Socioeconomic Impact Assessment of Deforestation of ... · densities. Figure 2 depicts the classification of vegetation density on the satellite images of the year 2000, performed

Environmental Energy and Economic Research (2017) 1(3):287-298

DOI 10.22097/eeer.2017.86996.1005

Socioeconomic Impact Assessment of Deforestation of Hyrcanian

Forests on Local Communities

Sahar Rezaiana*, Seyed Ali Jozib

a Department of Environment, Islamic Azad University, Shahrood Branch, Shahrood, Iran b Department of Environment, Islamic Azad University, North Tehran Branch, Tehran, Iran

Received: 9 January 2017 /Accepted: 19 May 2017

Abstract

The present study was conducted to assess socio-economic impacts of deforestation of

hyrcanian forests in two basins of Do-Hezar and Se-Hezar, northern Iran. To this end,

changes in the forest area were detected over the period between 1990 and 2006. This was

done by land use land cover maps, prepared from Landsat and IRS satellite images. The land

use changes were investigated by enhancement of the images at each time interval.

Furthermore, Normalized Difference Vegetation Index (NDVI) was also used to evaluate the

density of the land cover in the form of six greenness classes. Subsequently, the

socioeconomic impacts of deforestation in the basins were estimated using rapid assessment

matrix. Depending on the extent of destruction, the socioeconomic parameters affected by

destroyed or decreased forest area were identified and scored by RIAM Software. The

obtained results indicated that the greatest changes in forest area was occurred due to urban

development and expanded farmland areas over the years between 2000 and 2006, when the

greenness degree was also decreased. Acceding to the RIAM results, 3% of the deforestation

impacts in two basins was very negative (-E) and 23% was moderately negative (-c). Besides,

slightly negative (-A) impacts included 27% of the total negative effects. Although the

adverse effects of deforestation on socioeconomic status of the residents were not very

destructive, however, the impacts on the rural community could be important and noticeable.

Keywords: Socioeconomic impacts, Land use, Remote sensing, Rapid impact assessment

matrix, Deforestation.

Introduction

Due to the unique natural conditions of the local environment, hyrcanian forests in northern

Iran have long been a place for human habitation in the form of forest villages. The cultural

characteristics of people within or adjacent to the forests are somehow derived from the

conditions of their living environment. Human manipulation and exploitation of the fragile

ecosystem services of the hyrcanian forests has led to severe damages (Zarandian et al. 2016;

Akhani et al. 2010; Yousefpoor et al. 2016). These damages could be detected over time by

the use of change detection methods (Tajbakhsh et al. 2016; Azizi et al. 2016; Afshar et al.

2016). Different techniques and conceptual approaches have yet been proposed in many

* Corresponding Author Email: [email protected]

288 Rezaian and Jozi

studies worldwide for change detection of forest area. As such, Tian et al. (2013) developed a

novel region-based method for change detection using space borne panchromatic Cartosat-1

stereo imagery. They concluded that the proposed method well suited for Cartosat-1 imagery,

and the obtained height values can largely improve the change detection accuracy. In a

research by Pahari et al. (1999), future deforestation and total forest loss in the world was

predicted with regard to the growing rate of global population. According to their reports,

tropical and Sahelian Africa, Central America, Tropical Asia, and Tropical Latin America will

be the countries with the highest rate of deforestation by the year 2050. Panta et al. (2008)

used artificial neural network and Landsat images to analyze forest degradation and

deforestation trends in Chitwan District in Nepal, where there are several key habitats for

wildlife species. In another research, Robert et al. (2007) used the dense temporal stack of

Landsat Thematic Mapper (TM) imagery to develop a new model for change detection of

forest areas. They evaluated the accuracy of the proposed change detection method as 90%.

Baudouin et al. (2007) performed the change detection analysis of forest areas based on the

high resolution satellite images. Gartner et al. (2014), using the data from QuickBird (2005)

and WorldView2 (2011) as well as the object based change detection method, observed

changes in Populus euphratica trees over time. Bin et al. (2013) suggested illumination

correction algorithm to improve the accuracy of forest change detection by Landsat-derived

reflectance data. Desheng et al. (2008) proposed the use of spatial-temporal Markov Random

Fields (MRF) models to improve the accuracy of change detection results. Yuan et al. (2008)

used Li-Strahler geometric-optical model for change detection of forest land cover in Three

Gorges region, China.

The primary aim of the present study was to investigate changes in forest area and other

land use land cover classes in Do-Hezar and Se-Hezar basins based on remotely-sensed data

and over the period of 1990-2006. As the secondary goal, the socioeconomic impacts of

deforestation of hyrcanian forests at these basins were also studied.

Material and Methods

Study area

The study area (Do-Hezar and Se-Hezar Basins) is located in the northern Iran and at the

southern fringe of the Caspian Sea in Mazandaran Province, over the eastern longitude range

of 50°31'43" to 50°58'41" and the northern latitude range of 36°19'22" to 36°45'25". The

study area is extended over the approximate area of 77,443 ha, from which about 31,751 ha

are covered by hyrcanian forests and about 42,033 ha by rangelands. Agricultural and

horticultural lands occupy about 546 ha of the whole study area. The minimum altitude of the

study area is about 100 m from sea level and maximum altitude is about 4800 m at the peaks

of Alam Kooh and Takhteh Soleiman mountains. The basins are divided into 17 hydrological

units, each with 8 sub-basins along with a common sub-basin (multipurpose report of Forestry

Master Plan for basins 33 and 34, Tonekabon, Forests, Range and Watershed Management

Organization, 2006). Figure 1 depicts the divisions of the Do-Hezar and Se-Hezar basins.

Research procedure

In order to analyze the process of deforestation, as the main objective of the present study, the

satellite images of the study area in three time periods, including Landsat TM 1990, Landsat

ETM 2000, and IRS 2006, were used. The data was analyzed by ILWIS, IDRISI, ERDAS,

ArcGIS, Excel, and RIAM software. Before preparing the land use land cover maps of the

river basins, the satellite images were preprocessed. The preprocessing operations included

Environmental Energy and Economic Research (2017) 1(3):287-298 289

relative radiometric correction (RRC), modulation transfer function compensation (MTFC),

and geo-correction (GC) (Fang et al., 2014). Then, Chavez Method was used to perform

atmospheric corrections (reducing numerical value of dark pixels). This technique promotes

the accuracy of classifications on satellite images. The images used in this study did not

require atmospheric correction. For geometric correction of the images, they were imported

into the ERDAS software.

Figure 1. Location of hyrcanian forests in Do-Hezar and Se-Hezar Basins

Afterwards, the images were geo-referenced using Resampling Function of the software

and by the control points well distributed throughout the study area and based on the first

nearest neighbor method. The processing stage was initiated by the assortment of the satellite

images. In digital satellite imagery remote sensing, each pixel has its own numerical value,

which reflects the spectral behavior of the corresponding phenomenon on the Earth’s surface

(Ghulam 2010; Kotthaus et al. 2014; Liang 2004; Weng 2007). The separation of similar

spectra on the satellite images is known as satellite data classification (Dalponte et al., 2012).

Classification of satellite images is possible through two classification methods of supervised

and unsupervised (Mohammady et al., 2015; Nagarajan and Basil, 2014; Rahimi 2016;

Ayalew Mengistu et al. 2011). For supervised classification of pixels, ground training points

are necessary (Malmir et al., 2015; Ganguly et al. 2016; Berakhi et al. 2014; Sewnet 2016).

In this study, different land use classes were intended to separate as various polygons on

the satellite images. These polygons require their own training points, already identified based

on field studies, field sampling, and GPS software. After introducing the training points to the

software, different land use types were separated on the satellite images using maximum

likelihood method in ERDAS software. Accordingly, the land use land cover maps of the

river basins in three time intervals of 1990, 2000, and 2006 were prepared. The prepared land


use land cover maps were then cross-tabulated to detect the changes in the area of each land

use type over the study period.

It should be mentioned that in the post-processing step, the accuracy of the classifications

was checked by randomly-selected sample points in the river basins and IDRISI software. In

addition, NDVI (Normalized Difference Vegetation Index) was used to check the greenness

status of the region during 1990 to 2006 in ILWIS software. Accordingly, NDVI was obtained

in the desired bands of IRS, TM, and ETM images. Then, the land cover of the study area was

classified into 6 classes (no land cover, very poor land cover, poor land cover, fair land cover,

good land cover, and very good land cover). This was done by the method of threshold

densities. Figure 2 depicts the classification of vegetation density on the satellite images of

the year 2000, performed through slicing method (threshold densities) and image histogram

curve in ILWIS software.

Figure 2. Classification of vegetation density on the satellite images using histogram curve

After detecting the spatiotemporal changes of different land use types and vegetation cover in

the study area, the method of Rapid Impact Assessment Matrix (RIAM) was used to evaluate

the socioeconomic effects of the detected changes. RIAM consists of four components,

including physical/chemical, biological/ecological, social/cultural, and economic/operational

(Araujo et al., 2005). The process of RIAM method is summarized in the following equation

(Eq. 1).

a1 × a2 = aT (1)

b1 + b2 + b3 = bT

aT × bT = ES

Where; aT and bT are respectively the multiplication products of all A and all B scores, and

ES is the environmental score for the current conditions.

Figure 3 shows the process of the present study.


Figure 3. procedure of the present research

Result and Discussion

The results of this study showed that the area of both forest areas kept a declining trend

during 1990 to 2006. Most of the reduction in the forest lands was occurred during 2000 to

2006. In addition, settlements and agricultural lands showed an increase in both basins from

1990 to 2006, representing extensive land use changes in these years. The greatest changes

belong to the residential land use, occurred during 2006-2000. The area of the agricultural

lands had a declining trend during 1990-2000, while then; it notably increased during 2000-

2006. Comparing the rangeland areas of the basins during 1990-2000 showed an increasing

trend; this could be due to conversion of forest areas to rangelands in the upstream areas,

while the area of rangeland decreased during 2000-2006. Figure 4 shows the changes in

residential, agricultural, forest, rangeland, and water land use land cover classes during three

intervals of 1990, 2000, and 2006.

As the Figure 4 suggests, rangeland and forest areas include majority of both basins. For

accuracy assessment of the maps, it is required to compare them pixel by pixel with the

ground truth points. After classification of the satellite images, some training points were

positioned by GPS to assess the accuracy of the maps and to control the results. Table 1

shows the accuracy of the classifications.

The results showed that conversion of forest lands to residential areas accelerated during

1990-2000 which was due to the construction of villas in the region. Also, conversion of

forest areas to agricultural lands increased in the period of 1990-2006. During 1990-2000 and

2000-2006 periods, 21% and 27% of the forest areas in both basins were converted into r

rangelands. Conversion of agricultural land to residential areas in both basins doubled from

6% to about 12% during 1990-2006. At this period, the changing trend of rangelands to

residential areas fell from 28% down to 17%. In other words, during the 16-year period,


conversion of rangeland areas to agricultural lands has declined from 14% to 4% in both

basins. Based on the results, the value of NDVI in the basins ranged from 17% (minimum) to

62% (maximum) in 1990. This means that areas with rich land cover were in adjacent to the

areas with spare land cover in both basins. The NDVI map of the basins in 2000 indicated a

range between a minimum of 35% and a maximum of 50%, indicating normal vegetation

status in both basins.

Figure 4. Land use land cover maps of Do-Hezar and Se-Hezar basins in 1999, 2000, and

2006

Table1: Accuracy assessment of the prepared land use land cover maps

Classification accuracy Date and type of satellite image

0. 82 TM/1990

0. 80 ETM/2000

0. 78 IRS/2006

The results of 2006 showed the minimum change as 15% and the maximum change as

58%, indicating areas with rich and dense vegetation cover beside vegetation-free areas. The

NDVI status showed that no cover, very poor cover, and poor cover classes increased in the

period of 1990-2006. Most NDVI changes occurred during 1990-2000, which correspond to

accelerated land use land cover changes in this period. Figure 5 provides a comparison on the

greenness extent of both basins in three time periods.


Figure 5. Comparison of greening coverage in two basins in 2006

As the greenness degree of the basins shows, the quality of land cover in fair and good

classes was desirable in 1990, while in 2000, the area of spare land cover class was expanded

more than the other vegetation cover classes. This was due to the conversion of very poor,

poor, and fair land cover classes to spare land cover. In 2006, the area of very poor and fair

land cover classes was greater than the other classes and the area of good land cover was the

smallest. Also, there was found no patches of very good land cover in both basins. Figure 6

depicts the extent of land cover greenness in the two basins during three time intervals of

1990, 2000, and 2006. As observed, the density of vegetation cover declined in the period

between 1990 and 2006. This is more evident in the time period of 1990-2000.

In this paper, for socioeconomic impact assessment of the deforestation, first of all,

satellite images were used to map the current land use and the trend of land use land cover

changes. In a similar study, Siddiqui et al., (2004) prepared the map of a forest area using

ETM satellite images. They also used logistic regression model to predict the spatial pattern

of deforestation and to find possible reasons of this deterioration.

The results of the present study revealed a declining trend in the area of hyrcanian forests

in the basins. On the contrary, the area of rangelands increased from 46490 ha in 1990 to

499369 ha in 2000, which could be due to degradation of forest land cover in the upstream

areas and its conversion to rangeland. However, the area of rangelands decreased to 43576ha

in 2006. The area of agricultural fields and residential lands increased during 1990-2006. This

was mainly due to conversion of rangelands to built-up areas, as a result of extensive

unplanned urban development , particularly during the period of 2000-2006.

The socioeconomic impact assessment of land degradation in the study area showed that

loss of hyrcanian forests is the most important adverse effect of land use changes over the

study period, which is attributed to the dependence of the livelihood of the local communities

to these forests.

Figure 7 illustrates the scores of possible socioeconomic effects of land use changes in the

study area over the study period. It should be mentioned that A to E letters indicate the degree

of effects on the receiving environment, ranging from slight effect (A) to considerable effect

(E). The positive and negative nature of the effects is shown by + and – symptoms.

Reviewing the flooding records revealed a return period of 20 to 50 years in both basins.

This would be a serious threat to the dense residential and commercial areas, and livelihood of

local communities, as well as animal husbandry and farmers, especially in villages located on

the floodways. In the event of flooding, majority of the farmlands will be flooded and

consequently, the farmlands will gradually convert into the barren lands due to loss of soil

fertility and top soil.

No cover Very poor poor Moderate Good Very good

0

10000

20000

30000

40000

50000

60000

The green

degree of

year 1990The green

degree of

year 2000The green

degree of

year 2006


Figure 6. vegetation density of Do-Hezar and Se-Hezar Basins in 1990, 2000, and 2006

Socioeconomic effects


Sociocultural (SC) analysis of land degradation showed that alignment with macro policies,

with a score of -81, would be very negative impact (-E) of land use change in the study area.

Furthermore, tourism decline, increased flooding, reduced aesthetic quality, and landscape

quality, with the score range of -19 to -39, would be the most important moderately negative

effects (-C) of conversion of land use. In addition, reduced local population, increased

immigration, declining health indicators, increased disease, reduced quality of drinking water,

culture invasion, indiscriminate constructions, and poor quality of groundwater, with a total

score ranges between -10 and -18, are the most important negative changes (-B) imposed by

land use change. Other parameters (literacy, education, population displacement, public

participation, educational facilities, roads and transport, as well as cultural, historical, and

archaeological heritage, and traffic), with a score range of -1 to -9 will be slightly affected (-

A) by the deforestation, which could be neglected. Among economic/operational (EO),

employment, income, livestock, and real estate value will be moderately affected (-C) by

deforestation. In addition, negative impact (-B) is expected to impose on agriculture, local

land use, community services, future development plans, new infrastructures, aquaculture,

and revenue from the sale of timber. Slight negative effects (-A), but negligible, are also

expected on health services, beekeeping, and handicrafts. According to the results, the

negative effects of deforestation in the social/cultural parameters are greater than

economic/operational parameters. The results also showed that 3% of the impacts were very

negative (-E), 23% moderately negative (-C), 37% negative (-B), and 27% slightly negative

and negligible (-A). In addition, no significant adverse effect (-D) is expected to impose on

any social/cultural and economic parameters in the two basins. The very negative impacts (-E)

of deforestation, in comparison to negative impacts (-B) and moderately negative impacts (-

C), was very limited in both basins. Therefore, this impact cannot be considered destructive

and significant in the socioeconomic section in the basins.

Conclusion

The research findings revealed a declining trend of Hyrcanian forest over the study period

from 1990 to 2006. In addition, residential areas and agricultural lands were expanded in both

basins from 1990 to 2006. The increasing trend of rangeland areas could be attributed to

conversion of forest areas in the upstream areas. According to the results of RIAM, the major

negative socio-cultural and economic impacts of forest degradation in both basins include

tourism decline, increased risk of flooding, declined aesthetic quality, loss of landscape

quality, unemployment, lower income of residents, lower value of real estates, and reducing

animal husbandry. Accordingly, it would be of utmost importance to conserve natural

ecosystem of the hyrcanian forests in order to conserve tourist destinations and increase the

income of local communities. Do-Hezar and Se-Hezar Basins in Tonkabon City have a good

potential for ecotourism development due to its exquisite scenery, unique landscapes, rivers,

castles, and mountains.

Land use changes in the past three decades, particularly conversion of forest land cover to

rangelands and farmlands, have increased the risk of flooding of farmlands in the study area.

Therefore, the saturated fertile top-soil will be washed; its fertility will be lost, and gradually

converted into barren lands. This will affect the income of local communities, whose

livelihood absolutely depends on forest, farming, and animal husbandry.

Among the negative effects of deforestation in these basins can be mentioned to

immigration, population displacement, reduction of public participation, poor social welfare

(educational, health, and transport facilities) and social services, declined health indicators,

and poor quality of drinking water.


Figure 7. Environmental and socio-economic effects of land use land cover changes in the

study area.

According to the research findings, deforestation will leave no significant socioeconomic

on none of the basins; however, the role of Do-Hezar and Se-Hezar beautiful and rich forests

in the livelihood of the residents cannot be ignored. Given the potentials of the region, the

source of income for a rural family varies in both basins and includes a variety of sources

such as agricultural activities, livestock, services, labor, handicrafts, and other jobs. However,

several factors constitute household income in most rural families; for example, a family may

be a farmer or a herder, and also a handcrafter. Due to the low income of the local

communities, the head of household has to earn revenue from redouble efforts. In this context,

a comprehensive and dynamic management can help identifying the potentials of local

0

1

2

3

4

5

6

7

8

-E -D -C -B -A N A B C D E

Nu

mb

er

of

eff

ect

s

Effects range

Socio-cultual effect

0

1

2

3

4

5

6

7

8


Nu

mb

er

of

eff

ect

s

Range of effects

Economic-executive effect

0

5

10

15


Nu

mb

er

of

eff

ect

s

Range of effects

Socio-cultual effect Economic-executive effect


environment in order to find new ways of livelihood for the locals in a way to leave the least

possible adverse effects on the environment while securing the income of local communities.

In this respect, attempts should be made towards conservation of ecosystem services. Among

suitable measures should be preventing the trafficking of timber in the forest. Establishment

of multipurpose cooperatives with members from local communities, replacement of

traditional husbandry with semi-industrial animal husbandry units, assistance to self-

sufficiency of local communities by granting loans with low interest rates, rehabilitation of

native industry, establishment of small breweries for dairy products, supporting beekeeping,

silk rearing, poultry, and traditional handicrafts, providing opportunities for development of

ecotourism and increasing social welfare of local communities could be among other

measures recommended.

Acknowledgement

The authors hereby would like to express their sincere gratitude to the staff of Iran Forests,

Range and Watershed Management Organization for providing them with the input data.

Special thanks to all who have contributed in the present study.

References

Akhani, H., Djamali, M., Ghorbanalizadeh, A. and Ramezani, E. (2010). Plant biodiversity of

hyrcanian relict forests, Iran: an overview of the flora, vegetation, palaeoecology and conservation.

Pak. J. Bot., 42, 231-258.

Afshar, S., Saghafian, B. and Vosoughifar, H. R. (2016). Source apportionment of the sediments

entering dam using lithological and mineralogical studies. Global J. Environ. Sci. Manage., 2(3),

265-274. DOI: 10.7508/gjesm.2016.03.006

Araujo, S.J., Aslam, H., Tear, G. and Casanova, J. (2005). Mummy/cystic encodes an enzyme required

for chitin and glycan synthesis, involved in trachea, embryonic cuticle and CNS development -

Analysis of its role in Drosophila tracheal morphogenesis. Dev. Biol., 288(1), 179-193.

Ayalew Mengistu, D., Kassahun Waktola, D. and Woldetsadik, M. (2011). Detection and analysis of

land-use and land-cover changes in the Midwest escarpment of the Ethiopian Rift Valley. J. Land

Use Sci., 7(3), 239-260.

Azizi, A., Malakmohamadi, B. and Jafari, H. R. (2016). Land use and land cover spatiotemporal

dynamic pattern and predicting changes using integrated CA-Markov model. Global J. Environ.

Sci. Manage., 2 (3), 223-234. DOI: 10.7508/gjesm.2016.03.002.

Berakhi, R. O., Oyana, T.J. and Adu-Prah, S. (2014). Land use and land cover change and its

implications in Kagera river basin, East Africa. Afr. Geogr. Rev., 34(3), 209-231.

Dalponte, M., Bruzzone, L. and Gianelle, D. (2012). Tree species classification in the Southern Alps

based on the fusion of very high geometrical resolution multispectral/hyperspectral images and

LiDAR data. Remote Sens. Environ., 123, 258-270.

Desclée, B., Bogaert, P. and Defourny, P. (2006). Forest change detection by statistical object-based

method. Remote Sens. Environ. 102(1–2): 1-11.

Fang, L., Wang, M., Li, D. and Pan, J. (2014). CPU/GPU near real-time preprocessing for ZY-3

satellite images: Relative radiometric correction, MTF compensation, and geocorrection. ISPRS J.

Photogramm. Remote Sens., 87, 229-240.

Ganguly, K.l., Kumar, R., Mruthyunjaya Reddy, K., Jagadeeswara Rao, P., Raj Saxena, M. and Ravi

Shankar, G. (2016). Optimization of spatial statistical approaches to identify land use/land cover

change hot spots of Pune region of Maharashtra using remote sensing and GIS techniques.

Geocarto International, In press. DOI: 10.1080/10106049.2016.1178813.

Gärtner, Ph., Förster, M., Kurban, A. and Kleinschmit, B. (2014). Object based change detection of

Central Asian Tugai vegetation with very high spatial resolution satellite imagery. Int. J. Appl.

Earth Obs. Geoinf., 31, 110-121.

http://dx.doi.org/10.1016/j.ydbio.2005.09.031

http://www.sciencedirect.com/science/article/pii/S0034425712001320










Ghulam, A. (2010). Calculating surface temperature using Landsat thermal imagery. Ph.D. thesis,

Department of Earth & Atmospheric Sciences, and Center for Environmental Sciences, Saint Louis

University.

Ijas, A., Kuitunen, M.T. and Jalava, K. (2009). Developing the RIAM method (rapid impact

assessment matrix) in the context of impact significance assessment. Environ. Impact Assess. Rev.,

30 (2), 82–89.

Kennedy, R.E., Cohen, W.B. and Schroeder, T.A. (2007). Trajectory-based change detection for

automated characterization of forest disturbance dynamics. Remote Sens. Environ. 110(3), 370-

386.

Kotthaus, S., Smith, T.E.L., Wooster, M.J. and Grimmond, C. S. B. (2014). Derivation of an urban

materials spectral library through emittance and reflectance spectroscopy. ISPRS J Photogramm

Remote Sens., 94, 194-212. DOI: 10.1016/j.isprsjprs.2014.05.005

Liang, Sh. (2004).Quantitative Remote Sensing of Land Surfaces. Wiley Pub.

Weng, Q. (2007). Remote Sensing of Impervious Surfaces. CRC Press Book.

Liu, D., Song, K., Townshend, J.R.G. and Gong, P. (2008). Using local transition probability models

in Markov random fields for forest change detection. Remote Sens. Environ. 112 (5), 2222-2231.

Malmir, M., Kheirkhah Zarkesh, M.M., Monavari, S.M., Jozi, S.A. and Sharifi, E. (2015). Urban

development change detection based on Multi-Temporal Satellite Images as a fast tracking

approach—a case study of Ahwaz County, southwestern Iran. Environ. Monitor. Assess., 187, 108.

In press, Doi:10.1007/s10661-015-4295-y.

Mohammady, M., Moradi, H.R., Zeiniv, H. and Temme, A.J. A. M. (2015). A comparison of

supervised, unsupervised and synthetic land use classification methods in the north of Iran. Int. J.

Environ. Sci. Technol., 12(5), 1515–1526.

Nagarajan, M. and Basil, G. (2014). Remote sensing- and GIS-based runoff modeling with the effect

of land-use changes (a case study of Cochin corporation). Nat. Hazards, 73(3), 2023–2039.

Tajbakhsh, M., Memarian, H., Shahrokhi, Y., (2016). Analyzing and modeling urban sprawl and land

use changes in a developing city using a CA-Markovian approach. Global. J. Environ. Sci.

Manage., 2(4), 397-410. DOI: 10.22034/gjesm.2016.02.04.009.

Tan, B., Masek, J.G., Wolfe, R., Gao, F., Huang, C., Vermote, E.F., Sexton, J.O. and Ederer, G.

(2013). Improved forest change detection with terrain illumination corrected Landsat

images. Remote Sens. Environ. 136, 469-483.

Pahari, K. and Murai, S. (1999). Modelling for prediction of global deforestation based on the ground

of human population. ISPRS J. Photogramm. Remote Sens., 54(1999), 317-324.

Panta, M., Kim, K. and Joshi, C. (2007). Temporal mapping of deforestation and forest degradation in

Nepal: Applications to forest conservation. Forest Ecol. Manag., 256, 1587-1595.

Rahimi, A. (2016). A methodological approach to urban land-use change modeling using infill

development pattern- a case study in Tabriz, Iran. Ecol. Proc., 5:1, in press. DOI: 10.1186/s13717-

016-0044-6.

Sewnet A. (2016). Land use/cover change at Infraz watershed by using GIS and remote sensing

techniques, northwestern Ethiopia. Int. J. Riv. Bas. Manage., 14 (2), 133-142.

Siddiqui, M.N., Jamil, Z. and Afsar, J. (2004). Monitoring changes in riverine forests of Sindh-

Pakistan using remote sensing and GIS techniques. Adv. Space Res. 33(3), 333-337.

Tian, J., Reinartz, P., d’Angelo, P. and Ehlers, M. (2013). Region-based automatic building and forest

change detection on Cartosat-1 stereo imagery. ISPRS J. Photogramm. Remote Sens.,79, 226-239.

Yousefpoor, M., Rostamie Shahraji, T., Eslam Bonyad, A., and Salahi, M. (2016). Modeling human

population patterns on tree density. Global J. Environ. Sci. Manage., 2(3), 311-318. DOI:

10.7508/gjesm.2016.03.01.

Zarandian, A., Baral, H., Yavari, A.R., Jafari, H.R., Stork, N.E., Ling, M.A. and Amirnejad, H.

(2016). Ecosystem services, land use, household surveys, Participatory, approach, rapid methods.

Forests, 7(3), 51-78. DOI: 10.3390/f7030051.

Zeng, Y., Schaepman, M.E., Wu, B., Clevers, J.G.P.W. and Bregt, A.K. (2008). Scaling-based forest

structural change detection using an inverted geometric-optical model in the Three Gorges region

of China. Remote Sens. Environ., 112(12), 4261-4271.

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Socioeconomic Impact Assessment of Deforestation of ... · densities. Figure 2 depicts the classification of vegetation density on the satellite images of the year 2000, performed