly contributes to flood monitoring

Assessment and Monitoring of VIIRS-DNB and SQML-L light Pollution in Lahore-Pakistan

The usage of artificial light is excessive and improper. Earth's night picture has changed significantly from space and studies have shown that over-exposure to artificial light in the night can influence animals, the environment and human beings. The purpose of this study was to monitor and measure skylights of Lahore City and temporary light pollution from 2012-2019. The Suite-Day/Night band of the Visible Image Radiometer was used for time changes analysis with GIS and Remote Sensing tools. Indicators were established as a table tool through zonal statistics, and a field survey was also undertaken to measure the Sky-Glow of Lahore with Sky Quality Meter-L. The results suggest that from 2012 to 2019, light pollution rose by 23.43 percent. Results suggest that around 53.99% of Lahore suffered from light pollution. The number of lights in Lahore has increased by 161.82 percent between 2012 and 2019. In the study period, the mean night light and the standard night light deviation were 127.87 and 98.22 percent, respectively. Lahore's night sky was heavily polluted by light. Lahore's average skylight is 17.15 meters above sea level, which means low quality skies at night. This research aims to provide people an insight into light pollution and the causes of local light pollution. Furthermore, this study aims to enhance public attention to light pollution mitigation attempts by governments and politicians. Full Tex

Assessment and Monitoring of VIIRS-DNB and SQML-L light Pollution in Lahore-Pakistan

The usage of artificial light is excessive and improper. Earth's night picture has changed significantly from space and studies have shown that over-exposure to artificial light in the night can influence animals, the environment and human beings. The purpose of this study was to monitor and measure skylights of Lahore City and temporary light pollution from 2012-2019. The Suite-Day/Night band of the Visible Image Radiometer was used for time changes analysis with GIS and Remote Sensing tools. Indicators were established as a table tool through zonal statistics, and a field survey was also undertaken to measure the Sky-Glow of Lahore with Sky Quality Meter-L. The results suggest that from 2012 to 2019, light pollution rose by 23.43 percent. Results suggest that around 53.99% of Lahore suffered from light pollution. The number of lights in Lahore has increased by 161.82 percent between 2012 and 2019. In the study period, the mean night light and the standard night light deviation were 127.87 and 98.22 percent, respectively. Lahore's night sky was heavily polluted by light. Lahore's average skylight is 17.15 meters above sea level, which means low quality skies at night. This research aims to provide people an insight into light pollution and the causes of local light pollution. Furthermore, this study aims to enhance public attention to light pollution mitigation attempts by governments and politicians. Full Tex

Utilization of cereal-based husks to achieve sustainable development goals: Treatment of wastewater, biofuels, and biodegradable packaging

International audienceBackground: Food waste management (FWM) has gained much attention from industries, research institutions, and national, and international organizations over the past few years because of the significant potential of food waste products to produce valuable products and control environmental pollution, so to find out the sustainable approaches to utilize these waste materials is a current focus of researchers these days.Scope and approach: This review aims to explore the potential of cereal-based husks as waste products from 5 major cereal grains including wheat, rice, corn, barley, and millet to achieve goals 6 (clean water and sanitation), goal 7 (affordable and clean energy), goal 9 (industry, innovation, and infrastructure), and goal 12 (responsible consumption and production) out of the 17 sustainable development goals (SDGs) as per the agenda given by the united nations to make the world more healthy and developed with minimum environmental pollution.Key findings and conclusion: Cereal-based husks can be utilized to make adsorbents to remove heavy metals, dyes, antibiotics, pesticides, and other petrol contaminants from wastewater. In the sense of clean and affordable energy, these husks can be utilized to produce biogas (methane), organic bio-oil, biodiesel, and bioethanol. Moreover, the biopolymeric matrices of these husks can be efficiently used to make bioplastics, biocomposite films, biodegradable foams, and packaging paper for food and other industries

Improvement in Photovoltaic Performance of Dye Sensitized Solar Cell Using Activated Carbon-TiO2 Composites-Based Photoanode

In this study, the activated carbon (AC) is synthesized from the oil fly ash (FA). Noncarbonaceous impurities were eliminated by acidic treatment of oil FA. Brunauer-Emmett-Teller and scanning electron microscopy energy-dispersive spectroscopy (SEM-EDS) analyses are used to characterize the AC. The AC is then mixed with TiO2 in different concentrations to form hybrid photoanodes for dye sensitized solar cells. The dispersion of AC in TiO2 is confirmed by transmission electron microscopy analysis. Solar cells are fabricated by employing the composite films as the working electrode. The UV-Visible absorption spectroscopy, photocurrent-voltage characteristic and electrochemical impedance spectroscopy analyses are carried out to examine the performance of solar cells. The results indicate that the concentration of AC in photoanode strongly affects the efficiency of solar cells. The solar cell fabricated with 0.05% AC-TiO2 photoanode, shows the highest efficiency of 5.45%, which is 61% greater than unmodified photoanode (3.38%). Density functional theory quantum modeling technique is used to compute the band gap of TiO2 and C-TiO2 clusters. 2011-2012 IEEE.Scopu

A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

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

Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data

Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection and risk mapping to examine moisture variability, land cover modifications, area changes and proximity-based threats over two decades. The random forest algorithm attained the highest accuracy (89.5%) for land cover classification based on rigorous k-fold cross-validation, with a training accuracy of 91.2% and a testing accuracy of 87.3%. This demonstrates the model’s effectiveness and robustness for wetland vulnerability modeling in the study area, showing 11% shrinkage in open water bodies since 2000. Inventory risk zoning revealed 30% of present-day wetland areas under moderate to high vulnerability. The cellular automata–Markov (CA–Markov) model predicted continued long-term declines driven by swelling anthropogenic pressures like the 29 million population growth surrounding Khinjhir Lake. The research demonstrates the effectiveness of integrating satellite data analytics, machine learning algorithms and spatial modeling to generate actionable insights into wetland vulnerability to guide conservation planning. The findings provide a robust baseline to inform policies aimed at ensuring the health and sustainable management and conservation of Khinjhir Lake wetlands in the face of escalating human and climatic pressures that threaten the ecological health and functioning of these vital ecosystems