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The urbanization rate is at its peak bringing economic boost and complex issues related to urban development. Many countries are promoting and implementing urban sustainability with a significant focus on promoting public transport systems. United Nations Sustainable Development Goals 2030 (UN SDG Agenda) provides a path for countries worldwide to achieve urban sustainability. The advancement of the Google Earth Engine and remote sensing data help monitor urban sustainability research. The study's core objective was to investigate the serviceability of the proposed public bus transport system in Bahawalpur for 2030. The results for existing LULC revealed that about 18.5 km2 of the urban area has increased from 2010 to 2020 with a decrease of 1 km2 in vegetation and 19 km2 in the barren land, respectively. This study also focuses on LULC prediction for 2030 based on a machine learning algorithm using Modules for Land Use Change Evaluation (MOLUSCE). The prediction map for 2030 was generated to understand the overall trend of LULC change detection. The LULC prediction results for the year 2030 indicate that urban area increased from 31.5 to 70 km2 while vegetation cover decreased from 219 to 192 km2. Also, soil (barren land) was reduced from 64 to 52 km2. Furthermore, a detailed road network was created and service area analysis was performed for proposed bus stops. The results indicate that serviceability for the built-up area was 38% while the unserved area was observed at about 62%. The study spatially identified the area that appeared to be more vulnerable to future city expansion. The majority of the buildup area was concentrated around the city center, which was served by bus stations; nonetheless, a huge region surrounding important highways remains undeveloped. It can be concluded that the use of geospatial approach and future simulations can be useful in future planning for urban development, road planning, and achieving sustainability in transportation. [ABSTRACT FROM AUTHOR]

The existing fresh water resources are insufficient to fulfill the water requirements for different uses such as drinking, agriculture irrigation and for industrialization purposes. Present study carried out for the identification of groundwater potential zones by using Geographical Information System and Remote Sensing Techniques in the district Chakwal, a water scarce area lying in the Pothohar region of Punjab province, Pakistan. The land-use/land cover, geology, rainfall, drainage density and slope data have been incorporated to identify groundwater potential zones. Freely available landsat satellite data for landuse/landcover classification and Digital Elevation Model (DEM) for the extraction of slope, drainage network have been used. However, available geological maps for the study area were incorporated as an input raster layers in analyses. Weighted Overlay analyses were performed to produce the resultant map showing the spatial distribution pattern of ground water potential. Results showed the water potential zones as; (i) unsuitable area=2756 sq.km, (ii) moderately suitable area=3256 sq.km and (iii) suitable land=3256 sq.km. North side of the district shows better water potential in comparison to the south of study area. This can be due to hard rocks in the south of study area causing inference in ground water recharge. The present highlights the usefulness of processed remote sensing satellite data and GIS techniques used for the production of ground water potential zone map. [ABSTRACT FROM AUTHOR]

Urban Heat Island (UHI) is related to the theory of higher atmosphere and surface temperature taking place in the city area as compared to the nearby rural areas. The study results explain the advantages of green spaces in urban areas. Landsat TM/OLI concept of 1998 and 2017 years was attained from USGS for this work. To find out the land-use and land cover pattern supervised classification, the maximum prospect process is adopted. A total of four classes were mapped for the study area which includes built-up, barren land, vegetation, and water bodies. Effective sensor brightness temperature has been acquired from spectral radiance by using Plank's reverse function. On the basis of surface emissivity, Normalize Difference Vegetation Index (NDVI) classes are used to find out the ultimate temperature of the land surface (LST). The maximum temperature was recorded in the built-up area and barren land of the city and minimum temperatures were noted in areas with a greater cover of vegetation. The presence of UHI temperature is obvious from the land surface temperature views. An increase of 2 °C in surface temperature is recorded in district Lahore during the last two decades. Rise of 2.2 °C and 2.4 °C surface temperature has taken place in Faisalabad and Multan Districts in the last 19 years, respectively. In the establishment of a correlation among the LST, built-up, and vegetation, there is a positive sign present in their relation. With the increase in the built-up area, LST does increase as well. Moreover, with the reduction in vegetation cover, the LST is also raised. The study has revealed that there is a need for proper planning for the durable management of urbanization. [ABSTRACT FROM AUTHOR]

Increase in population is escalating many issues, these days one of them is the rapid growth of industries which reached at its peak level as well alarming situation for clean environment. Due to the increases in population the process of transformation materialized from foliage as well as residential areas to industrial. Thus, hasty growth of urbanization directly affects the condition of existence resources which will raise many life-threatening environmental disquiets. This research is based upon the assessment of foliage land loses due to industrial growing trend under current environmental situation by using GIS and remote sensing based approach for the population census years 1998 and 2017. Land transfiguration identified by the help of satellite-based classification and geospatial analysis approach. Results showed that in 2017 the built-up area of Sheikhupura has been increased up to 170.90% and temporally 10.59% foliage land engaged by industries in the light of exceeded built up scenario. Furthermore, the population of the study area increases by 52.02% with respect to the population of 1998. The research shows the main concern for environmental aspect by the help of inverse distance weight modeling (IDWM) approach for evaluation of yearly (1998 to 2017) rainfall spread in millimeters (mm) on real time meteorological data, derived results showed the temporal rainfall spread on the study area, additionally it recorded up to 200 millimeters according to its annual average base. Due to rainfall which happened in the industrialized area, unswervingly cause for environmental pollution, while research also resolute the situation of the ground water (drain, canal and river) pattern as well spatial arrangement of industries shows the serious concern towards the environmental protection. [ABSTRACT FROM AUTHOR]

Pakistan is a major tea consumer country and ranked as one of the largest importer of tea worldwide. This research has been conducted over district Mansehra and district Abbottabad in Khyber Pakhtunkhwa province of Pakistan where the most favourable conditions for tea cultivation already exist and National Tea Research Institute has done successful experiments to cultivate high quality tea. High tech approach is adopted to meet the objectives of this research by using the remotely sensed data i.e. Aster DEM, Land sat8 Imagery. Results showed that in study area 13.24% (778.3 sq.km.) is highly suitable, 33.44% (1966 sq.km.) is suitable, 30.71% area (1805 sq.km.) is moderately suitable, 16.13% area (948.5 sq.km.) is less suitable, and 6.46% (379.3 sq.km.) is not suitable for tea cultivation. This geomatics based model and approach could be used to identify more areas for tea cultivation to meet country's demand. [ABSTRACT FROM AUTHOR]

People of Asia and Africa have been facing floods while carrying out their agricultural activities because their permanent settlements are built along the rivers. Floods affect people and other living organisms by crop nutrient reduction, desertification, salinization, organic substance wastage, and soil structure degradation. Most importantly, floods can destroy vegetation and plants, which are nutrition resources for human and animals. Because of seasonal lows in the Arabian Sea and known westerly waves, northern regions of Pakistan experienced heavy rainfall at the start of September 2014, which caused overflow and flash floods by the rivers Jhelum, Ravi, and Chenab. Many cities in Punjab, along riversides, suffered because of floods that inundated large areas of rural and urban areas in 16 districts. The present work aimed to assess and quantify the change in the vegetation cover in the Hafizabad district due to the 2014 flood and its recovery in 2015 by adopting the geographic information system and remote sensing approach. Remote sensing and satellite images enable us to analyze the current state and the past state over a large geographical area. Supervised classification methodology was applied to three Landsat satellite images, such as pre-flood 2014 land cover classification, for post-flood land cover classification, and land cover classification of the year 2015 to assess the current state of recovery. It was found that 52.7 km" of the vegetation area has expanded to the flood-affected area of 541 km" in a time span of 1 year only. Moreover, the barren land has decreased by 19 percent, which resulted in an increase in the built-up area (9.5 percent) and fish farms (59 percent). [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1007/s12517-023-11749-8.Arabian Journal of Geosciences (2023) 16:628https://doi.org/10.1007/s12517-023-11749-8In the original version of this article, second corresponding authorname is incorrect. It should be ''Ahmad" not "Ahamd".The original article has been corrected.By Muhammad Nabeel Khalid; Muhammad Nasar Ahmad; Muhammad Asif Javed and Sajid Rashid AhmadReported by Author; Author; Author; Author [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

The changing climate and global warming have rendered existing surface water insufficient, which is projected to adversely influence the irrigated farming systems globally. Consequently, groundwater demand has increased significantly owing to increasing population and demand for plant-based foods especially in South Asia and Pakistan. This study aimed to determine the potential areas for groundwater use for agriculture sector development in the study area Lower Dir District. ArcGIS 10.4 was utilized for geospatial analysis, which is referred to as Multi Influencing Factor (MIF) methodology. Seven parameters including land cover, geology, soil, rainfall, underground faults (liniment) density, drainage density, and slope, were utilized for delineation purpose. Considering relative significance and influence of each parameter in the groundwater recharge rating and weightage was given and potential groundwater areas were classified into very high, high, good, and poor. The result of classification disclosed that the areas of 113.10, 659.38, 674.68, and 124.17 km2 had very high, high, good, and poor potential for groundwater agricultural uses, respectively. Field surveys for water table indicated groundwater potentiality, which was high for Kotkay and Lalqila union councils having shallow water table. However, groundwater potentiality was poor in Zimdara, Khal, and Talash, characterized with a very deep water table. Moreover, the study effectively revealed that remote sensing and GIS could be developed as potent tools for mapping potential sites for groundwater utilization. Furthermore, MIF technique could be a suitable approach for delineation of groundwater potential zone, which can be applied for further research in different areas. [ABSTRACT FROM AUTHOR]

In this study, we investigate stand-alone and combined Pleiades high-resolution passive optical and ALOS PALSAR active Synthetic Aperture Radar (SAR) satellite imagery for aboveground biomass (AGB) estimation in subtropical mountainous Chir Pine (Pinus roxburghii) forest in Murree Forest Division, Punjab, Pakistan. Spectral vegetation indices (NDVI, SAVI, etc.) and sigma nought HV-polarization backscatter dB values are derived from processing optical and SAR datasets, respectively, and modeled against field-measured AGB values through various regression models (linear, nonlinear, multi-linear). For combination of multiple spectral indices, NDVI, TNDVI, and MSAVI2 performed the best with model R2/RMSE values of 0.86/47.3 tons/ha. AGB modeling with SAR sigma nought dB values gives low model R2 value of 0.39. The multi-linear combination of SAR sigma nought dB values with spectral indices exhibits more variability as compared with the combined spectral indices model. The Leave-One-Out-Cross-Validation (LOOCV) results follow closely the behavior of the model statistics. SAR data reaches AGB saturation at around 120–140 tons/ha, with the region of high sensitivity around 50–130 tons/ha; the SAR-derived AGB results show clear underestimation at higher AGB values. The models involving only spectral indices underestimate AGB at low values (< 60 tons/ha). This study presents biomass estimation maps of the Chir Pine forest in the study area and also the suitability of optical and SAR satellite imagery for estimating various biomass ranges. The results of this work can be utilized towards environmental monitoring and policy-level applications, including forest ecosystem management, environmental impact assessment, and performance-based REDD+ payment distribution. [ABSTRACT FROM AUTHOR]

This study aims to monitor the flash flood response of Vidor/Wadore hill torrent in Pakistan by the integration of Personal Computer Storm Water Management Model PCSWMM (hydrologic) and HEC-RAS 5.x (hydraulic) models. The method leverages remote sensing and GIS derive estimates of measured and inferred parameters of Vidor rural catchment to quantify the flash flood events of the last four years: 2014–2017. The calibration of the PCSWMM is performed using the sensitivity-based radio tuning calibration (SRTC) tool. The Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), and relative error (RE) values were found between 0.75–0.97, 0.94–0.98, and −0.22–−0.09 respectively. The statistical indicators prove the accuracy of PCSWMM for rural catchments. The runoff response of Vidor torrent is also analyzed for 0.5/12.7, 1.5/38.1, and 2.0/50.8-inch/mm rainfall hyetographs. The generated hydrographs are used to simulate 2D-module in HEC-RAS 5.x for floodplain demarcation in the piedmont area. The accuracy of the flood extent is analyzed using spatial overlay analogy in the ArcGIS environment by comparing simulated and historically available flood extents. The simulated flood extent shows 76% accuracy with historic flood extent. The impact of flash flood events shows wheat, maize, and fruit orchards are the most effected agriculture in piedmont area. The results revealed that the integration of hydrological, hydraulic, and geospatial modeling approaches can be used to model a full picture of catchment response during flash flood events. [ABSTRACT FROM AUTHOR]

• RS based crop irrigation performance monitoring as an independent check. • Efficiency assessment of predefined canal rotation plan. • Near real time monitoring based on open source dataset. • Flexible irrigation water allocation for improving adequacy and productivity. This study comprehensively used Remote Sensing (RS) and GIS for independently monitoring crop water adequacy in response to existing irrigation management system in Bari Doab, Pakistan. Canals' rotation plan is developed before start of each cropping season to distribute water on fixed turn basis. The study combines the moderate (Landsat-8) and low resolution (MODIS) satellite imagery to assess near real time crop water deficit in canal command areas (CCAs) of the study area. Landsat imagery were used to develop crop classification and determine cropping pattern using time series of NDVI. MODIS' NDVI product was employed to monitor the current crop health and develop annual reference NDVI cycle based on last 13 years' imagery. Climatic data of WMO stations combined with DEM have also been used to determine reference evapotranspiration based on Penman-Monteith method. These inputs were combined to estimate average crop water deficit in CCAs for ongoing cropping season on 08-day interval, the interval parallel to the canal rotation plan. RS based reflectance crop coefficients (K CR) of current and reference crop cycle were developed to calculate potential and actual evapotranspiration, and then translated their difference into crop water deficit. Lag time of 08–10 days is limitation of the study due to satellites overpass time, but it can still prove very helpful considering the crop season spanned over 160–180 days. Results revealed that maximum crop water deficit (up to 9 mm) occurred in CCAs during crop growing (mid) stage. Crop water deficit showed inverse relation when plotted against intense rainfall months. Crop water deficit results could be helpful for irrigation managers to modify canals' rotation plan in near real time according to crop health and growing stage. It would not only help in achieving equitable water distribution but also assuring supplies when required by the crops. [ABSTRACT FROM AUTHOR]