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Spatiotemporal Monitoring for Deforestation and Forest Degradation Activities in Selected Areas of Khyber Pakhtunkhwa (KPK)

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The global significance of forest ecosystems requires precise determination of the amount of carbon stored in different forest ecosystems. Regular monitoring of forests can aid in designing efficient climate change control strategies at national and global scale specially in reducing emissions from deforestation and degradation strategies. This research is designed to focus on determining deforestation of study area from 2001 to 2011 using Remote Sensing (RS) and Geographic Information System (GIS) techniques. This research provided rate and amount of degradation of forests in the study area and was quite helpful in formulating a strategy to earn carbon credits consistently and, therefore, will help in the uplifting of the standards of local population.
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International Journal of Geosciences, 2016, 7, 1191-1207
http://www.scirp.org/journal/ijg
ISSN Online: 2156-8367
ISSN Print: 2156-8359
DOI: 10.4236/ijg.2016.710089 October 27, 2016
Spatiotemporal Monitoring for Deforestation
and Forest Degradation Activities in Selected
Areas of Khyber Pakhtunkhwa (KPK)
Saif ur Rehman Khalid, Mobushir Riaz Khan, Muhammad Usman*, Muhammad Waqar Yasin,
Muhammad Shahid Iqbal
Department of Geo-Informatics, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan
Abstract
The global significance of forest ecosystems requires precise determination
of the
amount of carbon stored in different forest ecosystems. Regular monitoring of forests
can aid in designing efficient climate change control strategies at national and global
scale specially in reducing emissions from deforestation and degradation st
rategies.
This research is designed to focus on determining deforestation of study area from
2001 to 2011 using Remote Sensing (RS) and Geographic Information System (GIS)
techniques. This research provided rate and amount of degradation of forests in the
study area and was quite helpful in formulating a strategy to earn carbon credits co
n-
sistently and, therefore, will help in the uplifting of the standards of local population.
Keywords
Deforestation, Degradation, Carbon Credits, Climate Change
1. Introduction
One of the most debated and provocative science issues of 21st century is Global
Warming. Report of Intergovernmental Panel on Climate Change (IPCC) on this issue,
proclaims that the scientific concerns and suspicions of global warming are fundamen-
tally determined. According to this report, there is 20 cm rise in sea level and 0.6˚C rise
in global temperature during the last century
i.e.
20th century. The IPCC report also
suggests that by the end of this century, global temperature could rise by 1.4˚C to 5.8˚C
and sea level could rise by between 20 cm and 88 cm if situation remains unchanged
[1].
How to cite this paper:
Khalid, S. ur R
.,
Khan,
M.R., Usman, M., Yasin, M.W.
and
Iqbal
, M.S. (2016) Spatiotemporal Mon
i-
toring for Deforestation and Forest Degra-
dation Activities in Selected Areas of Khyber
Pakhtunkhwa (KPK)
.
International Journal
of
Geosciences
,
7
, 1191-1207.
http://dx.doi.org/10.4236/ijg.2016.710089
Received:
April 4, 2016
Accepted:
October 24, 2016
Published:
October 27, 2016
Copyright © 201
6 by authors and
Scientific
Research Publishing Inc.
This work is licensed under the Creative
Commons Attribution
International
License (CC BY
4.0).
http://creativecommons.org/licenses/by/4.0/
Open Access
S. ur R. Khalid et al.
1192
This is not the matter of national interest within the boundary of a country now; ra-
ther it is a global responsibility of human kind and all the nations will have to realize it
globally and act upon collectively and effectively and should be taken seriously to save
our planet [2].
Extensive concern about global climate change has led to attention in reducing emis-
sions of carbon dioxide (CO2) and under certain conditions, in counting additional
carbon captivated in soils and vegetation as part of encouraging the emissions reduc-
tions. Increasing the amount of carbon removed by and stored in forests can be one op-
tion for slowing the rise of greenhouse gas concentrations in the atmosphere, and the-
reby providing possible mitigation of adverse effects of change in climate [3].
According to the Government of Pakistan 4.2 million ha of area is covered by forest
in Pakistan
i.e.
, only about 4.8 percent of total land area. Forest area of Khyber Pakh-
tunkhwa (KPK) is 1.21 million hectares,
i.e.
, 40 percent of total forest area of the coun-
try [4].
Forests are an essential part of daily lives of the countryside population living close to
forested areas of Khyber Pakhtunkhwa (KPK). Timber, firewood, forest soil, pastures,
and raw goods for industries of cottage, medicinal or edible plants and royalty expenses
are the main advantages obtained by local people from these forests. Rural livelihood,
most sturdily of those at the lowest of the socio-economic scale is affected by degrada-
tion of the forests [5].
The basic idea behind REDD is in fact simple,
i.e.
countries that are prepared and
capable of reducing emissions from deforestation should be monetarily compensated
for doing so. To curb global deforestation, previous methodologies have so far been not
successfully operational owing to lack of any visible monetary benefits to the local pop-
ulations. However, REDD provides a new structure to permit deforesting countries to
halt this historic trend. REDD is chiefly about emissions reductions and could concur-
rently address climate change as well as sustainable rural development [6].
In order to qualify for earning carbon credits there are certain perquisites that have
to be fulfilled such as:
Stable forest area for a reasonable period of time.
Deforestation and forest degradation should be calculated.
The local population should have sustainable livelihoods.
It is, therefore, required to continuously observe numerous changes occurring in the
forest areas, to make optimal policies for better utilization of forests. Conventional me-
thodologies such as surveying are not only labor intensive but also costly. Whereas, cut-
ting edge technology of RS and GIS provides us with the capability to efficiently moni-
tor and manage the forests. Advances in RS and GIS data availability, quality, and type
can possibly alleviate the current challenges of large-area monitoring and detailed ex-
aminations of subtle forest modifications which are the main hindrances in the under-
standing of the scale and pace of forest change. Digital remotely sensed imagery is now
a standard instrument in the collection of the professional forest manager because the
relationship of technology and need has finally arisen [7].
S. ur R. Khalid et al.
1193
2. Statement of the Problem
Keeping in view the above mentioned issues pertaining to the adverse effects of carbon
emissions being accumulated in the environment, it is required to control such emis-
sions. Since the forests act as carbon sinks by absorbing CO2 from the environment,
therefore, sustainable management of the forested areas as well as wellbeing of the local
populations is needed. The UN’s REDD initiative provides options for the regions
which have these natural carbon sinks to earn credits from those regions which are
causing carbon emissions through their industries. The REDD have certain conditions
to be fulfilled before earning the carbon credits. These conditions demand sustainable
forest areas as well as providing livelihood to the local populations. This demands reg-
ular monitoring of forest areas and assessment of afforestation activities for the preser-
vation from damaging and deforestation.
Essentially the main objective of this study is to provide rate and amount of degrada-
tion of forests in the study area and hence to formulate a strategy to earn carbon credits
consistently and, therefore, help in the uplifting of the standards of local population.
Therefore, the following goals are required to be achieved by this research.
Preparation of baseline data for implementation of REDD.
Land-cover map including deforestation/afforestation estimation maps of the study
area.
How much forest cover changes in the form of deforestation/afforestation in study
area has occurred in the last 10 years?
Comprehensive analysis on the basis of research study of selected forest area of
Khyber Pakhtunkhwa province.
3. Study Area
The area under study consists of 3 northern districts of KPK province of Pakistan
i.e
.
Kohistan, Shangla and Batgram which include 7 tehsils
i.e.
Pattan, Bisham, Chakisar,
Maroong, Palas, Allai and Batagram, as shown in Figure 1. This area is located in
north-western side of Pakistan. Its total area is 7126.2 km2. Its borders meet Kohistan
Division to the North, Sawat Division to the West and it is surrounded by Mansehra
Division to the East and South.
4. Methodology
ASTER images were collected since it has 15 meter spatial resolution (VNIR bands) and
90 meter spatial resolution (Thermal Bands) which is finer as compared to LANDSAT
TM data which has 30 meter spatial resolution of visible/infrared range and 120 meter
resolution for thermal band. Two datasets of ASTER_14DMO images of year 2001 and
2011 comprising of total 23 images were used (10 images from 2001 and 13 images
from 2011). Google Earth Images of study area were used online for hybrid classifica-
tion since these are very high resolution images and covers different period of time
(historical images). So high resolution images
i.e.
Geo Eye 0.5, Quickbird 0.61 and
SPOT 2.5 images of year 2001 and 2011 were used for hybrid classification. Radiometric
S. ur R. Khalid et al.
1194
Figure 1. Map of study area.
correction of ASTER images (VNIR) was carried out to derive ecologically relevant ve-
getation metrics. Top of atmosphere reflectance was calculated to use as the input for
the Normalized Difference Vegetation Index (NDVI). Top of atmosphere reflectance
corrects for two sets of factors and for this purpose “aster_radiance_vnir_hhn.gmd”
model as shown in Figure 2, was used to convert an input visible near-infrared (VNIR)
image in scaled radiance (with bands 1 and 2 collected with the high gain setting and
band 3N collected with the normal gain setting) to true radiance using the unit conver-
sion coefficients and finally converted into top of atmosphere by using graphical model
as shown in Figure 3.
Variations in solar illumination influenced by properties such as the solar elevation
angle and earth-sun distance.
The influence of atmospheric haze and aerosols on the signal detected by the sensor.
By correcting for these factors, surface reflectance should characterize the land fea-
tures themselves.
Change detection in land use and land cover can be done by different ways. Each
method has its own advantages and disadvantages. Most effective and simple technique
is determining change detection through image classifications. In this process different
classes are assigned to the pixels of remotely sensed data. The chief objective of this
process is to recognize between different classes of land cover e.g. forest area, bare land,
agricultural, vegetation, water bodies and urban area etc.
Change detection in land use and land cover can be done by different ways. Each
S. ur R. Khalid et al.
1195
Figure 2. ERDAS graphical model for conversion of sensor DN values to top of atmosphere radiance.
Figure 3. ERDAS graphical model for conversion radiance to top of atmosphere reflectance.
S. ur R. Khalid et al.
1196
method has its own advantages and disadvantages. Most effective and simple technique
is determining change detection through image classifications. In this process different
classes are assigned to the pixels of remotely sensed data. The chief objective of this
process is to recognize between different classes of land cover e.g. forest area, bare land,
agricultural, vegetation, water bodies and urban area etc.
Change detection in land use and land cover can be done by different ways. Each
method has its own advantages and disadvantages. Most effective and simple technique
is determining change detection through image classifications. In this process different
classes are assigned to the pixels of remotely sensed data. The chief objective of this
process is to recognize between different classes of land cover e.g. forest area, bare land,
agricultural, vegetation, water bodies and urban area etc.
In classification method used for change detection, each temporal image is catego-
rized independently and then after classification these images are compared to other
corresponding images. If the resultant pixels have same land cover class label then it is
determined as no change and in the case of difference it is labeled as change [8]. A hy-
brid classification method was adopted in which first of all unsupervised classification
method using ISODATA algorithm was exercised on the datasets of both the years as
described below, then a separability analysis was done and final unsupervised classified
images were chosen for further process. Now a supervised classification using maxi-
mum likelihood was done by taking training samples from high resolution images of
Google Earth.
5. Results and Discussion
Spatiotemporal analysis exposed a number of output maps at tehsil level Different out-
put maps were produced showing study area, classified maps for the year 2001 and
2011 as shown in Figure 4 and Figure 5 one-to-one; total forest area in 2001 and 2011
as shown in Figure 6 and Figure 7 respectively, forest area, Dense Forest area, agricul-
ture area, total deforestation as shown in Figure 8, total afforestation as shown in Fig-
ure 9, deforestation of forest class as shown in Figure 10; afforestation of forest class as
shown in Figure 11; deforestation of Dense Forest class as shown in Figure 12; affore-
station of Dense Forest class as shown in Figure 13 and finally agriculture area map for
the year 2001 and 2011 as shown in Figure 14 and Figure 15 respectively were pro-
duced. Also graphical representation of dense forest, forest class and union agriculture
class at union level for the year 2001 and 2011.
All these maps and graphs show that over the 10 year period, Deforestation and For-
est Degradation occurred considerably largely at the places where population is more.
Hence it clearly shows socioeconomic activities linked with deforestation or degradation
of forests in the area. Since forest was divided into two classes
i.e.
Forest and Dense
Forest, it has been observed that generally Dense Forests have been converted into for-
est due to individual cutting of trees and forest class was at some places have been
eliminated and converted into bare land class. In general, Dense Forest and forest
classes have been reduced and bare land has been increased.
S. ur R. Khalid et al.
1197
Figure 4. Classified map of 2001.
Figure 5. Classified map of 2011.
S. ur R. Khalid et al.
1198
Figure 6. Map of total forest in 2001.
Figure 7. Map of total forest in 2011.
S. ur R. Khalid et al.
1199
Figure 8. Map of total deforestation.
Figure 9. Map of total afforestation.
S. ur R. Khalid et al.
1200
Figure 10. Map of total deforestation (forest class).
Figure 11. Map of total afforestation (forest class).
S. ur R. Khalid et al.
1201
Figure 12. Map of total deforestation (dense forest class).
Figure 13. Map of total afforestation (dense forest class).
S. ur R. Khalid et al.
1202
Figure 14. Map of agriculture for 2001.
Figure 15. Map of agriculture for 2011.
S. ur R. Khalid et al.
1203
Water in the area has also been increased in the area, this is because in datasets of
year 2001, Snow was more prominent in the area and since pure snow was eliminated
in the images due to the processes applied on the images for atmospheric correction.
And this problem was not in the images of 2011 and it shows water and snowy area
preserved in classified images as it is, hence it gives wrong impression that what has
been increased. So by visual inspection and logic, it has been established that there is
generally no change in water class.
Figure 2 shows that the deforestation occurred in all the three studied districts of
KPK which needs to be addressed before applying for earning carbon credits under
REDD program. The main reasons for deforestation are heavy snowfall, landslides,
flash flooding and earthquake. District wise discussion can be divided into three por-
tions emphasizing each district. Kohistan is the most affected region. The area is the
least developed area in terms of literacy rate among the entire KPK region. Its literacy
rate is only 14.5%. Due to less education, together with less development there are less
opportunities of employment are available.
Two tehsils in Kohistan district come under study area
i.e.
Palas and Pattan. Compa-
ratively Palas has more tendency in deforestation and degradation of forest. Union
council areas of Peach Bela, Shaman and Shared in Palas tehsil have been severely af-
fected due to deforestation. However tehsil Pattan shows improvement in forestation.
Figure 16 shows that severe deforestation occurred in valleys of Palas tehsil where these
forests have been converted to agriculture lands, out of the studied districts, Shangla
was mostly affected by 8 Oct 2005 earthquake [9]. The main reasons for deforestation
are heavy snowfall, landslides, flash flooding and earthquake.
The local people need to be provided with education, alternate sources of fuel, em-
ployment opportunities. Two important economic factors need to be strengthened in
the areas which are controlled mining at sites identified by geological survey and forest
conservation authorities and establishing fish hatcheries in the cold waters of the area.
By stopping illegal mining and establishment of legal mines under the supervision of
geological survey and forest conservation authorities will provide employment to the
local people along with forest clearing for illegal mines. Similarly the area needs to be
provided with expertise for fish nurseries and fish farming. This study also shows that
the process of forest degradation is different at different areas. Therefore, it is required
to establish the local institutional authorities. Batagram district as shown in Figure 17,
although affected badly in 8 October 2005 earthquake, however least affected area as far
as deforestation and degradation of forests is concerned.
At some places in tehsil Batagram as shown Figure 18, even one can observe affore-
station in many areas especially in areas of union council Batta Mohri, Kuza Banda,
Paimal Sharif, Pashora, Raj Dahari, Shamlai, Tarand and Thakot. However, even then a
little deforestation can be observed in tehsil Alai. Similar reason as stated above can be
sensed in Allai tehsil, where poverty, lack of education, non-availability of energy
sources and unemployment in this tehsil is on the increase. Moreover tehsil Allai was
also effected severely from 8 Oct 2005 earthquake.
S. ur R. Khalid et al.
1204
Figure 16. Deforestation and afforestation in Kohistan district.
Figure 17. Deforestation and afforestation in Batagram district.
Figure 18. Deforestation and afforestation in Batagram district.
0
2000
4000
6000
8000
10000
12000
14000
16000
Barsharyal
Bataira Bala
Batairspayeen
Khas Sari
Kolai
Kota Kot
Kuz Paro
Mada Khail
Mareen
Peach Bela
Qillah
Shaman
Shara Kot
Shared
Shilken Abad
Bankhad
Chowa Dara
Dobair Khas
Dobair Pain
Jijal
Keyal
Mandraza
Pattan
Ranolia
Sagayon
Palas
Pattan
Total Forest 2001
Total Forest 2011
0
2000
4000
6000
8000
10000
Botial
Dandai
Maira
Bunerwal
Chakisar
Opel
Sarkul
Behlool Khail
Hasham Khel Dab
Martung Khass
Bisham
Chiksar
Martung
Total Forest 2001
Total Forest 2011
S. ur R. Khalid et al.
1205
Accuracy assessment was performed and for this purpose 150 stratified random
point throughout classified image of year 2001 were selected. Same point were verified
by Google Earth high resolution images of the same area as reference points and then
user and producer accuracies were measured, procedure was repeated for classified im-
age of year 2011as shown in Table 1 and Table 2 respectively. The overall accuracies
for classified images of year 2001 and year 2011 were achieved which is 93.33% for both
the images. Conditional Kappa for each class for images of year 2001 and 2011 were al-
so calculated as shown in Table 3 and Table 4 respectively.
6. Conclusion
On the basis of this research it is concluded that medium resolution remote sensing da-
ta provide an efficient means to monitor the forest areas over time, which is a prerequi-
site to earn carbon credits under REDD program. Hybrid classification aids in identi-
fying features on earth surface by combining the advantages of supervised and unsu-
pervised classifications; atmospheric processing reduces errors and uncertainties in
Table 1. Accuracy assessment of classified image 2001.
Class Names Reference
Totals
Classified
Totals
Number
Correct
Producer’s
Accuracy
User’s
Accuracy
Water 10 11 10 100.00% 90.91%
Built-up 7 9 5 71.43% 55.56%
Bare Land 15 15 13 86.67% 86.67%
Forest 36 33 33 91.67% 100.00%
Agriculture 16 13 13 81.25% 100.00%
Dense Forest 66 69 66 100.00% 95.65%
Totals 150 150 140
Overall Classification Accuracy = 93.33%.
Table 2. Accuracy assessment of classified image 2011.
Class Name Reference
Totals
Classified
Totals
Number
Correct
Producer’s
Accuracy
User’s
Accuracy
Water 14 15 14 100.00% 100.00%
Built-up 15 14 13 86.61% 92.81%
Bare Land 18 17 17 95.00% 100.00%
Agriculture 15 16 14 95.00% 90.91%
Forest 29 30 27 94.12% 90.27%
Dense Forest 59 58 55 93.27% 94.68%
Totals 150 150 140
Overall Classification Accuracy = 93.33%.
S. ur R. Khalid et al.
1206
Table 3. Kappa (K) statistics for classified image 2001.
Class Name Kappa (K)
Water 0.9026
Built-up 0.5338
Bare Land 0.8519
Forest 1
Agriculture 1
Dense Forest 0.9224
Overall Kappa Statistics = 0.9071.
Table 4. Kappa (K) statistics for classified image 2011.
Class Name Kappa (K)
Water 0.9413
Built-up 0.92505
Bare Land 1
Agriculture 0.9026
Forest 0.89185
Thick Forest 0.94264
Overall Kappa Statistics = 0.9280.
geospatial analysis. The forest areas of KPK have a great potential for earning carbon
credits under REDD program. The areas as described in discussion need to be regularly
monitored; remedial measure to counter deforestation should be taken; local people
should be provided with education; and infrastructure should be strengthened particu-
larly in energy and sanitation (water conservation/purification). The areas of transition
(from dense forests to scattered forests) should be monitored and afforestation activi-
ties should be conducted in these areas. Illegal forest cuttings should be strictly con-
trolled through strong law enforcement. Urbanization trends in the district of Batagram
need to be institutionalized.
Acknowledgements
The authors thank to Dr. Mobushir Riaz Khan, associate professor at Department of
Geo-informatics, Pir Mehr Ali Shah Arid Agriculture University Rawalpindi, Pakistan
for his valuable scientific and technical support, also for providing data set for this re-
search.
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Districts Disaster Risk Management Plan, Building Enabling Governance and Institutions for Earthquake Response (BEGIN-ER)
NDMA (2007) 2007 Districts Disaster Risk Management Plan, Building Enabling Governance and Institutions for Earthquake Response (BEGIN-ER). ERRA 2007.
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