Tree Species Composition in Tropical Forest Remnants of Highly Deforested Regions: The Case of the Huasteca Potosina Region, Mexico

Deforestation and fragmentation of tropical forest are the most serious threats to biodiversity. In the lowlands of the Huasteca Potosina region, Mexico, more than 95,000 ha of tropical forest have disappeared. This paper analyses tree species composition in tropical forest patches of this highly deforested region, located in northeast of Mexico. At present, only 57 remnants of arboreal vegetation larger than ten hectares still remain standing, which cover 6117 ha. These decreasing fragments host 140 floristic species, representing 42 families and 85 genera. Compared with previous inventories, these data show a sharp drop in species composition. Most of the forest remnants contain less than 50% of the floristic diversity recorded by previous surveys, particularly in the medium subperennial tropical forest.

An agent-based model of agricultural land expansion in the mountain forest of Timor Island,Indonesia

The Mutis-Timau Forest Complex,located on Timor Island,Indonesia,is a mountainous tropical forest area that gradually decreases due to deforestation and forest degradation.Previous modelling studies based on patterns indicate that deforestation primarily occurs at lower elevations and near the boundaries of forests and settlements,often associated with shifting cultivation by local farmers.This study adopts a process-based modelling approach,specifically the agent-based model,to simulate land changes,particularly farmers'expansion of agricultural land around the Mutis mountain forest.The underlying concept of this agent-based approach is the interaction between the human and environmental systems.Farmers,representing the human system,interact with the land,which represents the environmental system,through land use decision-making mechanisms.The research was conducted in the Community Forest of the Timor Tengah Utara District,one of the sites within the Mutis-Timau Forest Complex with the highest deforestation rate.Land use change simulations were performed using agent-based modelling from 1999 to 2030,considering the socio-economic conditions of farmers,spatial preferences,land use decisions,and natural transitions.The results revealed that the agricultural area increased by 14%under the Business as Usual scenario and 5%under the Reducing Emission from Deforestation and Forest Degradation scenario,compared to the initial agricultural area of 245 hectares.The probability of farmers deciding to extend agricultural activities was positively associated with the number of livestock maintained by farmers and the size of the village area.Conversely,the likelihood of farmers opting for agricultural extensification decreased with an increase in the area of private land and the farmer's age.These findings are crucial for the managers of the Mutis-Timau Forest Complex and other relevant stakeholders,as they aid in arranging actions to combat deforestation,designing proper forest-related policies,and providing support for initiatives such as reducing emissions from deforestation and forest degradation programs or further incentive schemes.

Characterizing selected soil attributes of different land-use management to assess reforestation benefits of deforested riparian buffers

Introduction:The information available on the sensitivity of soil biotic and abiotic attributes,which can be used to track the impact of reforestation in riparian buffers,is often insufficient to refine management practices and convince stakeholders of the benefits of reforestation.Methods:In this study,conducted in Victoria,Australia,the changes in soil biotic and abiotic attributes,organic carbon(OC),mineral nitrogen(MN),total dissolved solutes(TDS)and pH were characterised to assess the impact of land-use change from bare riparian(BR)to reforested riparian(RR).Additionally,the benefits of revegetating a deforested creek bank with regard to salinity abatement and C-sequestration potentials were assessed.Results:The TDS depletion in the RR strips varied spatiotemporally from 65 to 169 mg/L,the net OC deposition from 16 to 19 g C/kg soil and MN deposition from 1.2 to 2.1 g N/kg soil,respectively.Additionally,the net changes in pH from alkaline to near neutral condition varied by 0.4 to 1.0 pH units.Approximately 30%to 60%of the net OC depletion after deforestation was redeposited under RR over 3 to 6 years.The TDS depletion after land-use changed from BR to RR ranged from 15 to 32%over 3 to 6 years.Conclusion:The soil attributes OC,MN and TDS characteristics under different land-use practices varied spatiotemporally.This information may be useful to convince stakeholders to undertake reforestation of creek banks for salinity abatement,and that change in land-use has the potential to increase C sequestration at a farm scale.

Has global deforestation accelerated due to the COVID-19 pandemic?

As the COVID-19 pandemic unfolded,questions arose as to whether the pandemic would amplify or pacify tropical deforestation.Early reports warned of increased deforestation rates;however,these studies were limited to a few months in 2020 or to selected regions.To better understand how the pandemic infl uenced tropical deforestation globally,this study used historical deforestation data(2004–2019)from the Terra-i pantropical land cover change monitoring system to project expected deforestation trends for 2020,which were used to determine whether observed deforestation deviated from expected trajectories after the fi rst COVID-19 cases were reported.Time series analyses were conducted at the regional level for the Americas,Africa and Asia and at the country level for Brazil,Colombia,Peru,the Democratic Republic of Congo and Indonesia.Our results suggest that the pandemic did not alter the course of deforestation trends in some countries(e.g.,Brazil,Indonesia),while it did in others(e.g.,Peru).We posit the importance of monitoring the long-term eff ects of the pandemic on deforestation trends as countries prioritize economic recovery in the aftermath of the pandemic.

Taxonomic and community composition of epigeal arthropods in monoculture and mixed tree species plantations in a deciduous forest of Ghana

Tropical forests provide several ecosystem services and functions and support approximately two-thirds of the world’s biodiversity but are seriously threatened by deforestation.Approaches to counteract this menace have revolved around aff orestation with several or a single tree species.We thus investigated how plantation forests with either a single or several tree species infl uenced arthropod taxonomic and community composition using pitfall traps to sample selected groups of epigeal arthropods(Araneae,Coleoptera,Orthoptera and Hymenoptera)and with environmental variables assessed simultaneously.Our results revealed 54 taxonomic groups with signifi cantly higher taxonomic richness,activity density,and diversity in the mixed stands than in the monoculture stands.The significant differences in community composition were mainly driven by families including Lycosidae,Formicidae,Staphylinidae,Scotylidae,Hydrophilidae,Gryllidae and Scarabaeidae and were explained by distinct habitat characteristics(canopy openness,litter depth,deadwood volume,and tree height).While the diverse tree communities and heterogeneous vegetation structure off ered food and habitat resources for diverse arthropod groups,the allelopathic nature coupled with homogenous stand characteristics of the Tectona grandis stands in the monoculture suppressed the growth of understorey vegetation that could otherwise serve as food and habitat resources for arthropods,which might have led to limited activities and diversity of arthropods in the monoculture plantation stands.The fi ndings thus highlight the need to promote mixed tree plantations in degraded tropical areas,especially when restoring biodiversity is the prime management focus.

The Climate Response to Global Forest Area Changes under Different Warming Scenarios in China

Human activities have notably affected the Earth’s climate through greenhouse gases(GHG), aerosol, and land use/land cover change(LULCC). To investigate the impact of forest changes on regional climate under different shared socioeconomic pathways(SSPs), changes in surface air temperature and precipitation over China under low and medium/high radiative forcing scenarios from 2021 to 2099 are analyzed using multimodel climate simulations from the Coupled Model Intercomparison Project Phase 6(CMIP6). Results show that the climate responses to forest changes are more significant under the low radiative forcing scenario. Deforestation would increase the mean, interannual variability, and the trend of surface air temperature under the low radiative forcing scenario, but it would decrease those indices under the medium/high radiative forcing scenario. The changes in temperature show significant spatial heterogeneity. For precipitation, under the low radiative forcing scenario, deforestation would lead to a significant increase in northern China and a significant decrease in southern China, and the effects are persistent in the near term(2021–40), middle term(2041–70), and long term(2071–99). In contrast, under the medium/high radiative forcing scenario, precipitation increases in the near term and long term over most parts of China, but it decreases in the middle term, especially in southern, northern,and northeast China. The magnitude of precipitation response to deforestation remains comparatively small.

Impact of forest governance and enforcement on deforestation and forest degradation at the district level:A study in West Bengal State,India

According to the United Nations Environmental Programme(UNEP),the world loses 1.0×106hm2forest land through deforestation annually.About 1.6×106people who depend on forests for livelihood are negatively affected by deforestation and forest degradation.The paper attempts to study the impact of forest governance,enforcement and socio-economic factors on deforestation and forest degradation at the local level in West Bengal State,India.The study was based on questionnaire survey data during 2020–2021 collected from three western districts(Purulia,Bankura,and Paschim Medinipur)where deforestation and poverty rates are higher than other districts in West Bengal State.The total number of selected villages was 29,and the total sample households were 693.A stratified random sampling technique was used to collect data,and a questionnaire was followed.Forest governance and enforcement indices were constructed using United Nation Development Programme(UNDP)methodology and a step-wise logistic regression model was used to identify the factors affecting deforestation and forest degradation.The result of this study showed that four factors(illegal logging,weak forest administration,encroachment,and poverty)are identified for the causes of deforestation and forest degradation.It is observed that six indices of forest governance(rule of law,transparency,accountability,participation,inclusiveness and equitability,and efficiency and effectiveness)are relatively high in Purulia District.Moreover,this study shows that Purulia and Bankura districts follow medium forest governance,while Paschim Medinipur District has poor forest governance.The enforcement index is found to be highest in Purulia District(0.717)and lowest for Paschim Medinipur District(0.257).Finally,weak forest governance,poor socio-economic conditions of the households,and weak enforcement lead to the deforestation and forest degradation in the study area.Therefore,governments should strengthen law enforcement and encourage sustainable forest certification schemes to combat illegal logging.

Assessment of Land Use and Land Cover Changes (LULC) in the North Talihya River Watershed (Lubero Territory, Eastern DR Congo)

On the Equator, the Talihya North watershed is a vast area of nearly 581.7 km2 that extends from the Cool Highlands on the Congo-Nile Crest in Lubero Territory to the Rift Valley in the Virunga National Park in Beni Territory. This vast territory has an agropastoral vocation. Indeed, agricultural activities combined with the high population density in this watershed generate modifications in the landscape structure. The objective of this paper is to study the dynamics of land use from 1987 to 2020. To achieve this, two Landsat TM+ and ETM+ images and one Sentinel-2 image were analyzed. After the classification of the images based on the Maximum Likelihood algorithm, this study shows that two processes are evident in the landscape of the North Talihya watershed: deforestation and savannization. Forests that occupied 253.11 km2 in 1987 have decreased to 201.12 km2 in 2001 and to 123.04 km2 in 2020. These area balances indicate that the natural forest formations in the North Talihya watershed have been converted primarily to croplands and fallows. The estimated annual deforestation rate between 1987 and 2020 is 2.18%. With this high rate of deforestation, mechanisms to restore degraded forest ecosystems in this watershed will need to be put in place. The sustainable management of residual forest ecosystems that have escaped human pressure is necessary for the conservation of biodiversity for future generations.

VTOL UAVs for Forest Monitoring and Management via Remote Sensing: A Case Study in Khao Yai National Park, Thailand

The utilization of UAVs (Unmanned Aerial Vehicles) has experienced a remarkable upsurge in various industries, including forestry. Their capacity to expeditiously and effectively cover large tracts of land has resulted in their widespread adoption as a valuable forest management and monitoring tool. The versatility of UAVs extends to their capability to perform quick and efficient surveys of large areas, inventory of tree species, and monitoring of forest health. This research paper reports on the successful utilization of VTOL (Vertical Takeoff and Landing) UAV that was designed and built at the IESSD (Institute of Earth Science and Sustainable Development) located in the AAA (Asia Aviation Academy) at KMITL (King Mongkut’s Institute of Technology Ladkrabang) Prince of Chumphon Campus, Thailand. The VTOL UAV is employed for resource and environmental missions, as well as forest monitoring by using remote sensing technology. VTOL UAVs are used for aerial surveillance to conduct air photography, data collection, and processing for resource and environmental missions. This research paper presents a comprehensive analysis of the areas at risk of deforestation and forest encroachment in a particular region of Khao Yai National Park in Thailand, highlighting the potential for the resulting photographs to inform evidence-based decision-making and facilitate sustainable forest management practices. This study offers recommendations to develop VTOL UAVs remote sensing capabilities and mitigate deforestation and forest encroachment in Khao Yai National Park.

Anthropogenic Climate Change, Deforestation and Renewable Energy

Since the Industrial Revolution, greenhouse gas (GHG) emission, especially global CO2 emission (GCE) has greatly increased with the growth of global human population (GHP), which has caused climate change. Both GCE and GHP are positively related with the rise of global mean surface temperature (GMST). Our empirical research shows that the impact from GCE on GMST is 7.72 times of that from TSI, and the impact from GHP on GMST is 7.9 times of that from TSI. The growth rate of global surface temperature was slower from 1998 to 2012, which was mainly caused by the decadal cooling of the tropical Pacific Ocean-La Niña. Deforestation and vegetation degradation affect climate change, feedback radiation between the atmosphere and the biosphere account for 30% of the variation in global surface radiation and precipitation. The share of renewable energy remains small in primary energy consumption due to their disadvantages.

Land Use Land Cover Dynamics of Oba Hills Forest Reserve, Nigeria, Employing Multispectral Imagery and GIS

Land use Land cover (LULC) has undergone progressive changes worldwide over the years. However, there is limited information available about these changes in Oba Hills Forest Reserve, Nigeria. The existing spatial analysis of the forest excluded important land use classes like settlements. Therefore, this study aimed at assessing the dynamics of LULC in Oba Hills Forest Reserve between 1987 and 2019. Images from Landsat 5, Landsat 7, and Landsat 8 for the years 1987, 2001, 2013, and 2019 were obtained and subjected to preprocessing and classification using the maximum likelihood algorithm, change detection, and Normalized Differential Vegetation Index (NDVI). The coordinates of specific benchmark locations and other points were acquired for ground-truthing and developing Digital Elevation Model (DEM). Three distinct LULC classes were identified: forest, bare land (including open spaces, agriculture, rocks, and grasslands), and built-up areas. The forest cover in the reserve gradually decreased from 56% in 1987 to 47% in 2019, resulting in a total area loss of 455.4 hectares. Correspondingly, the other LULC classes experienced exponential expansion. Bare land increased from 44% in 1987 to 52% in 2019, while the built-up area expanded by 57.28 hectares. These changes are attributed to prevalent anthropogenic activities such as agriculture, grazing, logging, firewood collection, and population growth within the catchment area. The declining NDVI values in the forest reserve, from 0.52 to 0.44 within the years of assessment, further substantiated the substantial loss of forest cover. The DEM and topographical map highlighted notable steep slopes and elevations of up to over 550 m above sea level (asl) within the reserve, which have implications for forest growth and dynamics. In conclusion, this study reveals extensive rates of forest cover changes into bare land, primarily for agriculture, and settlements, and offers further recommendations to reverse the trend.

Comparing the soil quality changes of different land uses determined by two quantitative methods

Soil quality is one of the most important environmental factors in sustaining the global biosphere and developing sustainable agricultural practices. A study was initiated in Wolong Nature Reserve, Sichuan Province, China to elucidate the soil quality changes of natural secondary succession, forest planting and agricultural practices after deforestation in the humid mountainous region. The soil qualities of six land use types (natural forestland, grassland, shrub land, secondary forestland, cultivated land and reforested land) were compared using two quantitative methods: the integrated soil quality index(QI) and soil deterioration index(DI). The QI values of natural forestland, grassland, shrub land, secondary forestland, cultivated land, reforested land were 0.8039, 0.3277, 0.9127, 0.6881, 0.0285 and 0.3183, respectively. The DI values were 0%, -14%, 12%, 1%, -26% and -18% respectively. Both indexes suggested that shrub land can restore soil properties. To compare the two methods more directly, a deduced index QI′ based on QI value was developed. The results showed that DI and QI′had a very high linear correlation coefficient (r=0.9775) despite the values were different. Both methods were efficient in evaluating the soil quality levels and DI was a more simple way in soil quality assessment, while QI could show more ecological meanings.

Numerical Simulation for the Impact of Deforestation on Climate in China and Its Neighboring Regions

In this paper, the CCMOB model is used to study the effect of the deforestation on the climate of China and its neighboring regions. On the assumption that the forest in China would be replaced by the vegetation(such as grassland), the distribution of the albedo changed was calculated. The initial fields used were taken from the FGGE zonal mean data on 16 July, 1979. In the control simulation, the observed albedo data were used to modify the physical parameters of the original model. The control and sensitive experiments were run each for 210 days, in which the external forcing fields were fixed in July. As a result, we find that the East Asian Monsoon, Hadley cell and troposphere easterly jet are weakened for the deforestation in China. The precipitation and cloud amount over China are also decreased. The changes in evaporation and surface temperature are small. The results also show that the deforestation in China exerts a remarkable effect on the climate in the neighboring regions of China.

Lost in transition： Forest transition and natural forest loss in tropical China

The term forest transition refers to a change in forest cover over a given area from a period of net forest area loss to a period of net gain. Whether transitioning from deforestation to reforestation can lead to improved ecosystem services, depends on the quality and characteristics of the newly established forest cover. Using publicly available data, we examine forest transition in two regions of tropical China: Hainan Island and Xishuangbanna. We found that the overall increase of forest cover in both areas during the1980 s was due to an increase in plantation forests rather than to increases in the area covered by natural forest. We also found a time lag between the increase in overall forest cover and an increase in natural forest. On Hainan Island, natural forest continued to decline beyond the point in time when overall forest cover had started to increase, and only began to recover ten years after the turning point in 1978. In Xishuangbanna, where the transition point occurred ten years later, the decline of natural forest cover is still going on. These divergent trends underlying forest transition are concealed by the continued practice to apply the term "forest" broadly, without distinguishing between natural forests and planted forests.Due to the use of undiscriminating terminology, the loss of natural forest may go unnoticed, increasing the risk of plantation forests displacing natural forests in the course of forest transition. Our findings are important for programs related to forest management and ecosystem services improvement, including reforestation and Payments for Ecosystem Services programs.

Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico

As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring strategies using geospatial technology.Among statistical methods for mapping biomass,there is a nonparametric approach called k-nearest neighbor(kNN).We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone.Satellite derived,climatic,and topographic predictor variables were combined with the Mexican National Forest Inventory(NFI)data to accomplish the purpose.Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique.The results indicate that the Most Similar Neighbor(MSN)approach maximizes the correlation between predictor and response variables(r=0.9).Our results are in agreement with those reported in the literature.These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation(REDD+).

Assessment of causes and future deforestation in the mountainous tropical forest of Timor Island, Indonesia

The Mutis-Timau Forest Complex,one of the remaining mountainous tropical forest areas in Timor Island,eastern Indonesia that covers an area of 31,984 ha,tends to decrease gradually.Efforts to secure mountain forest functions and counteract the negative impact of declining forest areas are often constrained by data uncertainty on factors contributing to deforestation.For this reason,this study attempts to develop models of deforestation and predict future deforestation in the Mutis-Timau Forest Complex.We constructed models of deforestation that describe the relationship between deforestation and factors contributing to deforestation using spatial statistical models.In this model,we used the deforestation data for the 1987-2017 period obtained from a previous study as dependent variables and the potential causes of deforestation generated from Geographic Information System spatial analysis as independent variables.Using the probability of deforestation derived from the model,we predicted future deforestation under two different scenarios,namely,business-as-usual(as the reference scenario)and reducing emission fromdeforestation and forest degradation.Our findings showed that a positive relationship exists between probability of deforestation,distance to the settlement,and population density variables,whereas a negative relationship exists between likelihood of deforestation,elevation,slope,distance to the road,distance to the savanna,and forest management unit variables.During the 2017-2030 period,under the business-as-usual scenario,the Mutis-Timau Forest Complex will lose 1327.65 ha in forest area with an annual deforestation rate of 0.54%.Meanwhile,under the reducing emission from deforestation and forest degradation scenario,the overall forest loss was estimated to be 1237.11 ha with an annual deforestation rate of 0.50%.The predicted area of avoided deforestation in 2017-2030 under the reducing emission from deforestation and forest degradation scenario was 90.54 ha.Such data and information are important for the Mutis-Timau Forest Complex authority in prioritizing actions for combating deforestation and designing appropriate forest-related policies and supporting data for reducing emission from deforestation and forest degradation programme or other incentive schemes in reducing deforestation.

A system dynamics model for billion trees tsunami afforestation project of Khyber Pakhtunkhwa in Pakistan:Model application to afforestation activities

As part of the global effort to plant billion trees,an afforestation project is launched in Pakistan in Khyber Pakhtunkhwa(KP)province to conserve existing forests and to increase area under forest cover.The present study is designed to build a Systems'model by incorporating major activities of the Billion Tree Tsunami Afforestation Project(BTTAP)with special focus on afforestation activities to estimate the growth in forest area of KP.Availability of complete dataset was a challenge.To fix the model,the raw data taken from the project office has been utilized.Planning Commission Form 1-Phase I&II helped us with additional information.We relied on the data available for one and half period of the project as rest of the data is subject to the completion of the project.Our results show that the project target to enhance area under forest differs from the target to afforest area under the project.The system dynamics'model projection shows that the forest area of KP would be 23.59 million hectares at the end of the BTTA project,thus having an increase of 3.29%instead of 2%that has been initially proposed.However,the results show that the progress to meet the target in some afforestation classes is slow as compared to other categories.Farm forestry,plantation on communal lands and owners'plantation need special focus of the authority.Deforestation would affect 0.02 million hectares area of the project.The model under study may be used as a reference model that can be replicated to other areas where billion tree campaigns are going on.

The Extent and Drivers of Deforestation and Forest Degradation in Masito-Ugalla Ecosystem, Kigoma Region, Tanzania

Deforestation and forest degradation has been observed to be rampant in Masito-Ugalla ecosystem, Kigoma Region, western part of Tanzania. This paper therefore, intended to assess the extent of deforestation and forest degradation in the area, and to determine their causes. A total of 101 respondents were considered as the sample size for this study. The methods used for data collection were household questionnaire interviews, in-depth interviews, focus group discussions, analysis of satellite images and direct observation. The findings indicated that deforestation was occurring in the study area. Satellite data revealed diminished closed woodland, bushed grassland, forest and thickets between 1990 and 2014. On the contrary, settlement area, cultivated land and open woodland had increased during the same time frame. Proximate factors causing deforestation and forest degradation included agricultural expansion, wood extraction and expansion of settlement area. Underlying factors included population growth, poverty, poor levels of education, lack of employment, corruption and embezzlement of public funds by politicians and senior government officials;and high demand for fuel-wood. Biophysical drivers like incidences of unplanned wildfires and socio trigger events notably civil strife were also important. In order to minimize the problem and based on the factors augmenting deforestation and forest degradation in the Masito-Ugalla ecosystem and their coupled negative consequences, effective environmental conservation education, increased patrols, effective law enforcement and provision of alternative energy sources are necessary.

Spatial and temporal dynamics of Pai forest vegetation in Pakistan assessed by RS and GIS

Pai, an arid forest in Sindh Province of Pakistan, is important for the environmental, social, economic development and conservation of ecosystems of the province. Considering the significance of the forest for Sindh and the calls from the local population for its deforestation, we quantified the spatial and temporal variation in the vegetation of the forest and land surface temperature （LST） using optical and thermal Landsat satellite data. Our analysis of temporal （1987-2014） images with ArcGIS 10.1 revealed that the dense forest area was greatest at 725 ha （37 % of the total forest area） during 2013 while it was smallest at 217 ha （11%） in 1992. The sparse forest area peaked during 1987 at 1115 ha （58 %） under shrubs whereas it was smallest at 840 ha （43 %） in 1992, and the maximum deforestation of Pai forest occurred during 1992. Spatial change in vegetation over a period of about 27 years （1987-2014） revealed that vegetation increased on an area of 735 ha （37 %）, decreased on 427 ha （22 %）, and there was no change on 808 ha （41%） of the forest. Variation in temperature between shaded （dense forest） and unshaded areas （bare land） of the forest was from 6 to 10 ℃. While the temperature difference between areas with sparse forest and bare land ranged from 4 to 6℃. An inverse relationship between LST and NDVI of Pai forest with coefficients of determination of 0.944 and 0.917 was observed when NDVI was plotted against minimum and maximum LST, respectively. The vegetation in the forest increased with time and the areas of more dense Pai forest supported lower surface temperature and thus air temperature.

Forest cover dynamics in Palas Valley Kohistan, Hindu Kush-Himalayan Mountains, Pakistan

Forest cover change in the mountainous region is driven by a variety of anthropogenic and natural factors.The Hindu Kush-Himalayan Mountains has experienced a considerable vegetation cover change due to intensive human activities,such as population growth,proximate causes,accessibility,unstable political situations,government policy failure and poverty.The present study seeks to find out the impact of population growth and road network expansion on forest cover of Palas valley based on remotely sensed data and employing geospatial techniques.Changes in forest cover were determined by classifying time-series satellite images of Landsat and Sentinel 2 A.The images of October 1980,2000,2010 and 2017 were classified into six land cover classes and then the impact of population growth and accessibility on forest cover was analyzed.Furthermore,forest cover and land-use change detection map was prepared using classified images of 1980 and 2017.The data were collected mainly from field visits(ground verification),census reports,Communication and Works Department,Kohistan.Satellite imageries were obtained from the United States Geological Survey’s websites and classified in ERDAS imagine 2014 and ESRI ArcGIS 10.2.1 using supervised classification-maximum likelihood algorithm.Result of this study revealed that a substantial reduction in forest cover has taken place mainly in the proximity of human settlements.On the average,during the study period,annually more than 460 hectares of forest area has been converted into other uses.