9 results on '"Aslam, Rana Waqar"'
Search Results
2. Identification of time-varying wetlands neglected in Pakistan through remote sensing techniques
- Author
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Aslam, Rana Waqar, Shu, Hong, Yaseen, Andaleeb, Sajjad, Asif, and Abidin, Syed Zain Ul
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- 2023
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3. Landslide hazard, susceptibility and risk assessment (HSRA) based on remote sensing and GIS data models: a case study of Muzaffarabad Pakistan
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Ahmad, Muhammad Nasar, Shao, Zhenfeng, Aslam, Rana Waqar, Ahmad, Israr, Liao, Ming, Li, Xianyi, and Song, Yang
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- 2022
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4. Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data.
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Aslam, Rana Waqar, Shu, Hong, Naz, Iram, Quddoos, Abdul, Yaseen, Andaleeb, Gulshad, Khansa, and Alarifi, Saad S.
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WETLANDS , *OPTICAL remote sensing , *REMOTE sensing , *MACHINE learning , *RANDOM forest algorithms , *ARTIFICIAL neural networks , *ENVIRONMENTAL health - Abstract
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. [ABSTRACT FROM AUTHOR]
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- 2024
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5. Flood Disaster Mapping Using Geospatial Techniques: A Case Study of the 2022 Pakistan Floods †.
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Sajjad, Asif, Lu, Jianzhong, Aslam, Rana Waqar, and Ahmad, Muhammad
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GEOSPATIAL data ,REMOTE sensing ,LANDSAT satellites ,FLOODS ,EMERGENCY management - Abstract
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. [ABSTRACT FROM AUTHOR]
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- 2023
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6. The Cellular Automata Approach in Dynamic Modelling of Land Use Change Detection and Future Simulations Based on Remote Sensing Data in Lahore Pakistan.
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Ahmad, Muhammad Nasar, Zhenfeng Shao, Javed, Akib, Islam, Fakhrul, Ahmad, Hafiz Haroon, and Aslam, Rana Waqar
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LAND use ,REMOTE sensing ,CELLULAR automata ,ZONING ,URBAN planning - Abstract
Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic and environmental problems. Therefore, it is noteworthy to monitor land use/land cover (LULC) change detection and future LULC patterns in Lahore. The present study focuses on evaluating the current extent and modeling the future LULC developments in Lahore, Pakistan. Therefore, the semi-automatic classification model has been applied for the classification of Landsat satellite imagery from 2000 to 2020. And the Modules of Land Use Change Evaluation (MOLUSCE) cellular automata (CA-ANN) model was implemented to simulate future land use trends for the years 2030 and 2040. This study project made use of Landsat, Shuttle Radar Topography Mission Digital Elevation Model, and vector data. The research methodology includes three main steps: (i) semi-automatic land use classification using Landsat data from 2000 to 2020; (ii) future land use prediction using the CA-ANN (MOLUSCE) model; and (iii) monitoring change detection and interpretation of results. The research findings indicated that there was a rise in urban areas and a decline in vegetation, barren land, and water bodies for both the past and future projections. The results also revealed that about 27.41% of the urban area has been increased from 2000 to 2020 with a decrease of 42.13% in vegetation, 2.3% in barren land, and 6.51% in water bodies, respectively. The urban area is also expected to grow by 23.15% between 2020 and 2040, whereas vegetation, barren land, and water bodies will all decline by 28.05%, 1.8%, and 12.31%, respectively. Results can also aid in the long-term, sustainable planning of the city. It was also observed that the majority of the city's urban area expansion was found to have occurred in the city's eastern and southern regions. This research also suggests that decision-makers and municipal Government should reconsider city expansion strategies. Moreover, the future city master plans of 2050 must emphasize the relevance of rooftop urban planting and natural resource conservation. [ABSTRACT FROM AUTHOR]
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- 2023
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7. A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan.
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Ahmad, Adeel, Ahmad, Sajid Rashid, Gilani, Hammad, Tariq, Aqil, Zhao, Na, Aslam, Rana Waqar, and Mumtaz, Faisal
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REMOTE sensing ,FOREST mapping ,REMOTE-sensing images ,FOREST reserves ,MAXIMUM likelihood statistics ,RANDOM forest algorithms ,CLASSIFICATION algorithms - Abstract
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]
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- 2021
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8. Comparison of Multi-Year Reanalysis, Models, and Satellite Remote Sensing Products for Agricultural Drought Monitoring over South Asian Countries.
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Shahzaman, Muhammad, Zhu, Weijun, Ullah, Irfan, Mustafa, Farhan, Bilal, Muhammad, Ishfaq, Shazia, Nisar, Shazia, Arshad, Muhammad, Iqbal, Rashid, and Aslam, Rana Waqar
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AGRICULTURAL remote sensing ,DROUGHT management ,DROUGHTS ,SOIL moisture ,AGRICULTURAL productivity ,WATER storage ,REMOTE sensing - Abstract
The substantial reliance of South Asia (SA) to rain-based agriculture makes the region susceptible to food scarcity due to droughts. Previously, most research on SA has emphasized the meteorological aspects with little consideration of agrarian drought impressions. The insufficient amount of in situ precipitation data across SA has also hindered thorough investigation in the agriculture sector. In recent times, models, satellite remote sensing, and reanalysis products have increased the amount of data. Hence, soil moisture, precipitation, terrestrial water storage (TWS), and vegetation condition index (VCI) products have been employed to illustrate SA droughts from 1982 to 2019 using a standardized index/anomaly approach. Besides, the relationships of these products towards crop production are evaluated using the annual national production of barley, maize, rice, and wheat by computing the yield anomaly index (YAI). Our findings indicate that MERRA-2, CPC, FLDAS (soil moisture), GPCC, and CHIRPS (precipitation) are alike and constant over the entire four regions of South Asia (northwest, southwest, northeast, and southeast). On the other hand, GLDAS and ERA5 remain poor when compared to other soil moisture products and identified drought conditions in regions one (northwest) and three (northeast). Likewise, TWS products such as MERRA-2 TWS and GRACE TWS (2002–2014) followed the patterns of ERA5 and GLDAS and presented divergent and inconsistent drought patterns. Furthermore, the vegetation condition index (VCI) remained less responsive in regions three (northeast) and four (southeast) only. Based on annual crop production data, MERRA-2, CPC, FLDAS, GPCC, and CHIRPS performed fairly well and indicated stronger and more significant associations (0.80 to 0.96) when compared to others. Thus, the current outcomes are imperative for gauging the deficient amount of data in the SA region, as they provide substitutes for agricultural drought monitoring. [ABSTRACT FROM AUTHOR]
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- 2021
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9. A comparative analysis of methods and tools for low impact development (LID) site selection.
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Gulshad, Khansa, Szydłowski, Michał, Yaseen, Andaleeb, and Aslam, Rana Waqar
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COMPARATIVE method , *COMPARATIVE studies , *MULTIPLE criteria decision making , *EVIDENCE gaps , *RUNOFF , *REMOTE sensing - Abstract
The site selection for Low Impact Development (LID) practices is a significant process. It affects the effectiveness of LID in controlling stormwater surface runoff, volume, flow rate, and infiltration. This research paper presents a comprehensive review of various methods used for LID site selection. It starts by introducing different methods and tools. Three main methods: index-based methods, GIS-based multi-criteria decision analysis (MCDA), and multi-criteria models and tools, are discussed in detail. A comparative analysis of these methods is then conducted based on ten different criteria. These criteria include the number of variables, data properties, the scale of analysis, benefits maximization approach, multi-attribute decision analysis, user-friendliness, community and stakeholder participation, and the validation methods. This comparison reveals limitations in each method. These include inadequate data availability and quality, lack of evaluation methods, comprehensive assessment criteria and spatial explicitness. These challenges underscore the need for future research to prioritize spatial clarity, broaden criteria, improve data quality through standardization, incorporate field visits and remote sensing for robust results, integrate big data, and develop web-based, open-source tools for enhanced accessibility. These key strategies provide valuable insights for advancing LID site selection methods. • Reviews key LID site selection methods. • Comparative analysis based on multiple factors. • Highlights method limitations, prompting further research in LID site selection. • Recommends improvements and new methods to address research gaps. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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