https://orcid.org/0000-0003-0429-765X
Retraction
After this article [1] was published, concerns were raised regarding inconsistencies in the reporting of the forest cover results. Specifically:
- The forest cover results differ between Fig 1, Fig 2, Table 3, and subsections 3.1 “Forest cover maps” and 3.2 “Forest cover change” of the Results section.
- In the Table 3 October 1980 to 2021 and Fig 2 October 1980 to 2020 results, the deforestation percentage result does not appear to correspond to the reported time period and forest cover results.
- Differences were noted in the reporting of the time periods and collection years for the forest cover data. Table 3 reports October 2000 to 2021, Fig 2 reports October 2000 to 2020, and Fig 4 reports 2001–2020.
Co-corresponding author NA stated that the results in Fig 1 and subsection 3.1 “Forest cover maps” represent the area of the largest single forest patch for each respective year and do not show the total forest cover, whilst Table 3 and subsection 3.2 “Forest cover change” show total forest cover for the entire study area.
Regarding the Table 3 October 1980 to 2021 and Fig 2 October 1980 to 2020 results, co-corresponding author NA stated the deforestation area of 6509 ha and the deforestation percentage of 1.4% are incorrect and should instead be 7109 ha and 1.18%, and that the Fig 2 October 2000 to 2020 deforestation result of 4265 ha should instead be 4965 ha.
Regarding the differences in reported time periods, co-corresponding author NA stated that in Table 3, “October 2000 to 2021” and “October 1980 to 2021” are incorrect and should instead read “October 2000 to 2020” and “October 1980 to 2020”. The authors did not comment on the 2001–2020 time period reported in Fig 4.
Co-corresponding author NA stated that in subsection 3.4 “Decay model forecast” of the Results section in [1], the population density is incorrectly written as “0.28 million in 1851” and should instead read that the population density was 0.28 million in 1951.
In light of the extent of the above concerns, which bring into question the reliability of the reported results, the PLOS One Editors retract this article.
NA did not agree with the retraction. ZU, BA, AA, and KS either did not respond directly or could not be reached.
In addition to the above concerns, the PLOS One Editors identified multiple citation errors in this article [1].
3 Mar 2026: The PLOS One Editors (2026) Retraction: Population growth poses a significant threat to forest ecosystems: A case study from the Hindukush-Himalayas of Pakistan. PLOS ONE 21(3): e0343943. https://doi.org/10.1371/journal.pone.0343943 View retraction
Figures
Abstract
Human population growth and the accompanying increase in anthropogenic activities pose a significant threat to forest ecosystems by reducing the natural services these ecosystems provide. Malam Jabba, located in the District Swat of Pakistan’s Hindukush-Himalayan temperate zone, is known for its ecotourism, skiing, timber-producing tree species, medicinal plants, and unique biodiversity. However, a large portion of Swat Valley’s population depends on the Malam Jabba forests for timber and fuelwood. This study investigates how deforestation rates have increased in response to the growing human population in Malam Jabba, District Swat. To monitor forest cover changes, we used remote sensing (RS) and geographic information systems (GIS) tools. Vegetation analysis was conducted using the Normalized Difference Vegetation Index (NDVI) based on multi-temporal satellite imagery from 1980, 2000, and 2020. Using a decay model, we calculated the deforestation rate from 1980 to 2020 and projected future rates using MATLAB, based on anticipated population growth. Our results show that over the last two decades, the average annual deforestation rate rose from 0.7% to 1.93%, coinciding with a population increase from 1.2 million to 2.3 million at a growth rate of 9% per year. Projections indicate that the deforestation rate will increase to 2.5% annually over the next 20 years, given the predicted 11.6% yearly population growth. Population growth in District Swat has severely endangered nearby forest ecosystems, and further increases in human activity, such as unsustainable tourism, fuel and timber collection, and urbanization, will likely exacerbate this trend. Based on our findings, we recommend: (i) the implementation of reforestation programs and sustainable forest resource use; (ii) the development of a long-term forest management plan that maintains equilibrium between forest density and population pressure; and (iii) prioritizing areas with extreme human impact for in-situ conservation efforts.
Citation: Alam N, Ullah Z, Ahmad B, Ali A, Syed K (2024) RETRACTED: Population growth poses a significant threat to forest ecosystems: A case study from the Hindukush-Himalayas of Pakistan. PLoS ONE 19(11): e0302192. https://doi.org/10.1371/journal.pone.0302192
Editor: Gouranga Lal Dasvarma, Flinders University, AUSTRALIA
Received: March 31, 2024; Accepted: October 4, 2024; Published: November 25, 2024
Copyright: © 2024 Alam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: We confirm that all relevant data are available in the manuscript.
Funding: Funded by Higher Education Commission of Pakistan (HEC) through Start-up Research Grant Program (SRGP) – R&D Division No: 21-1095/SRGP/R&D/HEC/2016.
Competing interests: Dear Editor, We, the authors of the manuscript entitled "Population growth poses a significant threat to forest ecosystems: a case study from the Hindukush-Himalayas of Pakistan," hereby express our collective agreement to submit this article for publication in PLOS ONE. We confirm that all authors have reviewed and approved the final version of the manuscript. We wish to declare that there are no competing interests among the authors regarding the publication of this article. Additionally, we would like to bring to your attention that our institutions are geographically located in Research 4 Life countries (Group B), thereby qualifying for inclusion in partnership models for various PLOS journals, including PLOS ONE. As such, researchers affiliated with these institutions are exempt from publication fees at PLOS ONE. Thank you for considering our submission. Sincerely, Dr. Naveed Alam Dr. Bilal Ahmad Dr. Ahmad Ali Dr.Zahid Ullah Kashmala Syed This does not change our commitment to PLOS ONE’s policies on sharing data and materials.
1. Introduction
Forests are vital ecosystems, providing a range of ecological and socioeconomic services that support approximately 60% of global biodiversity [1–3]. More than two billion people depend on forests for essential needs such as food, fuelwood, shelter, medicine, and livestock forage [4]. Beyond these basic services, forests play a crucial role in maintaining ecosystem health, mitigating climate change, and supporting economic opportunities through ecotourism [5–7]. Ecotourism, one of the fastest-growing industries globally, offers significant potential for boosting rural economies and providing livelihoods for local communities [8]. Additionally, under the Kyoto Protocol and the United Nations Framework Convention on Climate Change, forests are recognized as essential for carbon storage, further underscoring their global significance [9, 10]. Despite these critical functions, forest ecosystems worldwide are rapidly diminishing due to anthropogenic activities [11, 12]. This is particularly true in the Hindu Kush-Himalayan region, where natural resources are degrading at an alarming rate, yet these areas have received less international attention compared to other ecosystems [13, 14]. Given these challenges, regular forest cover assessments are necessary to track changes and predict future environmental impacts [15–17].
In response, modern technologies such as Geographic Information Systems (GIS) and remote sensing (RS) have emerged as valuable tools for monitoring forest cover [18]. These techniques allow researchers to analyze long-term changes in land use and forest composition using multi-temporal and multispectral satellite data [19]. By integrating RS data into GIS software, researchers can quantify vegetation changes over large areas, offering crucial insights into the effects of human activities on forest health [20, 21]. The ATCOR tool in ERDAS software can correct the topographic and atmospheric errors in satellite images, improve accuracy and reduce error [18]. Normalized Difference Vegetation Index (NDVI) distinguishes green vegetation from other land features based on chlorophyll content [19]. ArcGIS model builder analyzes the NDVI data further for a comprehensive forest cover quantification [20]. For the development of conservation and management policy, large-scale monitoring of forest resources is essential, whereas small-scale monitoring is required for its implementation [22, 23]. Human population growth is directly related to economic insecurity, social problems, unsustainable resource consumption, and anthropogenic disturbances in agricultural expansion, unsustainable ecotourism, urbanization, and deforestation [24]. The decay model is the most accurate method for predicting carrying capacity because it considers the strain that growing populations, under linear population growth, places on natural resources, such as an increase in the rate of deforestation and other environmental disturbances [25]. The decay model determines the relationship between exponential population density growth and deforestation.
This study focuses on the impacts of human population growth and associated anthropogenic activities on the forests of Malam Jabba, District Swat, Pakistan. The region, known for its unique biodiversity, medicinal plants, and timber resources, is facing increasing pressures from human activities such as unsustainable tourism, urbanization, and fuelwood collection [26]. We aim to quantify changes in forest cover over the past four decades and assess the relationship between population growth, deforestation rates, and forest health. Specifically, we address the following research questions: (i) How do population growth and human activities affect forest cover? (ii) What is the relationship between deforestation and human-induced disturbances? (iii) Can a decay model effectively predict future forest health? To address these questions, we propose a workflow that combines RS and GIS technologies, along with a decay model to predict deforestation trends. This approach will not only enhance our understanding of forest cover changes in the Hindu Kush-Himalayan region but also contribute to the development of more accurate models for forest management and conservation.
2. Material and methods
2.1 Study area
The study area Malam Jabba (34° 47’ 57” N, 72° 34’ 19” E) is situated in Hindukush- Himalayas mountains at elevations between 2000 to 3000 meters above sea level in District Swat of Northern Pakistan. It is a well-known summer resort surrounded by dense, moist temperate coniferous forests containing numerous commercially, medicinally, and ecologically significant plant species, providing a habitat for hundreds of birds and animals. Malam Jabba is a mountainous union council in Swat District, comprising 14 villages and approximately 23,000 inhabitants, however, Malam Jabba Forest is a protected forest, allowing legal rights of local community, facing severe illegal cutting and deforestation pressures from the nearby city of Mingora and surrounding villages of District Swat [27]. Historically, this region has been home to the Gandhara civilization and its rich culture, where lush green forests and rangelands were likely the primary reasons for their settlements. Malam Jabba forests are experiencing immense anthropogenic pressure in the form of fuel and timber wood collection due to human population growth in the nearby central city of District Swat; heavy grazing, unmanaged ecotourism activities, and agriculture expansion have further threatened the local ecosystem [28, 29].
2.2 Research approach and methodology
Remote Sensing (RS) data were acquired and processed by using Geographic Information System (GIS) to evaluate long-term forest degradation for the years 1980, 2000, and 2020 [18–20]. Data collected by optical satellites with a high resolution are an essential source for various fields of research, including natural resource management, land cover change detection, conservation biology, and population studies [19]. However, satellite imagery can be affected by atmospheric and terrain-induced errors [30]. ATCOR tool in ERDAS was used for satellite imagery from 1980, 2000, and 2020 to remove atmospheric and topographic effects. During the atmospheric correction, different values from metadata were utilized, including solar azimuth angle, elevation angle, solar zenith angle, and the angle between the sun and moon [31]. Normalized Divergence Vegetation Index (NDVI) was used to detect forest cover. The NDVI indices were selected from the unsupervised classification menu in ERDAS, and the algorithm was executed on the corrected images from 1980, 2000, and 2020 at regular intervals to obtain NDVI values. Specifically, red and infrared pixel-level brightness values from Landsat images were utilized for this purpose. The results fell from -1 to 1, with positive values indicating more biomass in plants and negative values indicating less biomass. This index was utilized for all vegetation types to obtain forest biomass values. In order to examine NDVI values for forest cover, Global Positioning System (GPS) and Google Earth training samples were gathered. After comparing a training sample with NDVI-classified images from 1980, 2000, and 2020, we obtained maps and NDVI values for the corresponding imageries, with a maximum value of 0.7 for forest classes [32, 33].
2.3 Data collection
Primary Data were collected from U.S. Geological Survey (USGS). Landsat-5 (TM) images for the year 1980, Landsat-7 (ETM+) images for the year 2000, Landsat-8 (OLI) images for the year 2020, and Digital Elevation Model (DEM) data with a resolution of 30m x 30m from USGS Earth Explorer were collected to evaluate forest degradation for the years 1980, 2000, and 2020 (Table 1) [30]. To confirm satellite data, field trips were conducted to obtain the Global Positioning System (GPS) data on the ground positioning of vegetation cover [32]. Population data was collected from census report-2017 of Pakistan Bureau of Statistics, Islamabad, as a secondary data to correlate population growth with forest degradation [34].
2.4 Data processing
Satellite images were processed by first creating a composite through image stacking, where each image contained unique spectral bands to capture different wavelengths of the electromagnetic spectrum. A projection transformation using the UTM WGS-84 41N coordinate system was applied to ensure spatial consistency for accurate analysis. After transformation, the "clip" tool in ArcGIS was used to isolate the study area, focusing the analysis on the specific region of interest. The study area was divided into sub-scenes with a pixel size of 1334 x 1511 for each color channel in the original dataset [30], enabling detailed analysis at a consistent resolution.
Normalized Difference Vegetation Index (NDVI) statistics were computed using multitemporal satellite images from 1980, 2000, and 2020 (Table 2). NDVI values were derived from the reflectance of vegetation, which is strongest in the near-infrared and weakest in the red part of the spectrum due to chlorophyll absorption. This index helps differentiate areas of forest vegetation from non-vegetated or barren surfaces, providing insight into changes in forest cover over time [32].
ArcGIS’s model builder was used for analyzing NDVI values to calculate forest cover and correct the topographical errors. The tool facilitates the creation, editing, and management of geoprocessing models; it also permits multiple geoprocessing tools to be utilized sequentially in the automation of various tasks. Various tools from the ArcTools box were used to construct the model for this study, including a raster calculator, a clip tool, a raster to polygon tool, a select tool, an added field tool, a geometry calculator, a smoothing tool, a layer to KM tool, and a tool for discovering topological flaws [35, 36]. In the study’s model, blue represents the input of satellite images being added with some tool from the toolbox in ArcGIS, and green is the resultant data after calculation in ArcGIS.
2.5 Statistical analysis and future forecast
The normalized difference vegetation index for assessing vegetation cover, is represented by the equation [32, 33].
NIR = the brightness value of the near-infrared band.
For predicting future population growth and its effect on forest cover in the form of deforestation, MATLAB 7.10 software was used [37]. Using the exponential growth model equation [38, 39], we determined the rates of population increase and deforestation for the next two decades.
3. Results
3.1 Forest cover maps
Using NDVI values, forest cover for the years 1980, 2000, and 2020 was mapped in both 2D and 3D formats through ArcMap and ArcScene, as shown in Fig 1. ArcMap was used to generate 2D forest maps at a scale of 1:125,000, while ArcScene, combined with DEM data, was used to create 3D visualizations for the corresponding years. The total forest cover was measured in polygon areas, with 8,298.04 hectares in 1980, 2,357.54 hectares in 2000, and 609.55 hectares in 2020, as calculated through ArcGIS (Table 3).
2D & 3D map of forest cover in the consistent years 1980, 2000 & 2020. Scale. 1: 125,000, polygon area in 1980: 8298.039313, polygon area in 2000: 2357.536113, polygon area in 2020: 609.553980.
3.2 Forest cover change
Deforestation, defined as the transformation of forested areas into barren land, occurred at different rates during the study period. Between 1980 and 2000, forest cover declined at an annual rate of 0.7%, reducing from 15,003 hectares to 12,859 hectares. From 2000 to 2020, deforestation accelerated, with an average annual rate of 1.93%, further decreasing forest cover to 7,894 hectares. Over the entire period from 1980 to 2020, a total of 6,509 hectares of forest were lost, at an average annual rate of 1.4% (Table 3 and Fig 2).
3.3 Population growth and anthropogenic pressure
Over the past two decades, the human population in Southeast Asia has increased significantly, and in the study area, the population has grown at an average annual rate of 9.1% (Figs 3 and 4). This rapid population growth has exerted mounting pressure on forest resources, as seen in the decrease in forest cover from 12,859 hectares in 2000 to 7,894 hectares in 2020. This corresponds to an average deforestation rate of 1.93% per year.
According to the 2017 census report from the Pakistan Bureau of Statistics, Swat District’s population is now 2,309,570. The population growth rate between 1980 and 2000 was 8.9%, rising to 9.1% by 2020. The exponential/decay model projects that the population will increase to 5.7 million by 2040, with anthropogenic activities accelerating deforestation at a rate of 2.5% per year. By 2040, it is expected that forest cover will decrease by an additional 4,000 hectares.
3.4 Decay model forecast
Using the decay model, as shown in Fig 5, we forecasted that District Swat’s population density, which was 0.28 million in 1851 and 2.31 million in 2021, will reach 5.7 million by 2040. Forest cover, which was 15,003 hectares in 1980, has declined to 12,859 hectares in 2000 and 7,894 hectares in 2020. The model predicts that by 2040, forest cover will further reduce to 4,000 hectares as deforestation rates increase due to population pressure and related anthropogenic activities (Fig 6).
4. Discussion
Northern Pakistan’s Hindukush-Himalayan mountains are replete with temperate forests, and in addition to their many other benefits, these forests are among the country’s primary natural resources [40, 41]. To meet their basic needs, local communities depend on forest resources, such as firewood, animal feed, building materials, food, and medicine. In addition, the sale of forest products like fuelwood, timber, medicinal plants, wild fruits, and vegetables provides a means of subsistence [14, 26, 42]. Despite the tremendous importance of forests for human society, forest ecosystems are threatened due to increasing human density and the unsustainable use of natural resources [14, 23, 24]. It is essential to determine the current status forest ecosystem and identify the significant drivers of deforestation for sustainable forest management [16]. The use of GIS and RS and proven to be the most convenient source of studying land use changes over a large area [18–20]. However, terrain and atmospheric effects degrade the quality of the images, which can be corrected with ERDAS software [30]. The Normalized Difference Vegetation Index (NDVI) was used to delineate the forest cover from RS satellite imagery across considerable time intervals [31], where the Decay model of exponential growth rate was able to determine the future projection and correlation of two parameters [37]. Our findings sought to describe the phenomenon of increased deforestation that has occurred in the study area over the last 40 years. From October 1980 to October 2000, the rate of deforestation was 0.7% per year, reducing the total forest area from 15,003 hectares to 12,859 hectares. From October 2000 to October 2020, the total forest cover decreased to 7,894 hectares at a rate of 1.93 percent annually. From October 1980 to October 2020, the average deforestation rate was 1.4%, indicating that the rate of deforestation is significantly higher than that reported by many authors in the nearby Hindukush-Himalayan mountains [43–45]. Deforestation is a major ecological issue in Northern Pakistan’s Hindukush-Himalayas Mountains. The main drivers of deforestation is population density, distance from the main town, and administration boundary [44].
Our findings indicate that population growth is the primary cause of deforestation in the study area. According to the 2017 district census report [34], the population of Swat district increased from 0.7 to 2.3 million over the last 40 years at an annual rate of 9% [46]. The decay model projection shows that the Swat district population will increase exponentially to 5.4M at a rate of 11.6% per year and predict a further loss of 2.5% per annum [43]. Population growth indicates that forest resources will be more at risk if long-term management planning in the study area is not implemented [47, 48]. Numerous authors have previously reported on the severe effects of anthropogenic activities resulting from population growth. According to our findings, the population density of the study area will increase exponentially to 5.4 M with an average annual increase rate of 11.6% by 2040, while the forest density will further decrease to 4000 ha, as predicted by the decay model. Application of decay model by using software MATLAB has been explained in various biological studies [49]. Time series analysis of satellite data is a highly effective method for detecting changes, particularly in monitoring urban expansion [50], however, the use of software MATLAB in the current study for modelling forest cover with population growth is an novel approach. The local people have limited sources of income, and non-timber forest products (NTFPs) are their primary income source. Continued practices of deforestation will further worsen their poverty and life standard. The carrying capacity of forests declines at a rate proportional to the pressures exerted on them by a growing population; nevertheless, an equilibrium level of forest cover can be maintained as a function of the population [51]. Unplanned and uncontrolled urbanization that has led to overpopulation in urban areas has had a catastrophic effect on the structure and function of ecosystems and has upset the natural ecological equilibrium [52]. Human population growth is associated with numerous anthropogenic activities that substantially contribute to deforestation, such as socioeconomic conditions that lead to illegal logging, agricultural expansion to feed hungry mouths, and increased fuel wood collection to meet rising energy demands [53]. Although ecotourism determines the economic value of the forest ecosystem (52), unsustainable tourism disturbance impacts the forest vegetation, and forest sites exposed to unsustainable tourism affect vegetation structure and diversity [54]. It is evident from different studies that unsustainable ecotourism have a negative impact on the environment including biodiversity and forest cover. Excessive visitors may disturb the fragile soils, vegetations and can cause wildlife conflicts [55]. Thus, it is essential to improve the conservation effort [44]. Priority areas for biodiversity conservation should be identified based on human population pressure, habitat status, and management activities [47]. The confluence of climate change and human population pressure threatens the resilience of ecosystem services [48]. Reforestation programs will be necessary to increase the amount of forest, and local communities should be urged to use forest resources sustainably. Alternative methods should also be introduced to lessen the demand for forest resources [4, 56].
5. Conclusion
The study highlights the ecological and socioeconomic importance of forests, emphasizing their role in sustaining biodiversity, mitigating climate change, and supporting human livelihoods. Despite these benefits, forest ecosystems, particularly in the Hindu Kush-Himalayas, are under severe threat from human activities, leading to significant forest cover loss. The application of remote sensing (RS) and geographic information systems (GIS) provides a reliable method for monitoring these changes, with findings indicating a rapid decline in forest cover over the last four decades. To detect forest cover change over the past three decades, multispectral satellite images were analyzed using geospatial information systems (GIS) and remote sensing (RS) techniques to evaluate the effects of various anthropogenic activities in the study area. Supervised land cover classification and normalized difference vegetation indices (NDVIs) were applied to Landsat 5, 7, and 254 8 geospatial satellite images from 1980, 2000, and 2020. The study found that the forest area in Malam Jabba has diminished at an alarming rate, primarily driven by population growth, unsustainable resource consumption, and anthropogenic pressures such as deforestation, agricultural expansion, and unsustainable ecotourism. The data reveals that from 1980 to 2020, forest cover decreased from 15,003 hectares to 7,894 hectares, with the deforestation rate accelerating over time. Projections using the decay model suggest that without immediate intervention, forest cover could reduce to as little as 4,000 hectares by 2040, while the local population is expected to nearly triple. The findings underscore the critical need for effective forest management and conservation strategies to mitigate the ongoing loss of forest resources. Sustainable forest management, reforestation, and controlled ecotourism are essential to preserving these ecosystems and improving the livelihoods of local communities. As human population pressures and climate change continue to exacerbate environmental degradation, immediate action is required to halt deforestation and promote the sustainable use of forest resources. Effective policy measures must be implemented at both the local and national levels to ensure the long-term health of the forest ecosystem and the well-being of the people who depend on it. To ensure the long-term viability of natural forest resources, it is advised that local citizens, non-governmental organizations (NGOs), and the government departments responsible for conservation and management should work together to ensure community-based sustainable forest management. The government should provide alternative timber and fuel wood options to the local community. Awareness of the local community is essential for sustainable ecotourism, using natural resources, and successful joint forest management programs. The government should designate as a site for in-situ conservation the moist temperate forest of the study area, which contains timber trees, abundant floral diversity, and economically significant medicinal plants with sustainable ecotourism which will help to conserve these valuable forests. Furthermore, Hindukush-Himalayas forests are experiencing huge anthropogenic pressure and it is difficult to study all parameters of human intervention in a single study, therefore addressing the broader context and challenges of the areas for further research will enhance the reliability and application of the current findings.
Supporting information
S1 Fig.
A -I. 2D and 3D forest cover maps for the year 1980, 2000 and 2020, ArcGIS model builder.
https://doi.org/10.1371/journal.pone.0302192.s001
(DOCX)
Acknowledgments
We would like to express our sincere gratitude to all individuals and organizations who contributed to this research.
References
- 1.
Food and Agriculture Organization. World agriculture: towards 2015/2030: an FAO perspective. FAO; 2003.
- 2. Haines-Young R, Potschin M. The links between biodiversity, ecosystem services and human well-being. Ecosystem Ecology: a new synthesis. 2010;1:110–139.
- 3. Felton A, Felton AM, Wam HK, Witzell J, Wallgren M, Löf M, et al. Forest biodiversity and ecosystem services from spruce-birch mixtures: The potential importance of tree spatial arrangement. Environmental Challenges. 2022;6:100407.
- 4. Raza G, Shaukat Ali, Hussain A, Abbas Q, Khan G, Khan M, et al. Dependence of rural livelihoods on forest resources in Naltar Valley, a dry temperate mountainous region, Pakistan. Global Ecology and Conservation. 2019;20:e00765.
- 5. Messier C, Bauhus J, Doyon F, Maure F, Sousa-Silva R, Nolet P, et al. The functional complex network approach to foster forest resilience to global changes. Forest Ecosystems. 2019;6(1):1–16.
- 6. Zhang L, Sun P, Huettmann F, Liu S. Where should China practice forestry in a warming world?. Global Change Biology. 2022;28(7):2461–2475. pmid:34962005
- 7. Ahmad B, Wang YH, Hao J, Liu YH, Bohnett E, Zhang KB. Variation of Carbon Density Components with Overstory Structure of Larch Plantations in Northwest China and its Implication for Optimal Forest Management. Forest Ecology and Management. 2021;496:119399.
- 8. Nega D, Damtie M, Workie A, Gebrewold Weldesenbet E. An assessment of ecotourism potential for Community Based Ecotourism Development: The case of Alemsaga Priority State Forest, South Gondar, Ethiopia. African Journal of Hospitality, Tourism and Leisure. 2019;8(4):1–18.
- 9. Nizami SM. The inventory of the carbon stocks in sub tropical forests of Pakistan for reporting under Kyoto Protocol. Journal of Forestry Research. 2012;23(3):377–384.
- 10. Ahmad B, Ali A, Rabbi F, Alam N, Siraj M, Mehmood K, et al. Estimation of Wood Volume, Carbon Stock and Rotation Age In Subtropical Pine Forests of Malakand, Khyber Pakhtunkhwa. Pakistan Journal of Botany. 2022;54(5).
- 11. Watson JE, Evans T, Venter O, Williams B, Tulloch A, Stewart C, et al. The exceptional value of intact forest ecosystems. Nature ecology & evolution. 2018;2(4):599–610. pmid:29483681
- 12.
Fearnside PM. Deforestation in Brazilian Amazonia. Oxford University Press; 2017.
- 13. Walker B. Conserving biological diversity through ecosystem resilience. Conservation biology. 1995;9(4):747–752.
- 14. Hamayun M. Ethnobotanical profile of Utror and Gabral valleys, district Swat, Pakistan. Ethnobotanical Leaflets. 2005;2005(1):9.
- 15. Ustin SL, Roberts DA, Gamon JA, Asner GP, Green RO. Using Imaging spectroscopy to study ecosystem processes and properties. AIBS Bulletin. 2004;54(6):523–534.
- 16. Liu SQ, Bo Z, Xia CZ, Ahmad B, Ming Z, Jian C, et al. Forest biomass carbon pool dynamics in Tibet Autonomous Region of China: Inventory data 1999–2019. Plos one. 2021;16(5):e0250073. pmid:33939719
- 17. IUCN Species Survival Commission, Kyne PM, Simpfendorfer CA. A collection and summarization of available data on deep water chondrichthyans: Biodiversity life history and fisheries.
- 18. Faruque MJ, Vekerdy Z, Hasan MY, Islam KZ, Young B, Ahmed MT, et al. Monitoring of land use and land cover changes by using remote sensing and GIS techniques at human-induced mangrove forests areas in Bangladesh. Remote Sensing Applications: Society and Environment. 2022;25:100699.
- 19. Joshi C, De Leeuw J, van Duren IC. Remote sensing and GIS applications for mapping and spatial modelling of invasive species. Proceedings of ISPRS. 2004;35:B7.
- 20. Wang X, Zhang B, Zhang K, Zhou J, Ahmad B. The spatial pattern and interactions of woody plants on the temperate savanna of Inner Mongolia, China: The effects of alternating seasonal grazing-mowing regimes. PLoS ONE. 2015;10:e0133277. pmid:26196956
- 21. Thapa A, Cohen J, Guffey S, Higgins-D’Alessandro A. A review of school climate research. Review of educational research. 2013;83(3):357–385.
- 22. Chazdon RL, Guariguata MR. Natural regeneration as a tool for large‐scale forest restoration in the tropics: prospects and challenges. Biotropica. 2016;48(6):716–730.
- 23. Koehl M, Paivinen R. Definition of a system of nomenclature for mapping European forests and for compiling a Pan-European Forest Information System. EUR 16416.Luxembourg: Office of Official Publications of the European Countries; 1996.
- 24. Tong M, Yan Z, Chao L. Research on a grey prediction model of population growth based on a logistic approach. Discrete Dynamics in Nature and Society. 2020.
- 25. Qureshi S, Yusuf A, Aziz S. Fractional numerical dynamics for the logistic population growth model under Conformable Caputo: a case study with real observations. Physica Scripta. 2021;96(11):114002.
- 26. Sher H, Al yemeni M. Economically and ecologically important plant communities in high altitude coniferous forest of Malam Jabba, Swat, Pakistan. Saudi Journal of Biological Sciences. 2011;18(1):53–61.
- 27. Ali S, Jan BH, Ali A, Tariq M. Sociological analysis of threat to biodiversity and agricultural land conversion into commercial use due to tourism in district Swat, Khyber Pakhtunkhwa. Remittances Review. 2024; 9(2): 355–376.
- 28. Iqbal I, Hamayun M (2004) Studies on the traditional uses of plants of Malam Jabba valley, District Swat, Pakistan. Ethnobotanical Leaflets. 2004; (1): 15.
- 29. Rashid A, Swati MF, Sher H, Al-Yemeni MN. Phyto ecological evaluation with detailed floristic appraisal of the vegetation around Malam Jabba, Swat, Pakistan. Asian Pacific Journal of Tropical Biomedicine. 2011; 1(6): 461–467.
- 30. Kneissl T, Van Gasselt S, Neukum G. Measurement of strike and dip of geologic layers from remote sensing data—New software tool for ArcGIS. 41st Annual Lunar and Planetary Science Conference. 2010;1533:1640.
- 31. Rihan M, Naikoo MW, Ali MA, Usmani TM, Rahman A. Urban heat island dynamics in response to land-use/land-cover change in the coastal city of Mumbai. Journal of the Indian Society of Remote Sensing. 2021;49(9):2227–2247.
- 32.
Abdullah MJY. Application of Remote Sensing for Vegetation Cover Change Assessing Based on (NDVI) index. Doctoral dissertation, Sudan University of Science & Technology; 2022.
- 33.
Population-census report 2017. Pakistan Bureau of Statistics. https://www.pbs.gov.pk/content/population-census.
- 34. Islam M, Jimmy AN, Alam M, Khan NA. The use of multi-temporal Landsat normalized difference vegetation index (NDVI) data for assessing forest cover change of Lawarchara National Park. Environment, Development and Sustainability. 2021;23(12):17702–17722.
- 35. Nugroho RH. Aplikasi ArcGIS Model Builder untuk Analisis Intensitas Pemanfaatan Ruang. Geo Spatial Proceeding. 2021.
- 36. Khan F, Das B, Mishra RK. An automated land surface temperature modelling tool box designed using spatial technique for ArcGIS. Earth Science Informatics. 2022;15(1):725–733.
- 37. Okeke F, Karnieli A. Methods for fuzzy classification and accuracy assessment of historical aerial photographs for vegetation change analyses. Part II: Practical application. International Journal of Remote Sensing. 2007;27(9):1825–1838.
- 38. Barnes RFW. Deforestation trends in tropical Africa. African Journal of Ecology. 1990;28(3):161–173.
- 39. Letcher RA, Croke BFW, Jakeman AJ. Integrated assessment modeling for water resource allocation and management: A generalised conceptual framework. Environmental Modelling & Software. 2007;22(5):733–742.
- 40. Galicia L, Zarco-Arista AE. Multiple ecosystem services, possible trade-offs and synergies in a temperate forest ecosystem in Mexico: a review. International Journal of Biodiversity Science, Ecosystem Services & Management. 2014;10(4):275–288.
- 41. Giergiczny M, Jacobsen J, Glenk K, Meyerhoff J, Abildtrup J, Agimass F, et al. Shaping the future of temperate forests in Europe: why outdoor recreation matters.
- 42.
Abdullah SN, Malik KF, Noor R, Arif M, Khan W. Ethnobotanical diversity of Moist Temperate Mountain Forests: A Case Study from Ayubia National Park, Western Himalayas, Pakistan. In: Biodiversity, Conservation and Sustainability in Asia. Springer, Cham; 2022. pp. 857–871.
- 43. Shehzad K, Qamer FM, Murthy MSR, Abbas S, Bhatta LD. Deforestation trends and spatial modelling of its drivers in the dry temperate forests of northern Pakistan—A case study of Chitral. Journal of Mountain Science. 2014;11(5):1192–1207.
- 44. Ahmad A, Nizami SM. Carbon stocks of different land uses in the Kumrat valley, Hindu Kush Region of Pakistan. Journal of forestry research. 2015;26(1):57–64.
- 45. Qasim M, Hubacek K, Termansen M, Khan A. Spatial and temporal dynamics of land use pattern in District Swat, Hindu Kush Himalayan region of Pakistan. Applied Geography. 2011;31(2):820–828.
- 46. Shi H, Singh A, Kant S, Zhu Z, Waller E. Integrating habitat status, human population pressure, and protection status into biodiversity conservation priority setting. Conservation biology. 2005;1273–1285.
- 47. Aukema JE, Pricope NG, Husak GJ, Lopez-Carr D. Biodiversity areas under threat: overlap of climate change and population pressures on the world’s biodiversity priorities. PloS one. 2017;12(1):e0170615. pmid:28125659
- 48. Misra AK, Lata K, Shukla JB. Effects of population and population pressure on forest resources and their conservation: a modeling study. Environment, development and sustainability. 2014;16(2):361–374.
- 49. Anița S, Arnăutu V, Capasso V. An Introduction to Optimal Control Problems in Life Sciences and Economics: From Mathematical Models to Numerical Simulation with MATLAB. Springer Science & Business Media. 2011; ISBN: 978-88-470-1774-8.
- 50. Sharifi A, Hosseingholizadeh M. The effect of rapid population growth on urban expansion and destruction of green space in Tehran from 1972 to 2017. Journal of the Indian Society of Remote Sensing 2019; 47(7): 1063–1071.
- 51. Alshaybani SAA, Aydın M. Effects of Population Pressure on Forest Resources in Trabzon-Turkey and How to Conserve them. Journal of Global Scientific Research. 2022;7(4):2217–2226.
- 52. Niu L, Cheng Z. Impact of tourism disturbance on forest vegetation in Wutai Mountain, China. Environmental monitoring and assessment. 2019;191(2):1–11. pmid:30656422
- 53. Krieger DJ. Economic value of forest ecosystem services: a review.
- 54. Legese D, Diriba A. The Effect of Population Dynamics On Forest Cover In Daro Lebu Wereda, West Hararghe Zone, Oromia Region, Ethiopia.
- 55. Sobhani P, Esmaeilzadeh H, Sadeghi SMM, Marcu MV. Estimation of Ecotourism Carrying Capacity for Sustainable Development of Protected Areas in Iran. International Journal of Environmental Research and Public Health. 2022;19(3):1059. pmid:35162082
- 56. Isbell F, Calcagno V, Hector A, Connolly J, Harpole WS, Reich PB, et al. High plant diversity is needed to maintain ecosystem services. Nature. 2011;477(7363):199. pmid:21832994