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Understanding the spatial growth of cities is crucial for proactive planning and sustainable urbanization. The largest and most densely inhabited megapolis of Pakistan, Karachi, has experienced massive spatial growth not only in the core areas of the city, but also in the city’s suburbs and outskirts over the past decades. In this study, the land use/land cover (LULC) in Karachi was classified using Landsat data and the random forest algorithm from the Google Earth Engine cloud platform for the years 1990, 2000, 2010, and 2020. Land use/land cover classification maps as well as an urban sprawl matrix technique were used to analyze the geographical patterns and trends of urban sprawl. Six urban classes, namely, the primary urban core, secondary urban core, sub-urban fringe, scatter settlement, urban open space, and non-urban area, were determined for the exploration of urban landscape changes. Future scenarios of LULC for 2030 were predicted using a CA–Markov model. The study found that the built-up area had expanded in a considerably unpredictable manner, primarily at the expense of agricultural land. The increase in mangroves and grassland and shrub land proved the effectiveness of afforestation programs in improving vegetation coverage in the study area. The investigation of urban landscape alteration revealed that the primary urban core expanded from the core districts, namely, the Central, South, and East districts, and a new urban secondary core emerged in Malir in 2020. The CA–Markov model showed that the total urban built-up area could potentially increase from 584.78 km2 in 2020 to 652.59 km2 in 2030. The integrated method combining remote sensing, GIS, and an urban sprawl matrix has proven invaluable for the investigation of urban sprawl in a rapidly growing city.
Muhammad Baqa; Fang Chen; Linlin Lu; Salman Qureshi; Aqil Tariq; Siyuan Wang; Linhai Jing; Salma Hamza; Qingting Li. Monitoring and Modeling the Patterns and Trends of Urban Growth Using Urban Sprawl Matrix and CA-Markov Model: A Case Study of Karachi, Pakistan. Land 2021, 10, 700 .
AMA StyleMuhammad Baqa, Fang Chen, Linlin Lu, Salman Qureshi, Aqil Tariq, Siyuan Wang, Linhai Jing, Salma Hamza, Qingting Li. Monitoring and Modeling the Patterns and Trends of Urban Growth Using Urban Sprawl Matrix and CA-Markov Model: A Case Study of Karachi, Pakistan. Land. 2021; 10 (7):700.
Chicago/Turabian StyleMuhammad Baqa; Fang Chen; Linlin Lu; Salman Qureshi; Aqil Tariq; Siyuan Wang; Linhai Jing; Salma Hamza; Qingting Li. 2021. "Monitoring and Modeling the Patterns and Trends of Urban Growth Using Urban Sprawl Matrix and CA-Markov Model: A Case Study of Karachi, Pakistan." Land 10, no. 7: 700.
Prescribed burning is a common strategy for minimizing forest fire risk. Fire is introduced under specific environmental conditions, with explicit duration, intensity, and rate of spread. Such conditions deviate from those encountered during the fire season. Prescribed burns mostly affect surface fuels and understory vegetation, an outcome markedly different when compared to wildfires. Data on prescribed burning are crucial for evaluating whether land management targets have been reached. This research developed a methodology to quantify the effects of prescribed burns using multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) imagery in the forests of southeastern Australia. C-band SAR datasets were specifically used to statistically explore changes in radar backscatter coefficients with the intensity of prescribed burns. Two modeling approaches based on pre- and post-fire ratios were applied for evaluating prescribed burn impacts. The effects of prescribed burns were documented with an overall accuracy of 82.3% using cross-polarized backscatter (VH) SAR data under dry conditions. The VV polarization indicated some potential to detect burned areas under wet conditions. The findings in this study indicate that the C-band SAR backscatter coefficient has the potential to evaluate the effectiveness of prescribed burns due to its sensitivity to changes in vegetation structure.
Aqil Tariq; Hong Shu; Qingting Li; Orhan Altan; Mobushir Khan; Muhammad Baqa; Linlin Lu. Quantitative Analysis of Forest Fires in Southeastern Australia Using SAR Data. Remote Sensing 2021, 13, 2386 .
AMA StyleAqil Tariq, Hong Shu, Qingting Li, Orhan Altan, Mobushir Khan, Muhammad Baqa, Linlin Lu. Quantitative Analysis of Forest Fires in Southeastern Australia Using SAR Data. Remote Sensing. 2021; 13 (12):2386.
Chicago/Turabian StyleAqil Tariq; Hong Shu; Qingting Li; Orhan Altan; Mobushir Khan; Muhammad Baqa; Linlin Lu. 2021. "Quantitative Analysis of Forest Fires in Southeastern Australia Using SAR Data." Remote Sensing 13, no. 12: 2386.
Pakistan is a flood-prone country and almost every year, it is hit by floods of varying magnitudes. This study was conducted to generate a flash flood map using analytical hierarchy process (AHP) and frequency ratio (FR) models in the ArcGIS 10.6 environment. Eight flash-flood-causing physical parameters were considered for this study. Five parameters were based on the digital elevation model (DEM), Advanced Land Observation Satellite (ALOS), and Sentinel-2 satellite, including distance from the river and drainage density slope, elevation, and land cover, respectively. Two other parameters were geology and soil, consisting of different rock and soil formations, respectively, where both layers were classified based on their resistance against water percolation. One parameter was rainfall. Rainfall observation data obtained from five meteorological stations exist close to the Chitral District, Pakistan. According to its significant importance in the occurrence of a flash flood, each criterion was allotted an estimated weight with the help of AHP and FR. In the end, all the parameters were integrated using weighted overlay analysis in which the influence value of the drainage density was given the highest value. This gave the output in terms of five flood risk zones: very high risk, high risk, moderate risk, low risk, and very low risk. According to the results, 1168 km2, that is, 8% of the total area, showed a very high risk of flood occurrence. Reshun, Mastuj, Booni, Colony, and some other villages were identified as high-risk zones of the study area, which have been drastically damaged many times by flash floods. This study is pioneering in its field and provides policy guidelines for risk managers, emergency and disaster response services, urban and infrastructure planners, hydrologists, and climate scientists.
Hassan Waqas; Linlin Lu; Aqil Tariq; Qingting Li; Muhammad Baqa; Jici Xing; Asif Sajjad. Flash Flood Susceptibility Assessment and Zonation Using an Integrating Analytic Hierarchy Process and Frequency Ratio Model for the Chitral District, Khyber Pakhtunkhwa, Pakistan. Water 2021, 13, 1650 .
AMA StyleHassan Waqas, Linlin Lu, Aqil Tariq, Qingting Li, Muhammad Baqa, Jici Xing, Asif Sajjad. Flash Flood Susceptibility Assessment and Zonation Using an Integrating Analytic Hierarchy Process and Frequency Ratio Model for the Chitral District, Khyber Pakhtunkhwa, Pakistan. Water. 2021; 13 (12):1650.
Chicago/Turabian StyleHassan Waqas; Linlin Lu; Aqil Tariq; Qingting Li; Muhammad Baqa; Jici Xing; Asif Sajjad. 2021. "Flash Flood Susceptibility Assessment and Zonation Using an Integrating Analytic Hierarchy Process and Frequency Ratio Model for the Chitral District, Khyber Pakhtunkhwa, Pakistan." Water 13, no. 12: 1650.
The geographical concentration of criminal violence is closely associated with the social, demographic, and economic structural characteristics of neighborhoods. However, few studies have investigated homicide patterns and their relationships with neighborhoods in South Asian cities. In this study, the spatial and temporal patterns of homicide incidences in Karachi from 2009 to 2018 were analyzed using the local indicators of spatial association (LISA) method. Generalized linear modeling (GLM) and geographically weighted Poisson regression (GWPR) methods were implemented to examine the relationship between influential factors and the number of homicides during the 2009–2018 period. The results demonstrate that the homicide hotspot or clustered areas with high homicide counts expanded from 2009 to 2013 and decreased from 2013 to 2018. The number of homicides in the 2017–2018 period had a positive relationship with the percentage of the population speaking Balochi. The unplanned areas with low-density residential land use were associated with low homicide counts, and the areas patrolled by police forces had a significant negative relationship with the occurrence of homicide. The GWPR models effectively characterized the varying relationships between homicide and explanatory variables across the study area. The spatio-temporal analysis methods can be adapted to explore violent crime in other cities with a similar social context.
Salma Hamza; Imran Khan; Linlin Lu; Hua Liu; Farkhunda Burke; Syed Nawaz-Ul-Huda; Muhammad Baqa; Aqil Tariq. The Relationship between Neighborhood Characteristics and Homicide in Karachi, Pakistan. Sustainability 2021, 13, 5520 .
AMA StyleSalma Hamza, Imran Khan, Linlin Lu, Hua Liu, Farkhunda Burke, Syed Nawaz-Ul-Huda, Muhammad Baqa, Aqil Tariq. The Relationship between Neighborhood Characteristics and Homicide in Karachi, Pakistan. Sustainability. 2021; 13 (10):5520.
Chicago/Turabian StyleSalma Hamza; Imran Khan; Linlin Lu; Hua Liu; Farkhunda Burke; Syed Nawaz-Ul-Huda; Muhammad Baqa; Aqil Tariq. 2021. "The Relationship between Neighborhood Characteristics and Homicide in Karachi, Pakistan." Sustainability 13, no. 10: 5520.
Environmental managers and policymakers increasingly discuss trade-offs between ecosystem services (ESs). However, few studies have used nonlinear models to provide scenario-specific land-use planning. This study determined the effects of different future land use/land cover (LULC) scenarios on ESs in the Yili River Valley, China, and analyzed the trade-offs and synergistic response characteristics. We simulated land-use changes in the Yili River Valley during 2020–2030 under three different scenarios using a patch-generating land-use simulation (PLUS) model—business as usual (BAU), economic development (ED), and ecological conservation (EC). Subsequently, we evaluated the water yield (WY), carbon storage (CS), soil retention (SR), and nutrient export (NE) ESs by combining the PLUS and integrated valuation of ecosystem services and trade-offs (InVEST) models, thus exploring multiple trade-offs among these four ESs at a regional scale. For the BAU scenario, there are some synergistic effects between WY and SR in the Yili River Valley, in addition to significant trade-off effects between CS and NE. For the ED scenario, the rapid expansion of cropland and constructed land is at the expense of forested grassland, leading to a significant decline in ESs. For the EC scenario, the model predicted that the cumulative regional net future carbon storage, cumulative water retention, and cumulative soil conservation would all increase due to ecological engineering and the revegetation of riparian zones and that formerly steep agricultural land can be effective in improving ESs. Meanwhile, the trade-off effect would be significantly weakened between CS and NE. These results can inform decision makers on specific sites where ecological engineering is implemented. Our findings can enhance stakeholders’ understanding of the interactions between ESs indicators in different scenarios.
Mingjie Shi; Hongqi Wu; Xin Fan; Hongtao Jia; Tong Dong; Panxing He; Muhammad Fahad Baqa; Pingan Jiang. Trade-Offs and Synergies of Multiple Ecosystem Services for Different Land Use Scenarios in the Yili River Valley, China. Sustainability 2021, 13, 1577 .
AMA StyleMingjie Shi, Hongqi Wu, Xin Fan, Hongtao Jia, Tong Dong, Panxing He, Muhammad Fahad Baqa, Pingan Jiang. Trade-Offs and Synergies of Multiple Ecosystem Services for Different Land Use Scenarios in the Yili River Valley, China. Sustainability. 2021; 13 (3):1577.
Chicago/Turabian StyleMingjie Shi; Hongqi Wu; Xin Fan; Hongtao Jia; Tong Dong; Panxing He; Muhammad Fahad Baqa; Pingan Jiang. 2021. "Trade-Offs and Synergies of Multiple Ecosystem Services for Different Land Use Scenarios in the Yili River Valley, China." Sustainability 13, no. 3: 1577.
The objective of this study is to adopt a methodology for analysing spatial patterns of danger of forest fire at Margalla Hills, Islamabad, Pakistan. The work is concentrated on burnt areas using Landsat data and to classify forest fire severity with different parameters (climatic, vegetation, topography and human activities). In addition to these four variables, the extent of the burned areas was measured. Statistical analysis at each fire scene was used to measure the effect on the variables. To calculate the fire severity ratio correlated to each variable, logistic and stepwise regressions were used. The results showed that the burned areas have increased at a rate of 25.848 ha/day (R2 = 0.98) if the number of total days since the start of fire has increased. As a result, forest density, distance to roads, average quarterly maximum temperature and average quarterly mean wind speed were highly correlated with the fire severity. Only average quarterly maximum temperature and forest density affected the size of the burnt areas. Prediction maps indicate that 53% of forests are in the very low severity level (0.25–0.45), 25% in the low level (0.45–0.65) and 22% in high and very high levels (>0.65).
Aqil Tariq; Hong Shu; Saima Siddiqui; B. G. Mousa; Iqra Munir; Adel Nasri; Hassan Waqas; Linlin Lu; Muhammad Fahad Baqa. Forest fire monitoring using spatial-statistical and Geo-spatial analysis of factors determining forest fire in Margalla Hills, Islamabad, Pakistan. Geomatics, Natural Hazards and Risk 2021, 12, 1212 -1233.
AMA StyleAqil Tariq, Hong Shu, Saima Siddiqui, B. G. Mousa, Iqra Munir, Adel Nasri, Hassan Waqas, Linlin Lu, Muhammad Fahad Baqa. Forest fire monitoring using spatial-statistical and Geo-spatial analysis of factors determining forest fire in Margalla Hills, Islamabad, Pakistan. Geomatics, Natural Hazards and Risk. 2021; 12 (1):1212-1233.
Chicago/Turabian StyleAqil Tariq; Hong Shu; Saima Siddiqui; B. G. Mousa; Iqra Munir; Adel Nasri; Hassan Waqas; Linlin Lu; Muhammad Fahad Baqa. 2021. "Forest fire monitoring using spatial-statistical and Geo-spatial analysis of factors determining forest fire in Margalla Hills, Islamabad, Pakistan." Geomatics, Natural Hazards and Risk 12, no. 1: 1212-1233.
Turbidity, relating to underwater light attenuation, is an important optical parameter for water quality evaluation. Satellite estimation of turbidity in alpine rivers is challenging for common ocean color retrieval models due to the differences in optical properties of the water bodies. In this study, we present a simple two-band semi-analytical turbidity (2BSAT) retrieval model for estimating turbidity in five alpine rivers with varying turbidity from 1.01 to 284 NTU. The model was calibrated and validated, respectively, while using one calibration dataset that was obtained from the Three Parallel Rivers basin and two independent validation datasets that were obtained from the Kaidu River basin and the Yarlung Zangbo River basin. The results show that the model has excellent performance in deriving turbidity in alpine rivers. We verified the consistency of the simulated reflectance and satellite-based reflectance and calibrated the 2BSAT model for the specified bands of high spatial resolution satellites in order to achieve the goal of remote sensing monitoring. It is concluded that the model can be used for the quantitative monitoring of turbidity in alpine rivers using satellite images. Based on the model, we used the Sentinel-2 images from one year to identify the seasonal patterns of turbidity of five alpine rivers and the Landsat series images from 1989 to 2018 to analyze the turbidity variation trends of these rivers. The results indicate that the turbidity of these alpine rivers usually presents the highest level in summer, followed by spring and autumn, and the lowest in winter. Meanwhile, the variation trends of turbidity over the past 30 years present distinctly different characteristics in the five rivers.
Weihua Liu; Siyuan Wang; Ruixia Yang; Yuanxu Ma; Ming Shen; Yongfa You; Kai Hai; Muhammad Fahad Baqa. Remote Sensing Retrieval of Turbidity in Alpine Rivers based on high Spatial Resolution Satellites. Remote Sensing 2019, 11, 3010 .
AMA StyleWeihua Liu, Siyuan Wang, Ruixia Yang, Yuanxu Ma, Ming Shen, Yongfa You, Kai Hai, Muhammad Fahad Baqa. Remote Sensing Retrieval of Turbidity in Alpine Rivers based on high Spatial Resolution Satellites. Remote Sensing. 2019; 11 (24):3010.
Chicago/Turabian StyleWeihua Liu; Siyuan Wang; Ruixia Yang; Yuanxu Ma; Ming Shen; Yongfa You; Kai Hai; Muhammad Fahad Baqa. 2019. "Remote Sensing Retrieval of Turbidity in Alpine Rivers based on high Spatial Resolution Satellites." Remote Sensing 11, no. 24: 3010.