Geographic Information System Tools Research Articles

The Study of Septic Tank Proximity on Coliform, E. Coli, and Organic Contamination in Shallow Groundwater: a Case Study of DKI Jakarta

Groundwater in DKI Jakarta is subject to microbiological contamination. Approximately 32% of the population in DKI Jakarta relies on groundwater as an alternative source of clean water, highlighting the urgent need to address and prevent contamination. This study aims to investigate the effect of the separation distance between septic tanks and wells on the levels of Total coliform (TC), Escherichia coli (EC), and organic parameter contamination in shallow groundwater. Groundwater quality data, including TC, EC, and organic parameters from 265 monitoring points in DKI Jakarta, was obtained from the Jakarta Environment Agency in 2022. A quantitative descriptive approach, incorporating spatial mapping of bacterial and organic contamination using Geographic Information System (GIS) tools, was employed. MANOVA analysis using SPSS 26 was conducted to determine the relationship between septic tank distances and shallow water contamination levels. Results indicate that the separation distance between septic tanks and wells significantly influences TC and EC contamination in Jakarta Pusat, Jakarta Timur, and Jakarta Barat. Organic contamination, however, is significantly affected only in Jakarta Pusat and Jakarta Timur. In contrast, Jakarta Utara was identified as the most contaminated region, with 84% of groundwater samples showing TC contamination, 55% exhibiting Escherichia coli (EC) contamination, and 69% affected by organic pollution. However, these contaminations were not proven to be influenced by the proximity of septic tanks. This is likely due to the area's coastal location, where hydrogeological conditions and diverse pollution sources play a more significant role in groundwater contamination. Improvements in sanitation infrastructure could significantly enhance groundwater quality, particularly in highly vulnerable areas such as Jakarta Utara.

Assessment of land suitability and crop water requirements for irrigated sugarcane in the Kuraz Irrigation Scheme, Lower Omo Basin, Ethiopia

ABSTRACT Assessing land suitability is crucial for sustainable agricultural production. This study focuses on evaluating the suitability of land for irrigated sugarcane cultivation and determining crop water requirements within the Kuraz Irrigation Scheme in Ethiopia. A combination of geographic information system (GIS) and analytical hierarchy process (AHP) tools was used to classify land suitability and support decision-making by considering factors such as soil pH, texture, temperature, rainfall, land use-land cover, and slope. The AquaCrop model was utilized to simulate current and future scenarios for biomass, canopy cover, yield, maximum evapotranspiration, and water productivity of sugarcane crops. The model was calibrated for irrigated sugarcane during the 2015–2016 crop season, revealing that the majority of the study area is highly suitable for sugarcane cultivation. Specifically, 28% of the area was classified as highly suitable, 38% as moderately suitable, 21% as marginally suitable, and 13% as unsuitable. These findings highlight the potential for expanding sugarcane cultivation to boost yields and profitability. However, the study also underscores the need to consider potential environmental impacts, such as water usage and soil degradation from agricultural expansion. Additionally, social factors, including labor practices and the possible displacement of local communities, must be taken into account when expanding sugarcane cultivation.

Macroscopic evaluation of road traffic safety using Qgis: A case study of Tuyen Quang province

Road traffic safety in Vietnam is one of the top social concerns due to the complicated situation of traffic accidents, requiring in-depth research to find appropriate solutions. Macroscopic evaluation of traffic safety is one of the effective ways to prevent and reduce road traffic accidents. The paper presents a new method based on an Equivalent Severity Score (ESS) to evaluate road traffic safety in an area using a geographic information system (GIS) tool, namely QGIS. The road traffic accident (RTA) data from 2021 to 2023 in Tuyen Quang province, Vietnam, was used to analyze and test this method. First, the RTA data was divided by the administrative units of Tuyen Quang, using several evaluation indicators, including the number of traffic accidents, fatalities, serious injuries, and slight injuries. Then, the ESS was determined based on these evaluation indicators to measure the seriousness of RTA across the administrative units. Finally, the ranking of road traffic safety of administrative units is presented by using QGIS software. From there, the traffic authorities can easily understand areas where RTA occurs more seriously and provide reasonable solutions. The results of the paper will help traffic authorities have more basis in making decisions related to solutions to solve traffic problems not only in Tuyen Quang province of Vietnam but also in other localities

QGIS: an effective tool in assessing the quantity and quality of groundwater resources

ABSTRACT The study demonstrates the utilization of the Quantum Geographic Information System (QGIS) package, an open-source geographic information system (GIS) tool for evaluating the quantity and quality of groundwater resources. This research employed data from public sources pertaining to several sampling stations in the Indian city of Dehradun as its case study and converted this information into a digitized format. The use of QGIS allows for processing and analysis of huge data, leading to the creation of informative maps that depict the spatial distribution of groundwater characteristics. By employing spatial analysis techniques, the study aimed to gain insights into the state of groundwater resources, including their availability, variations in level, and suitability for drinking and irrigation purposes. By visualizing and analyzing the groundwater data using QGIS, the study contributes to the broader goal of managing water resources efficiently and helps policymakers, water resource managers, and other stakeholders make informed decisions regarding groundwater management, allocation, and conservation strategies.

Application of GIS Tools to analyze changes in road network parameters: The case of Berkane province

Road network management is an aspect of infrastructure heritage preservation that requires special attention from the departments concerned, in order to accurately predict maintenance, reinforcement and planning interventions, which requires effective methods for visualizing and tracking the network. This research aims to integrate techniques of the Geographic Information System (GIS) for modeling, classifying and analyzing the evolution of two parameters: Traffic and pavement deterioration. In this context, a case study examining GIS spatial representation options visualized these two parameters on national, regional and provincial roads in the Berkane province in Morocco based on the results of traffic investigations and visual surveys of pavement deterioration between 2016 and 2022. This study will provide road network managers and decision-makers with the information they need to make the right decisions regarding future maintenance programs.

Assessment of soil erosion and sediment delivery ratio in the Arghandab Catchment, Kandahar Province, Afghanistan by using GIS-based RUSLE method

A significant proportion of Arghandab Catchment in Kandahar Province, Afghanistan has been under potential degradation threat due to soil erosion. This study assessed the extent of soil erosion and estimated the sediment delivery ratio (SDR) in the catchment by employing the Revised Universal Soil Loss Equation (RUSLE), utilizing the Geographic Information System (GIS) and the Remote Sensing (RS) techniques. Data, related to rainfall erosivity (R factor), soil erodibility (K factor), slope length and steepness (LS factor), cover management (C factor), and support practices (P factor), employed for the RUSLE model were processed using the GIS tools and R-Studio software. Average RUSLE factor values estimated in the studied area ranged from 51.8 to 124 MJ mm ha−1 h−1 year−1, 0.03725 t ha h ha−1 MJ−1 mm−1, 9.2, 0.445, and 0.75 for R, K, LS, C, and P factors, respectively. The results revealed that the average annual soil loss from the catchment was 6.81 t ha−1 yr−1, ranging from 0.002 t ha−1 yr−1 in the flat areas up to 94.7 t ha−1 yr−1 in the hilly and mountainous regions. Soil classes of slight, moderate, high, very high, severe, and very severe covered areas of 20.1% (263,542.3 ha), 12.2% (160,286.5 ha), 22.8% (298,740.6 ha), 20.3% (265,546.8 ha), 17.6% (231,224.4 ha) and 6.9% (90,879.9 ha), respectively. The computed SDR for Dahla Reservoir located in the catchment was found to be in between 24.2% and 36%. LS factor was identified as the most crucial variable associated with soil erosion. The findings from this study can be applied when designing effective conservation strategies in the management of erosion and water management.

"Developing a Holistic Strategy Based on GIS and Story Mapping for Learning and Communicating Geography "

The Geographic Information System (GIS) offers a vast array of applications, tools, techniques, and digital resources in geographic science, including digital cartography, remote sensing, virtual globes and web viewers. Recently, these technologies have gained prominence due to their high scientific and educational value. They provide an ideal platform for digital learning and for fostering curricular and methodological innovation in Geography education, with significant potential for independent distance learning. Based on practical activities and student satisfaction surveys, this paper demonstrates that modern GIS tools and story mapping are powerful mechanisms for understanding geographic and land-planning concepts. They also provide a more engaging and pedagogical way to communicate a holistic vision of the world. The results show an increase in student motivation and an improvement in space skills. The findings highlight that innovative methods for sharing visually georeferenced and geographic content can significantly enhance the teaching and learning experience connected with real-world problems.

Application of GIS in road safety analysis: A case study of NH66 corridor in Kollam

Road accidents are a significant concern globally, impacting public safety and infrastructure. This study investigated the application of Geographic Information System (GIS) technology to analyze road safety along the most extended bypass in Kerala, India. Through a detailed review of existing methodologies and the integration of GIS tools, the research identified and characterized accident black spots - areas with a high frequency of accidents. The methodology involved collecting and analyzing secondary and primary data. Secondary data used in the study was the accident data obtained from District Crime Records Bureau (DCRB) of the Police Department and primary data included road characteristics and traffic parameters collected from the study site. Preliminary analysis of secondary data revealed insights into the temporal and demographic aspects of accidents, common types of collisions, and other significant contributing factors leading to accidents. Different approaches were used to identify accident black spots: reactive and proactive. The reactive approach used for black spot determination employed by Ministry of Road Transport and Highways (MoRTH) specifications and the Weighted Severity Index method which relied upon accident data for the analysis. The proactive approach employed the Total Weight and Weighted Overlay Method in GIS to rank the black spots without depending upon the accident data, thereby aiming at proactively identifying potential safety issues for incident prevention. The study’s findings revealed areas with high accident susceptibility, facilitating targeted interventions and remedial measures, including implementing speed breakers, service roads, separate turning lanes at junctions, and enhanced traffic signage at accident-prone locations. Additionally, the study identified the potential of GIS technologies in improving road safety management practices, data visualization, and decision-making processes. Overall, the study underscores the importance of leveraging GIS technology for comprehensive road safety analysis and management, with implications for policy formulation, infrastructure planning, and accident prevention strategies.

Análise Multicritério para Seleção de Técnicas Compensatórias de Drenagem Urbana e Recomendação de sua Distribuição Espacial para a Cidade de Itajubá – MG

Objective - This study evaluated the applicability of the integration of multicriteria analysis methods with Geographic Information System tools to identify suitable locations for the implementation of sustainable drainage measures. Methodology – The multicriteria analysis method known as Analytical Hierarchical Process (AHP) was employed alongside QGIS software to evaluate bioretention, detention basins, permeable pavements, individual reservoirs, and green/storage roofs in a case study conducted in the city of Itajubá, MG. Originality/relevance – Decision-makers continue to face challenges in identifying optimal locations for implementing sustainable urban drainage techniques that account for technical and spatial constraints, posing a persistent barrier to their widespread adoption. Results – It was observed that detention basins are more suitable for peripheral areas and individual reservoirs and green/storage roofs systems for urban areas. Bioretention was not very suitable for urban areas, due to space restrictions. The most influential criterion overall was “solid input”, except for the green/storage roof system, which prioritized the resident population. Social and environmental contributions - This work provides a consolidated methodology for creating maps that can be used by decision makers to choose the most suitable locations for the application of sustainable urban drainage techniques, contributing to the reduction of flooding and diffuse pollution in urban centers.

Decadal Change in Land Use and Land Cover Dynamics: A Comprehensive Assessment of the Kharun Catchment

Land Use and Land Cover (LULC) changes exert profound influences on environmental and resource dynamics, particularly in developing regions experiencing rapid urbanization and agricultural expansion. This study investigates LULC changes over a 30-year period (1992–2022) in the Kharun catchment, part of the upper Mahanadi basin in Chhattisgarh, India. Encompassing 4,143.17 km² across six districts, the catchment plays a critical role in supporting agricultural and urban activities. Landsat imagery from 1992, 2002, 2012, and 2022 was analyzed using supervised classification with Remote Sensing (RS) and Geographic Information System (GIS) tools to categorize six LULC types: water bodies, forests, urban areas, upland paddy fields, lowland paddy fields, and barren land. The results reveal dynamic and significant LULC changes. In 1992, water bodies (2.00%), forests (4.86%), and urban areas (1.92%) occupied smaller portions of the landscape, while barren land accounted for 28.64%. By 2002, water bodies slightly increased to 2.12%, forests decreased to 3.98%, urban areas expanded to 3.21%, and barren land reduced to 27.80%. Urban expansion became pronounced by 2012, with urban areas rising to 6.42%, while forests further declined to 3.11%, and barren land reduced to 26.30%. By 2022, water bodies expanded to 2.63%, forests declined significantly to 2.77%, and urban areas surged to 9.35%, indicative of extensive deforestation and urbanization. Barren land continued to decrease, covering 24.15%. These findings highlight the urgent need for integrated land-use management strategies that balance developmental needs with environmental conservation. Policy frameworks should prioritize reforestation initiatives, sustainable urban planning, and soil and water conservation practices to mitigate the adverse effects of deforestation and land degradation. Regular and systematic monitoring of LULC dynamics at the catchment scale is essential to support informed decision-making, ensuring ecological integrity and socio-economic sustainability in rapidly developing regions.

GIS-based analysis of anti-Asian hate speech and its socioeconomic and ideological drivers in the United States during the early COVID-19 pandemic

Anti-Asian racism and xenophobia surged on social media during the COVID-19 pandemic, fueling discrimination and violence against Asians and Asian Americans in the United States. This study aims to understand the spatial patterns of Anti-Asian hate speech (AAHS) and identify the socioeconomic and ideological factors that may influence its emergence. Using Geographic Information System (GIS) tools, including hotspot analysis, Ordinary Least Squares (OLS), and Geographically Weighted Regression (GWR), we analyzed several socioeconomic and ideological factors to understand their impact on the spatial variability of AAHS risk levels. Our models show AAHS hotspots tend to cluster in suburban areas near the metropolitan and micropolitan regions. Our OLS model identifies three significant socioeconomic factors: the poverty level is negatively correlated with the AAHS risk level, but both high education level and diversity index demonstrate positive correlations with the AAHS risk level. The GWR model further explored local spatial patterns of how these factors influence AAHS levels. We suggest that anti-Asian sentiments may be driven by dominant groups fearing the loss of their identity and socioeconomic security. Additionally, in diverse areas, pandemic-related crises can exacerbate existing intergroup tensions, with structural inequalities further deepening divisions. Policymakers can leverage these findings to develop strategies aimed at mitigating the factors contributing to hate speech and effectively combating it. Future studies can expand on our findings to develop more robust theoretical frameworks for understanding these patterns.

System for forecasting the lake dynamics in Russian Arctic using satellite images based on NextGIS Web

The article discusses the important geoecological problem of predicting the dynamics of thermokarst lakes in the Russian Arctic as intensive sources of natural greenhouse gas emissions, which is considered as one of the factors of current climate change. The purpose of the work is to consider the development of a system for forecasting the dynamics of lake areas using entropy-randomized machine learning algorithms and tools of the NextGIS Web geographic information system. The procedure for processing information to predict the dynamics of lakes is considered. Data from remote measurements of the areas of thermokarst lakes in the Arctic zone of Russia, obtained from Landsat satellite images over the past several decades, and climate data determined by reanalysis of meteorological data for the same period are used as retrospective information for forecasting. The system is implemented on the basis of the NextGIS Web geographic information system, which allows the inclusion of randomized modeling applications using the Python language.

The First Fifth-Generation District Heating and Cooling System in Kazakhstan: Planning and Design

This paper focuses on the potential for integrating Geographic Information System (GIS) software into Fifth-Generation District Heating and Cooling (5GDHC) systems to promote efficient and sustainable energy management, particularly in Kazakhstan. By reviewing the key literature, we identify three main areas where GIS software enhances the planning of 5GDHC systems: decision-making in the context of energy market regulations, operational data utilization, and modeling/simulations for technical design. The technical design emphasizes the role of heat pumps, ultra-low temperature district heating (ULTDH) networks, and end-user buildings. Previous research has explored various methodologies for integrating network and demand-side strategies, developing sustainable district heating and cooling (DHC) systems, and mapping urban areas suitable for DHC deployment. However, none has presented an open-source model incorporating GIS-based decision-making in designing 5GDHC systems. This study, for the first time, addresses this gap through a case study conducted in the Northern Industrial Zone of Karaganda, Kazakhstan, demonstrating how GIS-enhanced modeling can be effectively applied in a developing, industry-driven economy. We outline the mathematical framework for comparing existing simulation tools and developing a custom model suited to the region’s needs. Additionally, we discuss validation and calibration methods, which remain underexplored in the current literature. The proposed model incorporates waste heat recovery from local sources, including nearby wastewater treatment plants, showcasing a sustainable energy solution for the industrial park. The results indicate that a well-structured 5GDHC system, supported by GIS tools, can markedly enhance energy efficiency and sustainability, presenting a scalable and adaptable approach for other regions in Kazakhstan and beyond.

Detection of changes in water salinity based on patrol monitoring and remote sensing methods

A significant number of rivers heavily influenced by anthropogenic pressures are not monitored (or monitored infrequently). For this reason, there is a need to develop modern methods allowing for the ongoing observation of water quality parameters, of which salinity is a key one. As a result of patrol monitoring information on changes in conductivity in the longitudinal profile of the Oder were obtained. The aim of this study is to correlate these results with Sentinel – 2 satellite imagery (VIS + NIR bands) to verify the hypothesis that remote sensing methods can be used to detect salinity changes in inland flowing waters. For this purpose data acquired with the multi-parameter probe during field expeditions, remote sensing methods and Geographical Information Systems tools (such as inter-bands algebra, pixel value extraction) and statistical methods were used. We concluded that the best spectra for salinity detection is Green as well as Salinity Index (SI) mostly showed statistically significant correlations. Remote sensing can be successfully used to detect changes in the salinity of flowing waters.

Study of monitoring of water bodies using remote sensing data and GIS technologies (Talimarjan water reservoir)

Abstract The study of water bodies using remote sensing data and Geographic Information System (GIS) technologies is crucial for effective water resource management. This research focuses on monitoring the Talimarjan water reservoir, a vital water source in Uzbekistan, by leveraging satellite imagery and GIS tools. Remote sensing offers a cost-effective and comprehensive approach to assess various parameters, including surface area, water quality, and seasonal fluctuations in water levels. By utilizing multi-temporal satellite data, changes in the reservoir’s volume and surrounding vegetation were analyzed. The integration of GIS technologies facilitated the mapping and spatial analysis of these changes, enabling the identification of trends and potential risks, such as sedimentation and water loss. The combination of remote sensing and GIS provides valuable insights into the health and sustainability of the water reservoir, aiding in decision-making for water management authorities. This study demonstrates the effectiveness of these technologies in environmental monitoring, highlighting their potential for broader applications in the management of water resources in arid regions. Through accurate, real-time data, remote sensing and GIS help in ensuring sustainable water use, promoting efficient resource management, and mitigating the impacts of climate change on water bodies.

Percursos de pedestres: um índice a partir das inter-relações entre caminhabilidade e conforto ambiental

Abstract The study of environmental comfort in open urban spaces has undergone constant updates in the last decades. In the meantime, the specific analysis of these aspects within the context of walkability stands out as an area with considerable potential to be investigated. In this context, this article aims to explore walkability in pedestrian routes and its relationship with environmental comfort, proposing a more systematized approach for its application in the strategic planning of cities. Therefore, we proposed the construction of a Pedestrian Walkability and Comfort Index (ICCP), composed of three partial indices that consider thermal, acoustic, and ergonomic aspects of pedestrian paths. The methodology is based on street visualization tools and Geographic Information System (GIS) tools to spatialize and systematize the interactions between walkability and environmental comfort. The research concludes that ICCP is a valid tool for measuring walkability at meso and micro scales, and it can be replicated in other urban contexts. Furthermore, the development and application of ICCP also highlight that rethinking walkability with environmental comfort in urban areas requires an integrated approach to the entire mobility system and urban infrastructure of cities.

An Assessment of Land Use Land Cover Using Machine Learning Technique

This research paper presents a comprehensive assessment of the built-up area in Mysuru City over the decade spanning from 2010 to 2020, employing advanced geospatial techniques. The study aims to analyze the spatiotemporal patterns of urban expansion, land-use dynamics, and associated factors influencing the city’s built environment. Remote sensing imagery, Geographic Information System (GIS) tools, and machine learning algorithms are leveraged to process and interpret satellite data for accurate land-cover classification. The methodology involves the acquisition and preprocessing of multi-temporal satellite imagery to delineate and map the built-up areas at different time intervals. Land-use change detection techniques are employed to identify and quantify alterations in urban morphology over the specified period. Additionally, socio-economic and environmental variables are integrated into the analysis to discern the drivers of urban growth. The outcomes of this research contribute valuable insights into urbanization dynamics and land-use planning strategies, facilitating informed decision-making for sustainable urban development.

Study of using GIS technologies in forestry cadastre and monitoring for environmental sustainability

Abstract The integration of Geographic Information Systems (GIS) technologies in the forestry cadastre and monitoring practices in Uzbekistan represents a significant advancement in sustainable forest management. This study explores the application of GIS in enhancing the accuracy, efficiency, and comprehensiveness of forestry cadastre, which is crucial for the effective management, conservation, and restoration of forest resources in Uzbekistan. Through spatial analysis, remote sensing, and data integration, GIS technologies offer detailed insights into forest cover, land use changes, and biodiversity, providing a robust framework for decision-making and policy formulation. The research highlights how GIS tools facilitate the precise mapping of forest boundaries, monitoring of illegal logging activities, and assessment of reforestation efforts. Furthermore, the study examines the challenges and opportunities in implementing GIS in Uzbekistan’s forestry sector, considering the country’s unique geographical and ecological context. The findings suggest that adopting GIS technologies can significantly contribute to achieving Uzbekistan’s environmental sustainability goals and improving the management of its forestry resources.

Crop, semi-natural, and water features of the cotton agroecosystem as indicators of risk of infestation of two plant bug (Hemiptera: Miridae) pests.

This study considers concepts and tools of landscape ecology and geographic information systems (GIS) to prioritize insect monitoring in large-scale crops, using the cotton agroecosystem of the Texas Gulf Coast and two plant bug species (Creontiades signatus Distant and Pseudatomoscelis seriatus (Reuter) [Hemiptera: Miridae]) as a case study. The two species differed in host plants and time span as cotton pests. C. signatus and P. seriatus abundance in early growth of cotton were regressed on landscape metrics. Comparisons of three approaches to select landscape variables in stepwise multiple regressions were made across spatial scales and two weeks of insect data extracted from monitoring of 21 cotton fields, years 2010 through 2013. The spatial variation of plant bug abundance and the landscape features were substantial, aiding the regression approach. For full stepwise regression models using 18 landscape variables, regression model fit using C. signatus data was modestly better in week one of sampling when C. signatus adults and young nymphs were detected (R 2 range of 0.56 to 0.82), as compared with model fit at week two (R 2 range of 0.49 to 0.77). The smallest scale (2.5 km radius) models had the greatest number of variables selected and highest R 2, while two broader scales (5 and 10 km) and truncating the models to three variables produced a narrower range of R 2s (0.49 to 0.62) and more consistent entry of variables. Wetland composition had a consistent positive association with C. signatus abundance, supporting its association with seepweeds which are common in coastal wetlands. When selected, the composition of cotton and grassland/shrubland/pasture also had a positive association with C. signatus abundance. Aggregation metrics were also relevant, but composition metrics in the models were arguably more easily utilized in prioritizing insect monitoring. In contrast, there were few significant regressions using P. seriatus data, possibly due to the widespread distribution of its weedy host plants and lower abundance. Overall, selected landscape features served as indicators of C. signatus infestation potential in cotton particularly grown near coastal wetlands, but landscape features were not useful for P. seriatus infestation potential in cotton.

Population growth poses a significant threat to forest ecosystems: A case study from the Hindukush-Himalayas of Pakistan.

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.