Use of GIS and Remote Sensing Data to Understand the Impacts of Land Use/Land Cover Changes (LULCC) on Snow Leopard (Panthera uncia) Habitat in Pakistan

Khan, Tauheed Ullah; Mannan, Abdul; Hacker, Charlotte E.; Ahmad, Shafiq; Siddique, Muhammad Amir; Khan, Barkat Ullah; Din, Emad Ud; Chen, Minhao; Zhang, Chao; Nizami, Moazzam; Luan, Xiaofeng

doi:10.3390/su13073590

Cited by 26 publications

(20 citation statements)

References 83 publications

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“…However, the study does mention that it used pixel-based classification, but without reporting the method, and it also did not mention the forest change statistics. The following study, by Khan et al [80], used Landsat 5 and Landsat 8 remotely sensed images to map the alpine forests existing in the northern part of Pakistan (GB, KP, and AJK), possessing an area of 85,435.4 km 2 . They used a maximum likelihood classifier to map the forests for 2000, 2010, and 2020.…”

Section: Thatta Districtmentioning

confidence: 99%

“…In this forest ecoregion, five studies were conducted [62,[78][79][80]103]. These studies were performed in a relatively larger region than other studies, which is why they fall under two or more forest ecoregions.…”

Section: Mixed Forestsmentioning

confidence: 99%

“…This is the forest ecoregion in which the largest number of studies were conducted. Almost all studies reported forest declination trends, except two studies [45,80], which reported an overall increase in forest cover. Out of all these studies, two [47,94] did not perform forest change assessment.…”

Section: Western Himalayan Subalpine Conifer Forestsmentioning

confidence: 99%

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

Ahmad

Gilani

et al. 2021

Forests

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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.

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Section: Thatta Districtmentioning

confidence: 99%

Section: Mixed Forestsmentioning

confidence: 99%

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

Ahmad

Gilani

et al. 2021

Forests

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“…Driving forces are the factors that affect LULC changes at the local scale, and therefore they should be locally investigated and addressed (Eastman 2009;Khan et al 2021). In simulating future LULC, LCM differentiates between static and dynamic variables, where the rst are stable in time while the latter are temporally changing, and therefore recalculate every time-step (Table 3).…”

Section: Prediction Of Future Lulc and Associated Driving Forcesmentioning

confidence: 99%

Historical and future land use and land cover changes in the Fincha watershed, Ethiopia

Regasa

Nones

2021

Preprint

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The increasing human pressure on African regions is recognizable from land use land cover (LULC) changes maps, as derived from satellite imagery. Using the Ethiopian Fincha watershed as a case study, the present work focuses on i) identifying historical LULC change in the period 1989-2019; ii) estimating LULC in the next thirty years, combining Geographical Information Systems (GIS) with Land Change Modelling (LCM). Landsat5/8 images were combined with field evidence to map LULC in three reference years (1989, 2004, 2019), while the Multi-Layer Markov Chain (MPL-MC) model of LCM was applied to forecast LULC in 2030, 2040 and 2050. The watershed was classified into six classes: waterbody, grass/swamp, built-up, agriculture; forest and shrub. The results have shown that, in the past 30 years, the Fincha watershed experienced a reduction of forest and shrubs due to ever-increasing agricultural activities, and such a trend is also expected in the future. In addition, the decrease in areas covered by natural forests can drive to an increase in soil erosion, fostering the siltation in the water reservoirs located in the basin. The study pointed out the urgency of taking actions in the basin to counteract such changes, which can eventually drive to a less sustainable environment.

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“…According to Seto et al [8], Mukete et al [9], and Khan et al [10], the land-use and land-cover change (LULCC) caused by urban sprawl disrupts ecohydrology, external geodynamics, soil texture, local climates, and geomorphology, which in turn influences local ecological processes, including the local climate and the carbon stored in trees. For instance, Angel et al [11] observed that an increase of 10% in the urban area led to an estimated 11% increase in CO 2 emissions.…”

Section: Introductionmentioning

confidence: 99%

Urban Growth Patterns and Forest Carbon Dynamics in the Metropolitan Twin Cities of Islamabad and Rawalpindi, Pakistan

Mannan

Khan

et al. 2021

Sustainability

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The unchecked and unplanned expansion of urban areas has led to the conversion of millions of green areas to gray areas. The recent urban growth patterns of Pakistan’s metropolitan twin cities, Islamabad and Rawalpindi, is a matter of concern for the surrounding green areas. The present study aimed to categorize and quantify the land-use and land-cover change (LULCC) patterns and the corresponding impacts on the forest carbon dynamics around Islamabad and Rawalpindi. Multispectral satellite images for the year 1990 (Landsat 5 TM) and 2020 (Landsat 8 OLI) were used to determine, quantify, and compare the LULCC inside and around the twin metropolitan cities. Field inventory surveys in the reserved forests of Rawalpindi and Islamabad were also conducted to determine the amount of stored carbon in these forests. Our results showed an accelerated annual urban expansion (i.e., an increase in the built-up area) of 16.49% and 26.72% in Rawalpindi and Islamabad, respectively, during the study period. Similarly, the amount of barren land and agricultural land was reduced at an annual rate of 2.08% and 2.18%, respectively, in Rawalpindi and 0.25% and 1.04% in Islamabad. A reduction in the area of barren mountains also occurred at an annual of 2.26% in Islamabad, while it increased by 4.16% in Rawalpindi. The amount of carbon stored in the reserved forests of Islamabad stood at 139.17 ± 12.15 Mg C/ha while that of Rawalpindi was 110.4 ± 13.79 Mg C/ha. In addition, total stored forest carbon was found to have decreased from 544.70 Gg C to 218.05 Gg C in Rawalpindi, while in Islamabad it increased from 2779.64 Gg C to 3548.16 Gg C. Investment in ecological urban planning, sustainable cities, and appropriate land-use planning is recommended to curb the degradation and conversion of the surrounding green areas of Rawalpindi and Islamabad.

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Use of GIS and Remote Sensing Data to Understand the Impacts of Land Use/Land Cover Changes (LULCC) on Snow Leopard (Panthera uncia) Habitat in Pakistan

Cited by 26 publications

References 83 publications

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

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

Historical and future land use and land cover changes in the Fincha watershed, Ethiopia

Urban Growth Patterns and Forest Carbon Dynamics in the Metropolitan Twin Cities of Islamabad and Rawalpindi, Pakistan

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